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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Nedasadat Taheri Sepehr Tabrizchi Shaahin Angizi Arman Roohi School of Computing University of Nebraska-Lincoln Lincoln NE USA Department of Electrical and Computer Engineering University of Illinois Chicago IL USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Wireless Sensor Networks (WSN) are integral to diverse applications, ranging from environmental monitoring to urban smart infrastructure. In the realm of WSNs, security remains a critical challenge owing to the complex nature of the sensor environment. As a result, WSN security has become a research focus in recent years. In this paper, we introduce ChaoSen, a novel image sensor system incorporating analog chaotic circuits within the sensor, thereby enhancing the overall system security. The system utilizes a scrambler module, which intricately intertwines with the chaotic encryption process, to reduce the predictability of pixel values and enhance the security of the system. Comparative evaluations demonstrate that the system achieves an NPCR value of 99.5562% and a UACI of 35.81900%, indicating high sensitivity to input changes and significant alteration in pixel intensity. Our approach also demonstrates its resilience against common cyber attacks, balancing enhanced security with resource efficiency.

关键词： Image sensors Wireless sensor networks Sensitivity Statistical analysis Circuits Encryption Security Environmental monitoring Cyberattack Resilience

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Design of COVID19 Detection for Risk Identification using Deep Learning Approach 2

Design of COVID19 Detection for Risk Identification using De...

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2nd IEEE International Conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Chaturvedi, Abhay Rohan, J. Saktheeswaran, Murali Samson Isaac, J. Kathan, Ambujam Sekhar, G.G. Raja Gla University Department of Electronics and Communication Engineering Mathura India Kpr Institute of Engineering and Technology Department of Chemical Engineering Coimbatore India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India Akt Memorial College of Engineering and Technology Department of Electronics and Communication Engineering Kallakurichi India Koneru Lakshmaiah Education Foundation Department of Electrical and Electronics Engineering Guntur India

ISBN: (纸本)9798350347456

Coronavirus infection is caused by one of the global issues of a deadly virus. The letters 'CO' stands for different types of symptoms, 'VI' for infection easily spread, and 'D' for everybody. Detection and diagnosis are based on various symptoms, and because the virus primarily affects the lung area of the human body, most people test their health condition using CT scans (Computed Tomography). The existing method Support Vector Machine Classifier (SVM)-based image processing technique, identifies CT (Computed Tomography) lung images from patients, which has drawbacks such as a lower positive rate and a less accurate affecting region. So, in the proposed method, the lung CT (Computed Tomography) image is used to classify deep learning. Preprocessing, Segmentation, and Classification are all steps in the digital image processing process. The first step image is obtained by taking the input CT (Computed Tomography) image and processing it into various regions while obtaining the deep level of the specified area using preprocessing bilateral filter. The second step is segmentation using K-means clustering the image segments into different regions or objects like effect and normal regions. This main object is removing the unwanted area. The third step Classification method is based on deep learning. The image is classified with different mathematical functions and identified as the extract part of the lung image. Output results are distinguished sickness and non-illness areas in the lung. The difference in the dark level of each locale is recorded and used as a highlight to identify infection areas identified by low fluctuation contrast with non-COVID-19 affected areas. Simulation is used to validate the proposed method's performance. The proposed method has an accuracy of 84.35% and a false rate of 15.65%. © 2023 IEEE.

关键词： COVID-19

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN)

Multi-Controller Architecture Design and Features in Softwar...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： V. Prabhu P. Balamurugan Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Networking paradigm known as “Software-Defined Networking” (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can't be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done.

关键词： Performance evaluation Scalability Process control computer architecture Telecommunication traffic Coherence Software Reliability Software defined networking Mobile computing

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YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count

YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detec...

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IEEE Southeastcon

作者： Mahmudul Islam Masum Hugo Riggs Preyojon Dey Alicia Boymelgreen Arif I. Sarwat School of Computing and Information Sciences FIU Miami USA Department of Electrical and Computer Engineering FIU Miami USA Department of Mechanical and Materials Engineering FIU Miami USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Accurate object detection is critical in marine and ecological research, where microscopic imaging provides insights into organism health and environmental conditions. This paper presents a comparative study of object detection using YOLOv5 and YOLOv8 for three distinct classes: artemia, cyst, and excrement. Our methodology involves bounding box annotation, contour extraction, and fluorescence-based intensity measurements to assess nanoparticle uptake in Artemia. In this comparative study, we analyze the performance of these models in terms of accuracy, precision, recall, etc. where YOLOv5 exhibits higher detection rates for Artemia and excrement, demonstrating remarkable robustness in identifying these classes despite challenging image conditions. However, YOLOv8 outperforms YOLOv5 for cyst detection, suggesting it may better handle this specific class. This suggests that YOLOv5 offers greater versatility and adaptability in detection tasks while YOLOv8 may struggle in difficult situations and may need further fine-tuning or specialized training to enhance its performance. The results show insights into the suitability of YOLOv5 and YOLOv8 for detecting objects in challenging marine environments, with implications for applications such as ecological research.

