This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. The LBP descriptor is utilized to extract facial features for fast face detection. The eye detection algorithm reduces the false face detection rate. The detected facial image is then processed to correct the orientation and increase the contrast, therefore, maintains high facial recognition accuracy. Finally, the PCA algorithm is used to recognize faces efficiently. Large databases with faces and non-faces images are used to train and validate face detection and facial recognition algorithms. The algorithms achieve an overall true-positive rate of 98.8% for face detection and 99.2% for correct facial recognition.
{"title":"Real-Time Face Detection and Recognition in Complex Background","authors":"Xin Zhang, T. Gonnot, J. Saniie","doi":"10.4236/JSIP.2017.82007","DOIUrl":"https://doi.org/10.4236/JSIP.2017.82007","url":null,"abstract":"This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. The LBP descriptor is utilized to extract facial features for fast face detection. The eye detection algorithm reduces the false face detection rate. The detected facial image is then processed to correct the orientation and increase the contrast, therefore, maintains high facial recognition accuracy. Finally, the PCA algorithm is used to recognize faces efficiently. Large databases with faces and non-faces images are used to train and validate face detection and facial recognition algorithms. The algorithms achieve an overall true-positive rate of 98.8% for face detection and 99.2% for correct facial recognition.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"30 1","pages":"99-112"},"PeriodicalIF":0.0,"publicationDate":"2017-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88433847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.
{"title":"Sound-Environment Monitoring Method Based on Computational Auditory Scene Analysis","authors":"M. Kawamoto","doi":"10.4236/JSIP.2017.82005","DOIUrl":"https://doi.org/10.4236/JSIP.2017.82005","url":null,"abstract":"Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"960 ","pages":"65-77"},"PeriodicalIF":0.0,"publicationDate":"2017-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72495684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Up to now, the Mean Square Error (MSE) criteria, the residual Inter-Symbol Interference (ISI) and the Bit-Error-Rate (BER) were used to analyze the equalization performance of a blind adaptive equalizer in its convergence state. In this paper, we propose an additional tool (additional to the ISI, MSE and BER) for analyzing the equalization performance in the convergence region based on the Maximum Time Interval Error (MTIE) criterion that is used for the specification of clock stability requirements in telecommunications standards. This new tool preserves the short term statistical information unlike the already known tools (BER, ISI, MSE) that lack this information. Simulation results will show that the equalization performance of a blind adaptive equalizer obtained in the convergence region for two different channels is seen to be approximately the same from the residual ISI and MSE point of view while this is not the case with our new proposed tool. Thus, our new proposed tool might be considered as a more sensitive tool compared to the ISI and MSE method.
{"title":"A New Equalization Performance Analyzing Method for Blind Adaptive Equalizers Inspired by Maximum Time Interval Error","authors":"Guilad Suissa, M. Pinchas","doi":"10.4236/JSIP.2017.82004","DOIUrl":"https://doi.org/10.4236/JSIP.2017.82004","url":null,"abstract":"Up to now, the Mean Square Error (MSE) criteria, the residual Inter-Symbol Interference (ISI) and the Bit-Error-Rate (BER) were used to analyze the equalization performance of a blind adaptive equalizer in its convergence state. In this paper, we propose an additional tool (additional to the ISI, MSE and BER) for analyzing the equalization performance in the convergence region based on the Maximum Time Interval Error (MTIE) criterion that is used for the specification of clock stability requirements in telecommunications standards. This new tool preserves the short term statistical information unlike the already known tools (BER, ISI, MSE) that lack this information. Simulation results will show that the equalization performance of a blind adaptive equalizer obtained in the convergence region for two different channels is seen to be approximately the same from the residual ISI and MSE point of view while this is not the case with our new proposed tool. Thus, our new proposed tool might be considered as a more sensitive tool compared to the ISI and MSE method.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"1 1","pages":"42-64"},"PeriodicalIF":0.0,"publicationDate":"2017-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76093623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present in this paper a method for enhancing equalization of a dynamic channel. A dynamic channel is characterized and modeled by a high relative velocity between transmitter and receiver and fast changes of environment conditions for wave propagation. Based on Jakes model, an auto-regressive model (AR) [1] for such a dynamic system, i.e., a time variant channel is developed. More specifically, the enhanced equalization method we are proposing is a combination of a multi-stage time and frequency domain equalizer with a feed-forward loop. The underlined wok presents a unified approach to the equalization method that employs both time and frequency domains data to enhance the equalization scheme. In an OFDM system, the channel coefficients for each tap, in time domain for consecutive blocks, are partially independent thus correlated. Such correlation can improve the channel estimation if it is taken into account. The method in this paper enhances the performance of equalization by dynamically selecting the number of previous OFDM symbols based on the Doppler frequency. In order to decrease the complexity of the system model, we utilize the autocorrelation and Doppler frequency to dynamically select the previous OFDM symbols that will be stored in the memory. In addition to deriving earlier results in a unified manner, the approach presented also leads to enhanced performance results without imposing any restrictions or limitations on the OFDM system such as increasing the number of pilots or cyclic prefix.
