2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)最新文献

Cloud Based Smart Vehicle Tracking System 基于云的智能车辆跟踪系统

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534843

Tamilvizhi T, S. R, Krishnaraj N

{"title":"Cloud Based Smart Vehicle Tracking System","authors":"Tamilvizhi T, S. R, Krishnaraj N","doi":"10.1109/iCCECE52344.2021.9534843","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534843","url":null,"abstract":"Cloud Computing is internet-based computing for Optimal Resource management Techniques (ORMT). A mobile cloud resource access in Vehicular Ad hoc Network (VANET) presents the resource speed predicting system, resource tracking, resource monitoring, and resource discovery. It also implements the landmark mobile routing service and distance calculation algorithm. This technique is to know the speed of a particular vehicle by the owner, passengers, or people who care for the passengers and are willing to know the speed of their vehicle. Here resource act as a vehicle and monitoring Quality of Service (QoS) parameters are speed and time. This system predicts an over-speed vehicle and sends the message to that particular vehicle. The vehicle tracking system benefits the people in two ways to save the waiting time, one way for public users and another way for inside passengers for a specific vehicle through VANET. The Way 1 tracks the vehicle’s current location for an outside passenger in smart cities. Way 2 indicates the vehicle’s current location route map for inside passengers. In monitoring a vehicle’s loading, it monitors all passengers and also luggage of the vehicle. The resource discovery technique is gathering all the information of vehicles through VANET in Smart Cities.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129139754","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: 11

COVID19 Impact on Medical Clinics, Analysing and Devising a Safety Software Model for Clinics covid - 19对医疗诊所的影响，分析和设计诊所安全软件模型

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534853

M. Fetaji, B. Fetaji, Azir Aliu, Halil Snopçe, I. Zeqiri, Mirlanda Ebibi, Maaruf Ali

{"title":"COVID19 Impact on Medical Clinics, Analysing and Devising a Safety Software Model for Clinics","authors":"M. Fetaji, B. Fetaji, Azir Aliu, Halil Snopçe, I. Zeqiri, Mirlanda Ebibi, Maaruf Ali","doi":"10.1109/iCCECE52344.2021.9534853","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534853","url":null,"abstract":"The focus of this research is on investigating and identifying what constitutes a safe software model of a medical clinic that will enable and enhance its protection for healthcare providers and its patients by devising a model of a software system. There is a gap in the published research regarding the understanding of impacting factors of COVID19 in the safe functioning of a medical clinic. This Case Study investigated several impacting factors. The aims of this research project are: increasing the safety effectiveness of both the hospital personnel and patients. Next, to solve the problems with tracking the illnesses of patients, a more rigorous database indexing system was implemented with data provenance. With this technique in the database, it kept track of illnesses (when, why, in whom did it appear or any kind of other developments), login details (when, who is logged in, logged out), which is more important especially for medical systems. Also, which doctors interacted with the patients (what drug have been administered, their performance, percentage of efficacy, successfully or unsuccessfully) and other data mining information. Because this project is envisioned to be continually supported, it should evolve and deal with the problems encountered in modern hospitals. The system has been evaluated on its functionalities and the safety usability, on two groups of users: those with a computer science background and those from a different field. Afterwards regression analyses were used to determine the impact. The research study contributed with identified impacting factors for such systems, analyses of the improvement in conceptual increase of safety and usability. Insights and recommendations are provided.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121146895","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

Devising an Optimisation Testing Methodology and Its Analyses 设计一种优化测试方法及其分析

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534854

B. Fetaji, M. Fetaji, M. Ebibi, Maaruf Ali

引用次数: 0

Multi-cell Interference Management in In-band D2D Communication under LTE-A Network LTE-A网络下带内D2D通信的多小区干扰管理

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534849

Koushik Modak, S. Rahman

{"title":"Multi-cell Interference Management in In-band D2D Communication under LTE-A Network","authors":"Koushik Modak, S. Rahman","doi":"10.1109/iCCECE52344.2021.9534849","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534849","url":null,"abstract":"Device-to-Device (D2D) communication is an active research area. As a part of this active research area, Device-to-Device (D2D) communication is largely exploited in Out-band non-cellular technologies, such as, Bluetooth or Wi-Fi network. However, it has not been fully incorporated into existing cellular networks. Interference management is the main challenge of this technology as it generates both intra and inter-cell interference resulting in severe network performance degradation. eNodeBs with high transmit power usually affects D2D user equipments (UEs) with high interference. It usually incurs severe interference to the cellular UEs and to the base station (eNB). The scenario becomes more critical in case of multi-cell environment, which is the main research focus in this paper. In order to encourage and increase frequent use of D2D communications, some changes in the network configuration are required for today’s networking scenario. Flexible multi-cell D2D communication is required to reduce the network load. Interference management techniques are necessary in parallel to make the communication smooth, efficient and effective.This paper reviews multi-cell interference in In-Band D2D communications and investigates interference mitigation techniques in scenarios where two or more similar or different devices under same eNB or from two different eNBs can be connected as a D2D pair without compromising user experience and quality of service standard. These issues cannot be guaranteed by the current applications operated on unlicensed frequency band. The research also addresses the following related issues: mode selection, resource allocation (both for cellular and D2D environment), power control (both for eNB and D2D pair), and flexible frequency allocation techniques. The research aims to look at other issues, such as, achieving high SINR, improved system capacity, better throughput and transmission rate.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117209401","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: 4

