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Journal articles on the topic 'Novel recovery algorithms'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Liang, Rui Hua, Xin Peng Du, Qing Bo Zhao, and Li Zhi Cheng. "Sparse Signal Recovery Based on Simulated Annealing." Applied Mechanics and Materials 321-324 (June 2013): 1295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1295.

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Sparse signal recovery is a hot topic in the fields of optimization theory and signal processing. Two main algorithmic approaches, i.e. greedy pursuit algorithms and convex relaxation algorithms have been extensively used to solve this problem. However, these algorithms cannot guarantee to find the global optimum solution, and then they perform poorly when the sparsity level is relatively large. Based on the simulated annealing algorithm and greedy pursuit algorithms, we propose a novel algorithm on solving the sparse recovery problem. Numerical simulations show that the proposed algorithm has

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Zhu, Ying, Yong Xing Jia, Chuan Zhen Rong, and Yu Yang. "Study on Compressed Sensing Recovery Algorithms." Applied Mechanics and Materials 433-435 (October 2013): 322–25. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.322.

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Abastruct. Compressive sensing is a novel signal sampling theory under the condition that the signalis sparse or compressible.In this case,the small amount of signal values can be reconstructed when signal is sparse or compressible.This paper has reviewed the idea of OMP,GBP and SP,given algorithms and analyzed the experiment results,suggested some improvements.

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Battiston, Adrian, Inna Sharf, and Meyer Nahon. "Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle." International Journal of Robotics Research 38, no. 10-11 (2019): 1286–306. http://dx.doi.org/10.1177/0278364919867397.

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An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned

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An, Qi, Li Wang та Nana Zhang. "Novel Iterative Reweighted ℓ1 Minimization for Sparse Recovery". Mathematics 13, № 8 (2025): 1219. https://doi.org/10.3390/math13081219.

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Data acquisition and high-dimensional signal processing often require the recovery of sparse representations of signals to minimize the resources needed for data collection. ℓp quasi-norm minimization excels in exactly reconstructing sparse signals from fewer measurements, but it is NP-hard and challenging to solve. In this paper, we propose two distinct Iteratively Re-weighted ℓ1 Minimization (IRℓ1) formulations for solving this non-convex sparse recovery problem by introducing two novel reweighting strategies. These strategies ensure that the ϵ-regularizations adjust dynamically based on the

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Wang, Runsong, Xuelian Li, Juntao Gao, Hui Li, and Baocang Wang. "Quantum rotational cryptanalysis for preimage recovery of round-reduced Keccak." Quantum Information & Computation 23, no. 3&4 (2023): 223–34. http://dx.doi.org/10.26421/qic23.3-4-3.

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The Exclusive-OR Sum-of-Product (ESOP) minimization problem has long been of interest to the research community because of its importance in classical logic design (including low-power design and design for test), reversible logic synthesis, and knowledge discovery, among other applications. However, no exact minimal minimization method has been presented for more than seven variables on arbitrary functions. This paper presents a novel quantum-classical hybrid algorithm for the exact minimal ESOP minimization of incompletely specified Boolean functions. This algorithm constructs oracles from s

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Shukla, Vasundhara, and Preety D. Swami. "Sparse Signal Recovery through Long Short-Term Memory Networks for Compressive Sensing-Based Speech Enhancement." Electronics 12, no. 14 (2023): 3097. http://dx.doi.org/10.3390/electronics12143097.

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This paper presents a novel speech enhancement approach based on compressive sensing (CS) which uses long short-term memory (LSTM) networks for the simultaneous recovery and enhancement of the compressed speech signals. The advantage of this algorithm is that it does not require an iterative process to recover the compressed signals, which makes the recovery process fast and straight forward. Furthermore, the proposed approach does not require prior knowledge of signal and noise statistical properties for sensing matrix optimization because the used LSTM can directly extract and learn the requ

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Acharya, Deep Shekhar, and Sudhansu Kumar Mishra. "Optimal Consensus Recovery of Multi-agent System Subjected to Agent Failure." International Journal on Artificial Intelligence Tools 29, no. 06 (2020): 2050017. http://dx.doi.org/10.1142/s0218213020500177.

