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Journal articles on the topic 'Deployment error estimation'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Gutierrez, Edgar Andres, Ivan Fernando Mondragon, Julian D. Colorado, and Diego Mendez Ch. "Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations." Plants 11, no. 13 (2022): 1636. http://dx.doi.org/10.3390/plants11131636.

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This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a

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Lin, Feilong, Wenbai Li, and Liyong Yuan. "Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks." Sensors 18, no. 10 (2018): 3338. http://dx.doi.org/10.3390/s18103338.

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Process parameter estimation, to a large extent, determines the industrial production quality. However, limited sensors can be deployed in a traditional wired manner, which results in poor process parameter estimation in hostile environments. Industrial wireless sensor networks (IWSNs) are techniques that enrich sampling points by flexible sensor deployment and then purify the target by collaborative signal denoising. In this paper, the process industry scenario is concerned, where the workpiece is transferred on the belt and the parameter estimate is required before entering into the next pro

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Rolling, Craig A., Yuhong Yang, and Dagmar Velez. "COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS." Econometric Theory 35, no. 6 (2018): 1089–110. http://dx.doi.org/10.1017/s0266466618000397.

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Estimating a treatment’s effect on an outcome conditional on covariates is a primary goal of many empirical investigations. Accurate estimation of the treatment effect given covariates can enable the optimal treatment to be applied to each unit or guide the deployment of limited treatment resources for maximum program benefit. Applications of conditional treatment effect estimation are found in direct marketing, economic policy, and personalized medicine. When estimating conditional treatment effects, the typical practice is to select a statistical model or procedure based on sample data. Howe

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Liu, Yu-Sun, Shingchern You, and Yu-Chun Lai. "Machine Learning-Based Channel Estimation Techniques for ATSC 3.0." Information 15, no. 6 (2024): 350. http://dx.doi.org/10.3390/info15060350.

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Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances wit

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Hsiao, Chiu-Han, Frank Yeong-Sung Lin, Hao-Jyun Yang, et al. "Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors." Sensors 21, no. 21 (2021): 7121. http://dx.doi.org/10.3390/s21217121.

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As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below

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Park, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho, and Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction." Canadian Journal of Civil Engineering 36, no. 4 (2009): 580–91. http://dx.doi.org/10.1139/l08-129.

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With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link

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Madsen, Tatiana, Hans-Peter Schwefel, Lars Mikkelsen, and Annelore Burggraf. "Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data." Sensors 22, no. 11 (2022): 4111. http://dx.doi.org/10.3390/s22114111.

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Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccu

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McLoughlin, Benjamin, Harry Pointon, John McLoughlin, Andy Shaw, and Frederic Bezombes. "Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments." Sensors 18, no. 7 (2018): 2274. http://dx.doi.org/10.3390/s18072274.

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Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisa

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Rathore, Kapil Singh, Sricharan Vijayarangan, Preejith SP, and Mohanasankar Sivaprakasam. "A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation." Sensors 23, no. 3 (2023): 1599. http://dx.doi.org/10.3390/s23031599.

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Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimat

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Aljohani, Nader, Tierui Zou, Arturo S. Bretas, and Newton G. Bretas. "Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers." Applied Sciences 11, no. 10 (2021): 4419. http://dx.doi.org/10.3390/app11104419.

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In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions conside

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He, Peng, Quan Zhou, Libing Bai, Songlin Xie, and Weijing Zhang. "A Current Sharing State Estimation Method of Redundant Switched-Mode Power Supply Based on LSTM Neural Network." Applied Sciences 12, no. 7 (2022): 3303. http://dx.doi.org/10.3390/app12073303.

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Redundant Switched-mode Power supplies (SMPSs) are commonly used to improve electronic systems’ reliability, and accurate estimation of the current sharing state is significant for evaluating the system’s health. Currently, the current sharing state estimation is mainly realized by using current sensors to detect each branch’s current, and the deployment and maintenance costs are high. In this paper, a method for power supply current sharing state estimation based on LSTM recurrent neural network is proposed. By taking advantage of subtle differences in the inherent spectral characteristics of

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Ukani, Neema Amish, and Saurabh S. Chakole. "Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective." Journal of Physics: Conference Series 2327, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2327/1/012026.

