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International Conference on VLSI Design

作者： Santosh Kumar Ayan Palchaudhuri Department of Electronics & Communication Engineering School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar Argul Khordha Odisha India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Decimal operands expressed in BCD is often the convenient data format used in embedded systems and human-centric applications. For optimized arithmetic computation on hardware where binary arithmetic can be conveniently processed, input BCD operands should first be converted to binary. This paper explores the versatility of FPGA specific logic primitives, namely the dual output Look-Up Tables (LUTs) and carry chain for efficient realization of parallel and pipelined architectures to optimize throughput and resource utilization for BCD to binary conversion architectures. Primitive instantiation was adopted to ensure design optimization, which involved direct configuration of FPGA logic primitives. The utility ratio of the configured FPGA primitives was increased to extract the best performance. Experimental results demonstrate a good trade-off in speed and area for our proposed architectures when compared with existing designs. We have investigated multiple implementation variants for the addition tree, which serves as a crucial functional logic block in the converter design.

关键词： Embedded systems computer architecture Very large scale integration Throughput Table lookup Logic Field programmable gate arrays Adders Optimization Arithmetic

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Enhancing Mitochondrial Image Segmentation with Extended U-Net

Enhancing Mitochondrial Image Segmentation with Extended U-N...

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International Conference on computer and Information Technology (ICCIT)

作者： Sakib Abdul Ahad Hussain Nyeem Department of Electrical and Electronic Engineering Green University of Bangladesh Dhaka Bangladesh Department of Electrical Electronic and Communication Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh

Accurate segmentation of mitochondria from electron microscopy data holds crucial significance in unraveling intricate cellular structures and functions. In this paper, we present a comprehensive approach to enhance the semantic segmentation of mitochondria through the integration of an extended U-Net model. Our proposed model introduces an additional layer, encompassing both encoder and decoder components, optimized for capturing intricate mitochondrial features. Moreover, we delve into the impact of employing diverse loss functions, training epochs, and dataset augmentation to assess their effects on segmentation performance. The Mean Intersection over Union (Mean IoU) and Dice Coefficient (DSC) metric serves as our indicator of segmentation quality. Our experimental findings highlight the effectiveness of the extended model and underscore the importance of tailored loss functions and training strategies, culminating in substantial advancements in mitochondria segmentation accuracy.

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models

Primeval Identification and Diagnosis of Alzheimer's Disease...

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International Conference on Advanced Computing and communication Systems (ICACCS)

作者： K. L. Sujitha Lynsha Helena Pratheeba HP Rajeshwari R Kottaimalai Ramaraj Pallikonda Rajasekaran Murugan Thilagaraj M Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections.

关键词： Solid modeling Three-dimensional displays Parkinson's disease Magnetic resonance imaging Resonance Convolutional neural networks Proposals Alzheimer's disease Older adults Medical diagnostic imaging

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Revolutionizing Waste Management Through Intelligent Garbage Segregation Bin 3

Revolutionizing Waste Management Through Intelligent Garbage...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Veerabhadraswamy, K.M. Kumar, Karthik Indhuja, A. Barve, Amit Kannadasan, B. Bakhare, Ruhi Government Engineering College Mosalehosahalli Department of Electronics and Communication Engineering Karnataka Hassan573 212 India REVA University School of Civil Engineering Karnataka Bangalore560 064 India SNS College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641 035 India Parul University Parul Institute of Technology Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat 391 760 India Kannadasan Balakrishnan Research Foundation Kalieswari Nagar Department of Civil Architectural Engineering and Planning Tamil Nadu Peraiyur625 703 India Dr. Ambedkar Institute of Management Studies and Research Deekshabhoomi Maharashtra Nagpur440 010 India

ISBN: (纸本)9798350343632

Efficient waste management stands as a paramount concern in the current environmental landscape. The proper handling, sorting, transportation, and disposal of waste materials are essential to mitigate environmental hazards. The separation of trash into several categories allows for a more accurate evaluation. Conventional manual waste segregation systems necessitate a significant amount of human labor, time, and money. This study proposes a waste management system integrated using advanced technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI), allowing for easy and cost-effective garbage segregation and tracking. The technique is intended to separate garbage into several categories such as glass, plastic, cardboard, paper, metal, and trash. A sample dataset acquired from Kaggle was rigorously processed to design and evaluate the system, which includes operations like as image resizing and rescaling. The DenseNet, is a Deep Learning (DL) model used for accurate garbage recognition. This unique smart segregation bin makes use of the potential of AI and IoT technology. The model is embedded in a controller, allowing it to properly recognize different waste items and trigger the associated actuator for accuracy sorting. A user-friendly Graphical User Interface (GUI) structure makes the entire process visible to the public. This interface enables users to watch the system's operation in real-time, providing a clear perspective of garbage identification and sorting. By revolutionizing waste management through intelligent garbage segregation, this system enhances the efficiency of waste sorting, contributing to environmental preservation and sustainable waste management practices. © 2023 IEEE.