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Multi-Agent Systems are susceptible to external disturbances, sensor failures or collapse of communication channel/media. Such failures disconnect the agent network and thereby hamper the consensus of the system. Quick recovery of consensus is vital to continue the normal operation of an agent-based system. However, only limited works in the past have investigated the problem of recovering the consensus of an agent-based system in the event of a failure. This work proposes a novel algorithmic approach to recover the lost consensus, when an agent-based system is subject to the failure of an age

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Jo, Hwi-Jeong, Heewoo Lee, Jihoon Choi, and Wookyung Lee. "Hybrid Deterministic Sensing Matrix for Compressed Drone SAR Imaging and Efficient Reconstruction of Subsurface Targets." Remote Sensing 17, no. 4 (2025): 595. https://doi.org/10.3390/rs17040595.

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Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. C

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Malik, Jameel, Ahmed Elhayek, and Didier Stricker. "WHSP-Net: A Weakly-Supervised Approach for 3D Hand Shape and Pose Recovery from a Single Depth Image." Sensors 19, no. 17 (2019): 3784. http://dx.doi.org/10.3390/s19173784.

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Hand shape and pose recovery is essential for many computer vision applications such as animation of a personalized hand mesh in a virtual environment. Although there are many hand pose estimation methods, only a few deep learning based algorithms target 3D hand shape and pose from a single RGB or depth image. Jointly estimating hand shape and pose is very challenging because none of the existing real benchmarks provides ground truth hand shape. For this reason, we propose a novel weakly-supervised approach for 3D hand shape and pose recovery (named WHSP-Net) from a single depth image by learn

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Zhang, Hongyang, Zhouchen Lin, Chao Zhang, and Junbin Gao. "Relations Among Some Low-Rank Subspace Recovery Models." Neural Computation 27, no. 9 (2015): 1915–50. http://dx.doi.org/10.1162/neco_a_00762.

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Recovering intrinsic low-dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, a lot of work has modeled subspace recovery as low-rank minimization problems. We find that some representative models, such as robust principal component analysis (R-PCA), robust low-rank representation (R-LRR), and robust latent low-rank representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations.

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Song, Chen, Jiarui Deng, Zehao Liu, Bingnan Wang, Yirong Wu, and Hui Bi. "Complex-Valued Sparse SAR-Image-Based Target Detection and Classification." Remote Sensing 14, no. 17 (2022): 4366. http://dx.doi.org/10.3390/rs14174366.

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It is known that synthetic aperture radar (SAR) images obtained by typical matched filtering (MF)-based algorithms always suffer from serious noise, sidelobes and clutter. However, the improvement in image quality means that the complexity of SAR systems will increase, which affects the applications of SAR images. The introduction of sparse signal processing technologies into SAR imaging proposes a new way to solve this problem. Sparse SAR images obtained by sparse recovery algorithms show better image performance than typical complex SAR images with lower sidelobes and higher signal-to-noise

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Liu, Cheng, Tong Wang, Kun Liu, and Xinying Zhang. "A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects." Remote Sensing 14, no. 16 (2022): 3906. http://dx.doi.org/10.3390/rs14163906.

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Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade significantly. Reducing the grid interval can mitigate off-grid effects, but brings strong column coherence of the dictionary, heavy computational load, and heavy storage load. A sparse Bayesian learning approach is proposed to mitigate the off-grid effects in the paper. The algorithm employs an eff

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Chekousari, Maryam Mehraban. "Enhancing Compressed Sensing with Graph Structural Constraints: A Novel Approach to Active Learning in Measurement Matrices." Asian Journal of Research in Computer Science 17, no. 9 (2024): 92–102. http://dx.doi.org/10.9734/ajrcos/2024/v17i9501.

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Compressed sensing on the graph, signals can be approximated by the graph and with the nodes containing information, so compressed sensing can collect information distributed on nodes or links. Also, compressed sensing on the graph becomes important due to the high cost of examining parameters one by one and the unavailability of information on some of them directly in the graph. In this article, by using the idea of active learning and random walking, a method has been introduced to improve the construction of the measurement matrix in the field of the graph, so that information from the gr

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Meng, Dandan, Xianpeng Wang, Mengxing Huang, Chong Shen, and Guoan Bi. "Weighted Block Sparse Recovery Algorithm for High Resolution DOA Estimation with Unknown Mutual Coupling." Electronics 7, no. 10 (2018): 217. http://dx.doi.org/10.3390/electronics7100217.

