The thought of decomposition and modeling have important position in signal processing . This paper take the thought of decomposition as main line , introducting the concept of transforming cells and some common tranformations in signal processing . The ARMA model is introduced in this paper and its application in spectral estimation . Thus , it is crucial to develop a forensic scheme that is able to decode the information from the received signals only . The very first step of communication forensic detector is to determine which kind of modulation is in use , which is an intermediate step between signal detection and demodulation . A modulation forensic detector is not only useful for security or military purposes , but also for many other civilian applications . The reason of why students do not understand well phase modulation and demodulation after learning course of high frequency electronics is , to some extent , that there were not corresponding experiments in the experimental teaching program . This paper introduces principle of phase modulation and demodulation , sets forth experimental scheme based on VI to simulate the phase modulation and demodulation , and develop phase modulation and demodulation experiments by using present experimental circuits for frequency modulation and demodulation , exploits VI suitable for simulation and real experiments according to their different demands , and inspects the VI with simulation and practical experiment . Resultants of the VI to operate were proved to be correct , further it was proved that design of the VI and the experimental scheme all were correct . The demodulator extracts the amplitude of the AM square signal by measuring the period of a reference signal that is altered by the AM signal itself , as already suggested in a previous paper but for AM sinusoidal signals . The demodulation and digitization are carried out simultaneously via a digital timer and without requiring a rectifier , a mixer , a low-pass filter , or an analog-to-digital converter . In addition , a new method to extract the amplitude of the AM square signal , which can not be applied to the sinusoidal case , is experimentally presented . We present the error-free demodulation of a 10Gb/s DPSK-signal based on a novel Delayed Interferometer with ultra-small footprint . The device is fabricated on thick um-scale SOI platform and exhibits similar dimension with nanophotonic based DIs . The demodulator is integrated with IQ mixers , 13GHz QVCO , frequency synthesizers and baseband AGC , for an overall power consumption of 60mW from a 1V supply . The entire demodulator chip occupies 1.275 × 1.19mm2 and enables error free demodulation up to 2.5Gbps and BER of 1E-09 up to 3Gbps . To the best of authors ' knowledge , this demonstrates the maximum throughput at minimum power budget among all types of CMOS multi-gigabit demodulators . On such technique is multi-user detection . Multi-user Detection ( MUD ) is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference ( MAI ) . In this paper , we will show the role of matched filter used as pre-processing tool for MUD in DS-CDMA system . Multiuser Detection is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference . The authors have presented the Bit-error rate ( BER ) achieved by linear multi-user detectors : Matched filter ( which treats the MAI as AWGN ) , Decorrelating and MMSE . The equipment adopts the following technical proposal that the IC card read-write equipment is composed of a read-write module and an antenna plate , and the read-write module is composed of a master plate and an input/output plate . Compared with the prior art , an ARM9 circuit is adopted on a hardware platform , an RF radio-frequency circuit adopts a single integrated circuit mode to achieve various radio-frequency card coding and decoding and modulation-demodulation compatible treatment , which has high reliability and solves the problems of disturbance and matching of the original mode . The IC card read-write equipment can be compatible to treat radio-frequency cards in three international standards of ISO14443 type A , ISO14443 type B and FELTCA ( TYPE C ) , software layers are higher , multiple application systems can be treated , and the price is cheap . The advantage of this type of interferometer is easy multiplexing , low cost and the possibility of flexible sensor design . The article describes the design of a sensor with multiple outputs of 3x3 coupler used for passive phase demodulation . The sensor has been tested on the railway track attached to the railway bridge . Single-carrier frequency division multiple access ( SC-FDMA ) is an attractive air interface scheme for broadband wireless communications . For the coherent demodulation of SC-FDMA systems , the receiver performs channel estimation to obtain the channel frequency response by using the reference signals multiplexed in the transmitted signal . In this paper , we propose an iterative frequency domain channel estimation scheme for SC-FDMA systems . We also characterized DGD and dispersion tolerances . To the author 's best knowledge , this is the first experimental demonstration of multi-bit-per-symbol modulation/demodulation using an incoherent ASE light source . The postprocessing includes : modeling of the RO signal frequency from refractivity climatology and the subsequent adjustment of this model by use of feedback which makes the postprocessing model-independent ; internal ( as in PLL ) removal of NDM and the use of externally recorded NDM bit sequence . Statistical comparison of the refractivity inversion results demonstrates that external demodulation of RO signals reduces the inversion bias in the tropical lower troposphere by about 0.5 % . A