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IMAGE-RECONSTRUCTION FROM PROJECTIONS UNDER WAVELET CONSTRAINTS

IEEE TRANSACTIONS ON signal processing 1993年第12期41卷 3579-3584页

作者： SAHINER, B YAGLE, AE Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

First, we discuss how the wavelet transform can be used to perform spatially-varying filtering of an image, suppressing noise locally in smooth regions of the image, and we discuss detection of such regions in a noise-corrupted image. Second, we show how to compute the minimum mean-square estimate of an image given: 1) noisy projections of the image;2) statistics of additive noise in the projections;and 3) constraints on wavelet coefficients of the image. Examples illustrate the resulting procedure.

关键词： Image reconstruction Low pass filters signal to noise ratio Speech processing signal processing signal resolution signal processing algorithms Image coding Spatial resolution Filtering

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Nonlinear maximum likelihood estimation of autoregressive time series

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IEEE TRANSACTIONS ON signal processing 1995年第12期43卷 2909-2919页

作者： McWhorter, LT Scharf, LL Department of Electrical and Computer Engineering University of Colorado Boulder CO USA

In this paper, we describe an algorithm for finding the exact, nonlinear, maximum likelihood (ML) estimators for the parameters of an autoregressive time series. We demonstrate that the ML normal equations can be written as an interdependent set of cubic and quadratic equations in the AR polynomial coefficients. We present an algorithm that algebraically solves this set of nonlinear equations for low-order problems. For high-order problems, we describe iterative algorithms for obtaining a ML solution.

关键词： Maximum likelihood estimation Nonlinear equations Iterative algorithms Parameter estimation Polynomials Reflection Statistics Probability Computer errors signal processing algorithms

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Sequential Change-Point Detection for High-Dimensional and Non-Euclidean Data

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4498-4511页

作者： Chu, Lynna Chen, Hao Iowa State Univ Dept Stat Ames IA 50011 USA Univ Calif Davis Dept Stat Davis CA 95616 USA

In many applications, it is often of practical and scientific interest to detect anomaly events in a streaming sequence of high-dimensional or non-Euclidean observations. We study a non-parametric framework that utilizes nearest neighbor information among the observations to detect changes in an online setting. It can be applied to data in arbitrary dimension and non-Euclidean data as long as a similarity measure on the sample space can be defined. We consider new test statistics under this framework that can detect anomaly events more effectively than the existing test while keeping the false discovery rate controlled at a fixed level. Analytic formulas approximating the average run lengths of the new approaches are derived to make them fast applicable to modern datasets. Simulation studies are provided to support theoretical results. The proposed approach is illustrated with an analysis of the NYC taxi dataset.

关键词： Delays Indexes Streaming media signal processing algorithms Image edge detection Wearable sensors Surveillance Anomaly detection online change-point streaming data graph-based tests non-parametric

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Do algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3187-3200页

作者： Wang, Guanyi Lou, Mengqi Pananjady, Ashwin Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we establish fundamental limits that depend on the geometry of the problem instance, and show that a natural projected power method exhibits local convergence to the statistically near-optimal neighborhood of the solution. We complement these results with end-to-end analyses of two important special cases given by path and tree sparsity in a general basis, showing initialization methods and matching evidence of computational hardness. Overall, our results indicate that several of the phenomena observed for vanilla sparse PCA extend in a natural fashion to its structured counterparts.

关键词： Principal component analysis Computational modeling Estimation Indexes Vectors signal processing algorithms Iterative methods structured sparsity nonconvex iterative optimization computational hardness

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Digital signal processing system for digital watermarking implementation

Digital signal processing system for digital watermarking im...

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International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： S. Zoican Telecommunication Department Politechnica University of Bucharest Romania

In general, the watermarking processes become vulnerable when the attacker knows the watermark insertion algorithm. This paper presents a watermark insertion algorithm for which the watermark can be recovered. A key code (namely - buyer key or buyer code) and image key are introduced. Therefore, recovery of the watermark authenticates the particular owner of the multimedia object and identities the buyer.

关键词： Digital signal processing Watermarking Authentication Pixel Degradation signal processing algorithms Cryptography Copyright protection Data mining Humans

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A versatile DSP-system for student-projects on embedded real-time audio signal processing

A versatile DSP-system for student-projects on embedded real...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： H. Kruger T. Lotter P. Vary Institute of Communication Systems and Data Processing RWTH Aachen University of Technology Aachen Germany

In this contribution, a new software environment for an embedded system is presented that is part of a laboratory for teaching students in digital audio signal processing. This environment enables even students with only a limited background in programming embedded technology to achieve remarkable implementations of realtime projects, including runtime user interaction. Hardware and algorithm related programming issues are separated for audio processing and interactive messaging. While the hardware related part is provided along with the software environment, the students can completely focus on algorithmic aspects. A well defined interface builds the bridge between the two programming components. Examples are presented to demonstrate the flexibility and efficiency of the given software platform. The underlying projects were defined, implemented and finally demonstrated by students in an interactive real-time demonstration.

