Computers & Electrical Engineering最新文献

{"title":"Performance improvement of MIMOOFDM communication systems based on non-linear equalization","authors":"Khaled Ramadan","doi":"10.1016/j.compeleceng.2025.110287","DOIUrl":"10.1016/j.compeleceng.2025.110287","url":null,"abstract":"<div><div>This paper introduces a nonlinear equalizer for Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems based on Discrete Wavelet Transform (DWT). It is referred to as the Joint Non-Linear Combining Zero Forcing-Successive Interference Cancellation (JNLCZF-SIC) equalizer. Unlike traditional equalizers, the JNLCZF-SIC equalizer performs both equalization and Carrier Frequency Offset (CFO) correction simultaneously. The main advantage of the proposed JNLCZF-SIC equalizer over existing methods is its ability to reduce simulation times without compromising performance by minimizing the number of arithmetic operations required. To assess its effectiveness, we evaluate its Bit-Error-Rate (BER) and computational load against standard equalization techniques such as Linear Zero-Forcing (LZF), Linear Minimum Mean Squared Error (LMMSE), and MMSE-Successive Interference Cancellation (MMSE-SIC) equalizers. Simulation results reveal that the JNLCZF-SIC equalizer not only achieves a lower BER but also significantly decreases computational complexity. This improvement in simulation time is particularly beneficial for large-scale MIMO systems, where DWT is employed to accelerate processing by breaking down the signal into multiple frequency bands, optimizing the equalization process. The equalizer's ability to simultaneously handle equalization and CFO compensation with reduced computational demands makes it highly suitable for real-time applications and large-scale implementations. Additionally, a closed-form expression for the count of arithmetic operations for the proposed equalizer in a general 2^σ × 2^σ MIMO configuration is provided.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110287"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Underwater target detection algorithm based on multi-scale feature fusion","authors":"Xiao Chen ,&nbsp;Qi Yang ,&nbsp;Xiaoqi Ge ,&nbsp;Jiayi Chen ,&nbsp;Haiyan Wang","doi":"10.1016/j.compeleceng.2025.110327","DOIUrl":"10.1016/j.compeleceng.2025.110327","url":null,"abstract":"<div><div>Underwater target detection technology is an important way to realize the biological monitoring of the marine ranching. Nevertheless, since light absorption of the water and particles in the water will have a scattering effect on the light, the majority of the captured underwater optical images have low contrast and color bias, which blurs the local details of the measured biological targets. In addition, due to the mutual occlusion of underwater organisms, which will lead to a higher rate of missed targets detection. To address the above problems, we propose a new target detection algorithm for complex underwater environments based on the YOLOv7 (You Only Look Once Version 7). Firstly, in order to enhance the underwater image quality, while improving the color bias and reducing the image noise. By comparing different image enhancement methods, RGHS (Relative Global Histogram Stretching) is used to enhance the quality of dataset. Secondly, in order to realize the network's perception of biological detail features, the MIS (Multi-Gradient Interaction Structure) is proposed. The structural innovation aims to mitigate the challenges posed by target proximity and overlap. Finally, after the backbone feature extraction, we propose the multiscale feature fusion structure. It facilitates the cross-channel communication of semantic information, thereby enhancing the network's capability to express features. The experimental results indicate that our proposed algorithm has achieved a detection accuracy improvement of 1.35 % compared to the original YOLOv7 (You Only Look Once Version 7). Simultaneously, it has reduced instances of missed detections and misjudgments of underwater targets, surpassing common object detection algorithms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110327"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement framework for the consistent and fast measurement of conducted grid emissions in the 9-500 kHz range","authors":"Alexander Gallarreta ,&nbsp;Jon González-Ramos ,&nbsp;Stefano Lodetti ,&nbsp;Peter Davis ,&nbsp;Igor Fernández ,&nbsp;David de la Vega ,&nbsp;Itziar Angulo ,&nbsp;Amaia Arrinda","doi":"10.1016/j.compeleceng.2025.110314","DOIUrl":"10.1016/j.compeleceng.2025.110314","url":null,"abstract":"<div><div>There is currently no standardized measurement framework for the characterization of conducted emissions in the low-voltage grid for the 9–500 kHz range. Several methods are proposed for the 9–150 kHz band in power quality standards, while the only method defined for the 150–500 kHz range is not designed for power quality measurements. The different configuration of the existing methods prevents consistency of results in the entire band.