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Based on weighted block sparse recovery, a high resolution direction-of-arrival (DOA) estimation algorithm is proposed for data with unknown mutual coupling. In our proposed method, a new block representation model based on the array covariance vectors is firstly formulated to avoid the influence of unknown mutual coupling by utilizing the inherent structure of the steering vector. Then a weighted 1l -norm penalty algorithm is proposed to recover the block sparse matrix, in which the weighted matrix is constructed based on the principle of a novel Capon space spectrum function for increasing t

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Li, Yangyang, Jianping Zhang, Guiling Sun, and Dongxue Lu. "The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing." Journal of Electrical and Computer Engineering 2019 (July 14, 2019): 1–8. http://dx.doi.org/10.1155/2019/6950819.

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This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization

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KANKAM, Kunrada, Prasit CHOLAMJİAK, and Watcharaporn CHOLAMJİAK. "A modified parallel monotone hybrid algorithm for a finite family of $\mathcal{G}$-nonexpansive mappings apply to a novel signal recovery." Results in Nonlinear Analysis 5, no. 3 (2022): 393–411. http://dx.doi.org/10.53006/rna.1122092.

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In this work, we investigate the strong convergence of the sequences generated by the shrinking projection method and the parallel monotone hybrid method to find a common fixed point of a finite family of $\mathcal{G}$-nonexpansive mappings under suitable conditions in Hilbert spaces endowed with graphs. We also give some numerical examples and provide application to signal recovery under situation without knowing the type of noises. Moreover, numerical experiments of our algorithms which are defined by different types of blurred matrices and noises on the algorithm to show the efficiency and

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Li, Yinhai, Fei Wang, and Xinhua Hu. "Deep-Learning-Based 3D Reconstruction: A Review and Applications." Applied Bionics and Biomechanics 2022 (September 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/3458717.

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In recent years, deep learning models have been widely used in 3D reconstruction fields and have made remarkable progress. How to stimulate deep academic interest to effectively manage the explosive augmentation of 3D models has been a research hotspot. This work shows mainstream 3D model retrieval algorithm programs based on deep learning currently developed remotely, and further subdivides their advantages and disadvantages according to the behavior evaluation of the algorithm programs obtained by trial. According to other restoration applications, the main 3D model retrieval algorithms can

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Malyk, I., and Y. Litvinchuk. "ABOUT ONE APPROACH TO THE CONSTRUCTION OF SELF-ADAPTIVE ALGORITHMS BASED ON DISTRIBUTION MIXTURES." Bukovinian Mathematical Journal 11, no. 2 (2023): 183–89. http://dx.doi.org/10.31861/bmj2023.02.18.

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This article presents a novel approach for constructing self-optimizing algorithms designed to estimate parameters (hyperparameters) in complex systems, with a broader application to classical genetic and evolutionary algorithms. The central theme of this paper revolves around the exploration of multimodality in the objective function and advocates the effectiveness of employing distribution mixtures as opposed to single-peaked distributions in traditional scenarios. A significant focus of this research involves addressing the challenge of determining the dimensionality of the mixture and deve

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Zhao, Hongwei, Zichun Zhang, Xiaozhu Shi, and Yihui Yin. "A novel demodulation algorithm for VHF Data Broadcast signals in multi-sources augmentation navigation system." International Journal of Distributed Sensor Networks 16, no. 3 (2020): 155014772091477. http://dx.doi.org/10.1177/1550147720914770.

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The augmentation navigation system based on multi-source information fusion can significantly improve position accuracy, and the multi-source information is usually transmitted through VHF Data Broadcast . Aiming at the burst characteristics of VHF Data Broadcast, this article proposed a novel demodulation algorithm based on open-loop structure. When a VHF Data Broadcast burst is detected, the timing recovery should be finished first, and the value of cross-correlation between the timing-recovered signal and the local training symbol is calculated to complete the frame synchronization. Then, t

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KHONSARI, A., H. SARBAZI-AZAD, and M. OULD-KHAOUA. "A Performance Model of Software-Based Deadlock Recovery Routing Algorithm in Hypercubes." Parallel Processing Letters 15, no. 01n02 (2005): 153–68. http://dx.doi.org/10.1142/s012962640500212x.

