Maximum a posteriori estimation
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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.
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
The ACC is the annual conference of the American Automatic Control Council (AACC, the U.S. national member organization of the International Federation for Automatic Control (IFAC)). The ACC is internationally recognized as a premier scientific and engineering conference dedicated to the advancement of control theory and practice. The ACC brings together an international community of researchers and practitioners to discuss the latest findings in automatic control. The 2020 ACC technical program will
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
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.
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.
Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.
Proceedings of the 28th IEEE Conference on Decision and Control,, 1989
A computational algorithm is presented for obtaining the maximum a posteriori estimates of the states of a stochastic dynamical system by programming a neural network. It is well known that for real-time control implementations, especially in such applications as multitarget tracking and vision-guided robots, the computational requirements for solving such state estimation problems attain particular significance, and parallel processing techniques ...
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006
We consider the problem of jointly estimating the number as well as the parameters of two-dimensional sinusoidal signals, observed in the presence of an additive colored noise field. In this framework we consider the problem of least squares estimation of the parameters of 2-D sinusoidal signals observed in the presence of an additive noise field, when the assumed number of ...
2013 7th International Conference on Application of Information and Communication Technologies, 2013
The objective of the paper is to present a segmentation method, using maximum a posteriori probability (MAP) estimator, with application in decision making, based on change detection and diagnosis. Some experimental results obtained by Monte-Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, that make the ...
IEEE Transactions on Image Processing, 1998
We propose a method for the reconstruction of signals and images observed partially through a linear operator with a large support (e.g., a Fourier transform on a sparse set). This inverse problem is ill-posed and we resolve it by incorporating the prior information that the reconstructed objects are composed of smooth regions separated by sharp transitions. This feature is modeled ...
2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515), 2003
It is well known that as the iterations of the maximum likelihood algorithm are continued, density estimates become more and more noisy. In situations where some prior knowledge about the object distribution is available, one may utilize such information in the reconstruction procedure for improving the reconstruction. Fixed prior based image reconstruction process produces over- smooth reconstruction due to the ...
Signal Processing on Manifolds
IEEE Authoring Part 4: Paper Structure
"Approximation- Beyond the Tyranny of Digital Computing," (Rebooting Computing)
Some Recent Work in Computational Intelligence for Software Engineering
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
IROS TV 2019- How to Build a Robot: Vision Based Estimation of Driving Energy for Planetary Rovers
Advances on Many-objective Evolutionary Optimization - IEEE WCCI 2012
APEC 2011- Methode Electronics at APEC 2011
IMS 2014:Active 600GHz Frequency Multiplier-by-Six S-MMICs for Submillimeter-Wave Generation
A 28GHz CMOS Direct Conversion Transceiver with Packaged Antenna Arrays for 5G Cellular Systems: RFIC Industry Showcase 2017
IROS TV 2019- Macau- Episode 2- Robots Connecting People
IMS 2014: A 600 GHz Low-Noise Amplifier Module
A Fully Integrated 75-83GHz FMCW Synthesizer for Automotive Radar Applications with -97dBc/Hz Phase Noise at 1MHz Offset and 100GHz/mSec Maximal Chirp Rate: RFIC Industry Showcase 2017
Audience Development For Your Conference - Lea Miller - Ignite: Sections Congress 2017
Micro-Apps 2013: Design Methodology for GaAs MMIC PA
Massive MIMO Active Antenna Arrays for Advanced Wireless Communications: IEEE CAS lecture by Dr. Mihai Banu
Dr. Scott Fish
MIRAI Program and the New Super-high Field NMR Initiative in Japan - Applied Superconductivity Conference 2018
IEEE Medal of Honor Recipient (2007): Thomas Kailath
A computational algorithm is presented for obtaining the maximum a posteriori estimates of the states of a stochastic dynamical system by programming a neural network. It is well known that for real-time control implementations, especially in such applications as multitarget tracking and vision-guided robots, the computational requirements for solving such state estimation problems attain particular significance, and parallel processing techniques are highly useful. The performance of the algorithm has been investigated by conducting several numerical experiments. It appears to be useful for handling state estimation problems arising in real-world applications.<<ETX>>
We consider the problem of jointly estimating the number as well as the parameters of two-dimensional sinusoidal signals, observed in the presence of an additive colored noise field. In this framework we consider the problem of least squares estimation of the parameters of 2-D sinusoidal signals observed in the presence of an additive noise field, when the assumed number of sinusoids is incorrect. In the case where the number of sinusoidal signals is under-estimated we show the almost sure convergence of the least squares estimates to the parameters of the dominant sinusoids. In the case where the number of sinusoidal signals is over-estimated, the estimated parameter vector obtained by the least squares estimator contains a sub-vector that converges almost surely to the correct parameters of the sinusoids. Based on these results, we prove the strong consistency of a large family of model order selection rules
The objective of the paper is to present a segmentation method, using maximum a posteriori probability (MAP) estimator, with application in decision making, based on change detection and diagnosis. Some experimental results obtained by Monte-Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, that make the object of investigation in other papers, are presented to prove the effectiveness of the approach.
