Artykuły w czasopismach: „Over-constrained temporal networks”

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Lange, Trent E., and Michael G. Dyer. "Parallel reasoning in structured connectionist networks: Signatures versus temporal synchrony." Behavioral and Brain Sciences 19, no. 2 (June 1996): 328–31. http://dx.doi.org/10.1017/s0140525x00042953.

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Shastri & Ajjanagadde (1993) (S&A) argue convincingly that both structured connectionist networks and parallel dynamic inferencing are necessary for reflexive reasoning - a kind of inferencing and reasoning that occurs rapidly, spontaneously, and without conscious effort, and which seems necessary for everyday tasks such as natural language understanding. As S&A describe, reflexive reasoning requires a solution to thedynamic binding problem, that is, how to encode systematic and abstract knowledge and instantiate it in specific situations to draw appropriate inferences. Although symbolic artificial intelligence systems trivially solve the dynamic binding problem using computers' registers and pointers, it has remained a difficult problem for connectionist systems (see Fodor & Pylyshyn 1988). S&A's temporal synchrony solution to the dynamic binding problem using synchronous firing of argument units and the entities that are bound to them illustrates one way in which connectionist networks can do thisusing a constrained but important class of long-term knowledge rules. Their structured connectionist solution allows dynamic inferencing to proceed in parallel and therefore has a number of advantages for reflexive reasoning over most other connectionist and symbolic systems.

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Xiang, Liuyu, Jundong Zhou, Jirui Liu, Zerun Wang, Haidong Huang, Jie Hu, Jungong Han, Yuchen Guo, and Guiguang Ding. "ReMoNet: Recurrent Multi-Output Network for Efficient Video Denoising." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2786–94. http://dx.doi.org/10.1609/aaai.v36i3.20182.

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While deep neural network-based video denoising methods have achieved promising results, it is still hard to deploy them on mobile devices due to their high computational cost and memory demands. This paper aims to develop a lightweight deep video denoising method that is friendly to resource-constrained mobile devices. Inspired by the facts that 1) consecutive video frames usually contain redundant temporal coherency, and 2) neural networks are usually over-parameterized, we propose a multi-input multi-output (MIMO) paradigm to process consecutive video frames within one-forward-pass. The basic idea is concretized to a novel architecture termed Recurrent Multi-output Network (ReMoNet), which consists of recurrent temporal fusion and temporal aggregation blocks and is further reinforced by similarity-based mutual distillation. We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image-Signal-Processor (ISP) noise. The experimental results show that ReMoNet is both effective and efficient on video denoising. Moreover, we show that ReMoNet is more robust under higher noise level scenarios.

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Chen, Jingkai, Yuening Zhang, Cheng Fang, and Brian C. Williams. "Generalized Conflict-Directed Search for Optimal Ordering Problems." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 22, 2021): 46–54. http://dx.doi.org/10.1609/socs.v12i1.18550.

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Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively decouple the problem is to judiciously order the events such that decisions can be made over sequences of tasks. As many problems encountered in practice are over-constrained, we must instead find relaxed solutions in which certain requirements are dropped. This motivates a formulation of optimality with respect to the costs of relaxing constraints and the problem of finding an optimal ordering under which this relaxing cost is minimum. In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning. Due to its ability to reason over generalized conflicts, GCDO is much more efficient in finding high-quality total orders than the previous conflict-directed approach CDITO. We demonstrate this by benchmarking on temporal network configuration problems, which involves managing networks over time and makes necessary tradeoffs between network flows against CDITO and Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two orders of magnitude more benchmark problems to optimality and twice the problems compared to CDITO and MILP within a runtime limit, respectively.

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Ale, Laha, Ning Zhang, Scott A. King, and Jose Guardiola. "Spatio-temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (June 9, 2021): 1–21. http://dx.doi.org/10.1145/3448613.

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A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision-making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in terms of computing and storage to accomplish various tasks. Moreover, these cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to network congestion when massive numbers of tasks offload through the core network. Therefore, by decentralizing resources spatially close to IoT devices, mobile edge computing (MEC) can reduce latency and improve service quality for a smart city, where service requests can be fulfilled in proximity. As the service demands exhibit spatial-temporal features, deploying MEC servers at optimal locations and allocating MEC resources play an essential role in efficiently meeting service requirements in a smart city. In this regard, it is essential to learn the distribution of resource demands in time and space. In this work, we first propose a spatio-temporal Bayesian hierarchical learning approach to learn and predict the distribution of MEC resource demand over space and time to facilitate MEC deployment and resource management. Second, the proposed model is trained and tested on real-world data, and the results demonstrate that the proposed method can achieve very high accuracy. Third, we demonstrate an application of the proposed method by simulating task offloading. Finally, the simulated results show that resources allocated based upon our models’ predictions are exploited more efficiently than the resources are equally divided into all servers in unobserved areas.

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Wang, Jiangkun, Ogbodo Mark Ikechukwu, Khanh N. Dang, and Abderazek Ben Abdallah. "Spike-Event X-ray Image Classification for 3D-NoC-Based Neuromorphic Pneumonia Detection." Electronics 11, no. 24 (December 13, 2022): 4157. http://dx.doi.org/10.3390/electronics11244157.

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The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency.

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Sasse, T. P., B. I. McNeil, and G. Abramowitz. "A novel method for diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle data using neural networks." Biogeosciences Discussions 9, no. 11 (November 1, 2012): 15329–80. http://dx.doi.org/10.5194/bgd-9-15329-2012.

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Abstract. The ocean's role in modulating the observed 1–7 Pg C yr−1 inter-annual variability in atmospheric CO2 growth rate is an important, but poorly constrained process due to sparse spatio-temporal ocean carbon measurements. Here, we investigate and develop a non-linear empirical approach to predict inorganic CO2 concentrations (total carbon dioxide (CT) and total alkalinity (AT) in the global ocean mixed-layer from hydrographic properties (temperature, salinity, dissolved oxygen and nutrients). The benefit of this approach is that once the empirical relationship is established, it can be applied to hydrographic datasets that have better spatio-temporal coverage, and therefore provide an additional constraint to diagnose ocean carbon dynamics globally. Previous empirical approaches have employed multiple linear regressions (MLR), and relied on ad-hoc geographic and temporal partitioning of carbon data to constrain complex global carbon dynamics in the mixed-layer. Synthesising a new global CT/AT carbon bottle dataset consisting of ~33 000 measurements in the open ocean mixed-layer, we develop a neural network based approach to better constrain the non-linear carbon system. The approach classifies features in the global biogeochemical dataset based on their similarity and homogeneity in a self-organizing map (SOM; Kohonen, 1988). After the initial SOM analysis, which includes geographic constraints, we apply a local linear optimizer to the neural network which considerably enhances the predictive skill of the new approach. We call this new approach SOMLO, or self-organizing multiple linear output. Using independent bottle carbon data, we compare a traditional MLR analysis to our SOMLO approach to capture the spatial CT and AT distributions. We find the SOMLO approach improves predictive skill globally by 19% for CT, with a global capacity to predict CT to within 10.9 μmol kg−1 (9.2 μmol kg−1 for AT. The non-linear SOMLO approach is particularly powerful in complex, but important regions like the Southern Ocean, North Atlantic and equatorial Pacific where residual standard errors were reduced between 25–40% over traditional linear methods. We further test the SOMLO technique using the Bermuda Atlantic time-series (BATS) and Hawaiian ocean (HOT) datasets, where hydrographic data was capable of explaining 90% of the seasonal cycle and inter-annual variability at those multi-decadal time-series stations.

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Onisawa, Takehisa. "Special Issue on Selected Papers in SCIS & ISIS 2004 – No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (May 20, 2005): 225. http://dx.doi.org/10.20965/jaciii.2005.p0225.

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The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.

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8

Chang, Yuhu, Yingying Zhao, Mingzhi Dong, Yujiang Wang, Yutian Lu, Qin Lv, Robert P. Dick, Tun Lu, Ning Gu, and Li Shang. "MemX." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (June 23, 2021): 1–23. http://dx.doi.org/10.1145/3463509.

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This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX.

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9

Sasse, T. P., B. I. McNeil, and G. Abramowitz. "A novel method for diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle data using neural networks." Biogeosciences 10, no. 6 (June 27, 2013): 4319–40. http://dx.doi.org/10.5194/bg-10-4319-2013.

