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MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure

IEEE communications Letters 2025年第6期29卷 1220-1224页

作者： Janu, Dimpal Mushtaq, Faisel Mandia, Sandeep Singh, Kuldeep Kumar, Sandeep Malaviya National Institute of Technology Electronics and Communication Engineering department Jaipur302017 India SRM University AP Electronics and Communication Engineering department Amaravati522502 India Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur302017 India Thapar Institute of Engineering and Technology Patiala147004 India Bharat Electronics Ltd Central Research Lab Ghaziabad201010 India

In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based co-operative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability Pd, sensing error, and classification accuracy (CA). © 1997-2012 IEEE.

关键词： Fading channels

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High Speed Re-Settable Delay Line TDC in 90nm CMOS technology 3

High Speed Re-Settable Delay Line TDC in 90nm CMOS Technolog...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Charishma, Pandala Mohd, Ziauddin Jahangir Kumar, A Krishna Sekhar, Paidimarry Chandra Chaitanya Bharathi Institute Technology Electronics and Communication Engineering Hyderabad India Osmania University College of Engineering Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350354171

This work presents a re-settable, high speed, delay- line time to digital converters (TDC). The proposed TDC is designed using a re-settable buffer in 90 nm CMOS process. Unlike the conventional delay line TDC used in the (All Digital Phase Locked Loops) ADPLLs, that allow a residue from a previous cycle delay measurement to be accommodated in the present cycle delay measurement, the proposed TDC flushes the residue of previous cycle and only the present cycle delay is measured. Comparison is done for average power, power spectral density, dynamic resolution and noise floor for pre layout and post layout simulations. © 2024 IEEE.

关键词： CMOS integrated circuits

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A new approach for non-contact, automated measurement of respiration rate using structured light 9

A new approach for non-contact, automated measurement of res...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Dagdanpurev, Sumiyakhand Davaasuren, Darkhijav Enkhjargal, Adiyabat Renchin-Ochir, Amartuvshin National University of Mongolia School of Information Technology and Electronics Department of Electronics and Communication Engineering Ulaanbaatar Mongolia

ISBN: (纸本)9798350391367

The respiration rate is one of the essential indicators of human health. Therefore, respiration rates can be used to diagnose several diseases, such as pneumonia. At medical facilities, breaths are measured by a respiration belt or manually counted by medical professionals. However, during the influenza season, hospitals and medical professionals are overloaded and easy, rapid and reliable respiration rate measurement is essential. In this study, we proposed to measure respiration rate utilizing a generated structured light pattern in 10 seconds. We used a simple web camera and mini projector. There are 18 participants (5-45 years old) were enrolled in this study. As a result, the correlation coefficient between the actual values and the experimental values was 0.91. The proposed method appears promising for non-contact, rapid, reliable respiration rate measurement. © 2024 IEEE.

关键词： non-contact respiration rate measurement Respiration rate structured light

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Real-Time 3D Bounding Box Estimation with RCNN-Resnet101 and Adaptive Projection Matrices 4

Real-Time 3D Bounding Box Estimation with RCNN-Resnet101 and...

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4th International Conference on Advances in Electrical, Computing, communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, Utsav Parihar, Pratik Vaithiyanathan, Dhandapani Manigandan, Muniraj National Institute of Technology Delhi Department of Electronics and Communication Engineering Delhi110036 India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350343670

In recent years, there has been a surge in demand for precise object detection and 3D bounding box estimation across various applications in computer vision, ranging from autonomous driving to robotics and augmented reality. This paper introduces a method specifically designed for real-time 3D bounding box estimation using a monocular camera. In contrast to conventional approaches that directly estimate 3D bounding boxes, our method begins by calculating accurate 2D bounding boxes. Subsequently, it employs a combination of mathematical modelling to predict 3D bounding boxes. The primary goal of this research is to deliver a practical and efficient solution for achieving accurate 3D bounding box estimation, capitalizing on the widespread availability of monocular camera hardware. Within our study, we independently generate the Projection Matrix, ensuring the robustness and applicability of our method across diverse datasets. This underscores the effectiveness of our approach in accurately estimating 3D object properties. Notably, our methodology demonstrates satisfactory results on both the Kitti and Cityscapes datasets, further emphasizing its proficiency in accurately estimating 3D object properties. © 2024 IEEE.