关键词： Accuracy Camera Data Processing DenseNet Garbage Loss User Interface

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Hierarchical Attention Mechanism for Domain-invariant Spoken Language Identification via Inter-domain Alignment and Intra-domain Language Discrimination

Hierarchical Attention Mechanism for Domain-invariant Spoken...

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engineering Informatics, IEEE

作者： Urvashi Goswami H Muralikrishna Sujeet Kumar A. D. Dileep Veena Thenkanidiyoor School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Department of Electronics and Communication Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India Department of Computer Science and Engineering National Institute of Technology Goa Goa India

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

Modern methods for spoken language identification (LID) have demonstrated promising results when trained on large datasets. However, their effectiveness is considerably impacted by the discrepancies between the training and testing distributions. Unsupervised domain adaptation (UDA) addresses the domain discrepancies by aligning feature distributions across source and target domains without using labeled target data, thereby ensuring the model performs consistently across both domains. This alignment, however, may come at the cost of reduced model discriminability. To enhance performance under domainmismatched conditions, a LID system should learn domaininvariant representations of speech while maintaining language discriminability at every level, from initial segment-level representations to final utterance-level representations. To address this issue, in this paper, we propose a hierarchical attention network that simultaneously enhances model domain invariance through inter-domain alignment and improves model’s language discriminability using intra-domain contrastive discrimination. The performance on the target domain shows that the proposed method exceeds the effectiveness of the top-performing existing approaches.

关键词： Training Adaptation models Computational modeling System performance Refining Speech enhancement Transformers Data models Optimization Testing

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation 7

Multi-Layer Perceptron Regression Model for Material Charact...

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7th International Youth Conference on Radio electronics, Electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Chaudhari, Tilesh Sanjay Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Indian Institute of Technology Creative and Advanced Research Based on Nanomaterials Laboratory Department of Chemical Engineering Hyderabad India School of Engineering and Applied Science Bennett University U.P. Greater Noida India

ISBN: (纸本)9798331531836

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an R2 of 99.991% and MSE of 6.65 × 10-5, with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions. © 2025 IEEE.

关键词： Linear regression

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Novel simulator designed for grounded negative inductance with lossless characteristics incorporated with single OTRA

Memories - Materials, Devices, Circuits and Systems

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Memories - Materials, Devices, Circuits and Systems 2023年 6卷

作者： Banerjee, Khushi Ghosh, Mourina Sarkar, Chittajit Biring, Sajal Department of Electronics and Communication Engineering Asansol Engineering College W.B Asansol713305 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Guwahati India Department of Electronic Engineering Ming Chi University of Technology Taishan New Taipai City24301 Taiwan

This article introduces a novel design of a simulator for grounded lossless negative inductance by using an active element -single Operational Trans Resistance Amplifier (OTRA) and four passive components. Without interrupting the condition of realization of inductance, the value of the simulated inductance can be independently administered by a MOS based resistor. For validation of the analytical elucidation PSPICE simulation results are used. Moreover, sensitivity analysis, Monte-Carlo simulation, temperature analysis, and % of total harmonic distortion (%THD) are also investigated to verify the functionality of the proposed circuit. As an application claim of the projected configuration, an inductance nullification circuit is also implemented that exposes that the proposed negative inductance simulator may be used to cancel or reduce the effective inductance in a circuit. The Analog Design Environment tool of Cadence Virtuoso is employed for designing the layout of the OTRA. © 2023 The Authors

关键词： Inductance

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A Novel Optimized Deep Learning Model for Canola Crop Yield Prediction on Edge Devices

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood electronics 2024年第2期2卷 436-444页

作者： Valarezo-Plaza, Stephany Torres-Tello, Julio Singh, Keshav Shirtliffe, Steve Deivalakshmi, S. Ko, Seok-Bum University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Agriculture and Agri Food Canada in Lethbridge Lethbridge Research and Development Centre LethbridgeABT1J 4B1 Canada University of Saskatchewan Department of Plant Sciences SaskatoonSKS7N 5A9 Canada National Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli620015 India

