2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)最新文献_第6页

A Roughness Grading Method for Skin Surface Microstructure Using Deep Learning for the Assessment of Atopic Dermatitis 一种基于深度学习的皮肤表面微观结构粗糙度分级方法用于特应性皮炎的评估

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212652

Tatsuki Ohta, Yuma Miyaji, Tetsushi Koide, Kenta Nakamoto, Y. Hayashida, Y. Aoyama

引用次数: 0

A MR-Based Self-Learning System of Basic Cutting for Vegetables 基于mr的蔬菜基本切块自学系统

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212891

Miku Kato, M. Makino

引用次数: 0

A State-Space Approach for Adaptive Notch Digital Filters with Unbiased Parameter-Estimation 一种无偏参数估计自适应陷波数字滤波器的状态空间方法

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212531

Y. Hinamoto, S. Nishimura

引用次数: 0

An Efficient Neural Network Design for Image Super-Resolution with Knowledge Distillation 基于知识蒸馏的图像超分辨率神经网络设计

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212926

Tuan Nghia Nguyen, X. Nguyen, Kyujoong Lee, Hyuk-Jae Lee

{"title":"An Efficient Neural Network Design for Image Super-Resolution with Knowledge Distillation","authors":"Tuan Nghia Nguyen, X. Nguyen, Kyujoong Lee, Hyuk-Jae Lee","doi":"10.1109/ITC-CSCC58803.2023.10212926","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212926","url":null,"abstract":"This paper proposes a new neural network design for efficient image super-resolution inference. Employing complex-simple sub-networks, the proposed design samples feature to dynamically choose an execution path, leading to considerable computation reduction. However, uniformly random sampling generally causes a large accuracy drop due to highly different feature maps obtained by the sub-networks. To address the problem, we propose two simple yet effective techniques considering both the training and inference stages. First, Knowledge Distillation is utilized during training to minimize the feature map difference. Second, a gradient image which is obtained with the well-known Sobel filter guides the sampling by assigning points on edge and texture regions to the complex sub-network. The experimental results show that the proposed design reduces 50% of computations when only 20% of feature maps are computed by the complex sub-network. More importantly, the proposed sampling method enhances the restoration accuracy by 0.3 dB on average compared to the uniformly random sampling method.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117321719","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

Proposal for Dry Eye Detection Caused by Contact Lenses Using a Smartphone with a Ring Light and Deep Learning Technology 使用环光智能手机和深度学习技术检测隐形眼镜引起的干眼症的建议

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212584

Kaito Okazaki, M. Hasegawa

引用次数: 0

A Memory Based Concurrence Detector for SPAD ToF Image Sensors 基于内存的SPAD ToF图像传感器并发检测器

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212957

Jongha Park, Seong-ook Jung

引用次数: 0

CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy Pattern CAESAR:利用稀疏性和冗余模式的CNN加速器

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212679

Seongwoo Kim, Yongjun Kim, Gwang-Jun Byeon, Seokin Hong

{"title":"CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy Pattern","authors":"Seongwoo Kim, Yongjun Kim, Gwang-Jun Byeon, Seokin Hong","doi":"10.1109/ITC-CSCC58803.2023.10212679","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212679","url":null,"abstract":"Convolutional Neural Networks (CNN) have shown outstanding performance in many computer vision applications. However, CNN Inference on mobile and edge devices is challenging due to high computation demands. Recently, many prior studies have tried to address this challenge by reducing the data precision with quantization techniques, leading to abundant redundancy in the CNN models. This paper proposes CAESAR, a CNN accelerator that eliminates redundant computations to reduce the computation demands of CNN inference. By analyzing the computation pattern of the convolution layer, CAESAR predicts the location where the redundant computations occur and removes them in the executions. After that, CAESAR remaps the remaining effectual computations on the processing elements originally mapped to the redundant computations so that all processing elements are fully utilized. Based on our evaluation with a cycle-level microarchitecture simulator, CAESAR achieves an overall speedup of up to 2.13x and saves energy by 78% over the TPU-like baseline accelerator.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127205139","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

Virtual Memory Support for PIM with Table-Based Management 基于表管理的PIM虚拟内存支持

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212961

Seung Jae Yong, Eui-Young Chung

引用次数: 0

A VR-Based Squat Self-Study System with Superimposing Motion on Model and Post-Checking Viewing from All Angles 基于vr模型运动叠加、多角度后检的深蹲自学系统

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212491

Masahiro Watatani, M. Makino

引用次数: 0

Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders 心理健康的机器学习:检测精神障碍的七种方法的系统研究

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212609

Muhammad Nadeem, Junaid Rashid, Hyeonjoon Moon, Arailym Dosset

{"title":"Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders","authors":"Muhammad Nadeem, Junaid Rashid, Hyeonjoon Moon, Arailym Dosset","doi":"10.1109/ITC-CSCC58803.2023.10212609","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212609","url":null,"abstract":"Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124362708","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