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Abstract Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiomete

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Le, Nam Tuan, and Yeong Min Jang. "Photography Trilateration Indoor Localization with Image Sensor Communication." Sensors 19, no. 15 (2019): 3290. http://dx.doi.org/10.3390/s19153290.

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Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the a

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Gupta, Akarsh, Yashwant Kashyap, and Panagiotis Kosmopoulos. "Tether Force Estimation Airborne Kite Using Machine Learning Methods." Wind 5, no. 1 (2025): 5. https://doi.org/10.3390/wind5010005.

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This paper explores the potential of Airborne Wind Energy Systems to revolutionize wind energy generation, demonstrating advancements over current methods. Through a series of controlled field experiments and the application of classical machine learning techniques, we achieved significant improvements in tether force estimation. Our XGBoost model, for example, demonstrated a notable reduction in error in predicting the tether force that can be extracted at a particular location, with a root mean square error of 52.3 Newtons and a mean absolute error of 32.1 Newtons, coupled with a R2 error, w

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Wang, Rui, Xikang Xu, Qi Zhou, et al. "State of Health Estimation for Lithium-Ion Batteries Using Enhanced Whale Optimization Algorithm for Feature Selection and Support Vector Regression Model." Processes 13, no. 1 (2025): 158. https://doi.org/10.3390/pr13010158.

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Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperatur

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Son, Younggill, and Woongchul Choi. "Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach." Batteries 10, no. 6 (2024): 191. http://dx.doi.org/10.3390/batteries10060191.

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With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at

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Mottakin, Khairul, Kiran Davuluri, Mark Allison, and Zheng Song. "SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization." Sensors 24, no. 19 (2024): 6327. http://dx.doi.org/10.3390/s24196327.

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Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with sm

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Wu, Yufei, Xiaofei Ruan, Yu Zhang, Huang Zhou, Shengyu Du, and Gang Wu. "Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision." Symmetry 11, no. 4 (2019): 585. http://dx.doi.org/10.3390/sym11040585.

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The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the p

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Ai, Xiaofeng, Yuqing Zheng, Zhiming Xu, and Feng Zhao. "Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network." Remote Sensing 14, no. 4 (2022): 1006. http://dx.doi.org/10.3390/rs14041006.

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Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effe

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Vidal-Valladares, Matías G., and Marcos A. Díaz. "A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats." Remote Sensing 14, no. 5 (2022): 1101. http://dx.doi.org/10.3390/rs14051101.

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This article presents a feasibility analysis to remotely estimate the geo-location of a femto-satellite only using two station-CubeSats and the communication link between the femto-satellite and each CubeSat. The presented approach combines the Time Difference Of Arrival (TDOA) and Angle Of Arrival (AOA) methods. We present the motivation, the envisioned solution together with the constraints for reaching it, and the best potential sensitivity of the location precision for different (1) deployment scenarios of the femto-satellite, (2) precisions in the location of the CubeSats, and (3) precisi

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Thimmaiah, Sudha H., and Mahadevan G. "A Radio Signal Strength Based Localization Error Optimization Technique for Wireless Sensor Network." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 839. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp839-847.

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Wireless Sensor Networks (WSN) is useful in collecting data from various sensor devices that are distributed over a network which is generally positioned in a stationary manner. Wireless sensor based communication system is an ever growing sector in the industry of communication. Wireless infrastructure is a network that enables correspondence between various devices associated through an infrastructure protocol. Finding the position or location of sensor node (Localization) is an important factor in sensor network for proving efficient service to end user. The existing technique proposed so f

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Sudha, H. Thimmaiah, and G. Mahadevan. "A Radio Signal Strength Based Localization Error Optimization Technique for Wireless Sensor Network." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 839–47. https://doi.org/10.11591/ijeecs.v11.i3.pp839-847.

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Wireless Sensor Networks (WSN) is useful in collecting data from various sensor devices that are distributed over a network which is generally positioned in a stationary manner. Wireless sensor based communication system is an ever growing sector in the industry of communication. Wireless infrastructure is a network that enables correspondence between various devices associated through an infrastructure protocol. Finding the position or location of sensor node (Localization) is an important factor in sensor network for proving efficient service to end user. The existing technique proposed so f

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Mallary, C., C. J. Berg, John R. Buck, and Amit Tandon. "Detection and estimation of rainfall from broadband acoustic signals." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A97. http://dx.doi.org/10.1121/10.0018293.

