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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham science Publishers.

关键词： Quantum computers

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A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things

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High-Confidence Computing 2024年第3期4卷 13-24页

作者： Rongxin Guo Zhenping Guo Zerui Lin Wenxian Jiang College of Engineering Huaqiao UniversityQuanzhou 362000China College of Computer Science and Technology Huaqiao UniversityXiamen 361021China

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence *** solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance(PBFT)in IoT scenarios,a hierarchical consensus protocol called DCBFT is *** all,we propose an improved k-sums algorithm to build a two-level consensus cluster,achieving an hierarchical management for IoT ***,A scalable two-level consensus protocol is proposed,which uses a multi-primary node mechanism to solve the single-point-of-failure *** addition,a data synchronization process is introduced to ensure the consistency of block data after view ***,A dynamic reputation evaluation model is introduced to update the nodes’reputation values and complete the rotation of consensus nodes at the end of each consensus *** experimental results show that DCBFT has a more robust dynamic and higher consensus ***,After running for some time,the performance of DCBFT shows some improvement.

关键词： IoT Hierarchical consensus Clustering Reputation evaluation model Practical byzantine fault tolerance

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Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique

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computer Systems science & engineering 2023年第8期46卷 2589-2599页

作者： P.M.Siva Raja S.Brinthakumari K.Ramanan Computer Science and Engineering Amrita College of Engineering and TechnologyNagercoil629901India Information Technology K.C College of Engineering and Management Studies and ResearchThaneIndia Computer Science and Engineering NPR College of Engineering and TechnologyDindigul629902India

Brain tumor detection and division is a difficult tedious undertaking in clinical image *** it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great *** is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain *** have a difficult time sorting and classifying tumors from multiple *** tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are *** clinical characteristics are then retrieved and analyzed statistically to identify brain *** use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative *** dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture *** segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.

关键词： Brain tumor detection image segmentation nonlinear teager kaiser filtering

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Prediction of Burn-Through Point in Sintering Process Based on Improved Hybrid Optimization Algorithm and TCN-BiGRU-AM 5

Prediction of Burn-Through Point in Sintering Process Based ...

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5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Jiayin Liao, Liefa Wu, Jiale College of Software Engineering Jiangxi University of Science and Technology Nanchang China College of Software Engineering Jiangxi University of Science and Technology Jiangxi Modern Polytechnic College Nanchang China

ISBN: (纸本)9798331528911

Sintering is a crucial upstream process in the steelmaking process, and accurately predicting the burning through point (BTP) is vital for the yield and quality of sintered ore. The rise of artificial intelligence and deep learning has introduced innovative data-driven methods for predicting the BTP. In this paper, we propose a model that combines Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism to predict the sintering end point. Before training the model, we employ an innovative Improved Grey Wolf Optimizer-Whale Optimization Algorithm (IGWO-WOA) to find the optimal hyperparameters for the model. To validate the proposed method, we conduct experiments using actual production data from a steel company, comparing the proposed model with pre-improvement algorithms and single models. The results indicate that the proposed method has good prediction accuracy, with a Mean Absolute Error (MAE) of 0.0306 and a Root Mean Square Error (RMSE) of 0.0619, confirming the effectiveness of the proposed method. © 2024 IEEE.

关键词： Steelmaking

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Fatigue driving detection of urban road at night based on multimodal information fusion

Advances in Transportation Studies

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Advances in Transportation Studies 2023年第SPECIAL ISSUE期2卷 171-188页

作者： Wang, W.X. Sun, B.G. Xia, R. College of Computer Engineering Chongqing College of Humanities Science & Technology Hechuan Chongqing401520 China

Due to the poor accuracy, low efficiency and poor stability of fatigue driving detection of urban road at night, this paper proposes a fatigue driving detection of urban road at night based on multimodal information fusion. Firstly, the multi parameter extraction of fatigue driving state of driver's eyes, mouth and head is completed;Then, based on multimodal information fusion rules, the weighted average method is used to measure fatigue parameters and achieve classification of fatigue state levels. Finally, the fatigue detection model of the neural network is established, and the driver's fatigue detection is completed through SVM model classification. The experimental results show that this method can effectively realize accurate detection of fatigue driving of urban roads at night. © 2023, Aracne Editrice. All rights reserved.

关键词： Fatigue testing

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LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

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Context-aware API recommendation using tensor factorization

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science China(Information sciences) 2023年第2期66卷 78-93页

作者： Yu ZHOU Chen CHEN Yongchao WANG Tingting HAN Taolue CHEN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Birkbeck University of London

An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces(APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose contextaware API recommendation using tensor factorization(CARTF), a novel API recommendation approach in considering programmers' working context. To this end, we use tensors to explicitly represent the queryAPI-context triadic relation. When a new query is made, CARTF harnesses word embeddings to retrieve similar user queries, based on which a third-order tensor is constructed. CARTF then applies non-negative tensor factorization to complete missing values in the tensor and the Smith-Waterman algorithm to identify the most matched context. Finally, the ranking of the candidate APIs can be derived based on which API sequences are recommended. Our evaluation confirms the effectiveness of CARTF for class-level and method-level API recommendations, outperforming state-of-the-art baseline approaches against a number of performance metrics, including SuccessRate, Precision, and Recall.

关键词： API recommendation tensor factorization context awareness word embedding intelligent software development

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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An Ophthalmic Evaluation of Central Serous Chorioretinopathy

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computer Systems science & engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar Department of Information Technology Jeppiaar Engineering CollegeChennai600119India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

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E-SWAN:Efficient Sliding Window Analysis Network for Real-Time Speech Steganography Detection

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computers, Materials & Continua 2025年第3期82卷 4797-4820页

作者： Kening Wang Feipeng Gao Jie Yang Hao Zhang School of Engineering and Technology Jiyang College of Zhejiang A & F UniversityZhuji311800China College of Mathematics and Computer Science Zhejiang A & F UniversityHangzhou311300China

With the rapid advancement of Voice over Internet Protocol(VoIP)technology,speech steganography techniques such as Quantization Index Modulation(QIM)and Pitch Modulation Steganography(PMS)have emerged as significant challenges to information *** techniques embed hidden information into speech streams,making detection increasingly difficult,particularly under conditions of low embedding rates and short speech *** steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech *** address these challenges,this paper proposes an Efficient Sliding Window Analysis Network(E-SWAN),a novel deep learning model specifically designed for real-time speech steganalysis.E-SWAN integrates two core modules:the LSTM Temporal Feature Miner(LTFM)and the Convolutional Key Feature Miner(CKFM).LTFM captures long-range temporal dependencies using Long Short-Term Memory networks,while CKFM identifies local spatial variations caused by steganographic embedding through convolutional *** modules operate within a sliding window framework,enabling efficient extraction of temporal and spatial *** results on the Chinese CNV and PMS datasets demonstrate the superior performance of *** conditions of a ten-second sample duration and an embedding rate of 10%,E-SWAN achieves a detection accuracy of 62.09%on the PMS dataset,surpassing existing methods by 4.57%,and an accuracy of 82.28%on the CNV dataset,outperforming state-of-the-art methods by 7.29%.These findings validate the robustness and efficiency of E-SWAN under low embedding rates and short durations,offering a promising solution for real-time VoIP *** work provides significant contributions to enhancing information security in digital communications.

关键词： Steganalysis speech convolutional sliding window deep learning

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