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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Yuan, Tengfei Wang, Pengjun Zhang, Yuejun Ren, Mingze Hu, Shuang Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang315211 China Wenzhou University Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350361834

Physical unclonable function (PUF) utilizes the tiny deviations in the chip manufacturing to generate unique identity keys. Traditional PUFs are generally implemented on dedicated circuit structures, which may not be effective in resource-constrained Internet environments. To solve the above problems, this paper proposes a software PUF based on Digital Signal Processor (DSP-PUF). The DSP-PUF uses the timing violation of the internal registers of the chip to generate random response. Primarily, the mechanism of generating abnormal information between timing paths and registers in DSP chip is analyzed. Secondly, the overclocking clock is applied to make the DSP work abnormally, and the unique and unclonable random response is generated according to the dependence of the response on the path delay. Eventually, DSP-28335 is selected as the DSP-PUF carrier, and the DSP-PUF response extraction is completed by computer, oscilloscope, DSP and other equipment. The test results indicate that the 12000 bit responses generated by DSP-PUF has passed the NIST randomness test, and the worst case reliability is 88.01% in the temperature range of 20° C-80 ° C. Moreover, the proposed DSP-PUF does not use additional circuit structure, and does not need to change the existing circuit structure. It is suitable for privacy information encryption in resource constrained Internet of things (IoT). © 2024 IEEE.

关键词： Data privacy

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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4D Structured Tensor Decomposition-Based Channel Estimation for RIS-Aided mmWave MIMO-NOMA System in Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 22298-22310页

作者： Cai, Wanyuan Li, Youming Wu, Yonghong Sheng, Menglei Qi, Qinke Guo, Qiang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Research Institute of Radiowave Propagation Qingdao266107 China Harbin Engineering University College of Information and Communication Engineering Harbin150000 China

Severe Doppler shift and the obstruction of the line-of-sight (LoS) path significantly decrease the communication performance. This article considers a downlink channel estimation problem for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) system in Internet of Vehicles (IoVs) with high-mobility scenarios. By introducing the concept of aggregated slot and half slot, a 5G subframe partitioning scheme without changing the standard 5G frame structure is first proposed to facilitate the formulation of the received signal. Then, the received signal is modeled as a quadrilinear tensor, meeting with a canonical polyadic decomposition (CPD) form, which separates angle of arrival (AoA), angle of departure (AoD), time delay, and Doppler shift into four corresponding factor matrices and avoids parameters coupling. Subsequently, by leveraging the Vandermonde structure of the factor matrix and the low-rank property of the mmWave channel, we design a four-dimension (4D) structured tensor decomposition-based method to decompose the tensor into four factor matrices in a closed-form solution, which can avoid initialization and iteration. Accordingly, the channel parameters can be extracted by a simple correlation-based estimator. After obtaining the channel parameters, we construct a least squares problem to obtain the channel path gain, which can avoid solving the scaling matrix. Finally, numerical experiments are conducted to confirm the effectiveness of the proposed algorithm, in which the Cramér-Rao bound (CRB) results for channel parameters are derived as the benchmark. © 2014 IEEE.

关键词： Least squares approximations

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A Novel Complexity Scoring Model for Non-Functional Requirements in Request for Proposal in Housing Industry Using FURPS Quality and Moscow Priority Model

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International Journal for Housing science and Its Applications 2024年第4期45卷 30-40页

作者： Rashid, Ekbal Mastorakis, Nikos E. Technical University of Sofia Sofia Bulgaria Sector of Electrical Engineering and Computer Science Hellenic Naval Academy Piraeus Greece English Language Faculty of Engineering Technical University of Sofia Sofia Bulgaria

In this research paper we have attempted to elicit Non-Functional Requirements (NFR) which may or not have been explicitly added to the Request for Proposal (RFP) in housing industry but is important for the success of the program. We have used real time RFPs for the elicitation of NFRs. The sales proposal process, also known as response to RFP process, refers to the methodical steps a vendor takes when developing a proposal in response to a buyer’s RFP which will have details of integrating systems, Software platforms, users, business outcomes, limitations, functional requirements, and may be nonfunctional requirements (NFR). There is some work done by researchers on assessing opaqueness of NFRs and traceability of NFR. But there has been no work on a complexity scoring model for NFRs which enables a vendor to respond to an RFP with best price and schedule. The paper proposes a novel complexity scoring model for NFRs in RFP in Housing industry by using FURPS (Functionality, Usability, Reliability, Performance, Supportability) quality attribute model in conjunction with MoSCoW(Must Have, Should Have, Could Have, Won’t Have) priority model by using mathematical formula. The working model is broken down in steps for Sales and pre-Sales teams of the Vendor and is ready for adoption. © 2024 International Association for Housing science. All rights reserved.

