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Design and Analysis of Microstrip Patch Antenna Using Metamaterial for Biomedical Applications

Design and Analysis of Microstrip Patch Antenna Using Metama...

引用

AI in Cybersecurity (ICAIC), international conference on

作者： Anshul Shrivastava Rajesh Kumar Kurmendra Department of Electronics and Communication Engineering North Eastern Regional institute of Science & Technology Nirjuli Arunachal Pradesh India Department of Electronics & Communication Engineering Rajiv Gandhi University Doimukh Arunachal Pradesh- India

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Over the past ten years, metamaterials research has attracted a lot of attention. Metamaterials are materials that have been created artificially and exhibit characteristics that cannot be seen in materials that naturally occur. Metamaterials occur in a wide range of forms and exhibit electromagnetic phenomena that are not present in natural materials. When creating a novel metamaterial, simulation can be used to examine both the overall property of the substance and each individual component. The researcher needs to be mindful of a few particular factors when modelling metamaterials. Multiple modeling methodologies are thus employed for the effective development of a gadget. Given that early-stage cancer can be successfully treated with well-established conventional treatments, remote malignant tumor diagnostics is essential to modern medicine’s capacity to treat cancer disorders. Such antennas have been designed so far for many types of diseases using a variety of ways.

关键词： Wireless communication Patch antennas Microstrip antennas Medical services Metamaterials Microstrip Tuning Monitoring Cancer Antenna radiation patterns

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MSRFormer: Hybrid Scale Self-Attention and Local Fast Convolution Transformer for Facial Expression Recognition

MSRFormer: Hybrid Scale Self-Attention and Local Fast Convol...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zi-Qiang Shen Yu-Yi Tang Jun-Feng Yan Yang Li Guo-Ying Zhao Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Department of Computer Science and Engineering Center for Machine Vision Research University of Oulu Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition.

关键词： Training Representation learning Sentiment analysis Accuracy Head Convolution Face recognition Speech recognition Transformers Speech processing

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An Improved Adaptive Whale Optimization Algorithm with Multi-Strategy for Optimization Problems

An Improved Adaptive Whale Optimization Algorithm with Multi...

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Consumer Electronics and Computer engineering (ICCECE), 2021 ieee international conference on

作者： Jiarui Zhang Qunfeng Liu Yunchao Zhi Kanghua Zhu Department of Computer Science and Technology Dongguan University of Technology

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

The Whale Optimization Algorithm (WOA) is indeed recognized for its effectiveness in solving complex optimization problems, including NP-hard problems. However, like many metaheuristic algorithms, it has its limitations. Insufficient population diversity and ineffective search strategies are common challenges that can reduce its performance. We urgently need to introduce effective strategies to alleviate these core shortcomings of WOA. To address the slow convergence, low accuracy, and local optima issues of WOA, this paper proposes an Improved Adaptive Whale Optimization Algorithm with multi-strategy(IWOA). Firstly, enhanced Sine chaotic map was introduced to improve the quality of the initial population. Secondly, the adaptive inertia weights are used to balance the global and local searches, so as to accelerate the convergence of the algorithm. Lastly, the Metropolis criterion is incorporated to strengthen the algorithm's ability to escape local optima. Datasets are used to verify the performance of the improved adaptive whale optimization algorithm in solving problems.

关键词： Flowcharts Accuracy NP-hard problem Heuristic algorithms Metaheuristics Simulated annealing Search problems Whale optimization algorithms Consumer electronics Convergence

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A Hybrid Deep Learning Framework for IoT Security Enhancement and Anomaly Detection

A Hybrid Deep Learning Framework for IoT Security Enhancemen...

引用

Integrated Circuits and Communication Systems (ICICACS), ieee international conference on

作者： D Baswaraj Abdur Rahman Deependra Pandey T Bhargavi Muslimbek Ismoilov Natha Deepthi Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India School of Business & Technology Emporia State University Emporia Kansas USA Department of Electronics and Communication Engineering Amity School of Engineering & Technology (ASET) Amity University Lucknow India Department of Information Technology St. Martins Engineering College Hyderabad India Department of Electrical Engineering Fergana Polytechnic Institute Fergana Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cyber threats targeting Internet-of-Things(IoT) devices have grown in frequency and severity alongside the IoT's fast uptake and usage in the past few years. Because of this, creating a model that can detect hostile attacks and lessen security risks in IoT-devices is becoming a very important subject. Botnets pose a significant risk because they disrupt the networks that Internet of Things (IoT) devices rely on to function. An method called GA-HDLAD, which stands for genetic-algorithm with hybrid deep learning(DL), was created in this study to advance security in the IoT environment by detecting botnets. Botnet detection makes use of hybrid deep learning, which combines attention concepts, feature-extraction-techniques(FETs), and recurrent-neural-networks(RNNs). The hybrid-DL approach can identify botnet attacks, which frequently use complicated patterns. In addition, the model's RNNs capture temporal dependencies, while FETs guarantee efficient feature extraction from geographical data. To choose the hyperparameters required by the HDL method, simulated annealing (SA) is employed. This paper compares the GA-HDLAD system to other detection approaches and finds that it outperforms them experimentally using a benchmark botnet dataset.

