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arXiv 2023年

作者： Ali Rushdi, Ali Muhammad Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University P. O. Box 80200 Jeddah21589 Saudi Arabia

A task frequently encountered in digital circuit design is the solution of a two-valued Boolean equation of the form ℎ(X, Y, Z) = 1, where ℎ: B2κ+m+n → B2 and X, Y, and Z are binary vectors of lengths κ, m, and n, representing inputs, intermediary values, and outputs, respectively. The resultant of the suppression of the variables Y from this equation could be written in the form g(X, Z) = 1 where g: B2κ+n → B2. Typically, one needs to solve for Z in terms of X, and hence it is unavoidable to resort to ‘big’ Boolean algebras which are finite (atomic) Boolean algebras larger than the two-valued Boolean algebra. Other situations which necessitate ‘big’ Boolean-equation-solving are the so-called ‘elementary problems of digital circuit design,’ which entail five vector-Boolean quantities X, Y, Z, s and t that belong to B2κ, B2m, B2n, B2l , and B2l , respectively. Consider a ‘parent’ combinational network C of two (vectorial) inputs X and Y and a vectorial output t(X, Y), and assume that network C consists of two subnetworks A and B, where subnetwork A has the single (vectorial) input X and the (vectorial) output Z(X), while network B has the two vectorial inputs Z(X) and Y and the (vectorial) output s(Z, Y), which is exactly the same as the (vectorial) output t(X, Y) of network C. The above arrangement involves three vectorial Boolean functions, namely Z(X), s(Z, Y) and t(X, Y). Three problems arise when one utilizes the information that s(Z(X), Y) and t(X, Y) are equal, together with knowledge of two of the three functions X(X), s(Z, Y) and t(X, Y) in order to deduce the third function. Two of these problems are natural examples why ‘big’ Boolean-equation-solving is warranted. Methods of solving ‘big’ Boolean equations can be broadly classified as algebraic, tabular, numerical and map methods. The most prominent among these classes are the algebraic and map methods. This paper surveys and compares these two types of methods as regards simplicity, efficiency, and usabil

关键词： Integrated circuit manufacture

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Pseudonym Mutable Based Privacy for 5G User Identity

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computer Systems Science & engineering 2021年第10期39卷 1-14页

作者： Rashid A.Saeed Mamoon M.Saeed Rania A.Mokhtar Hesham Alhumyani S.Abdel-Khalek Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of EngineeringAlzaiem Alazhari UniversityKhartoum11111Sudan Department of Communications and Electronics Engineering Faculty of EngineeringUniversity of Modern Sciences(UMS)Sana'a16784Yemen Department of Mathematics College of SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Privacy,identity preserving and integrity have become key problems for telecommunication *** privacy threats are expected in 5G networks considering the large number of devices that will be *** Internet of Things(IoT)and long-term evolution for machine type(LTE-m)are growing very fast with massive data traffic the risk of privacy attacks will be greatly *** all the above issues standards’bodies should ensure users’identity and privacy in order to gain the trust of service providers and *** such threats,5G specifications require a rigid and robust privacy *** research studies have addressed user privacy in 5G *** paper proposes a method to enhance user identity privacy in 5G systems through a scheme to protect the international mobile subscriber identity(IMSI)using a mutable mobile subscriber identity(MMSI)that changes randomly and avoids the exchange of *** maintains authentication and key agreement(AKA)structure compatibility with previous mobile generations and improves user equipment(UE)synchronization with home *** proposed algorithm adds no computation overhead to UE or the network except a small amount in the home subscriber server(HSS).The proposed pseudonym mutable uses the XOR function to send the MMSI from the HSS to the UE which is reducing the encryption overhead *** proposed solution was verified by ProVerif.

关键词： 5G MMSI IMSI AKA privacy user identity

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Deep Neural Networks for Halyomorpha Halys Detection

Deep Neural Networks for Halyomorpha Halys Detection

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Mediterranean Conference on control and Automation (MED)

作者： Alexandru Dinca Nicoleta Angelescu Loretta Ichim Dan Popescu Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania Faculty of Electrical Engineering Electronics and Information Technology Valahia University of Targoviste Targoviste Romania

Pest detection and identification in a timely manner is a crucial step for precision agriculture. Halyomorpha Halys is a common pest whose negative effects are known in agricultural areas and on various crops. The present work implemented and studied four performant neural networks, VGG19_BN, EfficientNetB7, DenseNet161, and ResNet152 for the detection of these insects. Although the detection of these insects in the natural environment through automated means, excluding traps, is a challenge, the results obtained are promising.

