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2018 联合国际先进工程与技术研究国际会议

作者： zhangchong wangcai caojingyao Chengdu Neusoft University

A new classification method based on binary coding is proposed for composite information consisting of a variety of basic information types, which takes the composition of composite information into account at the initial stage of design, so it has high efficiency and good scalability in search operation, and it is applied to a practical project successfully.

关键词： binary coding classification composite information

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Distributed QoS routing algorithm based on partheno-GA with binary coding

Distributed QoS routing algorithm based on partheno-GA with ...

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2007年中国智能自动化会议

作者：冯杰姜宁杨既春 Department of Mathematics Dalian University of Technology Operation Research Room Dalian Naval Academy Operation Research Room Dalian Naval Academy

Routing is a critical issue in networking *** basic problem of QoS routing is to satisfy some network parameters constrained of many conditions which is a NP-complete problem and QoS parameters are nondeterministic even *** practical heuristic algorithms are researched to solve such a *** on partheno-GA with binary coding,the network model and mathematical model of QoS routing are constructed,and the distributed QoS routing algorithm is put *** algorithm can be easily implemented through dividing the giant and complex network into some *** simulation result shows that the algorithm can meet increasing daily QoS requirements on Internet,and improve performance more 10 times in time and space complexity compared with the source routing algorithms.

关键词： binary coding partheno-GA QoS routing distributed algorithm simulation

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binary Modeling and Capacity-Approaching coding for the IM/DD Channel

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第5期71卷 2670-2684页

作者： Bahanshal, Sarah Abdel-Qader, Ahmad Chaaban, Anas Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper proposes a new, capacity-preserving vector binary channel (VBC) model, consisting of dependent binary noisy bit-pipes. Then, to simplify coding over this VBC, the paper proposes coding schemes with varying levels of complexity, building on the capacity of binary-symmetric channels (BSC) and channels with state. The achievable rates are compared to capacity and capacity bounds, showing that coding for the BSC with state over the VBC achieves rates close to capacity at moderate to high signal-to-noise ratio (SNR), whereas simpler schemes achieve lower rates at lower complexity. The presented coding schemes are realizable using capacity-achieving codes for binary-input channels, such as polar codes. Numerical results are provided to validate the theoretical results and demonstrate the applicability of the proposed schemes.

关键词： IM/DD channel peak constraint binary mod-eling binary coding achievable rate vector-binary channel

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Low Profile and Multiband Antenna Based on binary Encoding and Multiobjective Optimization With Artificial Intelligence

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INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION 2025年第1期2025卷

作者： Geng, Wenbo Tong, Guolin Xian Jiaotong Liverpool Univ Dept Commun & Networking Suzhou Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

This paper presents a low profile and multiband antenna based on binary encoding and multiobjective optimization using artificial intelligence (AI). The antenna adopts a single-arm helical structure on the top layer and two metal parasitic middle layers above the ground. The parasitic layers are composed of 10 x 10 rectangular squares, being similar as a chessboard, and they are coded as two sets of binary sequences, respectively, according to whether there is a metal sheet at the corresponding position. The multiobjective optimization to binary coding antenna with multiple effective bands, low profile, and high gains is carried out with three steps. Firstly, the discretized parameters are converted into continuous parameters, and the crude model of the antenna is optimized. Secondly, optimization algorithms under different weight assignments to the goals are applied to optimize the antenna and produce a large amount of sample data. AI is introduced to learn and train the model and then produce a large amount of sample data, and we finally get the optimal antenna structure that satisfies the multiobjective performance. The optimized prototype of the antenna is manufactured, and its impedance is well matched with the bandwidth of 1.43-2.494 GHz and 4.62-6.75 GHz. The measured maximum gain of prototype is 7.5 dBi with 10.98%lambda for profile.