direct-sequence spread-spectrum receiver using M-ary phase shift keyed signaling is presented with digital algorithms for coherent data demodulation , diversity combining , and frequency offset estimation . These algorithms are derived by applying the maximum-likelihood rule . In this paper , an application of a simplified second-order lattice-based adaptive IIR notch filter to binary FSK demodulation is presented . The design method and performance evaluation of the demodulator , based on mean-squared error analysis of the steady-state variable coefficient fluctuations of the adaptive notch filter are studied . When developing signal processing methods in the complex domain , there are two key issues : making use of the full statistical information and optimization . In this article , we review the necessary tools to address these two key issues and provide examples in filtering and blind source separation ( BSS ) that utilize these tools . The contrast function remains to be an open problem in blind source separation ( BSS ) when the number of source signals is unknown and/or dynamically changed . The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank . The problem of blind source separation ( BSS ) is investigated . Following the assumption that the time-frequency ( TF ) distributions of the input sources do not overlap , quadratic TF representation is used to exploit the sparsity of the statistically nonstationary sources . The blind source separation ( BSS ) problem under noise is known as a hard problem . The performance of separation algorithm degrades with the decrease of SNR significantly . Hyperspectral unmixing , as a blind source separation ( BSS ) problem , has been intensively studied from independence aspect in the last few years . However , independent component analysis ( ICA ) can not totally unmix all the materials out because the sources ( abundance fractions ) are not statistically independent . A conventional auto-associative neural network ( AANN ) is shown to have an intrinsic ability to solve the blind source separation ( BSS ) problem without special computations explicitly intended for BSS , except for a pruning mechanism to deal with the usual case in which the number of the sources is unknown ; each nonlinear hidden unit that has survived the pruning would recover one of the source signals . The feasibility of this non-information-theoretic approach is shown by computer simulation for twoand three-source examples involving various pdf 's for the independent sources . Independent Component Analysis ( ICA ) is a mathematical tool able to separate complex signals in statistically independent components . It solves the blind source separation problem ( BSS ) . The EEG satisfies most of the assumptions of ICA , so it may be an adequate signal for ICA for its use . Abstract Blind source separation ( BSS ) in communications applications is usually considered under the assumption that the observed or received signals are instantaneous linear mixtures of the individual sources without intersymbol interference ( ISI ) . Based on a more general structure for multi-input – multi-output channels , we present a method using fractional sampling and BSS to recover transmitted symbols in the presence of ISI . Abstract Blind Source Separation ( BSS ) is a task of separating a set of source signals from mixed signal without ( or very little information ) of both the sources and the mixing process . This paper addresses the problem of BSS in bio-acoustic mixed signals . Estimating appliance specific power consumption using a single measuring device , known as Non-Intrusive Load Monitoring ( NILM ) , is a challenging Blind Signal source Separation ( BSS ) problem . For the past two decades , numerous mathematical and pattern recognition techniques , including Fractional Hidden Markov Model ( FHMM ) , Gaussian Mixture Model ( GMM ) and Mean Shift Based Clustering Techniques ( MSBCT ) have been proposed to decompose the total power consumption of a household into appliance specific power signals . This paper presents the description and the operation of a system composed of an intelligent algorithm , that separates information and noise coming from different sources , implemented with embedded technology in a DSP ( digital signal processor ) , that interacts with field bus devices connected through a foundation field bus network . The technique used in this blind source separation ( BSS ) process was the independent component analysis ( ICA ) , that explores the possibility of separating mixed signals based on the fact that they are statistically independent . The algorithm and its implementation are presented , as well as the test results . In these cases we can tackle the separation problem by supervised adaptation algorithms , e.g. , the popular LMS- or RLS-type algorithms , rather than the more involved blind adaptation algorithms . In contrast , for blind estimation , such as in the blind source separation ( BSS ) scenario where both the propagation paths and the original source signals are unknown , the method of independent component analysis ( ICA ) is typically applied . Traditionally , the ICA method and supervised adaptation algorithms have been treated as different research areas . The sound source separation problem has challenged researchers in the field of acoustics for decades . Advances have been made in the field of blind source separation and various methods such as Independent Component Analysis , Higher Order Statistics and Blind Source Signal ( BSS ) separation via signal sparsity have been developed to separate non-moving sound sources . These BSS methods work by estimating the mixing parameters of the sound source mixture and demixing the sound