The Experts below are selected from a list of 6003 Experts worldwide ranked by ideXlab platform
T R N Rao - One of the best experts on this subject based on the ideXlab platform.
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a simple approach for construction of algebraic geometric codes from affine plane curves
International Symposium on Information Theory, 1993Co-Authors: Guiliang Feng, T R N RaoAbstract:The current algebraic geometric (AG) codes are based on the theory of algebraic geometric curves. In this paper, we present a novel approach for construction of AG codes without any background in algebraic geometry. Given an affine plane irreducible curve and its all rational points, based on the equation of this curve, we can find a sequence of monomial polynomials x/sup i/y/sup j/. Using the first r polynomials as a basis of dual code of a linear code called AG code, the designed minimum distance d of this AG code can be easily determined. For these codes a fast Decoding Procedure with complexity O(n/sup 7/3/) which can correct errors up to [(d-1)/2], is also shown. By this approach it is neither necessary to know the genus of curve nor find a basis of differential form. This approach can be easily understood by most engineers. Some examples are also shown, which indicate that the codes constructed by this approach are better than the current AG codes from same curves.
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Decoding algebraic geometric codes up to the designed minimum distance
IEEE Transactions on Information Theory, 1993Co-Authors: G L Feng, T R N RaoAbstract:A simple Decoding Procedure for algebraic-geometric codes C/sub Omega /(D,G) is presented. This Decoding Procedure is a generalization of Peterson's Decoding Procedure for the BCH codes. It can be used to correct any ((d*-1)/2) or fewer errors with complexity O(n/sup 3/), where d* is the designed minimum distance of the algebraic-geometric code and n is the codelength. >
Paul H Siegel - One of the best experts on this subject based on the ideXlab platform.
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efficient root finding algorithm with application to list Decoding of algebraic geometric codes
IEEE Transactions on Information Theory, 2001Co-Authors: Paul H SiegelAbstract:A list Decoding for an error-correcting code is a Decoding algorithm that generates a list of codewords within a Hamming distance t from the received vector, where t can be greater than the error-correction bound. In previous work by M. Shokrollahi and H. Wasserman (see ibid., vol.45, p.432-7, March 1999) a list-Decoding Procedure for Reed-Solomon codes was generalized to algebraic-geometric codes. Recent work by V. Guruswami and M. Sudan (see ibid., vol.45, p.1757-67, Sept. 1999) gives improved list Decodings for Reed-Solomon codes and algebraic-geometric codes that work for all rates and have many applications. However, these list-Decoding algorithms are rather complicated. R. Roth and G. Ruckenstein (see ibid., vol.46, p.246-57, Jan. 2000) proposed an efficient implementation of the list Decoding of Reed-Solomon codes. In this correspondence, extending Roth and Ruckenstein's fast algorithm for finding roots of univariate polynomials over polynomial rings, i.e., the reconstruct algorithm, we present an efficient algorithm for finding the roots of univariate polynomials over function fields. Based on the extended algorithm, we give an efficient list-Decoding algorithm for algebraic-geometric codes.
Andrzej Dziech - One of the best experts on this subject based on the ideXlab platform.
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multidimensional enhanced hadamard error correcting code in comparison with reed solomon code in video watermarking applications
WSEAS Transactions on Signal Processing archive, 2017Co-Authors: Jakob Wassermann, Andrzej DziechAbstract:Watermarking technology.play a central role in the digital right management for multimedia data. Especially a video watermarking is a real challenge, because of very high compression ratio (about 1:200). Normally the watermarks can barely survive such massive attacks, despite very sophisticated embedding strategies. It can only work with a sufficient error correcting code method. In this paper, the authors introduce a new developed Enhanced Multidimensional Hadamard Error Correcting Code (EMHC), which is based on well known Hadamard Code, and compare his performance with Reed-Solomon Code regarding its ability to preserve watermarks in the embedded video. The main idea of this new developed multidimensional Enhanced Hadamard Error Correcting Code is to map the 2D basis images into a collection of one-dimensional rows and to apply a 1D Hadamard Decoding Procedure on them. After this, the image is reassembled, and the 2D Decoding Procedure can be applied more efficiently. With this approach, it is possible to overcome the theoretical limit of error correcting capability of (d-1)/2 bits, where d is a minimum Hamming distance. Even better results could be achieved by expanding the 2D to 3D EMHC. A full description is given of encoding and Decoding Procedure of such Hadamard Cubes and their implementation into video watermarking Procedure.To prove the efficiency and practicability of this new Enhanced Hadamard Code, the method was applied to a video Watermarking Coding Scheme. The Video Watermarking Embedding Procedure decomposes the initial video through Multi-Level Interframe Wavelet Transform. The low pass filtered part of the video stream is used for embedding the watermarks, which are protected respectively by Enhanced Hadamard or Reed-Solomon Correcting Code. The experimental results show that EHC performs much better than RS Code and seems to be very robust against strong MPEG compression.