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Recent studies have revealed that deadlocks are generally infrequent in the network. Thus the hardware resources, e.g. virtual channels, dedicated for deadlock avoidance are not utilised most of the time. This consideration has motivated the development of novel adaptive routing algorithms with deadlock recovery. This paper describes a new analytical model to predict message latency in hypercubes with a true fully adaptive routing algorithm with progressive deadlock recovery. One of the main features of the proposed model is the use of results from queueing systems with impatient customers to

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Zhang, Rui, Di Xiao, and Yanting Chang. "A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing." Security and Communication Networks 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/1591206.

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This paper proposes a novel tamper detection, localization, and recovery scheme for encrypted images with Discrete Wavelet Transformation (DWT) and Compressive Sensing (CS). The original image is first transformed into DWT domain and divided into important part, that is, low-frequency part, and unimportant part, that is, high-frequency part. For low-frequency part contains the main information of image, traditional chaotic encryption is employed. Then, high-frequency part is encrypted with CS to vacate space for watermark. The scheme takes the processed original image content as watermark, fro

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Liu, Zhuoliu, Luwei Fu, Maojun Pan, and Zhiwei Zhao. "Lightweight Path Recovery in IPv6 Internet-of-Things Systems." Electronics 11, no. 8 (2022): 1220. http://dx.doi.org/10.3390/electronics11081220.

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In an Internet-of-Things system supported by Internet Protocol version 6 (IPv6), the Routing Protocol for Low-Power and Lossy Networks (RPL) presents extensive applications in various network scenarios. In these novel scenarios characterized by the access of massive devices, path recovery, which reconstructs the complete path of the packet transmission, plays a vital role in network measurement, topology inference, and information security. This paper proposes a Lightweight Path recovery algorithm (LiPa) for multi-hop point-to-point communication. The core idea of LiPa is to make full use of t

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23

Suantai, Suthep, Kunrada Kankam, Watcharaporn Cholamjiak, and Watcharaporn Yajai. "Parallel Hybrid Algorithms for a Finite Family of G-Nonexpansive Mappings and Its Application in a Novel Signal Recovery." Mathematics 10, no. 12 (2022): 2140. http://dx.doi.org/10.3390/math10122140.

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This article considers a parallel monotone hybrid algorithm for a finite family of G-nonexpansive mapping in Hilbert spaces endowed with graphs and suggests iterative schemes for finding a common fixed point by the two different hybrid projection methods. Moreover, we show the computational performance of our algorithm in comparison to some methods. Strong convergence theorems are proved under suitable conditions. Finally, we give some numerical experiments of our algorithms to show the efficiency and implementation of the LASSO problems in signal recovery with different types of blurred matri

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Likassa, Habte Tadesse. "New Robust Principal Component Analysis for Joint Image Alignment and Recovery via Affine Transformations, Frobenius and L2,1 Norms." International Journal of Mathematics and Mathematical Sciences 2020 (April 10, 2020): 1–9. http://dx.doi.org/10.1155/2020/8136384.

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This paper proposes an effective and robust method for image alignment and recovery on a set of linearly correlated data via Frobenius and L2,1 norms. The most popular and successful approach is to model the robust PCA problem as a low-rank matrix recovery problem in the presence of sparse corruption. The existing algorithms still lack in dealing with the potential impact of outliers and heavy sparse noises for image alignment and recovery. Thus, the new algorithm tackles the potential impact of outliers and heavy sparse noises via using novel ideas of affine transformations and Frobenius and

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Chen, Hongsheng, and Ke Shi. "Connectivity Recovery Based on Boundary Nodes and Spatial Triangle Fermat Points for Three-Dimensional Wireless Sensor Networks." Sensors 24, no. 24 (2024): 7876. https://doi.org/10.3390/s24247876.