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Abstract. The ocean's role in modulating the observed 1–7 Pg C yr−1 inter-annual variability in atmospheric CO2 growth rate is an important, but poorly constrained process due to current spatio-temporal limitations in ocean carbon measurements. Here, we investigate and develop a non-linear empirical approach to predict inorganic CO2 concentrations (total carbon dioxide (CT) and total alkalinity (AT)) in the global ocean mixed layer from hydrographic properties (temperature, salinity, dissolved oxygen and nutrients). The benefit of this approach is that once the empirical relationship is established, it can be applied to hydrographic datasets that have better spatio-temporal coverage, and therefore provide an additional constraint to diagnose ocean carbon dynamics globally. Previous empirical approaches have employed multiple linear regressions (MLR) and relied on ad hoc geographic and temporal partitioning of carbon data to constrain complex global carbon dynamics in the mixed layer. Synthesizing a new global CT/AT carbon bottle dataset consisting of ~33 000 measurements in the open ocean mixed layer, we develop a neural network based approach to better constrain the non-linear carbon system. The approach classifies features in the global biogeochemical dataset based on their similarity and homogeneity in a self-organizing map (SOM; Kohonen, 1988). After the initial SOM analysis, which includes geographic constraints, we apply a local linear optimizer to the neural network, which considerably enhances the predictive skill of the new approach. We call this new approach SOMLO, or self-organizing multiple linear output. Using independent bottle carbon data, we compare a traditional MLR analysis to our SOMLO approach to capture the spatial CT and AT distributions. We find the SOMLO approach improves predictive skill globally by 19% for CT, with a global capacity to predict CT to within 10.9 μmol kg−1 (9.2 μmol kg−1 for AT). The non-linear SOMLO approach is particularly powerful in complex but important regions like the Southern Ocean, North Atlantic and equatorial Pacific, where residual standard errors were reduced between 25 and 40% over traditional linear methods. We further test the SOMLO technique using the Bermuda Atlantic time series (BATS) and Hawaiian ocean time series (HOT) datasets, where hydrographic data was capable of explaining 90% of the seasonal cycle and inter-annual variability at those multi-decadal time-series stations.

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Song, Yawei, Jia Chen, and Rongxin Zhang. "Heart Rate Estimation from Incomplete Electrocardiography Signals." Sensors 23, no. 2 (January 4, 2023): 597. http://dx.doi.org/10.3390/s23020597.

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As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

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Hussain, Md Muzakkir, Ahmad Taher Azar, Rafeeq Ahmed, Syed Umar Amin, Basit Qureshi, V. Dinesh Reddy, Irfan Alam, and Zafar Iqbal Khan. "SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks." Sensors 23, no. 2 (January 6, 2023): 667. http://dx.doi.org/10.3390/s23020667.

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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Saha, Swapnil Sayan, Sandeep Singh Sandha, Luis Antonio Garcia, and Mani Srivastava. "TinyOdom." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (July 4, 2022): 1–32. http://dx.doi.org/10.1145/3534594.

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Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31× to 134× with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within ±0.1m and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models.

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Wells, K. C., D. B. Millet, N. Bousserez, D. K. Henze, S. Chaliyakunnel, T. J. Griffis, Y. Luan, et al. "Simulation of atmospheric N2O with GEOS-Chem and its adjoint: evaluation of observational constraints." Geoscientific Model Development Discussions 8, no. 7 (July 8, 2015): 5367–418. http://dx.doi.org/10.5194/gmdd-8-5367-2015.

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Abstract. We describe a new 4D-Var inversion framework for N2O based on the GEOS-Chem chemical transport model and its adjoint, and apply this framework in a series of observing system simulation experiments to assess how well N2O sources and sinks can be constrained by the current global observing network. The employed measurement ensemble includes approximately weekly and quasi-continuous N2O measurements (hourly averages used) from several long-term monitoring networks, N2O measurements collected from discrete air samples aboard a commercial aircraft (CARIBIC), and quasi-continuous measurements from an airborne pole-to-pole sampling campaign (HIPPO). For a two-year inversion, we find that the surface and HIPPO observations can accurately resolve a uniform bias in emissions during the first year; CARIBIC data provide a somewhat weaker constraint. Variable emission errors are much more difficult to resolve given the long lifetime of N2O, and major parts of the world lack significant constraints on the seasonal cycle of fluxes. Current observations can largely correct a global bias in the stratospheric sink of N2O if emissions are known, but do not provide information on the temporal and spatial distribution of the sink. However, for the more realistic scenario where source and sink are both uncertain, we find that simultaneously optimizing both would require unrealistically small errors in model transport. Regardless, a bias in the magnitude of the N2O sink would not affect the a posteriori N2O emissions for the two-year timescale used here, given realistic initial conditions, due to the timescale required for stratosphere–troposphere exchange (STE). The same does not apply to model errors in the rate of STE itself, which we show exerts a larger influence on the tropospheric burden of N2O than does the chemical loss rate over short (< 3 year) timescales. We use a stochastic estimate of the inverse Hessian for the inversion to evaluate the spatial resolution of emission constraints provided by the observations, and find that significant, spatially explicit constraints can be achieved in locations near and immediately upwind of surface measurements and the HIPPO flight tracks; however, these are mostly confined to North America, Europe, and Australia. None of the current observing networks are able to provide significant spatial information on tropical N2O emissions. There, averaging kernels are highly smeared spatially and extend even to the midlatitudes, so that tropical emissions risk being conflated with those elsewhere. For global inversions, therefore, the current lack of constraints on the tropics also places an important limit on our ability to understand extratropical emissions. Based on the error reduction statistics from the inverse Hessian, we characterize the atmospheric distribution of unconstrained N2O, and identify regions in and downwind of South America, Central Africa, and Southeast Asia where new surface or profile measurements would have the most value for reducing present uncertainty in the global N2O budget.

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Wells, K. C., D. B. Millet, N. Bousserez, D. K. Henze, S. Chaliyakunnel, T. J. Griffis, Y. Luan, et al. "Simulation of atmospheric N2O with GEOS-Chem and its adjoint: evaluation of observational constraints." Geoscientific Model Development 8, no. 10 (October 8, 2015): 3179–98. http://dx.doi.org/10.5194/gmd-8-3179-2015.

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Abstract. We describe a new 4D-Var inversion framework for nitrous oxide (N2O) based on the GEOS-Chem chemical transport model and its adjoint, and apply it in a series of observing system simulation experiments to assess how well N2O sources and sinks can be constrained by the current global observing network. The employed measurement ensemble includes approximately weekly and quasi-continuous N2O measurements (hourly averages used) from several long-term monitoring networks, N2O measurements collected from discrete air samples onboard a commercial aircraft (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container; CARIBIC), and quasi-continuous measurements from the airborne HIAPER Pole-to-Pole Observations (HIPPO) campaigns. For a 2-year inversion, we find that the surface and HIPPO observations can accurately resolve a uniform bias in emissions during the first year; CARIBIC data provide a somewhat weaker constraint. Variable emission errors are much more difficult to resolve given the long lifetime of N2O, and major parts of the world lack significant constraints on the seasonal cycle of fluxes. Current observations can largely correct a global bias in the stratospheric sink of N2O if emissions are known, but do not provide information on the temporal and spatial distribution of the sink. However, for the more realistic scenario where source and sink are both uncertain, we find that simultaneously optimizing both would require unrealistically small errors in model transport. Regardless, a bias in the magnitude of the N2O sink would not affect the a posteriori N2O emissions for the 2-year timescale used here, given realistic initial conditions, due to the timescale required for stratosphere–troposphere exchange (STE). The same does not apply to model errors in the rate of STE itself, which we show exerts a larger influence on the tropospheric burden of N2O than does the chemical loss rate over short (< 3 year) timescales. We use a stochastic estimate of the inverse Hessian for the inversion to evaluate the spatial resolution of emission constraints provided by the observations, and find that significant, spatially explicit constraints can be achieved in locations near and immediately upwind of surface measurements and the HIPPO flight tracks; however, these are mostly confined to North America, Europe, and Australia. None of the current observing networks are able to provide significant spatial information on tropical N2O emissions. There, averaging kernels (describing the sensitivity of the inversion to emissions in each grid square) are highly smeared spatially and extend even to the midlatitudes, so that tropical emissions risk being conflated with those elsewhere. For global inversions, therefore, the current lack of constraints on the tropics also places an important limit on our ability to understand extratropical emissions. Based on the error reduction statistics from the inverse Hessian, we characterize the atmospheric distribution of unconstrained N2O, and identify regions in and downwind of South America, central Africa, and Southeast Asia where new surface or profile measurements would have the most value for reducing present uncertainty in the global N2O budget.

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Chirkov, M., G. P. Stiller, A. Laeng, S. Kellmann, T. von Clarmann, C. Boone, J. W. Elkins, et al. "Global HCFC-22 measurements with MIPAS: retrieval, validation, climatologies and trends." Atmospheric Chemistry and Physics Discussions 15, no. 10 (May 27, 2015): 14783–841. http://dx.doi.org/10.5194/acpd-15-14783-2015.

