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Improved Simulated Annealing Genetic Algorithm based Low power mapping for 3D NoC 2

Improved Simulated Annealing Genetic Algorithm based Low pow...

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2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)

作者： Hanna He Fang Fang Wei Wang Hefei University of Technology School of Computer Science and Information Engineering Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine

Mapping of IP(Intellectual Property) cores onto NoC(Network-on-Chip) architectures is a key step in NoCbased designs. Energy is the key parameter to measure the designs. Therefore, we propose an Improved Simulated Annealing Genetic Alogrithm, abbreviated as ISAGA. The algorithm combines the parallelism of Genetic Algorithm(GA) and the local search ability of Simulated Annealing(SA). We improve the initial population selection of GA to get the lower power consumption mapping scheme. The experimental results show that compared with the GA, ISAGA has good convergence and can search the optimal solution quickly, which can effectively reduce the power consumption of the system. In the case of 124 IP cores, the average power consumption of the ISAGA is reduced by 32.0% compared with the GA.

关键词： NTD Improved Simulated Annealing Genetic Algorithm based Low power mapping for 3D NoC

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Design and realization of 3D NOC multicast router base on multicast rotational routing arithmetic 5

Design and realization of 3D NOC multicast router base on mu...

引用

5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Wang, Wei Li, Runfeng Fang, Fang Chen, Tian Ren, Fuji Liu, Jun Wu, Xi Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine 230009 China

ISBN: (纸本)9781479926961

Traditional 3D NoC router may encounter the situations of congestion and hot spot. And it also doesn't have the function of multicast. In this paper, we present a new routing arithmetic called Multicast Rotational Routing Arithmetic (MRRA). It can handle several destination addresses at the same time, recombine packets, sent the same data to the different destination nodes, and avoid the congestion. The proposed algorithm is implemented by the new multicast 3D NoC router which is also proposed in this paper. The 3D NoC router is designed in Verilog HDL, simulated by Modelsim, synthesized and prototyped by Synplify and FPGA respectively. The experiment results show that the multicast router of MRRA can achieve a very good performance in terms of throughput and delay. The increased area utilization also can be tolerated. © 2014 IEEE.

关键词： Network-on-chip

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SEMG-based gesture recognition by rotation forest-based extreme learning machine

SEMG-based gesture recognition by rotation forest-based extr...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Peng, Fulai Chen, Cai Zhang, Xikun Wang, Xingwei Wang, Changpeng Wang, Lin Chinese Academy of Sciences Shandong Institute of Advanced Technology Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

The motion information contained in surface electromyography (sEMG) signals contributes significantly to the prosthetic hand control. However, the accuracy and speed of gesture recognition from sEMG signals are still insufficient for natural control. In order to alleviate this problem, this paper propose a rotation forest-based extreme learning machine method (RoF-ELM) to improve the recognition performance based on sEMG signals. Firstly, the active motion segments were picked out and pre-processed by sliding window. Then, 104 features were extracted from each sample, and SVM-RFE method was used to reduce the feature space dimension. Finally, the RoF-ELM classification model was constructed and tested based on the EMG data for gestures Data Set containing a total of 72 recordings, each of which consists of six basic gestures. 100 trials were implemented to validate the performance of the proposed method, the results show that the RoF-ELM method have the highest accuracy (91.11%) across different subjects with relatively short runtime compared with decision tree (DT), ELM, random forest (RF), and RoF methods. © 2021 IEEE.

关键词： Decision trees

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A Novel Spatial-Temporal Convolutional Neural Network for Remote Photoplethysmography 12

A Novel Spatial-Temporal Convolutional Neural Network for Re...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Hu, Min Guo, Dong Wang, Xiaohua Ge, Peng Chu, Qian School of Computer and Information of Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China

ISBN: (纸本)9781728148526

Remote photoplethysmography (rPPG) using a simple consumer-level webcam has great potential for healthcare, human-computer interaction, affective computing, and many other fields. However, traditional methods based on the skin reflection model and signal processing achieved limited success. Thus, this paper presents a novel end-to-end spatial-temporal convolutional neural network for the rPPG recovering task: To preserve the details, the pooling layer is moved to the branch network, and the attention mechanism is used to enhance the backbone network performance. Moreover, the ConvLSTM was used to learn the relationship between frames. The manual features extraction and signal post-processing steps can be omitted through our end-to-end neural network. The experimental results conducted on PURE and COHFACE datasets show that the proposed method significantly improved the quality of the recovered rPPG signal. © 2019 IEEE.

关键词： Photoplethysmography

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Salient Foreground-Aware Network for Person Search 16th

Salient Foreground-Aware Network for Person Search

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Chen, Hongxu Zhang, Quan Lai, Jianhuang The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou510006 China

ISBN: (纸本)9783031202322

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification. In existing methods, the extracted features come from the low-quality proposals generated by the structure like Region Proposal Network (RPN), and convolution is used to learn local features in the process of extracting features while the receptive field cannot grasp the global structural information. We propose an end-to-end network embedded with our Salient Foreground-Aware Module (SFAM). Self-attention mechanism in SFAM allows for better capture of global information. Our network incorporates our embedding method for person detection and person re-identification, which can effectively optimize the process of extracting features and improve feature expression capabilities. We merge the above modules into our Salient Foreground-Aware Network (SFAN). Extensive experiments have shown that our SFAN significantly improves the performance of end-to-end models with acceptable time-consuming and achieves state-of-the-art results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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Lidar Mapping Optimization Based on Lightweight Semantic Segmentation

Lidar Mapping Optimization Based on Lightweight Semantic Seg...