sources by attributing the correct parameters to their respective sources . Furthermore , a learning method based on dynamic time warping is provided that makes it possible to automatically generate models . Finally , to demonstrate the performance and robustness of this approach , we have applied it to the recognition of hand gestures without any kind of signal preprocessing . A speaker recognition method based on support vector and multi-scale wavelet analysis is proposed and a frame model of it is constructed in this paper . Firstly , Multi-scale wavelet analysis is applied to the process of signal preprocess , based on it , the theory of multi-scale analysis is applied to separate speech and noise , and enhance the speech consequently . Secondly , in the feature extracting phase , Mel Frequency Cepstrum Coefficient and its difference are derived to be the characteristic parameters and then composed into feature vector sequences based on SVM . Results Our network includes a 14-row structure , and a 30-s period was extracted as input in order to be categorized which is followed by second and third period prior to the first 30-s period . Another consecutive period for temporal tissue was added which is not required to a signal preprocess and attribute data derivation phase . Our means of evaluating and improving our approach was to use input from the Sleep Heart Health Study ( SHHS ) , which is a large study field aimed at using research from numerous centers and people and which studies the records of specialist-rated polysomnography ( PSG ) . Wavelet transform has been emerged over recent years as a powerful time-frequency analysis and signal coding tool favored for the interrogation of complex non stationary signals . Its application to bio-signal processing has been at the forefront of these developments where it has been found particularly useful in the study of these , often problematic , signals : none more so than the Electrocardiogram ( ECG ) . In this paper , the emerging roles of the wavelet transform in the ECG preprocessing and noise removing step is discussed in detail . We have developed a Matlab/spl trade/ based software package for bio-signal analysis . The software is based on modular design and can thus be easily adapted to fit on analysis of various kind of time variant or event-related bio-signals . Summary In this chapter , we have studied the use of a powerful class of noncausal statistical models , the Gauss-Markov random fields , in 2D signal processing . These models were motivated through intuitive arguments regarding the importance of noncausality and the primacy of local dependence or Markovianity , as well as through the structures arising from numerical integration of PDEs . Researchers have proposed many effective methods in this aspect , this paper no longer mentions them but will introduce a new method . Whereas difference method has obtained good results in 1D signal processing , this paper manages to apply it to 2D signal processing , that is to say , dim point targets detection in infrared images of low SNR . A theoretical foundation to the notion of 2D transform and 2D signal processing is given , focusing on 2D group-based transforms , of which the 2D Haar and 2D Fourier transforms are particular instances . Conditions for separability of these transforms are established . In this study the authors focus on RADAR-data . The classification of RADAR-data shows various advantages and disadvantages compared with a classification based on optical data . Main advantages are the independence of atmospheric conditions , the independence of illumination and the penetration depth , which allows an observation of volumetric properties and surface properties . The algorithm first uses fuzzy logic reasoning ( FLR ) to obtain the priority of targets to radars , and designs a radar clustering algorithm based on the priority to form several subradar networks . The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target , which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model . Considering the uncertainty of the target RCS in practice , the chance-constraint programming ( CCP ) is used to balance power resource and tracking accuracy . This report presents a simple and direct method of solving the registration problem associated with multiple radar tracking . The registration proceedure is split into two distinct parts that are independent from one another . This renders antenna gain in certain directions and rejection in others , i.e. spatial filtering . Until recently , phased array systems exploited dedicated RF technologies leading to relatively costly systems , e.g. for nautical , airplane radar systems , and satellite communication . More recently , low-cost highly integrated beamforming concepts received considerable interest in academia but also industry , enabling consumer applications e.g. in base-stations for macro- and femto-cells , car-radar and 60GHz wideband radio links . We show the viability of GFDM for radar with communications systems and the benefits of using it over Orthogonal Frequency Division Multiplexing ( OFDM ) . Finally , we also present GFDM as a solution to mitigate inter-system interference in radar with communications ( RadCom ) systems , thus showing that GFDM may prove to be a better candidate than OFDM for RadCom applications . This method is validated with simulations and practical measurements at 24 GHz . In many wireless communications/radar applications , constant modulus sequences with low correlation sidelobes levels are desired . In this letter , we present an efficient optimization method to design them . Weak signal to noise ratio(SNR ) environment in battlefield becomes increasingly prominent , which