The Experts below are selected from a list of 1962 Experts worldwide ranked by ideXlab platform
Hideki Yagi - One of the best experts on this subject based on the ideXlab platform.
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single bit quantization of binary input continuous Output channels
International Symposium on Information Theory, 2017Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:A binary-input, memoryless channel with a continuous-valued Output quantized to one bit is considered. For arbitrary noise models, conditions on an optimal Quantizer, in the sense of maximizing mutual information between the channel input and the Quantizer Output, are given. This result is obtained by considering the “backward” channel and applying Burshtein et al.'s theorem on optimal classification. In this backward channel, there exists an optimal Quantizer for which the Quantizer preimage is convex. It is possible no optimal forward Quantizer is convex, but by working with the backward channel, the optimal Quantizer may be found. However, if the channel satisfies a certain condition, then a convex optimal forward Quantizer exists.
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1Quantization of Binary-Input Discrete Memoryless Channels, with Applications to LDPC Decoding
2016Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:Abstract—The quantization of the Output of a binary-input discrete memoryless channel to a smaller number of levels is considered. The optimal Quantizer, in the sense of maximizing mutual information between the channel input and the Quantizer Output, may be found by an algorithm with complexity which is quadratic in the number of channel Outputs. This is a concave optimization problem, and results from the field of concave optimization are invoked. The Quantizer design algorithm is a realization of a dynamic program. Then, this algorithm is applied to the design of message-passing decoders for low-density parity-check codes, over arbitrary discrete memoryless channels. A general, systematic method to find message-passing decoding maps which maximize mutual information at each iteration is given. This may contrasted with existing quantized message-passing algorithms which are heuristically derived. The method finds message-passing decoding maps similar to those given by Richardson and Urbanke’s Algorithm E. Using four bits per message, noise thresholds similar to belief-propagation decoding are obtained. Index Terms—discrete memoryless channel, channel quantiza-tion, mutual information maximization, LDPC decoding I
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finding the capacity of a quantized binary input dmc
International Symposium on Information Theory, 2012Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:Consider a binary-input, M-Output discrete memoryless channel (DMC) where the Outputs are quantized to K levels, with K < M. The subject of this paper is the maximization of mutual information between the input and Quantizer Output, over both the input distribution and channel Quantizer. This can be regarded as finding the capacity of a quantized DMC. An algorithm is given, which either finds the optimal input distribution and corresponding Quantizer, or declares a failure.
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channel Quantizers that maximize random coding exponents for binary input memoryless channels
International Conference on Communications, 2012Co-Authors: Hideki Yagi, Brian M. KurkoskiAbstract:The problem of finding the optimum Output Quantizer for a given discrete memoryless channel is investigated, where the Quantizer Output has fewer values than the channel Output. While mutual information has received attention as an objective function for optimization, the focus of this paper is use of the random coding exponent, which was originally derived by Gallager, as criteria. Two problems are addressed, where one problem is a partial problem of the other. The main result is a Quantizer design algorithm, and a proof that it finds the optimum Quantizer in the partial problem. The Quantizer design algorithm is based on a dynamic programming approach, and is an extension of a mutual-information maximization method. For the binary-input case, it is shown that the optimum Quantizer can be found with complexity that is polynomial in the number of channel Outputs.
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concatenation of a discrete memoryless channel and a Quantizer
Information Theory Workshop, 2010Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:The concatenation of an arbitrary discrete memoryless channel with binary input followed by a Quantizer is considered. For a restricted Quantizer alphabet size, it is shown that the maximum of the mutual information between the channel input and the Quantizer Output can be found by dynamic programming. Numerical examples are given to illustrate the results. This problem is shown to be an example of concave programming.
Mort Naraghipour - One of the best experts on this subject based on the ideXlab platform.
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scalable phy layer security for distributed detection in wireless sensor networks
Vehicular Technology Conference, 2012Co-Authors: Reza Soosahabi, Mort NaraghipourAbstract:The problem of binary hypothesis testing is considered in a bandwidth-constrained low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method we propose maps the Output of the Quantizer to one of the possible Quantizer Output levels randomly according to a probability matrix. This operation is similar to that of a discrete memoryless channel. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. A good suboptimal solution to this problem is found. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. As the number of levels in the Quantizer increases the performance loss of the secure system compared to insecure system is reduced. Compared to the existing data encryption methods, the proposed method is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.