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In recent years, wireless sensor networks have been widely used, especially in three-dimensional environments such as underwater and mountain environments. However, in harsh environments, wireless sensor networks may be damaged and split into many isolated islands. Therefore, restoring network connectivity to transmit data effectively in a timely manner is particularly important. However, the problem of finding the minimum relay nodes is NP-hard, so heuristics methods are preferred. This paper presents a novel connectivity recovery strategy based on boundary nodes and spatial triangle Fermat p

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Row, Ter-Chan, Wei-Ming Syu, Yen-Liang Pan, and Ching-Cheng Wang. "One Novel and Optimal Deadlock Recovery Policy for Flexible Manufacturing Systems Using Iterative Control Transitions Strategy." Mathematical Problems in Engineering 2019 (March 27, 2019): 1–12. http://dx.doi.org/10.1155/2019/4847072.

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This paper focuses on solving deadlock problems of flexible manufacturing systems (FMS) based on Petri nets theory. Precisely, one novel control transition technology is developed to solve FMS deadlock problem. This new proposed technology can not only identify the maximal saturated tokens of idle places in Petri net model (PNM) but also further reserve all original reachable markings whatever they are legal or illegal ones. In other words, once the saturated number of tokens in idle places is identified, the maximal markings of system reachability graph can then be checked. Two classical S3PR

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Teshima, Takeshi, Miao Xu, Issei Sato, and Masashi Sugiyama. "Clipped Matrix Completion: A Remedy for Ceiling Effects." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5151–58. http://dx.doi.org/10.1609/aaai.v33i01.33015151.

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We consider the problem of recovering a low-rank matrix from its clipped observations. Clipping is conceivable in many scientific areas that obstructs statistical analyses. On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-rank completion. However, the current theoretical guarantees for low-rank MC do not apply to clipped matrices, as the deficit depends on the underlying values. Therefore, the feasibility of clipped matrix completion (CMC) is not trivial. In this paper, we first provide a theoretical

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Alassafi, Madini O., Ishtiaq Rasool Khan, Rayed AlGhamdi, et al. "Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet." Healthcare 11, no. 16 (2023): 2280. http://dx.doi.org/10.3390/healthcare11162280.

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An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work

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Guo, Jianzhong, Cong Cao, Dehui Shi, et al. "Matching Pursuit Algorithm for Decoding of Binary LDPC Codes." Wireless Communications and Mobile Computing 2021 (October 31, 2021): 1–5. http://dx.doi.org/10.1155/2021/9980774.

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This paper presents a novel hard decision decoding algorithm for low-density parity-check (LDPC) codes, in which the stand matching pursuit (MP) is adapted for error pattern recovery from syndrome over GF(2). In this algorithm, the operation of inner product can be converted into XOR and accumulation, which makes the matching pursuit work with a high efficiency. In addition, the maximum iteration is theoretically explored in relation to sparsity and error probability according to the sparse theory. To evaluate the proposed algorithm, two MP-based decoding algorithms are simulated and compared

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Wang, Changlin, Zhonghua Lu, and Jiatong Yu. "Biological optimization of sustainable agricultural systems through genetic algorithms and nitrogen balance management." Molecular & Cellular Biomechanics 22, no. 3 (2025): 1073. https://doi.org/10.62617/mcb1073.

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In order to improve resource efficiency and enhance sustainability in biological systems, this study investigates the optimization of biomechanical processes by combining genetic algorithms (GA) with human performance and recovery management. The study aims to minimize injury risks and maximize recovery efficiency by utilizing GA to model biomechanical processes. To ensure a dynamic balance in physical performance, the study presents an ideal optimization framework in which human biomechanics is optimized for enhanced sports performance and injury prevention. The model considers factors such a

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Wu, Tianjun, Yuexiang Yang, Chi Wang, and Rui Wang. "Study on Massive-Scale Slow-Hash Recovery Using Unified Probabilistic Context-Free Grammar and Symmetrical Collaborative Prioritization with Parallel Machines." Symmetry 11, no. 4 (2019): 450. http://dx.doi.org/10.3390/sym11040450.

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Slow-hash algorithms are proposed to defend against traditional offline password recovery by making the hash function very slow to compute. In this paper, we study the problem of slow-hash recovery on a large scale. We attack the problem by proposing a novel concurrent model that guesses the target password hash by leveraging known passwords from a largest-ever password corpus. Previously proposed password-reused learning models are specifically designed for targeted online guessing for a single hash and thus cannot be efficiently parallelized for massive-scale offline recovery, which is deman

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Mutny, Mojmir, Johannes Kirschner, and Andreas Krause. "Experimental Design for Optimization of Orthogonal Projection Pursuit Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10235–42. http://dx.doi.org/10.1609/aaai.v34i06.6585.