is a challenge to radar detecting and tracking target . Based on the home and abroad research achievements of detect and track algorithm for weak SNR targets , a comprehensive and up-to-date survey of the detect and track techniques based on plot for radar weak SNR targets is presented . Ground-penetrating radar ( GPR ) measurement and its interpretation/analysis are challenging when soil is heterogeneous . Soil heterogeneity causes unwanted reflections ( i.e. , clutter ) that disturb reflections from objects of interest . Network distributed radar track fusion with hierarchical structure is enhanced for distributed data fusion , it has its own characteristic and difficult . In this paper , a two hierarchical structure based fusion frame is describe , simulation plat base on HLA is proposed , the component and framework is described , the process and realization during the whole fusion course for low -lever and high lever fusion center is analyzed , finally the demonstrated simulation is shown . We propose a strategy to evaluate the performance of a radar sounder for the subsurface exploration of the Europa icy crust and , in particular , the possibility to detect liquid water at the base of the ice shell . The approach integrates the information coming from experimental measurements of the dielectric properties of icy materials , thermal models related to different crustal scenarios , and numerical simulations of radar signal propagation . The radar response has been evaluated in terms of cumulative attenuation , signal-to-noise ratio ( SNR ) , and reflectivity . Scanning Laser Radar has been widely used in many military and civil areas . Usually there are relative movements between the target and the radar , so the moving target image modeling and simulation is an important research content in the field of signal processing and system design of scan-imaging laser radar . In order to improve the simulation speed and hold the accuracy of the image simulation simultaneously , a novel fast simulation algorithm is proposed in this paper . We argue in this paper that there are strong parallels between this threshold behavior and earlier Shannon 's justification for similar phenomenon in his notion of the capacity of the information transmission . Attempts of finding performance bounds for the compressive sensing with the standard information-theoretic tools were later made , e.g. by Wainwright et al. We outline sharp asymptotical bounds obtained in 1979 that were inspired by the Multi-Access Capacity Region theory and that can greatly enhance the current understanding of recovery of active inputs in sparse compressive sensing . These bounds were obtained for asymptotically optimal designs which include random designs and thus imply upper bounds for performance of recovery under arbitrary designs . It will be shown that these methods are especially advantageous to use when the data-length is short , or when the signal is a mixture of multiple hidden periods . The dictionary techniques were inspired by recent advances in sparsity based compressed sensing . Apart from the l1 norm based convex programs currently used in other applications , our dictionaries can admit l2 norm formulations that have linear and closed form solutions , even when the systems is under-determined .
The thought of decomposition and modeling have important position in signal processing . This paper take the thought of decomposition as main line , introducting the concept of transforming cells and some common tranformations in signal processing . The ARMA model is introduced in this paper and its application in spectral estimation .
Thus , it is crucial to develop a forensic scheme that is able to decode the information from the received signals only . The very first step of communication forensic detector is to determine which kind of modulation is in use , which is an intermediate step between signal detection and demodulation . A modulation forensic detector is not only useful for security or military purposes , but also for many other civilian applications .
The reason of why students do not understand well phase modulation and demodulation after learning course of high frequency electronics is , to some extent , that there were not corresponding experiments in the experimental teaching program . This paper introduces principle of phase modulation and demodulation , sets forth experimental scheme based on VI to simulate the phase modulation and demodulation , and develop phase modulation and demodulation experiments by using present experimental circuits for frequency modulation and demodulation , exploits VI suitable for simulation and real experiments according to their different demands , and inspects the VI with simulation and practical experiment . Resultants of the VI to operate were proved to be correct , further it was proved that design of the VI and the experimental scheme all were correct .
The demodulator extracts the amplitude of the AM square signal by measuring the period of a reference signal that is altered by the AM signal itself , as already suggested in a previous paper but for AM sinusoidal signals . The demodulation and digitization are carried out simultaneously via a digital timer and without requiring a rectifier , a mixer , a low-pass filter , or an analog-to-digital converter . In addition , a new method to extract the amplitude of the AM square signal , which can not be applied to the sinusoidal case , is experimentally presented .
We present the error-free demodulation of a 10Gb/s DPSK-signal based on a novel Delayed Interferometer with ultra-small footprint . The device is fabricated on thick um-scale SOI platform and exhibits similar dimension with nanophotonic based DIs .
