Matching pursuit algorithms
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting
The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
2000 10th European Signal Processing Conference, 2000
In this work we introduce an alternative implementation of matching pursuit (MP) using a genetic algorithm in the continuous space (GACS). MP is an attractive analysis approach in which the signal is sequentially decomposed into a linear expansion of atoms (functions) from a dictionary of waveforms so as to obtain a sparse representation. The main problem with MP is its ...
IWAENC 2012; International Workshop on Acoustic Signal Enhancement, 2012
This article discusses on an undetermined separation method based on the sparse multichannel representation of the sources in timefrequency domain. Sparse modeling assumes an ability to describe signals as linear combinations of few atoms from a pre-specified dictionary. As such, the choice of the dictionary that sparsifies the signals is crucial for the success of this task. A redundant dictionary ...
IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 2003., 2003
IEEE Transactions on Circuits and Systems for Video Technology, 1997
This paper addresses an approach to video coding utilizing an iterative nonorthogonal expansion technique called "matching pursuits" (MP) in combination with a new algorithm for selecting an appropriate coding technique at each frame in a sequence. This decision algorithm is called "selective update" and is based on an estimate of the amount and type of motion occurring between coded frames ...
Data Compression Conference, 2005
Summary form only given. The paper presents a low rate progressive 3D mesh compression scheme for simple, genus-zero 3D objects. The proposed scheme is based on signal representation using redundant expansions on the 2D-sphere. First, generic input data is re-sampled as a function on the 2D-sphere, and the signal value for each point on the regular grid is obtained by ...
Robotics History: Narratives and Networks Oral Histories: Larry Matthies
Impedance Matching: RF Boot Camp
Fast Broadband Impedance Matching with Automatic Circuit Synthesis: MicroApps 2015 - Keysight Technologies
Regular Expression Matching with Memristor TCAMs - Cat Graves - ICRC 2018
Stephen Weinstein accepts the IEEE Richard M. Emberson Award - Honors Ceremony 2016
LDA to Find User Archetypes for Search & Matching
IMS 2012 Microapps - Custom OFDM Validation of Wireless/Military DSP Algorithms and RF Components Daren McClearnon, Jin-Biao Xu, Agilent EEsof
Systems Considerations for POC Technology for Global Health - Arunan Skandarajah - IEEE EMBS at NIH, 2019
2013 IEEE Richard M. Emberson Award
Cultural Algorithms: Harnessing the Power of Social Intelligence 1
Julian Togelius: Algorithms That Play & Design Games
A Fully-Integrated SOI CMOS Complex-Impedance Detector for Matching Network Tuning in LTE Power Amplifier: RFIC Interactive Forum
Optimization Algorithms for Signal Processing
Cultural Algorithms: Harnessing the Power of Social Intelligence 2
Life Through the Eyes of a Machine
Comparing Partitions from Clustering Algorithms
V-Band Flip-Chip pHEMT Balanced Power Amplifier with CPWG-MS-CPWG Topology and CPWG Lange Couplers: RFIC Interactive Forum 2017
IMS 2014: Broadband Continuous-mode Power Amplifiers
In this work we introduce an alternative implementation of matching pursuit (MP) using a genetic algorithm in the continuous space (GACS). MP is an attractive analysis approach in which the signal is sequentially decomposed into a linear expansion of atoms (functions) from a dictionary of waveforms so as to obtain a sparse representation. The main problem with MP is its computation load, due to the necessarily large size of the dictionary. We propose instead to determine the optimal atom at each stage of the decomposition using a GACS, i.e. a genetic algorithm that requires no quantization of the solution parameters. Preliminary simulation results illustrate the potential benefits of this scheme.
This article discusses on an undetermined separation method based on the sparse multichannel representation of the sources in timefrequency domain. Sparse modeling assumes an ability to describe signals as linear combinations of few atoms from a pre-specified dictionary. As such, the choice of the dictionary that sparsifies the signals is crucial for the success of this task. A redundant dictionary is built through geometrically simulated room impulse responses in order to represent the mixing systems of sources propagating in convolutive environments. The property of the dictionary is analyzed and normalization criteria are proposed in order to: 1) mitigate the effect of mismatch between true and simulated mixing systems 2) mitigate the effect of non-ideal sparseness. Experimental analysis indicates that the proposed normalization combined with a modified greedy algorithm guarantees sparse estimation and source location detection. Index Terms - multi-channel detection, source separation, sparse signal, matching pursuit, source localization.
This paper addresses an approach to video coding utilizing an iterative nonorthogonal expansion technique called "matching pursuits" (MP) in combination with a new algorithm for selecting an appropriate coding technique at each frame in a sequence. This decision algorithm is called "selective update" and is based on an estimate of the amount and type of motion occurring between coded frames in a video sequence. This paper demonstrates that the matching pursuits approach is most efficient for video coding when motion compensation results in a prediction error which is well localized to the edges of moving objects. In the presence of global motion, such as panning and zooming, or in the presence of objects entering or leaving a scene, matching pursuits becomes less effective than orthogonal transform-based coding techniques like the block-based discrete cosine transform (DCT). The rate- distortion characteristics of matching pursuits and block-wise DCT coding are used to demonstrate how MP coding can be more efficient than block-wise DCT- based coding. When an appropriate combination of these nonorthogonal and orthogonal transforms are used for encoding a complete low bit-rate video sequence, improved overall compression efficiency can be achieved. Results are shown which demonstrate the effectiveness of a hybrid video codec based on this concept.