关键词： AI binary coding low profile multiband antenna multiobjective optimization

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A novel page clipping search engine based on page discussion topics

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KNOWLEDGE AND INFORMATION SYSTEMS 2019年第3期58卷 525-550页

作者： Chen, Lin-Chih Natl Dong Hwa Univ Dept Informat Management Hualien Taiwan

In this paper, we propose a page clipping search engine based on page discussion topics. Compared to other search engines, our search engine uses the page discussion topic instead of the search engine results page as the main result. After the user selects the topic of interest, our search engine will clip the relevant pages according to the selected topic and produce an integrated page result. The advantage of this topic-based integration page result is that the user can reduce the time it takes to decide whether the page content is relevant. Our results consist of two parts: the query-related discussion topics and the clipping results for relevant pages. We first use an adjusted N-gram language model and a hash method to produce discussion topics. At the same time, we use the idea of binary coding and mathematical set to organize related topics into a hierarchical topic tree with parent-child relationship. Next, we use a cost-effective genetic algorithm to produce the relevant page clipping results. This study has the following three advantages. The first is that we can find multiple clustering relationships, that is, a child topic can appear simultaneously in multiple parent topics. The second is that we propose a good topic generation method, that is, we cannot only produce better quality topics, but also produce the topic tree in a linear time. The third is that we propose a good clipping generation method, that is, we cannot only produce better quality clippings, but also produce a cost-effective solution.

关键词： N-gram language model Hash method Genetic algorithm binary coding Mathematical set

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Vote counting measures for ensemble classifiers

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PATTERN RECOGNITION 2003年第12期36卷 2743-2756页

作者： Windeatt, T Univ Surrey Sch Elect Engn Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Various measures, such as Margin and Bias/Variance, have been proposed with the aim of gaining a better understanding of why Multiple Classifier Systems (MCS) perform as well as they do. While these measures provide different perspectives for MCS analysis, it is not clear how to use them for MCS design. In this paper a different measure based on a spectral representation is proposed for two-class problems. It incorporates terms representing positive and negative correlation of pairs of training patterns with respect to class labels. Experiments employing MLP base classifiers, in which parameters are fixed but systematically varied, demonstrate the sensitivity of the proposed measure to base classifier complexity. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： decision level fusion multiple classifiers ensembles error-correcting binary coding

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Learning iterative quantization binary codes for face recognition

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NEUROCOMPUTING 2016年 214卷 629-642页

作者： Tian, Lei Fan, Chunxiao Ming, Yue Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Key Lab Work Safety Intelligent Monitorin 10 Xitucheng Rd Beijing 100876 Peoples R China

binary feature descriptors have been widely used in computer vision field due to their excellent discriminative power and strong robustness, and local binary patterns (LBP) and its variations have proven that they are effective face descriptors. However, the forms of such binary feature descriptors are pre-defined in the hand-crafted way, which requires strong domain knowledge to design them. In this paper, we propose a simple and efficient iterative quantization binary codes (IQBC) feature learning method to learn a discriminative binary face descriptor in the data-driven way. Firstly, similar to traditional LBP method, we extract patch-wise pixel difference vectors (PDVs) by computing and concatenating the difference between center patch and its neighboring patches. Then, inspired by multi-class spectral clustering and the orthogonal Procrustes problem, which both are widely used in image retrieval field, we learn an optimized rotation to minimize the quantization error of mapping data to the vertices of a zero-centered binary hypercube by using iterative quantization scheme. In other words, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors. And our IQBC can be used with unsupervised data embedding method such as principle component analysis (PCA) and supervised data embedding method such as canonical correlation analysis (CCA), namely IQBC-PCA and IQBC-CCA. Lastly, we cluster and pool these projected binary codes into a histogram-based feature that describes the co-occurrence of binary codes. And we consider the histogram-based feature as our final feature representation for each face image. We investigate the performance of our IQBC-PCA and IQBC-CCA on FERET, CAS-PEAL-R1, LFW and PaSC databases. Extensive experimental results demonstrate that our IQBC descriptor outperforms other state-of-the-art face descriptors. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Iterative quantization Feature learning binary coding Local descriptor Face recognition Face verification