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scalable phy layer security for distributed detection in wireless sensor networks
IEEE Transactions on Information Forensics and Security, 2012Co-Authors: Reza Soosahabi, Mort NaraghipourAbstract:The problem of binary hypothesis testing is considered in a bandwidth-constrained densely populated low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method maps the Output of the Quantizer to one of the possible Quantizer Output levels randomly according to a probability matrix. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. In the binary case the optimal solution is found and in the nonbinary case a good suboptimal solution is analytically obtained. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. The proposed method which may be considered a PHY-layer security scheme is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.
Brian M. Kurkoski - One of the best experts on this subject based on the ideXlab platform.
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single bit quantization of binary input continuous Output channels
International Symposium on Information Theory, 2017Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:A binary-input, memoryless channel with a continuous-valued Output quantized to one bit is considered. For arbitrary noise models, conditions on an optimal Quantizer, in the sense of maximizing mutual information between the channel input and the Quantizer Output, are given. This result is obtained by considering the “backward” channel and applying Burshtein et al.'s theorem on optimal classification. In this backward channel, there exists an optimal Quantizer for which the Quantizer preimage is convex. It is possible no optimal forward Quantizer is convex, but by working with the backward channel, the optimal Quantizer may be found. However, if the channel satisfies a certain condition, then a convex optimal forward Quantizer exists.
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1Quantization of Binary-Input Discrete Memoryless Channels, with Applications to LDPC Decoding
2016Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:Abstract—The quantization of the Output of a binary-input discrete memoryless channel to a smaller number of levels is considered. The optimal Quantizer, in the sense of maximizing mutual information between the channel input and the Quantizer Output, may be found by an algorithm with complexity which is quadratic in the number of channel Outputs. This is a concave optimization problem, and results from the field of concave optimization are invoked. The Quantizer design algorithm is a realization of a dynamic program. Then, this algorithm is applied to the design of message-passing decoders for low-density parity-check codes, over arbitrary discrete memoryless channels. A general, systematic method to find message-passing decoding maps which maximize mutual information at each iteration is given. This may contrasted with existing quantized message-passing algorithms which are heuristically derived. The method finds message-passing decoding maps similar to those given by Richardson and Urbanke’s Algorithm E. Using four bits per message, noise thresholds similar to belief-propagation decoding are obtained. Index Terms—discrete memoryless channel, channel quantiza-tion, mutual information maximization, LDPC decoding I
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finding the capacity of a quantized binary input dmc
International Symposium on Information Theory, 2012Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:Consider a binary-input, M-Output discrete memoryless channel (DMC) where the Outputs are quantized to K levels, with K < M. The subject of this paper is the maximization of mutual information between the input and Quantizer Output, over both the input distribution and channel Quantizer. This can be regarded as finding the capacity of a quantized DMC. An algorithm is given, which either finds the optimal input distribution and corresponding Quantizer, or declares a failure.
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channel Quantizers that maximize random coding exponents for binary input memoryless channels
International Conference on Communications, 2012Co-Authors: Hideki Yagi, Brian M. KurkoskiAbstract:The problem of finding the optimum Output Quantizer for a given discrete memoryless channel is investigated, where the Quantizer Output has fewer values than the channel Output. While mutual information has received attention as an objective function for optimization, the focus of this paper is use of the random coding exponent, which was originally derived by Gallager, as criteria. Two problems are addressed, where one problem is a partial problem of the other. The main result is a Quantizer design algorithm, and a proof that it finds the optimum Quantizer in the partial problem. The Quantizer design algorithm is based on a dynamic programming approach, and is an extension of a mutual-information maximization method. For the binary-input case, it is shown that the optimum Quantizer can be found with complexity that is polynomial in the number of channel Outputs.
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concatenation of a discrete memoryless channel and a Quantizer
Information Theory Workshop, 2010Co-Authors: Brian M. Kurkoski, Hideki YagiAbstract:The concatenation of an arbitrary discrete memoryless channel with binary input followed by a Quantizer is considered. For a restricted Quantizer alphabet size, it is shown that the maximum of the mutual information between the channel input and the Quantizer Output can be found by dynamic programming. Numerical examples are given to illustrate the results. This problem is shown to be an example of concave programming.
