2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)最新文献_第4页

On the Use of FPGAs to Implement CNNs: A Brief Review 利用fpga实现cnn:综述

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231243

Muhammad Arbab Arshad, Sakib Shahriar, A. Sagahyroon

引用次数: 4

A CMOS Current Starved VCO for Energy Harvesting applications 用于能量收集应用的CMOS电流饥渴压控振荡器

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231172

G. Almeida, Zhaochu Yang, P. Mendes, T. Dong

引用次数: 0

Effect of Preprocessing on Performance of Neural Networks for Microscopy Image Classification 预处理对神经网络显微图像分类性能的影响

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231071

A. Uka, X. Polisi, J. Barthès, A. Halili, Florenc Skuka, N. Vrana

{"title":"Effect of Preprocessing on Performance of Neural Networks for Microscopy Image Classification","authors":"A. Uka, X. Polisi, J. Barthès, A. Halili, Florenc Skuka, N. Vrana","doi":"10.1109/iCCECE49321.2020.9231071","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231071","url":null,"abstract":"Medical field depends heavily on understanding and analyzing microscopy images of cells to better diagnose diseases, to evaluate the effectiveness of various medical treatments and to determine their health under stress. The amount of data that needs to be analyzed has increased and computer assisted analysis has become crucial as it would be very labor intensive for the medical practitioners otherwise. Many of the images are acquired using brightfield microscopy with no staining in order to avoid all the side effects. The unstained images have some associating challenges as they suffer from random nonuniform illumination, low contrast, relatively high transparency of the cytoplasm. The initial challenge of the large amount of data calls for the use of deep learning algorithms, whereas the other structural challenges call for the need to carefully train the convolutional neural networks in order to have a reliable system of evaluation. We have prepared a dataset of 20.000 images and we have tested the trained models on datasets with different number of images (N=300-8000). Here is this work we present classification of the cell health using convolutional neural networks and monitor the effect of the preprocessing steps on the overall accuracy.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117052868","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: 7

Image Restoration on Residual Aggregation Network in Poor Weather Condition 恶劣天气条件下残差聚集网络图像恢复

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231197

Jing Wang

引用次数: 1

FPGA Implementations for Real-Time Processing of High-Frame-Rate and High-Resolution Image Streams 实时处理高帧率和高分辨率图像流的FPGA实现

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231119

U. Hudomalj, C. Mandla, M. Plattner

引用次数: 2

Detection of the Hardcoded Login Information from Socket Symbols 从套接字符号检测硬编码登录信息

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231177

Minami Yoda, Shuji Sakuraba, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga

{"title":"Detection of the Hardcoded Login Information from Socket Symbols","authors":"Minami Yoda, Shuji Sakuraba, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga","doi":"10.1109/iCCECE49321.2020.9231177","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231177","url":null,"abstract":"Internet of Things (IoT) for smart homes enhances the convenience of our life; however, it also introduces the risk of leakage of privacy data in the house. A user wants to protect their privacy data from leakage. However, the analysis of IoT devices requires technical knowledge; therefore, it is challenging for the users to detect any vulnerability by themselves. In this study, we propose a lightweight method to detect the hardcoded username and password in IoT devices using static analysis. This method can detect the 1st vulnerability from 2018 OWASP TOP 10 for the IoT device. The hardcoded login information can be obtained by comparing the user input with strcmp or strncmp. Thus, previous studies analyzed the symbols of strcmp or strncmp to detect the hardcoded login information. However, these studies require time because of the usage of complicated algorithms such as symbolic execution. To develop a lightweight algorithm, we focus on a network function, such as the socket symbol in firmware, because the IoT device is compromised when it is invaded by someone via the Internet. We propose two methods to detect the hardcoded login information, i.e., string search and socket search. In string searching, it finds a function that uses strcmp or strncmp symbol. In socket searching, it finds a function that is referenced by socket symbol. In the experiment, we measured the ability of our method by searching six firmware in the real world that has a backdoor. we ran three methods: string search, socket search, and whole search to compare two methods. As a result, all methods found login information from four of six firmware. Our method reduces an analysis time that when the whole search takes 38mins to complete, our methods finish 4-6min.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531020","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