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Bayesian optimization and kernelized bandit algorithms are widely used techniques for sequential black box function optimization with applications in parameter tuning, control, robotics among many others. To be effective in high dimensional settings, previous approaches make additional assumptions, for example on low-dimensional subspaces or an additive structure. In this work, we go beyond the additivity assumption and use an orthogonal projection pursuit regression model, which strictly generalizes additive models. We present a two-stage algorithm motivated by experimental design to first de

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Zhou, Mingliang, Qin Mao, Chen Zhong, Weiqin Zhang, and Changzhi Chen. "Spatial Error Concealment by Jointing Gauss Bayes Model and SVD for High Efficiency Video Coding." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (2019): 1954037. http://dx.doi.org/10.1142/s0218001419540375.

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This paper proposes a novel sparsity-based error concealment (EC) algorithm which integrates the Gauss Bayes model and singular value decomposition for high efficiency video coding (HEVC). Under the sequential recovery framework, pixels in missing blocks are successively reconstructed in Gauss Bayes mode. We find that the estimation error follows the Gaussian distribution in HEVC, so the error pixel estimation problem can be transferred to a Bayesian estimation. We utilize the singular value decomposition (SVD) technique to select sample pixels, which yields high estimation accuracy and reduce

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A, Kadar A., Parameshwaran Ramalingam, Rithanathith S, Gurugubelli Vasudeva Rao, and Lakshminarayanan G. "FPGA Implementation of Adaptive Sampling Algorithm for Space Applications." ECS Transactions 107, no. 1 (2022): 5839–46. http://dx.doi.org/10.1149/10701.5839ecst.

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Adaptive sampling is a signal processing technique used in various aerospace applications. Many adaptive algorithms used for instrumentation and telemetry systems process the signal in the frequency domain, which leads to high computational cost and power. ASA-m solves this problem by performing all operations in the time domain. It estimated the subsequent sampling frequency to collect meaningful information based on mean velocity prediction. This novel algorithm is implemented on the Spartan 3E FPGA board to study the device power and hardware utilization for real-time vibration signal datas

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Zhang, Yang, Chaoyue Chen, Wei Huang, et al. "Machine Learning-Based Radiomics of the Optic Chiasm Predict Visual Outcome Following Pituitary Adenoma Surgery." Journal of Personalized Medicine 11, no. 10 (2021): 991. http://dx.doi.org/10.3390/jpm11100991.

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Preoperative prediction of visual recovery after pituitary adenoma surgery remains a challenge. We aimed to investigate the value of MRI-based radiomics of the optic chiasm in predicting postoperative visual field outcome using machine learning technology. A total of 131 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (N = 79) and the non-recovery group (N = 52) according to visual field outcome following surgical chiasmal decompression. Radiomic features were extracted from the optic chiasm on preoperative coronal T2-weighted imaging. Least absolut

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Dodd, Peter J., Jeff J. Pennington, Liza Bronner Murrison, and David W. Dowdy. "Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation." Medical Decision Making 38, no. 8 (2018): 930–41. http://dx.doi.org/10.1177/0272989x18807438.

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Introduction. Cost-effectiveness models for infectious disease interventions often require transmission models that capture the indirect benefits from averted subsequent infections. Compartmental models based on ordinary differential equations are commonly used in this context. Decision trees are frequently used in cost-effectiveness modeling and are well suited to describing diagnostic algorithms. However, complex decision trees are laborious to specify as compartmental models and cumbersome to adapt, limiting the detail of algorithms typically included in transmission models. Methods. We con

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Xu, Huihui, and Fei Li. "Multilevel Pyramid Network for Monocular Depth Estimation Based on Feature Refinement and Adaptive Fusion." Electronics 11, no. 16 (2022): 2615. http://dx.doi.org/10.3390/electronics11162615.

