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Research on the Primary Features of the Internet of Things System and the Corresponding Data Communication Characteristics based on Sparse Coding and Joint Deep Neural Network

Jianping PAN, Wenzhun HUANG, Shanwen ZHANG
2016 International Journal of Future Generation Communication and Networking  
To enhance the robustness and efficiency of the current IOT systems, we adopt the sparse coded dictionary learning theory to detect the size of the data and optimize the compressive sensing technique to  ...  In this paper, we conduct research on issues related to the primary features of the Internet of things system and the corresponding data communication characteristics based on sparse coding and joint deep  ...  The Sparse Coding Algorithms and the Dictionary Learning The Introduction to Compressive Sensing In the reality environment, a large number of real signals and the image itself is not sparse or compressible  ... 
doi:10.14257/ijfgcn.2016.9.10.11 fatcat:s65rrtxzqndwrlcvl35u5igix4

Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing

Amirreza Farnoosh, Sarah Ostadabbas, Mehrdad Nourani
2017 Machine Learning in Health Care  
Our algorithm is also shown to be robust in presence of measurement error and limited training data, therefore efficiently addresses the challenges encountered in production of commercial in-shoe monitoring  ...  K < 10) based on a supervised dictionary learning approach.  ...  Introduction Compressive Sensing (CS) is an l 1 -norm minimization-based signal transformation, compression, and reconstruction approach that provides a sparse representation of the information presented  ... 
dblp:conf/mlhc/FarnooshON17 fatcat:atykh65yq5czlmop6yojn2iqvy

An Advanced Hybrid Technique of DCS and JSRC for Telemonitoring of Multi-Sensor Gait Pattern

2017 Sensors  
In this study, based on the joint sparsity of data, we proposed an advanced hybrid technique of distributed compressed sensing (DCS) and joint sparse representation classification (JSRC) for multi-sensor  ...  Then, the jointly compressed gait data are directly used to develop a novel neighboring sample-based JSRC model by defining the sparse representation coefficients-inducing criterion (SRCC), in order to  ...  Next, we evaluate the feasibility of our proposed method for multi-sensor gait classification based on the different compression ratios.  ... 
doi:10.3390/s17122764 pmid:29186066 pmcid:PMC5751380 fatcat:sjw2ef3jnfgnxfenndvsrjlgym

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Huajun Bai, Liang Wen, Yunfei Ma, Xisheng Jia
2022 Sensors  
Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data.  ...  It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically.  ...  [8] used lossless compression technology and Huffman coding encryption technology to provide effective means for remote monitoring security and compressibility of electrocardiography (ECG) data.  ... 
doi:10.3390/s22103884 pmid:35632293 pmcid:PMC9143168 fatcat:dm3edcrwwrax7jx35sbvkllivi

Multi-Scale 1-Bit Compressed Sensing Algorithm and Its Application in Coalmine Gas Monitoring System

Yonggang Xu, Yi Zhang, Gang Hua
2013 International Journal of Machine Learning and Computing  
Gas data, for example, acts an essential role in mass time-continuous data sets of coalmine monitoring systems, we propose a multi-scale 1-bit compressive sensing algorithm in this paper to effectively  ...  It is imperative to reduce load of the underground cable channel in coming Mining of Things for thousands of sensors.  ...  ACKNOWLEDGMENT This work specially thanks for communications office, dispatch center etc., Huaibei Coalmine Group, Anhui Prov.  ... 
doi:10.7763/ijmlc.2013.v3.350 fatcat:r45vrfwepbht5g2n2kuicu3bye

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

H.O.A. Ahmed, M.L.D. Wong, A.K. Nandi
2018 Mechanical systems and signal processing  
Spectral Kurtosis (SK) has been used effectively in the vibration-based condition monitoring of rotating machines.  ...  Various characteristic features can be observed from vibration signals that make it the best choice for machine condition monitoring.  ...  This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing  ... 
doi:10.1016/j.ymssp.2017.06.027 fatcat:z2nybtruurdp7h5jcjohhckx3m

Trends in Compressive Sensing for EEG Signal Processing Applications

Dharmendra Gurve, Denis Delisle-Rodriguez, Teodiano Bastos-Filho, Sridhar Krishnan
2020 Sensors  
An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes  ...  This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies.  ...  Acknowledgments: The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for providing the funding support.  ... 
doi:10.3390/s20133703 pmid:32630685 fatcat:yutyvj5msfdlnlvfmikmpeucyi