Reza Soosahabi - One of the best experts on this subject based on the ideXlab platform.
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Scalable PHY-Layer Security for Distributed Detection in Wireless Sensor Networks
2013Co-Authors: Reza Soosahabi, Student Member, Mort Naraghi-pourAbstract:Abstract—The problem of binary hypothesis testing is considered in a bandwidth-constrained densely populated low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method maps the Output of the Quantizer to one of the possible Quantizer Output levels randomly according to a probability matrix. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. In the binary case the optimal solution is found and in the nonbinary case a good suboptimal solution is analytically obtained. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. The proposed method which may be considered a PHY-layer security scheme is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack. Index Terms—Decentralized detection, decision fusion rule, information security, soft decision, wireless sensor networks. I
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scalable phy layer security for distributed detection in wireless sensor networks
Vehicular Technology Conference, 2012Co-Authors: Reza Soosahabi, Mort NaraghipourAbstract:The problem of binary hypothesis testing is considered in a bandwidth-constrained low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method we propose maps the Output of the Quantizer to one of the possible Quantizer Output levels randomly according to a probability matrix. This operation is similar to that of a discrete memoryless channel. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. A good suboptimal solution to this problem is found. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. As the number of levels in the Quantizer increases the performance loss of the secure system compared to insecure system is reduced. Compared to the existing data encryption methods, the proposed method is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.
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scalable phy layer security for distributed detection in wireless sensor networks
IEEE Transactions on Information Forensics and Security, 2012Co-Authors: Reza Soosahabi, Mort NaraghipourAbstract:The problem of binary hypothesis testing is considered in a bandwidth-constrained densely populated low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method maps the Output of the Quantizer to one of the possible Quantizer Output levels randomly according to a probability matrix. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. In the binary case the optimal solution is found and in the nonbinary case a good suboptimal solution is analytically obtained. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. The proposed method which may be considered a PHY-layer security scheme is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.
Nobutomo Matsunaga - One of the best experts on this subject based on the ideXlab platform.
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dynamic Quantizer design under communication rate constraints
IEEE Transactions on Automatic Control, 2016Co-Authors: Hiroshi Okajima, Kenji Sawada, Nobutomo MatsunagaAbstract:Feedback type dynamic Quantizers such as delta-sigma modulators are typically effective for encoding high-resolution data into lower resolution data. The dynamic Quantizers include a filter and a static Quantizer. When it is required to control under a communication rate constraint, the data rate of the Quantizer Output should be minimized appropriately by quantization. This technical note provides numerical methods for the complete design of a type of dynamic Quantizers, including the selection of all the Quantizer parameters in order to minimize a specific performance index and satisfy a communication constraint. The design method of the dynamic Quantizer is proposed using a particle swarm optimization (PSO) method. A part of the initial Quantizers in PSO are designed based on an invariant set analysis and an iteration algorithm. Effectiveness of the system with the proposed Quantizer is assessed through numerical examples.
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integrated design of filter and interval in dynamic Quantizer under communication rate constraint
IFAC Proceedings Volumes, 2011Co-Authors: Hiroshi Okajima, Kenji Sawada, Nobutomo MatsunagaAbstract:Abstract This paper proposes an design method of feedback type dynamic Quantizers under communication rate constraints. It is well known that feedback type dynamic Quantizers such as Delta/Sigma modulator are effective for encoding high resolution data into lower resolution data. The dynamic Quantizers include a set of a filter and a static Quantizer. When it is required to control under the communication rate constraint, the data size of signal should be minimized appropriately by quantization. In the field of control engineering, many dynamic Quantizer design methods have been proposed in terms of the filter design. However, design of the static Quantizer part has not been considered though it is also important to satisfy constraint of the data size. The quantization interval of the static Quantizer part is strongly related to the data size. In this paper, an integrated design method of the filter and the quantization interval is proposed under the communication rate constraint. The proposed method is derived based on our previous work which design the interval to guarantee the communication rate constraint. By our proposed Quantizer, the Quantizer Output satisfy the communication rate constraint and it gives good performance. The effectiveness of proposed Quantizer is shown by numerical examples.
