2021 IEEE International Conference on Electro Information Technology (EIT)最新文献

Material Texture Recognition using Ultrasonic Images with Transformer Neural Networks 基于变形神经网络的超声图像材料纹理识别

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491908

Xin Zhang, J. Saniie

{"title":"Material Texture Recognition using Ultrasonic Images with Transformer Neural Networks","authors":"Xin Zhang, J. Saniie","doi":"10.1109/EIT51626.2021.9491908","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491908","url":null,"abstract":"Material texture recognition by estimating the grain size has been extensively used for characterization of material structures. Ultrasonic inspection can approximate material grain size nondestructively with advantages of one-sided measurement, high penetration depth and inspection accuracy. In ultrasonic testing, the energy of signal attenuates as ultrasonic signal propagates through the material. This attenuation is due to scattering and absorption, which are functions of the frequency and grain size distribution. Therefore, the attenuation and scattering of ultrasonic echoes can be used to evaluate grain size for microscopic texture. In this paper we propose to use the transformer neural networks to learn grain scattering features for material textures recognition. The transformer neural network utilizes the multi-head attention mechanism to substantially reduce the computation cost. An ultrasonic testbed platform is assembled to acquire the 3D ultrasonic data cube to train the neural networks for texture analysis. The 3D data cube consists of a sequence of 2D ultrasonic C-scan images and is obtained from three different heat-treated steel blocks. Several state-of-the-art machine learning algorithms, the deep Convolutional Neural Networks (CNNs) and Support Vector Machine (SVM) were trained and compared to classify the grain scattering textures of three heat-treated steel blocks. To build a data-efficient automatic system for ultrasonic nondestructive evaluation (NDE) applications, a self-attention based transformer neural networks: Ultrasonic Texture Recognition Vision Transformer: UTRV Transformer, was proposed to classify material textures with high testing accuracy of 96.15%.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611625","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

Visually Impaired Indoor Navigation using YOLO Based Object Recognition, Monocular Depth Estimation and Binaural Sounds 基于YOLO的目标识别、单目深度估计和双耳声音的视障室内导航

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491913

Sukesh Davanthapuram, Xinrui Yu, J. Saniie

引用次数: 5

On the Equivalent Circuit Model of a 3D-Printed Conductive Electrifi Transmission Line on a Flexible NinjaFlex Substrate 柔性NinjaFlex基板上3d打印导电传输线的等效电路模型

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491832

H. Wolf, D. Mitra, R. Striker, B. Braaten

引用次数: 1

An Intelligent Approach to Reverse Engineer CAN Messages in Automotive Systems 汽车系统CAN信息逆向工程的智能方法

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491907

Mohamad Ali Mokhadder, Samar Bayan, Utayba Mohammad

{"title":"An Intelligent Approach to Reverse Engineer CAN Messages in Automotive Systems","authors":"Mohamad Ali Mokhadder, Samar Bayan, Utayba Mohammad","doi":"10.1109/EIT51626.2021.9491907","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491907","url":null,"abstract":"Most of the advanced features in today’s automobiles are performed by Electronic Control Units (ECUs) and an intra-vehicle communication network that allows these ECUs to exchange data. The most dominant intra-vehicle communication protocol is the Controller Area Network (CAN) protocol. The broadcast nature of CAN and the ability to access it through multiple interfaces in a vehicle, introduce an array of attack vectors that make vehicles vulnerable to cyber threats. CAN messages are proprietary to manufacturers, and their IDs and contents are guarded closely for intellectual property and security reasons. In this paper, an Automated Current-Based Fuzzing System (ACFS) is introduced. ACFS is a lightweight reverse engineering system that identifies CAN messages related to a specific user-vehicle interaction. It monitors and synchronizes variations in the data of CAN messages with current readings drawn from the vehicle’s battery. Then, it passes the current signal through frequency analysis and filtering stage and associate changes in the output signal with the CAN bus traffic. As a result, a small group of candidate messages, related to a specific user-vehicle interaction, e.g., turning headlights on, are identified. The candidate messages are then played back on the vehicle CAN bus to identify the correct and desired message ID and data. This process allows the user to control specific actions in the vehicle without deep knowledge of its internal setup and functionality, simply by accessing the CAN bus. The ACFS system was tested on a 2017 production prototype BreadBoard Vehicle (BBV) and was able to automatically extract many of the messages that control headlights, turn signals, and information cluster.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134413563","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

A Dual Approach for Preventing Blackhole Attacks in Vehicular Ad Hoc Networks Using Statistical Techniques and Supervised Machine Learning 利用统计技术和监督机器学习防止车辆自组织网络黑洞攻击的双重方法

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491885

Abiral Acharya, Jared Oluoch

{"title":"A Dual Approach for Preventing Blackhole Attacks in Vehicular Ad Hoc Networks Using Statistical Techniques and Supervised Machine Learning","authors":"Abiral Acharya, Jared Oluoch","doi":"10.1109/EIT51626.2021.9491885","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491885","url":null,"abstract":"Vehicular Ad Hoc Networks (VANETs) have the potential to improve road safety and reduce traffic congestion by enhancing sharing of messages about road conditions. Communication in VANETs depends upon a Public Key Infrastructure (PKI) that checks for message confidentiality, integrity, and authentication. One challenge that the PKI infrastructure does not eliminate is the possibility of malicious vehicles mounting a Distributed Denial of Service (DDoS) attack. We present a scheme that combines statistical modeling and machine learning techniques to detect and prevent blackhole attacks in a VANET environment.Simulation results demonstrate that on average, our model produces an Area Under The Curve (ROC) and Receiver Operating Characteristics (AUC) score of 96.78% which is much higher than a no skill ROC AUC score and only 3.22% away from an ideal ROC AUC score. Considering all the performance metrics, we show that the Support Vector Machine (SVM) and Gradient Boosting classifier are more accurate and perform consistently better under various circumstances. Both have an accuracy of over 98%, F1-scores of over 95%, and ROC AUC scores of over 97%. Our scheme is robust and accurate as evidenced by its ability to identify and prevent blackhole attacks. Moreover, the scheme is scalable in that addition of vehicles to the network does not compromise its accuracy and robustness.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133232448","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