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco, Kenneth E. Barner
2015 IEEE journal of biomedical and health informatics  
Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring.  ...  In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals.  ...  Barner for providing the right combination of supervision, encouragement, and support during my Ph.D. studies. I thank him for his efforts on helping me become a better researcher and writer.  ... 
doi:10.1109/jbhi.2014.2325017 pmid:24846672 fatcat:ea4dfl3r2bgxphvsecyvqweh6m

A High-Efficiency Fatigued Speech Feature Selection Method for Air Traffic Controllers Based on Improved Compressed Sensing

Yonggang Yan, Yi Mao, Zhiyuan Shen, Yitao Wei, Guozhuang Pan, Jinfu Zhu, Qiu-Hua Lin
2021 Journal of Healthcare Engineering  
For adapting a method to the specific field of fatigued speech, we propose an improved compressed sensing construction algorithm to decrease the reconstruction error and achieve superior sparse coding.  ...  provides a significant improvement in the precision of fatigue detection compared with current state-of-the-art approaches.  ...  Conclusions In order to quantitatively and fast detect fatigue condition of ATCs, we proposed a CS-based framework for detecting fatigue from speech of ATCs. en, an improved compressed sensing reconstruction  ... 
doi:10.1155/2021/2292710 pmid:34616528 pmcid:PMC8487830 fatcat:qd5wojpbajez3ersmbdphjzl4i

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

Xin Zhang, Jafar Saniie, Sasan Bakhtiari, Alexander Heifetz
2022 IEEE Access  
Analysis (EFA), Sparse Dictionary Learning (SDL), and a novel lightweight Thermography Compressive Sparse Autoencoder (TCSA).  ...  However, high-resolution PIT imaging results in the generation of a large volume of thermography data (~TB), which creates challenges for the storage and transmission of data.  ...  SPARSE DICTIONARY LEARNING (SDL) The SDL method [63] aims to find the sparse representation of the multivariate data by using optimized base vectors, called atoms.  ... 
doi:10.1109/access.2022.3141654 doaj:7cadb8a78e54458d9f59a4d930e7653c fatcat:7fiadlxfyrcenotcs44ufbzzbq

Embedding ML Algorithms onto LPWAN Sensors for Compressed Communications

Antoine Bernard, Aicha Dridi, Michel Marot, Hossam Afifi, Sandoche Balakrichenan
2021 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)  
Surprisingly, machine learning used in this context does not consume a lot of energy and it even leads to energy saving in the very constrained devices which are the sensors.  ...  To extend the efficiency of the data transmission by decreasing the traffic sent from sensors, this paper proposes a lossy compression method using known ML techniques.  ...  Classical compression approaches based on dictionaries or entropy coding have been adapted to IoT, like [12] where a specific dictionary is created for different kinds of data depending on their change  ... 
doi:10.1109/pimrc50174.2021.9569714 fatcat:67e7hzcd25fibkyftgx6pk6p3e

Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method [article]

Ye Xue, Vincent Lau
2021 arXiv   pre-print
One is to do an alternative optimization of both the dictionary and the sparse code; the other way is to optimize the dictionary by restricting it over the orthogonal group.  ...  Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works of dictionary learning are in an offline manner.  ...  We compress each reading in the mini-batch by the sparse code at each iteration after obtaining the dictionary D t .  ... 
arXiv:2103.01484v2 fatcat:4k6cte25y5ho3hnlybwmbz4nca

4D Seismic History Matching Incorporating Unsupervised Learning [article]

Clement Etienam
2019 arXiv   pre-print
The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques.  ...  A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed.  ...  Thanks are due to Schlumberger for providing the reservoir simulator.  ... 
arXiv:1905.07469v1 fatcat:rrf7vy334fcfpmteohzqes5eea

Spatiotemporal Sparse Bayesian Learning With Applications to Compressed Sensing of Multichannel Physiological Signals

Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Zhouyue Pi, Bhaskar D. Rao
2014 IEEE transactions on neural systems and rehabilitation engineering  
However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic of many physiological signals.  ...  Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous  ...  In this paper the use of CS for data compression/de-compression is considered.  ... 
doi:10.1109/tnsre.2014.2319334 pmid:24801887 fatcat:42xjjserbrhohckgvyso4wynvm

Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network

Muhammad Sajjad, Irfan Mehmood, Sung Baik
2014 Sensors  
The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR.  ...  In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors.  ...  The scores listed in Table 3 justify the superiority of the sparse-coding and dictionary-learning methods used by the proposed technique.  ... 
doi:10.3390/s140203652 pmid:24566632 pmcid:PMC3958298 fatcat:zou3sq7ksnbmtkue4lsqj6iyqm
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