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Successive Wyner-Ziv Coding for the binary CEO Problem Under Logarithmic Loss

IEEE TRANSACTIONS ON COMMUNICATIONS 2019年第11期67卷 7512-7525页

作者： Nangir, Mahdi Asvadi, Reza Chen, Jun Ahmadian-Attari, Mahmoud Matsumoto, Tad KN Toosi Univ Technol Fac Elect Engn Tehran *** Iran Univ Tabriz Fac Elect & Comp Engn Tehran *** Iran Shahid Beheshti Univ Fac Elect Engn Tehran *** Iran McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada Japan Adv Inst Sci & Technol Sch Informat Sci Nomi 9231292 Japan Univ Oulu Ctr Wireless Commun Oulu 90014 Finland

The L-link binary Chief Executive Officer (CEO) problem under logarithmic loss is investigated in this paper. A quantization splitting technique is applied to convert the problem under consideration to a (2L - 1)-step successive Wyner-Ziv (WZ) problem, for which a practical coding scheme is proposed. In the proposed scheme, Low-Density Generator-Matrix (LDGM) codes are used for binary quantization while Low-Density Parity-Check (LDPC) codes are used for syndrome generation;the decoder performs successive decoding based on the received syndromes and produces a soft reconstruction of the remote source. The simulation results indicate that the rate-distortion performance of the proposed scheme can approach the theoretical inner bound based on binary-symmetric test-channel models.

关键词： binary CEO problem binary quantization successive decoding syndrome decoding Wyner-Ziv problem quantization splitting logarithmic loss

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Granger Causality of Gaussian Signals from binary or Non-uniformly Quantized Measurements 24

Granger Causality of Gaussian Signals from Binary or Non-uni...

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24th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Ahmadi, Salman Nair, Girish N. Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

This paper studies the assessment of Granger causality between jointly Gaussian signals from binary/non-uniformly quantized measurements. A necessary and sufficient condition is proposed for Granger causality inference under binary quantization. Furthermore, sufficient conditions are introduced to infer Granger causality between jointly Gaussian signals through non-uniformly quantized measurements. No assumptions are made on the identifiability of the jointly Gaussian random processes through the quantized observations. Copyright (C) 2021 The Authors.

关键词： Granger causality jointly Gaussian random processes non-uniform quantization binary quantization

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A Knowledge Based System for Minimum Rectangle Nesting

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13th International Conference on Knowledge-Based Intelligent Information and Engineering Systems

作者： Chmaj, Grzegorz Pozniak-Koszalka, Iwona Kasprzak, Andrzej Wroclaw Univ Technol Dept Syst & Comp Networks Fac Elect PL-50370 Wroclaw Poland

ISBN: (纸本)9783642045943

Nesting algorithms deal with the optimal placement of shapes ill specified regions Subject to specified constraints. In this paper, a complex algorithm for solving two-dimensional nesting problem is proposed. Arbitrary geometric shapes are first quantized into a binary form. These binary representations are subsequently processed by operators which nest the shapes in a rectangle of minimum area. After nesting is completed, the binary representations are converted back to the original geometric form. Investigations have shown that the nesting effect is driven by quantization accuracy. Therefore, better accuracy is possible given more computing time. However, the proposed knowledge based system call significantly reduce the time of nesting, by intelligently pairing shapes, based oil prior knowledge of their form.

关键词： nesting knowledge based system binary quantization

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Efficient binary Multi-view Subspace Learning for Instance-Level Image Retrieval 27th

Efficient Binary Multi-view Subspace Learning for Instance-L...

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27th International Conference on Neural Information Processing

作者： Wu, Zhijian Li, Jun Xu, Jianhua Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9783030638191;9783030638207

The existing hashing methods mainly handle either the feature based nearest-neighbour search or the category-level image retrieval, whereas a few efforts are devoted to instance retrieval problem. Besides, although multi-view hashing methods are capable of exploring the complementarity among multiple heterogeneous visual features, they heavily rely on massive labeled training data, and somewhat affects the real-world applications. In this paper, we propose a binary multi-view fusion framework for directly recovering a latent Hamming subspace from the multi-view features. More specifically, the multi-view subspace reconstruction and the binary quantization are integrated in a unified framework so as to minimize the discrepancy between the original multi-view high-dimensional Euclidean space and the resulting compact Hamming subspace. In addition, our method is amenable to efficient iterative optimization for learning a compact similarity-preserving binary code. The resulting binary codes demonstrate significant advantage in retrieval precision and computational efficiency at the cost of limited memory footprint. More importantly, our method is essentially an unsupervised learning scheme without any labeled data involved, and thus can be used in the cases when the supervised information is unavailable or insufficient. Experiments on public benchmark and large-scale datasets reveal that our method achieves competitive retrieval performance comparable to the state-of-the-art and has excellent scalability in large-scale scenario.

关键词： Instance retrieval Multi-view fusion Multi-view subspace reconstruction binary quantization

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Design Method Of Frequency-Agile Radar Frequency Hopping Sequence Based On CNN Network And Chaotic Sequence

Design Method Of Frequency-Agile Radar Frequency Hopping Seq...

