Relevant bibliographies by topics / Binary vector quantization (BVQ)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Binary vector quantization (BVQ)'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Binary vector quantization (BVQ)"

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Chen, Liquan, Zhaofa Chen, Tianyu Lu, and Aiqun Hu. "A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization." Security and Communication Networks 2023 (April 8, 2023): 1–14. http://dx.doi.org/10.1155/2023/4911338.

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Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.
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Ku, Ning-Yun, Shun-Chieh Chang, and Sha-Hwa Hwang. "Binary Search Vector Quantization." AASRI Procedia 8 (2014): 112–17. http://dx.doi.org/10.1016/j.aasri.2014.08.019.

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Kwak, Nae Joung, Soung Pil Ryu, Heak Bong Kwon, and Jae Hyeong Ahn. "The Improved Binary Tree Vector Quantization Using Spatial Sensitivity of HVS." Key Engineering Materials 277-279 (January 2005): 254–58. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.254.

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In this paper, we proposed an improved binary tree vector quantization in special consideration of the area of spatial sensitivity which is an important characteristic of the human visual system. We regarded spatial sensitivity as a function of the human visual system, which works using variations of the three primary colors in blocks of input images. In addition, we applied the weight derived from HVS spatial sensitivity to the process of splitting nodes using eigenvectors in binary tree vector quantization. The test results showed that the proposed method provided better visual quality and greater PSNR than conventional methods.
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Xiaolin Wu. "Optimal binary vector quantization via enumeration of covering codes." IEEE Transactions on Information Theory 43, no. 2 (1997): 638–45. http://dx.doi.org/10.1109/18.556119.

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Journal, Baghdad Science. "An algorithm for binary codebook design based on the average bitmap replacement error (ABPRE)." Baghdad Science Journal 8, no. 2 (2011): 684–88. http://dx.doi.org/10.21123/bsj.8.2.684-688.

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In this paper, an algorithm for binary codebook design has been used in vector quantization technique, which is used to improve the acceptability of the absolute moment block truncation coding (AMBTC) method. Vector quantization (VQ) method is used to compress the bitmap (the output proposed from the first method (AMBTC)). In this paper, the binary codebook can be engender for many images depending on randomly chosen to the code vectors from a set of binary images vectors, and this codebook is then used to compress all bitmaps of these images. The chosen of the bitmap of image in order to compress it by using this codebook based on the criterion of the average bitmap replacement error (ABPRE). This paper is suitable to reduce bit rates (increase compression ratios) with little reduction of performance (PSNR).
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Hameed, Maha A. "An algorithm for binary codebook design based on the average bitmap replacement error (ABPRE)." Baghdad Science Journal 8, no. 2 (2011): 684–88. http://dx.doi.org/10.21123/bsj.2011.8.2.684-688.

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In this paper, an algorithm for binary codebook design has been used in vector quantization technique, which is used to improve the acceptability of the absolute moment block truncation coding (AMBTC) method. Vector quantization (VQ) method is used to compress the bitmap (the output proposed from the first method (AMBTC)). In this paper, the binary codebook can be engender for many images depending on randomly chosen to the code vectors from a set of binary images vectors, and this codebook is then used to compress all bitmaps of these images. The chosen of the bitmap of image in order to compress it by using this codebook based on the criterion of the average bitmap replacement error (ABPRE). This paper is suitable to reduce bit rates (increase compression ratios) with little reduction of performance (PSNR).
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Lei, Shi. "Deburr Algorithm of Binary Image Based on Outline Trace." Advanced Materials Research 811 (September 2013): 422–25. http://dx.doi.org/10.4028/www.scientific.net/amr.811.422.

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Sari, Jayanti Yusmah, and Rizal Adi Saputra. "Pengenalan Finger Vein Menggunakan Local Line Binary Pattern dan Learning Vector Quantization." Jurnal ULTIMA Computing 9, no. 2 (2018): 52–57. http://dx.doi.org/10.31937/sk.v9i2.790.

