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In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

History of engineering & technology --- geometric calibration --- long- and short-period errors --- equivalent bias angles --- sparse recovery --- linear array push-broom sensor --- deep learning --- signal detection --- modulation classification --- the single shot multibox detector networks --- the multi-inputs convolutional neural networks --- medical image registration --- similarity measure --- non-rigid transformation --- computational efficiency --- registration accuracy --- signal denoising --- singular value decomposition --- Akaike information criterion --- reaction wheel --- micro-vibration --- permutation entropy (PE) --- weighted-permutation entropy (W-PE) --- reverse permutation entropy (RPE) --- reverse dispersion entropy (RDE) --- time series analysis --- complexity --- sensor signal --- tensor principal component pursuit --- stable recovery --- tensor SVD --- ADMM --- kalman filter --- nonlinear autoregressive --- neural network --- noise filtering --- multiple-input multiple-output (MIMO) --- frequency-hopping code --- dual-function radar-communications --- information embedding --- mutual information (mi) --- waveform optimization --- spectroscopy --- compressed sensing --- inverse problems --- dictionary learning --- image registration --- large deformation --- weakly supervised --- high-order cumulant --- cyclic spectrum --- decision tree–support vector machine --- wind turbine --- gearbox fault --- cosine loss --- long short-term memory network --- indoor localization --- CSI --- fingerprinting --- Bayesian tracking --- image reconstruction --- computed tomography --- nonlocal total variation --- sparse-view CT --- low-dose CT --- proximal splitting --- row-action --- brain CT image --- audio signal processing --- sound event classification --- nonnegative matric factorization --- blind signal separation --- support vector machines --- brain-computer interface --- motor imagery --- machine learning --- internet of things --- pianists --- surface inspection --- aluminum ingot --- mask gradient response --- Difference of Gaussian --- inception-v3 --- EEG --- sleep stage --- wavelet packet --- state space model --- image captioning --- three-dimensional (3D) vision --- human-robot interaction --- Laplacian scores --- data reduction --- sensors --- Internet of Things (IoT) --- LoRaWAN --- n/a --- decision tree-support vector machine

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• Reference Manager
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In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

History of engineering & technology --- geometric calibration --- long- and short-period errors --- equivalent bias angles --- sparse recovery --- linear array push-broom sensor --- deep learning --- signal detection --- modulation classification --- the single shot multibox detector networks --- the multi-inputs convolutional neural networks --- medical image registration --- similarity measure --- non-rigid transformation --- computational efficiency --- registration accuracy --- signal denoising --- singular value decomposition --- Akaike information criterion --- reaction wheel --- micro-vibration --- permutation entropy (PE) --- weighted-permutation entropy (W-PE) --- reverse permutation entropy (RPE) --- reverse dispersion entropy (RDE) --- time series analysis --- complexity --- sensor signal --- tensor principal component pursuit --- stable recovery --- tensor SVD --- ADMM --- kalman filter --- nonlinear autoregressive --- neural network --- noise filtering --- multiple-input multiple-output (MIMO) --- frequency-hopping code --- dual-function radar-communications --- information embedding --- mutual information (mi) --- waveform optimization --- spectroscopy --- compressed sensing --- inverse problems --- dictionary learning --- image registration --- large deformation --- weakly supervised --- high-order cumulant --- cyclic spectrum --- decision tree-support vector machine --- wind turbine --- gearbox fault --- cosine loss --- long short-term memory network --- indoor localization --- CSI --- fingerprinting --- Bayesian tracking --- image reconstruction --- computed tomography --- nonlocal total variation --- sparse-view CT --- low-dose CT --- proximal splitting --- row-action --- brain CT image --- audio signal processing --- sound event classification --- nonnegative matric factorization --- blind signal separation --- support vector machines --- brain-computer interface --- motor imagery --- machine learning --- internet of things --- pianists --- surface inspection --- aluminum ingot --- mask gradient response --- Difference of Gaussian --- inception-v3 --- EEG --- sleep stage --- wavelet packet --- state space model --- image captioning --- three-dimensional (3D) vision --- human-robot interaction --- Laplacian scores --- data reduction --- sensors --- Internet of Things (IoT) --- LoRaWAN

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This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains.

Information technology industries --- Computer science --- tourism big data --- text mining --- NLP --- deep learning --- clinical named entity recognition --- information extraction --- multitask model --- long short-term memory --- conditional random field --- relation extraction --- entity recognition --- long short-term memory network --- multi-turn chatbot --- dialogue context encoding --- WGAN-based response generation --- BERT word embedding --- text summary --- reinforce learning --- FAQ classification --- encoder-decoder neural network --- multi-level word embeddings --- BERT --- bidirectional RNN --- cloze test --- Korean dataset --- machine comprehension --- neural language model --- sentence completion --- primary healthcare --- chief complaint --- virtual medical assistant --- spoken natural language --- disease diagnosis --- medical specialist --- protein-protein interactions --- deep learning (DL) --- convolutional neural networks (CNN) --- bidirectional long short-term memory (bidirectional LSTM) --- dialogue management --- user simulation --- reward shaping --- conversation knowledge --- multi-agent reinforcement learning --- language modeling --- classification --- error probability --- error assessment --- logic error --- neural network --- LSTM --- attention mechanism --- programming education --- neural architecture search --- word ordering --- Korean syntax --- adversarial attack --- adversarial example --- sentiment classification --- dual pointer network --- context-to-entity attention --- text classification --- rule-based --- word embedding --- Doc2vec --- paraphrase identification --- encodings --- R-GCNs --- contextual features --- sentence retrieval --- TF−ISF --- BM25 --- partial match --- sequence similarity --- word to vector --- word embeddings --- antonymy detection --- polarity --- text normalization --- natural language processing --- deep neural networks --- causal encoder --- question classification --- multilingual --- convolutional neural networks --- Natural Language Processing (NLP) --- transfer learning --- open information extraction --- recurrent neural networks --- bilingual translation --- speech-to-text --- LaTeX decompilation --- word representation learning --- word2vec --- sememes --- structural information --- sentiment analysis --- zero-shot learning --- news analysis --- cross-lingual classification --- multilingual transformers --- knowledge base --- commonsense --- sememe prediction --- attention model --- ontologies --- fixing ontologies --- quick fix --- quality metrics --- online social networks --- rumor detection --- Cantonese --- XGA model --- delayed combination --- CNN dictionary --- named entity recognition --- deep learning NER --- bidirectional LSTM CRF --- CoNLL --- OntoNotes --- toxic comments --- neural networks