{"title":"Low Complexity Dynamic Channel Equalization in OFDM with High Frequency Mobile Network Technologies","authors":"Redhwan Mawari, M. Zohdy","doi":"10.4236/JSIP.2017.82003","DOIUrl":"https://doi.org/10.4236/JSIP.2017.82003","url":null,"abstract":"We present in this paper a method for enhancing equalization of a dynamic channel. A dynamic channel is characterized and modeled by a high relative velocity between transmitter and receiver and fast changes of environment conditions for wave propagation. Based on Jakes model, an auto-regressive model (AR) [1] for such a dynamic system, i.e., a time variant channel is developed. More specifically, the enhanced equalization method we are proposing is a combination of a multi-stage time and frequency domain equalizer with a feed-forward loop. The underlined wok presents a unified approach to the equalization method that employs both time and frequency domains data to enhance the equalization scheme. In an OFDM system, the channel coefficients for each tap, in time domain for consecutive blocks, are partially independent thus correlated. Such correlation can improve the channel estimation if it is taken into account. The method in this paper enhances the performance of equalization by dynamically selecting the number of previous OFDM symbols based on the Doppler frequency. In order to decrease the complexity of the system model, we utilize the autocorrelation and Doppler frequency to dynamically select the previous OFDM symbols that will be stored in the memory. In addition to deriving earlier results in a unified manner, the approach presented also leads to enhanced performance results without imposing any restrictions or limitations on the OFDM system such as increasing the number of pilots or cyclic prefix.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"56 1","pages":"17-41"},"PeriodicalIF":0.0,"publicationDate":"2017-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83174900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper mainly studies Weather Stations part of the wind power station. The use of wind energy in practice is carried out using the facilities of the wind in which the kinetic energy of the windscreen flow is converted into mechanical energy wind speed, then electrical energy alternator. The effective operation of the wind turbine is dependent on the direction of the wind. Speed air density, which in turn depends on the temperature and humidity. Thus, the speed of the wind worked effectively in its composition must include the weather. Meteorological station also performs the role of prevention. When the sharp wind speed or increase wind speed above the maximum value, it sends a signal to the lock assembly wind to prevent wind turbine technology from damage. The work of the meteorological stations design as part of the Wind Energy Station is considered. The complex technical devices are used for its implementation. A set of technical means used to its implementation and designed system consists of a temperature, humidity, wind speed, wind direction and rain gauge sensors that are connected to PIC16f876A microcontroller.
{"title":"Digital Weather Stations as a Part of Wind Power Station","authors":"Fawzy M. Al Zureiqat","doi":"10.4236/JSIP.2017.81002","DOIUrl":"https://doi.org/10.4236/JSIP.2017.81002","url":null,"abstract":"This paper mainly studies Weather Stations part of the wind power station. The use of wind energy in practice is carried out using the facilities of the wind in which the kinetic energy of the windscreen flow is converted into mechanical energy wind speed, then electrical energy alternator. The effective operation of the wind turbine is dependent on the direction of the wind. Speed air density, which in turn depends on the temperature and humidity. Thus, the speed of the wind worked effectively in its composition must include the weather. Meteorological station also performs the role of prevention. When the sharp wind speed or increase wind speed above the maximum value, it sends a signal to the lock assembly wind to prevent wind turbine technology from damage. The work of the meteorological stations design as part of the Wind Energy Station is considered. The complex technical devices are used for its implementation. A set of technical means used to its implementation and designed system consists of a temperature, humidity, wind speed, wind direction and rain gauge sensors that are connected to PIC16f876A microcontroller.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"8 1","pages":"9-16"},"PeriodicalIF":0.0,"publicationDate":"2017-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82374797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Takialddin A. Al Smadi, I. Al-Khawaldeh, Kalid Al Smadi
Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically stage transforms overlap stored on a digital computer. Different three-dimensional images restore and develop the system for the expansion of the three-dimensional scenes and camera movement parameters. This article talks about these kinds of digital image processing algorithms as the reconstruction of three-dimensional model of the scene. In the present state, many such algorithms need to be improved in this paper proposing one of the options to improve the accuracy of such reconstruction.
