Review
Neurosciences
Biao Sun, Wenfeng Zhao
Summary: This article provides a comprehensive survey of literature on compressed sensing of neurophysiology signals, discussing its applications, technical challenges, and prospects in neural signal transmission.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Engineering, Electrical & Electronic
Jinn Ho, Wen-Liang Hwang
Summary: This paper investigates the issue of ambiguity in dictionary estimation and the sensitivity of true sparse vectors and signals to dictionary perturbations in compressed sensing. The study shows that inherent ambiguity in dictionary estimation cannot be resolved even when the desired sparse vector can be obtained. By imposing conditions on the perturbation of the true dictionary, the sparse vector and signal can be stably estimated within the bounds of the dictionary perturbation.
Article
Engineering, Electrical & Electronic
Xianglong You, Jiacheng Li, Zhongwei Deng, Kai Zhang, Hang Yuan
Summary: This paper proposes a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. The triaxial vibration signals are compressed using hybrid and joint measurement matrices, and the fused spectra are employed for sparse-representation-based classification with the batch matching pursuit algorithm. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis and provide evidence for professional vibration analysis by reconstructing the compressed vibration data. The method is validated with high accuracies in two practical case studies, 99.73% and 96.70% respectively.
Article
Computer Science, Information Systems
Workneh Wolde Hailemariam, Pallavi Gupta
Summary: A secure compressed sensing system design approach is proposed in this study, which uses a novel deterministic sensing matrix to sense and transmit fingerprint images. The experimental results show that the proposed method can achieve high compression ratios without damaging the fingerprint minutiae and possesses sufficient randomness and resistance against attacks.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Qianru Jiang, Sheng Li, Liping Chang, Xiongxiong He, Rodrigo C. de Lamare
Summary: In this work, compressed sensing techniques based on prior knowledge are investigated for supporting telemedicine. The prior knowledge obtained by computing the probability of appearance of non-zero elements in each row of a sparse matrix is utilized in sensing matrix design and recovery algorithms. A robust sensing matrix is designed by jointly reducing the average mutual coherence and the projection of the sparse representation error. A Probability-Driven Normalized Iterative Hard Thresholding algorithm is developed as the recovery method, which exploits the prior knowledge and provides performance benefits. Simulations for synthetic data and endoscopy images of different organs demonstrate the superior performance of the proposed methods compared to previous algorithms.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Multidisciplinary
Chao-Lung Yang, Hendrik Tampubolon, Aji Setyoko, Kai-Lung Hua, Mohammad Tanveer, Wei Wei
Summary: A framework of skeleton-based pervasive healthcare monitoring was proposed in this study, which employs secure edge-fog-cloud computing to manage computation and storage resources, address latency, cyber-attack, and privacy-preserving concerns. Experimental results demonstrate that the framework performs well compared to other methods on the dataset and exhibits better computation performance in terms of frame per second.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Chemistry, Analytical
Aiguo Wang, Shenghui Zhao, Huan-Chao Keh, Guilin Chen, Diptendu Sinha Roy
Summary: This study proposes a clustering guided hierarchical framework for discriminating human activities. By introducing an activity confusion index based on clustering, the confusion between activities is quantitatively measured automatically, leading to the design of a hierarchical activity recognition framework to reduce recognition errors between similar activities.
Article
Computer Science, Information Systems
Menghao Hu, Mingxuan Luo, Menghua Huang, Wenhua Meng, Baochen Xiong, Xiaoshan Yang, Jitao Sang
Summary: This paper introduces a new dataset for wearable device-based human activity recognition (HAR), which includes multiple sensor data and labels of participants' health status. It demonstrates the importance of multimodal fusion in activity recognition and provides baselines for further research using this dataset.
MULTIMEDIA SYSTEMS
(2023)
Article
Remote Sensing
Gongwei Xiao, Genyou Liu, Jikun Ou, Guolin Liu, Shengliang Wang, Jiachen Wang, Ming Gao
Summary: This study discusses key issues in the processing of global tropospheric grid data, presents the use of compressed sensing for sparse reconstruction, and proposes the mini-batch K-SVD algorithm to speed up calculations. Experimental results demonstrate that compressed sensing yields more accurate solutions than traditional spherical harmonic expansion, saving real-time transmission costs and enabling data encryption and compression.
Article
Computer Science, Information Systems
Yiwei Zhang, Bin Gao, Daili Yang, Wai Lok Woo, Houlai Wen
Summary: This article presents a novel semisupervised learning method, VFDT, for wearable sensors to recognize human activities. The method efficiently reduces computational time and storage by generating three VFDTs and using unlabeled examples. The method is embedded into wearable devices for online learning and shows similar performance as offline learning.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Yan Zhang, Jie Li, Xinyue Li, Bin Wang, Tiange Li
Summary: This study proposes a method based on compressed sensing to remove stripe noise in the camera's imaging process. By establishing the measurement matrix of the image with stripe noise and defining the relationships between the corresponding coefficients of adjacent scales, the removal of stripe noise and preservation of image texture details are achieved.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Multidisciplinary
Yi Liang, Zhilong Hou, Ling Yu
Summary: This paper proposes a block-sparse compressed sensing framework for the identification of moving forces, which can reconstruct the forces in the frequency domain. The improved block orthogonal matching pursuit algorithm enhances the accuracy and robustness of the identification process.
Article
Engineering, Electrical & Electronic
Yingtong Chen, Shoujin Lin
Summary: A preconditioning method is proposed in this study to improve the signal recovery accuracy of CS systems by simultaneously enhancing the RIP of an equivalent dictionary and maintaining the total noise energy, resulting in improved performance of the CS system in a total noise environment.
Article
Engineering, Electrical & Electronic
Yuqing Yang, Peng Xiao, Nikos Deligiannis
Summary: This paper presents a new method for underwater source localization by combining the matched field processing method (MFP) with 1-bit compressive sensing (1-bit CS). The Fixed Point Continuation (FPC) method and a deep neural network (DNN) are used to solve the 1-bit recovery problem and evaluate their performance in source localization. Additionally, a simple average technique is proposed to improve the robustness of signal recovery to noise added in the binary measurements.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Computer Science, Hardware & Architecture
Jixin Liu, Ruxue Zhang, Guang Han, Ning Sun, Sam Kwong
Summary: This paper proposes a method for video action recognition that protects visual privacy using compressed sensing, while balancing operational efficiency with recognition accuracy. The method utilizes a convolutional 3D network model and PCA to reduce temporal complexity, and integrates a sparse representation-based classification algorithm to improve recognition performance. Experiments show the method's robustness in video action recognition tasks and its ability to adequately protect visual privacy.
JOURNAL OF SYSTEMS ARCHITECTURE
(2021)