Design and implementation for partition dynamically vector quantization chip by YU Ning-mei, WANG Dong-fang, LIAO Yu-min, FU Yun-qiang

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Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering by Thommas K. S. Flores, Morsinaldo Medeiros, Marianne Silva, Daniel G. Costa, Ivanovitch Silva

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Technologies of network audio retrieval by ZHANG Wei-qiang, LIU Jia

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Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling by Yeonjin Park, Jungpyo Hong

“…To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. …”
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Lossless image authentication algorithm based on adaptive combinations of image basic blocks by Jian-gao LUO, Guo-qiang HAN, Yan WO, Xiang-yang LI

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Online multi‐object tracking based on time and frequency domain features by Mahbubeh Nazarloo, Meisam Yadollahzadeh‐Tabari, Homayun Motameni

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Latent space improved masked reconstruction model for human skeleton-based action recognition by Enqing Chen, Xueting Wang, Xin Guo, Ying Zhu, Dong Li

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Low-complexity optimal MPSK detection for spatial modulation by Hong-zhi MEN, Ming-lu JIN

“…The low-complexity optimal SM detection algorithm for MQAM signal detection had been proposed,but no similar method was found for MPSK signal.The problem of low-complexity detection for MPSK signal was considered in SM systems.Utilizing 2-D vector quantization of ML demodulation and the property of MPSK constellation,a low-complexity optimal detection which is independent to modulation order was developed.Since the approach avoids the exhaustive searching on signal constellation space,the computational complexity can be significantly reduced.The proposed detection algorithm can provide the identical performance with ML-optimum detector and has lower computational complexity.Therefore,it has both theoretical and practical significance.The proposed algorithm is of great significance in the large antenna and green communication technology.…”
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Hybrid precoding method for mmWave MIMO systems based on limited feedback by Ruonan YOU, Peng PAN, Dan ZHANG, Haiquan WANG

“…A hybrid precoding algorithm for millimeter wave (mmWave) MIMO systems without knowledge of channel state information at the transmitter was proposed based on the limited feedback.Specifically, the joint optimization problem of analog precoding and digital precoding was firstly separated into two independent optimization problems, which correspond to the analog precoding and digital precoding respectively.Based on this analog precoding codebook, the best precoding matrix was selected by analog precoding in the designed analog precoding codebook.Secondly, according to the obtained analog precoding matrix, the digital precoding matrix was obtained through the least square method, and then, the codeword closest to it in the random vector quantization codebook was selected as the feedback digital precoding matrix.Finally, the receiver feeds the index values of the analog and digital precoding matrices back to the transmitter.Simulation results show that the proposed algorithm can achieve better balance between complexity and performance.…”
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Implementation of a low‐power LVQ architecture on FPGA by Najoua Chalbi, Mohamed Boubaker, Mohamed Hedi Bedoui

“…This study presents an architecture‐optimising methodology for embedding an learning vector quantization (LVQ) neural network on an field programmable gate array (FPGA) device. …”
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An overview of the CSI feedback based on deep learning for massive MIMO systems by Muhan CHEN, Jiajia GUO, Xiao LI, Shi JIN

“…The massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information (CSI).Due to the significant increase in the number of antennas,the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently,deep learning (DL) has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly,the background and significance of the CSI feedback were expounded.Then,a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally,a further prospect on the development trend of the CSI feedback based on DL was made.…”
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Recognizing Cursive Typewritten Text Using Segmentation-Free System by Mohammad S. Khorsheed

“…The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. …”
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Feedback bit allocation in distributed antenna array systems by Shuai ZHANG, Peng PAN, Cui WANG

“…In a frequency division duplex communication system,precoding at the base station usually requires terminal feedback channel state information.In a distributed array system,the channel conditions between the terminal and different access nodes in the cell are different because multiple access nodes are arranged at different locations in the cell.When the resources for feedback channel state information are limited,the allocation method of feedback bit needs to be optimized to improve the overall performance of the system.In a multi-user distributed array system,an access node selection method based on distance threshold was adopted.Based on this method,the quantization characteristics of the random vector quantization codebook and the Taylor expansion method were used to derive the system quantization capacity loss,and then an approximate expression was given.Based on this expression,a feedback bit allocation method was proposed.Compared with other allocation methods,the method of this paper was more universal because it did not limit the number of access nodes selected by the user.The simulation results show that the strategy proposed is superior to the traditional equal-bit allocation scheme and can obtain better performance when the feedback resources are limited.…”
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Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm by Changhong Zhu, Zhenjun Guo, Jie Ke

“…This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. …”
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Petri Net Model for Serious Games Based on Motivation Behavior Classification by Moh. Aries Syufagi, Mochamad Hariadi, Mauridhi Hery Purnomo

“…This modeling employs Learning Vector Quantization (LVQ) for optimizing the motivation behavior input classification of the player. …”
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Advanced in Islanding Detection and Fault Classification for Grid-Connected Distributed Generation using Deep Learning Neural Network by Rusvaira Qatrunnada, Novizon Novizon, Mardini Hasanah, Tuti Angraini, Anton Anton

“…The use of an Artificial Neural Network (ANN) based on the learning vector quantization (LVQ) technique is proposed in this paper for fault classification and islanding detection in grid-connected distributed generators. …”
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Dimensionality cutback and deep learning algorithms efficacy as to the breast cancer diagnostic dataset by Gennady Chuiko, Denys Honcharov

“…Two clustering algorithms that use neurons (Self Organized Map, SOM, and Learning Vector Quantization, LVQ) have also shown similar results. …”
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