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Jeff Nystuen contributed major advances in our understanding of the physical acoustics of rain falling on the ocean, culminating in his pioneering work quantifying rainfall from the shape of the acoustic spectrum. Ma and Nystuen [JAOT (2005)] exploited Vagle’s wind spectrum to self-calibrate hydrophones for long deployments as acoustic rain gauges. Their algorithm predominantly relied on 3 narrowband frequencies to detect rainfall, and correlated the rainfall amount with the power spectral density (PSD) at 5kHz. Recent research at UMass Dartmouth built upon the foundational work of Nystuen, Ma

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Li, Ling, Ruiwen Gu, Chongyang Wang, Junliang Xing, Xinchun Yu, and Xiao-Ping Zhang. "Multi-View 3D Human Pose Estimation with Weakly Synchronized Images." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 4833–41. https://doi.org/10.1609/aaai.v39i5.32511.

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Multi-view 3D human pose estimation (MHPE) is an important research task in computer vision. To maintain consistency during the data collection, hardware synchronization devices are commonly used to connect cameras, ensuring that images from different views are captured simultaneously. However, synchronizing with extra devices has two apparent limitations: the hardware is i) usually expensive and ii) less flexible for deployment in outdoor open scenarios. Suppose the model can improve its tolerance for the time differences in multi-view image capture. In that case, the difficulty and cost of d

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Nakutis, Žilvinas, and Paulius Kaškonas. "A Contemplation on Electricity Meters In-Service Surveillance Assisted by Remote Error Monitoring." Energies 13, no. 20 (2020): 5245. http://dx.doi.org/10.3390/en13205245.

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In this paper, remote error monitoring techniques for electricity meters are overviewed suggesting their utilization for in-service surveillance assistance. It is discussed that in-service error observation could provide valuable input, contributing to the timely detection of batches of meters reaching nonconformance status. The payback period analysis of the deployment of a remote error monitoring solution is considered. However, it is pointed out that such an analysis lacks input information describing the relationship between the remote monitoring system’s performance and its ability to det

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Onakoya, Adegbemi Babatunde, and Hassan Akolade Alayande. "Macroeconomic Variables, the Oil, and the Agricultural Sectors in Nigeria." Asian Social Science 16, no. 1 (2019): 69. http://dx.doi.org/10.5539/ass.v16n1p69.

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The present study examined the impact of the macroeconomic variables and the oil sector on the performance of the agricultural sector between 1981and 2017 in Nigeria. The study adopted a three-stage estimation approach. The initial step in this estimation was the conduct of descriptive statistics and stationarity tests of the variables. Some of the series were stationary at level and some others at the first difference which informed the deployment of the Auto regressive distributed lag (ARDL) technique for model estimation. The third stage was the post-estimation of the model in order ascerta

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Kaboli, Ali, Farzad Dadras Javan, Italo Aldo Campodonico Avendano, et al. "Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation." Energies 17, no. 17 (2024): 4401. http://dx.doi.org/10.3390/en17174401.

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This study proposes a simulation-based methodology for estimating the energy saving achievable through the implementation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump). In this methodology, the operation of the heating supply unit each day is initiated at a different time, aiming at achieving the desired setpoint upon (and not before) the expected arrival of the occupants. It requires the estimation of the ramp-up duration (the time it takes the heating system to bring the indoor temperature to the desired setpoint), which can be provided by machine lea

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Benallal, Abdellah, Nawal Cheggaga, Amine Hebib, and Adrian Ilinca. "LSTM-Based State-of-Charge Estimation and User Interface Development for Lithium-Ion Battery Management." World Electric Vehicle Journal 16, no. 3 (2025): 168. https://doi.org/10.3390/wevj16030168.

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State-of-charge (SOC) estimation is pivotal in optimizing lithium-ion battery management systems (BMSs), ensuring safety, performance, and longevity across various applications. This study introduces a novel SOC estimation framework that uniquely integrates Long Short-Term Memory (LSTM) neural networks with Hyperband-driven hyperparameter optimization, a combination not extensively explored in the literature. A comprehensive experimental dataset is created using data of LG 18650HG2 lithium-ion batteries subjected to diverse operational cycles and thermal conditions. The proposed framework demo

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Hidayah, Entin, Gusfan Halik, and Minarni Nur Trilita. "Rain Station Network Analysis in the Sampean Watershed: Comparison of Variations in Data Aggregation." Geosfera Indonesia 7, no. 1 (2022): 96. http://dx.doi.org/10.19184/geosi.v7i1.29160.