Design Flow for Real-Time Face Mask Detection Using PYNQ System-on-Chip Platform 基于PYNQ片上系统平台的实时人脸检测设计流程

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491842

Tianyang Fang, Xin Huang, J. Saniie

引用次数: 8

Towards Tackling Common Web Application Vulnerabilities Using Secure Design Patterns 使用安全设计模式解决常见的Web应用程序漏洞

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491919

A. Ratnaparkhi, Yi Liu

引用次数: 2

A Chaotic-based Encryption/Decryption System for Secure Video Transmission 基于混沌的安全视频传输加/解密系统

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491868

Xin Huang, David Arnold, Tianyang Fang, J. Saniie

{"title":"A Chaotic-based Encryption/Decryption System for Secure Video Transmission","authors":"Xin Huang, David Arnold, Tianyang Fang, J. Saniie","doi":"10.1109/EIT51626.2021.9491868","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491868","url":null,"abstract":"Ultrasonic communication is an alternative communication method of transmitting information through solids. Sending video streams can deliver more information than a single sensor, and video monitoring shows great potential in ultrasonic communication applications. An efficient and secure cryptosystem is needed to protect the sensitive video stream. In this paper, we propose a novel Chaotic-based encryption scheme utilizing 1-D and 2-D iteration models for secure video streaming. This algorithm is based on the Arnold Cat Map and the Logistic Map and has good confusion and diffusion properties. The Arnold Cat Map transforms the dataset into a pseudo-random state over several iterations and is reversible, while the Logistic Map introduces a specific external key to replace and recover the pixels value during encryption and decryption. Both the encryption and decryption processes are presented and formulated in our cryptosystem scenario. The proposed method maintains a good encryption quality, provides key sensitivity and has low correlation between pixels. The results of a secured video frame using separate Chaotic Maps and the novel encryption scheme are compared and discussed. Experiments and analysis demonstrate that the Chaotic-based algorithm is best suited for the ultrasonic video communication system and is resilient to security attacks.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127470992","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

Intelligent Manufacturing with Digital Twin 数字孪生的智能制造

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491874

D. Möller, H. Vakilzadian, Weyan Hou

{"title":"Intelligent Manufacturing with Digital Twin","authors":"D. Möller, H. Vakilzadian, Weyan Hou","doi":"10.1109/EIT51626.2021.9491874","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491874","url":null,"abstract":"The age of digital transformation will have a significant impact on industry and society. Designing, developing, and manufacturing products is more based on the influence of the fourth technological wave with its possibilities through digital transformation. The digital twin is commonly known as a key enabler for digital transformation at all scales – for large and small businesses, for organizations and individuals, globally and locally. The new challenges through digital transformation are seen as an opportunity to achieve higher levels of solutions to handle the different innovative technological methods with regard to quality in production in the manufacturing industry. Due to the complexity of intelligent manufacturing, an advanced and complex tool is required to (i) investigate, (ii) monitor, and (iii) simulate the intelligent manufacturing processes in real-time. The tool to achieve this goal is the digital twin, which works in parallel to sense, monitor, and control manufacturing devices and cyber-physical production systems across the manufacturing plant network infrastructure. The digital twin performs real-time optimization and evaluates the metrics of the execution of the intelligent manufacturing system. This paper introduces intelligent manufacturing and the digital twin as key enablers for the digital transformation in intelligent manufacturing.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128955795","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: 6

Size Efficient Key-Value Type Context Sharing in Mobile Edge Computing 移动边缘计算中高效键值类型上下文共享

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491881

Samuel Sungmin Cho, Myoungkyu Song

{"title":"Size Efficient Key-Value Type Context Sharing in Mobile Edge Computing","authors":"Samuel Sungmin Cho, Myoungkyu Song","doi":"10.1109/EIT51626.2021.9491881","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491881","url":null,"abstract":"Mobile Edge Computing (MEC) aims to extend the edge of cloud computing networks in remote servers. MEC promises advantages such as fast response time and balancing processing load because MEC devices can process the information between the user devices and cloud servers intelligently and dynamically depending on the situation. An increasing number of Internet of Things (IoT) devices share information with cloud servers. MEC can address issues arising from situations where billions of IoT devices share information with other devices, MEC, and cloud servers. In this case, a load balancing model and architecture are needed for better storage and communication bandwidth control to avoid problems such as data congestion among MEC servers. Considering that the Key-value type context is one of the most popular representations for IoT information sharing, a new approach to address the communication load imbalance in MEC is needed. This paper proposes a novel model and architecture to share key-value type context among mobile devices, cloud servers, and MEC servers. We use probabilistic data structures, Bloomier Filters, to reduce the key-value type information footprint. The data quality degradation caused by false positives can be controlled and eliminated with a particular type of Bloomier filter and a common dictionary shared by devices and servers. We analyze our approach to show the proposed model’s performance, architecture, and algorithm, demonstrating that we can reduce the footprint size to represent the key-value type context information with zero or practically zero data degradation.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121292804","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