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On the Design of Capacity-Achieving Distributions for Discrete-Time Poisson Channel with Low-Precision ADCs

IEEE Transactions on Vehicular technology 2025年

作者： Li, Qianqian Li, Lintao Liu, Lixiang Yang, Lei Gong, Caihong Li, Hua Hao, Shiya Dai, Xiaoming University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Beijing100094 China Zhongyuan University of Technology School of Electronic and Infomation Engineering Zhengzhou451191 China University of Science and Technology Beijing Shunde Innovation School Foshan528399 China

This paper investigates the design of the capacity-achieving input distribution for the discrete-time Poisson channel (DTPC) under dark current effects with low-precision analog-to-digital converters (ADCs). An efficient algorithm integrating the Newton-Raphson and Blahut-Arimoto (BA) methods is proposed to jointly optimize the input distribution and amplitude levels under both peak and average power constraints. Additionally, the Karush-Kuhn-Tucker (KKT) conditions are established to provide necessary and sufficient conditions for the optimality of the obtained capacity-achieving distribution. Simulation results illustrate that the proposed algorithm attains 72% and 83% of the theoretical capacity at 5 dB for 1-bit and 2-bit quantized DTPC, respectively. Furthermore, for a DTPC with finite-precision quantization (i.e., log2K bits), the capacity can be achieved by a non-uniform discrete input distribution with support for K mass points, under the given power constraints. © 1967-2012 IEEE.

关键词： analog-to-digital converter Capacity-achieving distribution discrete-time Poisson channel non-uniform discrete input distribution

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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Raphael Henshaw Ekpo Victor Chukwudi Osamor Ambrose A. Azeta Kayode Akindeji Adeyinka Elizabeth Orelaja Department of Computer and Information Sciences Covenant University Ota Ogun State Nigeria Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia Department of Electrical Power Engineering Faculty of Engineering and The Built Environment Durban University of Technology South Africa Department of Computer Science and Quantitative Methods Clarksville Tennesse U.S.A

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers’ model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset.

关键词： Surveys Pediatrics Hospitals Computational modeling Predictive models Benchmark testing Data models

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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Information technology (ACIT)

作者： Ahmed BaniMustafa Hazem Qattouz Sattam Almatarneh Khalid Mansour Raed Alazaidah Olla Bulkrock Computer Information Science Dept Higher Colleges of Technology Abu Dhabi UAE Software Engineering Dept Princess Sumaya University for Technology Amman Jordan Data Science & Artificial Intelligence Dept Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Faculty of Computer Studies Arab-Open University Bahrain Bahrain

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment.

关键词： Support vector machines Plant diseases Pathogens Machine learning algorithms Pesticides Soil Cameras Real-time systems Mobile applications Convolutional neural networks

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Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Relationship between Organization's Physical and Psychological Environment and Employees' Mental Satisfaction: An Empirical Analysis of Employee Turnover in software Industry

Relationship between Organization's Physical and Psychologic...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Sadia Khalid Usman Qamar Uzair Rahseed Wasi Haider Butt Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9781665464789

With all the awareness and work on mental health being done, the software industry is often left out. The study aims to find commonly reported physical and psychological factors linked to employee's mental satisfaction and loyalty to the organization and then, in their light, gauge the current scenario of software houses of Lahore, Pakistan. The study finds literature published since 2000 and develops a questionnaire to look into environment provided by software houses. The findings show how the physical environment is likeable for the employees but their loyalty and mental health is being compromised by a number of psychological factors, including biased rules and limited growth opportunities and how this impacts their desire to quit. Also, the findings of this study prove that software industry needs to take the mental wellbeing of its employees seriously as qualitative software products influence all walks of life.

关键词： Industries Employee welfare Decision making Organizations Switches Mental health software

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Imperceptible and Reliable Adversarial Attack 1

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4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Image Stitching Based on Multi-Scale Meshes

Image Stitching Based on Multi-Scale Meshes

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IEEE International Conference on Image Processing

作者： Yixuan Li Haotian Zhao Qi Jia Nan Pu International School of Information Science & Engineering Dalian University of Technology School of Software & Microelectronics Peking University School of Computer Science University of Trento

Generating high-quality stitching images with a natural structure is a challenging task in computer vision. Recent image stitching methods based on warps failed to suppress the distortion of the images. They often bend the salient lines in the image, which is inconsistent with human perception. In this paper, we succeed in proposing a novelty model called multi-perspective warps for natural image stitching which is related to the density of feature points in the images. With it we can get more precise matching results. Three new energy terms are developed to stitch quality to specify and balance the expected for aligning the vertices of the multi-scale mesh, which can constrain the transformation of the mesh. We also explore and introduce three feature point reconstruction algorithms to enrich the features in the images. Extensive experiments demonstrate that the proposed method outperforms most state-of-the-arts by effectively preserving the linear structure in the image and improving the robustness.

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Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation

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computer Modeling in engineering & Sciences 2022年第4期131卷 73-95页

作者： Jia Chen Zhiqiang He Dayong Zhu Bei Hui Rita Yi Man Li Xiao-Guang Yue School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Department of Economics and Finance/Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHongkongChina Rattanakosin International College of Creative Entrepreneurship Rajamangala University of Technology RattanakosinNakhon Pathom73170Thailand Department of Computer Science and Engineering School of SciencesEuropean University CyprusNicosia1516Cyprus CIICESI ESTGPolit′ecnico do Porto4610-156FelgueirasPortugal

Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment *** 2015,U-Net-based approaches have been widely used formedical image *** purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature ***,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level *** high-level information can make the segmentationmore *** this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level *** MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive *** proposed MU-Net architecture is evaluated based on three different medical imaging *** experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different *** the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.

关键词： Medical image segmentation MU-Net(multi-path upsampling convolution network) U-Net clinical diagnosis encoder-decoder networks

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Deep Learning-Driven Simultaneous Layout Decomposition and Mask Optimization

Deep Learning-Driven Simultaneous Layout Decomposition and M...

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作者： Zhong, Wei Hu, Shuxiang Ma, Yuzhe Yang, Haoyu Ma, Xiuyuan Yu, Bei DUTRU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China

Combining multiple patterning lithography (MPL) and optical proximity correction (OPC) pushes the limit of 193-nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization (MO) process to select the best candidate for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and MO (LDMO) flow. In this article, we propose a convolutional neural network (CNN)-based prediction and integrate it into our new high-performance LDMO framework. The optimization process can be considerably improved as the decomposition quality has been inferred in the early phase. To facilitate the network training and ensure better estimation accuracy, we develop sampling strategies for both layout and decomposition. Moreover, we enhance the layout sampling approach by adopting autoencoder to distance evaluation that promises superior sampling results. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms. © 1982-2012 IEEE.

关键词： Manufacture

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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