Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342469
Tsuhan Chen, P. Vaidyanathan
For an analysis/synthesis filter bank to achieve perfect reconstruction, the synthesis polyphase matrix should be equal to an inverse of the analysis polyphase matrix E(z). Therefore, the problem of perfect reconstruction filter banks is same as the inversion of the multi-input multi-output transfer function E(z). Using state-space notations, it has been shown that the inversion can be achieved by using time-reversed filters given proper initial conditions. In this paper, we extend the idea of time-reversed inversion to the case of time-varying filter banks. Using the state-space framework, we show perfect reconstruction is always guaranteed, no matter how often the filter bank varies with time. This framework covers both maximally-decimated filter banks and under-decimated ones. We also show how the overhead of transmitting initial conditions can be avoided.<>
{"title":"Time-reversed inversion for time-varying filter banks","authors":"Tsuhan Chen, P. Vaidyanathan","doi":"10.1109/ACSSC.1993.342469","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342469","url":null,"abstract":"For an analysis/synthesis filter bank to achieve perfect reconstruction, the synthesis polyphase matrix should be equal to an inverse of the analysis polyphase matrix E(z). Therefore, the problem of perfect reconstruction filter banks is same as the inversion of the multi-input multi-output transfer function E(z). Using state-space notations, it has been shown that the inversion can be achieved by using time-reversed filters given proper initial conditions. In this paper, we extend the idea of time-reversed inversion to the case of time-varying filter banks. Using the state-space framework, we show perfect reconstruction is always guaranteed, no matter how often the filter bank varies with time. This framework covers both maximally-decimated filter banks and under-decimated ones. We also show how the overhead of transmitting initial conditions can be avoided.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115163202","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342525
C. Lindquist
A variety of standard basis functions have long been used for characterizing signals including Fourier, Walsh, Haar, and Slant. Optimum Karhunen-Loeve basis functions derived from correlation data can also be used. Radial basis functions are a new type. They are based on actual data rather than predefined or generated sets. This paper relates radial basis functions and standard basis functions. It reviews how they are derived and illustrates their broad use with EKG and stock market examples. They have great utility and may become as common as their standard predecessors.<>
{"title":"Radial basis function transforms and their use in signal processing","authors":"C. Lindquist","doi":"10.1109/ACSSC.1993.342525","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342525","url":null,"abstract":"A variety of standard basis functions have long been used for characterizing signals including Fourier, Walsh, Haar, and Slant. Optimum Karhunen-Loeve basis functions derived from correlation data can also be used. Radial basis functions are a new type. They are based on actual data rather than predefined or generated sets. This paper relates radial basis functions and standard basis functions. It reviews how they are derived and illustrates their broad use with EKG and stock market examples. They have great utility and may become as common as their standard predecessors.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"476 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123055365","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342475
H. Sawhney
Digital storage of images and videos requires staggering amounts of storage. A mere hour long digitized video can occupy a staggering 30GB of storage. With the increasing dependence of a variety of applications on digital storage, the need for automatic and semiautomatic annotation and analysis tools for intelligent video data indexing and manipulation will be strongly felt. Manual annotation of this data will be practically infeasible and insipid. The paper presents highlights of some of the important problems to be solved for automating the process. Computer vision techniques with a human-in-the-loop approach can provide a powerful workbench of algorithms and techniques for the task.<>
{"title":"Motion video annotation and analysis: an overview","authors":"H. Sawhney","doi":"10.1109/ACSSC.1993.342475","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342475","url":null,"abstract":"Digital storage of images and videos requires staggering amounts of storage. A mere hour long digitized video can occupy a staggering 30GB of storage. With the increasing dependence of a variety of applications on digital storage, the need for automatic and semiautomatic annotation and analysis tools for intelligent video data indexing and manipulation will be strongly felt. Manual annotation of this data will be practically infeasible and insipid. The paper presents highlights of some of the important problems to be solved for automating the process. Computer vision techniques with a human-in-the-loop approach can provide a powerful workbench of algorithms and techniques for the task.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127159900","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342386
L. T. McWhorter, L. Scharf
We investigate the properties of quadratic covariance bounds for parametric estimators. The Cramer-Rao, Bhattacharyya (1946), and Barankin (1949) bounds have this quadratic structure and the properties of these bounds are uniquely determined by their respective score functions. We enumerate some characteristics of score functions which generate tight bounds. We also introduce projection operator and integral/kernel representations for this class of quadratic covariance bounds. These representations are useful as analysis and synthesis tools. We also address the issue of efficiency for this class of bounds.<>
