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High-performance 5.5 THz coplanar waveguide-fed circular dielectric resonator antenna: a dual-band solution for advanced THz applications

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Optical and Quantum Electronics 2024年第12期56卷 1-19页

作者： Rathinakumar, Perinbaraj Vijayalakshmi, K. Department of Signal and Image Processing Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

An improved circular dielectric resonator antenna (CDRA) with a gain of 5.5 THz and excitation via a keyhole-shaped coplanar waveguide (CPW) is detailed in this research. The structure's feed and ground materials are silver, the substrate is silicon dioxide, and the dielectric resonator antenna (DRA) is silicon. The DRA is 5 μm in radius and 15 Εm in height, and it is placed on a substrate that is 90 × 90 × 20 μm in size. An S11 value of –16.328 dB and a principal resonant frequency of 5.5 THz indicate that the antenna matches impedances admirably and scatters signals with minimal effect. Furthermore, the possibility of dual-band operation is demonstrated by a secondary resonance at 8.6 THz with an S11 of –6.7614 dB. With an operational bandwidth centered on 5.6 THz and a range of 5.0–6.2 THz, it is highly adaptable and may be used for a variety of applications. With a radiation efficiency of 68% and a peak gain of around 19 dB, this antenna exhibits a multi-lobe radiation pattern that is both highly gainful and highly directed. Phased array antennas, terahertz imaging systems, and autonomous vehicles and unmanned aerial vehicles (UAVs) can all benefit from these. Antenna performance is improved due to the creation of orthogonal and higher-order modes, which increase radiation efficiency and lead to high gain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Antenna lobes

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Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model

Strategic Planning of Rural Telecommunication Infrastructure...

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ISPRS Technical Commission I Midterm Symposium on Intelligent Sensing and Remote Sensing Application

作者： Chaabane, Ferdaous Réjichi, Safa Salem, Houssem Ben Elmabrouk, Hassen Tupin, Florence COSIM laboratory SUP’COM Carthage University Tunisia Department of Image and Signal Processing Telecom ParisTech France

The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy. © Author(s) 2024.

关键词： Topology

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Training Deep Neural Classifiers with Soft Diamond Regularizers 23

Training Deep Neural Classifiers with Soft Diamond Regulariz...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Adigun, Olaoluwa Kosko, Bart Signal and Image Processing Institute Department of Electrical and Computer Engineering Los AngelesCA90089-2564 United States

ISBN: (纸本)9798350374889

We introduce new soft diamond regularizers that both improve synaptic sparsity and maintain classification accuracy in deep neural networks. These parametrized regularizers outperform the state-of-the-art hard-diamond Laplacian regularizer of Lasso regression and classification. They use thick-tailed symmetric alpha-stable (Sa S) bell-curve synaptic weight priors that are not Gaussian and so have thicker tails. The geometry of the diamond-shaped constraint set varies from a circle to a star depending on the tail thickness and dispersion of the prior probability density function. Training directly with these priors is computationally intensive because almost all Sa S probability densities lack a closed form. A precomputed lookup table removed this computational bottleneck. We tested the new soft diamond regularizers with deep neural classifiers on the three datasets CIFAR-10, CIFAR-100, and Caltech-256. The regularizers improved the accuracy of the classifiers. The improvements included 4.57% on CIFAR-10, 4.27% on CIFAR-100, and 6.69% on Caltech-256. They also outperformed L2 regularizers on all the test cases. Soft diamond regularizers also outperformed L1 lasso or Laplace regularizers because they better increased sparsity while improving classification accuracy. Soft-diamond priors substantially improved accuracy on CIFAR-10 when combined with dropout, batch, or data-augmentation regularization. © 2024 IEEE.

关键词： Probability density function

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A Review on the Detection of the Novel Coronavirus Using Machine Learning Techniques 8th

A Review on the Detection of the Novel Coronavirus Using Mac...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Patibandla, Anitha Rawal, Kirti Sethi, Gaurav Department of ECE Lovely Professional University Punjab India Signal and Image Processing Domain Lovely Professional University Punjab India

ISBN: (纸本)9789819765805

The novel Coronavirus which has emerged in late 2019 has impacted the lives of the people around the world and has raised new questions in the healthcare sector. There has been extensive research in the area of COVID-19 detection considering its effect on people’s lives everywhere on the planet. The disease has indicated an intensive need for study in the area of Biomedical image processing by the utilisation of Artificial Intelligence and Machine learning strategies. The utmost affected parts in the human body are the lungs. Although RTPCR test can specify whether an individual has Covid or not, it is not a reliable technique since it has low sensitivity and depends on the operator. CT scan of the lungs reveal the exact information about the intensity of the disease in most of the cases and has proved to be reliable. Large datasets are available for carrying out research by using CT scan imagery. This paper presents an overview of the various methodologies used by various authors in the detection of COVID-19. Application of different Machine learning techniques for the dataset of CT images is proposed and performance metrics will be evaluated in the proposed work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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Improved Distance vector based Kalman Filter localization algorithm for wireless sensor network

Improved Distance vector based Kalman Filter localization al...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Mani, Rahma Messous, Sana Campobello, Giuseppe Battaglia, Filippo Liouane, Noureddine University of Monastir Laboratory of Automatic Signal and Image Processing Monastir5019 Tunisia University of Messina Department of Engineering Messina Italy National Engineering School of Monastir University of Monastir Laboratory of Automatic Signal and Image Processing Tunisia

