2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)最新文献_第2页

Multi Modal Deep Learning Based on Feature Attention for Prediction of Blood Clot Elasticity 基于特征关注的多模态深度学习预测血凝块弹性

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212605

Jiseon Moon, Sang-il Ahn, M. Joo, K. Park, H. Baac, Jitae Shin

{"title":"Multi Modal Deep Learning Based on Feature Attention for Prediction of Blood Clot Elasticity","authors":"Jiseon Moon, Sang-il Ahn, M. Joo, K. Park, H. Baac, Jitae Shin","doi":"10.1109/ITC-CSCC58803.2023.10212605","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212605","url":null,"abstract":"Blood clot is formed inside a blood vessel with various reasons. Carotid artery is major blood vessel in the neck that supply blood to the brain. If blood clot is harden in carotid artery, blood clot of carotid artery can block the blood vessel and make narrowed blood vessel. Therefore, it is essential to predict the coagulation of blood clot in blood vessels. In this paper, we propose the method to determine the coagulation progress of blood clot. We use different two data which are waveform of blood clot and frequency spectra data obtained by applying the Fourier transform to the waveform data. And then feature vectors are extracted from two different data. We apply an encoder block network for waveform data and propose a feature attention network for frequency spectra data. The extracted feature vectors are classified into 3 stages of coagulation progress through multi-modal deep learning. Through the proposed method, we show a meaningful result with an accuracy of 98% in determining the stage of coagulation of blood clot.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"71 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":"134565721","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

P300-Based Partial Face Recognition With xDAWN Spatial Filter and Covariance Matrix 基于p300的xDAWN空间滤波和协方差矩阵部分人脸识别

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212494

Ingon Chanpornpakdi, Toshihisa Tanaka

{"title":"P300-Based Partial Face Recognition With xDAWN Spatial Filter and Covariance Matrix","authors":"Ingon Chanpornpakdi, Toshihisa Tanaka","doi":"10.1109/ITC-CSCC58803.2023.10212494","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212494","url":null,"abstract":"Face cognition is one of the most crucial cognition processes in social interaction. In the study of face cognition, rapid serial face cognition (RSVP), the presentation of target and non-target images, is often used to understand the cognition mechanism. When a person perceives the target image, the event-related potential (ERP) is evoked. To identify the target image or the event of interest of a person, the classification model machine learning is introduced. However, the machine learning model that works the best when applied to ERP is still in question. This study aimed to investigate the simplest machine learning model that performs best when comparing six classification models applied to ERP peak evoked during the partial face cognition task. The six models used in this investigation were linear discrimination analysis (LDA), xDAWN filter + linear support vector machine (SVM), xDAWN filter + LightGBM, xDAWN covariance matrix + tangent space + linear SVM, xDAWN covariance matrix + tangent space + LightGBM, and xDAWN covariance matrix + minimum distance to mean (MDM). As a result, we found that the xDAWN covariance matrix improved the classification performance compared to combining the xDAWN filter with the same classification models. In addition, the combination of the xDAWN covariance matrix and MDM provided the best performance in participant-dependent cross-validation. In contrast, the xDAWN covariance matrix, tangent space, and LightGBM provided the most promising performance in the participant-independent cross-validation.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"183 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":"131643127","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

Analysis of the Effect of Feature Denoising from the Perspective of Corruption Robustness 基于腐败鲁棒性的特征去噪效果分析

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212895

Hyunha Hwang, Se-Hun Kim, Mincheol Cha, Min-Ho Choi, Kyujoong Lee, Hyuk-Jae Lee

引用次数: 0

Development of Computer-Aided Diagnosis System Using Single FCN Capable for Indicating Detailed Inference Results in Colon NBI Endoscopy 结肠NBI内窥镜中单FCN计算机辅助诊断系统的开发

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212877

Daisuke Katayama, Yongfei Wu, Tetsushi Koide, Toru Tamaki, Shigeto Yoshida, Shin Morimoto, Yuki Okamoto, S. Oka, Shinji Tanaka

引用次数: 0

Real-Time Inference Platform for Object Detection on Edge Device 边缘设备目标检测实时推理平台

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212984

Kwonseung Bok, Sang-Seol Lee, Aeri Kim, Sujin Han, Kyungho Kim

{"title":"Real-Time Inference Platform for Object Detection on Edge Device","authors":"Kwonseung Bok, Sang-Seol Lee, Aeri Kim, Sujin Han, Kyungho Kim","doi":"10.1109/ITC-CSCC58803.2023.10212984","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212984","url":null,"abstract":"Deep Neural Networks (DNNs) that perform object detection, such as autonomous driving, facial recognition, and medical healthcare, have received great attention recently. Due to the large amounts of data used in object detection DNNs, a cloud computing system for AI with centralized computing power and storage capacity has been used. However, with the increasing number of edge devices in the IoT trend and the growing amount of data, cloud-based AI encounters a challenge of network latency in processing real-time inference. In this paper, we propose a platform consisting of an edge device with a DNN inference accelerator and an optimized network to address the latency issues and achieve real-time inference of DNNs. The proposed platform adopts SqueezeNet, which is suitable for mobile devices due to its smaller network size than other DNNs. Post Training Quantization compresses the pre-trained SqueezeNet model size without accuracy loss. With the compressed network, an xczu3eg chip-based MPSoC board that includes an AI accelerator is used as the edge device. To further improve inference throughput, multi-threading is also used to reduce the latency between the Processing System(PS) and Programmable Logic(PL). Through the proposed platform, we achieve a 55 frame-per-second(fps) throughput, which is a sufficient real-time object detection inference performance.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"17 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":"132954191","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

