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Research on Beamspace Channel Estimation Method Based on DISTA 基于DISTA的波束空间信道估计方法研究

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00089

Juanyi Zheng, Yuanyuan Lv, Jinyu Mu, Lirong Xing, Pei Jie

引用次数: 0

Research on Collaborative Computational Offload Strategy Based on Improved Ant Colony Algorithm in Edge Computing 边缘计算中基于改进蚁群算法的协同计算卸载策略研究

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00093

Haibo Ge, Jiajun Geng, Yu An, Haodong Feng, Ting Zhou, Chaofeng Huang

引用次数: 0

Automatic Gain Control Circuit Design for Wireless RF Receiver 无线射频接收机自动增益控制电路设计

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00076

Jin Wu, Haoran Feng, Xiangyang Shi, He Wen

引用次数: 0

Implementation of Node Classification Algorithm Based on Graph Neural Network 基于图神经网络的节点分类算法实现

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00079

Jin Wu, Wenting Pang, Haoran Feng, Zhaoqi Zhang

引用次数: 0

Design of Memory System for Recursive Neural Network Hardware Accelerator 递归神经网络硬件加速器存储系统设计

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00085

Youyao Liu, Xinxin Liu, Kai Zhou, Qifei Shi

{"title":"Design of Memory System for Recursive Neural Network Hardware Accelerator","authors":"Youyao Liu, Xinxin Liu, Kai Zhou, Qifei Shi","doi":"10.1109/icnlp58431.2023.00085","DOIUrl":"https://doi.org/10.1109/icnlp58431.2023.00085","url":null,"abstract":"With the remarkable effectiveness of recurrent neural network (RNN) in speech recognition, machine translation and other fields, more and more scholars at home and abroad have begun to pay attention to the research of cyclic neural network acceleration. In recent years, due to the increase of the scale of the recurrent neural network, the software can speed up the network through the weight pruning network model compression technology. The acceleration of the cyclic neural network does not only stay in the aspect of software acceleration, but also in the aspect of hardware, the acceleration strategy includes the design of RNN accelerator based on GPU, FPGA and special ASIC circuit. The storage system almost determines the upper limit of the working efficiency of the accelerator. When the input data cannot be provided to the computing unit in time, the computing unit has to enter the idle state frequently, resulting in low working efficiency. Therefore, storage systems with continuous data feeds are very important for accelerators. This paper proposes a mapping mechanism of MVM operations on hardware operation units, and proposes a storage system with continuous data feeds.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80590737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Collision-Reducible Adaptive Data Rate Algorithm for Low-cost LoRa Gateways 一种低成本LoRa网关的可碰撞自适应数据速率算法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00087

Honggang Wang, Peidong Pei, Ruoyu Pan, Lihua Jie, Ruixue Yu, Kai Wu

{"title":"A Collision-Reducible Adaptive Data Rate Algorithm for Low-cost LoRa Gateways","authors":"Honggang Wang, Peidong Pei, Ruoyu Pan, Lihua Jie, Ruixue Yu, Kai Wu","doi":"10.1109/ICNLP58431.2023.00087","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00087","url":null,"abstract":"LoRa (Long Range), a wireless communication technology designed for Low Power Wide Area Networks (LPWAN), facilitates diverse IoT applications and inter-device communication by virtue of its openness and adaptable network deployment. However, the conventional static link transmission scheme employed in practical LoRa network deployment fails to fully exploit the available channel resources in dynamic channel environments, resulting in suboptimal network performance. To address this issue, this paper proposes a more efficient Adaptive Data Rate (ADR) algorithm tailored for low-cost gateways. This algorithm incorporates fuzzy support vector machine (FSVM) to accurately classify link quality and employs distinct link adaptation algorithms based on varying link qualities. Notably, the algorithm considers both link-level performance and MAC layer performance. Experimental measurements demonstrate that our proposed algorithm surpasses the standard LoRaWAN ADR algorithm in terms of packet reception rate (PRR) and network throughput in both single end device (ED) and multi EDs scenarios. Specifically, in multi-EDs scenarios, the proposed algorithm yields a remarkable 34.12% improvement in throughput and a significant 26% enhancement in packet reception rate compared to the LoRaWAN ADR algorithm. These findings demonstrate the substantial enhancements achieved by the proposed algorithm in terms of network throughput and packet reception rate.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74936604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Apple Leaf Disease Recognition Based on Improved Convolutional Neural Network with an Attention Mechanism 基于改进卷积神经网络的苹果叶片病害识别