关键词： complexity scoring model construction cost FURPS housing linear regression MoSCoW NFR RFP scatter plot

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Enhancing Trustworthiness and Interoperability of Electronic Voting Systems through Blockchain Bridges

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 749-760页

作者： Rexha, Blerim Neziri, Vehbi Dervishi, Ramadan Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosovo10000 Department of Computer Science and Engineering UBT Higher Education Institution Prishtina Kosovo10000 Department of Computer Science Universum Higher Education Institution Prishtina Kosovo10000

Decentralized applications leveraging blockchain technology are gaining widespread adoption within the decentralized applications ecosystem. Interoperability, a fundamental concept facilitating seamless data and processing power exchange across diverse blockchain networks, is paramount in this context. The primary objective of this paper is to explore the transformative potential of "blockchain bridges" in facilitating secure and transparent electronic voting processes across multiple blockchain networks. The study employs a comprehensive analysis of various approaches, including atomic exchanges, sidechains, cross-chain bridges, token wrappers, and interledger protocols. The selection of a specific method is guided by the unique requirements and privacy considerations of the electronic voting use case. The application of two distinct blockchains serves as a practical demonstration, illustrating the principles of blockchain bridges in real-world scenarios. The research reveals that blockchain bridges not only streamline the exchange of data between diverse blockchain networks but also establish a dual decentralization paradigm. This paradigm enables the creation of openly maintained, purpose-specific, decentralized ledgers for electronic voting. The integration of blockchain bridges significantly reduces the risk of fraud, instilling greater confidence in the accuracy of election results. Thus, by presenting a comprehensive array of approaches and emphasizing their practical application, this research contributes to advancing the understanding and implementation of blockchain technology in the critical domain of electronic voting. © 2023, Ital Publication. All rights reserved.

关键词： Blockchain

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Extended-Attention Applied in Selected Stages 7

Extended-Attention Applied in Selected Stages

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Zeyu Todo, Yuki Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798350350890

The Self-attention mechanism was widely applied in computer Vision (CV) field. However, while Self-attention mechanism brings high accuracy to Transformer, it also brings a huge amount of calculation. To further enhance accuracy and decrease computational load, in this paper, two refinements were implemented. Firstly, two matrices were introduced to enhance the extraction of image information for Extended-attention method. The capacity of Transformer was enhanced by these matrices to capture crucial information in complex tasks, the final accuracy and generalization performance were significantly improved. Secondly, the impact of implementing the aforementioned Self-attention mechanism at various stages of the Transformer is investigated. Intriguingly, our findings reveal that selectively implementing this method at specific stages leads to a significant enhancement in the model's performance. © 2024 IEEE.

关键词： Deep learning Different Stages Self-attention Transformer

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Resource Allocation and Edge Computing for Dual Hop Communication in Satellite Assisted UAVs enabled VANETs

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 108-127页

作者： Hamdi, Mustafa Maad Rashid, Sami Abduljabbar Nafea, Ahmed Adil Department of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Johor Batu Pahat86400 Malaysia Department of Artificial Intelligence College of Computer Science and IT University of Anbar Ramadi Iraq

VANETs are highly attractive and is used in maximum of the applications of cross-regional communication. To increase the coverage of the vehicular network, Unmanned Arial Vehicles (UAVs) are introduced, and they get connected with the satellite networks to perform heterogeneous communication. With the help of this connectivity, the communication quality of ground level to air medium is increased. Currently the vehicle usage is highly increased and as a results of communication link failure, improper resource allocation are arises whither abruptly assumes a stability about a network with that increases an energy consumption and communication delay in the heterogeneous networks. In these conditions, thus study is idea of Resource Allocation and Edge Computing for Dual Hop Communication (RAEDH) in introduced in satellite assisted UAVs enabled VANETs. The major sections of the approach are UAV assisted mobile computing, resource allocation among the vehicles and the UAVs, and dual communication among the vehicles and the *** these methods the input resources are properly allocated and that reduces the power utility and communication delay. Initially, the vehicular network is established, incorporating trusted components like TA, RSU, and CRS. Subsequently, mobile edge computing reduces energy consumption through computation offloading and optimized UAV trajectory selection. Resource allocation, facilitated by whale optimization, ensures effective utilization across vehicles. The implementation of this method is done in NS3, and the scenario is analyzed using two parameters like number of vehicles and its speed. The output parameters that remain thought-out over a performance examination stay throughput, end-to-end delay, energy efficiency, packet loss, packet delivery ratio, and routing overhead, and as well those results are compared with the earlier methods. Finally, dual-hop transmission between vehicles and UAVs enhances delivery ratio and throughput. From

关键词： Satellites

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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