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检索条件"丛书名=Lecture Notes in Electrical Engineering"

共 22048 条记录，以下是21-30 订阅

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A Current-Mode-Logic-Based PFD-Charge Pump Circuit for Low-Reference Spur PLLs 27th

A Current-Mode-Logic-Based PFD-Charge Pump Circuit for Low-R...

引用

27th International Symposium on VLSI Design and Test

作者： Prabaharan, Priyadharshini Kathiah, Saravanan Raju, S. Aniruddhan, Sankaran Indian Inst Technol Madras Chennai India Thiagarajar Coll Engn Madurai India

ISBN: (纸本)9789819783830;9789819752690;9789819752683

In this paper, phase-frequency detector and charge pump circuits using current mode logic (CML) have been proposed for use in phase-locked loops (PLL). The primary advantages are lower voltage swings and consequently faster current switching. This reduces clock feedthrough and charge injection which in turn achieves low PLL reference spurs. The overall PLL system has been demonstrated via simulations. The PLL uses the proposed tri-state CML phase-frequency detector, CML charge pump, a second-order passive loop filter at the transistor level, an ideal 1-GHz voltage-controlled oscillator, and frequency divider. The reference frequency is 50 MHz. The circuits are designed in TSMC 65 nm process, and consume 0.81 mW from a 1.2-V power supply. In simulations, the PLL exhibits an in-band phase noise of -112dBc/Hz at a 100-kHz offset, a reference spur of -72.2 dBc, and a lock time of 2.6 mu s.

关键词： Current mode logic Charge pump Phase-locked loop High speed Low-phase noise Low-reference spur

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Resume Parser Using Machine Learning 5th

Resume Parser Using Machine Learning

引用

5th International Conference on Data Science, Machine Learning and Applications

作者： Srinitha, M. Mounika, N. Kiran, K. Sai Priya, V. Sathya Lanke, Pallavi BV Raju Inst Technol Dept Comp Sci & Engn Narsapur 502313 Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Resume parsing is a crucial process for recruiters to identify suitable candidates for job vacancies. However, manual resume screening can be a time-consuming and tedious task, especially when dealing with a large volume of resumes. The use of machine learning techniques in resume parsing can automate this process and help recruiters quickly identify relevant candidates. This paper presents an abstract of a review of different approaches for resume parsing using machine learning techniques. We discuss the challenges of resume parsing, the preprocessing steps involved, the feature extraction techniques used, and the evaluation of different machine learning algorithms used for resume parsing. The "genism" library may summarize resumes for this system to use, and once classification has been completed as per the job description, prospective candidates may be rated using clustering methods to determine which resumes are most similar to the specified job description.

关键词： Resume parser Machine learning Gensim library Job description and internet-based recruitment

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Lung Cancer Prediction Using Variational Autoencoders and Early Stopping for Neural Network Clustering and Optimal Tuning 5th

Lung Cancer Prediction Using Variational Autoencoders and Ea...

引用

5th International Conference on Data Science, Machine Learning and Applications

作者： Britto, Christopher Francis Mahatma Gandhi Univ Dept Comp Sci & IT Byrnihat Meghalaya India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Lung cancer is a major public health concern, and accurate prediction models are essential for early detection and effective treatment. However, existing prediction models often suffer from low accuracy and robustness, especially in the presence of outliers and overfitting. This research study proposes a novel approach for lung cancer prediction that integrates variational autoencoders (VAEs), early stopping, neural network clustering, and optimal tuning. The proposed model was evaluated on a dataset of 10,000 lung cancer patients. The model achieved an accuracy of 92.1% on the test set, which is a significant improvement over the accuracy of previous models. The proposed model also showed improved robustness to outliers and overfitting. The main contributions of this work include the development of a new method for predicting lung cancer that combines various techniques such as Variational Autoencoders (VAEs), early stopping, neural network clustering, and optimal tuning. The proposed approach achieves higher accuracy compared to previous models and demonstrates improved robustness to potential issues like outliers and overfitting. This study represents a significant advancement in the field of lung cancer prediction. The proposed model offers the potential to revolutionize early detection and treatment strategies, leading to improved patient care and outcomes. Additionally, it serves as a foundation for future research in lung cancer diagnostics and management, paving the way for further breakthroughs in this crucial area. This study paves the way for personalized medicine approaches in lung cancer treatment by enabling more precise patient stratification and targeted therapies. The proposed model's potential for early detection translates to significant cost savings for healthcare systems and improved quality of life for patients.

