IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.05.013

Md Shahriar Nazim , Arbil Chakma , Md. Ibne Joha, Syed Samiul Alam, Md Minhazur Rahman, Miftahul Khoir Shilahul Umam, Yeong Min Jang

引用次数: 0

Multiple object detection and tracking in autonomous vehicles: A survey on enhanced affinity computation and its multimodal applications 自动驾驶车辆中的多目标检测与跟踪：增强关联计算及其多模态应用综述

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.06.005

Muhammad Adeel Altaf , Min Young Kim

{"title":"Multiple object detection and tracking in autonomous vehicles: A survey on enhanced affinity computation and its multimodal applications","authors":"Muhammad Adeel Altaf , Min Young Kim","doi":"10.1016/j.icte.2025.06.005","DOIUrl":"10.1016/j.icte.2025.06.005","url":null,"abstract":"<div><div>Three-dimensional (3D) object tracking is crucial in computer vision applications, particularly in autonomous driving, robotics, and surveillance. Despite advancements, effectively utilizing multimodal data to improve multi-object detection and tracking (MODT) remains challenging. This study introduces ACMODT, an affinity computation-based multi-object detection and tracking framework that integrates camera (2D) and LiDAR (3D) data for enhanced MODT performance in autonomous driving. This approach leverages EPNet as a backbone, utilizing 2D–3D feature fusion for accurate proposal generation. A deep neural network (DNN) extracts robust appearance and geometric features, while an improved affinity computation module combines Refined Boost Correlation Features (RBCF) and 3D-Extended Geometric IoU (3D-XGIoU) for precise object association. Motion prediction is refined using a Kalman filter (KF), and Gaussian Mixture Model (GMM)-based data association ensures consistent tracking. Experiments on the KITTI car tracking benchmark for quantitative analysis and the RADIATE dataset for visualization demonstrate that our method achieves superior tracking accuracy and precision compared to state-of-the-art multi-object tracking (MOT) approaches, proving its effectiveness for real-time object tracking.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 809-818"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing data harvesting systems: Performance quantification of Cloud–Edge-sensor networks using queueing theory 增强数据采集系统：使用排队理论的云边缘传感器网络的性能量化

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.04.017

Jose Wanderlei Rocha , Eder Gomes , Vandirleya Barbosa , Arthur Sabino , Luiz Nelson Lima , Gustavo Callou , Francisco Airton Silva , Eunmi Choi , Tuan Anh Nguyen , Dugki Min , Jae-Woo Lee

{"title":"Enhancing data harvesting systems: Performance quantification of Cloud–Edge-sensor networks using queueing theory","authors":"Jose Wanderlei Rocha , Eder Gomes , Vandirleya Barbosa , Arthur Sabino , Luiz Nelson Lima , Gustavo Callou , Francisco Airton Silva , Eunmi Choi , Tuan Anh Nguyen , Dugki Min , Jae-Woo Lee","doi":"10.1016/j.icte.2025.04.017","DOIUrl":"10.1016/j.icte.2025.04.017","url":null,"abstract":"<div><div>This study investigates a Cloud–Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model’s applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud–edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 597-602"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-term blood glucose prediction using deep learning-based noise reduction 基于深度学习的降噪长期血糖预测