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Abstract. We report on HCFC-22 data acquired by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) in reduced spectral resolution nominal mode in the period from January 2005 to April 2012 from version 5.02 level-1b spectral data and covering an altitude range from the upper troposphere (above cloud top altitude) to about 50 km. The profile retrieval was performed by constrained nonlinear least squares fitting of measured limb spectral radiances to modelled spectra. The spectral ν4-band at 816.5 ± 13 cm−1 was used for the retrieval. A Tikhonov-type smoothing constraint was applied to stabilise the retrieval. In the lower stratosphere, we find a global volume mixing ratio of HCFC-22 of about 185 pptv in January 2005. The linear growth rate in the lower latitudes lower stratosphere was about 6 to 7 pptv yr−1 in the period 2005–2012. The obtained profiles were compared with ACE-FTS satellite data v3.5, as well as with MkIV balloon profiles and in situ cryosampler balloon measurements. Between 13 and 22 km, average agreement within −3 to +5 pptv (MIPAS–ACE) with ACE-FTS v3.5 profiles is demonstrated. Agreement with MkIV solar occultation balloon-borne measurements is within 10–20 pptv below 30 km and worse above, while in situ cryosampler balloon measurements are systematically lower over their full altitude range by 15–50 pptv below 24 km and less than 10 pptv above 28 km. Obtained MIPAS HCFC-22 time series below 10 km altitude are shown to agree mostly well to corresponding time series of near-surface abundances from NOAA/ESRL and AGAGE networks, although a more pronounced seasonal cycle is obvious in the satellite data, probably due to tropopause altitude fluctuations and subsidence of polar winter stratospheric air into the troposphere. A parametric model consisting of constant, linear, quasi-biennial oscillation (QBO) and several sine and cosine terms with different periods has been fitted to the temporal variation of stratospheric HCFC-22 for all 10° latitude/1 to 2 km altitude bins. The relative linear variation was always positive, with relative increases of 40–70% decade−1 in the tropics and global lower stratosphere, and up to 120% decade−1 in the upper stratosphere of the northern polar region and the southern extratropical hemisphere. In the middle stratosphere between 20 and 30 km, the observed trend is not consistent with the age of stratospheric air-corrected trend at ground, but stronger positive at the Southern Hemisphere and less strong increasing in the Northern Hemisphere, hinting towards changes in the stratospheric circulation over the observation period.

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Chirkov, M., G. P. Stiller, A. Laeng, S. Kellmann, T. von Clarmann, C. D. Boone, J. W. Elkins, et al. "Global HCFC-22 measurements with MIPAS: retrieval, validation, global distribution and its evolution over 2005–2012." Atmospheric Chemistry and Physics 16, no. 5 (March 15, 2016): 3345–68. http://dx.doi.org/10.5194/acp-16-3345-2016.

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Abstract. We report on HCFC-22 data acquired by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) in the reduced spectral resolution nominal observation mode. The data cover the period from January 2005 to April 2012 and the altitude range from the upper troposphere (above cloud top altitude) to about 50 km. The profile retrieval was performed by constrained nonlinear least squares fitting of modelled spectra to the measured limb spectral radiances. The spectral ν4-band at 816.5 ± 13 cm−1 was used for the retrieval. A Tikhonov-type smoothing constraint was applied to stabilise the retrieval. In the lower stratosphere, we find a global volume mixing ratio of HCFC-22 of about 185 pptv in January 2005. The rate of linear growth in the lower latitudes lower stratosphere was about 6 to 7 pptv year−1 in the period 2005–2012. The profiles obtained were compared with ACE-FTS satellite data v3.5, as well as with MkIV balloon profiles and cryosampler balloon measurements. Between 13 and 22 km, average agreement within −3 to +5 pptv (MIPAS – ACE) with ACE-FTS v3.5 profiles is demonstrated. Agreement with MkIV solar occultation balloon-borne measurements is within 10–20 pptv below 30 km and worse above, while in situ cryosampler balloon measurements are systematically lower over their full altitude range by 15–50 pptv below 24 km and less than 10 pptv above 28 km. MIPAS HCFC-22 time series below 10 km altitude are shown to agree mostly well to corresponding time series of near-surface abundances from the NOAA/ESRL and AGAGE networks, although a more pronounced seasonal cycle is obvious in the satellite data. This is attributed to tropopause altitude fluctuations and subsidence of polar winter stratospheric air into the troposphere. A parametric model consisting of constant, linear, quasi-biennial oscillation (QBO) and several sine and cosine terms with different periods has been fitted to the temporal variation of stratospheric HCFC-22 for all 10°-latitude/1-to-2-km-altitude bins. The relative linear variation was always positive, with relative increases of 40–70 % decade−1 in the tropics and global lower stratosphere, and up to 120 % decade−1 in the upper stratosphere of the northern polar region and the southern extratropical hemisphere. Asian HCFC-22 emissions have become the major source of global upper tropospheric HCFC-22. In the upper troposphere, monsoon air, rich in HCFC-22, is instantaneously mixed into the tropics. In the middle stratosphere, between 20 and 30 km, the observed trend is inconsistent with the trend at the surface (corrected for the age of stratospheric air), hinting at circulation changes. There exists a stronger positive trend in HCFC-22 in the Southern Hemisphere and a more muted positive trend in the Northern Hemisphere, implying a potential change in the stratospheric circulation over the observation period.

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Yang, Xuan, Bing Zhang, Zhengchao Chen, Yongqing Bai, and Pan Chen. "A Multi-Temporal Network for Improving Semantic Segmentation of Large-Scale Landsat Imagery." Remote Sensing 14, no. 19 (October 10, 2022): 5062. http://dx.doi.org/10.3390/rs14195062.

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With the development of deep learning, semantic segmentation technology has gradually become the mainstream technical method in large-scale multi-temporal landcover classification. Large-scale and multi-temporal are the two significant characteristics of Landsat imagery. However, the mainstream single-temporal semantic segmentation network lacks the constraints and assistance of pre-temporal information, resulting in unstable results, poor generalization ability, and inconsistency with the actual situation in the multi-temporal classification results. In this paper, we propose a multi-temporal network that introduces pre-temporal information as prior constrained auxiliary knowledge. We propose an element-wise weighting block module to improve the fine-grainedness of feature optimization. We propose a chained deduced classification strategy to improve multi-temporal classification’s stability and generalization ability. We label the large-scale multi-temporal Landsat landcover classification dataset with an overall classification accuracy of over 90%. Through extensive experiments, compared with the mainstream semantic segmentation methods, our proposed multi-temporal network achieves state-of-the-art performance with good robustness and generalization ability.

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MOUHOUB, MALEK. "A HOPFIELD-TYPE NEURAL NETWORK BASED MODEL FOR TEMPORAL CONSTRAINTS." International Journal on Artificial Intelligence Tools 13, no. 03 (September 2004): 533–45. http://dx.doi.org/10.1142/s0218213004001673.

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In this paper we present an approximation method based on discrete Hopfield neural network (DHNN) for solving temporal constraint satisfaction problems. This method is of interest for problems involving numeric and symbolic temporal constraints and where a solution satisfying the constraints of the problem needs to be found within a given deadline. More precisely the method has the ability to provide a solution with a quality proportional to the allocated process time. The quality of the solution corresponds here to the number of satisfied constraints. This property is very important for real world applications including reactive scheduling and planning and also for over constrained problems where a complete solution cannot be found. Experimental study, in terms of time cost and quality of the solution provided, of the DHNN based method we propose provides promising results comparing to the other exact methods based on branch and bound and approximation methods based on stochastic local search.

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Yoshimoto, Atsushi, and Patrick Asante. "Inter-Temporal Aggregation for Spatially Explicit Optimal Harvest Scheduling under Area Restrictions." Forest Science 67, no. 5 (September 17, 2021): 587–606. http://dx.doi.org/10.1093/forsci/fxab025.

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Abstract We propose a new approach to solve inter-temporal unit aggregation issues under maximum opening size requirements using two models. The first model is based on Model I formulation with static harvest treatments for harvest activities. This model identifies periodic harvest activities using a set of constraints for inter-temporal aggregation. The second model is based on Model II formulation, which uses dynamic harvest treatments and incorporates periodic harvest activities directly into the model formulation. The proposed approach contributes to the literature on spatially constrained harvest scheduling problems as it allows a pattern of unit aggregation to change across multiple harvests over time, as inter-temporal aggregation under a maximum opening size requirement over period-specific duration. The main idea of the proposed approach for inter-temporal aggregation is to use a multiple layer scheme for a set of spatial constraints, which is adapted from a maximum flow specification in a spatial forest unit network and a sequential triangle connection to create fully connected feasible clusters. By dividing the planning horizon into period-specific durations for different spatial aggregation patterns, the models can complete inter-temporal spatial aggregation over the planning horizon under a maximum opening size requirement per duration.

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Martinez-Lorenzo, Jose, Jeff Hudack, Yutao Jing, Michael Shaham, Zixuan Liang, Abdullah Al Bashit, Yushu Wu, et al. "Preliminary Experimental Results of Context-Aware Teams of Multiple Autonomous Agents Operating under Constrained Communications." Robotics 11, no. 5 (September 9, 2022): 94. http://dx.doi.org/10.3390/robotics11050094.

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This work presents and experimentally tests the framework used by our context-aware, distributed team of small Unmanned Aerial Systems (SUAS) capable of operating in real time, in an autonomous fashion, and under constrained communications. Our framework relies on a three-layered approach: (1) an operational layer, where fast temporal and narrow spatial decisions are made; (2) a tactical layer, where temporal and spatial decisions are made for a team of agents; and (3) a strategical layer, where slow temporal and wide spatial decisions are made for the team of agents. These three layers are coordinated by an ad hoc, software-defined communications network, which ensures sparse but timely delivery of messages amongst groups and teams of agents at each layer, even under constrained communications. Experimental results are presented for a team of 10 small unmanned aerial systems tasked with searching for and monitoring a person in an open area. At the operational layer, our use case presents an agent autonomously performing searching, detection, localization, classification, identification, tracking, and following of the person, while avoiding malicious collisions. At the tactical layer, our experimental use case presents the cooperative interaction of a group of multiple agents that enables the monitoring of the targeted person over wider spatial and temporal regions. At the strategic layer, our use case involves the detection of complex behaviors, i.e., the person being followed enters a car and runs away, or the person being followed exits the car and runs away, which require strategic responses to successfully accomplish the mission.

