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Improving e-Health Security with Lightweight Cryptography for Enhanced Protection

Improving e-Health Security with Lightweight Cryptography fo...

引用

International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Bhuvaneshwari A J Kaythry P Jegadish Kumar K. J Department of ECE Sri SivaSubramaniya Nadar College of Engineering Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Securing medical data in e-health environments is imperative for maintaining trust and privacy. This abstract presents an innovative algorithm tailored for this purpose, employing advanced cryptographic techniques such as Cellular Automata and Circular Shift techniques. The methodology emphasizes robust protection against unauthorized access and differential attacks, which is crucial for safeguarding medical images from cyber threats. Rigorous testing, including the NIST test, validates the algorithm's reliability and suitability for e-Health applications. The comparative analysis highlights its superior security and processing efficiency over existing methods, positioning it as a leading solution for enhancing security, trust, and privacy in digital healthcare settings. The study's importance lies in addressing the critical need for secure handling of medical image data, ensuring both confidentiality and integrity. Its uniqueness lies in the innovative integration of cryptographic techniques tailored for e-Health, promising practical utility in fortifying security across healthcare sectors.

关键词： Wireless communication Privacy Automata signal processing algorithms NIST Cryptography Reliability Protection Biomedical imaging Testing

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DBRNN: Robust Speech Bandwidth Expansion Via High Fidelity Dilated Bidirectional Recurrent Neural Network

DBRNN: Robust Speech Bandwidth Expansion Via High Fidelity D...

引用

International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Tasnia Noshin Orin Taieba Taher Nursadul Mamun Robust Speech Processing Laboratory (RSPL) Chittagong University of Engineering and Technology Chittagong

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Speech Bandwidth Extension (SBE) is critical for enhancing speech quality and intelligibility in bandwidth-constrained telecommunications, where limited frequency range often leads to degraded audio clarity. However, state-of-the-art algorithms face challenges such as inadequate high-frequency signal reconstruction, limited robustness in noisy conditions, and inefficiencies in capturing complex temporal dependencies. To address these, this study introduces a Dilated Bidirectional Recurrent Neural Network (DBRNN)-based SBE framework, organized into Encoder, Bottleneck, and Decoder stages. The bottleneck integrates Intra-chunk and Inter-chunk processing techniques, effectively modeling long and short-term sequences to optimize high-frequency signal reconstruction. Evaluations on IEEE datasets reveal that the proposed approach outperforms baseline methods, achieving significant improvements in metrics like STOI, PESQ, LSD, and SOPM, while enhancing speech naturalness and intelligibility. By leveraging advanced neural network architectures, this work addresses the limitations of traditional algorithms, offering a robust and efficient solution for enriching communication experiences across diverse platforms and environments.

关键词： Adaptation models Recurrent neural networks Noise Bandwidth Computer architecture Speech enhancement Robustness signal reconstruction Telecommunications Spectrogram

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Intelligent Audio Analysis and processing Technology for Music Education

Intelligent Audio Analysis and Processing Technology for Mus...

引用

Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Youqian Zhong Jinan University Guangzhou Guangdong China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Teachers should actively utilize multimedia technology to optimize music teaching methods, aiming to transform traditional music classrooms into a dynamic and innovative learning environment. In this digital age, multimedia technology not only enriches teaching methods, but also injects new vitality into music education, making abstract music theories intuitive and easy to understand, and complex music techniques vividly displayed. Among them, intelligent audio analysis technology, as a cutting-edge music education auxiliary tool, is gradually receiving widespread attention from the education industry. This technology relies on advanced algorithms such as machine learning (ML) and deep learning (DL) to accurately quantify, analyze, and evaluate audio signals. This article innovatively proposes an intelligent audio analysis and processing method based on music education. This method fully utilizes the powerful capabilities of DL technology to efficiently and accurately identify and analyze audio. The experimental results show that using this music teaching method based on intelligent audio analysis and processing can significantly improve students' learning interest and participation.

