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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Sarwar, Muhammad Imran Maghrabi, Louai A. The Superior University Lahore Department of Computer Science and It Lahore54500 Pakistan College of Engineering University of Business and Technology Department of Software Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350373363

Current electric grids were designed a century ago for simple and limited needs. An electric grid helps the power generation company (PGC) supply electricity to homes and industrial consumers. Traditional electric grids have one-way interactions that make it difficult to cater to ever-changing and increased demand. The Smart Grids (SGs) introduced the bidirectional flow of electricity and the exchange of information between consumers and PGCs. SG is a combination of networks of communication, controls, computers, and automations that allow its integration with emerging technologies. Blockchain (BC) is a digital distributed ledger (DDL) that ensures the security, reliability and immutability of data. The integration of BC and SGs is an emerging idea that could also provide a platform for community-based electricity trading. This study aims to review community-based energy trading with BC-based SGs and discusses the first ever community-based peer-to-peer (P2P) solar energy trading project, Quartierstrom, by the Swiss Federal Office of Energy (SFOE), Switzerland. The findings of this study contribute a valuable reference to the knowledge body and enhance the comprehension of P2P energy trading for both new and experienced researchers, enabling them to identify new opportunities and challenges. © 2023 IEEE.

关键词： Blockchain

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Reversible data hiding based on pixel segmentation and prediction for improving hiding capacity

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Signal, Image and Video Processing 2024年第6-7期18卷 5043-5059页

作者： Huang, Cheng-Ta Weng, Chi-Yao Zheng, Guo-Ting Weng, Hao-Yu International Bachelor Program in Informatics Yuan Ze University Taoyuan320315 Taiwan Department of Information Management Yuan Ze University Taoyuan320315 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi City600355 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900391 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem, many reversible data hiding (RDH) techniques are developed to combine pixel prediction and histogram shifting to increase the embedding payload. In this paper, a RDH scheme based on chessboard prediction and pixel partitioning is proposed for enhancing the hiding payload. Pixel partitioning is used to separate a pixel into two parts: most significant bit and least significant bit. Then, the chessboard predictor is adopted to generate prediction errors and separate the pixels. Thereafter, histogram shifting is applied to embed the secret messages into prediction errors. Due to the high similarity of adjacent pixels in an image, the generated prediction errors are highly close to zero, resulting in a large embedding space. The experimental results indicate that the proposed method has superior performance in embedding capacity with acceptable image visual quality compared to existing schemes since it adopts the adaptive adjustment strategy to achieve this improved balance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pixels

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Study on the Role of Adaptive Power Control in Jamming Mitigation for Remote Keyless Entry Systems 29

Study on the Role of Adaptive Power Control in Jamming Mitig...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Alkofahi, Hamza Altarawneh, Amani Jordan University of Science and Technology Department of Software Engineering Irbid Jordan Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798331517816

Remote keyless entry (RKE) systems have become a standard feature in modern vehicles, offering the convenience of wireless control. However, this convenience is accompanied by significant security vulnerabilities, particularly susceptibility to jamming attacks. These attacks disrupt communication between the key fob and the vehicle, potentially leading to unauthorized access and vehicle theft. This study investigates the effectiveness of adaptive power control as a countermeasure to jamming attacks on RKE systems and examines the influence of jammer placement on data transmission success rates. We conducted experiments using a custom-built jammer, transmitter, and receiver to simulate real-world scenarios. Our findings demonstrate that jammer proximity significantly affects transmission success, and while increasing key fob power can initially mitigate jamming, its effectiveness diminishes beyond a certain threshold. © 2024 IEEE.

关键词： Jamming

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Dynamic Hand Gesture Recognition Based on Short-Term Sampling Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 110-120页

作者： Wenjin Zhang Jiacun Wang Fangping Lan the Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07740 USA

Hand gestures are a natural way for human-robot *** based dynamic hand gesture recognition has become a hot research topic due to its various *** paper presents a novel deep learning network for hand gesture *** network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive *** learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow *** two entities are fused and fed into a convolutional neural network(Conv Net)for feature *** Conv Nets for all groups share *** learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is *** new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia *** with other models,our model produced very competitive *** robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.

关键词： Convolutional neural network(ConvNet) hand gesture recognition long short-term memory(LSTM)network short-term sampling transfer learning

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech Technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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IoT Security and Privacy Challenges from the Developer Perspective 14th

IoT Security and Privacy Challenges from the Developer Persp...

