This book addresses one of the key problems in signal processing , the problem of identifying statistical properties of excursions in a random process in order to simplify the theoretical analysis and make it suitable for engineering applications . Precise and approximate formulas are explained , which are relatively simple and can be used for engineering applications such as the design of devices which can overcome the high initial uncertainty of the self-training period . An energy-efficient sensor node processor ( SNP ) is presented for intelligent sensing in Internet of Things ( IoT ) applications . To achieve ultralow energy consumption and satisfying performance , the proposed processor incorporates an ARM Cortex-M0 RISC core and diverse hardware accelerators , including discrete wavelet packet transform engine , finite-impulse-response filtering engine , fast Fourier transform engine , and coordinate rotation digital computer engine , to accelerate signal processing tasks . At the architecture level , dual-bus architecture with automatic bus sensing and reconfigurable memory access scheme are proposed . The measuring system is based on 16 -bit sigma-delta analog/digital converter , microcontroller and personal computer . A special digital smoothing polynomial filter is used for signal processing . The example of real measured and evaluated signal is also shown . The subject of this thesis is the original study of the application of the multi-layer perceptron architecture to channel equalization in digital communications systems . Both theoretical analyses and simulations were performed to explore the performance of the perceptron-based equalizer ( including the decision feedback equalizer ) . Channel equalization is a key technology in optical communications . Equalization has a good performance in dealing with multi-path disturbance and Inter Symbol Interference (ISI),and is suitable for data transmission . Traditional methods for channel equalization often involve cascading , a pipelined A/D converter and a FIR filter . Given the nature of pipelined converters , a new method is proposed that performs partial equalization within the pipelined A/D thus reducing the number of taps required for the FIR filter . The channel equalization problem for staggered ( or offset ) modulation formats is considered . It is shown that due to the relative time offset of the in-phase and quadrature components , the received signal must be sampled at twice the baud rate of the corresponding non-staggered signal . Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems . In this study , we consider IIR channel both for system identification and channel equalization purposes . We focus on four different approaches : In this paper authors have addressed the problems associated with blind adaptation technique used for channel equalization purposes . Blind adaptation techniques shows very poor convergence property in contrast to the supervised techniques which uses training sequences e.g. least mean square algorithm . Neural networks have been studied for channel equalization purposes with quite promising results . However , not a lot of published results are available for their performance in realistic mobile systems , such as GSM ( Global System for Mobile communications ) . In addition , we propose a new channel frequency response initialization method to solve the permutation indeterminacy and complex uncertain scaling problems in ICA-based channel equalization . The proposed initialization method shows faster convergence speed than the conventional random initialization method in the channel equalization process . Finally , we examine the number of OFDM symbols required for the proposed ICA-based channel equalization . The demodulating unit transforms a received signal to a signal of a frequency area . The channel equalizing unit performs channel equalizing by using the signal of the frequency area demodulated by the channel equalizing unit and a previously estimated channel . The channel estimating unit estimates the signal of the frequency area demodulated by the demodulating unit , and a high priority transmission signal and a low priority transmission signal . In this paper , we investigate the use of some classical techniques for accelerating the convergence of the LMS algorithm in widely linear ( WL ) adaptive processing . The application of channel equalizing is considered . Simulation results permit to verify that the normalized LMS strategy presents the best performance as far as a reduction of the trade-off between the convergence rate and steady-state error is concerned . We demonstrate an efficient wavelength-division-multiplexed ( WDM ) equalizer structure that uses a reconfigurable opto-very large scale integrated processor to steer/reshape many optical beams simultaneously , thus , achieving channel-by-channel equalization . The opto-VLSI processor consists of an array of liquid crystal ( LC ) cells independently addressed by a VLSI circuit to generate a reconfigurable reflective digital hologram . Then it brings forward an improved designing method for coefficients of equalization filter based on Fourier Transform . Finally an evaluating method for channel equalization effect in noise conditions is brought forwards . The effectiveness of above methods is validated through analyzing of equalization result . Abstract We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter ( RD-LKF ) by introducing the butterfly finite-impulse-response ( FIR ) filter in our previously proposed RD-LKF method . Along with the fast polarization tracking , it can also simultaneously compensate the inter-symbol interference ( ISI ) effects including residual chromatic dispersion and polarization mode dispersion . For cancelling the effect of phase error accurately and obtaining good performance , a new CPE ( Common Phase Error ) cancellation scheme is proposed for OFDM(Orthogonal Frequency Division Multiplexing ) system , which corrects the CPE with two steps in frequency synchronization and channel equalizer , and estimates the CPE using all pilots in an OFDM symbol including CP ( Continual Pilots ) and SP ( Scattered Pilots ) . The analysis and the simulations show that the proposed CPE correction structure with two steps adapts to the CPE correction in OFDM system , and the new CPE estimation algorithm using all pilots can obtain more accurate results than the conventional method , especially , under the severe channel conditions . The former is highly dependent on the number of LCs , and the latter the number of all the basic patterns . In order to achieve good noise performance , a small number of the basic patterns is preferred . These properties of the DRNN are theoretically analyzed and confirmed through computer simulations . Meanwhile combining the two adjacent synchronization code searching technology , the algorithm can extract the watermark without the help from the origin digital audio signal . Simulation results show that the proposed watermarking scheme is not only inaudible and robust against common signals processing such as MP3 compression , noise addition , resampling , and re-quantization etc . , but also robust against the desynchronization attacks such as random cropping , amplitude variation , pitch shifting , and jittering etc . The bits obtained from two different images or for two different keys K will generally be different ( uncorrelated ) . However , for the same key K , two images that can be matched after applying gray scale operations , such as lossy compression , recoloring , filtering , noise adding , gamma correction , and simple geometrical operations including rotation and scaling , the extracted N-tuple will be almost the same . We also explain how the extracted N-tuple can be further utilized for synthesizing a Gaussian sequence that gradually changes with increasing number of errors in the extracted bits . In the extraction procedure , the watermark can be extracted from the watermarked image without the requirement of the original host image or the original watermark image . Experimental results , compared with the related existing methods , show that the proposed color image scheme has stronger robustness against most common attacks such as image compression , filtering , cropping , noise adding , blurring , scaling and sharpening etc . Noise could also mimic natural sounds ( e.g. , seismic impulses that are similar to tail beats ) , or attract marine mammals into dangerous areas ( e.g. , attracting killer whales to fishing gear ) . Given the paucity of research available on noise effects in marine mammals , standards may ... An effective and sustainable reduction of highly annoyed people caused by noise is only possible with further scientific endeavors in the area of methods development and research of noise effects . Soundscape suggests exploring noise in its complexity , its ambivalence , and its approach toward sound to consider the conditions and purposes of its production , perception , and evaluation , to understand evaluation of noise as a holistic approach . The proposed method , regulating the quantization step intelligently by utilizing energy characteristic of the audio signals , is remarkably effective in several audio signals . Experimental results show that the proposed watermarking algorithm is imperceptible and robust against various signal processing such as re-sampling , re-quantifying , noising adding , low pass filtering and MP3 compression . This paper presents a method for signal denoising in the case of varying noise proportional to the local signal intensity . The signals are processed in a wavelet domain with a non-uniform threshold adjusted to the noise level . The complexity of this optimal detector grows exponentially with the number of observations and as a remedy , we propose a number of approximations to it . The problem under study is a fundamental one and it has applications in signal denoising , anomaly detection , and spectrum sensing for cognitive radio . We illustrate the results in the context of the latter . We show that efficient posterior sampling from the DAE can be achieved via Metropolis-Hastings , which allows the Monte Carlo EM algorithm to be used . We demonstrate competitive results for signal denoising , image deblurring and image devignetting . Our method is an example of combining the representation power of deep learning with uncertainty quantification from Bayesian statistics . One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio ( SNR ) scenarios . In this paper , we investigate the signal denoising problem for multi-component linear frequency modulated ( LFM ) signals . An effective time-frequency ( TF ) analysis-based approach is proposed . Both theoretical analysis and empirical evidence confirm that the approximate message passing ( AMP ) algorithm can be interpreted as recursively solving a signal denoising problem : at each AMP iteration , one observes a Gaussian noise perturbed original signal . Retrieving the signal amounts to a successive noise cancellation until the noise variance decreases to a satisfactory level . Estimating single-trial evoked potentials ( EPs ) corrupted by the spontaneous electroencephalogram ( EEG ) can be regarded as signal denoising problem . Sparse coding has significant success in signal denoising and EPs have been proven to have strong sparsity over an appropriate dictionary . Wavelet transforms are , generally , less prone to the problem than the pure spatial or frequency domain transforms , but there is still much room for improvements . In this paper , we propose a point-wise adaptive wavelet transform for signal denoising applications . It is very efficient in denoising higher frequency regions , without compromising the performance on smooth , lower frequency , regions . The author has introduced a new efficient algorithm called “ weighted mediated morphological filters ” for signal/image denoising [ 1 ] . Adaptive version of such operators has been applied in 1-D for adaptive ECG processing [ 2 ] . A novel approach is put forward to detect and analyze voltage stability by combining wavelet transform with pattern recognition technique . In signal denoising process , the statistic rule is proposed to determine the threshold of each order of wavelet space , which can determine the decomposition level adaptively , increasing the signal-noise-ratio . The wavelet transform coefficients as feature vector are presented for extracting disturbance signal . In this paper , we drive the wavelet threshold error function , adopt the particle swarm optimization ( PSO ) and genetic algorithm ( GA ) to determine the optimal wavelet denoising threshold , the results show that our methods have better performance in signal denoising processing Matching Pursuit ( MP ) techniques have recently been considered for signal denoising purposes . MP can reconstruct a clean signal from a noisy observation by greedily evaluating a least-squares ( LS ) estimate as a linear combination of elements ( atoms ) of a dictionary . Owing to the characteristic of wavelet transform , the detecting signal could be decomposed into different frequency bands by the Mallat decomposing algorithm . In the process of the efficient signal detecting and denoising , a measuring frequency band was set up , and the information within this band was separated and other space vectors were set to zero . So , the useful information with the frequency could be extracted while the rest were removed . These phenomena cause the frequently useful time-dependent pattern to be completely destroyed and become very difficult for further interpretation . An appropriate method of signal preprocessing may help the researcher to enhance some features and to obtain valuable information . Two simple algorithms of fetal heart rate filtering are described . The interface is based on EEG signal analyses , which extract features that can be classified as specific mental activities . First , a signal preprocessing is performed from the EEG data , filtering noise , using a spatial filter to increase the scalp signal resolution , and extracting relevant features . Then , different classifier models are proposed , evaluated and compared . For this purpose , BCI systems rely on signal processing and machine learning algorithms to decode the brain activity . This chapter provides an overview of the main steps required to do such a process , including signal preprocessing , feature extraction and selection , and decoding . Given the large amount of possible methods that can be used for these processes , a comprehensive review of them is beyond the scope of this chapter , and it is focused instead on the general principles that should be taken into account , as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems . Finite-impulse response ( FIR ) Filter is widely used in wireless sensor networks as a signal pre-processing step . Because sensor nodes require a long working periods and ultra-low cost , traditional FIR structures are in applicable as multipliers occupy too much die size for such node ’s chips . In this paper , we design energy efficient Finite Impulse Response ( FIR ) filter , which is widely used in wireless sensor networks as a signal pre-processing step because sensor nodes require a long working periods . Finite impulse response ( FIR ) filter is a type of digital filters . Finite-impulse response ( FIR ) Filter is widely used in wireless sensor networks as a signal pre-processing step . Because sensor nodes require a long working periods and ultra-low cost , traditional FIR structures are inapplicable as multipliers occupy too much die size for such node 's chips . Mental task among 5 subjects was processed to acquire motor imagery EEG signal . In order to improve the signal-to-noise ratio ( SNR ) of EEG , filters in time domain , frequency domain and special domain are applied in signal preprocess . Additionally , power spectral density ( PSD ) was extracted with sliding windows from processed signal as features for pattern recognition . As it is possible to extract further information about the health state by fusioning data of different biosensors , the wireless link based on IEEE 802.15.4 was extended by a synchronisation mechanism enabling synchronous sampling of the individual sensors . An adaptive application of algorithms for signal pre-processing and analysis allows the reduction of the transferred data . In this paper , we propose a signal strength exchange ( SSE ) system as an efficient key generation system and a synchronized quantization ( SQ ) method as a part of the SSE system that synchronizes data blocks in the quantization phase . The SQ method eliminates the signal pre-processing phase by performing a multi-bit conversion directly from the channel characteristics of the measurement results . Synchronization is carried out between the two authorized nodes to ensure sameness of the produced keys so it can eliminate the error-correcting phase . In addition , data and signals required for offline indicator design are obtained and