关键词： Deep learning Integrated circuits Botnet Field effect transistors Simulated annealing Feature extraction Internet of Things Security Hardware design languages Anomaly detection

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Hybrid Offline Passive Grammatical Inference and Online Planning for Non-Markovian Tasks

Hybrid Offline Passive Grammatical Inference and Online Plan...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mahyar Alinejad Alvaro Velasquez Yue Wang George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando Florida Dept. of Computer Science University of Colorado Boulder Colorado Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando Florida

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Planning in non-Markovian environments often requires inferring task structures, such as reward machines, through interactions with the environment. Traditional active grammatical inference methods, like Angluin’s L * algorithm, depend on continuous querying to learn task structures for the underlying planning objectives. In contrast, we propose a hybrid approach that combines passive grammatical inference, using the Regular Positive and Negative Inference (RPNI) algorithm, with online planning. By leveraging pre-collected positive and negative trajectories, RPNI learns a deterministic finite automaton (DFA) that captures the task structure, significantly reducing the need for real-time interactions. Subsequently, online planning is conducted over the product MDP, which integrates the environment with the learned DFA. This hybrid methodology minimizes the cost of online interactions and improves learning efficiency in complex environments. Our approach outperforms baseline algorithms in terms of runtime and sample complexity, and is well-suited for real-world scenarios where task structures are implicit, and interactions with the environment are expensive.

关键词： Runtime Learning automata Signal processing algorithms Signal processing Inference algorithms Real-time systems Planning Complexity theory Trajectory Speech processing

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INN-based Secure Steganography Using Lost Information as Adversarial Perturbations

INN-based Secure Steganography Using Lost Information as Adv...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fei Shang Weixiang Zhao Xiangui Kang Z. Jane Wang School of Computer Science and Engineering Guangdong Key Lab of Information Security Sun Yat-sen University Guangzhou China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently image steganography methods based on invertible neural networks (INNs) demonstrated the capability to automatically embed and extract secret messages while maintaining high visual quality in stego images. However, there remain concerns about security and invertibility of such methods. In this paper, for the first time, we introduce adversarial hiding into INN-based image steganography method to simultaneously perform steganographic embedding and adversarial perturbation generation, resulting in improved security. Our method enhances the invertibility of the INN structure: It utilizes the lost information of the INN to generate perturbations, which are then combined with the gradient of the cover image to produce an adversarial stego image. Also, a learnable noise layer is proposed to mitigate information loss caused by rounding and truncation during image storage. Therefore, the proposed method significantly improves security while enhancing extraction performance of INN-based steganography approach, as supported by our experimental results. For example, the steganalysis detection accuracy of SRNet decreases from 96.86% to 51.77% at a payload of 0.2 bits per pixel (bpp).

关键词： Training Steganography Visualization Perturbation methods Noise Neural networks Security Image storage Speech processing Payloads

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Partial Order Reduction on the Ambient Calculus for Modeling Intra-regional Truck Transportation

Partial Order Reduction on the Ambient Calculus for Modeling...

引用

international conference on Mechatronics (ICM)

作者： Toru Kato Atsuya Maruyama Faculty of Informatics and Cyber Informatics Research Institute Kindai University HigashiOsaka Japan Graduate School of Science and Engineering Kindai University HigashiOsaka Japan

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

We are working on a system that simulates the delivery routes of cargoes with the aim of improving the loading efficiency of truck freight transportation. To this aim we are using the ambient calculus, a family of process algebra, for modeling freight systems. The simulator generates all possible delivery plans consolidating cargoes by the function of enumerating all non-deterministic choice in processes. However, using naive methods to plan all consolidation patterns could easily lead to a combinatorial explosion problem. Therefore, we propose an algorithm that applies a method called partial order reduction that is usually used in model checking, so that we can simulate intra-regional medium-lot cargo transportation systems on a practical scale.