关键词： 2D to 3D Bounding Box Autonomous Navigation Depth Estimation Object Detection Single Shot Detection

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Smart Milk Grading System for Quality Assessment and Adulteration Detection using IoT 7

Smart Milk Grading System for Quality Assessment and Adulter...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Dhana Lakshmi, N. Siva Priyanka, S. Satya Sai Ram, M. Bhasker, D. Chaitanya Bharathi Institute of Technology Electronics and Communication Engineering Hyderabad India RVR and JC College of Engineering Electronics and Communication Engineering Guntur India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Ensuring the consumption of high-quality milk is particularly crucial for infants, especially in environments plagued by pollution. This paper introduces the Smart Milk Grading System (SMGS), an innovative IoT-based approach employing machine learning algorithms to accurately assess milk quality and tackle adulteration issues. The system incorporates an advanced liquid quality analyzer powered by Arduino, which effectively detects milk adulteration by monitoring key parameters such as pH, temperature, humidity, total dissolved solids (TDS), and gas levels. The collected data is then analyzed using six different machine learning classification algorithms. Notably, the Random Forest Classifier demonstrates exceptional accuracy in identifying milk adulteration, making it the most effective among the algorithms tested. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Low Profile Turbinella Shaped Antenna for 5G Millimeter Wave Applications

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Progress In Electromagnetics Research C 2023年 132卷 129-144页

作者： Ayyappan, Madhusudhanan Nair Gaonkar, Abhijeet Patel, Pragati Department of Electronics and Communication Engineering National Institute of Technology Goa 403401 India

This article investigates a Turbinella-shaped super wideband monopole antenna designed to accommodate the attributes of the fifth-generation (5G) technology which is the enhanced Mobile Broadband (eMBB). The antenna is designed to work with the current millimetre wave bands, including n77, n78, and n258, and it provides the increased data rate needed for eMBB applications. The proposed antenna comprises a Turbinella-shaped patch, a 50 Ω tapered feed line, and a multi-slotted partial ground plane. The self-similarity and space-filling nature of circular geometrical fractal is employed in a novel way to acquire antenna compactness and broadband performances. Further with the design of a tuning fork-shaped Defective Ground Structure (DGS), super wideband characteristics to incorporate 5G millimeter bands are obtained. The proposed antenna has a compact size of 0.25λ × 0.32λ along with a bandwidth of 173.33% along the frequency ranging from 3 to 41.97 GHz and has achieved a compactness of 81%. Moreover, the fundamental dimension limit theorem is used to demonstrate the antenna’s compactness. Time domain analysis is also studied in this article. © 2023, Electromagnetics Academy. All rights reserved.

关键词： Time domain analysis

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Edge Enhanced Densely Connected Convolutional Network for Liver Tumor Segmentation 10

Edge Enhanced Densely Connected Convolutional Network for Li...

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10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Kumari, L. Krishna Anusuya, V. Ramalakshmi, K. Rajalakshmi, R. Theivanathan, G. Lakshmi, A. Ramco Institute of Technology Electronics and Communication Engineering Virudhunagar India Ramco Institute of Technology Department of Information Technology Virudhunagar India P.S.R Engineering College Electronics and Communication Engineering Sivakasi India Vellammal Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350384369

Diagnosing liver sickness is a substantial curative exertion in underdeveloped nations. The previous liver aberration finding approaches have observed a great concept for restraint parameters but low in accuracy. The abrasion on the liver hasn't been notorious obviously with previous representations, so progressive, effectual, and genuine liver sickness discovery is vital. To overwhelm the boundaries of traditional approaches, this method suggests deep liver irregularity discovery with neural networks having a thick connection called as Densely Connected Convolutional Networks (DCCN). The pre-processing has been accomplished through median filtering, and training is executed over DCCN conventional methods with the performance metrics of 92.34 % accuracy, 91.54 %, Precision and 93.65 % of recall. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Bundle Branch Block Detection Using Multivariate Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5643-5654页

作者： Khan, Sibghatullah Inayatullah Pachori, Ram Bilas Sreenidhi Institute of Science and Technology Department of Electronics and Communication Engineering Hyderabad501301 India Indian Institute of Technology Indore Department of Electrical Engineering Indore453552 India