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21

Bruhwiler, L. M. P., A. M. Michalak, and P. P. Tans. "Spatial and temporal resolution of carbon flux estimates for 1983–2002." Biogeosciences 8, no. 5 (May 26, 2011): 1309–31. http://dx.doi.org/10.5194/bg-8-1309-2011.

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Abstract. We discuss the spatial and temporal resolution of monthly carbon flux estimates for the period 1983–2002 using a fixed-lag Kalman Smoother technique with a global chemical transport model, and the GLOBALVIEW data product. The observational network has expanded substantially over this period, and the flux estimates are better constrained provided by observations for the 1990's in comparison to the 1980's. The estimated uncertainties also decrease as observational coverage expands. In this study, we use the Globalview data product for a network that changes every 5 yr, rather than using a small number of continually-operating sites (fewer observational constraints) or a large number of sites, some of which may consist almost entirely of extrapolated data. We show that the discontinuities resulting from network changes reflect uncertainty due to a sparse and variable network. This uncertainty effectively limits the resolution of trends in carbon fluxes, and is a potentially significant source of noise in assimilation systems that allow changes in observation distribution between assimilation time steps. The ability of the inversion to distinguish, or resolve, carbon fluxes at various spatial scales is examined using a diagnostic known as the resolution kernel. We find that the global partition between land and ocean fluxes is well-resolved even for the very sparse network of the 1980's, although prior information makes a significant contribution to the resolution. The ability to distinguish zonal average fluxes has improved significantly since the 1980's, especially for the tropics, where the zonal ocean and land biosphere fluxes can be distinguished. Care must be taken when interpreting zonal average fluxes, however, since the lack of air samples for some regions in a zone may result in a large influence from prior flux estimates for these regions. We show that many of the TransCom 3 source regions are distinguishable throughout the period over which estimates are produced. Examples are Boreal and Temperate North America. The resolution of fluxes from Europe and Australia has greatly improved since the 1990's. Other regions, notably Tropical South America and the Equatorial Atlantic remain practically unresolved. Comparisons of the average seasonal cycle of the estimated carbon fluxes with the seasonal cycle of the prior flux estimates reveals a large adjustment of the summertime uptake of carbon for Boreal Eurasia, and an earlier onset of springtime uptake for Temperate North America. In addition, significantly larger seasonal cycles are obtained for some ocean regions, such as the Northern Ocean, North Pacific, North Atlantic and Western Equatorial Pacific, regions that appear to be well-resolved by the inversion.

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Carn, S. A., and T. M. Lopez. "Opportunistic validation of sulfur dioxide in the Sarychev Peak volcanic eruption cloud." Atmospheric Measurement Techniques 4, no. 9 (September 1, 2011): 1705–12. http://dx.doi.org/10.5194/amt-4-1705-2011.

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Abstract. We report attempted validation of Ozone Monitoring Instrument (OMI) sulfur dioxide (SO2) retrievals in the stratospheric volcanic cloud from Sarychev Peak (Kurile Islands) in June 2009, through opportunistic deployment of a ground-based ultraviolet (UV) spectrometer (FLYSPEC) as the volcanic cloud drifted over central Alaska. The volcanic cloud altitude (~12–14 km) was constrained using coincident CALIPSO lidar observations. By invoking some assumptions about the spatial distribution of SO2, we derive averages of FLYSPEC vertical SO2 columns for comparison with OMI SO2 measurements. Despite limited data, we find minimum OMI-FLYSPEC differences within measurement uncertainties, which support the validity of the operational OMI SO2 algorithm. However, our analysis also highlights the challenges involved in comparing datasets representing markedly different spatial and temporal scales. This effort represents the first attempt to validate SO2 in a stratospheric volcanic cloud using a mobile ground-based instrument, and demonstrates the need for a network of rapidly deployable instruments for validation of space-based volcanic SO2 measurements.

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Verma, Shreeya, Julia Marshall, Christoph Gerbig, Christian Rödenbeck, and Kai Uwe Totsche. "The constraint of CO2 measurements made onboard passenger aircraft on surface–atmosphere fluxes: the impact of transport model errors in vertical mixing." Atmospheric Chemistry and Physics 17, no. 9 (May 5, 2017): 5665–75. http://dx.doi.org/10.5194/acp-17-5665-2017.

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Abstract. Inaccurate representation of atmospheric processes by transport models is a dominant source of uncertainty in inverse analyses and can lead to large discrepancies in the retrieved flux estimates. We investigate the impact of uncertainties in vertical transport as simulated by atmospheric transport models on fluxes retrieved using vertical profiles from aircraft as an observational constraint. Our numerical experiments are based on synthetic data with realistic spatial and temporal sampling of aircraft measurements. The impact of such uncertainties on the flux retrieved using the ground-based network and those retrieved using the aircraft profiles are compared. We find that the posterior flux retrieved using aircraft profiles is less susceptible to errors in boundary layer height, compared to the ground-based network. This finding highlights a benefit of utilizing atmospheric observations made onboard aircraft over surface measurements for flux estimation using inverse methods. We further use synthetic vertical profiles of CO2 in an inversion to estimate the potential of these measurements, which will be made available through the IAGOS (In-service Aircraft for a Global Observing System) project in the future, in constraining the regional carbon budget. Our results show that the regions of tropical Africa and temperate Eurasia, that are under-constrained by the existing surface-based network, will benefit the most from these measurements, with a reduction of posterior flux uncertainty of about 7 to 10 %.

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Lin, J. T., M. Y. Liu, J. Y. Xin, K. F. Boersma, R. Spurr, R. Martin, and Q. Zhang. "Influence of aerosols and surface reflectance on satellite NO2 retrieval: seasonal and spatial characteristics and implications for NOx emission constraints." Atmospheric Chemistry and Physics 15, no. 19 (October 9, 2015): 11217–41. http://dx.doi.org/10.5194/acp-15-11217-2015.

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Abstract. Satellite retrievals of vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) normally do not explicitly account for aerosol optical effects and surface reflectance anisotropy that vary with space and time. Here, we conduct an improved retrieval of NO2 VCDs over China, called the POMINO algorithm, based on measurements from the Ozone Monitoring Instrument (OMI), and we test the importance of a number of aerosol and surface reflectance treatments in this algorithm. POMINO uses a parallelized LIDORT-driven AMFv6 package to derive tropospheric air mass factors via pixel-specific radiative transfer calculations with no look-up tables, taking slant column densities from DOMINO v2. Prerequisite cloud optical properties are derived from a dedicated cloud retrieval process that is fully consistent with the main NO2 retrieval. Aerosol optical properties are taken from GEOS-Chem simulations constrained by MODIS aerosol optical depth (AOD) data. MODIS bi-directional reflectance distribution function (BRDF) data are used for surface reflectance over land. For the present analysis, POMINO level-2 data for 2012 are aggregated into monthly means on a 0.25° long. × 0.25° lat. grid. POMINO-retrieved annual mean NO2 VCDs vary from 15–25 × 1015 cm−2 over the polluted North China Plain (NCP) to below 1015 cm−2 over much of western China. Using POMINO to infer Chinese emissions of nitrogen oxides leads to annual anthropogenic emissions of 9.05 TgN yr−1, an increase from 2006 (Lin, 2012) by about 19 %. Replacing the MODIS BRDF data with the OMLER v1 monthly climatological albedo data affects NO2 VCDs by up to 40 % for certain locations and seasons. The effect on constrained NOx emissions is small. Excluding aerosol information from the retrieval process (this is the traditional "implicit" treatment) enhances annual mean NO2 VCDs by 15–40 % over much of eastern China. Seasonally, NO2 VCDs are reduced by 10–20 % over parts of the NCP in spring and over northern China in winter, despite the general enhancements in summer and fall. The effect on subsequently constrained annual emissions is between −5 and +30 % with large seasonal and spatial dependence. The implicit aerosol treatment also tends to exclude days with high pollution, since aerosols are interpreted as effective clouds and the respective OMI pixels are often excluded by cloud screening; this is a potentially important sampling bias. Therefore an explicit treatment of aerosols is important for space-based NO2 retrievals and emission constraints. A comprehensive independent measurement network with sufficient spatial and temporal representativeness is needed to further evaluate the different satellite retrieval approaches.

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D’Souza, Ollencio, Subhas Mukhopadhyay, and Michael Sheng. "Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge." Sensors 22, no. 21 (October 24, 2022): 8143. http://dx.doi.org/10.3390/s22218143.

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The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific “wake up” triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the “edge”, where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains.