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As a traditional computer vision task, monocular depth estimation plays an essential role in novel view 3D reconstruction and augmented reality. Convolutional neural network (CNN)-based models have achieved good performance for this task. However, in the depth map recovered by some existing deep learning-based methods, local details are still lost. To generate convincing depth maps with rich local details, this study proposes an efficient multilevel pyramid network for monocular depth estimation based on feature refinement and adaptive fusion. Specifically, a multilevel spatial feature generat

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Mukhoty, Bhaskar, Debojyoti Dey, and Purushottam Kar. "Corruption-Tolerant Algorithms for Generalized Linear Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9243–50. http://dx.doi.org/10.1609/aaai.v37i8.26108.

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This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data. SVAM extends to tasks such as least squares regression, logistic regression, and gamma regression, whereas many existing works on learning with label corruptions focus only on least squares regression. SVAM is based on a novel variance reduction technique that may be of independent interest and works by iteratively solving weighted MLEs over variance-altered versions of the GLM objective. SVAM offers provable model recovery

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Li, Yanyang, Jindong Wang, Haiyang Zhao, Chang Wang, and Qi Shao. "Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors." Sensors 24, no. 1 (2023): 167. http://dx.doi.org/10.3390/s24010167.

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Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel app

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Cao, Wu, Wen Ren, Zhenyu Zhang, Weiqiang Huang, Jun Zou, and Guangzu Liu. "Direction of Arrival Estimation Based on DNN and CNN." Electronics 13, no. 19 (2024): 3866. http://dx.doi.org/10.3390/electronics13193866.

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The accuracy of Direction of Arrival (DOA) estimation primarily depends on the precision of the data. When the receiver uses a low-precision analog-to-digital converter (ADC), traditional DOA estimation algorithms exhibit poor accuracy. To face the challenge of multi-target DOA estimation in scenarios with low-precision ADC quantized sampling, this paper proposes a novel DOA estimation algorithm for quantized signals based on classification problems. A deep learning network was constructed using Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), divided into the quantized si

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Haider, Hassaan, Jawad Ali Shah, Kushsairy Kadir, and Najeeb Khan. "Sparse Reconstruction Using Hyperbolic Tangent as Smooth l1-Norm Approximation." Computation 11, no. 1 (2023): 7. http://dx.doi.org/10.3390/computation11010007.

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In the Compressed Sensing (CS) framework, the underdetermined system of linear equation (USLE) can have infinitely many possible solutions. However, we intend to find the sparsest possible solution, which is -norm minimization. However, finding an norm solution out of infinitely many possible solutions is NP-hard problem that becomes non-convex optimization problem. It has been a practically proven fact that norm penalty can be adequately estimated by norm, which recasts a non-convex minimization problem to a convex problem. However, norm non-differentiable and gradient-based minimization algo

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Zhao, Shengjie, Jianchen Zhu, and Di Wu. "Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery." Traitement du Signal 37, no. 5 (2020): 723–32. http://dx.doi.org/10.18280/ts.370504.

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Compressive sensing (CS) is a novel paradigm to recover a sparse signal in compressed domain. In some overcomplete dictionaries, most practical signals are sparse rather than orthonormal. Signal space greedy method can derive the optimal or near-optimal projections, making it possible to identify a few most relevant dictionary atoms of an arbitrary signal. More practically, such projections can be processed by standard CS recovery algorithms. This paper proposes a signal space subspace pursuit (SSSP) method to compute spare signal representations with overcomplete dictionaries, whenever the se

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Gürses, Dildar, Pranav Mehta, Sadiq M. Sait, and Ali Riza Yildiz. "African vultures optimization algorithm for optimization of shell and tube heat exchangers." Materials Testing 64, no. 8 (2022): 1234–41. http://dx.doi.org/10.1515/mt-2022-0050.

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Abstract Nature-inspired optimization algorithms named meta-heuristics are found to be versatile in engineering design fields. Their adaptability is also used in various areas of the Internet of things, structural design, and thermal system design. With the very rapid progress in industrial modernization, waste heat recovery from the power generating and thermal engineering organization is an imperative key point to reduce the emission and support the government norms. However, the heat exchanger is the component applied in various heat recovery processes. Out of the available designs, shell a

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Xu, Renjie, Ting Yun, Lin Cao, and Yunfei Liu. "Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing." Forests 11, no. 3 (2020): 257. http://dx.doi.org/10.3390/f11030257.

