The Experts below are selected from a list of 5994 Experts worldwide ranked by ideXlab platform
Youngsu Park - One of the best experts on this subject based on the ideXlab platform.
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On-Load Motor Parameter Identification Using Univariate Dynamic Encoding Algorithm for Searches
IEEE Transactions on Energy Conversion, 2008Co-Authors: Youngsu ParkAbstract:Parameter identification of an induction motor has long been studied either for vector control or fault diagnosis. This paper addresses parameter identification of an induction motor under on-load operation. For estimating electrical and mechanical parameters in the motor model from the on-load data, unmeasured initial states and load torque profile have to be also estimated for state evaluation. Since gradient of cost function for the auxiliary variables are hard to be derived, direct optimization methods that rely on computational capability should be employed. In this paper, the univariate dynamic Encoding Algorithm for searches (uDEAS), recently developed by the authors, is applied to the identification of whole unknown variables with measured voltage, current, and velocity data. Profiles of motor parameters estimated with uDEAS are reasonable, and estimation time is 2 s on average, which is quite fast as compared with other direct optimization methods.
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Parameter optimization for SVM using dynamic Encoding Algorithm
2005Co-Authors: Youngsu ParkAbstract:In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic Encoding Algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization Algorithm which is based on variable length binary Encoding method. This method has less computation time than genetic Algorithm (GA) based and grid search based methods and better performance on flnding global optimal value than gradient based methods. It is very e-cient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.
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Support Vector Machine Parameter tuning using Dynamic Encoding Algorithm for handwritten digit recognition
2005 5th International Conference on Information Communications & Signal Processing, 2005Co-Authors: Youngsu ParkAbstract:In this paper, we propose a support vector machine parameter tuning Algorithm using dynamic Encoding Algorithm for handwritten digit recognition. This method uses dynamic Encoding Algorithm for search (DEAS) which is recently proposed optimization Algorithm based on variable binary Encoding length. The radius/margin bound is used for the estimation of the support vector machine generalization performance. When the radius/margin bound is not convex form or different from real error rate for test data set, n-poled validation error rate can be used for parameter tuning. The proposed method can be applied to the case which is hard to find gradient information of radius/margin bound. Moreover, the proposed method is a more efficient Algorithm compared with GA Algorithm and grid search in computation time
Antonio Ortega - One of the best experts on this subject based on the ideXlab platform.
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Distributed Encoding Algorithm for Source Localization in Sensor Networks
EURASIP Journal on Advances in Signal Processing, 2010Co-Authors: Antonio OrtegaAbstract:We consider sensor-based distributed source localization applications, where sensors transmit quantized data to a fusion node, which then produces an estimate of the source location. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to attain a certain source localization accuracy. We propose a distributed Encoding Algorithm that is applied after quantization and achieves significant rate savings by merging quantization bins. The bin-merging technique exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply the Algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate significant rate savings (e.g., over 30%, 5 nodes, and 4 bits per node) when our novel bin-merging Algorithms are used.
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IPSN - Quantizer design and distributed Encoding Algorithm for source localization in sensor networks
IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks 2005., 2005Co-Authors: Antonio OrtegaAbstract:In this paper, we propose a quantizer design Algorithm that is optimized for source localization in sensor networks. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that this goal can be achieved more efficiently when "application-specific" quantizers are used. Our proposed quantizer design Algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We also propose a distributed Encoding Algorithm that is applied after quantization and achieves rate savings by merging quantization bins without any degradation of localization performance. The merging technique in the Encoding Algorithm exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these Algorithms to a system where an acoustic sensor model is employed for localization. For this case, we introduce the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our quantizer over traditional quantizer designs. In addition, they show rate savings (32.8%, 5 nodes, 4 bits per node) when our novel bin-merging Algorithms are used. Our results also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.
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Quantizer design and distributed Encoding Algorithm for source localization in sensor networks
IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks 2005., 2005Co-Authors: Antonio OrtegaAbstract:In this paper, we propose a quantizer design Algorithm that is optimized for source localization in sensor networks. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that this goal can be achieved more efficiently when "application-specific" quantizers are used. Our proposed quantizer design Algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We also propose a distributed Encoding Algorithm that is applied after quantization and achieves rate savings by merging quantization bins without any degradation of localization performance. The merging technique in the Encoding Algorithm exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these Algorithms to a system where an acoustic sensor model is employed for localization. For this case, we introduce the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our quantizer over traditional quantizer designs. In addition, they show rate savings (32.8%, 5 nodes, 4 bits per node) when our novel bin-merging Algorithms are used. Our results also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.
Tomoharu Shibuya - One of the best experts on this subject based on the ideXlab platform.
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ISITA - Generalization of Lu's linear time Encoding Algorithm for LDPC codes
2012Co-Authors: Kazuki Kobayashi, Tomoharu ShibuyaAbstract:In this paper, we propose a new Encoding Algorithm applicable to any linear codes over arbitrary finite field whose computational complexity is O(w(H)) where w(H) denotes the number of non-zero elements in a parity check matrix H of a code. The proposed Algorithm is essentially equivalent to the linear time Encoding Algorithm presented by Lu et al. when the maximum column weight δ∗ of H is less than or equal to 3, and is regarded as a natural generalization of the Algorithm when δ∗ > 3. Moreover, the proposed Algorithm can encode any linear codes defined by sparse parity check matrices, such as LDPC codes, with O(n) complexity where n denotes the code length.
