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Generalised analysis of monomial-based viterbi and viterbi algorithms for NDA carrier phase estimation

ELECTRONICS LETTERS 2006年第9期42卷 542-544页

作者： Gappmair, W. Graz Univ Technol Inst Commun Networks & Satellite Commun Inffeldgasse 12 A-8010 Graz Austria

For non-data-aided carrier phase recovery of M-PSK modulated signals, viterbi and viterbi suggested a feedforward estimator based on a modification of the Mth power nonlinearity. With respect to the jitter variance, they presented a closed-form solution for even integer values of the generic design parameter mu, 0 <= mu <= M - 2. It is shown that their analysis can be simplified considerably as well as extended to any real value of mu > - M - 2. Furthermore, it is verified that the Cramer-Rao lower bound, as the theoretical limit, is solely approached for larger values of the signal-to-noise ratio and negligible carrier frequency offsets. Finally, to minimise the jitter performance, the closed-form solution is used for optimisation of the design parameter.

关键词： feedforward jitter maximum likelihood estimation optimisation phaseestimation phase shift keying synchronisation Cramer-Rao lower bound M-PSK signal modulation Mth power nonlinearity NDA carrier phase recovery viterbi algorithms closed-form solution fee

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Frequency line tracking using HMM-based schemes

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2003年第2期39卷 439-449页

作者： Paris, S Jauffret, C Univ Toulon & Var Signal Informat & Syst Lab F-83957 La Garde France

This paper deals with fluctuating line tracking, present on the so-called lofargram encountered in any passive sonar system. Considering such a line as a random walk modeled by a first-order Markov chain, we have recourse to the hidden Markov models (HMMs) arsenal. More precisely, we propose to track a frequency line with viterbi and Forward-Backward algorithms. The originality of this work comes from the fact that a "probabilistic integration of the spectral power" approach allows us to construct a signal-to-noise (SNR)-knowledge-free method. Intensive simulations reveal no loss of performance.

关键词： fluctuations hidden Markov models maximum likelihood estimation sonar detection sonar signal processing sonar target recognition sonar tracking HMM-based frequency line tracking SNR viterbi algorithms first-order Markov chain random walk modeling forward

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New processing elements: Max and sum_max with applications

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MICROPROCESSORS AND MICROSYSTEMS 1998年第2期22卷 103-110页

作者： Chou, WK Providence Univ Dept Comp Sci & Informat Management Shalu 43301 Taiwan

Conventional neurons (or processing elements) perform the weighted summation. However, due to the limitations of this kind of neurons, it is difficult to use neural networks to attack the problems related to dynamic programming. In this paper, two new processing elements are designed to make the applications of neural networks more extensive. The proposed computing element, max, accepts multiple weighted inputs and produces dual outputs, which is derived from the modification of CTMAXNET (W.K. Chou, D.Y.Y, Yun, A constant-time neural network for multiple selection of extreme values, Journal of Information Science and Engineering 9 (1993) 445-459 [3]). The processing element, sum_max, is the combination of the weighted-summation neuron and max and can support more powerful operations. Both elements have been used to integrate a massively parallel architecture to perform the viterbi algorithm (or the decoding problem in the hidden Markov model), which can reduce the time complexity of viterbi algorithms from O((NT)-T-2) to O(T), as shown in this paper, where N is the number of states and T is the length of the observation sequence. (C) 1998 Elsevier Science B.V.

关键词： neural networks CTMAXNET neurons (or processing elements) weighted summation hidden Markov models (HMMs) viterbi algorithms

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Analysis of Nucleotide Sequence with Normal and Affected Cancer Liver Cells Using Hidden Markov Model 5

Analysis of Nucleotide Sequence with Normal and Affected Can...

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5th IEEE International Conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Mayilvaganan, M. Rajamani, R. PSG Coll Arts & Sci Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9781479939756

The nucleotide sequence of biological databases is growing long terms of quantity, memory and complexity, managing these databases is becoming very complex. In this paper focuses Hidden Markov Model (HMM), has increased on the Pattern recognition domain primarily because of its strong mathematical basis and the ability to adapt to unknown of nucleotide sequence of normal and cancer affected liver cells as are pictorially represented by finite state machine. It is a finite automaton with a fixed number of states which are trained to maximize the probability of the observation sequence by using viterbi algorithm and forward algorithm. The work will be focused and analyzed about performance of DNA gene liver cancer database and normal liver cell data set from ncbi DNA data set. Each amino acid can have character variables and also assigned numeric number and its corresponding pair combination of sequence are represented in a graph. The proposed HMM system is validated with two different nucleotide values for analyse the performance and get the simulated output using viterbi and forward algorithms implemented in Mat Lab Tool.

关键词： Hidden Markov Model viterbi algorithms Forward algorithms Pub Chem of liver and Cancer DNA dataset

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Dim Line Tracking Using Deep Learning for Autonomous Line Following Robot 6th

Dim Line Tracking Using Deep Learning for Autonomous Line Fo...

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6th Computer Science On-Line Conference (CSOC)

作者： Matczak, Grzegorz Mazurek, Przemyslaw West Pomeranian Univ Technol Dept Signal Proc & Multimedia Engn 26 Kwietnia 10 St PL-71126 Szczecin Poland

ISBN: (纸本)9783319572611

The proposed approach improves preprocessing of image data for the line following robot. The tracking algorithm uses TrackBefore-Detect algorithm using viterbi algorithm. Proposed technique uses deep learning for the estimation of the line and background area. The segmentation improves detection of weak line on the image disturbed by numerous additive patterns and Gaussian noise.