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application of enhanced hadamard error correcting code in video watermarking and his comparison to reed solomon code
MATEC Web of Conferences, 2017Co-Authors: Andrzej Dziech, Jakob WassermannAbstract:Error Correcting Codes are playing a very important role in Video Watermarking technology. Because of very high compression rate (about 1:200) normally the watermarks can barely survive such massive attacks, despite very sophisticated embedding strategies. It can only work with a sufficient error correcting code method. In this paper, the authors compare the new developed Enhanced Hadamard Error Correcting Code (EHC) with well known Reed-Solomon Code regarding its ability to preserve watermarks in the embedded video. The main idea of this new developed multidimensional Enhanced Hadamard Error Correcting Code is to map the 2D basis images into a collection of one-dimensional rows and to apply a 1D Hadamard Decoding Procedure on them. After this, the image is reassembled, and the 2D Decoding Procedure can be applied more efficiently. With this approach, it is possible to overcome the theoretical limit of error correcting capability of ( d -1)/2 bits, where d is a Hamming distance. Even better results could be achieved by expanding the 2D EHC to 3D. To prove the efficiency and practicability of this new Enhanced Hadamard Code, the method was applied to a video Watermarking Coding Scheme. The Video Watermarking Embedding Procedure decomposes the initial video trough multi-Level Interframe Wavelet Transform. The low pass filtered part of the video stream is used for embedding the watermarks, which are protected respectively by Enhanced Hadamard or Reed-Solomon Correcting Code. The experimental results show that EHC performs much better than RS Code and seems to be very robust against strong MPEG compression.
Micaela Liberti - One of the best experts on this subject based on the ideXlab platform.
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automatic Decoding of input sinusoidal signal in a neuron model high pass homomorphic filtering
Neurocomputing, 2018Co-Authors: Simone Orcioni, Alessandra Paffi, Francesca Camera, Francesca Apollonio, Micaela LibertiAbstract:Abstract A processing technique for Decoding the information transferred from a sinusoidal input to the output spike sequence of a neuron model is a desirable tool for understanding the encoding principles of neuronal systems. An automatic Decoding Procedure, already proposed by the authors, is based on an improved version of the Signal to Noise Ratio (SNR) calculation and requires a knowledge of both spontaneous (in absence of input signal) and stimulated (in presence of input signal) neuronal activities. In this work, an automatic Decoding Procedure based on high-pass homomorphic filtering is developed that provides performances comparable or better than that obtained with the improved SNR. The advantages of not requiring the neuronal spontaneous activities, as most SNR methods do, are a Procedure simplification, a reduction of the amount of data needed to decode the information, and the possibility of application to contexts where the neuronal spontaneous activity is not available.
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automatic Decoding of input sinusoidal signal in a neuron model improved snr spectrum by low pass homomorphic filtering
Neurocomputing, 2017Co-Authors: Simone Orcioni, Alessandra Paffi, Francesca Camera, Francesca Apollonio, Micaela LibertiAbstract:Abstract The principles on how neurons encode and process information from low-level stimuli are still open questions in neuroscience. Neuron models represent useful tools to answer this question but a sensitive method is needed to decode the input information embedded in the neuron spike sequence. In this work, we developed an automatic Decoding Procedure based on the SNR spectrum improved by low-pass homomorphic filtering. The Procedure was applied to a stochastic Hodgkin Huxley neuron model forced by a low-level sinusoidal signal in the range 50 Hz–300 Hz. It exhibited very high performance, in terms of sensitivity and precision, in automatically Decoding the input information even when using a relatively small number of model runs (≈ 200). This could provide a fast and valid Procedure to understand the encoding mechanisms of low-level sinusoidal stimuli used by different types of neurons.
Bhaskar D. Rao - One of the best experts on this subject based on the ideXlab platform.
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Iterative joint source-channel Decoding of speech spectrum parameters over an additive white Gaussian noise channel
IEEE Transactions on Audio Speech and Language Processing, 2006Co-Authors: A.d. Subramaniam, W.r. Gardner, Bhaskar D. RaoAbstract:In this paper, we show how the Gaussian mixture modeling framework used to develop efficient source encoding schemes can be further exploited to model source statistics during channel Decoding in an iterative framework to develop an effective joint source-channel Decoding scheme. The joint probability density function (PDF) of successive source frames is modeled as a Gaussian mixture model (GMM). Based on previous work, the marginal source statistics provided by the GMM is used at the encoder to design a low-complexity memoryless source encoding scheme. The source encoding scheme has the specific advantage of providing good estimates to the probability of occurrence of a given source code-point based on the GMM. The proposed iterative Decoding Procedure works with any channel code whose decoder can implement the soft-output Viterbi algorithm that uses a priori information (APRI-SOVA) or the BCJR algorithm to provide extrinsic information on each source encoded bit. The source decoder uses the GMM model and the channel decoder output to provide a priori information back to the channel decoder. Decoding is done in an iterative manner by trading extrinsic information between the source and channel decoders. Experimental results showing improved Decoding performance are provided in the application of speech spectrum parameter compression and communication.