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Gourdji, S. M., A. I. Hirsch, K. L. Mueller, V. Yadav, A. E. Andrews, and A. M. Michalak. "Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study." Atmospheric Chemistry and Physics 10, no. 13 (July 8, 2010): 6151–67. http://dx.doi.org/10.5194/acp-10-6151-2010.

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Abstract. A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO2 measurements across the continent, used with no auxiliary information or prior estimates of the flux distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic data inversions do not consider all potential issues associated with using actual measurement data, e.g. systematic transport errors or problems with the boundary conditions, they help to highlight the impact of inversion setup choices, and help to provide a baseline set of CO2 fluxes for comparison with estimates from future real-data inversions.

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Collin, A. M., M. Andel, D. James, and J. Claudet. "THE SUPERSPECTRAL/HYPERSPATIAL WORLDVIEW-3 AS THE LINK BETWEEN SPACEBORNE HYPERSPECTRAL AND AIRBORNE HYPERSPATIAL SENSORS: THE CASE STUDY OF THE COMPLEX TROPICAL COAST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1849–54. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1849-2019.

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Abstract. Earth observation of complex scenes, such as coastal fringes, is based on a plethora of optical sensors constrained by trade-offs between spatial, spectral, temporal and radiometric resolution. The spaceborne hyperspectral EO-1 Hyperion sensor (decommissioned in 2017) was able to acquire imagery with 10&amp;thinsp;nm spectral (220 bands) at 30&amp;thinsp;m spatial resolutions over 1424.5&amp;thinsp;km2 scenes. Conversely, the widespread unmanned airborne vehicle (UAV) hyperspatial DJI Mavic Pro camera can collect only natural-coloured imagery of 100&amp;thinsp;nm spectral (3 bands) but at 0.1&amp;thinsp;m spatial resolution over &amp;sim;10&amp;thinsp;km2 scenes (with a single battery and calm meteo-marine conditions). The spaceborne WorldView-3 (WV3), featured by 60&amp;thinsp;nm spectral (16 bands) at 0.3&amp;thinsp;m spatial resolution (when pansharpened) over 1489.6&amp;thinsp;km2 scenes, has the capacity to bridge both sensors. This study aims at testing the spectral and spatial performances of the WV3 to discriminate 10 complex coastal classes, ranging from ocean, reefs and terrestrial vegetation in Moorea Island (French Polynesia). Our findings show that geometrically- and radiometrically-corrected 0.3-m 16-band WV3 bands competed with (30-m) 167-band Hyperion performance for classifying 10 coastal classes with 2-neuron artificial neural network modelling, while being able to segment objects seized by 0.1-m (3-band) UAV. Unifying superspectral and hyperspatial specificities, the WV3 also leverages hypertemporal resolution, that is to say 1-day temporal resolution, rivalling UAV’s one.

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Super, Ingrid, Hugo A. C. Denier van der Gon, Michiel K. van der Molen, Stijn N. C. Dellaert, and Wouter Peters. "Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2." Geoscientific Model Development 13, no. 6 (June 18, 2020): 2695–721. http://dx.doi.org/10.5194/gmd-13-2695-2020.

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Abstract. We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx, and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2 m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N=44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1 km×1 km resolution. We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences <2 %, hourly temporal r2=0.21–0.95), while reported errors in the underlying parameters allow a full covariance structure to be created readily. Propagating this error structure into atmospheric mole fractions shows a strong dominance of a few large sectors and a few dominant uncertainties, most notably the emission ratios of the various gases considered. If the prior emission ratios are either sufficiently well-known or well constrained from a dense observation network, we find that including observations of co-emitted species improves our ability to estimate emissions per sector relative to using CO2 mole fractions only. Nevertheless, the total CO2 emissions can be well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating solutions from day-to-day leads to largest uncertainty reduction and smallest CO2 residuals over the 14 consecutive days considered. Although we can technically estimate the temporal distribution of some emission categories like shipping separate from their total magnitude, the controlling parameters are difficult to distinguish. Overall, we conclude that our new system looks promising for application in verification studies, provided that reliable urban atmospheric transport fields and reasonable a priori emission ratios for CO2 and its co-emitted species can be produced.

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Zhu, Wei-Dong, Chu-Yi Qian, Nai-Ying He, Yu-Xiang Kong, Zi-Ya Zou, and Yu-Wei Li. "Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China." Sustainability 14, no. 14 (July 20, 2022): 8894. http://dx.doi.org/10.3390/su14148894.

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The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

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Tian, Wen, Qin Fang, Xuefang Zhou, and Fan Yang. "The Method of Trajectory Selection Based on Bayesian Game Model." Sustainability 14, no. 18 (September 14, 2022): 11491. http://dx.doi.org/10.3390/su141811491.

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To cope with the problem that most of the en-route spatial-temporal resource allocation in the collaborative trajectory options program (CTOP) only considers the air traffic control system command center (ATCSCC) while ignoring the needs of the airlines, which results in the loss of fairness, this study explores resource allocation methods oriented to airline trajectory preferences with optional trajectory and entry slots of flights over the flow constrained area (FCA) as the research object. Using game theory to analyze airline trajectory preference information and a Bayesian game model based on mixed strategies is constructed, the process of incomplete information game among airlines is studied. The equilibrium theory is used to solve the guarantee strategy of airline trajectory selection, which makes the airline trajectory selection strategy robust and provides a basis for the selection of schemes for ATCSCC to implement en-route network resource allocation under the CTOP. Experimental analysis was carried out to verify the feasibility of the method based on the actual operation data of high-altitude sectors of Shanghai. The results show that the solution obtained by the game can provide airlines with flight trajectory and entry slots over the FCA that are more in line with their actual operational needs and which provide data reference for the ATCSCC to select the final plan in multiple global Pareto optimal solutions in the subsequent process of the CTOP so as to better play the decision-making role of airlines in the CTOP while improving the fairness of en-route resource allocation.

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Schmidt, Henrik. "Environmentally adaptive acoustic communication and navigation for underice autonomous operation." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A109. http://dx.doi.org/10.1121/10.0015709.

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The Arctic acoustic environment has changed dramatically, with multi-year ice being less abundant and the influx of warmer Pacific water, the so-called “Beaufort Lens,” creating a local maximum in the sound speed at a depth of 50–80 m. In contrast to the classic Arctic sound speed profile which had a monotonic increase in sound speed yielding a surface duct with strong ice interaction, the warm water lens creates a lower duct supporting extremely efficient propagation to long ranges. On the other hand, this double duct environment is decremental to short range communication and navigation of underwater vehicles. Thus, for a transmitter in one channel, the lens creates distinct shadow zones in the other channel at ranges between 1 and 4 km, which are the typical operational ranges of small AUVs, severely affecting navigation and communication performance. In addition, the temporal variability of is significant due to internal waves. During ICEX20 MIT and WHOI demonstrated an integrated communication and navigation concept where a network of ice-moored modem buoys with GPS tracking were used to track an under-ice AUV using regular CTD updates of optimal ranging, which in combination with a novel dynamic model constrained navigation fusion engine demonstrating GPS-grade navigation accuracy over several hours of operation. Using an EOF framework for updating the onboard environmental awareness on the AUV, the concept supports autonomous depth selection for optimal communication connectivity. [Work supported by ONR and UWDC/ASL.]

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Baruch, Zdravko, Greg Guerin, Irene Martín-Forés, Samantha Munroe, Ben Sparrow, and Andrew J. Lowe. "Shifts in floristic composition and structure in Australian rangelands." PLOS ONE 17, no. 12 (December 14, 2022): e0278833. http://dx.doi.org/10.1371/journal.pone.0278833.

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Monitoring shifts in vegetation composition over time is essential for tracking biodiversity changes and for designing ecosystem management strategies. In Australia, the Terrestrial Ecosystem Research Network (TERN) provides a continent-wide network of monitoring sites (AusPlots) that can be used to assess the shifts in vegetation composition and structure of Australian Major Vegetation Groups (MVGs). Here we use time-series site data to quantify the extent and rate of MVG shifts between repeat visits and to recommend the most appropriate sampling frequency for specific MVGs. The research area spans a ~1,500 km latitudinal gradient within south/central Australia from arid rangelands in the north to Mediterranean vegetation in the south. The standardized AusPlots protocol was employed to repeatedly survey 103 one-hectare plots, assessed between 2011 and 2019. Floristic and growth form dissimilarities between visits were calculated with distance metrics and then regressed against survey interval. Multivariate ordination was used to explore temporal floristic shifts. Rank-dominance curves were used to display variations in species’ importance. Between repeated visits, sites exhibited high variability for all vegetation parameters and trajectories. However, several trends emerged: (a) Species composition moved away from baseline linearly with intervals between surveys. (b) The rate of species turnover was approximately double in communities that are herbaceous versus woody-dominated. (c) Species abundances and growth forms shift at different speeds. All floristic and structural metrics shifted between re-visits, with varying magnitude and speed, but herbaceous-dominated plots showed higher floristic dynamism. Although the expanse, logistics, and the short time between visits constrained our analysis and interpretation, our results suggest that shorter revisit intervals may be appropriate for herbaceous compared to woody systems to track change most efficiently.