ISBN: (纸本)9798350347074

Localization is a crucial concern in many wireless sensor networks (WSN) applications. Moreover, getting accurate information about the geographic positions of nodes in the sensing field is necessary in order to make the collected data useful and meaningful. In this context, the heuristic distance vector called DV-Distance is widely used for range-free localization in multi-hop WSN thanks to its advantages such as its simplicity and good accuracy. However, its localization accuracy may be relatively low due to environmental conditions. In this paper, we propose an improvement of the DV-Hop algorithm based on distance computation via a received signal strength indicator (RSSI) and a simple polynomial approximation technique. The proposed technique consists of RSSI measurement for distance estimation between sensor nodes thanks to low cost and simplicity compared to other techniques based on time and angle of arrival, i.e. TOA, AOA and TDOA which require more specialized equipment. However, as RSSI values may be affected by conditions environment such as noise, we propose to use the Kalman Filter in order to deal with this issue and improve localization accuracy. Test results proved that the proposed technique has a higher localization accuracy than DV-Distance by up to 50% and by up to 18% when compared with the state-of-the-art algorithms cited above and other DV-Hop-based localization methods. © 2023 IEEE.

关键词： Kalman filters

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Sign Language Recognition using Spiking Neural Networks 2

Sign Language Recognition using Spiking Neural Networks

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Bidirectional Backpropagation Autoencoding Networks for image Compression and Denoising 22

Bidirectional Backpropagation Autoencoding Networks for Imag...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Adigun, Olaoluwa Kosko, Bart Signal and Image Processing Institute Department of Electrical and Computer Engineering Los AngelesCA90089-2564 United States

ISBN: (纸本)9798350345346

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional auto encoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional auto encoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and de noising. The models trained on the MNIST handwritten-digit and CIFAR-IO image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%. © 2023 IEEE.

关键词： autoencoders bidirectional au-toencoders Bidirectional backpropagation bidirectional networks large language models variational autoencoders

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Fundamental Survey on Neuromorphic Based Audio Classification

arXiv

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arXiv 2025年

作者： Basu, Amlan Chaudhari, Pranav Di Caterina, Gaetano Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification. © 2025, CC BY.

关键词： Benchmarking

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Maintenance dynamic scheduling for a Tunisian railway transport system

Maintenance dynamic scheduling for a Tunisian railway transp...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Mellouli, Sarra Mhalla, Anis Dutilleul, Simon Collart Messaoud, Hassani Laboratory of Automatic Signal and Image Processing Laratsi Monastir Tunisia Laboratory of Automation Electrical Systems Environment Laese Monastir Tunisia Estas Laboratory University Gustave Eiffel Cosys Department Lille France

ISBN: (纸本)9798350361025

The developments reported in this paper are devoted to the dynamic scheduling of rail transport systems. In this context, our study focuses on implementing an algorithm for solving railway planning with predictable or unexpected demands. The system modeling process uses P-time stochastic Petri nets. In order to improve the efficiency and punctuality of rail transport. A method for minimizing disruptions by managing maintenance is proposed. The paper's contribution is a scheduling algorithm that prioritizes maintenance tasks according to a dynamic criticality. This dynamic criterion incorporates real-time information provided by a supervisory rail system layer (a SCADA system). The STPN is used for modeling system operation. It supplies a mathematical framework for disturbance detection and prognosis. The presented work constitutes a first step towards adapting the digital twin concepts for the maintenance of weakly instrumented railway infrastructures. © 2024 IEEE.

关键词： Petri nets

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Training Deep Neural Classifiers with Soft Diamond Regularizers

Training Deep Neural Classifiers with Soft Diamond Regulariz...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute Los Angeles California

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

We introduce new soft diamond regularizers that both improve synaptic sparsity and maintain classification accuracy in deep neural networks. These parametrized regularizers outperform the state-of-the-art hard-diamond Laplacian regularizer of Lasso regression and classification. They use thick-tailed symmetric alpha-stable $(\mathcal{S}\alpha \mathcal{S})$ bell-curve synaptic weight priors that are not Gaussian and so have thicker tails. The geometry of the diamond-shaped constraint set varies from a circle to a star depending on the tail thickness and dispersion of the prior probability density function. Training directly with these priors is computationally intensive because almost all $\mathcal{S}\alpha \mathcal{S}$ probability densities lack a closed form. A precomputed lookup table removed this computational bottleneck. We tested the new soft diamond regularizers with deep neural classifiers on the three datasets CIFAR-10, CIFAR-100, and Caltech-256. The regularizers improved the accuracy of the classifiers. The improvements included 4.57% on CIFAR-10, 4.27% on CIFAR-100, and 6.69% on Caltech-256. They also outperformed $L_{2}$ regularizers on all the test cases. Soft diamond regularizers also outperformed $L_{1}$ lasso or Laplace regularizers because they better increased sparsity while improving classification accuracy. Soft-diamond priors substantially improved accuracy on CIFAR-10 when combined with dropout, batch, or data-augmentation regularization.

关键词： Training Accuracy Sparse approximation Stars Tail Machine learning Probability density function Diamond Table lookup Residual neural networks

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