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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2003年第4期7卷 344-355页

作者： Sharma, SK Irwin, GW Queens Univ Belfast Sch Elect & Elect Engn Intelligent Syst & Control Grp Belfast Antrim North Ireland

A new chromosome encoding method, named fuzzy coding, is proposed for representing real number parameters in a genetic algorithm. Fuzzy coding provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree of membership. Thus, it represents the knowledge associated with each parameter and is an indirect method of encoding compared with alternatives, where the parameters are directly represented in the encoding. Fuzzy coding is described and compared with conventional binary coding, gray coding, and floating-point coding. Two test examples, along with neural identification of a nonlinear pH process from experimental data, are studied. It is shown that fuzzy coding is better than the conventional methods and is effective for parameter optimization in problems where the search space is complicated.

关键词： binary coding floating-point coding fuzzy coding genetic algorithm gray coding neural networks nonlinear identification

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Enhanced embedded zerotree wavelet algorithm for lossy image coding

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IET IMAGE PROCESSING 2019年第8期13卷 1364-1374页

作者： Boujelbene, Rania Boubchir, Larbi Ben Jemaa, Yousra Univ Tunis El Manar U2S Lab Tunis Tunisia Univ Paris 08 LIASD Lab St Denis France

Embedded zerotree wavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE-EZW), allowing to achieve a high compression performance in terms of peak-signal-to-noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well-known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000.

关键词： wavelet transforms binary codes trees (mathematics) data compression image coding peak-signal-to-noise ratio lossy image compression binary coding conventional EZW improved EZW schemes natural image coding applications medical image coding applications lossy image coding embedded zerotree wavelet algorithm effective coding technique low-bit-rate image compression enhanced EZW high compression performance hierarchical trees JPEG2000 SPIHT

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An improved vector quantization method using deep neural network

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 72卷 178-183页

作者： Jiang, Wenbin Liu, Peilin Wen, Fei Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Air Force Engn Univ Air Control & Nav Inst Xian 710000 Peoples R China

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR). (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Deep neural network Vector quantization Auto-encoder binary coding

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Modulation and coding for linear Gaussian channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 1998年第6期44卷 2384-2415页

作者： Forney, GD Ungerboeck, G Motorola Inc Informat Syst Grp Mansfield MA 02048 USA IBM Corp Div Res Zurich Res Lab CH-8803 Ruschlikon Switzerland

Shannon's determination of the capacity of the linear Gaussian channel has posed a magnificent challenge to succeeding generations of researchers. This paper surveys how this challenge has been met during the past half century. Orthogonal minimum-bandwidth modulation techniques and channel capacity are discussed binary coding techniques for lo low-signal-to-noise ratio (SNR) channels and nonbinary coding techniques for high-SNR channels are reviewed. Recent developments, which now allow capacity to be approached on any linear Gaussian channel, are surveyed. These new capacity-approaching techniques include turbo coding and decoding, multilevel coding, and combined coding/precoding for intersymbol-interference channels.

关键词： binary coding block codes canonical channel capacity coding gain convolutional codes lattice codes multicarrier modulation nonbinary coding orthogonal modulation precoding shaping gain single-carrier modulation trellis codes

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Search Efficient binary Network Embedding

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2021年第4期15卷 1–27页

作者： Zhang, Daokun Yin, Jie Zhu, Xingquan Zhang, Chengqi Univ Sydney Discipline Business Analyt City Rd Camperdown NSW 2006 Australia Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Glades Rd Boca Raton FL 33431 USA Univ Technol Sydney Fac Engn & Informat Technol Australian Artificial Intelligence Inst Broadway Ultimo NSW 2007 Australia

Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity search, which often involves finding nearest neighbors measured by distance or similarity in a continuous vector space. In this article, we propose a search efficient binary network embedding algorithm called binaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. binaryNE learns binary node representations using a stochastic gradient descent-based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support faster node similarity search than using Euclidean or other distance measures. Extensive experiments and comparisons demonstrate that binaryNE not only delivers more than 25 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods. The binary codes learned by binaryNE also render competitive performance on node classification and node clustering tasks. The source code of the binaryNE algorithm is available at https://***/daokunzhang/binaryNE.

关键词： Network embedding binary coding similarity search efficiency

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Interactive Exploration for Continuously Expanding Neuron Databases

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METHODS 2017年 115卷 100-109页

作者： Li, Zhongyu Metaxas, Dimitris N. Lu, Aidong Zhang, Shaoting Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA Rutgers State Univ Dept Comp Sci New Brunswick NJ USA

This paper proposes a novel framework to help biologists explore and analyze neurons based on retrieval of data from neuron morphological databases. In recent years, the continuously expanding neuron databases provide a rich source of information to associate neuronal morphologies with their functional properties. We design a coarse-to-fine framework for efficient and effective data retrieval from large-scale neuron databases. In the coarse-level, for efficiency in large-scale, we employ a binary coding method to compress morphological features into binary codes of tens of bits. Short binary codes allow for realtime similarity searching in Hamming space. Because the neuron databases are continuously expanding, it is inefficient to re-train the binary coding model from scratch when adding new neurons. To solve this problem, we extend binary coding with online updating schemes, which only considers the newly added neurons and update the model on-the-fly, without accessing the whole neuron databases. In the finegrained level, we introduce domain experts/users in the framework, which can give relevance feedback for the binary coding based retrieval results. This interactive strategy can improve the retrieval performance through re-ranking the above coarse results, where we design a new similarity measure and take the feedback into account. Our framework is validated on more than 17,000 neuron cells, showing promising retrieval accuracy and efficiency. Moreover, we demonstrate its use case in assisting biologists to identify and explore unknown neurons. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Neuron Morphology Large-Scale Retrieval binary coding Online Updating User Interaction

