Data, Signal and Image Processing and Applications in Sensors II
With the rapid advances in sensor technology, a vast and ever-growing amount of data in various domains and modalities is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among...
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| description | With the rapid advances in sensor technology, a vast and ever-growing amount of data in various domains and modalities is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensor signals’ data, further enhancement of the sensor signals acquired, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is needed. The processing of the data in themselves and the consequent extraction of useful information are also vital and included in the scope of this Special Issue. This SI of Sensors is aimed at highlighting advances in the development, testing, and application of data, signal, and image processing algorithms and techniques to all types of sensors and sensing methodologies. Experimental and theoretical results along with review papers, in as much detail as possible, are also considered. Some examples of the topics to be covered in this SI include the following: Ambient assisted living; Biomedical signal and image analysis; Machine learning in signal and image processing; Multimodal information processing for healthcare, monitoring, and surveillance; Real-time signal and image processing algorithms and architectures; Remote sensing processing; Sensors and smart sensors for IoT devices; Signal and image processing and understanding; Wearable sensor signal processing and its applications. |
| doi_str_mv | 10.3390/books978-3-7258-1562-3 |
| format | Book |
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However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensor signals’ data, further enhancement of the sensor signals acquired, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is needed. The processing of the data in themselves and the consequent extraction of useful information are also vital and included in the scope of this Special Issue. This SI of Sensors is aimed at highlighting advances in the development, testing, and application of data, signal, and image processing algorithms and techniques to all types of sensors and sensing methodologies. Experimental and theoretical results along with review papers, in as much detail as possible, are also considered. Some examples of the topics to be covered in this SI include the following: Ambient assisted living; Biomedical signal and image analysis; Machine learning in signal and image processing; Multimodal information processing for healthcare, monitoring, and surveillance; Real-time signal and image processing algorithms and architectures; Remote sensing processing; Sensors and smart sensors for IoT devices; Signal and image processing and understanding; Wearable sensor signal processing and its applications.</description><identifier>ISBN: 3725815615</identifier><identifier>ISBN: 9783725815616</identifier><identifier>ISBN: 3725815623</identifier><identifier>ISBN: 9783725815623</identifier><identifier>DOI: 10.3390/books978-3-7258-1562-3</identifier><language>eng</language><publisher>MDPI - Multidisciplinary Digital Publishing Institute</publisher><subject>5P-Medicine ; artificial intelligence ; auditory illusion ; augmented reality ; automation ; autonomic nervous system (ANS) ; blur kernel rendering ; body movement cancelation ; brain effective connectivity ; brain–computer interface ; breathing frequency ; cardiovascular diseases ; cepstral analysis ; CFRP ; classification ; cloud model ; CNN ; cold pressor task (CPT) ; computer vision ; continuous wave ; continuous wavelet transform (CWT) ; Convolutional Neural Network (CNN) ; convolutional neural networks ; corona extinction voltage ; cosine similarity ; damage area detection ; damaged old photo ; data embedding ; decision tree ; deep learning ; Deep Learning (DL) ; Dempster–Shafer evidence theory ; dense matching ; digital health ; Doppler radar ; DTF ; EEG ; electrical discharges ; electroencephalogram (EEG) ; Electronics and communications engineering ; Electronics engineering ; electrooculogram (EOG) ; EMG ; endurance exercise ; Engineering: general ; face detection ; feature extraction ; feature selection ; FitzHugh–Nagumo neuron ; FMCW laser ranging ; Gaussian Naive Bayes (GNB) ; genetic algorithm ; groundtruth ; gyroscope sensor ; heart rate variability ; heartbeat ; Hellinger distance ; human perception of materials ; hyperspectral images ; IALM ; image analysis ; image classification and reconstruction ; image protection ; inertial sensor ; instance normalization ; internet of things ; IoT devices monitoring ; IoT security ; k-means clustering ; k-Nearest Neighbors (kNN) ; k-nearest neighbours ; KNN ; labeling ; low cost ; low pressure ; low-cost solution ; machine learning ; Machine Learning (ML) ; material translation ; MEG ; memristive device ; mining cables ; mobile bio-sensing for medicine ; monitoring of elderly ; more electric aircraft ; motion blur model ; multi-task learning ; multimedia systems ; multiple radars ; n/a ; neural network ; neural style transfer ; neuromorphic circuit ; neuron-like oscillator ; noise reduction ; normalization ; octave illusion ; pain assessment ; pain induction ; patient empowerment technologies ; PCP ; PD denoising ; PDC ; person detection ; PHL transform ; photogrammetry ; physiological signals ; physiological time delay ; PhysioNet motor imagery ; picket fence effect ; point cloud ; power spectrum ; principal components analysis ; probabilistic neural network ; pulsed thermography ; radar ; Random Forest (RF) ; real-time detection ; respiration ; respiratory rate ; Robust PCA ; RPCA ; seizure detection ; semantic segmentation ; semi-supervised learning ; sensor data fusion ; shadow effect ; signal analysis ; signal processing ; singular spectrum analysis (SSA) ; Softmax ; spectrum correction ; spectrum leakage ; spinal cord injury ; stochastic dynamics ; Support Vector Machine (SVM) ; SVM ; synchronization ; synthetic blurry thermal image ; Teager–Kaiser Energy Operator ; Technology, Engineering, Agriculture, Industrial processes ; Technology: general issues ; temporal action detection ; thermal detector ; thermal image deblurring ; transfer learning ; Two-Stage Hybrid Model ; variable frequency ; video understanding ; visual corona ; vital signal detection ; VMD ; watermarking ; wavelet threshold ; white Gaussian noise ; window size</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>306,776,780,782,27902,55285</link.rule.ids></links><search><contributor>Cabral dos Santos Reis, Manuel José</contributor><title>Data, Signal and Image Processing and Applications in Sensors II</title><description>With the rapid advances in sensor technology, a vast and ever-growing amount of data in various domains and modalities is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensor signals’ data, further enhancement of the sensor signals acquired, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is needed. The processing of the data in themselves and the consequent extraction of useful information are also vital and included in the scope of this Special Issue. This SI of Sensors is aimed at highlighting advances in the development, testing, and application of data, signal, and image processing algorithms and techniques to all types of sensors and sensing methodologies. Experimental and theoretical results along with review papers, in as much detail as possible, are also considered. Some examples of the topics to be covered in this SI include the following: Ambient assisted living; Biomedical signal and image analysis; Machine learning in signal and image processing; Multimodal information processing for healthcare, monitoring, and surveillance; Real-time signal and image processing algorithms and architectures; Remote sensing processing; Sensors and smart sensors for IoT devices; Signal and image processing and understanding; Wearable sensor signal processing and its applications.</description><subject>5P-Medicine</subject><subject>artificial intelligence</subject><subject>auditory illusion</subject><subject>augmented reality</subject><subject>automation</subject><subject>autonomic nervous system (ANS)</subject><subject>blur kernel rendering</subject><subject>body movement cancelation</subject><subject>brain effective connectivity</subject><subject>brain–computer interface</subject><subject>breathing frequency</subject><subject>cardiovascular diseases</subject><subject>cepstral analysis</subject><subject>CFRP</subject><subject>classification</subject><subject>cloud model</subject><subject>CNN</subject><subject>cold pressor task (CPT)</subject><subject>computer vision</subject><subject>continuous wave</subject><subject>continuous wavelet transform (CWT)</subject><subject>Convolutional Neural Network (CNN)</subject><subject>convolutional neural networks</subject><subject>corona extinction voltage</subject><subject>cosine similarity</subject><subject>damage area detection</subject><subject>damaged old photo</subject><subject>data embedding</subject><subject>decision tree</subject><subject>deep learning</subject><subject>Deep Learning (DL)</subject><subject>Dempster–Shafer evidence theory</subject><subject>dense matching</subject><subject>digital health</subject><subject>Doppler radar</subject><subject>DTF</subject><subject>EEG</subject><subject>electrical discharges</subject><subject>electroencephalogram (EEG)</subject><subject>Electronics and communications engineering</subject><subject>Electronics engineering</subject><subject>electrooculogram (EOG)</subject><subject>EMG</subject><subject>endurance exercise</subject><subject>Engineering: general</subject><subject>face detection</subject><subject>feature extraction</subject><subject>feature selection</subject><subject>FitzHugh–Nagumo neuron</subject><subject>FMCW laser ranging</subject><subject>Gaussian Naive Bayes (GNB)</subject><subject>genetic