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Beyond Trends: Tiktok's Educational Symphony by Unmasking the Digital Revolution

Beyond Trends: Tiktok's Educational Symphony by Unmasking th...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ghani, Miharaini Md Mustafa, Wan Azani Bin Hashim, Mohd Ekram Alhafis Hanafi, Hafizul Fahri Alzubaidi, Laith H. Universiti Sains Malaysia School of Communication Penang Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau Malaysia Universiti Pendidikan Sultan Idris Faculty of Art Sustainability and Creative Industry Department of Creative Multimedia Perak Tanjong Malim Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Tanjong Malim Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

In the fast-paced digital age, TikTok has emerged as an unlikely protagonist in the realm of education, ushering in a bite-sized learning revolution. This comprehensive study delves into the captivating phenomenon of TikTok's educational content, unveiling its transformative impact on learners across generations and disciplines. Drawing from extensive empirical research and expert insights, the article will explore the intricate interplay between TikTok's snackable video format and its ability to foster knowledge acquisition and skill development. It illuminates how this platform's unique amalgamation of entertainment and education has redefined traditional learning paradigms, empowering users to consume and share knowledge in a highly engaging and democratized manner. Beyond trends unravels the cognitive mechanisms underpinning the effectiveness of bite-sized learning, shedding light on its potential to cater to diverse learning styles and attention spans. It examines the platform's role in democratizing access to education, enabling content creators and subject matter experts to reach unprecedented global audiences. Moreover, It provides a critical analysis of the challenges and opportunities that arise as bite-sized learning gains traction, offering invaluable insights for educators, policymakers, and stakeholders invested in shaping the future of education. With its multidisciplinary approach and forward-thinking perspectives, Beyond Trends serves as a comprehensive guide to navigating the digital learning landscape, empowering readers to harness the transformative potential of TikTok and its bite-sized educational content. © 2023 IEEE.

关键词： E-learning

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Optimization of Transmit Beamforming Using Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces

Optimization of Transmit Beamforming Using Channel Covarianc...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Khin Thandar Kyaw Wiroonsak Santipach Kritsada Mamat Kamol Kaemarungsi Kazuhiko Fukawa Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Information and Communications Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

We propose an unsupervised beamforming neural network (BNN) to optimize transmit beamforming in downlink multiple input single output (MISO) channels. Reconfigurable intelligent surface (RIS) panels are used to assist user equipment that does not receive sufficiently s trong s ignals f rom t he base station. To avoid frequent beam updates, the proposed BNN is based on slow-changing channel covariances and is different from most other BNNs that utilize instant channel state information. Numerical simulations show that the proposed BNN can achieve much higher sum rates than zeroforcing beamforming does when the system is heavily loaded and can drastically reduce computation time for large-scale communication systems.

关键词： Array signal processing Neural networks Reconfigurable intelligent surfaces MISO communication Downlink Numerical simulation Telecommunications

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Whisper of Understanding: Age Differences in Algorithmic Literacy Across Generations

Whisper of Understanding: Age Differences in Algorithmic Lit...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ghani, Miharaini Md Mustafa, Wan Azani Hashim, Mohd Ekram Alhafis Bin Hanafi, Hafizul Fahri Alabdeli, Haider Universiti Sains Malaysia School of Communication Penang Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau Malaysia Universiti Pendidikan Sultan Idris Faculty of Art Sustainability and Creative Industry Department of Creative Multimedia Perak Tanjong Malim Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Tanjong Malim Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