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Evaluation of the genetic algorithm parameters on the optimization performance: a case study on pump-and-treat remediation design

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TOP 2010年第2期18卷 303-320页

作者： Gungor-Demirci, Gamze Aksoy, Aysegul Middle E Tech Univ Dept Environm Engn TR-06531 Ankara Turkey

In this study, the impacts of different crossover and encoding schemes on the performance of a genetic algorithm (GA) in finding optimal pump-and-treat (P&T) remediation designs are investigated. For this purpose, binary and Gray encodings of the decision variables are tested. Uniform and two-point crossover schemes are evaluated for two different crossover probabilities. Analysis is performed for two P&T system optimization scenarios. Results show that uniform crossover operator with Gray encoding outperforms the other alternatives for the complex problem with higher number of decision variables. On the other hand, when a simpler problem, which had a lower number of decision variables, is solved, the efficiency of GA is independent of the encoding and crossover schemes.

关键词： Optimization Genetic algorithms Uniform crossover Two-point crossover binary coding Gray coding

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Bodyprint-A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

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SENSORS 2020年第18期20卷 5365页

作者： Avola, Danilo Cinque, Luigi Fagioli, Alessio Foresti, Gian Luca Pannone, Daniele Piciarelli, Claudio Sapienza Univ Dept Comp Sci I-00198 Rome Italy Sapienza Univ Dept Commun & Social Res I-00198 Rome Italy Univ Udine Dept Math Comp Sci & Phys I-33100 Udine Italy

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions;thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person's identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

关键词： person re-identification long short-term memory networks 2D skeletons RGB video sequences joints based meta-features binary coding hashing

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Phenotypic and genetic characterisation revealed the existence of several biotypes within the Neorautanenia brachypus (Harms) CA wild accessions in South East Lowveld, Zimbabwe

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BMC ECOLOGY 2019年第1期19卷 1-14页

作者： Nyarumbu, Trish. O. Kaseke, Tinotenda Gobvu, Vimbai Murungweni, Chrispen Mashingaidze, Arnold. B. Chikwambi, Zedias Hort Res Inst POB 810 Marondera Zimbabwe Chinhoyi Univ Technol Dept Biotechnol P Bag 7724 Chinhoyi Zimbabwe Chinhoyi Univ Technol Dept Anim Prod & Technol P Bag 7724 Chinhoyi Zimbabwe Chinhoyi Univ Technol Dept Crop Sci & Postharvest Technol P Bag 7724 Chinhoyi Zimbabwe

BackgroundLocal communities in the South Eastern Lowveld of Zimbabwe have adopted the feeding of livestock with Neorautanenia brachypus (Harms) C.A. tuber to mitigate against climate change. Differences within Neorautanenia brachypus (Harms) tuber flesh colour and preferences by cattle have been observed, suggesting possible diversity within the N. brachypus plant community. This study aimed at distinguishing the N. brachypus wild plant species through phenotypic and genetic characterization using morphological descriptors and random amplified polymorphic (RAPD) markers respectively. Leaf samples were selected using judgmental sampling techniques from wards 11-15 in Sengwe (Chiredzi district) for leaf morphology and molecular characterization. RAPD-PCR analysis was done using 18-screened random decamer primers to confirm the diversity in the plant population. The similarity of the biotypes was evaluated using binary coding on the basis of the presence or absence of a morphological indicator as well as distinct DNA amplicon fragments. Primer 7.0.13 was used to estimate morphological and genetic similarities using the unweighted pair group method with arithmetic average (UPGMA). The cluster number was estimated using the Elbow method part of the R ***, 14 biotype groups were identified from 96 accessions visually characterized basing of leaf characteristics. All the leaf biotypes displayed arcuate venation with differences observed for leaf shape, tip shape and leaf margins. The 14 biotypes clustered into six groups based on the binary data of the morphological characteristics. RAPD primers generated three hundred and sixty eight distinct amplicons with 77.5% being polymorphic from the 14 biotypes. The number of bands produced per primer ranged from four (OPF-02) to 44 (UBC-746). The PIC value ranged from 0.1327 to 0.1873 for the RAPD primers. Use of molecular markers collapsed the biotypes into five clusters. Both the leaf descriptors and RAPD ma

关键词： Tuber Random amplified polymorphic DNA (RAPD) Primer binary coding Dendrogram Genetic variability

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Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality

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NEUROINFORMATICS 2018年第3-4期16卷 339-349页

作者： Li, Zhongyu Butler, Erik Li, Kang Lu, Aidong Ji, Shuiwang Zhang, Shaoting Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA State Univ New Jersey Dept Ind & Syst Engn Piscataway NJ 08854 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

关键词： Neuron morphology Large-scale retrieval Deep learning binary coding Augmented reality

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