{"title":"Properties of quadratic covariance bounds","authors":"L. T. McWhorter, L. Scharf","doi":"10.1109/ACSSC.1993.342386","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342386","url":null,"abstract":"We investigate the properties of quadratic covariance bounds for parametric estimators. The Cramer-Rao, Bhattacharyya (1946), and Barankin (1949) bounds have this quadratic structure and the properties of these bounds are uniquely determined by their respective score functions. We enumerate some characteristics of score functions which generate tight bounds. We also introduce projection operator and integral/kernel representations for this class of quadratic covariance bounds. These representations are useful as analysis and synthesis tools. We also address the issue of efficiency for this class of bounds.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127160634","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342337
P. Powell
A prototype video monitoring system has been constructed to monitor and manage high occupancy vehicle lanes near San Diego, CA. A major project objective was to develop a system using off the shelf hardware and open system computer standards. We discuss the overall structure of the video system, and then how various hardware and software modules were selected and integrated into the system. Next we discuss how the open systems concepts aided and simplified the design and implementation of the system. Finally, we discuss how opens systems can aid in the development of Intelligent Vehicle Highway Systems (IVHS).<>
{"title":"Open systems on the highways","authors":"P. Powell","doi":"10.1109/ACSSC.1993.342337","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342337","url":null,"abstract":"A prototype video monitoring system has been constructed to monitor and manage high occupancy vehicle lanes near San Diego, CA. A major project objective was to develop a system using off the shelf hardware and open system computer standards. We discuss the overall structure of the video system, and then how various hardware and software modules were selected and integrated into the system. Next we discuss how the open systems concepts aided and simplified the design and implementation of the system. Finally, we discuss how opens systems can aid in the development of Intelligent Vehicle Highway Systems (IVHS).<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535069","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342514
A. Forman, D.B. Brown, J. Hughen, R.R. Pressley, A. R. Sanders, D. Sullivan
The paper describes the Advanced Research Projects Agency (ARPA) MUltiSensor Target Recognition System (MUSTRS). A smart sensor manager controls forward-looking infrared (FLIR) and millimeter wave (MMW) radar sensors to obtain multiple looks at targets on the ground. Targets in IR images are recognized using a variation on minimum average correlation energy (MACE) filtering and/or a model-based algorithm called key features. Radar data are processed using quadratic distance composite filtering techniques. Evidence is combined using a Bayesian method. The system has been designed to correctly classify time critical mobile targets with very low false alarm rates.<>
{"title":"MUltiSensor Target Recognition System (MUSTRS)","authors":"A. Forman, D.B. Brown, J. Hughen, R.R. Pressley, A. R. Sanders, D. Sullivan","doi":"10.1109/ACSSC.1993.342514","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342514","url":null,"abstract":"The paper describes the Advanced Research Projects Agency (ARPA) MUltiSensor Target Recognition System (MUSTRS). A smart sensor manager controls forward-looking infrared (FLIR) and millimeter wave (MMW) radar sensors to obtain multiple looks at targets on the ground. Targets in IR images are recognized using a variation on minimum average correlation energy (MACE) filtering and/or a model-based algorithm called key features. Radar data are processed using quadratic distance composite filtering techniques. Evidence is combined using a Bayesian method. The system has been designed to correctly classify time critical mobile targets with very low false alarm rates.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"297 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114389045","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342304
D. Gerlach, A. Paulraj
A central base station simultaneously communicates with several mobile users by allocating a separate time or frequency channel for each mobile to prevent undesired crosstalk. However, each time or frequency channel may be reused among several mobiles by means of an antenna array at the base station which points a separate beam at each user. The downlink beamformer would normally operate in an "open loop" mode, in which the base steers a mainlobe in the direction of each mobile. Such a system may operate effectively in a free space environment with no multipath. In the presence of scattering, open loop methods will not perform adequately. A new "closed loop" technique is presented in which each mobile user feeds back to the base estimates of the received signal amplitudes. Using feedback, the base station can achieve precision beamforming resulting in lower crosstalk and improved signal separation even in the presence of strong scattering environments.<>