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.05.009

Su-Jin Kim , Jun Sung Moon , Sung-Yoon Jung

{"title":"Long-term blood glucose prediction using deep learning-based noise reduction","authors":"Su-Jin Kim , Jun Sung Moon , Sung-Yoon Jung","doi":"10.1016/j.icte.2025.05.009","DOIUrl":"10.1016/j.icte.2025.05.009","url":null,"abstract":"<div><div>The Artificial Pancreas System (APS) is a device designed to monitor blood glucose levels in real-time and automatically regulate insulin for diabetes patients. Blood glucose prediction plays a crucial role in these systems by enabling proactive responses to glucose variations, thereby preventing risks such as hypoglycemia or hyperglycemia and assisting patients in managing their condition effectively. However, Continuous Glucose Monitoring (CGM) sensor data often contain significant sensor noise. Without effectively reducing the sensor noise, prediction accuracy can be severely compromised. Therefore, we first present a deep learning (DL) method for noise reduction in CGM data and, second, propose a long-term blood glucose prediction approach based on the system response function, utilizing a multi-input(e.g., blood glucose, carbohydrate (CHO) intake, and insulin). In this study, simglucose, based on the UVA-PADOVA simulator, was utilized to test and evaluate the proposed methods. As a result, we found that noise reduction using deep learning (DL) was significantly more effective than conventional filtering methods. Furthermore, the proposed long-term blood glucose prediction approach reliably tracked blood glucose fluctuations in custom scenarios and accurately predicted daily glucose patterns. Even in random scenarios, the proposed model accurately captured blood glucose trends, closely aligning with actual BG values and demonstrating remarkable performance.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 715-720"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time implementation of OFDM modulation for an OCC system: UNet-based equalizer for signal denoising and BER optimization OCC系统OFDM调制的实时实现：基于unet的信号去噪和误码率优化均衡器

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.06.002

Md Minhazur Rahman, Md Shahriar Nazim, Md. Ibne Joha, Yeong Min Jang

引用次数: 0

The journey to cloud as a continuum: Opportunities, challenges, and research directions 云之旅是一个连续体：机遇、挑战和研究方向

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.04.015

Md. Mahmodul Hasan , Tangina Sultana , Md. Delowar Hossain , Ashis Kumar Mandal , Thien-Thu Ngo , Ga-Won Lee , Eui-Nam Huh

{"title":"The journey to cloud as a continuum: Opportunities, challenges, and research directions","authors":"Md. Mahmodul Hasan , Tangina Sultana , Md. Delowar Hossain , Ashis Kumar Mandal , Thien-Thu Ngo , Ga-Won Lee , Eui-Nam Huh","doi":"10.1016/j.icte.2025.04.015","DOIUrl":"10.1016/j.icte.2025.04.015","url":null,"abstract":"<div><div>The rapid development of the Internet of Things (IoT) has driven a significant shift in computing architectures, leading to the rise of the cloud continuum—a flexible framework that combines cloud services with edge and fog computing. While existing survey papers have contributed valuable insights, they often focus narrowly on specific aspects of the continuum or do not fully address its evolving complexities. These limitations underscore the need for a comprehensive and up-to-date analysis of the field. This study bridges these gaps by presenting an extensive review of the cloud continuum, covering its role in enhancing resource management, improving real-time data processing, integrating machine learning approaches, and optimizing user experiences across diverse applications. We examine how edge devices, fog nodes, and cloud infrastructures synergize to enable decentralized data processing, reducing latency in critical areas such as smart cities, healthcare, and autonomous vehicles. Additionally, this study explores the integration of machine learning across edge, fog, and cloud layers, with a focus on inference and distributed learning methods. By highlighting how these technologies enhance efficiency, scalability, and intelligent decision-making, this review provides a holistic perspective on the cloud continuum. Our analysis offers valuable insights into future research directions, emphasizing innovations that can drive next-generation computing systems toward greater efficiency and adaptability.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 666-689"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoCL: EEG connectivity-guided contrastive learning for seizure detection 脑电图连接引导下的对比学习检测癫痫发作

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.06.004

Hyeon-Jin Im , Jiye Kim , Sunyoung Kwon

引用次数: 0

TMNet: Transformer-fused multimodal framework for emotion recognition via EEG and speech 基于脑电和语音的情感识别的多模态转换融合框架

IF 4.2 3区计算机科学

ICT Express Pub Date : 2025-08-01 DOI: 10.1016/j.icte.2025.04.007

Md Mahinur Alam , Mohamed A. Dini , Dong-Seong Kim , Taesoo Jun

引用次数: 0

Region-aware knowledge distillation between monocular camera-based 3D object detectors 基于单目摄像机的三维目标检测器的区域感知知识提取