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Stavrakou, T., J. F. Müller, M. Bauwens, I. De Smedt, M. Van Roozendael, A. Guenther, M. Wild, and X. Xia. "Isoprene emissions over Asia 1979–2012: impact of climate and land use changes." Atmospheric Chemistry and Physics Discussions 13, no. 11 (November 12, 2013): 29551–92. http://dx.doi.org/10.5194/acpd-13-29551-2013.

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Abstract. Due to the scarcity of observational constraints and the rapidly changing environment in East and Southeast Asia, isoprene emissions predicted by models are expected to bear substantial uncertainties. The aim of this study is to improve upon the existing bottom-up estimates, and investigate the temporal evolution of the fluxes in Asia over 1979–2012. To this purpose, we calculate the hourly emissions at 0.5° × 0.5° resolution using the MEGAN-MOHYCAN model driven by ECMWF ERA-Interim climatology. This study incorporates (i) changes in land use, including the rapid expansion of oil palms, (ii) meteorological variability according to ERA-Interim, (iii) long-term changes in solar radiation (dimming/brightening) constrained by surface network radiation measurements, and (iv) recent experimental evidence that South Asian tropical forests are much weaker isoprene emitters than previously assumed, and on the other hand, that oil palms hold a strong isoprene emission capacity. These effects lead to a significant lowering (factor of two) in the total isoprene fluxes over the studied domain, and to emission reductions reaching a~factor of 3.5 in Southeast Asia. The bottom-up annual isoprene emissions for 2005 are estimated at 7.0, 4.8, 8.3, 2.9 Tg in China, India, Indonesia and Malaysia, respectively. Changes in temperature and solar radiation are the major drivers of the interannual variability and trend in the emissions. An annual positive flux trend of 0.2% and 0.52% is found in Asia and China, respectively, through the entire period, related to positive trend in temperature and solar radiation. The impact of oil palm expansion in Indonesia and Malaysia is to enhance the trends over that region, e.g. from 1.17% to 1.5% in 1979–2005 in Malaysia. A negative emission trend is derived in India (−0.4%), owing to the negative trend in solar radiation data associated to the strong dimming effect likely due to increasing aerosol loadings. The bottom-up emissions are evaluated using top-down isoprene emission estimates derived from inverse modelling constrained by GOME-2/MetOp-A formaldehyde columns through 2007–2012. The satellite-based estimates appear to support our assumptions, and confirm the lower emission rate in tropical forests of Indonesia and Malaysia. Additional flux measurements are clearly needed to better characterize the spatial variability of emission factors. Finally, a decreasing trend in the top-down Chinese emissions inferred after 2007, is in line with the cooling episode recorded in China after that year, thus suggesting that the satellite HCHO columns are able to capture climate-induced changes in emissions.

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Chen, Junping, Jungang Wang, Ahao Wang, Junsheng Ding, and Yize Zhang. "SHAtropE—A Regional Gridded ZTD Model for China and the Surrounding Areas." Remote Sensing 12, no. 1 (January 2, 2020): 165. http://dx.doi.org/10.3390/rs12010165.

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A regional zenith tropospheric delay (ZTD) empirical model, referred to as SHAtropE (SHanghai Astronomical observatory tropospheric delay model—Extended), is developed and provides tropospheric propagation delay corrections for users in China and the surrounding areas with improved accuracy. The SHAtropE model was developed based on the ZTD time series of the continuous GNSS sites from the Crustal Movement Observation Network of China (CMONOC) and GNSS sites of surrounding areas. It combines the exponential and periodical functions and is provided as regional grids with a resolution of 2.5° × 2.0° in longitude and latitude. At each grid point, the exponential function converts the ZTD from the site height to the ellipsoid, and the periodical terms, including both annual and semi-annual periods, describe ZTD’s temporal variation. Moreover, SHAtropE also provides the predicted ZTD uncertainty, which is valuable in Precise Point Positioning (PPP) with ZTD being constrained for faster convergence. The data of 310 GNSS sites over 7 years were used to validate the new model. Results show that the SHAtropE ZTD has an accuracy of 3.5 cm in root mean square (RMS) quantity, which has a mean improvement of 35.2% and 5.4% over the UNB3m (5.4 cm) and GPT3 (3.7 cm) models, respectively. The predicted uncertainty of SHAtropE ZTD shows seasonal variations, where the values are larger in summer than in winter. By applying the SHAtropE model in the static PPP, the convergence time of GPS-only and BDS-only solutions are reduced by 8.1% and 14.5% respectively compared to the UNB3m model, and the reductions are 6.9% and 11.2% respectively for the GPT3 model. As no meteorological data are required for the implementation of the model, the SHAtropE could thus be a refined tropospheric model for GNSS users in mainland China and the surrounding areas. The method of modeling the ZTD uncertainty can also be used in further global tropospheric delay modeling.

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Chartier, Alex T., Thomas R. Hanley, and Daniel J. Emmons. "Long-distance propagation of 162 MHz shipping information links associated with sporadic E." Atmospheric Measurement Techniques 15, no. 21 (November 8, 2022): 6387–93. http://dx.doi.org/10.5194/amt-15-6387-2022.

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Abstract. Sporadic E layers form in the daytime midlatitude ionosphere as a result of wind shears in the mesosphere–lower-thermosphere compressing metallic ions of meteoric origin into dense, narrow sheets extending over hundreds or thousands of kilometers spatially. These layers are poorly observed, being too narrow to be properly resolved by incoherent scatter radar or path-integrated total electron content measurements. Sporadic E layer peak densities can be resolved by ionosondes and by rocket-borne Langmuir probes, but these techniques have major limitations in terms of spatial and temporal coverage, and (for many ionosondes) maximum density resolution. As a result, the density, occurrence, and spatial extent of sporadic E layers are not well constrained by observations. The maximum density of sporadic E is widely believed to be around 5–10×1011 electrons m−3 NmEs (equivalent to 6–9 MHz foEs), though there are a few isolated reports of layers extending beyond 20 MHz (Chandra and Rastogi, 1975; Maeda and Heki, 2014). Here, we identify sporadic E layers using a huge database of 29 million 162 MHz automatic identification system (AIS) shipping transmissions collected over 3 d by a United States Coast Guard (USCG) terrestrial monitoring network in the eastern United States and Puerto Rico. Within this dataset, most (>99 %) links are explained by line-of-sight, surface-wave, and tropospheric propagation, but a small population cannot be explained by these mechanisms. In total, 6677 signals were identified from ships located over 1000 km from the ground stations between 13 and 14 July 2021, and almost no long-distance links were received at night or at any time on 15 July. This coincides with intense (saturated) sporadic E in collocated ionosondes and in satellite radio occultation data. The density of these layers might exceed 27 MHz foEs or 9×1012 electrons m−3 NmEs. AIS transmissions potentially provide an excellent means of identifying dense sporadic E layers globally.

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Chalmandrier, Loïc, Camille Albouy, Patrice Descombes, Brody Sandel, Soren Faurby, Jens-Christian Svenning, Niklaus E. Zimmermann, and Loïc Pellissier. "Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago." Royal Society Open Science 5, no. 3 (March 2018): 171366. http://dx.doi.org/10.1098/rsos.171366.

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Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns.

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Stavrakou, T., J. F. Müller, M. Bauwens, I. De Smedt, M. Van Roozendael, A. Guenther, M. Wild, and X. Xia. "Isoprene emissions over Asia 1979–2012: impact of climate and land-use changes." Atmospheric Chemistry and Physics 14, no. 9 (May 12, 2014): 4587–605. http://dx.doi.org/10.5194/acp-14-4587-2014.

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Abstract. Due to the scarcity of observational constraints and the rapidly changing environment in East and Southeast Asia, isoprene emissions predicted by models are expected to bear substantial uncertainties. The aim of this study is to improve upon the existing bottom-up estimates, and to investigate the temporal evolution of the fluxes in Asia over 1979–2012. To this purpose, we calculate the hourly emissions at 0.5°×0.5° resolution using the MEGAN–MOHYCAN model driven by ECMWF ERA-Interim climatology. In order to remedy for known biases identified in previous studies, and to improve the simulation of interannual variability and trends in emissions, this study incorporates (i) changes in land use, including the rapid expansion of oil palms, (ii) meteorological variability according to ERA-Interim, (iii) long-term changes in solar radiation (dimming/brightening) constrained by surface network radiation measurements, and (iv) recent experimental evidence that South Asian tropical forests are much weaker isoprene emitters than previously assumed, and on the other hand, that oil palms have a strong isoprene emission capacity. These effects lead to a significant lowering (factor of 2) in the total isoprene fluxes over the studied domain, and to emission reductions reaching a factor of 3.5 in Southeast Asia. The bottom-up annual isoprene emissions for 2005 are estimated at 7.0, 4.8, 8.3, and 2.9 Tg in China, India, Indonesia and Malaysia, respectively. The isoprene flux anomaly over the whole domain and studied period is found to be strongly correlated with the Oceanic Niño Index (r = 0.73), with positive (negative) anomalies related to El Niño (La Niña) years. Changes in temperature and solar radiation are the major drivers of the interannual variability and trends in the emissions, except over semi-arid areas such as northwestern China, Pakistan and Kazakhstan, where soil moisture is by far the main cause of interannual emission changes. In our base simulation, annual positive flux trends of 0.2% and 0.52% throughout the entire period are found in Asia and China, respectively, related to a positive trend in temperature and solar radiation. The impact of oil palm expansion in Indonesia and Malaysia is to enhance the trends over that region, e.g., from 1.17% to 1.5% in 1979–2005 in Malaysia. A negative emission trend is derived in India (−0.4%), owing to the negative trend in solar radiation data associated with the strong dimming effect likely due to increasing aerosol loadings. The bottom-up emissions are compared to field campaign measurements in Borneo and South China and further evaluated against top-down isoprene emission estimates constrained by GOME-2/MetOp-A formaldehyde columns through 2007–2012. The satellite-based estimates appear to support our assumptions, and confirm the lower emission rate in tropical forests of Indonesia and Malaysia. Additional flux measurements are clearly needed to characterize the spatial variability of emission factors better. Finally, a decreasing trend in the inferred top-down Chinese emissions since 2007 is in line with recorded cooling in China after that year, thus suggesting that the satellite HCHO columns are able to capture climate-induced changes in emissions.