In today's digital landscape, algorithms play a pivotal role in shaping our experiences and decision-making processes. From social media feeds to online recommendations and search results, algorithms influence our daily interactions with technology. However, the understanding and literacy surrounding these algorithms vary significantly across different age groups, leading to a potential divide in how individuals engage with and comprehend the digital world. This research delves into the age differences in algorithmic literacy, exploring the contrasting experiences and challenges faced by younger generations, often referred to as "digital natives,"and older generations, termed "digital immigrants."By examining factors such as access to technology, educational opportunities, and cultural attitudes, this study aims to shed light on the root causes of these disparities. Through a multifaceted approach, including surveys, interviews, and observational studies, the research investigates the varying levels of algorithmic literacy across different age groups. It explores the potential advantages of younger generations, who have grown up immersed in digital environments, as well as the challenges faced by older generations, who may encounter resistance or difficulties in adapting to new technologies. Furthermore, this study examines the implications of age-related algorithmic literacy gaps, including the potential for digital exclusion, ethical concerns surrounding algorithmic bias and transparency, and the need for inclusive design and education initiatives. By highlighting these issues, the research aims to foster intergenerational dialogue and collaborative efforts to bridge the algorithmic literacy divide. Ultimately, this exploration of age differences in algorithmic literacy aims to contribute to the ongoing discourse on digital literacy, promoting a more inclusive and equitable digital landscape where individuals of all ages can engage with and critically understand t

关键词： Decision making

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Empowering Urban Mobility: Revolutionizing Public Transport with Efficient Planning of Electric Taxi Charging Stations Deployment in Power Distribution Networks

Empowering Urban Mobility: Revolutionizing Public Transport ...

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Smart Grid Conference (SGC)

作者： Babak Mohamadi SeyedJalal SeyedShenava Hossein Shayeghi Amir Mohammadian Peyman Zare University of Mohaghegh Ardabili and Ardabil Province Electricity Distribution Company (APED Co.) Ardabil Iran Department of Electrical and Computer Engineering Faculty of Engineering University of Mohaghegh Ardabili Ardabil Iran

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

With the increasing prevalence of Electric Taxi Public Transport Vehicles (ETPTVs) in urban transportation systems, the integration of Electric Taxi Public Transport Vehicle Charging Stations (ETPTVCS) into Power Distribution Networks (PDNs) has become a significant challenge. The rising demand for ETPTVs, driven by environmental goals and the transition towards cleaner urban mobility, adds new layers of complexity to maintaining the stability, reliability, and efficiency of distribution networks. This paper presents a comprehensive approach that addresses not only the optimal planning and deployment of ETPTVCSswithin PDNs, but also incorporates demand response programs (DRPs) and the Fuzzy Decision-Making Method (FDMM) has been used to select the optimal solution from the set of solutions of multi-objective optimization problems. From a technical standpoint, the study explores key issues such as voltage regulation, network losses, and load balancing, while also focusing on economic objectives like minimizing installation, operational, and charging costs, and improving network efficiency. To manage the complexity of station placement and scheduling, the Frilled Lizard Optimization (FLO) algorithm-a nature-inspired metaheuristic-is employed. The FLO algorithm is particularly effective in handling uncertainties related to fluctuating charging demands and dynamic network conditions. Simulation results confirm the algorithm's capability to enhance network performance, ensure reliable energy delivery, and reduce overall costs. The findings underscore the strategic importance of well-positioned ETPTVCSs, demonstrating that optimal placement can support the growing use of electric taxis while simultaneously improving both the technical and economic performance of PDNs.

关键词： Costs Heuristic algorithms Distribution networks Charging stations Demand response Planning Reliability Public transportation Optimization Genetic algorithms

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Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

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computers, Materials & Continua 2021年第8期68卷 1447-1465页

作者： T.Kowsalya R.Ganesh Babu B.D.Parameshachari Anand Nayyar Raja Majid Mehmood Department of Electronic and Communication Engineering Muthayammal Engineering CollegeRasipuramNamakkalTamilnaduIndia Department of Electronics and Communication Engineering SRM TRP Engineering CollegeTiruchirappalliTamilnaduIndia Department of Telecommunication Engineering GSSS Institute of Engineering and Technology for WomenMysuruIndia Faculty of Information Technology Graduate SchoolDuy Tan UniversityDa Nang550000Viet Nam Department of Information and Communication Technology School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive ***,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious *** order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious *** True Random Number Generator-Pseudo Random Number Generator(TRNG-PRNG)based key generation is proposed to generate the unpredictable keys,being highly difficult to predict by the ***,the hardware utilization of PRESENT architecture is optimized using the Dual port Read Only Memory(DROM).The proposed PRESENT-TRNGPRNG architecture supports the 64-bit input with 80-bit of key *** performance of the PRESENT-TRNG-PRNG architecture is evaluated by means of number of slice registers,flip flops,number of slices Look Up Table(LUT),number of logical elements,slices,bonded input/output block(IOB),frequency,power and *** input retrieval performances analyzed in this PRESENT-TRNG-PRNG architecture are Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Mean-Square Error(MSE).The PRESENT-TRNG-PRNG architecture is compared with three different existing PRESENT architectures such as PRESENT On-TheFly(PERSENT-OTF),PRESENT Self-Test Structure(PRESENT-STS)and PRESENT-Round Keys(PRESENT-RK).The operating frequency of the PRESENT-TRNG-PRNG is 612.208 MHz for Virtex 5,which is high as compared to the PRESENT-RK.

