Journal of Information Hiding and Multimedia Signal Processing最新文献_第6页

Real-Time Face Detection and Recognition in Complex Background 复杂背景下的实时人脸检测与识别

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2017-05-05 DOI: 10.4236/JSIP.2017.82007

Xin Zhang, T. Gonnot, J. Saniie

引用次数: 37

Sound-Environment Monitoring Method Based on Computational Auditory Scene Analysis 基于计算听觉场景分析的声环境监测方法

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2017-05-05 DOI: 10.4236/JSIP.2017.82005

M. Kawamoto

引用次数: 1

A New Equalization Performance Analyzing Method for Blind Adaptive Equalizers Inspired by Maximum Time Interval Error 基于最大时间间隔误差的盲自适应均衡器均衡性能分析新方法

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2017-05-05 DOI: 10.4236/JSIP.2017.82004

Guilad Suissa, M. Pinchas

引用次数: 1

Low Complexity Dynamic Channel Equalization in OFDM with High Frequency Mobile Network Technologies 基于高频移动网络技术的OFDM低复杂度动态信道均衡

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2017-05-05 DOI: 10.4236/JSIP.2017.82003

Redhwan Mawari, M. Zohdy

{"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}

引用次数: 0

Digital Weather Stations as a Part of Wind Power Station 数字气象站作为风力发电站的一部分

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2017-01-19 DOI: 10.4236/JSIP.2017.81002

Fawzy M. Al Zureiqat

引用次数: 0

Three-Dimensional Scenes Restore Using Digital Image 三维场景恢复使用数字图像

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2017-01-19 DOI: 10.4236/JSIP.2017.81001

Takialddin A. Al Smadi, I. Al-Khawaldeh, Kalid Al Smadi

引用次数: 0

Simulation Based Design Analysis of an Adjustable Window Function 基于仿真的可调窗函数设计分析

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2016-10-11 DOI: 10.4236/JSIP.2016.74019

Orvila Sarker, R. Khan

引用次数: 0

Evaluation of the Minimum Size of a Window for Harmonics Signals 谐波信号窗口最小尺寸的评估

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2016-10-11 DOI: 10.4236/JSIP.2016.74017

J. A. Reyes, C. S. Forgach

{"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}

引用次数: 6

Comparison of MRI Under-Sampling Techniques for Compressed Sensing with Translation Invariant Wavelets Using FastTestCS: A Flexible Simulation Tool 利用FastTestCS:一种灵活的仿真工具，比较MRI欠采样压缩感知技术与平移不变小波

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2016-10-11 DOI: 10.4236/JSIP.2016.74021

C. Baker

{"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}

引用次数: 1

Inspection of the Output of a Convolution and Deconvolution Process from the Leading Digit Point of View—Benford’s Law 从前导数的角度检验卷积和反卷积过程的输出——本福德定律

Journal of Information Hiding and Multimedia Signal Processing Pub Date : 2016-10-11 DOI: 10.4236/JSIP.2016.74020

M. Pinchas

{"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}

引用次数: 1