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Yao, Q., B. Tan, and Y. Huang. "FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 937–44. http://dx.doi.org/10.5194/isprs-archives-xli-b3-937-2016.

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Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

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Yao, Q., B. Tan, and Y. Huang. "FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 937–44. http://dx.doi.org/10.5194/isprsarchives-xli-b3-937-2016.

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Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

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40

Seyedhashemi, Hanieh, Jean-Philippe Vidal, Jacob S. Diamond, Dominique Thiéry, Céline Monteil, Frédéric Hendrickx, Anthony Maire, and Florentina Moatar. "Regional, multi-decadal analysis on the Loire River basin reveals that stream temperature increases faster than air temperature." Hydrology and Earth System Sciences 26, no. 9 (May 17, 2022): 2583–603. http://dx.doi.org/10.5194/hess-26-2583-2022.

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Abstract. Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data and difficulty in parsing effects of hydroclimate and landscape variability. Here, we address these issues using the physically based thermal model T-NET (Temperature-NETwork) coupled with the EROS semi-distributed hydrological model to reconstruct past daily stream temperature and streamflow at the scale of the entire Loire River basin in France (105 km2 with 52 278 reaches). Stream temperature increased for almost all reaches in all seasons (mean =+0.38 ∘C decade−1) over the 1963–2019 period. Increases were greatest in spring and summer, with a median increase of + 0.38 ∘C (range =+0.11 to +0.76 ∘C) and +0.44 ∘C (+0.08 to +1.02 ∘C) per decade, respectively. Rates of stream temperature increases were greater than for air temperature across seasons for the majority of reaches. Spring and summer increases were typically greatest in the southern part of the Loire basin (up to +1 ∘C decade−1) and in the largest rivers (Strahler order ≥5). Importantly, air temperature and streamflow could exert a joint influence on stream temperature trends, where the greatest stream temperature increases were accompanied by similar trends in air temperature (up to +0.71 ∘C decade−1) and the greatest decreases in streamflow (up to −16 % decade−1). Indeed, for the majority of reaches, positive stream temperature anomalies exhibited synchrony with positive anomalies in air temperature and negative anomalies in streamflow, highlighting the dual control exerted by these hydroclimatic drivers. Moreover, spring and summer stream temperature, air temperature, and streamflow time series exhibited common change points occurring in the late 1980s, suggesting a temporal coherence between changes in the hydroclimatic drivers and a rapid stream temperature response. Critically, riparian vegetation shading mitigated stream temperature increases by up to 0.16 ∘C decade−1 in smaller streams (i.e. < 30 km from the source). Our results provide strong support for basin-wide increases in stream temperature due to joint effects of rising air temperature and reduced streamflow. We suggest that some of these climate change-induced effects can be mitigated through the restoration and maintenance of riparian forests.

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Vergoz, Julien, Yves Cansi, Yoann Cano, and Pierre Gaillard. "Analysis of Hydroacoustic Signals Associated to the Loss of the Argentinian ARA San Juan Submarine." Pure and Applied Geophysics 178, no. 7 (January 8, 2021): 2527–56. http://dx.doi.org/10.1007/s00024-020-02625-7.

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AbstractOn November 15, 2017, an event related to the disappearance of the Argentine military submarine ARA San Juan was detected by two hydrophone triplet stations of the IMS network, established to enforce the nuclear test ban treaty (CTBT). From the two direct hydroacoustic arrivals recorded at 6000 and 8000 km from the localized wreckage, calculated location based on hydroacoustic data only is poorly constrained, and the associated uncertainties are large. In an attempt to interpret the recorded signals, an air dropped calibration grenade was conducted by the Argentine Navy two weeks later, on December 1, 2017, near the last known position of the submarine. From the comparison of temporal and spectral features of both events, we confirm the impulsive nature of the San Juan event. Array processing was performed with a progressive multi-channel correlation method (PMCC). Fine propagation details of direct arrivals are very well resolved in time-frequency space and thirteen secondary arrivals are revealed for the San Juan event, within the fifteen minutes following direct arrivals. The detections presented in this paper were calculated with DTK-PMCC software embedded in the NDC-In-A-Box virtual machine, and can be reproduced by any CTBTO principal user (Member State user which can access raw waveform data and data bulletins). All the identified late arrivals are associated to reflections or refractions from seamounts, islands and the South American continental Slope. The accurate identification of all the reflectors allows to significantly improve the source location accuracy: 95% confidence ellipse area has been reduced by a factor of 100 compared to location obtained from direct arrivals only, and the estimated location is 3.5 km from the known location of the wreckage. The originality of the relocation method is that it is based on the joint inversion of both San Juan and calibration events unknown parameters, and from the selection of only a well-chosen subset of secondary arrivals. Its calculation did not require either the need of advanced oceanographic specifications, or sophisticated methods requiring heavy computational means. Finally, a detailed cepstral analysis of the direct and secondary arrivals has allowed to detect the existence of a second impulse (doublet) in the signals associated to both San Juan and calibration events. Unlike the calibration event, the anisotropic character of the delays measured from the San Juan cepstra suggests that the 15 November signal was generated by two impulsive acoustic sources closely separated in space and time over scales comparable to the size of the submarine. This study demonstrates the capability of the hydroacoustic component of the IMS network to accomplish its mission of Comprehensive Nuclear-Test-Ban Treaty monitoring.

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Wang, James S., S. Randolph Kawa, G. James Collatz, Motoki Sasakawa, Luciana V. Gatti, Toshinobu Machida, Yuping Liu, and Michael E. Manyin. "A global synthesis inversion analysis of recent variability in CO2 fluxes using GOSAT and in situ observations." Atmospheric Chemistry and Physics 18, no. 15 (August 9, 2018): 11097–124. http://dx.doi.org/10.5194/acp-18-11097-2018.

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Abstract. The precise contribution of the two major sinks for anthropogenic CO2 emissions, terrestrial vegetation and the ocean, and their location and year-to-year variability are not well understood. Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 are expected to benefit from the increasing measurement density brought by recent in situ and remote CO2 observations. We uniquely apply a batch Bayesian synthesis inversion at relatively high resolution to in situ surface observations and bias-corrected GOSAT satellite column CO2 retrievals to deduce the global distributions of natural CO2 fluxes during 2009–2010. The GOSAT inversion is generally better constrained than the in situ inversion, with smaller posterior regional flux uncertainties and correlations, because of greater spatial coverage, except over North America and northern and southern high-latitude oceans. Complementarity of the in situ and GOSAT data enhances uncertainty reductions in a joint inversion; however, remaining coverage gaps, including those associated with spatial and temporal sampling biases in the passive satellite measurements, still limit the ability to accurately resolve fluxes down to the sub-continental or sub-ocean basin scale. The GOSAT inversion produces a shift in the global CO2 sink from the tropics to the north and south relative to the prior, and an increased source in the tropics of ∼ 2 Pg C yr−1 relative to the in situ inversion, similar to what is seen in studies using other inversion approaches. This result may be driven by sampling and residual retrieval biases in the GOSAT data, as suggested by significant discrepancies between posterior CO2 distributions and surface in situ and HIPPO mission aircraft data. While the shift in the global sink appears to be a robust feature of the inversions, the partitioning of the sink between land and ocean in the inversions using either in situ or GOSAT data is found to be sensitive to prior uncertainties because of negative correlations in the flux errors. The GOSAT inversion indicates significantly less CO2 uptake in the summer of 2010 than in 2009 across northern regions, consistent with the impact of observed severe heat waves and drought. However, observations from an in situ network in Siberia imply that the GOSAT inversion exaggerates the 2010–2009 difference in uptake in that region, while the prior CASA-GFED model of net ecosystem production and fire emissions reasonably estimates that quantity. The prior, in situ posterior, and GOSAT posterior all indicate greater uptake over North America in spring to early summer of 2010 than in 2009, consistent with wetter conditions. The GOSAT inversion does not show the expected impact on fluxes of a 2010 drought in the Amazon; evaluation of posterior mole fractions against local aircraft profiles suggests that time-varying GOSAT coverage can bias the estimation of interannual flux variability in this region.