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This research proposes finger vein recognition system using Local Line Binary Pattern (LLBP) method and Learning Vector Quantization (LVQ). LLBP is is the advanced feature extraction method of Local Binary Pattern (LBP) method that uses a combination of binary values from neighborhood pixels to form features of an image. The straight-line shape of LLBP can extract robust features from the images with unclear veins, it is more suitable to capture the pattern of vein in finger vein image. At the recognition stage, LVQ is used as a classification method to improve recognition accuracy, which has been shown in earlier studies to show better results than other classifier methods. The three main stages in this research are preprocessing, feature extraction using LLBP method and recognition using LVQ. The proposed methodology has been tested on the SDUMLA-HMT finger vein image database from Shandong University. The experiment shows that the proposed methodology can achieve accuracy up to 90%.
 Index Terms—finger vein recognition, Learning Vector Quantization, LLBP, Local Line Binary Pattern, LVQ.
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DAVIGNON, ANDRÉ. "Block classification scheme using binary vector quantization for image coding." International Journal of Electronics 68, no. 5 (1990): 667–73. http://dx.doi.org/10.1080/00207219008921210.

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Hidayat, Erwin Yudi, and Muhammad Farhan Radiffananda. "Pengenalan Tanda Tangan Menggunakan Learning Vector Quantization dan Ekstraksi Fitur Local Binary Pattern." CogITo Smart Journal 5, no. 2 (2019): 123. http://dx.doi.org/10.31154/cogito.v5i2.180.123-136.

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Tanda tangan merupakan salah satu biometrik pada karakteristik perilaku yang digunakan untuk mengenali seseorang sebagai sistem identifikasi. Meskipun unik, banyak terjadi kasus tanda tangan yang disalahgunakan dengan cara dipalsukan. Tidak mudah mengenali tanda tangan yang palsu dengan tanda tangan asli. Penelitian ini menerapkan algoritma Learning Vector Quantization, deteksi tepi Sobel, dan ekstraksi fitur Local Binary Pattern untuk mengidentifikasi tanda tangan. Hasil penelitian menunjukkan, jumlah data citra, iterasi, dan learning rate mempengaruhi akurasi dan waktu proses identifikasi. Dari percobaan yang dilakukan pada parameter yang berbeda-beda, akurasi yang didapat adalah 68% pada data latih dan pada data uji sebesar 54,6%.Kata kunci—identifikasi, Learning Vector Quantization, tanda tangan, pengenalan pola
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Dissertations / Theses on the topic "Binary vector quantization (BVQ)"

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Fan, Kuo-Lun, and 范國倫. "Binary Search & Mean Value Predictive Hybrid Fast Vector Quantization Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/54042830044642569713.

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碩士<br>立德管理學院<br>應用資訊研究所<br>91<br>Vector Quantization (VQ) is an effective technology for signal compression. In traditional VQ, most of the computation concentrates on searching the nearest codeword in the codebook for each input vector. We propose a fast VQ algorithm to reduce encoding time. There are two main parts in our proposed algorithm. One is pre-processing process and the other is practical encoding process. In pre-processing, we will generate some tables that we need to employ to practical encoding. On the second part, that is a practical encoding and we use the tables that generated previously and other techniques are added as well to speed up encoding time. This paper provides an effective algorithm to accelerate the encoding time. The proposed algorithm demonstrates the outstanding performance in terms of the time saving and arithmetic operations. Compared to full search algorithm, it saves more than 95% search time.
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Book chapters on the topic "Binary vector quantization (BVQ)"

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Kwak, Nae Joung, Soung Pil Ryu, Heak Bong Kwon, and Jae Hyeong Ahn. "The Improved Binary Tree Vector Quantization Using Spatial Sensitivity of HVS." In Key Engineering Materials. Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-958-x.254.

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Boutellaa, Elhocine, Farid Harizi, Messaoud Bengherabi, Samy Ait-Aoudia, and Abdenour Hadid. "Face Verification Using Local Binary Patterns and Maximum A Posteriori Vector Quantization Model." In Advances in Visual Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41914-0_53.