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The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the ch

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Okuboyejo, Damilola A., and Oludayo O. Olugbara. "Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning." Algorithms 15, no. 12 (2022): 443. http://dx.doi.org/10.3390/a15120443.

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The conventional dermatology practice of performing noninvasive screening tests to detect skin diseases is a source of escapable diagnostic inaccuracies. Literature suggests that automated diagnosis is essential for improving diagnostic accuracies in medical fields such as dermatology, mammography, and colonography. Classification is an essential component of an assisted automation process that is rapidly gaining attention in the discipline of artificial intelligence for successful diagnosis, treatment, and recovery of patients. However, classifying skin lesions into multiple classes is challe

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Wang, Hongyang, Zijing Zhang, Qingfeng Wang, Rui Feng, and Yuan Zhao. "Enhanced Measurement of Vortex Beam Rotation Using Polarization-Assisted Particle Swarm Optimization for Phase Retrieval." Photonics 10, no. 12 (2023): 1293. http://dx.doi.org/10.3390/photonics10121293.

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In detecting the rotation velocity of an object employing the rotational Doppler effect of vortex beams, atmospheric turbulence can easily cause phase distortion and spiral spectrum dispersion, consequently reducing velocity measurement accuracy. This study combines adaptive optical intelligence algorithms with polarization compensation information to propose a novel approach, the Stokes–Particle swarm optimization Gerchberg–Saxton (Stokes-PSO GS) algorithm, which integrates Stokes polarization information assistance and PSO for GS phase retrieval. The algorithm adjusts the phase and amplitude

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Liu, Chunsheng, Hong Shan, and Bin Wang. "Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence." Sensors 18, no. 9 (2018): 2974. http://dx.doi.org/10.3390/s18092974.

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One of the main challenges faced by wireless sensor network (WSN) localization is the positioning accuracy of the WSN node. The existing algorithms are arduous to use for dealing with the pulse noise that is universal and ineluctable in practical considerations, resulting in lower positioning accuracy. Aimed at this problem and introducing Bregman divergence, we propose in this paper a novel WSN localization algorithm via matrix completion (LBDMC). Based on the natural low-rank character of the Euclidean Distance Matrix (EDM), the problem of EDM recovery is formulated as an issue of matrix com

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Zhao, Li Ye. "Method Based on Wavelet and Empirical Mode Decomposition for Extracting the Gravity Signal." Applied Mechanics and Materials 668-669 (October 2014): 1076–80. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1076.

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The measurement data of the marine gravity contains a lot of noise, the low frequency part of which have a similar frequency with the gravity signal. It’s difficult to inhibit the noise of the measurement data and extract the gravity signal by classical algorithms. Therefore, in order to effectively eliminate the noise of the measurement gravity data and improve the accuracy of the extracted signal, based on algorithms of wavelet and Empirical Mode Decomposition (EMD), a novel method to extract the sea gravity anomaly signal is proposed. Firstly, the measurement gravity signal is decomposed in

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Krucoff, Max O., Katie Zhuang, David MacLeod, et al. "A novel paraplegia model in awake behaving macaques." Journal of Neurophysiology 118, no. 3 (2017): 1800–1808. http://dx.doi.org/10.1152/jn.00327.2017.

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Lower limb paralysis from spinal cord injury (SCI) or neurological disease carries a poor prognosis for recovery and remains a large societal burden. Neurophysiological and neuroprosthetic research have the potential to improve quality of life for these patients; however, the lack of an ethical and sustainable nonhuman primate model for paraplegia hinders their advancement. Therefore, our multidisciplinary team developed a way to induce temporary paralysis in awake behaving macaques by creating a fully implantable lumbar epidural catheter-subcutaneous port system that enables easy and reliable

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Zhao, Huihuang, Jianzhen Chen, Shibiao Xu, Ying Wang, and Zhijun Qiao. "Compressive sensing for noisy solder joint imagery based on convex optimization." Soldering & Surface Mount Technology 28, no. 2 (2016): 114–22. http://dx.doi.org/10.1108/ssmt-09-2014-0017.

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Purpose The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost. Design/methodology/approach Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is

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