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作者： Zhao, Zhihao Zhang, Wenquan Gu, Jianfeng Yang, Junjie Huang, Kai Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou510245 China UISEE Technology Inc. ShenZhen518000 China

Lidar Simultaneous Localization and Mapping (LiDAR-SLAM) algorithm with semantic information is an open research question and it is such a time consuming task. The related work that focus on real-time LiDAR-SLAM has poor accuracy. To solve these problems, a lightweight semantic segmentation network to assist in localization and mapping is proposed in this paper. The method uses the lidar point clouds generated by the simulator and annotated manually in real world as the original input. Then, the semantic cloud is segmented by the semantic segmentation network to obtain the semantic information. Finally, the semantic information obtained by the segmentation is used to assist the localization and mapping. © 2016 IEEE.

关键词： Optical radar

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Joint Feature Fusion Approach to Human Pose Estimation Using mmWave Radar 5th

Joint Feature Fusion Approach to Human Pose Estimation Usin...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Zhang, Jianxiong Cao, Zhongping Ding, Wen Chen, Rihui Guo, Xuemei Wang, Guoli School of Artificial Intelligence Sun-Yat-Sen University Zhuhai519000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China School of Computer Science and Engineering Sun-Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9789811989148

This paper mainly studies the use of millimeter wave (mmWave) radar for 3D human pose estimation. Although pioneering works have achieved remarkable success, the lack of stability of continuous data acquisition by mmWave radar is still an intractable problem in pose capturing. To mitigate this problem, a joint feature fusion method is proposed, which is based on the assumption that human pose in a motion sequence is domained by bone connection and specific motion cooperation mechanism. Specifically, we introduce a forget gate to suppress error features extracted due to the loss of information, and a select gate to select the most reasonable joint characteristics. Outputs of the two gates are fused to form an anti distortion mechanism of human pose estimation. Experiments show the effectiveness of our method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data acquisition

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Task-Adaptive Generative Adversarial Network Based Speech Dereverberation for Robust Speech Recognition 1

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18th National Conference on Man-machine Speech Communication, NCMMSC 2023

作者： Liu, Ji Li, Nan Ge, Meng Fu, Yanjie Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Nomi Japan

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

Reverberation is known to severely affect speech recognition performance when speech is recorded in an enclosed space. Deep learning-based speech dereverberation has been remarkably successful in recent years, achieving superior recognition performance for far-field speech applications. However, the output from conventional dereverberation systems cannot be guaranteed suitable for back-end recognition systems because of their different task goals. To bridge the gap between the front-end dereverberation and the back-end recognition, we propose a novel task-adaptive speech dereverberation generative adversarial network (GAN) based speech dereverberation model called Task-adaptive GAN. Specifically, we propose to replace the binary-valued discriminator in a regular generative adversarial network with a novel senone-predicted discriminator, and also introduce a well-designed recognition-aware generator as a dereverberation system. By doing so, the corresponding output distribution will be more suitable for the recognition task. Experimental results on the REVERB corpus show that our proposed approach achieves a relative 18.6% and 8.6% word error rate reduction than the traditional GAN-based baseline system on the simulated set and real set, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Improvement and optimization of consensus algorithm based on PBFT 4

Improvement and optimization of consensus algorithm based on...

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4th International Conference on Communications, Information System and Computer Engineering, CISCE 2022

作者： Zhao, Liang Li, Bin Zhou, Qinglei Chen, Xiaojie Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

This paper proposes an improved consensus algorithm based on PBFT(EBCR-PBFT). Firstly, The Modified Random Select(MRS) function is used to perform preliminary screening of network nodes, so as to solve the problem of multiple and redundant network nodes. Secondly, based on the idea of clustering, logical stratification of blockchain network nodes can reduce communication times and improve network efficiency. Thirdly, the core module of MRS is optimized by FPGA to further improve the efficiency of the algorithm. At the same time, the improved dynamic reputation evaluation model is introduced to evaluate and feedback the node behavior in time to ensure the smooth operation of the system. Experiments show that the proposed and optimized consensus algorithm can effectively improve the consensus efficiency, timely punish malicious nodes, increase the reliability of nodes, communication overhead, throughput, delay and other high performance, has a certain application value. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Semantic polarity detection of Chinese multiword expression in microblogging based on discriminative latent model

Semantic polarity detection of Chinese multiword expression ...

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作者： Sun, Xiao Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China

Extracting semantic polarity of Chinese Multiword Expression, especially some newly generated Multiword Expression from internet(such as weibo or microblog), is an important task for sentiment analysis of web texts or other real word text as some Multiword Expressions can express more integrative sentiments than words units. This paper proposes a method that contains a novel latent discriminative algorithm, which attempts to attack this problem by integrating discriminative model and latent value model. Although Chinese Multiword Expressions consist of multiple words, the semantic polarity of the Multiword Expression is not just simple integration of polarities of the component words, as some words can invert the affective polarity so the Multiword Expressions can have totally opposite semantic polarity, such as ironic texts. In order to capture the property of such Multiword Expressions, hidden semi-CRF which includes a latent valuable layer, which can be used to address dual-sequence labeling tasks synchronously, is adopted. The method is tested experimentally by adopting a manually labeled set of positive and negative Multiword Expressions from microblog or other internet resources, and the experiments have shown very promising results, which is comparable to the best value ever reported. © 2014-IOS Press and the authors. All rights reserved.

关键词： Sentiment analysis

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