The demodulator is integrated with IQ mixers , 13GHz QVCO , frequency synthesizers and baseband AGC , for an overall power consumption of 60mW from a 1V supply . The entire demodulator chip occupies 1.275 × 1.19mm2 and enables error free demodulation up to 2.5Gbps and BER of 1E-09 up to 3Gbps . To the best of authors ' knowledge , this demonstrates the maximum throughput at minimum power budget among all types of CMOS multi-gigabit demodulators .
On such technique is multi-user detection . Multi-user Detection ( MUD ) is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference ( MAI ) . In this paper , we will show the role of matched filter used as pre-processing tool for MUD in DS-CDMA system .
Multiuser Detection is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference . The authors have presented the Bit-error rate ( BER ) achieved by linear multi-user detectors : Matched filter ( which treats the MAI as AWGN ) , Decorrelating and MMSE .
The equipment adopts the following technical proposal that the IC card read-write equipment is composed of a read-write module and an antenna plate , and the read-write module is composed of a master plate and an input/output plate . Compared with the prior art , an ARM9 circuit is adopted on a hardware platform , an RF radio-frequency circuit adopts a single integrated circuit mode to achieve various radio-frequency card coding and decoding and modulation-demodulation compatible treatment , which has high reliability and solves the problems of disturbance and matching of the original mode . The IC card read-write equipment can be compatible to treat radio-frequency cards in three international standards of ISO14443 type A , ISO14443 type B and FELTCA ( TYPE C ) , software layers are higher , multiple application systems can be treated , and the price is cheap .
The advantage of this type of interferometer is easy multiplexing , low cost and the possibility of flexible sensor design . The article describes the design of a sensor with multiple outputs of 3x3 coupler used for passive phase demodulation . The sensor has been tested on the railway track attached to the railway bridge .
Single-carrier frequency division multiple access ( SC-FDMA ) is an attractive air interface scheme for broadband wireless communications . For the coherent demodulation of SC-FDMA systems , the receiver performs channel estimation to obtain the channel frequency response by using the reference signals multiplexed in the transmitted signal . In this paper , we propose an iterative frequency domain channel estimation scheme for SC-FDMA systems .
We also characterized DGD and dispersion tolerances . To the author 's best knowledge , this is the first experimental demonstration of multi-bit-per-symbol modulation/demodulation using an incoherent ASE light source .
The postprocessing includes : modeling of the RO signal frequency from refractivity climatology and the subsequent adjustment of this model by use of feedback which makes the postprocessing model-independent ; internal ( as in PLL ) removal of NDM and the use of externally recorded NDM bit sequence . Statistical comparison of the refractivity inversion results demonstrates that external demodulation of RO signals reduces the inversion bias in the tropical lower troposphere by about 0.5 % .
A direct-sequence spread-spectrum receiver using M-ary phase shift keyed signaling is presented with digital algorithms for coherent data demodulation , diversity combining , and frequency offset estimation . These algorithms are derived by applying the maximum-likelihood rule .
In this paper , an application of a simplified second-order lattice-based adaptive IIR notch filter to binary FSK demodulation is presented . The design method and performance evaluation of the demodulator , based on mean-squared error analysis of the steady-state variable coefficient fluctuations of the adaptive notch filter are studied .
When developing signal processing methods in the complex domain , there are two key issues : making use of the full statistical information and optimization . In this article , we review the necessary tools to address these two key issues and provide examples in filtering and blind source separation ( BSS ) that utilize these tools .
The contrast function remains to be an open problem in blind source separation ( BSS ) when the number of source signals is unknown and/or dynamically changed . The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank .
The problem of blind source separation ( BSS ) is investigated . Following the assumption that the time-frequency ( TF ) distributions of the input sources do not overlap , quadratic TF representation is used to exploit the sparsity of the statistically nonstationary sources .
The blind source separation ( BSS ) problem under noise is known as a hard problem . The performance of separation algorithm degrades with the decrease of SNR significantly .
Hyperspectral unmixing , as a blind source separation ( BSS ) problem , has been intensively studied from independence aspect in the last few years . However , independent component analysis ( ICA ) can not totally unmix all the materials out because the sources ( abundance fractions ) are not statistically independent .
A conventional auto-associative neural network ( AANN ) is shown to have an intrinsic ability to solve the blind source separation ( BSS ) problem without special computations explicitly intended for BSS , except for a pruning mechanism to deal with the usual case in which the number of the sources is unknown ; each nonlinear hidden unit that has survived the pruning would recover one of the source signals . The feasibility of this non-information-theoretic approach is shown by computer simulation for twoand three-source examples involving various pdf 's for the independent sources .