Bundle branch block (BBB) refers to cardiac condition that causes a delay in the path of electrical impulses, which makes it difficult for the heart to pump blood efficiently throughout the body. Early diagnosing BBB is important in cases where prior heart anomalies exist. Generally, the 12-lead electrocardiogram (ECG) is used to detect the BBB. To ease the ECG recording procedure, vectorcardiography (VCG) has been proposed with three leads ECG system. Manual diagnosis of BBB using ECG is subjective to the expertise of the doctor. To facilitate the doctors, in the present study, we have proposed a novel framework to automatically detect BBB from VCG signals using multivariate Fourier-Bessel series expansion-based empirical wavelet transform (MVFBSE-EWT). The MVFBSE-EWT is applied over the three channels of VCG signal, which results in the varying number of multivariate Fourier-Bessel intrinsic mode functions (MVFBIMFs). To process further, first six number of MVFBIMFs are selected due to their presence in the entire dataset. Each MVFBIMF is represented in higher dimensional phase space. From each phase space trajectory, fractal dimension (FD) is computed with three scales. The feature space is reduced with metaheuristic feature selection algorithm. © 2024 IEEE.

关键词： Intrinsic mode functions

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Multimodal MRI Analysis for Schizophrenia Classification Using Machine Learning Algorithm 4

Multimodal MRI Analysis for Schizophrenia Classification Usi...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Tamilarasi, S. Rajangam, Vijayarajan School of Electronics and Communication Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Centre for Healthcare Advancement Innovation and Research Chennai India

ISBN: (纸本)9798350387063

Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can lead to severe social and occupational failure. Structural Magnetic Resonance Imaging (sMRI) and resting-state Functional Magnetic Resonance Imaging (rs-fMRI) provide useful information about the neuroanatomical and functional changes associated with schizophrenia. This paper presents a unique approach to diagnosing schizophrenia by extracting energy, mean, and variance features from both sMRI and rs-fMRI data. Support Vector Machine (SVM) algorithm is used to conduct binary classification on sMRI -derived and rs-fMRI -derived features. sMRI -derived features detect dimensional structural variations in brain anatomy, whereas fMRI -derived features show dynamic functional activity patterns over time. Preliminary results show that both modalities have promising discriminating power, with fMRI -derived features outperforming sMRI-derived features. On a cohort of 106 schizophrenia patients and 160 healthy controls, our proposed technique achieved 93.5 % and 95.6 % classification accuracy for sMRI and rs-fMRI data, respectively. The classification accuracies from both modalities are compared to determine their diagnostic usefulness for schizophrenia. Integrating both modalities has the potential to improve the sensitivity and specificity of diagnostic algorithms for schizophrenia, resulting in better clinical results and customized treatment methods. These findings highlight the significance of multimodal MRI methods in schizophrenia diagnosis, © 2024 IEEE.

关键词： Diseases

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Automated liver segmentation from CT images using modified ResUNet

Gastroenterology and Endoscopy

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Gastroenterology and Endoscopy 2025年第2期3卷 93-104页

作者： Manjunath, R.V. Gowda N, Yashaswini Manu, H.M. Department of Electronics & Communication Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore 82 India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore-78 India

The Segmentation of liver from computed tomography (CT) images is a critical task in medical research particularly when employing deep learning methods. Most doctors prefer to use Computed tomography images for liver disease identification hence automatic liver segmentation plays a decisive role. To perform automatic segmentation of liver popular architectures such as modified ResUNet are specifically designed to capture multi-resolution features. In this study we proposed an automatic system that utilizes convolutional layers to efficiently extract features while maintaining spatial information. A modified Residual UNet model was developed for liver segmentation using the 3Dircadb dataset, the model demonstrated excellent performance by achieving a Dice Score of 93.08 %, accuracy of 98.57 %, Jaccard Index of 87.12 %, a volumetric overlap error of 12.87 %, and a relative volume difference of 14.91 % on 128x128 size images. These results highlight the effectiveness of the proposed method. The primary goal of this research is to apply deep learning techniques for liver segmentation and assess the model's performance using segmentation metrics. © 2025 The Authors

关键词： 128x128 Computed tomography Liver segmentation Resnet ResUNet Volumetric overlapping error

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