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The lack of rainfall-runoff accuracy is important for some applications. The choice of data aggregation that affects the estimation results is important at the level of accuracy. Some commonly used aggregations are daily, ten days, and monthly rainfall. This study aimed to compare the results of the estimation of the effect of data aggregation and to analyze the density of the rain gauge network in the Sampean watershed. The evaluation of the rain station network is carried out through the Kagan calculation. Rainfall data are from the rainfall data records for 20 years at 33 rain gauge station

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Wang, Meiling, Zhuoyue Cai, Yifan Chen, Shangqing Yang, Leilei Chen, and Qingsong Hu. "Shrimp Counting Algorithm Using a Small-Scale Labeling Model." Electronics 13, no. 23 (2024): 4737. https://doi.org/10.3390/electronics13234737.

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This study presents an algorithm for shrimp counting using a small-scale labeling model. The aim is to enhance shrimp farming efficiency, reduce biosecurity risks, and minimize manual counting errors. Experimental evaluations were conducted using Litopenaeus vannamei samples divided into four growth stages. A comparison was made between the classical threshold segmentation method, small-scale labeling density estimation based on FamNet, and our proposed small-scale labeling density estimation based on FamNet-S models. The results showed that the proposed FamNet-S-based small-scale labeling den

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Ni, Bo, Yu Wang, Lu Cheng, Erik Blasch, and Tyler Derr. "Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12417–25. https://doi.org/10.1609/aaai.v39i12.33353.

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Recently, Knowledge Graphs (KGs) have been successfully coupled with Large Language Models (LLMs) to mitigate their hallucinations and enhance their reasoning capability, e.g., KG-based retrieval-augmented framework. However, current KG-LLM frameworks lack rigorous uncertainty estimation, limiting their reliable deployment in applications where the cost of errors is significant. Directly incorporating uncertainty quantification into KG-LLM frameworks presents a challenge due to their more complex architectures and the intricate interactions between the knowledge graph and language model compon

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Tasadduq, Imran A., and Muhammad Rashid. "Toward Intelligent Underwater Acoustic Systems: Systematic Insights into Channel Estimation and Modulation Methods." Electronics 14, no. 15 (2025): 2953. https://doi.org/10.3390/electronics14152953.

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Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than

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Hammouda, Marwan, Anna Maria Vegni, and Valeria Loscrí. "On the Noise Effect of Fingerprinting-Based Positioning Error in Underwater Visible Light Networks." Sensors 21, no. 16 (2021): 5398. http://dx.doi.org/10.3390/s21165398.

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This paper assesses the performance of a localization technique for underwater visible light networks. The proposed approach is based on a fingerprinting technique, collecting the channel impulse responses from different wireless optical signals in the visible range. A local database related to the power level distribution within a maritime environment is built and exploited to estimate user position, e.g., a diver moving in a given space for underwater fish monitoring. In this paper, we investigate on the noise effect on the localization accuracy in underwater scenarios and for different wate

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Zhang, Zhe, ChenLu Shi, Xiao Lv, and ZiHong Ling. "Active control of interior road noise using the remote microphone technique." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 7 (2023): 916–20. http://dx.doi.org/10.3397/in_2022_0130.

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A multichannel feedforward headrest system for the active control of interior road noise in a vehicle cabin is built. The remote microphone technique is applied, which enables the estimation of the sound pressure responses at the passenger's ear positions without direct deployment of error microphones there. The optimal observation filter for the remote microphone technique is formulated in a so-called training stage using signals measured at two error microphones on the passenger's ears and an array of four to five monitoring microphones on the headrest, passenger seat and vehicle ceiling. Th

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Zhao, Licheng, Wei Guo, Jian Wang, et al. "An Efficient Method for Estimating Wheat Heading Dates Using UAV Images." Remote Sensing 13, no. 16 (2021): 3067. http://dx.doi.org/10.3390/rs13163067.