Independent Component Analysis ( ICA ) is a mathematical tool able to separate complex signals in statistically independent components . It solves the blind source separation problem ( BSS ) . The EEG satisfies most of the assumptions of ICA , so it may be an adequate signal for ICA for its use .
Abstract Blind source separation ( BSS ) in communications applications is usually considered under the assumption that the observed or received signals are instantaneous linear mixtures of the individual sources without intersymbol interference ( ISI ) . Based on a more general structure for multi-input – multi-output channels , we present a method using fractional sampling and BSS to recover transmitted symbols in the presence of ISI .
Abstract Blind Source Separation ( BSS ) is a task of separating a set of source signals from mixed signal without ( or very little information ) of both the sources and the mixing process . This paper addresses the problem of BSS in bio-acoustic mixed signals .
Estimating appliance specific power consumption using a single measuring device , known as Non-Intrusive Load Monitoring ( NILM ) , is a challenging Blind Signal source Separation ( BSS ) problem . For the past two decades , numerous mathematical and pattern recognition techniques , including Fractional Hidden Markov Model ( FHMM ) , Gaussian Mixture Model ( GMM ) and Mean Shift Based Clustering Techniques ( MSBCT ) have been proposed to decompose the total power consumption of a household into appliance specific power signals .
This paper presents the description and the operation of a system composed of an intelligent algorithm , that separates information and noise coming from different sources , implemented with embedded technology in a DSP ( digital signal processor ) , that interacts with field bus devices connected through a foundation field bus network . The technique used in this blind source separation ( BSS ) process was the independent component analysis ( ICA ) , that explores the possibility of separating mixed signals based on the fact that they are statistically independent . The algorithm and its implementation are presented , as well as the test results .
In these cases we can tackle the separation problem by supervised adaptation algorithms , e.g. , the popular LMS- or RLS-type algorithms , rather than the more involved blind adaptation algorithms . In contrast , for blind estimation , such as in the blind source separation ( BSS ) scenario where both the propagation paths and the original source signals are unknown , the method of independent component analysis ( ICA ) is typically applied . Traditionally , the ICA method and supervised adaptation algorithms have been treated as different research areas .
The sound source separation problem has challenged researchers in the field of acoustics for decades . Advances have been made in the field of blind source separation and various methods such as Independent Component Analysis , Higher Order Statistics and Blind Source Signal ( BSS ) separation via signal sparsity have been developed to separate non-moving sound sources . These BSS methods work by estimating the mixing parameters of the sound source mixture and demixing the sound sources by attributing the correct parameters to their respective sources .
Furthermore , a learning method based on dynamic time warping is provided that makes it possible to automatically generate models . Finally , to demonstrate the performance and robustness of this approach , we have applied it to the recognition of hand gestures without any kind of signal preprocessing .
A speaker recognition method based on support vector and multi-scale wavelet analysis is proposed and a frame model of it is constructed in this paper . Firstly , Multi-scale wavelet analysis is applied to the process of signal preprocess , based on it , the theory of multi-scale analysis is applied to separate speech and noise , and enhance the speech consequently . Secondly , in the feature extracting phase , Mel Frequency Cepstrum Coefficient and its difference are derived to be the characteristic parameters and then composed into feature vector sequences based on SVM .
Results Our network includes a 14-row structure , and a 30-s period was extracted as input in order to be categorized which is followed by second and third period prior to the first 30-s period . Another consecutive period for temporal tissue was added which is not required to a signal preprocess and attribute data derivation phase . Our means of evaluating and improving our approach was to use input from the Sleep Heart Health Study ( SHHS ) , which is a large study field aimed at using research from numerous centers and people and which studies the records of specialist-rated polysomnography ( PSG ) .
Wavelet transform has been emerged over recent years as a powerful time-frequency analysis and signal coding tool favored for the interrogation of complex non stationary signals . Its application to bio-signal processing has been at the forefront of these developments where it has been found particularly useful in the study of these , often problematic , signals : none more so than the Electrocardiogram ( ECG ) . In this paper , the emerging roles of the wavelet transform in the ECG preprocessing and noise removing step is discussed in detail .
We have developed a Matlab/spl trade/ based software package for bio-signal analysis . The software is based on modular design and can thus be easily adapted to fit on analysis of various kind of time variant or event-related bio-signals .