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3D palmprint recognition using complete block wise descriptor

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第29-30期79卷 21987-22006页

作者： Yang, Bing Xiang, Xueqin Yao, Jinliang Xu, Duanqing Hangzhou Dianzi Univ Sch Comp Sci & Technol Xiasha Higher Educ Zone Hangzhou 310018 Peoples R China Key Lab Brain Machine Collaborat Intelligence Zhe Xiasha Higher Educ Zone Hangzhou 310018 Peoples R China Zhejiang Univ Comp Sci Coll Yuquan Rd Hangzhou 310007 Peoples R China

Recent years have witnessed a growing interesting in developing automatic 3D palmprint recognition systems. The key problem of 3D palmprint recognition is to contrive an effective representation. In this paper, we propose a 3D palmprint recognition method by completely fusing of multiple block wise features. First, we propose a shape index multi-direction binary code strategy to extract 2D texture-level features of 3D palmprint. Then, we use the compact surface type binary code scheme to characterize 3D structure-level features of 3D palmprint. Finally, we integrate them together to construct the complete block wise descriptor for 3D palmprint recognition. Experiments conducted on the public available 3D palmprint database validate the effectiveness of our proposed method.

关键词： Bioinformatics 3D palmprint recognition Shape index binary coding Block wise descriptor

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Representation and processing of structures with binary sparse distributed codes

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2001年第2期13卷 261-276页

作者： Rachkovskij, DA VM Glushkov Cybernet Inst UA-03680 Kiev Ukraine

The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures, in this paper, we provide a comparative description of sparse binary distributed representation developed in the framework of the associative-projective neural network architecture and the more well-known holographic reduced representations of Plate and binary spatter codes of Kanerva. The key procedure in associative-projective neural networks is context-dependent thinning which binds codevectors and maintains their sparseness. The codevectors are stored in structured memory array which can be realized as distributed auto-associative memory. Examples of distributed representation of structured data are given. Fast estimation of the similarity of analogical episodes by the overlap of their codevectors is used in the modeling of analogical reasoning both for retrieval of analogs from memory and for analogical mapping.

关键词： sparse coding binary coding binding representation of structure hierarchical representation nested representation long-term memory analogy compositional distributed representations connectionist symbol processing analogical retrieval analogical mapping

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Sparse projections matrix binary descriptors for face recognition

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NEUROCOMPUTING 2018年 297卷 8-21页

作者： Fan, Chunxiao Tian, Lei Ming, Yue Hong, Xiaopeng Zhao, Guoying Pietikainen, Matti Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Ctr Machine Vis & Signal Anal Oulu 90014 Finland

In recent years, the binary feature descriptor has achieved great success in face recognition (FR) field, such as local binary pattern (LBP). It is well known that the high-dimensional feature representations can contain more discriminative information, therefore, it is natural for us to construct the high-dimensional binary feature for FR task. However, the high-dimensional representations would lead to high computational cost and overfitting. Therefore, an effective sparsity regularizer is necessary. In this paper, we introduce the sparsity constraint into the objective function of general binary codes learning framework, so that the problem of high computational cost and overfitting can be somehow solved. There are three main requirements in our objective function: First, we require that the high-dimensional binary codes have the minimized quantization loss compared with centered original data. Second, we require the projection matrices are sparse, so that the projection process would not take lots of computational resource even faced with high-dimensional original data. Third, for a mapping (hashing) function, the bit-independence and bit-balance are two excellent properties for generating discriminative binary codes. We also empirically show that the high-dimensional binary codes can obtain more discriminative ability by pooling process with an unsupervised clustering method. Therefore, a discriminative and low-cost Sparse Projection Matrix binary Descriptors (SPMBD) is learned by the data-driven way. Extensive experimental results on four public datasets show that our SPMBD descriptor outperforms other existing face recognition algorithms and demonstrate the effectiveness and robustness of the proposed methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Local descriptor Feature learning binary coding Sparse projection matrix Face recognition

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