{"title":"Three-Dimensional Scenes Restore Using Digital Image","authors":"Takialddin A. Al Smadi, I. Al-Khawaldeh, Kalid Al Smadi","doi":"10.4236/JSIP.2017.81001","DOIUrl":"https://doi.org/10.4236/JSIP.2017.81001","url":null,"abstract":"Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically stage transforms overlap stored on a digital computer. Different three-dimensional images restore and develop the system for the expansion of the three-dimensional scenes and camera movement parameters. This article talks about these kinds of digital image processing algorithms as the reconstruction of three-dimensional model of the scene. In the present state, many such algorithms need to be improved in this paper proposing one of the options to improve the accuracy of such reconstruction.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"34 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2017-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79026449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Window based Finite Impulse Response filters have the problem that in order to obtain better performance from these filters in terms of minimum stopband attenuation cost has to be paid for half main-lobe width and vice-versa. A solution of this contradictory behavior is to increase the length of the window which in turn requires more hardware hence increasing the cost of system. This paper proposes a novel window based on two shifted hyperbolic tangent functions. The proposed window contains an adjustable parameter, with the help of which desired time and frequency domain characteristics may be achieved for relatively shorter window length. The characteristics of the proposed window are compared with those of the two well-known adjustable windows namely Cosh window and Exponential window. MATLAB simulation results show that for the same value of window length, the proposed window provides improved output, and thus it makes a good compromise between minimum stopband attenuation and half main-lobe width compared to the windows mentioned previously.
{"title":"Simulation Based Design Analysis of an Adjustable Window Function","authors":"Orvila Sarker, R. Khan","doi":"10.4236/JSIP.2016.74019","DOIUrl":"https://doi.org/10.4236/JSIP.2016.74019","url":null,"abstract":"Window based Finite Impulse Response \u0000filters have the problem that in order to obtain better performance from these \u0000filters in terms of minimum stopband attenuation cost has to be paid for half \u0000main-lobe width and vice-versa. A solution of this contradictory behavior is to \u0000increase the length of the window which in turn requires more hardware hence \u0000increasing the cost of system. This paper proposes a novel window based on two \u0000shifted hyperbolic tangent functions. The proposed window contains an \u0000adjustable parameter, with the help of which desired time and frequency domain \u0000characteristics may be achieved for relatively shorter window length. The \u0000characteristics of the proposed window are compared with those of the two \u0000well-known adjustable windows namely Cosh window and Exponential window. MATLAB \u0000simulation results show that for the same value of window length, the proposed \u0000window provides improved output, and thus it makes a good compromise between \u0000minimum stopband attenuation and half main-lobe width compared to the windows \u0000mentioned previously.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"23 1","pages":"214-226"},"PeriodicalIF":0.0,"publicationDate":"2016-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80454678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.
{"title":"Evaluation of the Minimum Size of a Window for Harmonics Signals","authors":"J. A. Reyes, C. S. Forgach","doi":"10.4236/JSIP.2016.74017","DOIUrl":"https://doi.org/10.4236/JSIP.2016.74017","url":null,"abstract":"Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"29 1","pages":"175-191"},"PeriodicalIF":0.0,"publicationDate":"2016-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74726871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.