</div><div>This work analyzes drawbacks of the current standardized methods and summarizes the recently published research methods, to propose a measurement framework for the characterization of conducted emissions in the 9–500 kHz range. This framework solves the identified drawbacks by proposing a set of six measurement methods to obtain different metrics for consistent results in the whole range. For that, the use of a common method for obtaining root-mean-square values in the entire 9–500 kHz range is proposed to assess aggregated values and quasi-peak (QP) outputs. Two novel techniques (Approximated-QP and Conservative-QP) are also defined to cover the existing gap in the literature in the estimation of the QP spectra in the 150–500 kHz band. The measurement framework includes a time-frequency domain analysis method to detect and characterize impulsive emissions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110314"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The stochastic economic model for integrated energy system with carbon mechanism","authors":"Chong Cheng,&nbsp;Shufei Li","doi":"10.1016/j.compeleceng.2025.110304","DOIUrl":"10.1016/j.compeleceng.2025.110304","url":null,"abstract":"<div><div>As energy conversion technology progresses and evolves, interconnected energy networks with diverse attributes have emerged. The traditional model of energy networks has changed from isolated operation to the combined optimization and scheduling of integrated energy system. To efficiently decrease carbon dioxide emissions, it is necessary to vigorously develop green energy and fully consider carbon emission factors. To tackle the aforementioned issue, this paper proposes a stochastic optimal economic model within the carbon mechanism for integrated energy system. The model incorporates carbon trading, the volatility of renewable energy, and the deviation of real-time electricity prices into the objective function. By applying convex and robust optimization approaches, the primary problem is divided into the master problem and subproblem characterized by mixed-integer linearity, subsequently addressed iteratively. Convex optimization is mainly used to transform the deviation term of electricity price, and robust optimization is employed to find out the economically optimal scheme based on the worst scenario. Simulation results indicate that as the uncertainty in electricity market prices decreases, the electricity market cost can be reduced by up to 22.28 %, while the total cost of the integrated energy system can be reduced by up to 8.56 %. Finally, in the context of the carbon mechanism, the proposed stochastic optimization model was validated through simulations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110304"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of source of image degradation using a new hybrid dense F-LSTM network and image restoration","authors":"Poonam Yogesh Pawar,&nbsp;Dr. Bharati Sanjay Ainapure","doi":"10.1016/j.compeleceng.2025.110309","DOIUrl":"10.1016/j.compeleceng.2025.110309","url":null,"abstract":"<div><div>Image degradation has been garnering huge interest from researchers in the field of image processing and computer vision as images may contain several degradations that lead to different types of blurring, noise, and distortions. Hence, a new hybrid optimized network named DenseNet Fused Long Short Term Memory (Dense F-LSTM) is proposed for the identification of the source of image degradation. Initially, the input image with noise and blur is taken from the database and is subjected to noisy pixel identification, and identification of image source degradation, where the noise pixel identification is performed by Deep Convolutional Neural Network (DCNN), and the image source degradation is employed by proposed Dense F-LSTM. The identified noisy pixel is fed to the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter for noisy correction for obtaining the denoised images. Once the image source is identified, the identified source of image degradation and the denoised image is used for performing deblurring. Here, deblurring is done by kernel estimation, which is optimally selected employing Jaya Walrus Optimization (JWaO). The JWaO is formed by the integration of the Jaya Algorithm (JA) and Walrus Optimization Algorithm (WaOA). Both the denoised image and deblurred image are fused to gain the final output image. The devised Dense F-LSTM method provided superior identification performance and achieved a Second-Derivative-like Measure of Enhancement (SDME) is 59.956, Peak Signal Noise Ratio (PSNR) is 49.924 dB and Structural Similarity Index Measure (SSIM) is 0.979, and Mean Squared Error (MSE) of 0.2.