关键词： Mechatronics Algebra Loading Transportation Reinforcement learning Model checking Explosions Calculus Planning Logistics

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Predictive Analysis of Kidney Diseases with Artificial Intelligence and Data science

Predictive Analysis of Kidney Diseases with Artificial Intel...

引用

2025 international conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Eswar, G. Hariharan, C. Lavein, S. Akilandeswari, V. Velammal College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331521394

In the Field of Medical diagnosis, nowadays there are many new methods are getting introduced each day and also gets advanced by many people around the world. The Kidney is one of the most vital organ of a Human body that helps a lot by filtering blood and throws the waste toxic contents through excretory organs. But the food habits and other bad habits can affect the process of Kidney and further even affects kidney. As a result our kidney can also suffer from various disease. Most vital Kidney diseases can include Chronic Kidney Disease(CKD), Diabetic Nephropathy at the 2nd stage of Diabetes, and still many more. In the process of predicting of these diseases, we can use machine learning algorithms like Random Forest with Decision Tree and Supervised Learning algorithms we can train a model that can use the previous datasets to clarify if the new person is diagnosed with the particular Kidney disease. The Random Forest algorithm provides around 90% accuracy that is better in prediction of results for the collected information. Using these techniques we can detect the problem at early stage which can give a greater chance to prevent it from getting worse. © 2025 ieee.

关键词： Supervised learning

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Network Performance Monitoring and Bottleneck Detection Using Improved Recurrent Neural Network

Network Performance Monitoring and Bottleneck Detection Usin...

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Integrated Circuits and Communication Systems (ICICACS), ieee international conference on

作者： Layth Hussein Jasim R Priyadarshini Arun Kumar Katkoori S. Senthil Kumar Mukesh Soni Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Science and Engineering NITTE Meenakshi Institue of Technology Bengaluru India Department of Electronics and Communication Engineering CVR College of Engineering Hyderabad India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Monitoring network performance and bottleneck detection assist in identifying slowdowns, congestion points, and ineffectiveness in network traffic flow. Bottleneck detection helps to identify misconfiguration, overloaded links, and faulty performance which allow timely corrective actions. Therefore, ensuring smooth network performance is significant for managing security, reliability, and user experience. However, identifying bottlenecks in network and system performance is difficult because of the complexity of traffic patterns leads to inaccurate performance analysis. Therefore, this research proposes improved Recurrent Neural Network (RNN) for monitoring network performance and detecting bottleneck accurately by resolving sequence learning issues. The proposed improved RNN is evaluated using N_BaIoT dataset to determine the model performance. Then, the obtained data is pre-processed using min-max normalization to standardize data and one-hot encoding to encode the labels effectively. At last, the proposed improved RNN achieves a better accuracy of 99.99% accuracy compared to existing methods like hybrid Deep Neural Network (DNN) and Long Short-term Memory (LSTM) using N_BaIoT dataset.

关键词： Adaptation models Recurrent neural networks Accuracy Telecommunication traffic Encoding User experience Numerical models Integrated circuit modeling Monitoring Long short term memory

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ICAA-Mamba: Vision Mamba for Image Color Aesthetics Assessment

ICAA-Mamba: Vision Mamba for Image Color Aesthetics Assessme...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qinghao Xie Wenchao Jiang Zhihua Wang Qiuping Jiang School of Computer Science Guangdong University of Technology Guangzhou China Department of Engineering Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Image Color Aesthetics Assessment (ICAA) focuses on evaluating the aesthetic quality of color composition within images. This task involves analyzing and quantifying the visual appeal of color arrangements, taking into account factors such as harmony, contrast, and balance, to provide an objective assessment of color aesthetics. In this paper, we investigate the application of the State Space Model (Mamba) to the ICAA task, with a focus on exploring the perceptual capabilities of vision Mamba. To this end, we propose a novel framework that employs Mamba as the backbone to extract informative patterns from images, leveraging its global receptive field and linear complexity with respect to input length. Instead of relying solely on a final-layer feature, followed by fully connected layers for quality prediction, we exploit a comprehensive set of multi-scale features, thereby constructing a richer global representation of color information. To fuse these multi-scale features effectively, we introduce a Weighted Feature Fusion Module (WFFM), which adaptively assigns weights to emphasize salient color-related information. We conduct extensive experiments on two benchmark datasets, ICAA17K and SPAQ, successfully demonstrating its effectiveness for the ICAA task. The code is available at https://***/qinghaoya/icaa-mamba.

关键词： Deep learning Visualization Codes Image color analysis Fuses Signal processing Feature extraction Complexity theory Data mining Speech processing

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