Summary In this chapter , we have studied the use of a powerful class of noncausal statistical models , the Gauss-Markov random fields , in 2D signal processing . These models were motivated through intuitive arguments regarding the importance of noncausality and the primacy of local dependence or Markovianity , as well as through the structures arising from numerical integration of PDEs .
Researchers have proposed many effective methods in this aspect , this paper no longer mentions them but will introduce a new method . Whereas difference method has obtained good results in 1D signal processing , this paper manages to apply it to 2D signal processing , that is to say , dim point targets detection in infrared images of low SNR .
A theoretical foundation to the notion of 2D transform and 2D signal processing is given , focusing on 2D group-based transforms , of which the 2D Haar and 2D Fourier transforms are particular instances . Conditions for separability of these transforms are established .
In this study the authors focus on RADAR-data . The classification of RADAR-data shows various advantages and disadvantages compared with a classification based on optical data . Main advantages are the independence of atmospheric conditions , the independence of illumination and the penetration depth , which allows an observation of volumetric properties and surface properties .
The algorithm first uses fuzzy logic reasoning ( FLR ) to obtain the priority of targets to radars , and designs a radar clustering algorithm based on the priority to form several subradar networks . The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target , which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model . Considering the uncertainty of the target RCS in practice , the chance-constraint programming ( CCP ) is used to balance power resource and tracking accuracy .
This report presents a simple and direct method of solving the registration problem associated with multiple radar tracking . The registration proceedure is split into two distinct parts that are independent from one another .
This renders antenna gain in certain directions and rejection in others , i.e. spatial filtering . Until recently , phased array systems exploited dedicated RF technologies leading to relatively costly systems , e.g. for nautical , airplane radar systems , and satellite communication . More recently , low-cost highly integrated beamforming concepts received considerable interest in academia but also industry , enabling consumer applications e.g. in base-stations for macro- and femto-cells , car-radar and 60GHz wideband radio links .
We show the viability of GFDM for radar with communications systems and the benefits of using it over Orthogonal Frequency Division Multiplexing ( OFDM ) . Finally , we also present GFDM as a solution to mitigate inter-system interference in radar with communications ( RadCom ) systems , thus showing that GFDM may prove to be a better candidate than OFDM for RadCom applications . This method is validated with simulations and practical measurements at 24 GHz .
In many wireless communications/radar applications , constant modulus sequences with low correlation sidelobes levels are desired . In this letter , we present an efficient optimization method to design them .
Weak signal to noise ratio(SNR ) environment in battlefield becomes increasingly prominent , which is a challenge to radar detecting and tracking target . Based on the home and abroad research achievements of detect and track algorithm for weak SNR targets , a comprehensive and up-to-date survey of the detect and track techniques based on plot for radar weak SNR targets is presented .
Ground-penetrating radar ( GPR ) measurement and its interpretation/analysis are challenging when soil is heterogeneous . Soil heterogeneity causes unwanted reflections ( i.e. , clutter ) that disturb reflections from objects of interest .
Network distributed radar track fusion with hierarchical structure is enhanced for distributed data fusion , it has its own characteristic and difficult . In this paper , a two hierarchical structure based fusion frame is describe , simulation plat base on HLA is proposed , the component and framework is described , the process and realization during the whole fusion course for low -lever and high lever fusion center is analyzed , finally the demonstrated simulation is shown .
We propose a strategy to evaluate the performance of a radar sounder for the subsurface exploration of the Europa icy crust and , in particular , the possibility to detect liquid water at the base of the ice shell . The approach integrates the information coming from experimental measurements of the dielectric properties of icy materials , thermal models related to different crustal scenarios , and numerical simulations of radar signal propagation . The radar response has been evaluated in terms of cumulative attenuation , signal-to-noise ratio ( SNR ) , and reflectivity .
Scanning Laser Radar has been widely used in many military and civil areas . Usually there are relative movements between the target and the radar , so the moving target image modeling and simulation is an important research content in the field of signal processing and system design of scan-imaging laser radar . In order to improve the simulation speed and hold the accuracy of the image simulation simultaneously , a novel fast simulation algorithm is proposed in this paper .