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110309"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile node authentication with risk-based modified lattice cryptography enabled adaptive multifactor authentication for secure data transmission","authors":"Qureshi Imran M Hussain,&nbsp;Vijay Kale","doi":"10.1016/j.compeleceng.2025.110272","DOIUrl":"10.1016/j.compeleceng.2025.110272","url":null,"abstract":"<div><div>With the rapid proliferation of mobile services, the strategies for protecting the availability, confidentiality, and integrity of systems emerged to meet the demands of mobile networks. More specifically, the insecure mobile networks are more prone to different attacks and interrupt secure data communications between legitimate users. Despite the intensive research, the design of a secure and efficient multi-factor authentication scheme is still a challenging problem that ends with poor security, poor session management, and vulnerability to new and enhanced attacks. In order to tackle the challenges in the existing techniques, Risk-based modified lattice cryptography-enabled adaptive multifactor authentication (R-mLC-AMf) is proposed in the research. The ultimate intention of the research lies in the authentication of mobile nodes specifically in case of handover, or even reconfiguration with the same network that happens due to the risk factors. The proposed authentication in the research is performed with the AMf mechanism that authenticates the user with the help of multi-factors that evolve dynamically based on environmental factors. Specifically, the application of modified lattice cryptography into the research model works in encrypting the data and improves the security against attacks, addresses the scalability issues, and provides high computation efficiency. Further, the incorporation of Delegated Proof of Stake (DPoS) in the smart contract of blockchain addresses effective security purposes and offers the benefits of high transaction volume, low cost, faster confirmation time, and high efficiency. The Simulation results demonstrate that the proposed mechanism is effective even in the presence of malicious attacks in the network.Finally, the performance evaluation demonstrates that the proposed approach is efficient in terms of decryption time, encryption time, Genuine User Rate (GUR), memory, responsiveness, and time complexity that attained 1.091 ms, 0.895 ms, 0.87, 131.256 KB, 0.343 ms, and 0.497 ms respectively outperforming the other state-of-the-art approaches.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110272"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced fall prevention: A real-time hybrid analysis with smart walking stick & Edge-based IoMT","authors":"Pratik Bhattacharjee ,&nbsp;Indranil Sarkar ,&nbsp;Suparna Biswas","doi":"10.1016/j.compeleceng.2025.110312","DOIUrl":"10.1016/j.compeleceng.2025.110312","url":null,"abstract":"<div><div>Human fall poses a significant risk for the elderly. A fall can result in hospitalization or, tragically, death. The primary causes of falls among the elderly are often linked to a loss of balance or insufficient limb support.</div><div>While fall prevention is difficult, fall risk analysis can help predict and prevent future falls. The present work proposes a real-time 3-way hybrid fall risk factor analysis methodology, , employing a smart walking stick. It uses an Edge-based IoMT (Internet of Medical Things) architecture that is extendable to the Cloud. The smart walking stick has a 10 kg load cell (YZC-133) with HX711 (mounted on the grip) and a MPU 6050 kinematic sensor paired with an ESP8266 WiFi Micro Controller (MCU) to transfer the accelerometer, gyroscope and load cell data to the processing unit.</div><div>A Raspberry Pi-based edge device evaluates pressure (support) on the grip and walking patterns using an accelerometer, gyroscope, and load sensor signals connected with ESP 8266. The system could perform fine grain fall analysis by classifying the subjects into the risk categories of High/Medium/Low/None. The system used the clinically established parameters and tests for its multimodal analysis at low cost based on Timed Up and Go (TUG), Force test and Gait analysis modules. The individual results from each module were then combined to predict the final risk category. The long-term analysis is done on a cloud server and the system could predict falls with a maximum of 92% accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110312"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensure authentication and confidentiality in blockchain-based IoT with cryptanalysis and machine learning in 6G-enabled heterogeneous IoT-Blockchain","authors":"Dr. Bharati B Pannyagol ,&nbsp;Dr. Santosh L Deshpande","doi":"10.1016/j.compeleceng.2025.110303","DOIUrl":"10.1016/j.compeleceng.2025.110303","url":null,"abstract":"<div><div>Presently, the Internet of Things (IoT) is considered the major network, which includes multiple intelligent devices for interacting and transferring data throughout the Internet. The IoT is applicable in commercial, military, and industrial applications. With the popularity of IoT devices, security is the main issue for data privacy. Hence, authentication and secure data transmission are crucial parameters for the secure communication of IoT devices. Moreover, blockchain is employed for providing efficient data