stored in a database [ 9 ] . For this purpose NeuMonD provides data acquisition , signal processing and pre-processing , online visualization and recording of comments ( e.g. clinical events including time stamps ) required for later offline analysis . Furthermore , NeuMonD allows simultaneous and synchronized data re-cording from different clinical monitors . Modelling and analysis of surface Electromyogram ( sEMG ) signal has gained increasing attention in bio-signal processing for medical and healthcare applications . This research reports the study to examine the complexity in surface electromyogram signal measured from different muscles to identify the properties of muscles . Feature extraction is playing a major role in bio signal processing . Feature identification and selection has two approaches . In 1977 , Reichertz has defined medical informatics from the viewpoint of information logistics : to provide the right information at the right time at the right place [ 1 ; 2 ] . The analysis of medical information and (bio-)signal processing contributes substantially to an improved information provision . Therefore , medical informatics is an applied science in the best sense of the word . The development of bio-signal analysis systems , mostly , has become amajor research field due to technological progress in signal processing . Electrocardiography ( ECG ) had been amongst the most studied type of bio-signals for several decades . These new edge computing sensors for personalized healthcare must operate within a tight energy envelope ; addressing the ensuing challenge , we herein introduce an inexact and heterogeneous edge computing architecture , specifically tailored to the bio-DSP domain . We observe that bio-signal analysis applications present task-level parallelism , intensive computational hotspots and a high degree of resilience towards errors . These characteristics drive our new bio-DSP edge node architecture design composed of multiple processing cores , a Coarse-Grained Reconfigurable Array ( CGRA ) accelerator , and hardware-software co-design support to become resilient to a non-zero probability of bit-flips at runtime . Latest embedded bio-signal analysis applications , targeting low-power Wireless Body Sensor Nodes ( WBSNs ) , present conflicting requirements . On one hand , bio-signal analysis applications are continuously increasing their demand for high computing capabilities . On the other hand , long-term signal processing in WBSNs must be provided within their highly constrained energy budget . In this context , parallel processing effectively increases the power efficiency of WBSNs , but only if the execution can be properly synchronized among computing elements . To address this challenge , in this work we propose a hardware/software approach to synchronize the execution of bio-signal processing applications in multi-core WBSNs . This new approach requires little hardware resources and very few adaptations in the source code . An 8-channel ultra low power programmable wireless ExG ( ECG , EMG and EEG ) system-on-chip ( SoC ) design for bio-signal processing applications is presented in this paper . The proposed design consists of a capacitive coupled programmable gain instrumentation amplifier ( CC-PGIA ) with an improved transconductance of amplifier . Implantable biomedical systems usually operate in energy-limited environments and exhibit large variation of power consumption ranging from constant low-power ( bio-signal sensing ) to sporadic high-power ( stimulation and/or burst data transmission ) [ 1 ] . Thus , energy-efficient switching power converters that can support a wide range of load variation are necessary to meet this requirement . In this paper , we propose to convert high frequency signal to low frequency clock using 1/n series flip flop divider for down conversion mixed filtering in lock-in amplifier . This simple but novel idea will solve the physical problem to implement lock-in amplifier for low frequency signal DC level detection application in the fields of bio-signal sensing or nano-ampere signal detection application . The present invention for predicting the surface of the water phase using a biological signal of that , different conditions for the bio-signal-based automatic sleep stage classification system . The present invention to this , a variety of biological signals obtained through the respective channels in the bio-signal detection , bio-signal detector each for obtaining a variety of biological signals over a different channel using the EEG , EOG , EMG , PPG , SKT , GSR sensors subjected to signal processing in a manner suitable for feature extraction of a biological signal to the feature points for each of biological signal signal processing to extract a feature point representing the characteristics of the bio-signal and the feature extraction unit , a signal processing and feature extraction unit from each of the biological signal a subset generating unit for generating bio-signal combining basis , and classification of sleep stages ( REM , LS , DS ) through the combined biometric signals combined in the subset generation unit , and the performance evaluation of analyzing the performance evaluation of the combination according to the steps bio-signal-based automatic sleep stage classification to provide a system , when measuring another sleep stage analysis using the bio-electrical signals of different conditions including a The accuracy of the sleep stages optimum combination of biological signals in consideration of convenience makes it possible to accurately and easily predictable . Furthermore , one can determine whether the phenomenon corresponds to an activation or a de-activation of the brain region . In practice , however , there are many bio-signal processing problems to be solved : the artifact removal in the EEG , the demodulation of the EEG , the extraction of an EEG reference from the multi-channels , the determination of a sensible correlation co-efficient ( in which heart beat effects and breathing are eliminated ) and its statistical significance . In this paper , several innovations concerning the signal processing of simultaneously recorded fMRI , PULSE and fMRI are presented and applied in a case study on the generators of the alpha-rhythm An artificial neural network ( ANN ) approach for radar image processing is presented in this paper . A renewal concept of simple adaptive units as a foundation for network assembling allows one to design ANN-based feature extraction scheme for 2D-signal processing . It was shown that ANN implementing radial basis function ( RBF ) processing units can be applied for identification of radar targets described by the set of scatterers . After a short review of 1D linear systems the usual method for 2D signal processing will be reviewed . Its merits and disadvantages will be treated briefly . Furthermore , the whole convolver can be embedded in one single uniform bit-serial one-dimensional structure . This extremely pin-saving and VLSI-oriented design can also be used for recursive filters and for 2D signal processing . Programmability ( using a structure with varying precision and kernel size ) can be traded for simplicity and efficacy . We propose a new real-valued lapped transform for 2D-signal and image processing . Lapped transforms are particularly useful in block-based processing , since their intrinsically overlapping basis functions reduce or prevent block artifacts . Abstract We describe a new real-valued lapped transform for 2D-signal and image processing . Lapped transforms are particularly useful in block-based processing , since their overlapping basis functions reduce or prevent block artifacts . This opportunity is used to analyze some research directions in the last decades and to suggest what directions may be fruitful to develop . The paper undertakes the task of reviewing some of our laboratory applications in 1D/2D signal processing . We are referring to the wave propagation fuzzy modeling , the use of fuzzy logic in formant detection and speech synthesis , in Parkinson disease diagnosis using handwriting text analysis and in processing grayscale medical images . MATLAB toolbox and graphic user interface , EEGLAB is used for processing EEG data of any number of channels . Wavelet toolbox has been used for 2-D signal analysis . Spatio-temporal analysis of seismic records is of particular relevance in many geophysical applications , e.g. , vertical seismic profiles , plane-wave slowness estimation in seismographic array