关键词： Dual port read only memory hardware utilization lightweight cryptography malicious attackers present block cipher pseudo random number generator true random number generator

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Osprey Optimization Algorithm and Position-Aware Graph Neural Network for Enhanced Vehicle-to-Grid control System

Osprey Optimization Algorithm and Position-Aware Graph Neura...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： K. Venkatesh Gulshan Dhasmana Swapnil Uttamrao Deokar Chintan Thacker K G Saravanan R. K. Padmashini Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Departement of Mechanical Engineering Smt. Kashibai Navale College of Engineering Pune Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Department of Mechatronics Engineering Sona College of Technology Salem Tamil Nadu Department of Electrical and Electronics Engineering AMET Deemed to be University Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Optimized Vehicle-to-Grid (V2G) control System enhances energy management by leveraging Electric Vehicle (EV) as flexible storage units, enabling controlled energy exchange with the grid to improve power distribution and overall system efficiency. The system encounters difficulties in reducing total EV charging and discharging plan costs while maintaining high operational efficiency and power grid integrity throughout varying electrical circumstances. To address these challenges, this paper proposes a hybrid approach for optimized V2G control system. The proposed method integrates the Osprey Optimization Algorithm (OOA) and Position-Aware Graph Neural Network (PAGNN); therefore it is called the OOA-PAGNN technique. The main goal is to reduce total costs, enhance grid stability, and generate revenue for EV owners through an optimized V2G framework. OOA is utilized to optimize the charging and discharging schedules of EVs, ensuring effective energy flow management and enhancing grid stability. While, PAGNN is utilized to predict energy demand and generation by analyzing the spatial distribution of EVs, enabling optimized energy allocation. By then, the proposed method is implemented on the MATLAB platform and evaluated with various existing approaches such as Artificial Neural Network (ANN), Mixed-Integer Quadratic Programming (MIQP), Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), Recurrent Proximal Policy Optimization-Long Short-Term Memory (RPPO-LSTM) and Competitive Swarm Optimization-Imperialist Competitive Algorithm (CSO-ICA). The OOA-PAGNN model successfully optimizes V2G control while lowering expenses by -1.458$ while maintaining 98.6% system efficiency.

关键词： Vehicle-to-grid Costs Artificial neural networks Predictive models Power system stability control systems Stability analysis Power grids Mathematical models Optimization

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A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net

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computers in Biology and Medicine 2025年 188卷 109829-109829页

作者： MohammadiNasab, Poorya Khakbaz, Atousa Behnam, Hamid Kozegar, Ehsan Soryani, Mohsen School of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Medicine Danube Private University Krems Austria School of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Technology and Engineering-East of Guilan University of Guilan Guilan Rudsar Iran

Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to traditional mammography and has recently gained considerable attention. However, reviewing hundreds of ABUS slices imposes a high workload on radiologists, increasing review time and potentially leading to diagnostic errors. Consequently, there is a strong need for efficient computer-aided detection, CADe, systems. In recent years, researchers have proposed deep learning-based CADe systems to enhance mass detection accuracy. However, these methods are highly dependent on the number of training samples and often struggle to balance detection accuracy with the false positive rate. To reduce the workload for radiologists and achieve high detection sensitivities with low false positive rates, this study introduces a novel CADe system based on a self-supervised framework that leverages unannotated ABUS datasets to improve detection results. The proposed framework is integrated into an innovative 3-D convolutional neural network called DATTR2U-Net, which employs a multi-task learning approach to simultaneously train inpainting and denoising pretext tasks. A fully convolutional network is then attached to the DATTR2U-Net for the detection task. The proposed method is validated on the TDSCABUS public dataset, demonstrating promising detection results with a recall of 0.7963 and a false positive rate of 5.67 per volume that signifies its potential to improve detection accuracy while reducing workload for radiologists. The code is available at: ***/Pooryamn/SSL_ABUS. © 2025

关键词： Self-supervised learning

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