关键词： Lung Cancer Neural Network Optimal Tuning Clustering

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Managing Voltage Instability: Insights Into On-Load Tap Changer (OLTC) and Excitation System

Managing Voltage Instability: Insights Into On-Load Tap Chan...

引用

2024 Electric Power and Renewable Energy Conference

作者： Rashid, Bazila Kumar, Abdul Waheed Mufti, Mairaj Ud Din Natl Inst Technol Srinagar India

ISBN: (纸本)9789819779239;9789819779215;9789819779208

Voltage instability is a significant concern in power systems, particularly when the system is heavily loaded, experiences faults, or faces a shortage of reactive power. Ensuring the stability and security of a power system is a complex challenge that involves meeting various operational criteria and preparing for credible contingencies. Voltage instability is recognized as a significant contributor to power system insecurity and can lead to voltage collapse. Control devices, such as onload Tap Changers (OLTC) play a crucial role in maintaining voltage stability. The OLTCs are designed to adjust the voltage on the load side to match their reference voltages. However, when the load demand becomes excessively high, it can lead to secondary voltage instability. This phenomenon, known as the reverse action of on-load tap changers, which leads to voltage instability has been demonstrated in this study using theMATLAB App Designer. The impact of the excitation system on the voltage control has been explored in this study through MATLAB/SIMPOWER simulations.

关键词： Voltage instability On-Load tap changers (OLTC) Excitation system Voltage collapse Reactive power

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Analysis and Modeling of Self-heating and Substrate-Induced Transitions in 5-nm Stacked Nanosheet FET 27th

Analysis and Modeling of Self-heating and Substrate-Induced ...

引用

27th International Symposium on VLSI Design and Test

作者： Singh, Aditya Kumar Kumar, Vivek Patel, Jyoti Dasgupta, Sudeb Indian Inst Technol Roorkee IITR Roorkee 247667 Uttar Pradesh India Natl Inst Technol Uttarakhand Srinagar 246174 Garhwal India

ISBN: (纸本)9789819783830;9789819752690;9789819752683

Stacked nanosheet field effect transistors are the potential candidate for future CMOS-based design as a successor of the FinFET technology due to their improved short channel effect and higher effective width in the same area footprint. This research investigates the impact of self-heating and substrate effects on the small signal conductance and capacitance of FinFET and nanosheet devices using Synopsys TCAD. The presence of substrate introduces transitions in the conductance-frequency plot due to the substrate and self-heating effects in FinFET examined. Further, nanosheet devices show similar trends, with less pronounced substrate effects which is discussed first time in this work. Analyzing the effect of the type of substrate, voltage, work function, substrate doping, and electrode resistance variation provides further insights into the substrate-induced transition in the Nanosheet FET. These findings contribute to advancing nanoscale device technologies, particularly in RF applications.

关键词： Stacked nanosheet FET Substrate effect Self-heating effect

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A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language Processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

引用

5th International Conference on Data Science, Machine Learning and Applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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A System Module of Sentence Translator Using Neural Machine Translation 5th

A System Module of Sentence Translator Using Neural Machine ...

引用

5th International Conference on Data Science, Machine Learning and Applications

作者： Vaishnavi, Sadula Ramesh, D. Jawaharlal Nehru Technol Univ Jagtial India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Machine Translation (MT) has made significant advancements due to the rapid expansion of natural language processing. Even though several machine translation methods have been released in recent years, there hasn't been enough focus on automated and intelligent quality detection for translation outcomes. Neural Machine Translation (NMT), a machine translation method that is datadriven, is more effective in large corpora but with restricted corpus resources, there is still a sizable range of opportunity for advancement. To overcome these issues, design a system module of sentence translator using Neural Machine Translation is presented. This analysis will construct a translation system depending on the GRNN (Gated Recurrent Neural Network) deep learning algorithm and comprises the creation of the attention, pre-processing, coding, and decoding modules. This system does the Language translation with improved Accuracy and performance.