sharing and secure authentication in IoT. This research proposes authentication based on data protection (DPro_Auth) for blockchain-based IoT. Entities like users, IoT devices, blockchain, and a smart contract are utilized to attain authentication and confidentiality. The steps like initialization, registration, key generation, authentication, data verification, and access control are carried out. The initialization is based on the physical unclonable function-based chaotic pseudorandom number generators (PUF-CPRNG). Moreover, the pseudorandom number generator-based pseudo-randomly enhanced logistic map (PRNG–PELM) is employed in the key generation process. In addition, the memory, and computational time are used to validate the DPro_Auth, with the finest outcome of 5.277 MB, and 0.256 s are obtained.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110303"},"PeriodicalIF":4.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"G-VAE: Variational autoencoder-based adversarial attacks and defenses in industrial control systems","authors":"Lijuan Xu,&nbsp;Zhi Yang,&nbsp;Dawei Zhao,&nbsp;Fuqiang Yu,&nbsp;Yang Zhou,&nbsp;Hu Zhang","doi":"10.1016/j.compeleceng.2025.110290","DOIUrl":"10.1016/j.compeleceng.2025.110290","url":null,"abstract":"<div><div>The industrial control domain is increasingly focused on addressing the cybersecurity challenges posed by adversarial sample attacks. A key difficulty in such attacks on industrial control systems (ICS) is the failure to account for the complex dependencies among various features, making it challenging to learn the relationships between multiple sensors and establish constraints for representing multidimensional data in this domain. Additionally, defending against adversarial samples is hindered by the existence of multiple detection methods and the challenge of creating a defense model without being aware of adversarial samples beforehand. To tackle these challenges, this paper proposes a gated recurrent unit (GRU)-based variational autoencoder (VAE) method for both attacking and defending against adversarial samples. Our approach involves training a GRU model to understand the intrinsic interactions among sensors and then adding perturbations to generate adversarial samples that adhere to feature constraints. On the defense side, we introduce a VAE Feature Weight (VAE-FW) method, which operates without explicit information about the adversarial samples. To make sure that characteristics with the worst prediction outcomes do not dominate anomaly scores in VAE-FW, we equalize the prediction errors across various features. Experiments conducted on three real-world sensor datasets demonstrate that our adversarial attack method significantly enhances attack efficiency while confirming its effectiveness. Furthermore, our defense method, VAE-FW, detects anomalies with greater accuracy than current baseline anomaly detection methods, achieving an average increase of 28.8% in AUC values.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110290"},"PeriodicalIF":4.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Paradigm of Hallucinations in AI-driven cybersecurity systems: Understanding taxonomy, classification outcomes, and mitigations","authors":"Aditya K Sood ,&nbsp;Sherali Zeadally ,&nbsp;EenKee Hong","doi":"10.1016/j.compeleceng.2025.110307","DOIUrl":"10.1016/j.compeleceng.2025.110307","url":null,"abstract":"<div><div>The adoption of AI to solve cybersecurity problems is occurring exponentially. However, AI-driven cybersecurity systems face significant challenges due to the impact of hallucinations in Large Language Models (LLMs). In AI-driven cybersecurity systems, hallucinations refer to instances when an AI model generates fabricated, inaccurate, and misleading information that impacts the security posture of organizations. This failure to recognize and misreport security threats identifies benign activities as malicious, invents insights not grounded to actual cyber threats, and causes real threats to go undetected due to erroneous interpretations. Hallucinations are a critical problem in AI-driven cybersecurity because they can lead to severe vulnerabilities, erode trust in automated systems, and divert resources to address non-existent threats. In cybersecurity, where real-time, accurate insights are vital, hallucinated outputs—such as mistakenly generated alerts, can cause a misallocation of time and resources. It is crucial to address hallucinations by improving LLM accuracy, grounding outputs in real-time data, and implementing human oversight mechanisms to ensure that AI-based cybersecurity systems remain trustworthy, reliable, and capable of defending against sophisticated threats. We present a taxonomy of hallucinations in LLMs for cybersecurity, including mapping LLM responses to classification outcomes (confusion matrix components). Finally, we discuss mitigation strategies to combat hallucinations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110307"},"PeriodicalIF":4.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}