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This book focuses on both fundamental and applied research on nanogenerators. The triboelectric nanogenerator (TENG) is based on expanded Maxwell’s equations for a mechano-driven system, including the polarization density term Ps in a displacement vector owing to the electrostatic charges on medium surfaces as produced by effects such as triboelectrification. The TENGs have potential applications in blue energy, wearable devices, environmental protectioin, medical science, and security. Hybridized and coupled nanogenerators further expand the application of nanogenerators in energy stability and multi-functional sensing.
triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan
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This book focuses on both fundamental and applied research on nanogenerators. The triboelectric nanogenerator (TENG) is based on expanded Maxwell’s equations for a mechano-driven system, including the polarization density term Ps in a displacement vector owing to the electrostatic charges on medium surfaces as produced by effects such as triboelectrification. The TENGs have potential applications in blue energy, wearable devices, environmental protectioin, medical science, and security. Hybridized and coupled nanogenerators further expand the application of nanogenerators in energy stability and multi-functional sensing.
Technology: general issues --- triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan --- triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan
Choose an application
This book focuses on both fundamental and applied research on nanogenerators. The triboelectric nanogenerator (TENG) is based on expanded Maxwell’s equations for a mechano-driven system, including the polarization density term Ps in a displacement vector owing to the electrostatic charges on medium surfaces as produced by effects such as triboelectrification. The TENGs have potential applications in blue energy, wearable devices, environmental protectioin, medical science, and security. Hybridized and coupled nanogenerators further expand the application of nanogenerators in energy stability and multi-functional sensing.
Technology: general issues --- triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan
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Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions.
Medical equipment & techniques --- movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension --- movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension
Choose an application
Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions.
movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension
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