algorithm</subject><subject>groundtruth</subject><subject>gyroscope sensor</subject><subject>heart rate variability</subject><subject>heartbeat</subject><subject>Hellinger distance</subject><subject>human perception of materials</subject><subject>hyperspectral images</subject><subject>IALM</subject><subject>image analysis</subject><subject>image classification and reconstruction</subject><subject>image protection</subject><subject>inertial sensor</subject><subject>instance normalization</subject><subject>internet of things</subject><subject>IoT devices monitoring</subject><subject>IoT security</subject><subject>k-means clustering</subject><subject>k-Nearest Neighbors (kNN)</subject><subject>k-nearest neighbours</subject><subject>KNN</subject><subject>labeling</subject><subject>low cost</subject><subject>low pressure</subject><subject>low-cost solution</subject><subject>machine learning</subject><subject>Machine Learning (ML)</subject><subject>material translation</subject><subject>MEG</subject><subject>memristive device</subject><subject>mining cables</subject><subject>mobile bio-sensing for medicine</subject><subject>monitoring of elderly</subject><subject>more electric aircraft</subject><subject>motion blur model</subject><subject>multi-task learning</subject><subject>multimedia systems</subject><subject>multiple radars</subject><subject>n/a</subject><subject>neural network</subject><subject>neural style transfer</subject><subject>neuromorphic circuit</subject><subject>neuron-like oscillator</subject><subject>noise reduction</subject><subject>normalization</subject><subject>octave illusion</subject><subject>pain assessment</subject><subject>pain induction</subject><subject>patient empowerment technologies</subject><subject>PCP</subject><subject>PD denoising</subject><subject>PDC</subject><subject>person detection</subject><subject>PHL transform</subject><subject>photogrammetry</subject><subject>physiological signals</subject><subject>physiological time delay</subject><subject>PhysioNet motor imagery</subject><subject>picket fence effect</subject><subject>point cloud</subject><subject>power spectrum</subject><subject>principal components analysis</subject><subject>probabilistic neural network</subject><subject>pulsed thermography</subject><subject>radar</subject><subject>Random Forest (RF)</subject><subject>real-time detection</subject><subject>respiration</subject><subject>respiratory rate</subject><subject>Robust PCA</subject><subject>RPCA</subject><subject>seizure detection</subject><subject>semantic segmentation</subject><subject>semi-supervised learning</subject><subject>sensor data fusion</subject><subject>shadow effect</subject><subject>signal analysis</subject><subject>signal processing</subject><subject>singular spectrum analysis (SSA)</subject><subject>Softmax</subject><subject>spectrum correction</subject><subject>spectrum leakage</subject><subject>spinal cord injury</subject><subject>stochastic dynamics</subject><subject>Support Vector Machine (SVM)</subject><subject>SVM</subject><subject>synchronization</subject><subject>synthetic blurry thermal image</subject><subject>Teager–Kaiser Energy Operator</subject><subject>Technology, Engineering, Agriculture, Industrial processes</subject><subject>Technology: general issues</subject><subject>temporal action detection</subject><subject>thermal detector</subject><subject>thermal image deblurring</subject><subject>transfer learning</subject><subject>Two-Stage Hybrid Model</subject><subject>variable frequency</subject><subject>video understanding</subject><subject>visual corona</subject><subject>vital signal detection</subject><subject>VMD</subject><subject>watermarking</subject><subject>wavelet threshold</subject><subject>white Gaussian noise</subject><subject>window size</subject><isbn>3725815615</isbn><isbn>9783725815616</isbn><isbn>3725815623</isbn><isbn>9783725815623</isbn><fulltext>true</fulltext><rsrctype>book</rsrctype><creationdate>2024</creationdate><recordtype>book</recordtype><sourceid>V1H</sourceid><recordid>eNotj9FKAzEQRQMiqLV_IJIPMJrJZDbJm6VaXSgoVJ_LbDe7LK7J0vT_sVafLudyuHCFuAV9jxj0Q5PzVwnOK1TOkFdAlVF4Jq7wF48EdCHmpQyNpso6HYguxeMTH_hOboY-8Sg5tbL-5j7K933exaOb-lO5mKZx2PFhyKnIIclNTCXvi6zra3He8Vji_D9n4nP1_LF8Veu3l3q5WCt2GlEZ21hDgMCNZ-0sounIABIY51p2LVAEw9hVOnbMsYkafBtsNECGg8eZuPnbzTzFtG0zn_5uwaIHjT_5lUfy</recordid><startdate>2024</startdate><enddate>2024</enddate><general>MDPI - Multidisciplinary Digital Publishing Institute</general><scope>V1H</scope></search><sort><creationdate>2024</creationdate><title>Data, Signal and Image Processing and Applications in Sensors II</title></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a7033-24b425131ab8a074332f521351277da7d15e12a3f60efaaebe018d94e2152a983</frbrgroupid><rsrctype>books</rsrctype><prefilter>books</prefilter><language>eng</language><creationdate>2024</creationdate><topic>5P-Medicine</topic><topic>artificial intelligence</topic><topic>auditory illusion</topic><topic>augmented reality</topic><topic>automation</topic><topic>autonomic