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International Conference on Big Data, Information and Computer Network (BDICN)

作者： An, Pengfei Shang, Zhengguo Yan, Shi Wang, Dehao China Aerosp Sci & Ind Corp Beijing Inst Remote Sensing Equipment Res Inst 2 Beijing Peoples R China Beijing Inst Environm Features Sci & Technol Opt Radiat Lab Beijing Peoples R China

ISBN: (纸本)9781665484763

With the development of modern radar anti-jamming technology, frequency-agile radar has developed rapidly. To counter jamming signals, the operating frequency of the frequency-agile radar transmitting pulse has strong random performance. The chaotic sequence has great random performance, and the sequence generated by the chaotic sequence also has great randomness. Therefore, this paper first generates data through the chaotic sequence and binary quantization method and uses the autocorrelation performance as the criterion, The optimal threshold corresponding to the optimal frequency hopping sequence of the same chaotic sequence is screened out. The selected random Frequency hopping point sequence with better performance has great ambiguity function performance, that is, target detection performance. First, divide the value range of the optimal threshold, use the chaotic sequence and the optimal threshold as the standard data, turn the threshold optimization problem into a pattern recognition classification problem, and use the standard data as the training set to train the neural network. In the end, the CNN network has a great classification ability can better realize the Frequency hopping point sequence generation.

关键词： CNN network chaotic sequence binary quantization autocorrelation target detection

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Fast Training of Light binary Convolutional Neural Networks using Chainer and Cupy 12

Fast Training of Light Binary Convolutional Neural Networks ...

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12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Dogaru, Radu Dogaru, Ioana Univ Politehn Bucuresti Dept Appl & Informat Engn Bucharest Romania

ISBN: (纸本)9781728168432

Light binary convolutional neural networks (LB-CNN) are particularly useful when implemented in hardware technologies, such as FPGA. In this paper, such a network and its implementation using the Chainer machine learning framework is presented. Up to three convolutional layers, each provided with binary convolution kernels, can be defined forming a nonlinear expander for the images to be categorized. Effective training is done in a single output linear layer (Adaline) using the very fast extreme learning machine training with GPU acceleration. It is shown that a significant speedup of more than 60 times can be achieved while employing the CHAINER environment instead the more traditional Keras/Tensorflow. Such fast training allows easy tuning of the randomly generated binary kernels in a loop. Results for widely used datasets including MNIST, GTRSB, ORL, VGG etc. are given, showing very good compromise between accuracy and complexity. Particularly, for face recognition problems the model provides up to 100% accuracies. Such LB-CNN models are an excellent solutions for low-power applications where bit-quantization is an important option to reduce implementation complexity and energy.

关键词： machine learning convolutional neural network binary quantization FPGA Python extreme learning machine

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Reduced embedding complexity using BP message passing for LDGM codes

Reduced embedding complexity using BP message passing for LD...

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Conference on Security, Forensics, Steganography, and Watermarking of Multimedia Contents X

作者： Guenther, Peter Schoenfeld, Dagmar Winkler, Antje Tech Univ Dresden Fac Comp Sci Inst Syst Architecture D-01062 Dresden Germany

ISBN: (纸本)9780819469915

The application of Low-Density Generator Matrix (LDGM) Codes combined with Survey Propagation (SP) in steganography seems to be advantageous, since it is possible to approximate the coset leader directly. Thus, large codeword length can be applied, resulting in an embedding efficiency close to the theoretical upper bound of embedding efficiency. Since this approach is still quite complex, this paper deals with the application of Belief Propagation (BP) to LDGM Codes used to reduce the complexity of embedding while keeping the embedding efficiency constant.

关键词： steganography matrix embedding low-density codes belief propagation binary quantization

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Comparator based fast demodulation method for airborne secondary surveillance radar test 13

Comparator based fast demodulation method for airborne secon...

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2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Zhang Peng Wang Ping Wang Houjun Li Li Guo Lianping School of Automation Engineering University of Electronics Science and Technology of China Department of Aviation The 10th Research InstituteChina Electronics Technology Group

ISBN: (纸本)9781509050369;9781509050352

Secondary surveillance radar(SSR) is an important kind of airborne device of avionics system for various types of aircrafts, which bears the function of communication, navigation and identification during every flight mission. This paper proposes a digital demodulation method based on comparator binary quantization. The theory of demodulation, the hardware architecture and the diagram of logic design are described. The symbol error rate, the processing time and the test result are presented. Compared with traditional demodulation algorisms, the proposed method takes a distinct advantage of its processing speed and resource consumption, with which there is a great theoretical and practical value in the research, testing and daily maintenance of SSR.

关键词： Comparator Demodulation binary quantization SSR

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Invariant Attribute-Driven binary Bi-Branch Classification of Hyperspectral and LiDAR Images

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REMOTE SENSING 2023年第17期15卷 4255-4255页

作者： Zhang, Jiaqing Lei, Jie Xie, Weiying Li, Daixun Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

The fusion of hyperspectral and LiDAR images plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, such as Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we propose an Invariant Attribute-driven binary Bi-branch Classification (IABC) method, which is a unified network that combines a binary Convolutional Neural Network (CNN) and a GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structural representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrate the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for the accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling the real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

关键词： invariant graph convolutional network (GCN) convolutional neural network (CNN) binary quantization hyperspectral image (HSI) classification

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Evaluating the Randomness of Chaotic binary Sequences Via a Novel Period Detection Algorithm

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2022年第5期32卷

作者： Fan, Chunlei Ding, Qun Tse, Chi K. Heilongjiang Univ Elect Engn Coll Harbin 150080 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

When chaotic systems are applied to stream ciphers, chaotic real-valued sequences generally need to be converted into binary sequences with the purpose of encrypting data. However, the performance of binary sequences will be degraded under the joint influence of round-off and quantization errors. In this case, the randomness of some chaotic binary sequences may be weakened in a local range. Taking advantage of parallel computing, a fast period detection algorithm is designed to locate all local "periodicities" contained in chaotic binary sequences quickly and accurately. This algorithm evaluates the randomness of a chaotic binary sequence from a new perspective of periodicity which enriches the randomness test methods for binary sequences. Different logistic binary sequences are analyzed to demonstrate the effectiveness and practicability of the proposed algorithm.

关键词： Chaotic binary sequence local period binary quantization randomness test

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