processing and in sonar array processing . The goal is to estimate from a limited number of receivers the 2-D spectral signature of a group of events that are recorded on a linear array of receivers . Estimation of the incident waveform directions , or direction of arrival ( DOA ) estimation , is an essential problem in sonar array processing . A sonar array usually consists of a large number of elements , and the associated data processing complexity is extensive . World-wide a concern exists about the influence of man-made noise on marine life , and particularly of high power sonar . Most concern lies with marine mammals that use acoustics for hunting , communication and/or navigation . This includes the design and modeling of acoustical and vibrational transducers , arrays , and transduction systems in all media and frequency ranges ; instrumentation , metrology , and calibration ; measurement and computational techniques as they relate to acoustical phenomena and their utility ; and the engineering of materials and devices . EATC ’s recently sponsored special sessions attracted members from all areas and is an enabler for overlapping interests : single crystal piezoelectrics , transducer design , vector sensors , materials for high power sonar , micromachined silicon mics , multimedia , 3‐D audio , pro-audio , parametric sources , speakers , transduction , projection , materials processing and manufacturing , piezocomposites , sensor fusion , signal processing hardware , data communication , condition-based monitori ... A projector formulation for three-mode signal and noise subspaces is also given and the improvement of separation with the three-mode approach over a componentwise approach is shown . We study two applications for the proposed Higher Order Subspace approach : the reverberation problem in sonar , and the polarized seismo-acoustic wave separation problem . For the first application , we propose a three-mode version of the Principal Component Inverse algorithm ( IEEE Trans . ( Tele ) kinetic imaging ( e.g. kinetic sonar , radar , etc . ) provides visuo-acoustic feedback in which subject matter undergoes vibrational or other acoustic stimulation and the response to the stimulation is imaged . Specifically , the imaging uses active sonar to cause microscopic vibrations in the subject matter , while a special kind of lock-in amplifier is used to image the effects of these microscopic vibrations . We use data from an experiment of sediment transport conducted by the Applied Physics Laboratory at the University of Washington . We apply a variety of wavelet- based vector quantization data compression algorithms to acoustic sonar scans . We sue pruned tree-structured vector quantization ( PTSVQ ) with the generalized Breiman , Friedman , Olshen , and Stone algorithm to simultaneously prune trees that correspond to different wavelet subbands . A generic NNC ( neural network classifier ) capable of providing 1.9 billion programmable connections per second is described . Applications for these generic processors include image and speech recognition as well as sonar signal identification . To demonstrate the modularity and flexibility of the CCD ( charge coupled device ) NNCs , two generic multilayer system-level boards capable of both feedforward and feedback nets are presented . Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot , and the action that yields the lowest cost ( i.e. , the maximum information ) is selected . This technique is demonstrated via simulations , in-air sonar experiments , and underwater sonar experiments . Results are shown for ( 1 ) adaptive control of motion and ( 2 ) adaptive control of motion and scanning . Hence , they are well suited to the problem of training feedforward networks . In this paper , we describe a set of experiments performed on data from a sonar image classification problem . These experiments both 1 ) illustrate the improvements gained by using a genetic algorithm rather than backpropagation and 2 ) chronicle the evolution of the performance of the genetic algorithm as we added more and more domain-specific knowledge into it . Uniform or symmetric array is a common practice for most of the sonar array design , where the same performance transducer elements are used for numerical simulation and manufacture engineering conveniences . However , non‐uniform array designs show their advantages for special array applications . A common model for sonar clutter is that of the transmitted signal convolved with a colored Gaussian process relating to the sea-bottom profile . Rather surprisingly , this noise becomes strongly non-Gaussian if there are multiple realizations and if a realistic random phase is introduced - the univariate statistics remain Gaussian , but the joint probability density function is not .
This book addresses one of the key problems in signal processing , the problem of identifying statistical properties of excursions in a random process in order to simplify the theoretical analysis and make it suitable for engineering applications . Precise and approximate formulas are explained , which are relatively simple and can be used for engineering applications such as the design of devices which can overcome the high initial uncertainty of the self-training period .
An energy-efficient sensor node processor ( SNP ) is presented for intelligent sensing in Internet of Things ( IoT ) applications . To achieve ultralow energy consumption and satisfying performance , the proposed processor incorporates an ARM Cortex-M0 RISC core and diverse hardware accelerators , including discrete wavelet packet transform engine , finite-impulse-response filtering engine , fast Fourier transform engine , and coordinate rotation digital computer engine , to accelerate signal processing tasks . At the architecture level , dual-bus architecture with automatic bus sensing and reconfigurable memory access scheme are proposed .
The measuring system is based on 16 -bit sigma-delta analog/digital converter , microcontroller and personal computer . A special digital smoothing polynomial filter is used for signal processing . The example of real measured and evaluated signal is also shown .
The subject of this thesis is the original study of the application of the multi-layer perceptron architecture to channel equalization in digital communications systems . Both theoretical analyses and simulations were performed to explore the performance of the perceptron-based equalizer ( including the decision feedback equalizer ) .
Channel equalization is a key technology in optical communications . Equalization has a good performance in dealing with multi-path disturbance and Inter Symbol Interference (ISI),and is suitable for data transmission .
Traditional methods for channel equalization often involve cascading , a pipelined A/D converter and a FIR filter . Given the nature of pipelined converters , a new method is proposed that performs partial equalization within the pipelined A/D thus reducing the number of taps required for the FIR filter .
The channel equalization problem for staggered ( or offset ) modulation formats is considered . It is shown that due to the relative time offset of the in-phase and quadrature components , the received signal must be sampled at twice the baud rate of the corresponding non-staggered signal .
Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems . In this study , we consider IIR channel both for system identification and channel equalization purposes . We focus on four different approaches :
In this paper authors have addressed the problems associated with blind adaptation technique used for channel equalization purposes . Blind adaptation techniques shows very poor convergence property in contrast to the supervised techniques which uses training sequences e.g. least mean square algorithm .
Neural networks have been studied for channel equalization purposes with quite promising results . However , not a lot of published results are available for their performance in realistic mobile systems , such as GSM ( Global System for Mobile communications ) .
In addition , we propose a new channel frequency response initialization method to solve the permutation indeterminacy and complex uncertain scaling problems in ICA-based channel equalization . The proposed initialization method shows faster convergence speed than the conventional random initialization method in the channel equalization process . Finally , we examine the number of OFDM symbols required for the proposed ICA-based channel equalization .