nervous system (ANS)</topic><topic>blur kernel rendering</topic><topic>body movement cancelation</topic><topic>brain effective connectivity</topic><topic>brain–computer interface</topic><topic>breathing frequency</topic><topic>cardiovascular diseases</topic><topic>cepstral analysis</topic><topic>CFRP</topic><topic>classification</topic><topic>cloud model</topic><topic>CNN</topic><topic>cold pressor task (CPT)</topic><topic>computer vision</topic><topic>continuous wave</topic><topic>continuous wavelet transform (CWT)</topic><topic>Convolutional Neural Network (CNN)</topic><topic>convolutional neural networks</topic><topic>corona extinction voltage</topic><topic>cosine similarity</topic><topic>damage area detection</topic><topic>damaged old photo</topic><topic>data embedding</topic><topic>decision tree</topic><topic>deep learning</topic><topic>Deep Learning (DL)</topic><topic>Dempster–Shafer evidence theory</topic><topic>dense matching</topic><topic>digital health</topic><topic>Doppler radar</topic><topic>DTF</topic><topic>EEG</topic><topic>electrical discharges</topic><topic>electroencephalogram (EEG)</topic><topic>Electronics and communications engineering</topic><topic>Electronics engineering</topic><topic>electrooculogram (EOG)</topic><topic>EMG</topic><topic>endurance exercise</topic><topic>Engineering: general</topic><topic>face detection</topic><topic>feature extraction</topic><topic>feature selection</topic><topic>FitzHugh–Nagumo neuron</topic><topic>FMCW laser ranging</topic><topic>Gaussian Naive Bayes (GNB)</topic><topic>genetic algorithm</topic><topic>groundtruth</topic><topic>gyroscope sensor</topic><topic>heart rate variability</topic><topic>heartbeat</topic><topic>Hellinger distance</topic><topic>human perception of materials</topic><topic>hyperspectral images</topic><topic>IALM</topic><topic>image analysis</topic><topic>image classification and reconstruction</topic><topic>image protection</topic><topic>inertial sensor</topic><topic>instance normalization</topic><topic>internet of things</topic><topic>IoT devices monitoring</topic><topic>IoT security</topic><topic>k-means clustering</topic><topic>k-Nearest Neighbors (kNN)</topic><topic>k-nearest neighbours</topic><topic>KNN</topic><topic>labeling</topic><topic>low cost</topic><topic>low pressure</topic><topic>low-cost solution</topic><topic>machine learning</topic><topic>Machine Learning (ML)</topic><topic>material translation</topic><topic>MEG</topic><topic>memristive device</topic><topic>mining cables</topic><topic>mobile bio-sensing for medicine</topic><topic>monitoring of elderly</topic><topic>more electric aircraft</topic><topic>motion blur model</topic><topic>multi-task learning</topic><topic>multimedia systems</topic><topic>multiple radars</topic><topic>n/a</topic><topic>neural network</topic><topic>neural style transfer</topic><topic>neuromorphic circuit</topic><topic>neuron-like oscillator</topic><topic>noise reduction</topic><topic>normalization</topic><topic>octave illusion</topic><topic>pain assessment</topic><topic>pain induction</topic><topic>patient empowerment technologies</topic><topic>PCP</topic><topic>PD denoising</topic><topic>PDC</topic><topic>person detection</topic><topic>PHL transform</topic><topic>photogrammetry</topic><topic>physiological signals</topic><topic>physiological time delay</topic><topic>PhysioNet motor imagery</topic><topic>picket fence effect</topic><topic>point cloud</topic><topic>power spectrum</topic><topic>principal components analysis</topic><topic>probabilistic neural network</topic><topic>pulsed thermography</topic><topic>radar</topic><topic>Random Forest (RF)</topic><topic>real-time detection</topic><topic>respiration</topic><topic>respiratory rate</topic><topic>Robust PCA</topic><topic>RPCA</topic><topic>seizure detection</topic><topic>semantic segmentation</topic><topic>semi-supervised learning</topic><topic>sensor data fusion</topic><topic>shadow effect</topic><topic>signal analysis</topic><topic>signal processing</topic><topic>singular spectrum analysis (SSA)</topic><topic>Softmax</topic><topic>spectrum correction</topic><topic>spectrum leakage</topic><topic>spinal cord injury</topic><topic>stochastic dynamics</topic><topic>Support Vector Machine (SVM)</topic><topic>SVM</topic><topic>synchronization</topic><topic>synthetic blurry thermal image</topic><topic>Teager–Kaiser Energy Operator</topic><topic>Technology, Engineering, Agriculture, Industrial processes</topic><topic>Technology: general issues</topic><topic>temporal action detection</topic><topic>thermal detector</topic><topic>thermal image deblurring</topic><topic>transfer learning</topic><topic>Two-Stage Hybrid Model</topic><topic>variable frequency</topic><topic>video understanding</topic><topic>visual corona</topic><topic>vital signal detection</topic><topic>VMD</topic><topic>watermarking</topic><topic>wavelet threshold</topic><topic>white Gaussian noise</topic><topic>window size</topic><toplevel>online_resources</toplevel><collection>DOAB: Directory of Open Access Books</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cabral dos Santos Reis, Manuel José</au><format>book</format><genre>book</genre><ristype>BOOK</ristype><btitle>Data, Signal and Image Processing and Applications in Sensors II</btitle><date>2024</date><risdate>2024</risdate><isbn>3725815615</isbn><isbn>9783725815616</isbn><isbn>3725815623</isbn><isbn>9783725815623</isbn><abstract>With the rapid advances in sensor technology, a vast and ever-growing amount of data in various domains and modalities is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensor signals’ data, further enhancement of the sensor signals acquired, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is needed. The processing of the data in themselves and the consequent extraction of useful information are also vital and included in the scope of this Special Issue. This SI of Sensors is aimed at highlighting advances in the development, testing, and application of data, signal, and image processing algorithms and techniques to all types of sensors and sensing methodologies. Experimental and theoretical results along with review papers, in as much detail as possible, are also considered. Some examples of the topics to be covered in this SI include the following: Ambient assisted living; Biomedical signal and image analysis; Machine learning in signal and image processing; Multimodal information processing for healthcare, monitoring, and surveillance; Real-time signal and image processing algorithms and architectures; Remote sensing processing; Sensors and smart sensors for IoT devices; Signal and image processing and understanding; Wearable sensor signal processing and its applications.</abstract><pub>MDPI - Multidisciplinary Digital Publishing Institute</pub><doi>10.3390/books978-3-7258-1562-3</doi><oa>free_for_read</oa></addata></record> |
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| subjects | 5P-Medicine artificial intelligence auditory illusion augmented reality automation autonomic nervous system (ANS) blur kernel rendering body movement cancelation brain effective connectivity brain–computer interface breathing frequency cardiovascular diseases cepstral analysis CFRP classification cloud model CNN cold pressor task (CPT) computer vision continuous wave continuous wavelet transform (CWT) Convolutional Neural Network (CNN) convolutional neural networks corona extinction voltage cosine similarity damage area detection damaged old photo data embedding decision tree deep learning Deep Learning (DL) Dempster–Shafer evidence theory dense matching digital health Doppler radar DTF EEG electrical discharges electroencephalogram (EEG) Electronics and communications engineering Electronics engineering electrooculogram (EOG) EMG endurance exercise Engineering: general face detection feature extraction feature selection FitzHugh–Nagumo neuron FMCW laser ranging Gaussian Naive Bayes (GNB) genetic algorithm groundtruth gyroscope sensor heart rate variability heartbeat Hellinger distance human perception of materials hyperspectral images IALM image analysis image classification and reconstruction image protection inertial sensor instance normalization internet of things IoT devices monitoring IoT security k-means clustering k-Nearest Neighbors (kNN) k-nearest neighbours KNN labeling low cost low pressure low-cost solution machine learning Machine Learning (ML) material translation MEG memristive device mining cables mobile bio-sensing for medicine monitoring of elderly more electric aircraft motion blur model multi-task learning multimedia systems multiple radars n/a neural network neural style transfer neuromorphic circuit neuron-like oscillator noise reduction normalization octave illusion pain assessment pain induction patient empowerment technologies PCP PD denoising PDC person detection PHL transform photogrammetry physiological signals physiological time delay PhysioNet motor imagery picket fence effect point cloud power spectrum principal components analysis probabilistic neural network pulsed thermography radar Random Forest (RF) real-time detection respiration respiratory rate Robust PCA RPCA seizure detection semantic segmentation semi-supervised learning sensor data fusion shadow effect signal analysis signal processing singular spectrum analysis (SSA) Softmax spectrum correction spectrum leakage spinal cord injury stochastic dynamics Support Vector Machine (SVM) SVM synchronization synthetic blurry thermal image Teager–Kaiser Energy Operator Technology, Engineering, Agriculture, Industrial processes Technology: general issues temporal action detection thermal detector thermal image deblurring transfer learning Two-Stage Hybrid Model variable frequency video understanding visual corona vital signal detection VMD watermarking wavelet threshold white Gaussian noise window size |
| title | Data, Signal and Image Processing and Applications in Sensors II |
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