The escalating global demand for food, coupled with challenges in sustaining crop production, deteriorating ocean health, and depleting natural resources, underscores the critical role of agricultural technology. This article addresses the imperative of developing an optimal deep-learning model for predicting canola crop yield using hyperspectral images captured by drone flights. Our primary objective is to identify the most efficient model in terms of performance and size, considering the storage limitations on edge devices like Raspberry Pi 4 (RPi4). We start with the baseline 1D_CNN model, which achieves an R2 score of 0.82, and compress it into the proposed fs_model (fp32). To achieve the compression, we apply pruning through sparsity and feature selection using SHAP values. Further reduction in model size is accomplished by quantizing the weights of the proposed model to a lower precision, such as int16. This combined approach substantially decreases the proposed model's size by approximately 92.6% and inference time by approximately ×9013 in comparison to the baseline 1D_CNN model. In addition, we propose the novel fsp_model posit(8,3) that uses posit quantization to further reduce the computation requirements compared to the proposed fs_model (int16). Our findings indicate that the utilization of posit numbers enables us to shrink the model size to 94% of the original base model, while only reducing the R2 score by 5.7%. © 2024 IEEE.

关键词： Hyperspectral imaging

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Multi-variate hybrid modeling for pacific ocean acidification: predicting future pH trends and analyzing key biogeochemical drivers

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CSI Transactions on ICT 2024年第1期13卷 99-116页

作者： Vasanth, K. Kishore, R. K. Sugumaran, Vijayan Krishnamoorthy, Raja Ramdas, Rithvik Tadepalli, Satya Kiranmai Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thavapalayam Karur India Department of Decision and Information Sciences School of Business Administration Oakland University Rochester USA Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India

Ocean acidification, driven by rising atmospheric carbon dioxide levels, poses a significant threat to the health of marine ecosystems, particularly in the Pacific Ocean. This study employs a multi-variate hybrid machine learning approach to predict future pH trends within the Pacific and to analyze the influence of key biogeochemical drivers on these trends. Hybrid models, strategically combining the strengths of individual algorithms, were developed for predicting several ocean acidification parameters. A performance analysis demonstrated the superior accuracy of hybrid models compared to their counterparts. The predicted pH trends reveal a concerning shift towards increased acidity within the Pacific Ocean, highlighting the urgency of understanding and mitigating its impacts. In-depth analysis was conducted to identify the relative influence of key biogeochemical factors on the changing pH dynamics. This research aims to provide crucial insights for developing targeted mitigation strategies and protecting the vulnerable ecosystems of the Pacific Ocean from the escalating consequences of ocean acidification.

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Impact of IoT Data Integration on Real-Time Analytics for Smart City Management

Impact of IoT Data Integration on Real-Time Analytics for Sm...

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International Conference on communications and Signal Processing

作者： D. Lakshmi J. Jeyarani R. Suguna P. Muneeshwari G.Mary Valantina Suganya Jayaraman Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The paper in illustration will emphasize the necessity for an IoT big data and analytics on time fusion as a futuristic smart city management approach. The framework deals with the main problems observed in data assimilation, analytics, security and scalability. As a result, cities will enjoy the services and operations utilization of the urban data with the precision. Our approach is based upon the data collection with the use of the edge technologies, preprocessing, integration, and analysis of the data resulting in the creation of the tools of machine learning and the adoption of interoperability standards. The results have especially brought to the limelight the fantastic performance of the live analytics pipeline when considered as a whole, and the impressive throughput of 5,000 events per second alongside the very low latency of 50 milliseconds, indicating how the urban information flow is processed in the blink of an eye. With AI therein creating the precision rate of 88.7%-95.2%, which therefore means this level of an urban situation can pronounce a traffic congestion and air quality fluctuations. The situation where security systems that involve encryption, access controls, and privacy-preserving protocols, among other mechanisms, have been used for the management of urban data in a safe manner, away from any unauthorized people and hackers, is factual. For the sake of achieving scalability, the replacement of certain bottlenecks is applied in the system by means of horizontal scaling and optimal performance procedures assisting smart cities handle their increasing quantity of data. Usually the results are factorized to bring out the fact that the particular methodology is strong and thus can be used in real practice to transform and enhance urban resilience level.

关键词： Smart cities Scalability Pipelines Machine learning Transforms Throughput Real-time systems

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