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Area Efficient 0.009-mm2 28.1-ppm/°C 11.3-MHz ALL-MOS Relaxation Oscillator

IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2024年第10期32卷 1900-1907页

作者： Wijaya, Joshua Adiel Chen, Poki Kumar Pradhan, Lucky Shahid Bhatti, Ahmad Kajihara, Seiji National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan Kyushu Institute of Technology Fukuoka804-0015 Japan

This article presents an ultrasmall area on-chip relaxation oscillator with low-temperature sensitivity. In this design, a virtual resistor mainly composed of a complementary to absolute temperature (CTAT) voltage reference circuit is implemented to replace the real resistor for efficient temperature compensation, which counterbalances the inherent proportional to absolute temperature (PTAT) property of the original relaxation circuit of the oscillator. The conventional capacitor is also replaced with a MOS capacitor to complete the ALL-MOS oscillator circuit with two prime advantages, one of which is larger capacitance to area density, and the other is better matching with critical MOSFETs. Implemented in a 0.18-μm TSMC standard CMOS process, the proposed relaxation oscillator has achieved a temperature coefficient of 28.17 ppm/°C over the temperature range from -25°C to + 125°C at 11.39-MHz oscillation frequency. This circuit consumes 243.1μW under 1.3-V power supply. Along with the abovementioned excellent performance, the oscillator achieves an ultrasmall core chip area of 0.009 mm2, which is almost one order less than most of the prior arts' in the same process. © 1993-2012 IEEE.

关键词： Temperature

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A Comparative Analysis on Various Modified Deep Convolution Neural Networks on Maize Plant Leaf Disease Classification 1

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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Kumar, H. R. Sunil Poornima, K.M. Department of Computer Science and Engineering PES Institute of Technology and Management Shivamoggaa India Department of Computer Science and Engineering JNN College of Engineering Shivamoggaa India

ISBN: (数字)9789819973835

ISBN: (纸本)9789819973828

Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good health is very important for maximum yield. Maize is one of the major food crops in the world and contributes major part in food security. There are various reasons for leaf to get diseases that include pathogens like virus, bacteria, pests and even adverse environmental conditions. The idea of this paper is to take data set of maize leaf images with disease like blight, common rust and gray leaf spot along with healthy leaves, train it on different modified deep convolutional neural networks (DCNNs) and use the trained models to test new leaf images. Deep convolutional neural networks have been modified as per the requirements by introducing new epochs, iterations, activation functions and learning rates. In the proposed work, ten different modified networks have been implemented starting from a simple AlexNet to a more complex network like EfficientNet-b0. In this experiment two different data set have been used, one from a standard data set from Kaggle and another is created manually by capturing images from our neighborhood farm lands at different stage of growth. Finally, a comparative study on different networks with two different data set is carried out. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Energy-Efficient Encounter, Buffer and Contact Duration-Based Routing Protocol in Opportunistic Networks 2nd

Energy-Efficient Encounter, Buffer and Contact Duration-Bas...

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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Dutta, Amit Borah, Satya Jyoti Singh, Jagdeep Department of Computer Science and Engineering NERIST Nirjuli Itanagar India Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India

ISBN: (纸本)9789819736003

Opportunistic network (OppNet) is a type of advanced wireless mobile network that has evolved from Mobile Ad-hoc Networks (MANETs) and belongs to the category of Delay Tolerant Network (DTN). The unique features that make OppNet different from other wireless networks are its intermittent connectivity, unstable topology, no dedicated path to the destination from the source, and high node mobility. This further makes the process of data transmission a challenging task. For this a method called "store-carry-forward" is used in which the battery-operated intermediate nodes store the message, discover a suitable relay node, and forward the message to that node. This further leads to the depletion of the battery power or energy of the node in due course of time. In this context, there is a necessity to develop routing models by taking the current energy status of a node into consideration. In this paper, the authors proposed an energy-efficient version of a popular routing mechanism EBC, termed E-EBC where the selection of the next suitable node depends upon the battery power (referred to as Energy) of that node. This model is then compared with existing benchmark protocols- Epidemic, Prophet, EDR, E-Prophet, E-EDR, and EBC. Simulation results (using ONE Simulator) depict that the proposed model performs much better than that of the benchmark models respectively by around 42.73, 41.46, 58.19, 32.03, 65.99, and 44.77% in terms of average latency. The proposed protocol E-EBC is also compared against messages dropped by varying the TTL, Buffer Size, and number of nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Delay tolerant networks

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Hybrid Activation Functions in Deep Convolutional Neural Networks for Maize and Paddy Leaf Disease Recognition: A Transfer Learning Approach

Hybrid Activation Functions in Deep Convolutional Neural Net...