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Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observationa

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Deepika, Deepika, Om Prakash Sangwan, and Jai Bhagwan. "SaaS reusability assessment using machine learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 2123. https://doi.org/10.11591/ijai.v14.i3.pp2123-2131.

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With the aid of internet, cloud computing offers hardware and software resources as cloud service. Cloud computing is developing as an effective criterion for reusing. Software-as-a-service (SaaS) is one of the three cloud deployment models that provide on-demand software on a charge basis. A reusability model for SaaS needs to be developed in order to increase its benefits and effectively help end users. A product’s ability to be reused is essential to its easy and effective development. We have presented a software reusability estimation model in this paper. We have assessed the SaaS reusabi

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T P, Mohankumar. "Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 5, no. 1 (2024): 1–8. http://dx.doi.org/10.54060/jieee.2024.99.

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Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been propo

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Ravi Saharan. "Analysis of Crowd using CNN with Physical distance Status." Journal of Information Systems Engineering and Management 10, no. 38s (2025): 41–49. https://doi.org/10.52783/jisem.v10i38s.6820.

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Crowd analysis plays a critical role in public safety, resource allocation, and effective crowd management during large gatherings. This paper presents an enhanced convolutional neural network (CNN)-based model for crowd estimation that incorporates physical distance status, a key parameter for post-pandemic safety requirements. The proposed approach performs crowd density classification and individual count estimation, categorizing crowd levels into four classes based on count and proximity. The model is evaluated on the NWPU-Crowd dataset using standard performance metrics including accuracy

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Li, Y., L. Wang, J. Liu, P. Zhang, and Y. Lu. "ACCURACY EVALUATION OF MULTI-GNSS DOPPLER VELOCITY ESTIMATION USING ANDROID SMARTPHONES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 89–96. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-89-2022.

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Abstract. The number of smartphone users has increased dramatically as smartphones have become more widely available. The mass-market urgently requires the deployment of low-cost GNSS chips in smartphones to achieve continuous, high-precision velocity and location services. Currently, most GNSS velocity determination research relies on traditional geodetic receivers and focuses on comparing velocity determination methods and analyzing velocity determination influencing factors, while research into the direction of multi-GNSS velocity determination for smartphones is insufficient. To solve this

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Ortega, Andres, and Velio Tralli. "QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks." Sensors 22, no. 12 (2022): 4545. http://dx.doi.org/10.3390/s22124545.

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The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows t

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Alghanem, Hussah, and Alastair Buckley. "Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country." Energies 17, no. 8 (2024): 1812. http://dx.doi.org/10.3390/en17081812.

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Setting solar photovoltaic capacity targets and implementing supportive policies is a widespread strategy among nations aiming to achieve decarbonisation goals. However, policy implementation without a thorough understanding of the intricate relationship between social, economic, and land-use factors and solar photovoltaic deployment can lead to unintended consequences, including over- or underdeployment and failure to reach targets. To address this challenge, an investigation was conducted into the relationship between 36 factors and solar photovoltaic deployment across 143 countries from 200

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Hervis Santana, Yosvany, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, Wout Joseph, and David Plets. "Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation." Applied Sciences 12, no. 8 (2022): 3923. http://dx.doi.org/10.3390/app12083923.

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Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming,

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Rassoul, Abdelkader, Abderrahmane Belguerna, Hamza Daoudi, Zouaoui Chikr Elmezouar, and Fatimah Alshahrani. "On the Exact Asymptotic Error of the Kernel Estimator of the Conditional Hazard Function for Quasi-Associated Functional Variables." Mathematics 13, no. 13 (2025): 2172. https://doi.org/10.3390/math13132172.

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The goal of this research is to analyze the mean squared error (MSE) of the kernel estimator for the conditional hazard rate, assuming that the sequence of real random vector variables (Un)n∈N satisfies the quasi-association condition. By employing kernel smoothing techniques and asymptotic analysis, we derive the exact asymptotic expression for the leading terms of the quadratic error, providing a precise characterization of the estimator’s convergence behavior. In addition to the theoretical derivations and a controlled simulation study that validates the asymptotic properties, this work inc

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Jiang, Ye, Shuyan Xiao, Jian Liu, et al. "A Deterministic Sensor Deployment Method for Target Coverage." Journal of Sensors 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/2343891.

