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2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献

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Deep Learning Models for Cancer Classification from Microarray Gene Expression Profiles 基于微阵列基因表达谱的癌症分类深度学习模型
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201310
Aiguo Wang, Qi Hu
{"title":"Deep Learning Models for Cancer Classification from Microarray Gene Expression Profiles","authors":"Aiguo Wang, Qi Hu","doi":"10.1109/CCAI57533.2023.10201310","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201310","url":null,"abstract":"Gene expression profiles measured by microarray technology enables accurate identification of disease genes, prediction of cancers, and distinguishing tumor subtypes at the molecular level. However, these profiles are characterized by a small sample size and high dimensionality, which would inevitably degrade the performance of analysis models. In this study, we proposed a deep learning-based model to improve the prediction accuracy. Specifically, we first use the minimum redundancy maximum relevancy feature selector to discard irrelevant and noisy features. Then, we utilize a deep autoencoder to learn complex and nonlinear relationships among data. Finally, a predictor is trained on the latent representation to classify cancer. We conduct extensive experiments on four publicly available microarray datasets and compare the proposed model with six commonly used feature selectors using naïve bayes and decision tree in terms of accuracy and F1. Results demonstrate the superiority of the proposed model over its competitors.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"10 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120997965","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
Action Recognition System Based 5G Communication and Edge Computing for Service Robot 基于5G通信和边缘计算的服务机器人动作识别系统
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201242
Yanming Zhang, Cailun Wei, Gangcan Sun, Dawei Zhang
{"title":"Action Recognition System Based 5G Communication and Edge Computing for Service Robot","authors":"Yanming Zhang, Cailun Wei, Gangcan Sun, Dawei Zhang","doi":"10.1109/CCAI57533.2023.10201242","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201242","url":null,"abstract":"Action recognition is one of the most critical technologies for the robot to provide service excellently. In order to solve the problems of low accuracy, limited arithmetic power, and low intelligence of service robot's action recognition, an action recognition system based on 5G communication and edge computing is proposed. Communication and data interaction between robot and edge computing device is realized by using 5G routers and technologies such as NAT and Socket. A LightSlowfast network is designed for the service robot, which improves action recognition accuracy while reducing the computational effort. The experimental results show that the Light-Slowfast network achieves 72.9% accuracy on the HMDB51 dataset. The system performs well in practical tests, and the low latency of data transmission can satisfy the robot's demand for real-time action recognition.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131947410","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
Single-Photon Cameras Image Reconstruction Using Vision Transformer 基于视觉变换的单光子相机图像重建
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201259
Xingzheng Wang
{"title":"Single-Photon Cameras Image Reconstruction Using Vision Transformer","authors":"Xingzheng Wang","doi":"10.1109/CCAI57533.2023.10201259","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201259","url":null,"abstract":"Single-photon camera is a novel camera type that utilizes image sensor with photon-counting capability. Recently, the potential of such sensors to achieve high spatial resolutions (e.g., 10^9pixels/chip) and frame rates (e.g., 10^6frames/sec) is well-established. However, there is a significant difference in the output data between single-photon cameras and traditional CMOS sensor cameras. Therefore, conventional image reconstruction algorithms cannot be utilized. Vison Transformer has impressive performance on image processing tasks, and this paper will design a plug-and-play algorithm for reconstructing images from single-photon cameras and integrate image processing algorithms based on deep neural networks like ViT. The results demonstrate our methods can achieve improvement both in PSNR and SSIM.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127051915","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
Copyright Page 版权页
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/ccai57533.2023.10201282
{"title":"Copyright Page","authors":"","doi":"10.1109/ccai57533.2023.10201282","DOIUrl":"https://doi.org/10.1109/ccai57533.2023.10201282","url":null,"abstract":"","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123266746","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
Design and Practice of Digital Intelligent Operation and Maintenance Capabilities for Telecom Operators’ Backbone Networks 电信运营商骨干网数字化智能运维能力的设计与实践
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201288
Qiao Qiao
{"title":"Design and Practice of Digital Intelligent Operation and Maintenance Capabilities for Telecom Operators’ Backbone Networks","authors":"Qiao Qiao","doi":"10.1109/CCAI57533.2023.10201288","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201288","url":null,"abstract":"With the growing scale of operators’ backbone network and the continuous enrichment of carrying services, the business traffic is growing in a massive manner, and the complexity and workload of network traffic operation and maintenance are increasing. This paper aims to support “traffic management’’ and achieve the intelligent transformation and upgrading of operators’ backbone network operation dimension. It studies the automatic and intelligent means of backbone network traffic optimization, innovatively and comprehensively applies real-time route monitoring, network simulation modeling, multi factor combination analysis, network intelligent scheduling and other capabilities to achieve the whole closed-loop traffic automatic optimization scheme. Through practical application in the current network of operators, the traffic optimization cycle has been shortened from manual hourly and daily levels to minute levels, improving operation and maintenance efficiency and user experience, achieving good results.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129461647","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
External-Attentive Statistics Pooling for Text-Independent Speaker Verification 文本独立说话人验证的外部关注统计池
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201326
Lidong Pan, Chunhao He, Tieyuan Chang