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Control Instrumentation System Conference, CISCON 2023

作者： Sunilkumar, H.R. Poornima, K.M. Department of Computer Science and Engineering PES Institute of Technology Management Shivamoggaa India Department of Computer Science and Engineering JNN College of Engineering Shivamoggaa India

ISBN: (纸本)9789819758654

Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good health is very important for maximum yield. Maize and paddy are major food crops grown in India and contribute significant part in food security. There are various reasons for leaves to get diseases that include pathogens like virus, bacteria, pests and even adverse environmental conditions. The idea of proposed work is to take dataset of maize and paddy leaf images with diseases like corn blight, corn common rust, corn gray leaf spot, brown spot, hispa, leaf blast along with healthy leaves. Use transfer learning to train different Deep Convolutional Neural Networks (DCNNs) with hybrid activation functions and use the trained models to test new leaf images. Different activation functions like Rectified Linear Unit (ReLU), Exponential Linear Unit (ELU) and Leaky Rectified Linear Unit (Leaky-ReLU) have been combined and applied. Initially all DCNNs are trained by using respective original activation functions, later hybrid activation functions are applied with suitable epochs, learning rate, iterations. Different DCNNs like AlexNet, DarkNet-19, VGG-16, SqueezeNet, and ResNet-18 have been tested on maize leaf images and obtained accuracies from 75 to 97.5%. Further, Shuffle Net, DarkNet 53, and Resnet-50 have been used for paddy leaf images and obtained accuracies from 70 to 98%. Our study found that AlexNet and VGG-16 and ResNet-50 show promising improvements on classification accuracy with hybrid activation functions. A detailed ensemble of DCNNs is given at the end to check the performance of various hybrid activation functions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Performance analysis of precise energy consumption algorithm for smart home using hybrid renewable energy

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International Journal of Powertrains 2023年第1期12卷 1-14页

作者： Anguraj, Dinesh Kumar Sathya, C. Vinothini, C. Jayanthi, M. Rani, J. Vakula Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamil Nadu Coimbatore India Department of MCA CMR Institute of Technology Karnataka Bengaluru India

Energy demand is increased day by day, so there is a need for energy management, and it plays a vital part in the 21st century. At present, non-renewable energy is utilised most of the time. The people are not aware of the wastage of energy when their home appliances turned on the whole day. So, hybrid renewable energy is an alternate source for supplying continuous power for their smart home in the future. The collected renewable energy is utilised for the smart home usages. To minimise power consumption and to predict energy consumption precisely is a stimulating task in the future. This work proposes an innovative precise energy consumption algorithm (PECA) that is used to calculate the smart home power consumption accurately. PECA utilises smart plug, smart gateway, and mobile app administration platform to build and deploy a deep learning model. The smart plug and smart gateway combined into the complete distributed sensor network is to analyse, improve, and expand the energy consumption efficiently. Artificial intelligence is used to predict the energy usage data, optimise resource consumption, reduce the cost and maximise renewable energy usage. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Electric power utilization

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Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach 2

Eco-friendly Smart Sensor Adjustment using Deep Reinforcemen...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

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Customized Graph-Cut Segmentation with Superpixels for Snowfall Detection on Solar Panels 4

Customized Graph-Cut Segmentation with Superpixels for Snowf...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pavithra, V. Danisha, B.R. Divyadharshini, V. Koresh, H. James Deva Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

Snow accumulation on solar panels reduces their performance in energy generation regions. Hence certain image processing methods were developed for segmenting snow from the normal areas of solar panels. Graph Cut and Flood Fill are the popular semi-automatic method used for this application where it relies on detecting an efficient region for marking the segmentation process. Similarly, the manual intervention fails to provide consistent results in such methods. Therefore, a customized graph cut methodology is proposed in this work by combining super pixel-based feature extraction and k-means clustering. At first, the input images are preprocessed by dividing the pixels to form super pixels as a group to reduce the computational complexity without losing the meaningful information. Features like intensity, edge density, texture, and color are extracted from these super pixel groups for segmenting the snow-covered regions on a solar panel. Further, the extracted features are normalized by a k-means clustering algorithm for identifying the highest intensity regions to observe the snow-covered regions. A graph cut-based segmentation technique is applied at the end to ensure the precise boundary delineation. The work is also incorporated with several post-processing techniques for improving the segmentation mask quality. This proposed method is evaluated against Graph cut and flood fill and satisfied with an average dice similarity coefficient of 93.09% and 95.11%. © 2025 IEEE.

关键词： Image segmentation

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Enhancing COVID-19 Detection with YOLOv5 Object Detection Algorithm in Chest X-ray Images 3

Enhancing COVID-19 Detection with YOLOv5 Object Detection Al...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Duraisamy, Premkumar Arunkumar, D. Niranjani, V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381931

In the ongoing fight against COVID-19, accurate diagnostic tools are crucial. This study focuses on the detection of COVID-19, primarily by analyzing Chest X-ray images to identify and understand the disease. We introduce a new approach that utilizes the YOLOv5 (You Only Look Once) object detection algorithm. YOLOv5 is known for its ability to quickly identify objects, and we have fine-tuned it to perform effectively with X-ray images. The result is a model that excels at identifying intricate COVID-19-related patterns in X-ray images, achieving a high accuracy score of 98%. By carefully adjusting and training the model with specific data, our method achieves even higher accuracy. This modification makes YOLOv5 a powerful tool for automating the detection of COVID-19, offering hope for early detection and timely action in the ongoing global fight against the pandemic. © 2024 IEEE.

关键词： COVID-19

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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