关键词： Gated Recurrent Neural Network Machine Translation Neural Machine Translation

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Skin Disease Detection 5th

Skin Disease Detection

引用

5th International Conference on Data Science, Machine Learning and Applications

作者： Janokar, Sagar Joshi, Kalpesh Raikwar, Rajesh Kumbhar, Sahil Kumbhar, Akshay Kumbhar, Shashank Vishwakarma Inst Technol Elect & Telecommun Engn Pune Maharashtra India Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Dermatology is a specialized field of biology focused on diagnosing and treating skin conditions. The skin's diverse range of conditions can be influenced by factors like temperature, humidity, and climatic conditions, leading to regional and seasonal variations. However, the unique characteristics of human skin, such as unevenness, tone, and hairiness, make it challenging to mechanically synthesize and assess accurately. While some studies have used computer vision techniques to identify and simulate skin diseases, only a few have addressed the medical paradigm of the problem. In many remote areas, limited access to medical services causes patients to disregard early symptoms, potentially exacerbating their condition over time. As a result, an elevated demand is emerging for precise automated techniques to identify skin conditions. We developed a deep learning model capable of differentiating between normal skin and skin with ailments to address this requirement. Furthermore, our model is capable of categorizing significant varieties of skin conditions, including melanocytic nevi, melanoma, benign keratoses-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. A subclass of machine learning a deep learning, played a crucial role in the development of our model. By utilizing large datasets, deep learning significantly reduced the number of classifiers compared to traditional machine learning techniques. Convolutional neural network (CNN) algorithm, commonly employed for image categorization, was the backbone of our model. Through this research, we aim to promote and support the expansion of dermatology by providing a fast and accurate tool for skin disease diagnosis and classification.

关键词： Convolutional Neural Network (CNN) Skin Disease Deep Learning Training Testing and Evaluation Dermatoscopic pictures

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Current-Sensing ADC with Power Consumption of 57 nW and FoM of 336 fJ/Conversion at 28 nm CMOS Technology 27th

Current-Sensing ADC with Power Consumption of 57 nW and FoM ...

引用

27th International Symposium on VLSI Design and Test

作者： Singhal, Navin Santosh, M. Bose, S. C. Karmakar, Abhijit ChipSor Labs Pvt Ltd Jaipur Rajasthan India CSIR CEERI Pilani Rajasthan India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9789819783830;9789819752690;9789819752683

Neuron-based analog-to-digital converters (ADC) have proved to be very useful for many low-power applications. The choice of neuron and the architecture of ADC both play very important roles, especially for low-power, low-current sensing scenarios. In this paper, a novel neuron architecture based on the Leaky & Integrate Fire (LIF) model is proposed to realize a low-power, low-current sensing ADC with power scalability. ADC works for input signal ranging from 2 mu A to 5 mu A with scalable power supply ranging from 0.9 V to 0.5 V. The design is implemented in 28 nm technology CMOS process of UMC. The total power consumption of the circuit is found to be 57 nW and achieves FoM of 336 fJ/conversion for 7 bits of operation with a power supply of 0.5 V. The proposed design has the lowest power consumption and FoM among the reported current mode and neuromorphic ADCs.

关键词： ADC CMOS LIF Neuron Neuromorphic

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Lexical Interpretation of Visual Cues Using Deep Learning 5th

Lexical Interpretation of Visual Cues Using Deep Learning

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5th International Conference on Data Science, Machine Learning and Applications

作者： Budarapu, Amrita Jain, Komal Sree, S. Bindu Varshitha, T. Niveditha, B. Narayanamma Inst Technol & Sci Dept CSE AI&ML Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Lexical interpretation of visual cues is an approach for understanding spoken phrases by visually observing the movements and shapes of a speaker's lips. A comprehensive review of the existing methods exposes the limitations of traditional lip reading techniques in capturing both spatial and temporal dimensions of lip movements. To address this gap, this project presents an approach to advance lip reading efficacy by synergizing Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The system has achieved an accuracy of 97% on GRID dataset. The implications of this research extend to improved communication accessibility for individuals with hearing impairments as well as broader applications in areas such as criminal investigations and security.

关键词： Lexical interpretation Lip Reading CNN GRU Visual cues

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