Fully Integrated CMOS Wideband Power Amplifier for Fifth Generation Mobile Communications 第五代移动通信全集成CMOS宽带功率放大器

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212682

Bonghyuk Park, Hui-Dong Lee, Seunghyun Jang, Sunwoo Kong, Seunghun Wang, Jung-hwan Hwang

引用次数: 0

Multipath Cluster-Based Scatterer Recognition by Object Detection Techniques Using Panoramic Images 基于多路径聚类的全景图像目标检测技术散射体识别

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212703

Inocent Calist, Minseok Kim

{"title":"Multipath Cluster-Based Scatterer Recognition by Object Detection Techniques Using Panoramic Images","authors":"Inocent Calist, Minseok Kim","doi":"10.1109/ITC-CSCC58803.2023.10212703","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212703","url":null,"abstract":"Object detection is crucial in the field of wireless communication, as it helps in predicting the channel behavior and channel model parameters that are necessary for efficient communication. In recent years, various object detection techniques have been proposed for this purpose, ranging from traditional statistical methods to deep learning-based approaches. This paper provides a comprehensive review of object detection techniques for predicting wireless channel model parameters. Additionally, the paper discuss the advantages and limitations of different object detection frameworks such as YOLO, Faster R-CNN, and Mask R-CNN. Finally, the paper puts foward a conceptual introduction of a novel approach to utilize Faster R-CNN object detection technique andcomputer vision, to predict the scatterers and eventually estimate the channel characteristics of a wireless channel. The paper highlights the current challenges and future directions in object detection for wireless channel model parameters prediction. The dataset to train the deep learning model is generated from an example conference room environment panoramic images. The proposed approach can be applied in various wireless communication scenarios, such as 5G and beyond, to accurately predict the location of scatterers based on multipath clusters so as to optimize network design and improve the overall performance of the system.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"41 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":"115652516","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

Deep Reinforcement Learning Based Bus Stop-Skipping Strategy 基于深度强化学习的公交跳停策略

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212607

Mau-Luen Tham, Bee-Sim Tay, Kok-Chin Khor, S. Phon-Amnuaisuk

引用次数: 0

Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches 基于传感器的牛行为分类使用深度学习方法

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212958

S. Mekruksavanich, Ponnipa Jantawong, D. Tancharoen, A. Jitpattanakul

{"title":"Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches","authors":"S. Mekruksavanich, Ponnipa Jantawong, D. Tancharoen, A. Jitpattanakul","doi":"10.1109/ITC-CSCC58803.2023.10212958","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212958","url":null,"abstract":"The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"58 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":"114869499","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

Neural Networks Input Techniques to Maintain a Small Skew Angle in Bit-Patterned Magnetic Recording with a V-Shaped Read-Head Array v形读头阵列位模式磁记录中保持小倾斜角的神经网络输入技术

2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC) Pub Date : 2023-06-25 DOI: 10.1109/ITC-CSCC58803.2023.10212691

K. A. Fatika, S. Koonkarnkhai, P. Kovintavewat, C. Warisarn

{"title":"Neural Networks Input Techniques to Maintain a Small Skew Angle in Bit-Patterned Magnetic Recording with a V-Shaped Read-Head Array","authors":"K. A. Fatika, S. Koonkarnkhai, P. Kovintavewat, C. Warisarn","doi":"10.1109/ITC-CSCC58803.2023.10212691","DOIUrl":"https://doi.org/10.1109/ITC-CSCC58803.2023.10212691","url":null,"abstract":"The demand for enormous storage devices has kept increasing, leading to the development of various advanced technologies with a vast storage capacity. Extensive numbers of related research studies have been aiming at optimizing code design and algorithms analytically; however, enacting them on practical devices has been scarce. Achieving this demand might bring some obstacles called two-dimensional interference and skew angle (SA). To meet the challenge of the obstacle, we propose a SA detection method for bit-patterned magnetic recording systems by computing a specific target by three readback sequences before estimating the SA value and detecting the SA amount happening in the system using an application of neural network namely multilayer perceptron. An error correction code, low-density parity-check, is applied, and its decoder outputs a log-likelihood ratio whose probability density distribution is examined. The simulation results show that the sliding window technique can significantly provide a better bit error rate performance.","PeriodicalId":220939,"journal":{"name":"2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)","volume":"49 1 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":"116311548","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