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In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in

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Yu, Yun-Shuai, and Yeong-Sheng Chen. "A Measurement-Based Frame-Level Error Model for Evaluation of Industrial Wireless Sensor Networks." Sensors 20, no. 14 (2020): 3978. http://dx.doi.org/10.3390/s20143978.

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Industrial wireless sensor networks (IWSNs) are a key technology for smart manufacturing. To identify the performance bottlenecks in an IWSN before its real-world deployment, the IWSN must first be evaluated through simulations using an error model which accurately characterizes the wireless links in the industrial scenario within which it will be deployed. However, the traditional error models used in most IWSN simulators are not derived from the real traces observed in industrial environments. Accordingly, this study first measured the transmission quality of IEEE 802.15.4 in a one-day exper

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Abdullah, Cheman Mohammed, and Omar S. Abdulkareem. "Blood Pressure Prediction Using Deep Learning." Asian Journal of Research in Computer Science 18, no. 6 (2025): 130–46. https://doi.org/10.9734/ajrcos/2025/v18i6685.

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Although traditional cuff-based blood pressure (BP) monitoring is sporadic and laborious, BP is essential for cardiovascular health. We review deep learning approaches for cuff-less blood pressure estimation, such as CNNs, RNNs, Transformer models, and attention processes, and provide two new PPG-to-ABP waveform synthesis methods. The first (ASBP) maps one-dimensional PPG signals into arterial blood pressure waveforms using a VGG-16 encoder-decoder, while the second (SEANet) uses causal dilated convolutions in a calibration-free framework for continuous blood pressure estimation. Using correla

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Li, Mengqian, Mingduo Li, Jinhua Wang, Aoze Duan, Haotian Sun, and Qinggang Meng. "Optimization of a Time-of-Arrival-Ridge Estimation Iterative Model for Ultra-Wideband Positioning in a Long Linear Area." Sensors 25, no. 7 (2025): 2229. https://doi.org/10.3390/s25072229.

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Ultra-wideband (UWB) technology is widely used for high-precision indoor positioning due to its adaptability to various environments. However, in long linear areas, such as tunnels or corridors, the near-linear deployment of base stations caused by structural constraints significantly degrades UWB localization accuracy, rendering conventional algorithms ineffective. To address this issue, this study proposes a high-precision UWB+TOA-R positioning algorithm that incorporates Ridge estimation as a constraint condition. The algorithm introduces equivalent weights to refine the iterative computati

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Deng, Zhian, Jianxiang Feng, Shengao Wang, Zhiyu Qu, Jiahao Zhang, and Weijian Si. "An accurate and easy deployment array gain-phase error calibration method for DoA estimation in Wi-Fi network." Ad Hoc Networks 112 (March 2021): 102355. http://dx.doi.org/10.1016/j.adhoc.2020.102355.

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Chia, Harmon Lee Bruce. "Quantum computing and its revolutionary potential." Advances in Engineering Innovation 4, no. 1 (2023): 26–32. http://dx.doi.org/10.54254/2977-3903/4/2023022.

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The rapid emergence of quantum computing offers the potential to revolutionize numerous domains, promising computational advantages over classical counterparts. This study aimed to evaluate the performance, efficiency, and robustness of selected quantum algorithmsQuantum Variational Eigensolver (VQE), Quantum Fourier Transform (QFT), and Quantum Phase Estimation (QPE)on near-term quantum devices. Our benchmarking revealed that, despite promising theoretical benefits, the practical deployment of these algorithms remains challenged by noise, error rates, and hardware limitations. The VQE showed

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Narata, Ana Paula, Jordi Blasco, Luis San Roman, et al. "Early Results in Flow Diverter Sizing by Computational Simulation: Quantification of Size Change and Simulation Error Assessment." Operative Neurosurgery 15, no. 5 (2018): 557–66. http://dx.doi.org/10.1093/ons/opx288.

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Abstract BACKGROUND Sizing of flow diverters (FDs) stent in the treatment of intracranial aneurysms is a challenging task due to the change of stent length after implantation. OBJECTIVE To quantify the size change and assess the error in length prediction in 82 simulated FD deployments. METHODS Eighty-two consecutive patients treated with FDs were retrospectively analyzed. Implanted FD length was measured from angiographic images and compared to the nominal sizes of the implanted device. Length change was obtained by subtracting the nominal length from the real length and dividing by the nomin

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