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00024

Guangyuan Zhao, Xu Huang

{"title":"Apple Leaf Disease Recognition Based on Improved Convolutional Neural Network with an Attention Mechanism","authors":"Guangyuan Zhao, Xu Huang","doi":"10.1109/ICNLP58431.2023.00024","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00024","url":null,"abstract":"Traditional plant disease recognition algorithms have a complicated approach, difficult feature extraction, and low recognition accuracy. Based on the improved EfficientNetV2 model, this research classifies images of apple leaf disease. This study collected images of seven common apple leaf disease categories and one healthy category to address the present needs of various complex disease recognition scenarios. The disease images not only contain the common laboratory background but also add the background of the field growth environment of apple trees. And different recognition scenarios are further enriched by image enhancement techniques. For the model part, the processing of spatial feature information was strengthened while focusing on the channel feature information to ensure that the model focuses more on the subtle disease spot information for different disease classifications. The experimental results show that the accuracy of the model training recognition is 97.49%. To better evaluate this study, comparison experiments were conducted with five other popular convolutional neural network classification models, such as ResNet-50, DenseNet-121, Xception, MobileNet, and EfficientNet-B3. The improved models enhance the recognition accuracy of complex scenes and improve the model parameters and training speed. It provides a reference for apple leaf disease recognition and the development needs of smart agriculture.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85121251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Accurate Recognition of Kiwifruit Based on Improved YOLOv5 基于改进YOLOv5的猕猴桃准确识别

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00025

Sun Wei, Sun Yi Jun, Li Zhao Chen, Guo Jing

引用次数: 0

An Effective Algorithm for Direction-of-Arrival Estimation of Coherent Signals with ULA 一种有效的ULA相干信号到达方向估计算法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00031

Ziyu Mao, Bo Li, Lei Dong, Yani Qiao, Hao Sun, Yuji Li

{"title":"An Effective Algorithm for Direction-of-Arrival Estimation of Coherent Signals with ULA","authors":"Ziyu Mao, Bo Li, Lei Dong, Yani Qiao, Hao Sun, Yuji Li","doi":"10.1109/ICNLP58431.2023.00031","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00031","url":null,"abstract":"In the field of array signal processing, multiple signal classification (MUSIC) algorithm is a classical spectrum estimation algorithm. However, when there are coherent signals, the rank of signal covariance matrix is generally less than the number of signals, which makes the estimation inaccurate. Taking uniform linear array (ULA) as an example, this paper presents a high-precision DOA estimation algorithm by reconstructing noise subspace. This algorithm uses not only the auto-covariance but also cross-covariance information and constructs a new augmented matrix with the auto-covariance matrix. Noise subspace and eigenvalue matrix can be obtained by singular value decomposition of matrix. For more reliable data, on the basis of a large number of experiments, a noise subspace consisting of the eigenvectors corresponding to the new eigenvalue matrix is reconstructed, and finally the DOA estimation is obtained through spectrum peak search. It is shown by the simulation results show that the improved algorithm can maintain the accuracy well of DOA with effect even under the conditions of low signal-to-noise ratio and small number of snapshots.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88868554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What are You Posing: A gesture description dataset based on coarse-grained semantics 你在摆什么姿势:基于粗粒度语义的手势描述数据集

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00044

Luchun Chen, Guorun Wang, Yaoru Sun, Rui Pang, Chengzhi Zhang

引用次数: 0