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Tsai, Liang-Ting, Chih-Chien Yang, and Timothy Teo. "Weighting Imputation for Categorical Data." In Encyclopedia of Business Analytics and Optimization. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5202-6.ch241.

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This article aims to propose the Learning Vector Quantization (LVQ) approach to impute missing group membership and sampling weights in inferring the accuracy of population parameters of confirmatory factor analysis (CFA) models with categorical questionnaires. Survey data with missing group memberships, for example, gender, age, or ethnicity, are very familiar. However, the group memberships of examinees are critical for calculating the stratum sampling weights. Asparouhov (2005), Tsai and Yang (2008), and Yang and Tsai (2008) have described that appropriate imputation can further improve the precision of CFA model estimations. Questionnaires with categorical responses are not well established yet. In this study, a Monte Carlo simulation was conducted to compare the LVQ method with the other three existing methods (e.g., listwise-deletion, weighting-class adjustment, non-weighted). Four experimental factors, such as missing data rates, sampling sizes, disproportionate sampling, and different populations, were used to examine the performance of these four methods. The results showed that the LVQ method outperformed the other three methods in terms of accuracy of parameters of CFA model with binary or 5-category responses. The conclusion and discussion sections of this article provide for some practical guidelines.
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Conference papers on the topic "Binary vector quantization (BVQ)"

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Long-Jhe Yan and Shaw-Hwa Hwang. "The binary vector quantization." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537296.

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Allred, Rustin W., Richard W. Christiansen, and Douglas M. Chabries. "Adaptive vector quantization for binary images." In International Symposium on Optical Science and Technology, edited by Andrew G. Tescher. SPIE, 2000. http://dx.doi.org/10.1117/12.411536.

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Chang, L., and M. M. Bayoumi. "An economical binary tree structure for vector quantization." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150428.

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Yan, Long-Jhe, Shaw-Hwa Hwang, Shun-Chieh Chang, and Chi-Jung Huang. "Vector quantization based on a binary search-like algorithm." In 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2010. http://dx.doi.org/10.1109/isccsp.2010.5463333.

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Yeh, Chia L. "Delayed-Decision Binary Tree-Searched Vector Quantization For Image Compression." In SPIE 1989 Technical Symposium on Aerospace Sensing. SPIE, 1989. http://dx.doi.org/10.1117/12.960464.

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Park, Jihyun, Junghyun Kim, and Wonyoung Yoo. "TF-IDF based binary fingerprint search with vector quantization error compensation." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354613.

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Gerek, Oemer N., and Enis A. Cetin. "Coding of fingerprint images using binary subband decomposition and vector quantization." In Photonics West '98 Electronic Imaging, edited by Sarah A. Rajala and Majid Rabbani. SPIE, 1998. http://dx.doi.org/10.1117/12.298317.

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San-Segundo, R., R. Córdoba, J. Ferreiros, et al. "Efficient vector quantization using an n-path binary tree search algorithm." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-27.

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Engelsberger, Alexander, and Thomas Villmann. "Quantum-ready vector quantization: Prototype learning as a binary optimization problem." In ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2023. http://dx.doi.org/10.14428/esann/2023.es2023-108.

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A. Hameed, Maha. "Codebook Design Method for Astronomical Images Compression." In IX. International Scientific Congress of Pure, Applied and Technological Sciences. Rimar Academy, 2023. http://dx.doi.org/10.47832/minarcongress9-7.

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The searching in binary codebook of a combination the Block Truncation Coding and Vector Quantization methods BTC and VQ desires a time consuming process because the full codebook search for every input vector toward discovery the best matched codevector or code word, so, in this paper, at first, a small binary codebook has been designed then by applying a new way which has been adopted by rotating each codevector such as the idea of codevector rotation in four direction then classified all these codevectors depending on direction of rotation to involve four codebooks (i.e. I, II, III and IV). In this case, the time of coding procedure is decreased with very small Distortion for each block additionally to that, an efficiency of coding process is improved with increasing in the compression ratio
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Reports on the topic "Binary vector quantization (BVQ)"

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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.
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