The demodulating unit transforms a received signal to a signal of a frequency area . The channel equalizing unit performs channel equalizing by using the signal of the frequency area demodulated by the channel equalizing unit and a previously estimated channel . The channel estimating unit estimates the signal of the frequency area demodulated by the demodulating unit , and a high priority transmission signal and a low priority transmission signal .
In this paper , we investigate the use of some classical techniques for accelerating the convergence of the LMS algorithm in widely linear ( WL ) adaptive processing . The application of channel equalizing is considered . Simulation results permit to verify that the normalized LMS strategy presents the best performance as far as a reduction of the trade-off between the convergence rate and steady-state error is concerned .
We demonstrate an efficient wavelength-division-multiplexed ( WDM ) equalizer structure that uses a reconfigurable opto-very large scale integrated processor to steer/reshape many optical beams simultaneously , thus , achieving channel-by-channel equalization . The opto-VLSI processor consists of an array of liquid crystal ( LC ) cells independently addressed by a VLSI circuit to generate a reconfigurable reflective digital hologram .
Then it brings forward an improved designing method for coefficients of equalization filter based on Fourier Transform . Finally an evaluating method for channel equalization effect in noise conditions is brought forwards . The effectiveness of above methods is validated through analyzing of equalization result .
Abstract We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter ( RD-LKF ) by introducing the butterfly finite-impulse-response ( FIR ) filter in our previously proposed RD-LKF method . Along with the fast polarization tracking , it can also simultaneously compensate the inter-symbol interference ( ISI ) effects including residual chromatic dispersion and polarization mode dispersion .
For cancelling the effect of phase error accurately and obtaining good performance , a new CPE ( Common Phase Error ) cancellation scheme is proposed for OFDM(Orthogonal Frequency Division Multiplexing ) system , which corrects the CPE with two steps in frequency synchronization and channel equalizer , and estimates the CPE using all pilots in an OFDM symbol including CP ( Continual Pilots ) and SP ( Scattered Pilots ) . The analysis and the simulations show that the proposed CPE correction structure with two steps adapts to the CPE correction in OFDM system , and the new CPE estimation algorithm using all pilots can obtain more accurate results than the conventional method , especially , under the severe channel conditions .
The former is highly dependent on the number of LCs , and the latter the number of all the basic patterns . In order to achieve good noise performance , a small number of the basic patterns is preferred . These properties of the DRNN are theoretically analyzed and confirmed through computer simulations .
Meanwhile combining the two adjacent synchronization code searching technology , the algorithm can extract the watermark without the help from the origin digital audio signal . Simulation results show that the proposed watermarking scheme is not only inaudible and robust against common signals processing such as MP3 compression , noise addition , resampling , and re-quantization etc . , but also robust against the desynchronization attacks such as random cropping , amplitude variation , pitch shifting , and jittering etc .
The bits obtained from two different images or for two different keys K will generally be different ( uncorrelated ) . However , for the same key K , two images that can be matched after applying gray scale operations , such as lossy compression , recoloring , filtering , noise adding , gamma correction , and simple geometrical operations including rotation and scaling , the extracted N-tuple will be almost the same . We also explain how the extracted N-tuple can be further utilized for synthesizing a Gaussian sequence that gradually changes with increasing number of errors in the extracted bits .
In the extraction procedure , the watermark can be extracted from the watermarked image without the requirement of the original host image or the original watermark image . Experimental results , compared with the related existing methods , show that the proposed color image scheme has stronger robustness against most common attacks such as image compression , filtering , cropping , noise adding , blurring , scaling and sharpening etc .
Noise could also mimic natural sounds ( e.g. , seismic impulses that are similar to tail beats ) , or attract marine mammals into dangerous areas ( e.g. , attracting killer whales to fishing gear ) . Given the paucity of research available on noise effects in marine mammals , standards may ...
An effective and sustainable reduction of highly annoyed people caused by noise is only possible with further scientific endeavors in the area of methods development and research of noise effects . Soundscape suggests exploring noise in its complexity , its ambivalence , and its approach toward sound to consider the conditions and purposes of its production , perception , and evaluation , to understand evaluation of noise as a holistic approach .
The proposed method , regulating the quantization step intelligently by utilizing energy characteristic of the audio signals , is remarkably effective in several audio signals . Experimental results show that the proposed watermarking algorithm is imperceptible and robust against various signal processing such as re-sampling , re-quantifying , noising adding , low pass filtering and MP3 compression .
This paper presents a method for signal denoising in the case of varying noise proportional to the local signal intensity . The signals are processed in a wavelet domain with a non-uniform threshold adjusted to the noise level .
The complexity of this optimal detector grows exponentially with the number of observations and as a remedy , we propose a number of approximations to it . The problem under study is a fundamental one and it has applications in signal denoising , anomaly detection , and spectrum sensing for cognitive radio . We illustrate the results in the context of the latter .
We show that efficient posterior sampling from the DAE can be achieved via Metropolis-Hastings , which allows the Monte Carlo EM algorithm to be used . We demonstrate competitive results for signal denoising , image deblurring and image devignetting . Our method is an example of combining the representation power of deep learning with uncertainty quantification from Bayesian statistics .
One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio ( SNR ) scenarios . In this paper , we investigate the signal denoising problem for multi-component linear frequency modulated ( LFM ) signals . An effective time-frequency ( TF ) analysis-based approach is proposed .
Both theoretical analysis and empirical evidence confirm that the approximate message passing ( AMP ) algorithm can be interpreted as recursively solving a signal denoising problem : at each AMP iteration , one observes a Gaussian noise perturbed original signal . Retrieving the signal amounts to a successive noise cancellation until the noise variance decreases to a satisfactory level .
Estimating single-trial evoked potentials ( EPs ) corrupted by the spontaneous electroencephalogram ( EEG ) can be regarded as signal denoising problem . Sparse coding has significant success in signal denoising and EPs have been proven to have strong sparsity over an appropriate dictionary .
Wavelet transforms are , generally , less prone to the problem than the pure spatial or frequency domain transforms , but there is still much room for improvements . In this paper , we propose a point-wise adaptive wavelet transform for signal denoising applications . It is very efficient in denoising higher frequency regions , without compromising the performance on smooth , lower frequency , regions .
The author has introduced a new efficient algorithm called “ weighted mediated morphological filters ” for signal/image denoising [ 1 ] . Adaptive version of such operators has been applied in 1-D for adaptive ECG processing [ 2 ] .