Summary form only given. The paper presents a low rate progressive 3D mesh compression scheme for simple, genus-zero 3D objects. The proposed scheme is based on signal representation using redundant expansions on the 2D-sphere. First, generic input data is re-sampled as a function on the 2D-sphere, and the signal value for each point on the regular grid is obtained by performing nearest neighbor interpolation within four points from the initial 3D model. The model representation is then constructed using a matching pursuit algorithm, with an over-complete dictionary of atoms, defined on a sphere. In order to capture the particular characteristics of the 3D models efficiently, we propose a dictionary construction based on two generating functions, a Gaussian to capture low-frequency components, and a modified combination of a Gaussian and its second derivative to capture high-frequency components of the input signal. Compared to state-of-the-art encoders, our method has been shown to offer very good compression efficiency, but the performance is limited by the resampling step that maps the input model on the 2D-sphere. Matching pursuit has, however, the advantage of providing an intrinsically progressive scheme, that is also very flexible.
Cleaning of noise from signals is a classical and long-studied problem in signal processing. Algorithms for this task necessarily rely on an <i>a</i> <i>priori</i> knowledge about the signal characteristics, along with information about the noise properties. For signals that admit sparse representations over a known dictionary, a commonly used denoising technique is to seek the sparsest representation that synthesizes a signal close enough to the corrupted one. As this problem is too complex in general, approximation methods, such as greedy pursuit algorithms, are often employed. In this line of reasoning, we are led to believe that detection of the sparsest representation is key in the success of the denoising goal. Does this mean that other competitive and slightly inferior sparse representations are meaningless? Suppose we are served with a group of competing sparse representations, each claiming to explain the signal differently. Can those be fused somehow to lead to a better result? Surprisingly, the answer to this question is positive; merging these representations can form a more accurate (in the mean-squared-error (MSE) sense), yet dense, estimate of the original signal even when the latter is known to be sparse. In this paper, we demonstrate this behavior, propose a practical way to generate such a collection of representations by randomizing the Orthogonal Matching Pursuit (OMP) algorithm, and produce a clear analytical justification for the superiority of the associated Randomized OMP (RandOMP) algorithm. We show that while the maximum a posteriori probability (MAP) estimator aims to find and use the sparsest representation, the minimum mean- squared-error (MMSE) estimator leads to a fusion of representations to form its result. Thus, working with an appropriate mixture of candidate representations, we are surpassing the MAP and tending towards the MMSE estimate, and thereby getting a far more accurate estimation in terms of the expected lscr<sub>2</sub> -norm error.
The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictionary of time-frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In this paper, we apply matching pursuit to the derivation of signal expansions based on damped sinusoids. It is shown that expansions in terms of complex damped sinusoids can be efficiently derived using simple recursive filter banks. We discuss a subspace extension of the pursuit algorithm that provides a framework for deriving real-valued expansions of real signals based on such complex atoms. Furthermore, we consider symmetric and asymmetric two-sided atoms constructed from underlying one-sided damped sinusoids. The primary concern is the application of this approach to the modeling of signals with transient behavior such as music; it is shown that time-frequency atoms based on damped sinusoids are more suitable for representing transients than symmetric Gabor atoms. The resulting atomic models are useful for signal coding and analysis modification synthesis.
The overcomplete signal representation (OSR) is a recently established adaptive signal representation method. As an adaptive signal representation method, the OSR means that a given signal is decomposed onto a number of optimal basis components, which are found from an overcomplete basis dictionary via some optimization algorithms, such as the matching pursuit (MP), method of frame (MOF) and basis pursuit (BP). Such ideas are actually very close to or exactly the same as solving a minimum fuel (MF) problem. The MF problem is a well-established minimum L/sub 1/-norm optimization model with linear constraints. The BP-based OSR proposed by Chen and Donoho is exactly the same model as the MF model. The work of Chen and Donoho showed that the MF model could be used as a generalized method for solving an OSR problem and it outperformed the MP and the MOF. In this paper, the neural implementation of the MF model and its applications to the OSR are presented. A new neural network, namely the minimum fuel neural network (MFNN), is constructed and its convergence in solving the MP problem is proven theoretically and validated experimentally. Compared with the implementation of the original BP, the MFNN does not double the scales of the problem and its convergence is independent of initial conditions. It is shown that the MFNN is promising for the application in the OSR's of various kinds of nonstationary signals with a high time-frequency resolution and feasibility of real-time implementation. As an extension, a two-dimensional (2-D) MF model suitable for image data compression is also proposed and its neural implementation is presented.
We describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. affine (wavelet) frames. We propose a modification to the matching pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual (error) at every step and thereby leads to improved convergence. We refer to this modified algorithm as orthogonal matching pursuit (OMP). It is shown that all additional computation required for the OMP algorithm may be performed recursively.<<ETX>>
Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation cost function, which measures both, the reconstruction error and the number of elements in the representation. Simulation results suggest that the algorithm is comparable in performance to a commonly used alternative method.
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