关键词： Line following robot Track-Before-Detect viterbi algorithms Deep learning Convolutional Neural Networks

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Hierarchical statistical models for the fusion of multiresolution image data

Hierarchical statistical models for the fusion of multiresol...

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Conference on Neural, Morphological, and Stochastic Methods in Image and Signal Processing

作者： LAFERTE, JM HEITZ, F PEREZ, P FABRE, E INST RECH INFORMAT & SYST ALEATOIRES F-35042 RENNESFRANCE

ISBN: (纸本)0819419273

This paper presents a class of nonlinear hierarchical algorithms for the fusion of multiresolution image data in low-level vision. The approach combines nonlinear causal Markov models defined on hierarchical graph structures, with standard Bayesian estimation theory. Two random processes defined on simple hierarchical graphs (quadtrees or 'ternary graphs') are introduced to represent the multiresolution observations at hand and the hidden labels to be estimated. Two optimal algorithms (inspired from viterbi's algorithm) are developed on the quadtree structure. The first one gives the exact solution for the MAP (maximum a posteriori) estimator. The second one gives, on the same structure, the global minimum of an energy function more relevant than the MAP criterion. These algorithms are noniterative, estimates being obtained within two passes on the graph structure. They are compared to an extension of the multiscale algorithm proposed by Bouman et al. which is adapted here to multiresolution data fusion.

关键词： FUSION MARKOV MODELS MULTIRESOLUTION PROCESSING BAYESIAN ESTIMATION viterbi algorithms

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Automatic speech decomposition and speech coding using MDCT-based hidden Markov chain and wavelet-based hidden Markov tree models

Automatic speech decomposition and speech coding using MDCT-...

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Workshop on Applications of Signal Processing to Audio and Acoustics

作者： Tantibundhit, C Boston, JR Li, CC El-Jaroudi, A Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15261 USA

ISBN: (纸本)0780391543

Speech is decomposed into three different components. based on the idea of Daudet and Torresani (Signal Processing, vol. 82, no. 11, pp. 1595, 2002), as signal = tonal + transient + residual. The tonal and transient components identified using a small number of coefficients of the modified discrete cosine transform (.MDCT.) and the wavelet transform, respectively. Determinations of the significant MDCT and wavelet coefficients in the algorithm of Daudet and Torresani, referred as the D&T algorithm, ire achieved by thresholds. All MDCT coefficients are assumed to be independent as well as wavelet coefficients. However, the MDCT coefficients probably have statistical dependencies, namely the clustering, and persistence properties, and so do the wavelet coefficients. We propose a modification to the D&T algorithm, that can capture statistical dependencies by utilizing the hidden Markov model. The viterbi and the Maximum a Posteriori (MAP) algorithms, used to find file optimal state distribution, are applied to determine the significant MDCT and wavelet coefficients automatically. The modified algorithm was used to encode 43 monosyllabic Consonant-Vowel-Consonant (CVC) words and 3 sentences. Results showed that the modified algorithm improves the coding efficiency by 37% compared with the threshold method of D&T algorithm when equal numbers of significant coefficients;are used.

关键词： discrete cosine transforms hidden Markov models maximum likelihood estimation speech coding trees (mathematics) wavelet transforms MAP algorithms viterbi algorithms automatic speech decomposition clustering properties coding efficiency hidden Markov chai speech coding Hidden Markov models viterbi algorithm mdct(modified discrete cosine transform) discrete cosine transforms wavelet transforms Wavelet coefficients maximum likelihood estimation code efficiency Markov chain algorithms THRESHOLDING METHOD

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Reduced-State Decoding in Two-Way Relay Networks With Physical-Layer Network Coding

Reduced-State Decoding in Two-Way Relay Networks With Physic...

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IEEE Information Theory Workshop (ITW)

作者： To, Duc Choi, Jinho Swansea Univ Sch Engn Swansea SA2 8PP W Glam Wales

ISBN: (纸本)9781424482641

We consider the information exchange between two source nodes with the help of a relay node in a two-way relay network (TWRN). Based on physical-layer network coding (PNC), at the relay node, viterbi or BCJR algorithms based on a full-state trellis can be used for decoding. Since the relay node only needs to decode XORed message rather than two individual messages in PNC, we show that the decoding can be performed using a reduced-state trellis which results in a lower complexity. It is proved that if the source nodes use identical encoders, based on the reduced-state trellis, the diversity order is kept unchanged, which is then confirmed by simulation results.

关键词： BCJR algorithms viterbi algorithms viterbi decoding XORed message network coding physical-layer network coding radio networks reduced-state decoding reduced-state trellis code relay node source nodes trellis codes two-way relay networks

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State Estimation algorithms for Markov Chains Observed in Arbitrary Noise

State Estimation Algorithms for Markov Chains Observed in Ar...

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47th IEEE Conference on Decision and Control

作者： Malcolm, W. P. Australian Natl Univ Inst Math Sci Canberra ACT 0200 Australia

ISBN: (纸本)9781424431243

In this article we compute state estimation schemes for discrete-time Markov chains observed in arbitrary observation noise. Here we assume the observation noise distribution is known in advance. Appealing to a fundamental L-1 convergence result in [1] we propose to represent any practical observation noise model by a convex combination of Gaussian densities, that is, a mixture function that is itself a valid probability density function. To compute our state estimation schemes we use the techniques of reference probability, (see [2]). Here however, our Gaussian mixtures appear as sums in a product representation of Radon-Nikodym derivatives. The state estimation schemes we compute are;an information state recursion (filter), a general smoothing theorem, an M-ary detection scheme. A computer simulation is provided to indicate the performance of our recursive filter in a non-Gaussian observation noise scenario.

关键词： Gaussian-Mixture Distribution Martingales Reference Probability Filtering Smoothing Detection viterbi algorithms

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