关键词： YOLO Training Accuracy Biological system modeling Microscopy Pipelines Fluorescence Fisheries Robustness Organisms

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In-Memory Transformer Self-Attention Mechanism Using Passive Memristor Crossbar

In-Memory Transformer Self-Attention Mechanism Using Passive...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jack Cai Muhammad Ahsan Kaleem Roman Genov Mostafa Rahimi Azghadi Amirali Amirsoleimani Department of Electrical and Computer Engineering University of Toronto Toronto Canada College of Science and Engineering James Cook University QLD Australia Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Transformers have emerged as the state-of-the-art architecture for natural language processing (NLP) and computer vision. However, they are inefficient in both conventional and in-memory computing architectures as doubling their sequence length quadruples their time and memory complexity due to their self-attention mechanism. Traditional methods optimize self-attention using memory-efficient algorithms or approximate methods, such as locality-sensitive hashing (LSH) attention that reduces time and memory complexity from O(L 2 ) to O(L log L). In this work, we propose a hardware-level solution that further improves the computational efficiency of LSH attention by utilizing in-memory computing with semi-passive memristor arrays. We demonstrate that LSH can be performed with low-resolution, energy-efficient 0T1R arrays performing stochastic memristive vector-matrix multiplication (VMM). Using circuit-level simulation, we show our proposed method is feasible as a drop-in approximation in Large Language Models (LLMs) with no degradation in evaluation metrics. Our results set the foundation for future works on computing the entire transformer architecture in-memory.

关键词： Measurement Virtual machine monitors Memristors Stochastic processes Transformers In-memory computing Natural language processing

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Optimization of a Two-Level Thermoelectric System for Pavement Energy Harvesting

Optimization of a Two-Level Thermoelectric System for Paveme...

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2023 Annual Intermountain engineering, Technology and computing, IETC 2023

作者： Rohani, Ehsan Choi, Gwan S. Park, Philip Utah Valley University Engineering Department OremUT United States Texas A & M University Department of Electrical and Computer Engineering College StationTX United States University of Texas Rio Grande Valley Civil Engineering Department EdinburgTX United States

ISBN: (纸本)9798350335903

For this study, we designed and optimized a two-level thermoelectric pavement energy harvesting system. The optimization was evaluated based on the cost per unit of energy (/J) as the target function. The idea behind the two-level thermoelectric system is to use thermoelectric power generator (TEG) modules on different depths of pavement to maximize the heat exchange efficiency of temperature gradients created by both daytime and night-time conditions. As the temperature difference is the driving force for the flow of thermal energy through the TEG, and the TEG turns a certain percentage of thermal energy into electrical energy, the two extrema created by daytime and night-time temperatures have the most potential to generate electricity. Since the generated energy is entirely dependent on the temperature profile and thermal exchange between different elements of the device, and between the device and the environment below the surface, we had to develop tools to provide an accurate analysis of these exchanges. In addition, we created an accurate temperature profile by extrapolating from existing geological information. We calculated the associated costs and changes in total generated energy for each change in the design parameters using our evaluation model and determined their effect on the target function. © 2023 IEEE.

关键词： Pavements

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Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

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computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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Optimal Energy Management based on Dynamic Programming

Optimal Energy Management based on Dynamic Programming

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2024 International Conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Haripriya, D. Dahiwade, Mandakini Kotiyal, Arnav Al-Farouni, Mohammed Yadav, Kanchan Francis Raj, J. Relin Department of CSE Veltech Ranagarajan Dr.Saguntala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India Department of Basic Engineering Indira College of Engineering and Management Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350355338

The strategy based on reinforced learning will be able to present a solution that is close to the optimal solution achieved by using Dynamic Programming (DP), and the power distribution for hybrid vehicles also seems promising with DP. It addresses nonlinear dynamical systems' optimal control issues. However, DP cannot be applied to the majority of problems of practical interest due to its high complexity in terms of computation and dimensionality. In various energy policies, variability has not been addressed, and it is based on fixed driving profiles, which affect vehicle operation. The proposed study's goal is to create an optimal hybrid energy management system that generates more power at the lowest possible cost. The control strategy has been formed based on DP through the rule extraction process, and the fuel consumption must be minimized for the drive cycles. It has simple mathematical foundations, ensures the optimal solution for a given tolerance, and can be easily implemented in a short computer program. The strategy is evaluated in terms of real and predicted values, which makes the performance indices, and this comparison is done on different levels of time. © 2024 IEEE.

关键词： Hybrid power

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Sample-efficient robust multi-agent reinforcement learning in the face of environmental uncertainty 24

Sample-efficient robust multi-agent reinforcement learning i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Laixi Shi Eric Mazumdar Yuejie Chi Adam Wierman Department of Computing Mathematical Sciences California Institute of Technology CA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA

To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied-- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DR-NVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length.

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Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities 7

Robotic Restroom Hygiene Solutions with IoT and Recurrent Ne...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Nasreen, A. Kathija Shenbagapriya, M. Seeni, Senthil Kumar Veda, Parthiban Meenakshi, B. Murugan, S. Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science And Technology Centre For Yoga College of Engineering And Technology Chenagalpattu Chennai Tamil Nadu Kattankulathur India Sri Sairam Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

Public restrooms needs to be frequently cleaned to maintain public health and hygiene. Traditional bathroom cleaning techniques may not solve current cleanliness issues. This research proposes an advanced robotics, Internet of Things (IoT), and Recurrent Neural Networks (RNNs) solution to these problems. The proposed system uses autonomous robots with IoT sensors and cameras. Robots identify cleaning and maintenance needs in restrooms. A central system receives data from IoT sensors on cleanliness indicators, including toilet paper, soap, and foot movement. Recurrent Neural Networks (RNN) processes this data to predict and prioritize cleaning requirements. The RNN monitors toilet conditions in real time and reacts to changing use and cleaning needs. This dynamic technique optimizes cleaning resource allocation and maintains facility cleanliness. The device also warns cleaning personnel when certain areas need quick attention. This novel technique makes restroom care more cost-effective, responsive, and environmentally friendly by combining robots, IoT, and RNNs. This study advances smart facility management, which uses technology to improve public space cleanliness, user experience, and resource usage. © 2024 IEEE.

关键词： Internet of things

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