We propose a method for the reconstruction of signals and images observed partially through a linear operator with a large support (e.g., a Fourier transform on a sparse set). This inverse problem is ill-posed and we resolve it by incorporating the prior information that the reconstructed objects are composed of smooth regions separated by sharp transitions. This feature is modeled by a piecewise Gaussian (PG) Markov random field (MRF), known also as the weak-string in one dimension and the weak-membrane in two dimensions. The reconstruction is defined as the maximum a posteriori estimate. The prerequisite for the use of such a prior is the success of the optimization stage. The posterior energy corresponding to a PG MRF is generally multimodal and its minimization is particularly problematic. In this context, general forms of simulated annealing rapidly become intractable when the observation operator extends over a large support. Global optimization is dealt with by extending the graduated nonconvexity (GNC) algorithm to ill-posed linear inverse problems. GNC has been pioneered by Blake and Zisserman (1987) in the field of image segmentation. The resulting algorithm is mathematically suboptimal but it is seen to be very efficient in practice. We show that the original GNC does not correctly apply to ill-posed problems. Our extension is based on a proper theoretical analysis, which provides further insight into the GNC. The performance of the proposed algorithm is corroborated by a synthetic example in the area of diffraction tomography.
It is well known that as the iterations of the maximum likelihood algorithm are continued, density estimates become more and more noisy. In situations where some prior knowledge about the object distribution is available, one may utilize such information in the reconstruction procedure for improving the reconstruction. Fixed prior based image reconstruction process produces over- smooth reconstruction due to the penalizing nature of the potential. As the reconstruction process builds up, image properties like smoothness, frequency content etc., change and hence fixed prior based image reconstruction process may not serve the purpose. A new prior is proposed which is capable of producing improved reconstruction over those obtained by existing fixed prior based Bayesian algorithms. These are termed as dynamic priors, which unlike other priors modify itself recursively according to the noise level in the reconstruction. It is found that inclusion of prior knowledge in the reconstruction algorithm results in local minimums. In the present approach, appropriate prior energy is supplied to the estimate to overcome local minimums by scheduling Gibbs hyperparameter and subsequently the effect of prior is removed recursively as the estimate approaches global minimum.
In this paper, we present an efficient architecture of turbo decoder for IMT2000 system. We introduce a base 2 logarithmic maximum a-posteriori algorithm (log/sub 2/MAP) whose architecture is simpler than that of the conventional natural logarithmic MAP algorithm (log/sub e/MAP). With log/sub 2/MAP, we obtain a '2 function' which is simpler than the 'E function' used by log/sub e/MAP. In order to implement the architecture of the 2 function, we use approximated binary logarithmic algorithm (ABLA) which has been usefully adopted in DSP. Using ABLA, we can reduce the RAM size from 1 kbytes to 96 bytes, which can be implemented using combinational logic gates. Also, we design the simple normalization module by making all the branch metrics to have positive values. We introduce reverse interleaver and deinterleaver to calculate forward and reverse state metric simultaneously. Using our architecture, we obtained BER of 9.79/spl times/10/sup -7/ at Eb/No of 2 dB and 5th iterations for constraint length K=4, code rate R=1/2, jumping window of 512 bits and interleaver size of 1144 bits, i.e. data rate of 57.6 kbps.
The authors describe the development of a deterministic algorithm for obtaining the global maximum a posteriori probability (MAP) estimate from an image corrupted by additive Gaussian noise. The MAP algorithm requires the probability density function of the original undegraded image and the corrupting noise. It is assumed that the original image is represented by a compound model consisting of a 2-D noncausal Gaussian-Markov random field (GMRF) to represent the homogeneous regions and a line process model to represent the discontinuities. The MAP algorithm is written in terms of the compound GMRF model parameters. The solution to the MAP equations is realized by a deterministic relaxation algorithm that is an extension of the graduated nonconvexity (GNC) algorithm and finds the global MAP estimate in a small number of iterations. As a byproduct, the line process configuration determined by the MAP estimate produces an accurate edge map without any additional cost. Experimental results are given to illustrate the usefulness of the method.<<ETX>>
Quantitative analysis of brain magnetic resonance (MR) images requires methods of tissue classification. New Web and Internet technologies allow remote MR image classification and visualization through browser functionality. Our application uses a method of segmentation based on the Markov random field (MRF) modeling of maximum a-posteriori (MAP) probability. The result of the segmentation can also be visualised as a VRML (Virtual Reality Modeling Language) world.
We present and analyze a packet combining strategy for wireless networks with slow Rayleigh fading. The scheme is based on adding the current signal-to- noise ratio (SNR) as an overhead to the packet and packet combining using the maximum a posteriori (MAP) criterion. We consider single and multiple wireless hops, and we perform comparisons against the optimum case, the maximum ratio combiner (MRC). For the single wireless hop, we show that the error probability curve is very close to the optimum. In addition, we study the effect of selection and an alternative based on averaging over the channel statistics that has poorer error performance, but needs less processing and overhead. In multiple wireless hops, some nodes act as relays and the error probability increases. However, combining several branches, each with two hops, allows for diversity order equal to the number of branches. We demonstrate the performance of the proposed strategy by computer simulations. The fusing packet scheme that is presented in this paper is adequate for sensor networks
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