A novel approach is put forward to detect and analyze voltage stability by combining wavelet transform with pattern recognition technique . In signal denoising process , the statistic rule is proposed to determine the threshold of each order of wavelet space , which can determine the decomposition level adaptively , increasing the signal-noise-ratio . The wavelet transform coefficients as feature vector are presented for extracting disturbance signal .
In this paper , we drive the wavelet threshold error function , adopt the particle swarm optimization ( PSO ) and genetic algorithm ( GA ) to determine the optimal wavelet denoising threshold , the results show that our methods have better performance in signal denoising processing
Matching Pursuit ( MP ) techniques have recently been considered for signal denoising purposes . MP can reconstruct a clean signal from a noisy observation by greedily evaluating a least-squares ( LS ) estimate as a linear combination of elements ( atoms ) of a dictionary .
Owing to the characteristic of wavelet transform , the detecting signal could be decomposed into different frequency bands by the Mallat decomposing algorithm . In the process of the efficient signal detecting and denoising , a measuring frequency band was set up , and the information within this band was separated and other space vectors were set to zero . So , the useful information with the frequency could be extracted while the rest were removed .
These phenomena cause the frequently useful time-dependent pattern to be completely destroyed and become very difficult for further interpretation . An appropriate method of signal preprocessing may help the researcher to enhance some features and to obtain valuable information . Two simple algorithms of fetal heart rate filtering are described .
The interface is based on EEG signal analyses , which extract features that can be classified as specific mental activities . First , a signal preprocessing is performed from the EEG data , filtering noise , using a spatial filter to increase the scalp signal resolution , and extracting relevant features . Then , different classifier models are proposed , evaluated and compared .
For this purpose , BCI systems rely on signal processing and machine learning algorithms to decode the brain activity . This chapter provides an overview of the main steps required to do such a process , including signal preprocessing , feature extraction and selection , and decoding . Given the large amount of possible methods that can be used for these processes , a comprehensive review of them is beyond the scope of this chapter , and it is focused instead on the general principles that should be taken into account , as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems .
Finite-impulse response ( FIR ) Filter is widely used in wireless sensor networks as a signal pre-processing step . Because sensor nodes require a long working periods and ultra-low cost , traditional FIR structures are in applicable as multipliers occupy too much die size for such node ’s chips .
In this paper , we design energy efficient Finite Impulse Response ( FIR ) filter , which is widely used in wireless sensor networks as a signal pre-processing step because sensor nodes require a long working periods . Finite impulse response ( FIR ) filter is a type of digital filters .
Finite-impulse response ( FIR ) Filter is widely used in wireless sensor networks as a signal pre-processing step . Because sensor nodes require a long working periods and ultra-low cost , traditional FIR structures are inapplicable as multipliers occupy too much die size for such node 's chips .
Mental task among 5 subjects was processed to acquire motor imagery EEG signal . In order to improve the signal-to-noise ratio ( SNR ) of EEG , filters in time domain , frequency domain and special domain are applied in signal preprocess . Additionally , power spectral density ( PSD ) was extracted with sliding windows from processed signal as features for pattern recognition .
As it is possible to extract further information about the health state by fusioning data of different biosensors , the wireless link based on IEEE 802.15.4 was extended by a synchronisation mechanism enabling synchronous sampling of the individual sensors . An adaptive application of algorithms for signal pre-processing and analysis allows the reduction of the transferred data .
In this paper , we propose a signal strength exchange ( SSE ) system as an efficient key generation system and a synchronized quantization ( SQ ) method as a part of the SSE system that synchronizes data blocks in the quantization phase . The SQ method eliminates the signal pre-processing phase by performing a multi-bit conversion directly from the channel characteristics of the measurement results . Synchronization is carried out between the two authorized nodes to ensure sameness of the produced keys so it can eliminate the error-correcting phase .
In addition , data and signals required for offline indicator design are obtained and stored in a database [ 9 ] . For this purpose NeuMonD provides data acquisition , signal processing and pre-processing , online visualization and recording of comments ( e.g. clinical events including time stamps ) required for later offline analysis . Furthermore , NeuMonD allows simultaneous and synchronized data re-cording from different clinical monitors .
Modelling and analysis of surface Electromyogram ( sEMG ) signal has gained increasing attention in bio-signal processing for medical and healthcare applications . This research reports the study to examine the complexity in surface electromyogram signal measured from different muscles to identify the properties of muscles .
Feature extraction is playing a major role in bio signal processing . Feature identification and selection has two approaches .
In 1977 , Reichertz has defined medical informatics from the viewpoint of information logistics : to provide the right information at the right time at the right place [ 1 ; 2 ] . The analysis of medical information and (bio-)signal processing contributes substantially to an improved information provision . Therefore , medical informatics is an applied science in the best sense of the word .
The development of bio-signal analysis systems , mostly , has become amajor research field due to technological progress in signal processing . Electrocardiography ( ECG ) had been amongst the most studied type of bio-signals for several decades .
These new edge computing sensors for personalized healthcare must operate within a tight energy envelope ; addressing the ensuing challenge , we herein introduce an inexact and heterogeneous edge computing architecture , specifically tailored to the bio-DSP domain . We observe that bio-signal analysis applications present task-level parallelism , intensive computational hotspots and a high degree of resilience towards errors . These characteristics drive our new bio-DSP edge node architecture design composed of multiple processing cores , a Coarse-Grained Reconfigurable Array ( CGRA ) accelerator , and hardware-software co-design support to become resilient to a non-zero probability of bit-flips at runtime .
Latest embedded bio-signal analysis applications , targeting low-power Wireless Body Sensor Nodes ( WBSNs ) , present conflicting requirements . On one hand , bio-signal analysis applications are continuously increasing their demand for high computing capabilities . On the other hand , long-term signal processing in WBSNs must be provided within their highly constrained energy budget . In this context , parallel processing effectively increases the power efficiency of WBSNs , but only if the execution can be properly synchronized among computing elements . To address this challenge , in this work we propose a hardware/software approach to synchronize the execution of bio-signal processing applications in multi-core WBSNs . This new approach requires little hardware resources and very few adaptations in the source code .
An 8-channel ultra low power programmable wireless ExG ( ECG , EMG and EEG ) system-on-chip ( SoC ) design for bio-signal processing applications is presented in this paper . The proposed design consists of a capacitive coupled programmable gain instrumentation amplifier ( CC-PGIA ) with an improved transconductance of amplifier .