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Escribano, Jerónimo, Enza Di Tomaso, Oriol Jorba, Martina Klose, Maria Gonçalves Ageitos, Francesca Macchia, Vassilis Amiridis, et al. "Assimilating spaceborne lidar dust extinction can improve dust forecasts." Atmospheric Chemistry and Physics 22, no. 1 (January 14, 2022): 535–60. http://dx.doi.org/10.5194/acp-22-535-2022.

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Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.

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Baharani, Mohammadreza, and Hamed Tabkhi. "ATCN: Resource-Efficient Processing of Time Series on Edge." ACM Transactions on Embedded Computing Systems, March 21, 2022. http://dx.doi.org/10.1145/3524070.

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This paper presents a scalable deep learning model called Agile Temporal Convolutional Network (ATCN) for high-accurate fast classification and time series prediction in resource-constrained embedded systems. ATCN is a family of compact networks with formalized hyperparameters that enable application-specific adjustments to be made to the model architecture. It is primarily designed for embedded edge devices with very limited performance and memory, such as wearable biomedical devices and real-time reliability monitoring systems. ATCN makes fundamental improvements over the mainstream temporal convolutional neural networks, including residual connections to increase the network depth and accuracy, and the incorporation of separable depth-wise convolution to reduce the computational complexity of the model. As part of the present work, two ATCN families, namely T0, and T1 are also presented and evaluated on different ranges of embedded processors - Cortex-M7 and Cortex-A57 processor. An evaluation of the ATCN models against the best-in-class InceptionTime and MiniRocket shows that ATCN almost maintains accuracy while improving the execution time on a broad range of embedded and cyber-physical applications with demand for real-time processing on the embedded edge. At the same time, in contrast to existing solutions, ATCN is the first time-series classifier based on deep learning that can be run bare-metal on embedded microcontrollers (Cortex-M7) with limited computational performance and memory capacity while delivering state-of-the-art accuracy.

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Hu, Yuzhang, Wenhan Yang, Jiaying Liu, and Zongming Guo. "Deep Inter Prediction with Error-Corrected Auto-Regressive Network for Video Coding." ACM Transactions on Multimedia Computing, Communications, and Applications, April 2022. http://dx.doi.org/10.1145/3528173.

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Modern codecs remove temporal redundancy of a video via inter prediction, i.e. searching previously coded frames for similar blocks and storing motion vectors to save bit-rates. However, existing codecs adopt block-level motion estimation, where a block is regressed by reference blocks linearly and is doomed to fail to deal with non-linear motions. In this paper, we generate virtual reference frames with previously reconstructed frames via deep networks to offer an additional candidate, which is not constrained to linear motion structure and further significantly improves coding efficiency. More specifically, we propose a novel deep Auto-Regressive Moving-Average (ARMA) model, Error-Corrected Auto-Regressive Network (ECAR-Net), equipped with the powers of the conventional statistic ARMA models and deep networks jointly for reference frame prediction. Similar to conventional ARMA models, the ECAR-Net consists of two stages: Auto-Regression (AR) stage and Error-Correction (EC) stage, where the first part predicts the signal at the current time-step based on previously reconstructed frames while the second one compensates for the output of the AR stage to obtain finer details. Different from the statistic AR models only focusing on short-term temporal dependency, the AR model of our ECAR-Net is further injected with the long-term dynamics mechanism, where long temporal information is utilized to help predict motions more accurately. Furthermore, ECAR-Net works in a configuration-adaptive way, i.e. using different dynamics and error definitions for the Low Delay B and Random Access configurations, which helps improve the adaptivity and generality in diverse coding scenarios. With the well-designed network, our method surpasses HEVC on average \(5.0\% \) and \(6.6\% \) BD-rate saving for the luma component under the Low Delay B and Random Access configurations and also obtains on average \(1.54\% \) BD-rate saving over VVC. Furthermore, ECAR-Net works in a configuration-adaptive way, i.e. using different dynamics and error definitions for the Low Delay B and Random Access configurations, which helps improve the adaptivity and generality in diverse coding scenarios.

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46

Stocker, Julia Elina, Erfan Nozari, Marieke van Vugt, Andreas Jansen, and Hamidreza Jamalabadi. "Network controllability measures of subnetworks: implications for neurosciences." Journal of Neural Engineering, January 12, 2023. http://dx.doi.org/10.1088/1741-2552/acb256.

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Abstract Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand psychiatric disorders, a potentially large set of related variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics estimated over subnetworks would deviate, if access to the complete set of variables, as is common in neurosciences, cannot be taken for granted. In this paper, we use a host of simulations based on synthetic as well as structural MRI data to study the potential deviation of controllability metrics in sub- compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density. We find out, across all network types we test, that average and modal controllability are systematically, over- or underestimated depending on the number of nodes in the sub- and full network and the edge density. Finally, we provide formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.

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Ozsanlav-Harris, Luke, Larry R. Griffin, Mitch D. Weegman, Lei Cao, Geoff M. Hilton, and Stuart Bearhop. "Wearable reproductive trackers: quantifying a key life history event remotely." Animal Biotelemetry 10, no. 1 (August 10, 2022). http://dx.doi.org/10.1186/s40317-022-00298-8.

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AbstractAdvancements in biologging technology allow terabytes of data to be collected that record the location of individuals but also their direction, speed and acceleration. These multi-stream data sets allow researchers to infer movement and behavioural patterns at high spatiotemporal resolutions and in turn quantify fine-scale changes in state along with likely ecological causes and consequences. The scope offered by such data sets is increasing and there is potential to gain unique insights into a suite of ecological and life history phenomena. We use multi-stream data from global positioning system (GPS) and accelerometer (ACC) devices to quantify breeding events remotely in an Arctic breeding goose. From a training set of known breeders we determine the movement and overall dynamic body acceleration patterns indicative of incubation and use these to classify breeding events in individuals with unknown reproductive status. Given that researchers are often constrained by the amount of biologging data they can collect due to device weights, we carry out a sensitivity analysis. Here we explore the relative merits of GPS vs ACC data and how varying the temporal resolution of the data affects the accuracy of classifying incubation for birds. Classifier accuracy deteriorates as the temporal resolution of GPS and ACC are reduced but the reduction in precision (false positive rate) is larger in comparison to recall (false negative rate). Precision fell to 94.5%, whereas recall didn’t fall below 98% over all sampling schedules tested. Our data set could have been reduced by c.95% while maintaining precision and recall > 98%. The GPS-only classifier generally outperformed the ACC-only classifier across all accuracy metrics but both performed worse than the combined GPS and ACC classifier. GPS and ACC data can be used to reconstruct breeding events remotely, allowing unbiased, 24-h monitoring of individuals. Our resampling-based sensitivity analysis of classifier accuracy has important implications with regards to both device design and sampling schedules for study systems, where device size is constrained. It will allow researchers with similar aims to optimize device battery, memory usage and lifespan to maximise the ability to correctly quantify life history events.

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Fang, Cheng, Peng Yu, and Brian Williams. "Chance-Constrained Probabilistic Simple Temporal Problems." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 1 (June 21, 2014). http://dx.doi.org/10.1609/aaai.v28i1.9048.

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Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.

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Fontanier, Vincent, Matthieu Sarazin, Frederic M. Stoll, Bruno Delord, and Emmanuel Procyk. "Inhibitory control of frontal metastability sets the temporal signature of cognition." eLife 11 (May 30, 2022). http://dx.doi.org/10.7554/elife.63795.

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Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas’ specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

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Zhu, Xinting, Yu Lin, Yuxin He, Kwok-Leung Tsui, Pak Wai Chan, and Lishuai Li. "Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network." Frontiers in Artificial Intelligence 5 (June 13, 2022). http://dx.doi.org/10.3389/frai.2022.884485.

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With the dynamic air traffic demand and the constrained capacity resources, accurately predicting airport throughput is essential to ensure the efficiency and resilience of air traffic operations. Many research efforts have been made to predict traffic throughputs or flight delays at an airport or over a network. However, it is still a challenging problem due to the complex spatiotemporal dynamics of the highly interacted air transportation systems. To address this challenge, we propose a novel deep learning model, graph attention neural network stacking with a Long short-term memory unit (GAT-LSTM), to predict the short-term airport throughput over a national air traffic network. LSTM layers are included to extract the temporal correlations in the data, while the graph attention mechanism is used to capture the spatial dependencies. For the graph attention mechanism, two graph modeling methods, airport-based graph and OD-pair graph are explored in this study. We tested the proposed model using real-world air traffic data involving 65 major airports in China over 3 months in 2017 and compared its performance with other state-of-the-art models. Results showed that the temporal pattern was the dominate factor, compared to the spatial pattern, in predicting airport throughputs over an air traffic network. Among the prediction models that we compared, both the proposed model and LSTM performed well on prediction accuracy over the entire network. Better performance of the proposed model was observed when focusing on airports with larger throughputs. We also conducted an analysis on model interpretability. We found that spatiotemporal correlations in the data were learned and shown via the model parameters, which helped us to gain insights into the topology and the dynamics of the air traffic network.

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