We argue in this paper that there are strong parallels between this threshold behavior and earlier Shannon 's justification for similar phenomenon in his notion of the capacity of the information transmission . Attempts of finding performance bounds for the compressive sensing with the standard information-theoretic tools were later made , e.g. by Wainwright et al. We outline sharp asymptotical bounds obtained in 1979 that were inspired by the Multi-Access Capacity Region theory and that can greatly enhance the current understanding of recovery of active inputs in sparse compressive sensing . These bounds were obtained for asymptotically optimal designs which include random designs and thus imply upper bounds for performance of recovery under arbitrary designs .
It will be shown that these methods are especially advantageous to use when the data-length is short , or when the signal is a mixture of multiple hidden periods . The dictionary techniques were inspired by recent advances in sparsity based compressed sensing . Apart from the l1 norm based convex programs currently used in other applications , our dictionaries can admit l2 norm formulations that have linear and closed form solutions , even when the systems is under-determined .
Research on Signal Analysis and Processing Method Chapter 16 Modulation Forensics for Wireless Digital Communications in Frequency-Selective Fading Channels ∗ Phase modulation and demodulation experiments based on VI Timer-based Demodulator for AM Square Signals coming from Sensor Circuits DPSK-demodulation based on ultra-compact micron-scale SOI platform A 17pJ/bit broadband mixed-signal demodulator in 90nm CMOS Matched Filter as Pre-processing Tool for Multi-user Detection in DS-CDMA System Error Probability of Linear and Adaptive Linear Multi-User Detection IC card read-write equipment Compact Fiber Optic Trackside Sensor for Rail Vehicle Detection and Analysis Iterative Channel Estimation with Frequency Replacement for SC-FDMA Systems Ultimately phase-noise tolerant QPSK homodyne using a spectrum-sliced ASE light source Postprocessing of L1 GPS radio occultation signals recorded in open‐loop mode A coherent spread-spectrum RAKE-receiver with maximum-likelihood frequency estimation A lattice-based adaptive IIR notch filter and its application to FSK demodulation Optimization and Estimation of Complex-Valued Signals: Theory and applications in filtering and blind source separation Blind source separation with unknown and dynamically changing number of source signals Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques A Novel Grading Noise-Pretreatment Algorithm Based on Time-Frequency Blind Source Separation A Complexity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing A conventional auto-associative neural network separates blind sources without adding intentional algorithms other than pruning [Independent Components Analysis (ICA) in the study of electroencephalographic signals]. Blind separation and equalization using fractional sampling of digital communications signals A Comparative Study of Blind Source Separation for Bioacoustics Sounds based on FastICA, PCA and NMF A Very Deep One Dimensional Convolutional Neural Network (VDOCNN) for Appliance Power Signature Classification Embedded fastICA algorithm applied to the sensor noise extraction problem of foundation fieldbus network A Fundamental Relation Between Blind and Supervised Adaptive Filtering Illustrated for Blind Source Separation and Acoustic Echo Cancellation An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources Pattern recognition using temporal fuzzy automata Speaker Recognition Based on Support Vector Machines and Multi-Scale Wavelet Analysis A hybrid double-density dual-tree discrete wavelet transformation and marginal Fisher analysis for scoring sleep stages from unprocessed single-channel electroencephalogram. A new robust wavelet based algorithm for baseline wandering cancellation in ECG signals Software package for bio-signal analysis 15 Statistical algorithms for noncausal Gauss-Markov fields Application of difference method to dim point target detection in infrared images Two-dimensional wreath product group-based image processing Data compression-a possibility for the integration of remote sensing data in GISs Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster Based on Fuzzy Logic Reasoning Registration in Multi-Sensor Data Fusion and Tracking Beamforming techniques and RF transceiver design Non-Orthogonal Multicarrier Waveform for Radar With Communications Systems: 24 GHz GFDM RadCom On the Design of Constant Modulus Sequences with Low Correlation Sidelobes Levels Survey on Detect and Track Algorithm for Radar Weak Signal to Noise Ratio Targets Based on Plot Clutter Modeling for Ground-Penetrating Radar Measurements in Heterogeneous Soils Simulation for Hierarchical Structure and Distributed Radar Netting Radar Signal Penetration and Horizons Detection on Europa Through Numerical Simulations A fast 3D image simulation algorithm of moving target for scanning laser radar Recovery of sparse active inputs in general systems: A review The Nested Periodic Subspaces: Extensions of Ramanujan Sums for Period Estimation
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I2MTC OFC IWANN ISCAS IIH-MSP ICA KES Signal Processing and its Applications ISSCC
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