{"title":"External-Attentive Statistics Pooling for Text-Independent Speaker Verification","authors":"Lidong Pan, Chunhao He, Tieyuan Chang","doi":"10.1109/CCAI57533.2023.10201326","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201326","url":null,"abstract":"Speaker verification is an important biometric identification technique. In the neural network-based speaker feature extraction model, the pooling layer plays an important role. This layer aggregates frame-level features to obtain utterance-level features, and different pooling methods have different effects on the aggregation of frame-level features, which in turn affects the characterization ability of the final speaker features. In the existing work, some pooling methods with attention mechanisms have shown stronger feature aggregation capability than traditional pooling methods. In this paper, we combine a low-complexity External Attention with statistics pooling to design External-Attentive Statistics Pooling and propose Multi-Group External-Attentive Statistics Pooling considering the biological properties of human hearing. The two methods are used in text-independent speaker verification and tested on the VoxCeleb1 test set, VoxCeleb1-H, and VoxCeleb1-E. The test results show that the proposed method achieves more effective feature aggregation without significantly increasing the number of model parameters.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126971319","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 MMW Radar Indoor Mapping Method Based on Transfer Learning 一种基于迁移学习的毫米波雷达室内制图方法
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201250
Peiyan Tu, Tao He, Zhikai Yang, Zhanyu Zhu
{"title":"A MMW Radar Indoor Mapping Method Based on Transfer Learning","authors":"Peiyan Tu, Tao He, Zhikai Yang, Zhanyu Zhu","doi":"10.1109/CCAI57533.2023.10201250","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201250","url":null,"abstract":"A millimeter-wave radar indoor mapping method based on Transfer Learning to generate dense detection data as Lidar’s output is proposed in this paper. This method uses the NN model with CycleCAN architecture to learn the Lidar-like map pieces, to enhance the mmw radar mapping performance. With the ideal of Transfer Learning, the model is trained using simulated data generated by CARLA and deployed into physical system to improve the mmw radar mapping performance. Simulation and practice measurement experiments are carried out to prove the coefficients of this method, and the quantitative analysis is conducted to evaluate the mapping quality.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122278097","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
Design of over Temperature Protection Circuit Based on Hysteresis Comparison with High Precision 基于磁滞比较的高精度过温保护电路设计
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201314
Rui Jiang, Yahong Ma, Xiaojiao Fan, Rongrong Wang, Zhentao Huang, Weisu Li, Liu Yang, Yongsheng Dai
{"title":"Design of over Temperature Protection Circuit Based on Hysteresis Comparison with High Precision","authors":"Rui Jiang, Yahong Ma, Xiaojiao Fan, Rongrong Wang, Zhentao Huang, Weisu Li, Liu Yang, Yongsheng Dai","doi":"10.1109/CCAI57533.2023.10201314","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201314","url":null,"abstract":"In this paper, a high-precision over-temperature protection (OTP) circuit based on 55nm CMOS technology is proposed. By introducing external feedback, enable level with logic signal is generated to realize hysteresis of temperature signal. The bandgap reference circuit can work normally under a wide range of power supply voltage and temperature to ensure the stable working state and output of the over temperature protection circuit. The simulation results show that when the temperature exceeds 115°C, the chip stops working, and when the temperature drops to 88°C, the chip works normally, with a hysteresis interval of 27°C. Under different process angle conditions, the temperature coefficient can be kept in a small range, and the output conversion rate of the circuit is 24.99V/°C, which has the characteristics of high precision and high sensitivity. The circuit can suppress the drift of the threshold point caused by the process change, has high precision, and ensures the stability of the circuit performance.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132832577","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
LMSPNet: Improved Lightweight Network for Multi-Person Sitting Posture Recognition LMSPNet:用于多人坐姿识别的改进轻量级网络
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201258
Shuyang Jiao, Yubin Xiao, Xuan Wu, Yanchun Liang, Yi Liang, You Zhou
{"title":"LMSPNet: Improved Lightweight Network for Multi-Person Sitting Posture Recognition","authors":"Shuyang Jiao, Yubin Xiao, Xuan Wu, Yanchun Liang, Yi Liang, You Zhou","doi":"10.1109/CCAI57533.2023.10201258","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201258","url":null,"abstract":"Incorrect sitting posture may lead to health problems. Therefore, effective sitting posture recognition can remind individuals to maintain correct sitting posture and reduce discomfort. Traditional methods for sitting posture recognition have limitations in terms of high cost and slow inference speed. To address these issues, we propose a novel model called LMSPNet for multi-person sitting posture recognition. This model first employs the Light Convolution Core (LCC) to reduce the complexity of the model and then introduces Convolutional Block Attention Module (CBAM) to adaptively adjust the receptive field in the neural network to capture global contextual information, thereby enabling the model to better learn relationships between different channels. We construct the first human sitting posture dataset to evaluate the performance of LMSPNet. Experimental results demonstrate that, compared to the baseline models, our LMSPNet achieves state-of-the-art results with an accuracy of 99.57%. Therefore, our model is expected to become a powerful tool for multi-person sitting posture recognition.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115142968","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
Research on the Architecture and Implementation of In-Memory Computing 内存计算的体系结构与实现研究
2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201268
Rongkai Liu, Xiancheng Lin, Xiang Gao
{"title":"Research on the Architecture and Implementation of In-Memory Computing","authors":"Rongkai Liu, Xiancheng Lin, Xiang Gao","doi":"10.1109/CCAI57533.2023.10201268","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201268","url":null,"abstract":"The rapid growth of artificial intelligence and the bottleneck of computing force development have promoted the emergence of new computing architectures. In-memory computing, as a structure that breaks the traditional separation of memory and computing, is considered to be a method to break the bottleneck of computing force. This paper presents two typical parts of architecture design for in-memory computing, IM-A and IM-P. And it expounds the implementations of in-memory computing in various storage media. The challenges of in-memory computing in manufacture, design, and application are summarized. The development trend of in-memory computing is mainly to realize artificial intelligence algorithms.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131141538","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
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