Implantable biomedical systems usually operate in energy-limited environments and exhibit large variation of power consumption ranging from constant low-power ( bio-signal sensing ) to sporadic high-power ( stimulation and/or burst data transmission ) [ 1 ] . Thus , energy-efficient switching power converters that can support a wide range of load variation are necessary to meet this requirement .
In this paper , we propose to convert high frequency signal to low frequency clock using 1/n series flip flop divider for down conversion mixed filtering in lock-in amplifier . This simple but novel idea will solve the physical problem to implement lock-in amplifier for low frequency signal DC level detection application in the fields of bio-signal sensing or nano-ampere signal detection application .
The present invention for predicting the surface of the water phase using a biological signal of that , different conditions for the bio-signal-based automatic sleep stage classification system . The present invention to this , a variety of biological signals obtained through the respective channels in the bio-signal detection , bio-signal detector each for obtaining a variety of biological signals over a different channel using the EEG , EOG , EMG , PPG , SKT , GSR sensors subjected to signal processing in a manner suitable for feature extraction of a biological signal to the feature points for each of biological signal signal processing to extract a feature point representing the characteristics of the bio-signal and the feature extraction unit , a signal processing and feature extraction unit from each of the biological signal a subset generating unit for generating bio-signal combining basis , and classification of sleep stages ( REM , LS , DS ) through the combined biometric signals combined in the subset generation unit , and the performance evaluation of analyzing the performance evaluation of the combination according to the steps bio-signal-based automatic sleep stage classification to provide a system , when measuring another sleep stage analysis using the bio-electrical signals of different conditions including a The accuracy of the sleep stages optimum combination of biological signals in consideration of convenience makes it possible to accurately and easily predictable .
Furthermore , one can determine whether the phenomenon corresponds to an activation or a de-activation of the brain region . In practice , however , there are many bio-signal processing problems to be solved : the artifact removal in the EEG , the demodulation of the EEG , the extraction of an EEG reference from the multi-channels , the determination of a sensible correlation co-efficient ( in which heart beat effects and breathing are eliminated ) and its statistical significance . In this paper , several innovations concerning the signal processing of simultaneously recorded fMRI , PULSE and fMRI are presented and applied in a case study on the generators of the alpha-rhythm
An artificial neural network ( ANN ) approach for radar image processing is presented in this paper . A renewal concept of simple adaptive units as a foundation for network assembling allows one to design ANN-based feature extraction scheme for 2D-signal processing . It was shown that ANN implementing radial basis function ( RBF ) processing units can be applied for identification of radar targets described by the set of scatterers .
After a short review of 1D linear systems the usual method for 2D signal processing will be reviewed . Its merits and disadvantages will be treated briefly .
Furthermore , the whole convolver can be embedded in one single uniform bit-serial one-dimensional structure . This extremely pin-saving and VLSI-oriented design can also be used for recursive filters and for 2D signal processing . Programmability ( using a structure with varying precision and kernel size ) can be traded for simplicity and efficacy .
We propose a new real-valued lapped transform for 2D-signal and image processing . Lapped transforms are particularly useful in block-based processing , since their intrinsically overlapping basis functions reduce or prevent block artifacts .
Abstract We describe a new real-valued lapped transform for 2D-signal and image processing . Lapped transforms are particularly useful in block-based processing , since their overlapping basis functions reduce or prevent block artifacts .
This opportunity is used to analyze some research directions in the last decades and to suggest what directions may be fruitful to develop . The paper undertakes the task of reviewing some of our laboratory applications in 1D/2D signal processing . We are referring to the wave propagation fuzzy modeling , the use of fuzzy logic in formant detection and speech synthesis , in Parkinson disease diagnosis using handwriting text analysis and in processing grayscale medical images .
MATLAB toolbox and graphic user interface , EEGLAB is used for processing EEG data of any number of channels . Wavelet toolbox has been used for 2-D signal analysis .
Spatio-temporal analysis of seismic records is of particular relevance in many geophysical applications , e.g. , vertical seismic profiles , plane-wave slowness estimation in seismographic array processing and in sonar array processing . The goal is to estimate from a limited number of receivers the 2-D spectral signature of a group of events that are recorded on a linear array of receivers .
Estimation of the incident waveform directions , or direction of arrival ( DOA ) estimation , is an essential problem in sonar array processing . A sonar array usually consists of a large number of elements , and the associated data processing complexity is extensive .
World-wide a concern exists about the influence of man-made noise on marine life , and particularly of high power sonar . Most concern lies with marine mammals that use acoustics for hunting , communication and/or navigation .
This includes the design and modeling of acoustical and vibrational transducers , arrays , and transduction systems in all media and frequency ranges ; instrumentation , metrology , and calibration ; measurement and computational techniques as they relate to acoustical phenomena and their utility ; and the engineering of materials and devices . EATC ’s recently sponsored special sessions attracted members from all areas and is an enabler for overlapping interests : single crystal piezoelectrics , transducer design , vector sensors , materials for high power sonar , micromachined silicon mics , multimedia , 3‐D audio , pro-audio , parametric sources , speakers , transduction , projection , materials processing and manufacturing , piezocomposites , sensor fusion , signal processing hardware , data communication , condition-based monitori ...
A projector formulation for three-mode signal and noise subspaces is also given and the improvement of separation with the three-mode approach over a componentwise approach is shown . We study two applications for the proposed Higher Order Subspace approach : the reverberation problem in sonar , and the polarized seismo-acoustic wave separation problem . For the first application , we propose a three-mode version of the Principal Component Inverse algorithm ( IEEE Trans .
( Tele ) kinetic imaging ( e.g. kinetic sonar , radar , etc . ) provides visuo-acoustic feedback in which subject matter undergoes vibrational or other acoustic stimulation and the response to the stimulation is imaged . Specifically , the imaging uses active sonar to cause microscopic vibrations in the subject matter , while a special kind of lock-in amplifier is used to image the effects of these microscopic vibrations .
We use data from an experiment of sediment transport conducted by the Applied Physics Laboratory at the University of Washington . We apply a variety of wavelet- based vector quantization data compression algorithms to acoustic sonar scans . We sue pruned tree-structured vector quantization ( PTSVQ ) with the generalized Breiman , Friedman , Olshen , and Stone algorithm to simultaneously prune trees that correspond to different wavelet subbands .
A generic NNC ( neural network classifier ) capable of providing 1.9 billion programmable connections per second is described . Applications for these generic processors include image and speech recognition as well as sonar signal identification . To demonstrate the modularity and flexibility of the CCD ( charge coupled device ) NNCs , two generic multilayer system-level boards capable of both feedforward and feedback nets are presented .
Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot , and the action that yields the lowest cost ( i.e. , the maximum information ) is selected . This technique is demonstrated via simulations , in-air sonar experiments , and underwater sonar experiments . Results are shown for ( 1 ) adaptive control of motion and ( 2 ) adaptive control of motion and scanning .
Hence , they are well suited to the problem of training feedforward networks . In this paper , we describe a set of experiments performed on data from a sonar image classification problem . These experiments both 1 ) illustrate the improvements gained by using a genetic algorithm rather than backpropagation and 2 ) chronicle the evolution of the performance of the genetic algorithm as we added more and more domain-specific knowledge into it .
Uniform or symmetric array is a common practice for most of the sonar array design , where the same performance transducer elements are used for numerical simulation and manufacture engineering conveniences . However , non‐uniform array designs show their advantages for special array applications .
A common model for sonar clutter is that of the transmitted signal convolved with a colored Gaussian process relating to the sea-bottom profile . Rather surprisingly , this noise becomes strongly non-Gaussian if there are multiple realizations and if a realistic random phase is introduced - the univariate statistics remain Gaussian , but the joint probability density function is not .
Applications of Random Process Excursion Analysis An Ultralow-Voltage Sensor Node Processor With Diverse Hardware Acceleration and Cognitive Sampling for Intelligent Sensing Electronic system and signal processing for noninvasive seismocardiography examination Non-linear adaptive equalization based on a multi-layer perceptron architecture Linear Equalization and Decision Feedback Equalization in Indoor Visible Communication Hardware reduction by combining pipelined A/D conversion and FIR filtering for channel equalization CHANNEL EQUALIZATION WITH STAGGERED MODULATION FORMATS Development and applications of adaptive IIR and subband filters Use of logarthmic cost function for blind channel equalization On equalization with complex MLP network in the GSM environment Channel Equalization Using Independent Component Analysis in PDM-CO-OFDM Apparatus and method for estimating channel in receiving system based on hierarchical modulation Accelerating the convergence of the widely linear LMS algorithm for channel equalization Dynamic WDM equalizer using opto-VLSI beam processing Design and Implementation of Channel Equalization Method for Wideband Digital Array Radar Joint polarization tracking and channel equalization based on radius-directed linear Kalman filter Common Phase Error Cancellation Scheme for OFDM in Severe Channel A recurrent neural network with serial delay elements for memorizing limit cycles A robust digital audio watermarking scheme using wavelet moment invariance Secure Oblivious Hiding, Authentication, Tamper Proofing, and Verification Techniques Color image blind watermarking scheme based on QR decomposition Developing standards for protecting marine mammals from noise: Lessons from the development of standards for humans The need of collective expertise in noise control. Adaptive Digital Audio Watermarking Scheme Based on Digital Audio Characteristic Threshold Estimation for Wavelet Domain Filtering of Signal-dependent Noise A Bayesian approach to spectrum sensing, denoising and anomaly detection AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling An Effective LFM Signal Reconstruction Method for Signal Denoising Near Optimal Compressed Sensing Without Priors: Parametric SURE Approximate Message Passing Single-Trial Evoked Potential Estimating Based on Sparse Coding under Impulsive Noise Environment Point-Wise Adaptive Wavelet Transform for Signal Denoising ADAPTIVE WEIGHTED MEDIATED MORPHOLOGICAL FILTERS VERSUS NON- ADAPTIVE ONE Power fault using signal analysis with complex window and pattern recognition approach Determination of wavelet denoising threshold by PSO and GA Denoising using randomized matching pursuit: algorithmic improvements and GPU implementation Research on the Detecting of the Sulfur Dioxide Polluted Gas Based on Wavelet Transform Detection of fetal breathing movements with joint time-frequency analysis of cardiotocogram records Activation of a mobile robot through a brain computer interface General principles of machine learning for brain-computer interfacing. A Hardware Efficient FIR Filter for Wireless Sensor Networks Wireless sensor network specific low power FIR filter design and implementation on FPGA A novel hardware efficient FIR filter for wireless sensor networks Pattern recognition of motor imagery EEG signal in noninvasive brain-computer interface Wireless Body Sensor Network for low-power motion-tolerant syncronized vital sign measurment An Efficient Key Generation for the Internet of Things Based Synchronized Quantization Neurophysiological Monitoring - the Engineer Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal ICA based feature learning and feature selection Medical Informatics – Challenges of Applied Information Technology in Medicine (Medizinische Informatik – Herausforderungen der Informationstechnologie in der Medizin) Recognition of Cardiac Arrhythmia by Means of Beat Clustering on ECG-Holter Recordings Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications Hardware/software approach for code synchronization in low-power multi-core sensor nodes Ultra Low Power Programmable Wireless ExG SoC Design for IoT Healthcare System 12.3 PWM buck converter with >80% PCE in 45μA-to-4mA loads using analog-digital hybrid control for impiantale biomedical systems Low frequency clock synchronization technique for low signal to noise ratio (SNR) signal recoery from noise environment Bio-signals based automatic sleep-stage identification system Signal processing aspects of simultaneously recorded EEG, PULSE and fMRI Radar target identification based on feature extraction performed with RBF artificial neural networks A 1D Approach to 2D-Signal Processing Serial/Parallel Convolvers Lapped directional transform: a new transform for spectral image analysis A lapped directional transform for spectral image analysis and its application to restoration and enhancement A review of several applications of FL in modeling and in 1D/2D signal processing Role of EEG Lab for Analyzing Electroencephalogram Signal Estimation of the discrete Fourier transform, a linear inversion approach Beamspace transformation for data reduction using genetic algorithms SAKAMATA : A tool to avoid whale strandings The Engineering Acoustics Technical Committee Three-mode data set analysis using higher order subspace method: application to sonar and seismo-acoustic signal processing Rattletale: Phase-Coherent Telekinetic Imaging to Detect Tattletale Signs of Structural Defects or Potential Failure Lossy compression of acoustic backscatter data Real time CCD-based neural network system for pattern recognition applications Adaptive Mobile Robot Navigation and Mapping Training Feedforward Neural Networks Using Genetic Algorithms Non‐uniform array synthesis concept and theory. A non-Gaussian problem that arises in fused detection in clutter
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ISCAS (3) ICNN ICASSP NeurIPS ICRA ISCAS DATE MobiHealth ISSCC ISBI ICASSP SITIS IJCAI
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