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14th International Symposium on Ambient Intelligence, ISAmI 2023

作者： Shaheen, Yaqin Hornos, Miguel J. Rodríguez-Domínguez, Carlos Tulkarm Palestine Software Engineering Department Higher Technical School of Computer and Telecommunications Engineering Aynadamar Campus University of Granada Granada18071 Spain

ISBN: (纸本)9783031434600

Internet of things (IoT) is a highly-demand technological sector that can be applied to most of our life aspects, such as home, work, transportation, health, education and other important areas. At the same time, its heterogeneous nature, dynamism, intelligence, and mobility makes it vulnerable and unsafe in terms of security and privacy. Finding complete answers to the existing security and privacy concerns is a very challenging task for security experts due to the variety of platforms where the IoT is available. This paper demonstrates the major and critical challenges that facing developers through their journey of building and developing solutions for IoT-based systems (IoTSs). Developers are among the key players who directly engage with the IoTSs, along with manufacturers and users. Consequently, the goal of our study is to shed light on the main difficulties that developers have while attempting to manage the aspects related to security and privacy when they create and develop an IoTS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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A Comparative Analysis of Query Generation Methods for IoT Middleware Evaluation 17

A Comparative Analysis of Query Generation Methods for IoT M...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： De Silva, Ravindi Zaslavsky, Arkady Loke, Seng W. Jayaraman, Prem Prakash Deakin University School of Information Technology Melbourne Australia Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia

ISBN: (纸本)9798350351637

The rapid expansion of Internet of Things (IoT) applications necessitates the demand for efficient IoT middleware platforms, especially Context Management Platforms (CMPs) for accessing real-time contextual information. Current IoT middleware evaluation methods lack accessibility, posing a significant challenge for standardized performance analysis of CMPs. This paper introduces a novel scenario-based context query generation approach for assessing the data retrieval performance of a CMP. The proposed approach enables scene creation using either IoT data or real-world images, employing both data-driven and image-driven scene generation methods. This paper presents the challenges, limitations, and applicability of each approach in scene generation through a qualitative evaluation. The scenes are transformed into scene graphs, enabling inference to identify situations. Both approaches generate queries reflecting dynamic real-world situations, allowing for random query loads to evaluate CMPs fairly. This paper validates the proposed approach using real-world IoT datasets and camera images with a focus on bicycle riding safety use cases. The evaluation provides insights into the contextual completeness of scene graph representations crucial for accurately capturing real-world situations. The proposed approach generates queries of varying complexity, with the only difference being the underlying mechanism of scenario generation, providing a streamlined and user-friendly method for evaluating the data retrieval performance of CMPs. © 2024 IEEE.

关键词： Middleware

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Investigation of Latest CASE Tools for Database engineering: A Systematic Literature Review

Investigation of Latest CASE Tools for Database Engineering:...

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Yousaf, Nazish Arif, Madeha Awan, Misbah Mehboob Butt, Wasi Haider Nust College of Electrical and Mechanical Engineering Department of Computer Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350345933

computer Aided software engineering (CASE) tools aid in development of software at various stages. They provide automation of processes to some extent. Database handling requires reliability and integrity with security. Multiple CASE tools help in achieving all these, reduce the cost and time as well. Database implementations are made much easier with implementation of such tools. The purpose of this Systematic Literature Review (SLR) is to investigate various CASE Tools and analyze them categorizing into various domains. This paper has covered 49 researches between years 2015 to 2022, including 18 CASE tools belonging to 12 domains of Database development and maintenance in software development process. The paper highlights various domains in which CASE tools are being used. Moreover, multiple CASE tools for database handling that are frequently being used in the identified domains are also presented. After carrying out a parametric analysis, leading tools in top domains are identified. It is concluded that benefits of CASE tools in terms of database (DB) management automation, time and cost optimization are undeniable. They are the most important and core components of an efficient software development process. © 2022 IEEE.

关键词： software design

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Super-Resolution Based ROI Enhancement Approaches for Palmprint Recognition 8

Super-Resolution Based ROI Enhancement Approaches for Palmpr...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： MacAn, Huseyin Furkan Bingol, Ozkan Turk, Salih Ekinci, Murat Gumushane University Department of Management Information Systems Gümüşhane Turkey Gumushane University Department of Software Engineering Gümüşhane Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331531492

The sizes of regions of interest (ROI) obtained from images captured by cameras in palmprint recognition may vary depending on the camera specifications and the position of the hand. The regions of interest that recognition systems can use are typically rescaled through interpolation techniques and then defined as inputs to the system. In such cases, there may be some loss of biometric data. This study aims to enhance the quality of the palmprint ROI images, which are critical for palmprint recognition systems. To achieve this, various super-resolution techniques were employed to reconstruct low-resolution ROI images to the target sizes while preserving the distinctiveness of their biometric features. This approach is designed to optimize recognition accuracy and improve data reliability in palmprint recognition processes. © 2024 IEEE.

关键词： Biometrics

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Robust In-Vehicle Diagnostic-Based Intrusion Detection System

Robust In-Vehicle Diagnostic-Based Intrusion Detection Syste...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Shafee, Ahmed Awaad, Tasneem A. Moro, Ahmed Adams State University Department of Engineering and Computer Science AlamosaCO United States Ain Shams University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Siemens Eda Cairo Egypt Siemens Digital Industries Software Integrated Electrical Systems Segment Cairo Egypt

ISBN: (纸本)9798350364910

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable;attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks. © 2024 IEEE.

关键词： Robustness (control systems)

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