The Experts below are selected from a list of 15852 Experts worldwide ranked by ideXlab platform
Zhi Wang - One of the best experts on this subject based on the ideXlab platform.
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A new subband information fusion method for wideband DOA estimation using sparse Signal Representation
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Xiao-ping Zhang, Zhi WangAbstract:We present a new subband information fusion (SIF) method for wideband direction-of-arrival (DOA) estimation using single sparse Signal Representation of multiple frequency-based measurement vectors. The problem of wideband DOA estimation using SIF method is to jointly utilize all the frequency bin information to recover a single sparse indicative vector (SIV). The SIF method belongs to the sparse Signal Representation domain and therefore it will suffer from two cases of ambiguity: algebraic aliasing and spatial aliasing. We show that these two categories of ambiguity can be reduced by combining all the frequency components. The SIF algorithm is then proposed and the SIV is recovered iteratively. The numerical simulations are performed to illustrate that the SIF method has superior performances.
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ICASSP - A new subband information fusion method for wideband DOA estimation using sparse Signal Representation
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Xiao-ping Zhang, Zhi WangAbstract:We present a new subband information fusion (SIF) method for wideband direction-of-arrival (DOA) estimation using single sparse Signal Representation of multiple frequency-based measurement vectors. The problem of wideband DOA estimation using SIF method is to jointly utilize all the frequency bin information to recover a single sparse indicative vector (SIV). The SIF method belongs to the sparse Signal Representation domain and therefore it will suffer from two cases of ambiguity: algebraic aliasing and spatial aliasing. We show that these two categories of ambiguity can be reduced by combining all the frequency components. The SIF algorithm is then proposed and the SIV is recovered iteratively. The numerical simulations are performed to illustrate that the SIF method has superior performances.
Bart Preneel - One of the best experts on this subject based on the ideXlab platform.
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Efficient Implementation of a Buyer-Seller Watermarking Protocol Using a Composite Signal Representation
2020Co-Authors: Mina Deng, Tiziano Bianchi, Alessandro Piva, Bart PreneelAbstract:Buyer-seller watermarking protocols integrate watermarking techniques with cryptography, for copyright protection, piracy traci ng, and privacy protec- tion. In this paper, our main contribution is the development of an efficient buyer- seller watermarking protocol based on homomorphic public-key cryptosystem, and the use of composite Signal Representation in the encrypted domain to re- duce both the computational overhead and the large communication bandwidth which are due to the use of homomorphic public-key encryption schemes. Both complexity analysis and simulation results confirm the effic iency of the proposed solution, suggesting that this technique can be successful ly used in practical ap- plications.
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an efficient buyer seller watermarking protocol based on composite Signal Representation
ACM workshop on Multimedia and security, 2009Co-Authors: Mina Deng, Tiziano Bianchi, Alessandro Piva, Bart PreneelAbstract:Buyer-seller watermarking protocols integrate watermarking techniques with cryptography, for copyright protection, piracy tracing, and privacy protection. In this paper, we propose an efficient buyer-seller watermarking protocol based on homomorphic public-key cryptosystem and composite Signal Representation in the encrypted domain. A recently proposed composite Signal Representation allows us to reduce both the computational overhead and the large communication bandwidth which are due to the use of homomorphic public-key encryption schemes. Both complexity analysis and simulation results confirm the efficiency of the proposed solution, suggesting that this technique can be successfully used in practical applications.
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MM&Sec - An efficient buyer-seller watermarking protocol based on composite Signal Representation
Proceedings of the 11th ACM workshop on Multimedia and security - MM&Sec '09, 2009Co-Authors: Mina Deng, Tiziano Bianchi, Alessandro Piva, Bart PreneelAbstract:Buyer-seller watermarking protocols integrate watermarking techniques with cryptography, for copyright protection, piracy tracing, and privacy protection. In this paper, we propose an efficient buyer-seller watermarking protocol based on homomorphic public-key cryptosystem and composite Signal Representation in the encrypted domain. A recently proposed composite Signal Representation allows us to reduce both the computational overhead and the large communication bandwidth which are due to the use of homomorphic public-key encryption schemes. Both complexity analysis and simulation results confirm the efficiency of the proposed solution, suggesting that this technique can be successfully used in practical applications.
Karl Skretting - One of the best experts on this subject based on the ideXlab platform.
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Partial search vector selection for sparse Signal Representation
2020Co-Authors: Karl Skretting, John Hakon HusoyAbstract:In this paper a new algorithm for vector selection in Signal Representation problems is proposed, we call it Partial Search (PS). The vector selection problem is described, and one group of algorithms for solving this problem, the Matching Pursuit (MP) algorithms, is reviewed. The proposed algorithm is based on the Order Recursive Matching Pursuit (ORMP) algorithm, it extends ORMP by searching a larger part of the solution space in an effective way. The PS algorithm tests up to a given number of possible solutions and returns the best, while ORMP is a greedy algorithm testing and returning only one possible solution. A detailed description of PS is given. In the end some examples of its performance are given, showing that PS performs very well, that the Representation error is considerable reduced and that the probability of finding the optimal solution is increased compared to ORMP, even when only a small part of the solution space is searched.
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EUSIPCO - Topology Inference and Signal Representation Using Dictionary Learning
2019 27th European Signal Processing Conference (EUSIPCO), 2019Co-Authors: Mahmoud Ramezani-mayiami, Karl SkrettingAbstract:This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph Signal Representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and Signal Representation, when there is no information about the transform domain. Five performance measures are used to compare JGLSR with two conventional algorithms and show its higher performance.