Machine Learning for Smaller Firms: Challenges and Opportunities 小型企业的机器学习:挑战与机遇

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534852

T. H. Walcott, Maaruf Ali

引用次数: 2

Sequence Prediction Algorithm for the Diagnosis of Early Dementia Development 早期痴呆发展诊断的序列预测算法

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534844

K. Minhad, Araf Farayez, Kelvin Jian Aun Ooi, Mamun bin Ibne Reaz, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz

{"title":"Sequence Prediction Algorithm for the Diagnosis of Early Dementia Development","authors":"K. Minhad, Araf Farayez, Kelvin Jian Aun Ooi, Mamun bin Ibne Reaz, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz","doi":"10.1109/iCCECE52344.2021.9534844","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534844","url":null,"abstract":"Dementia is a combination of systematic symptoms of a long-term decline in human memory and thinking capabilities, typically caused by the aging process. The primary aim of this research is to employ a human activity prediction algorithm to distinguish between healthy subjects and dementia-affected patients, in order to provide diagnosis at the early stages of dementia. A new algorithm, viz. Sequence Prediction via All Discoverable Episodes (SPADE), is introduced in this research to find out distinct parameters that can be used to deliver successful diagnosis. Since dementia patients do not tend to have a recognisable activity pattern, this would make it difficult for the algorithm to function well. The experiment results establish a noticeable difference of 11% in the peak accuracy of sequence prediction performed between healthy adults and dementia-affected residents. SPADE has achieved an average accuracy of 80%, i.e. 12% improvement over M-SPEED in predicting future events. This is thus evidenced that the activity prediction algorithms possess the potentials to detect the early symptoms of dementia without using any expensive clinical procedures.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116658780","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: 2

Exploring Versatility of Primary Visual Cortex Inspired Feature Extraction Hardware Model through Various Network Architectures 基于不同网络架构的初级视觉皮层特征提取硬件模型的多功能性探索

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534841

Thi Diem Tran, Y. Nakashima

{"title":"Exploring Versatility of Primary Visual Cortex Inspired Feature Extraction Hardware Model through Various Network Architectures","authors":"Thi Diem Tran, Y. Nakashima","doi":"10.1109/iCCECE52344.2021.9534841","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534841","url":null,"abstract":"Improving the performance of the network architectures that mimic brain operation is a research trend. Optimizing the latency on hardware circuits of the artificial neural network continues investigating. In the third generation, the Spiking Neural Networks (SNNs) with biological plausibility and similarity to the functionality of the human brain are emerging. A more comprehensive study is expected to understand the inherent behavior of SNNs, especially under adversarial attacks. This study concatenates the proposed SLIT layer with the convolutional neural networks (CNNs) to degrade the latency of deep neural networks on the hardware platform. The input data modified with the SLIT layer is applied to interrogate the adversarial attack on Spiking Neural Network. We estimate new topology with MNIST and CIFAR-10 datasets. Latency of the inference phase on CNNs for image classification application is assessed on the chip ZC7Z020-1CLG484C FPGA. Reducing latency in the range of 2.6% to 16% is observed from the Vitis AI platform. With white-box adversarial attack applications on SNNs, the accuracy of the proposal is approximately 70% higher robustness than the previous works.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127823268","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

Interlayer Augmentation in a Classification Task 分类任务中的层间增强

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534840

Satoru Mizusawa, Y. Sei

引用次数: 1

Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN 利用CNN胸部x线成像识别COVID-19疾病

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534839

Md Gulzar Hussain, Shiren Ye

引用次数: 2

Sensor Pattern Noise Estimation using Non-textured Video Frames For Efficient Source Smartphone Identification and Verification 基于非纹理视频帧的传感器模式噪声估计用于有效的智能手机源识别和验证

2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2021-08-16 DOI: 10.1109/iCCECE52344.2021.9534850

Ashref Lawgaly, F. Khelifi, A. Bouridane, Somaya Al-Maaddeed

{"title":"Sensor Pattern Noise Estimation using Non-textured Video Frames For Efficient Source Smartphone Identification and Verification","authors":"Ashref Lawgaly, F. Khelifi, A. Bouridane, Somaya Al-Maaddeed","doi":"10.1109/iCCECE52344.2021.9534850","DOIUrl":"https://doi.org/10.1109/iCCECE52344.2021.9534850","url":null,"abstract":"Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for image authentication and source camera identification. The abundant information that the PRNU carries in terms of the frequency content makes it unique, and therefore suitable for identifying the source camera and detecting forgeries in digital images. However, PRNU estimation from smartphone videos is a challenging process due to the presence of frame-dependent information (very dark/very textured), as well as other non-unique noise components and distortions due to lossy compression. In this paper, we propose an approach that considers only the non-textured frames in estimating the PRNU because its estimation in highly textured images has been proven to be inaccurate in image forensics. Furthermore, lossy compression distortions tend to affect mainly the textured and high activity regions and consequently weakens the presence of the PRNU in such areas. The proposed technique uses a number of texture measures obtained from the Grey Level Cooccurrence Matrix (GLCM) prior to an unsupervised learning process that splits the feature space through training video frames into two different sub-spaces, i.e., the textured space and the non-textured space. Non-textured video frames are filtered out and used for estimating the PRNU. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art approach.","PeriodicalId":128679,"journal":{"name":"2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121468784","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: 5