{"title":"Comparison of MRI Under-Sampling Techniques for Compressed Sensing with Translation Invariant Wavelets Using FastTestCS: A Flexible Simulation Tool","authors":"C. Baker","doi":"10.4236/JSIP.2016.74021","DOIUrl":"https://doi.org/10.4236/JSIP.2016.74021","url":null,"abstract":"A sparsifying transform for use in \u0000Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic \u0000Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms \u0000(TIWT) have been shown to perform exceedingly well in CS by reducing repetitive \u0000line pattern image artifacts that may be observed when using orthogonal \u0000wavelets. To further establish its validity as a good sparsifying transform, \u0000the TIWT is comprehensively investigated and compared with Total Variation \u0000(TV), using six under-sampling patterns through simulation. Both trajectory and \u0000random mask based under-sampling of MRI data are reconstructed to demonstrate a \u0000comprehensive coverage of tests. Notably, the TIWT in CS reconstruction \u0000performs well for all varieties of under-sampling patterns tested, even for \u0000cases where TV does not improve the mean squared error. This improved Image \u0000Quality (IQ) gives confidence in applying this transform to more CS \u0000applications which will contribute to an even greater speed-up of a CS MRI \u0000scan. High vs low resolution time of flight MRI CS re-constructions are also \u0000analyzed showing how partial Fourier acquisitions must be carefully addressed \u0000in CS to prevent loss of IQ. In the spirit of reproducible research, novel \u0000software is introduced here as FastTestCS. It is a helpful tool to quickly \u0000develop and perform tests with many CS customizations. Easy integration and \u0000testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI \u0000datasets are shown to be efficiently distributed as a scalable solution for \u0000large studies. Comparisons in reconstruction computation time are made between \u0000the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a \u0000significant time savings factor of 60×. The addition of FastTestCS is proven to \u0000be a fast, flexible, portable and reproducible simulation aid for CS research.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"12 1","pages":"252-271"},"PeriodicalIF":0.0,"publicationDate":"2016-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74778897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the communication field, during transmission, a source signal undergoes a convolutive distortion between its symbols and the channel impulse response. This distortion is referred to as Intersymbol Interference (ISI) and can be reduced significantly by applying a blind adaptive deconvolution process (blind adaptive equalizer) on the distorted received symbols. But, since the entire blind deconvolution process is carried out with no training symbols and the channel’s coefficients are obviously unknown to the receiver, no actual indication can be given (via the mean square error (MSE) or ISI expression) during the deconvolution process whether the blind adaptive equalizer succeeded to remove the heavy ISI from the transmitted symbols or not. Up to now, the output of a convolution and deconvolution process was mainly investigated from the ISI point of view. In this paper, the output of a convolution and deconvolution process is inspected from the leading digit point of view. Simulation results indicate that for the 4PAM (Pulse Amplitude Modulation) and 16QAM (Quadrature Amplitude Modulation) input case, the number “1” is the leading digit at the output of a convolution and deconvolution process respectively as long as heavy ISI exists. However, this leading digit does not follow exactly Benford’s Law but follows approximately the leading digit (digit 1) of a Gaussian process for independent identically distributed input symbols and a channel with many coefficients.
{"title":"Inspection of the Output of a Convolution and Deconvolution Process from the Leading Digit Point of View—Benford’s Law","authors":"M. Pinchas","doi":"10.4236/JSIP.2016.74020","DOIUrl":"https://doi.org/10.4236/JSIP.2016.74020","url":null,"abstract":"In the communication field, during \u0000transmission, a source signal undergoes a convolutive distortion between its \u0000symbols and the channel impulse response. This distortion is referred to as \u0000Intersymbol Interference (ISI) and can be reduced significantly by applying a \u0000blind adaptive deconvolution process (blind adaptive equalizer) on the distorted \u0000received symbols. But, since the entire blind deconvolution process is carried \u0000out with no training symbols and the channel’s coefficients are obviously \u0000unknown to the receiver, no actual indication can be given (via the mean square \u0000error (MSE) or ISI expression) during the deconvolution process whether the \u0000blind adaptive equalizer succeeded to remove the heavy ISI from the transmitted \u0000symbols or not. Up to now, the output of a convolution and deconvolution \u0000process was mainly investigated from the ISI point of view. In this paper, the \u0000output of a convolution and deconvolution process is inspected from the leading \u0000digit point of view. Simulation results indicate that for the 4PAM (Pulse \u0000Amplitude Modulation) and 16QAM (Quadrature Amplitude Modulation) input case, \u0000the number “1” is the leading digit at the output of a convolution and \u0000deconvolution process respectively as long as heavy ISI exists. However, this \u0000leading digit does not follow exactly Benford’s Law but follows approximately \u0000the leading digit (digit 1) of a Gaussian process for independent identically \u0000distributed input symbols and a channel with many coefficients.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"154 1","pages":"227-251"},"PeriodicalIF":0.0,"publicationDate":"2016-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84881901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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