{"title":"Base station transmitter antenna arrays with mobile to base feedback","authors":"D. Gerlach, A. Paulraj","doi":"10.1109/ACSSC.1993.342304","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342304","url":null,"abstract":"A central base station simultaneously communicates with several mobile users by allocating a separate time or frequency channel for each mobile to prevent undesired crosstalk. However, each time or frequency channel may be reused among several mobiles by means of an antenna array at the base station which points a separate beam at each user. The downlink beamformer would normally operate in an \"open loop\" mode, in which the base steers a mainlobe in the direction of each mobile. Such a system may operate effectively in a free space environment with no multipath. In the presence of scattering, open loop methods will not perform adequately. A new \"closed loop\" technique is presented in which each mobile user feeds back to the base estimates of the received signal amplitudes. Using feedback, the base station can achieve precision beamforming resulting in lower crosstalk and improved signal separation even in the presence of strong scattering environments.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129591436","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342582
C. Lam
A bootstrap mean is calculated as follows. An artificial data set is generated by randomly sampling the original data set. A trimmed mean is then calculated for each of the artificial data set. These steps are repeated many times to produce a set of trimmed means. The bootstrap mean is the average of these trimmed means. The bootstrap mean is a more robust estimate of the true mean and the estimation of error is the usual standard deviation. Recent advances in fast computers make it feasible to calculate the bootstrap mean. A bootstrap mean filter was developed and tested using synthetic data with random noise added. Comparisons to mean, median, and trimmed-mean filters show that the bootstrap mean filter is superior in the removal of random noise and the retention of edge information. Implementation in special purpose hardware of this filter is desirable because of its heavy computational requirement. Some candidate solutions are suggested.<>
{"title":"The bootstrap mean filter for image restoration","authors":"C. Lam","doi":"10.1109/ACSSC.1993.342582","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342582","url":null,"abstract":"A bootstrap mean is calculated as follows. An artificial data set is generated by randomly sampling the original data set. A trimmed mean is then calculated for each of the artificial data set. These steps are repeated many times to produce a set of trimmed means. The bootstrap mean is the average of these trimmed means. The bootstrap mean is a more robust estimate of the true mean and the estimation of error is the usual standard deviation. Recent advances in fast computers make it feasible to calculate the bootstrap mean. A bootstrap mean filter was developed and tested using synthetic data with random noise added. Comparisons to mean, median, and trimmed-mean filters show that the bootstrap mean filter is superior in the removal of random noise and the retention of edge information. Implementation in special purpose hardware of this filter is desirable because of its heavy computational requirement. Some candidate solutions are suggested.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396158","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342389
H. Ge, D. Tufts, R. Kumaresan
We present some results of our analysis of Kumaresan's (1982) sparse linear prediction method for estimation of frequencies of sinusoids. Refinements of Kumaresan's method are proposed for the case of two sinusoids which are not close in frequency. When the data is corrupted by additive white Gaussian noise, the probability of correctly resolving ambiguities is used to evaluate the performance. Comparisons between statistical performance analyses and computer simulations demonstrate that the analyses are accurate.<>
{"title":"Ambiguity resolution in sparse linear prediction","authors":"H. Ge, D. Tufts, R. Kumaresan","doi":"10.1109/ACSSC.1993.342389","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342389","url":null,"abstract":"We present some results of our analysis of Kumaresan's (1982) sparse linear prediction method for estimation of frequencies of sinusoids. Refinements of Kumaresan's method are proposed for the case of two sinusoids which are not close in frequency. When the data is corrupted by additive white Gaussian noise, the probability of correctly resolving ambiguities is used to evaluate the performance. Comparisons between statistical performance analyses and computer simulations demonstrate that the analyses are accurate.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128777979","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}
Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342530
I. Gorodnitsky, B. Rao
Adaptive weighted norm extrapolation algorithms can provide superior performance for estimation of sparse signals from limited data. We present theoretical analysis results for a class of these algorithms that include a proof of the global convergence, the rate of convergence derivation, and characterization of the fixed points. We also propose a general class of adaptive weighted extrapolation algorithms and introduce a more general problem formulation which greatly expands the range of applications of the algorithm.<>
{"title":"Convergence analysis of a class of adaptive weighted norm extrapolation algorithms","authors":"I. Gorodnitsky, B. Rao","doi":"10.1109/ACSSC.1993.342530","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342530","url":null,"abstract":"Adaptive weighted norm extrapolation algorithms can provide superior performance for estimation of sparse signals from limited data. We present theoretical analysis results for a class of these algorithms that include a proof of the global convergence, the rate of convergence derivation, and characterization of the fixed points. We also propose a general class of adaptive weighted extrapolation algorithms and introduce a more general problem formulation which greatly expands the range of applications of the algorithm.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128649712","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：