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Topology Inference and Signal Representation Using Dictionary Learning
2019 27th European Signal Processing Conference (EUSIPCO), 2019Co-Authors: Mahmoud Ramezani-mayiami, Karl SkrettingAbstract:This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph Signal Representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and Signal Representation, when there is no information about the transform domain. Five performance measures are used to compare JGLSR with two conventional algorithms and show its higher performance.
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family of iterative ls based dictionary learning algorithms ils dla for sparse Signal Representation
Digital Signal Processing, 2007Co-Authors: Kjersti Engan, Karl Skretting, J H HusoyAbstract:The use of overcomplete dictionaries, or frames, for sparse Signal Representation has been given considerable attention in recent years. The major challenges are good algorithms for sparse approximations, i.e., vector selection algorithms, and good methods for choosing or designing dictionaries/frames. This work is concerned with the latter. We present a family of iterative least squares based dictionary learning algorithms (ILS-DLA), including algorithms for design of Signal dependent block based dictionaries and overlapping dictionaries, as generalizations of transforms and filter banks, respectively. In addition different constraints can be included in the ILS-DLA, thus we present different constrained design algorithms. Experiments show that ILS-DLA is capable of reconstructing (most of) the generating dictionary vectors from a sparsely generated data set, with and without noise. The dictionaries are shown to be useful in applications like Signal Representation and compression where experiments demonstrate that our ILS-DLA dictionaries substantially improve compression results compared to traditional Signal expansions such as transforms and filter banks/wavelets.
A.h. Tewfik - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - A Signal Representation approach for discrimination between full and empty hazelnuts
2007Co-Authors: Ibrahim Onaran, A.h. Tewfik, Nuri F. Ince, A. Enis CetinAbstract:We apply a sparse Signal Representation approach to impact acoustic Signals to discriminate between empty and full hazelnuts. The impact acoustic Signals are recorded by dropping the hazelnut shells on a metal plate. The impact Signal is then approximated within a given error limit by choosing codevectors from a special dictionary. This dictionary was generated from sub-dictionaries that are individually generated for the impact Signals corresponding to empty and full hazelnut. The number of codevectors selected from each sub-dictionary and the approximation error within initial codevectors are used as classification features and fed to a Linear Discriminant Analysis (LDA). We also compare this algorithm with a baseline approach. This baseline approach uses features which describe the time and frequency characteristics of the given Signal that were previously used for empty and full hazelnut separation. Classification accuracies of 98.3% and 96.8% were achieved by the proposed approach and base algorithm respectively. The results we obtained show that sparse Signal Representation strategy can be used as an alternative classification method for undeveloped hazelnut separation with higher accuracies.
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A Signal Representation approach for discrimination between full and empty hazelnuts
2007 15th European Signal Processing Conference, 2007Co-Authors: Ibrahim Onaran, A.h. Tewfik, Nuri F. Ince, Enis A. CetinAbstract:We apply a sparse Signal Representation approach to impact acoustic Signals to discriminate between empty and full hazelnuts. The impact acoustic Signals are recorded by dropping the hazelnut shells on a metal plate. The impact Signal is then approximated within a given error limit by choosing codevectors from a special dictionary. This dictionary was generated from sub-dictionaries that are individually generated for the impact Signals corresponding to empty and full hazelnut. The number of codevectors selected from each sub-dictionary and the approximation error within initial codevectors are used as classification features and fed to a Linear Discriminant Analysis (LDA). We also compare this algorithm with a baseline approach. This baseline approach uses features which describe the time and frequency characteristics of the given Signal that were previously used for empty and full hazelnut separation. Classification accuracies of 98.3% and 96.8% were achieved by the proposed approach and base algorithm respectively. The results we obtained show that sparse Signal Representation strategy can be used as an alternative classification method for undeveloped hazelnut separation with higher accuracies.
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Optimal subset selection for adaptive Signal Representation
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1996Co-Authors: M. Nafie, A.h. TewfikAbstract:A number of over-complete dictionaries such as wavelets, wave packets, cosine packets etc. have been proposed. Signal decomposition on such over-complete dictionaries is not unique. This non-uniqueness provides us with the opportunity to adapt the Signal Representation to the Signal. The adaptation is based on sparsity, resolution and stability of the Signal Representation. The computational complexity of the adaptation algorithm is of primary concern. We propose a new approach for identifying the sparsest Representation of a given Signal in terms of a given over-complete dictionary. We assume that the data vector can be exactly represented in terms of a known number of vectors.
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ICASSP - Optimal subset selection for adaptive Signal Representation
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1996Co-Authors: M. Nafie, A.h. TewfikAbstract:A number of over-complete dictionaries such as wavelets, wave packets, cosine packets etc. have been proposed. Signal decomposition on such over-complete dictionaries is not unique. This non-uniqueness provides us with the opportunity to adapt the Signal Representation to the Signal. The adaptation is based on sparsity, resolution and stability of the Signal Representation. The computational complexity of the adaptation algorithm is of primary concern. We propose a new approach for identifying the sparsest Representation of a given Signal in terms of a given over-complete dictionary. We assume that the data vector can be exactly represented in terms of a known number of vectors.
Ling Kok Ng - One of the best experts on this subject based on the ideXlab platform.
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Signal Representation based on instantaneous amplitude models with application to speech synthesis
IEEE Transactions on Speech and Audio Processing, 2000Co-Authors: Gang Li, Ling Kok NgAbstract:In this correspondence, a model for multicomponent Signal Representation is proposed. This model can avoid the difficulty in dealing with the time-varying phases and allow us to carry out an optimization procedure easily such that the synthetic Signal can be made as close to the original one as possible. This model is successfully applied to speech analysis/synthesis.