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Generalization of Lu's linear time Encoding Algorithm for LDPC codes
2012 International Symposium on Information Theory and its Applications, 2012Co-Authors: Kazuki Kobayashi, Tomoharu ShibuyaAbstract:In this paper, we propose a new Encoding Algorithm applicable to any linear codes over arbitrary finite field whose computational complexity is O(w(H)) where w(H) denotes the number of non-zero elements in a parity check matrix H of a code. The proposed Algorithm is essentially equivalent to the linear time Encoding Algorithm presented by Lu et al. when the maximum column weight δ* of H is less than or equal to 3, and is regarded as a natural generalization of the Algorithm when δ* >; 3. Moreover, the proposed Algorithm can encode any linear codes defined by sparse parity check matrices, such as LDPC codes, with O(n) complexity where n denotes the code length.
D Zhao - One of the best experts on this subject based on the ideXlab platform.
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fast intra Encoding Algorithm for high efficiency video coding
Signal Processing-image Communication, 2014Co-Authors: Liang Zhao, D ZhaoAbstract:The emerging High Efficiency Video Coding (HEVC) standard provides equivalent subjective quality with about 50% bit rate reduction compared to the H.264/AVC High profile. However, the improvement of coding efficiency is obtained at the expense of increased computational complexity. This paper presents a fast intra-Encoding Algorithm for HEVC, which is composed of the following four techniques. Firstly, an early termination technique for coding unit (CU) depth decision is proposed based on the depth of neighboring CUs and the comparison results of rate distortion (RD) costs between the parent CU and part of its child CUs. Secondly, the correlation of intra-prediction modes between neighboring PUs is exploited to accelerate the intra-prediction mode decision for HEVC intra-coding and the impact of the number of mode candidates after the rough mode decision (RMD) process in HM is studied in our work. Thirdly, the TU depth range is restricted based on the probability of each TU depth and one redundant process is removed in the TU depth selection process based on the analysis of the HEVC reference software. Finally, the probability of each case for the intra-transform skip mode is studied to accelerate the intra-transform skip mode decision. Experimental results show that the proposed Algorithm can provide about 50% time savings with only 0.5% BD-rate loss on average when compared to HM 11.0 for the Main profile all-intra-configuration. Parts of these techniques have been adopted into the HEVC reference software. HighlightsAn early termination technique for coding unit (CU) depth decision.The correlation of intra-prediction modes between neighboring PUs is exploited to accelerate the intra-mode decision.The TU depth range is restricted based on the probability of each TU depth.The probability of each case for the intra-transform skip mode is firstly studied.
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Fast intra-Encoding Algorithm for High Efficiency Video Coding
Signal Processing: Image Communication, 2014Co-Authors: Liang Zhao, Xiaopeng Fan, Siwei Ma, D ZhaoAbstract:The emerging High Efficiency Video Coding (HEVC) standard provides equivalent subjective quality with about 50% bit rate reduction compared to the H.264/AVC High profile. However, the improvement of coding efficiency is obtained at the expense of increased computational complexity. This paper presents a fast intra-Encoding Algorithm for HEVC, which is composed of the following four techniques. Firstly, an early termination technique for coding unit (CU) depth decision is proposed based on the depth of neighboring CUs and the comparison results of rate distortion (RD) costs between the parent CU and part of its child CUs. Secondly, the correlation of intra-prediction modes between neighboring PUs is exploited to accelerate the intra-prediction mode decision for HEVC intra-coding and the impact of the number of mode candidates after the rough mode decision (RMD) process in HM is studied in our work. Thirdly, the TU depth range is restricted based on the probability of each TU depth and one redundant process is removed in the TU depth selection process based on the analysis of the HEVC reference software. Finally, the probability of each case for the intra-transform skip mode is studied to accelerate the intra-transform skip mode decision. Experimental results show that the proposed Algorithm can provide about 50% time savings with only 0.5% BD-rate loss on average when compared to HM 11.0 for the Main profile all-intra-configuration. Parts of these techniques have been adopted into the HEVC reference software.
B. Siddik Yarman - One of the best experts on this subject based on the ideXlab platform.
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SYMPES Character Encoding Algorithm
2019 27th Signal Processing and Communications Applications Conference (SIU), 2019Co-Authors: Osman Korkmaz, B. Siddik YarmanAbstract:In this paper, SYMPES Character Encoding Algorithm, which bring a new solution to secure communication, is described. Although SYMPES is not an encryption Algorithm, it has an inherently trusted infrastructure. The character data is not send with SYMPES, it is only the indexes which are transmitted over the communication channel. In this way, due to the natural structure of the system, trusted communication infrastructure is provided and the data is compressed.
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SIU - SYMPES Character Encoding Algorithm
2019 27th Signal Processing and Communications Applications Conference (SIU), 2019Co-Authors: Osman Korkmaz, B. Siddik YarmanAbstract:In this paper, SYMPES Character Encoding Algorithm, which bring a new solution to secure communication, is described. Although SYMPES is not an encryption Algorithm, it has an inherently trusted infrastructure. The character data is not send with SYMPES, it is only the indexes which are transmitted over the communication channel. In this way, due to the natural structure of the system, trusted communication infrastructure is provided and the data is compressed.