Towards efficient wind energy monitoring: Learning more from open source data 迈向高效风能监测:从开源数据中学习更多

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231090

Alexander Marinšek, L. De Strycker

{"title":"Towards efficient wind energy monitoring: Learning more from open source data","authors":"Alexander Marinšek, L. De Strycker","doi":"10.1109/iCCECE49321.2020.9231090","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231090","url":null,"abstract":"Europe’s massive shift towards sustainable energy production has triggered a variety of new large scale projects, and wind energy is a crucial part of the effort to achieve a carbon-free future. However, because of their low financial impact and relatively high measurement campaign costs, small scale projects are often deemed impractical beforehand. To help small communities gain insight on the wind energy conditions in their surroundings, the present work briefly introduces a measuring station (MEST) concept based on affordable electronic components and proposes a solution to alleviating the effects of inevitable measurement data inconsistency on the energy yield analysis. By leveraging open source machine learning models and establishing a link with the publicly available ERA5-Land climate database, missing wind speed measurement data is reconstructed at an accuracy of up to 0.11 $frac{m}{s}$. The impact of data reconstruction on the estimated energy production of a wind turbine (WT) erected at the measuring location is then evaluated using the measurement data acquired by a MEST prototype and the ERA5-Land data recorded during October and November 2019. The results indicate that at a location experiencing moderate wind speeds, the estimated energy output of the WT is increased by up to 2 % in comparison with other data analysis procedures. Although the minute underestimation is not of great importance to the success of the analysis, the inaccuracies at higher wind speeds have a far more profound effect on the WT’s estimated energy output, and they can stop a potentially successful wind energy project from gaining further attention.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124740783","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

Temporal Image Forensic Analysis for Picture Dating with Deep Learning 基于深度学习的时间图像取证分析

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231160

F. Ahmed, F. Khelifi, Ashref Lawgaly, A. Bouridane

{"title":"Temporal Image Forensic Analysis for Picture Dating with Deep Learning","authors":"F. Ahmed, F. Khelifi, Ashref Lawgaly, A. Bouridane","doi":"10.1109/iCCECE49321.2020.9231160","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231160","url":null,"abstract":"Estimating the acquisition date of digital photographs is crucial in image forensics. The task of dating images by processing their contents should be reasonably accurate as this can be used in court to resolve high profile cases. The goal of temporal forensics analysis is to find out the links in time between two or more pieces of evidence. In this paper, the problem of picture dating is addressed from a machine learning perspective, precisely, by adopting a deep learning approach for the first time in temporal image forensics. In this work, the acquisition time of digital images is estimated in such a way that the analyst can identify the timeline of unknown digital photographs given a set of pictures from the same source whose time ordering is known. By applying Convolutional Neural Networks (CNN), namely the AlexNet and GoogLeNet architectures in both feature extraction and transfer learning modes, results have shown that the networks can successfully learn the temporal changes in the content of the digital pictures that are acquired from the same source. Interestingly, although images are divided into non-overlapping blocks in order to increase the number of training samples and feed CNNs, the obtained estimation accuracy has been from 80% to 88%. This suggests that the temporal changes in image contents, modelled by CNNs, are not dependent on block location. This has been demonstrated on a new database called ‘Northumbria Temporal Image Forensics’ (NTIF) database which has been made publicly available for researchers in image forensics. NTIF is the first public database that captures a large number of images at different timeslots on a regular basis using 10 different digital cameras. This will serve the research community as a solid ground for research on picture dating and other image forensics applications.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364247","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

Class-based Flow Scheduling Framework in SDN-based Data Center Networks sdn数据中心网络中基于类的流调度框架

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231052

Maiass Zaher, Aymen Hasan Alawadi, S. Molnár

引用次数: 0

Valent-Blocks: Scalable High-Performance Compilation of WebAssembly Bytecode For Embedded Systems 有价块:嵌入式系统WebAssembly字节码的可伸缩高性能编译

2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) Pub Date : 2020-08-17 DOI: 10.1109/iCCECE49321.2020.9231154

Fabian Scheidl

引用次数: 2