关键词： signal processing signal processing algorithms Hardware Embedded software Embedded system Education Digital signal processing Runtime environment Software algorithms Bridges

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A new array signal processing method via exploitation of cyclostationarity

A new array signal processing method via exploitation of cyc...

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Annual ASSP Workshop on Spectrum Estimation and Modeling

作者： Guanghan Xu T. Kailath Information Systems Laboratory University of Stanford Stanford CA USA

The authors consider the problem of direction finding of radar or sonar communication signals based on the data received by sensor arrays. Most communication (modulated) signals exhibit cyclostationarity corresponding to the underlying periodicity which may be carrier frequency or baud rate. By exploiting cyclostationarity, one can significantly improve the signal detection capability, i.e. null out other co-channel interferences and stationary background noise. They propose a new approach for array signal processing via exploitation of cyclostationarity, which is asymptotically exact for either narrow-band or broad-band sources. Moreover, the new technique has implementation advantages over the existing techniques. The simulation results indicate a significantly better performance than that of the prior algorithms.< >

关键词： Array signal processing Sensor arrays signal processing algorithms Radar signal processing Sonar Frequency signal detection Interference Background noise Narrowband

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Biosignal laboratory: a software tool for biomedical signal processing and analysis

Biosignal laboratory: a software tool for biomedical signal ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： D. Cuesta P. Mico M. Aboy D. Novak R. Brezny L. Samblas D. Pastor S. Sancho Department of Computer Science Polytechnic University of Valencia Alcoy Spain Biomedical Signal Processing Laboratory Electrical and Computer Engineering Portland State University Portland OR USA Department of Cybernetics Technical University of Prague Prague Czech Republic Department of Neurology The Motol University Hospital Prague Czech Republic

Biosignal processing and analysis is a field of great importance in current medical practice. In recent years, biomedical engineers have developed many algorithms and processing techniques in order to help doctors in the examination of many different biosignals, and to find new information embedded in them and not easily observable in the raw data. Since the number and variety of these techniques grows every day, it is becoming more and more difficult to be up to date in biosignal processing. Other aspects such as signals format, algorithms implementation and comparison, artifacts, etc, make the learning process in this field longer and longer, and in many occasions a lot of different software tools are needed to visualize or process biosignals. In this paper we present a program that consists of many of the current typical biosignal processing algorithms: filtering, domain transforms, basic operators, wave detection, and unlike other similar applications, it includes a pattern recognition block. The program can also be used just to exchange signals formats. The objective of this program is to become a usual tool both for medical practice and for biomedical educational purposes.

关键词： Laboratories Software tools Biomedical signal processing signal analysis signal processing algorithms Biomedical engineering Data engineering signal processing Software algorithms Visualization

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Comparison of reduced-rank signal processing techniques

Comparison of reduced-rank signal processing techniques

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Asilomar Conference on signals, Systems & Computers

作者： P.A. Zulch J.S. Goldstein J.R. Guerci L.S. Reed US AF Research Laboratory/SNRT Rome NY USA MIT Lincoln Lalboratory Lexington MA USA SAIC/ADT Arlington VA USA Department of Electrical Engineering University of Southern California Los Angeles CA USA

This paper compares several reduced-rank signal processing algorithms for adaptive sensor array processing. The comparisons presented here use Monte Carlo analysis to evaluate the algorithmic performance as a function of both rank and sample support when the covariance matrix is unknown and estimated from collected sensor data. The adaptive techniques considered are the principal components algorithm, the cross-spectral metric and the multistage Wiener filter. It is shown that the new multistage Wiener filtering technique provides more robust performance as a function of both rank and sample support.

关键词： signal processing signal processing algorithms Array signal processing Wiener filter Adaptive signal processing Sensor arrays Monte Carlo methods Performance analysis Algorithm design and analysis Covariance matrix

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Performance Comparison of Beat Frequency Extraction Algorithm and Cross Correlation Algorithm for FMCW Radar signal processing Implemented on LabVIEW and USRP

Performance Comparison of Beat Frequency Extraction Algorith...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jesrey Martin S. Macasero Olga Joy L. Gerasta Department of Electrical Engineering and Technology MSU-Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

There are two prominent ways of implementing signal processing of FMCW radar in SDRs, namely i) the Beat Frequency Extraction Algorithm and ii) Cross Correlation Algorithm. The former is done by numerical mixing of the reference signal and the received signal first and then performing an FFT to detect the beat frequency that is then processed to output range information. Moreover, the latter is done by using cross correlation on the reference signal and the received signal. The aim of this paper is to scrutinize these two algorithms when they are implemented on the NI-USRP SDR platform and determine which is best for FMCW radar target detection accuracy. This paper gives guidance on the pros and cons of choosing such algorithm when being implemented on the USRP platform with LabVIEW as the development environment. The implementation of the two systems are based on these conditions: i) Carrier signal is 2Ghz, ii) Loopback cable is RG58 with lengths= 6m, 8m, 14m, 22m, and iii) Output RF Power of device is 70mW. Given the performance criteria of this paper, the Beat Frequency algorithm performed best compared to the Cross-Correlation algorithm.

关键词： signal processing algorithms Radar Software algorithms Meters Correlation Hardware Radio frequency

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