关键词： Industries Deep learning Accuracy Machine learning algorithms Computational modeling Education Transforms Information age Multiple signal classification Optimization

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Quantum Multi-Path Communication Protocol Based on Maximum Flow Theory

Quantum Multi-Path Communication Protocol Based on Maximum F...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jihao Fan Liang Huang Jun Li Long Shi Yuwen Qian School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum networks are an actively researched and promising field, aiming to achieve efficient quantum information transmission by interconnecting quantum nodes. In large-scale quantum networks, end-to-end throughput is a critical factor that affects the overall performance of the network. The maximum flow problem, extensively studied in classical network theory, identifies a set of paths between the source and destination nodes that maximizes the total flow. This study extends the maximum flow problem to quantum networks, focusing on coordinating multiple paths for multi-path quantum communication. We propose a Quantum Multi-Path Communication Protocol (QMCP) that employs maximum flow theory to allocate transmission resources across multiple nodes efficiently, thus maximizing the total transmission capacity from the source to the destination. Our evaluation demonstrates that QMCP significantly enhances end-to-end throughput in quantum networks.

关键词： Quantum entanglement Simulation signal processing algorithms Focusing Information processing signal processing Throughput Routing protocols Quantum networks Speech processing

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Identical-Delay Based 2-D DOA and Frequency Joint Estimation With Sub-Nyquist Sampling for URA

Identical-Delay Based 2-D DOA and Frequency Joint Estimation...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liang Liu Xinyun Zhang Xinyi Zhou Lu Gan Jiancheng An Hongbin Li University of Electronic Science and Technology of China Chengdu China Yibin Institute of UESTC Yibin China Nanyang Technological University Singapore Stevens Institute of Technology Hoboken USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As spectrum congestion intensifies in wireless communication, efficient spectrum utilization through advanced sensing techniques has become increasingly important. This paper proposes a joint carrier frequency and two-dimensional (2-D) Direction of Arrival (DOA) estimation algorithm with signal recovery, utilizing identical-delay channels and sub-Nyquist sampling rates with a Uniform Rectangular Array (URA). Compared to multi-coset structures, the proposed method places identical-delay channels only along the edges of the URA, eliminating the need for additional ADCs and reducing hardware cost. Moreover, by leveraging tensor techniques, the spatial structure of the array is preserved, and the parameter pairing problem is avoided, leading to higher precision in estimation. Simulation results demonstrate the superior performance of the proposed method.

关键词： Wireless communication Direction-of-arrival estimation Tensors Two-dimensional displays signal processing algorithms Estimation Frequency estimation Hardware Sensors Speech processing

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Enhancing Induction Motor Reliability Through advanced Feature Selection and Diagnostic Models in Low-Load Conditions

Enhancing Induction Motor Reliability Through Advanced Featu...

引用

International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Chibuzo Nwabufo Okwuosa Jang-Wook Hur Department of Mechanical Engineering (Department of Aeronautics Mechanical and Electronic Convergence Engineering) Kumoh National Institute of Technology Gyeonsangbuk-do Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

In induction motors, stator windings are critical for optimal magnetic field distribution, influencing rotor bar induction and motor performance. Motor component failure is undesirable and often necessitates system overhauls. This paper presents a comparative analysis of feature selection techniques filter-based, wrapper-based, and embedded methodsfor diagnosing stator winding faults in squirrel cage induction motors and selecting a reliable cost-efficient feature selection technique with high efficiency. These techniques are applied using varying motor current data signals under low-load conditions. By leveraging advanced signal processing tools, the study demonstrated that the embedded techniques presented a superior computational efficiency and diagnostic model than the other techniques using the XGboost classifier algorithm. Preliminary results highlight the potential of these techniques to improve fault detection while addressing challenges such as noise and signal complexity. Future research will explore the integration of Explainable AI (XAI) to enhance transparency and trust in diagnostic models.

关键词： Induction motors Explainable AI Magnetic separation Noise Stator windings signal processing algorithms Rotors Transforms Feature extraction Information filters

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Performance Analysis of Normalized Min-Sum Decoder for LDPC using non-uniform Quantization

Performance Analysis of Normalized Min-Sum Decoder for LDPC ...

引用

International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Pargunarajan K Gandhiraj R Jayakumar M Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Verdant Advanced Antenna Systems Verdant Telemetry & Antenna Systems Coimbatore India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

Very high data rates and decoding computational complexity place strict constraints in 5G and 6G communications. In this paper, non-uniform Quantized Decoding algorithm is proposed for Low-density Parity Check codes (LDPC). In the proposed algorithm, the check node updating process is carried out using quantization, which reduces complexity. The Sum Product Algorithm (SPA) is iterative message passing decoding techniques offer the optimal performance for LDPC codes. Implementations use message-passing decoding with more precision, results extremely high complexity. In Iterative, message-passing (MP), trapping sets and absorbing sets causes low error floor. In this article non-uniform quantization proposed to overcome dynamic range of values over uniform quantization. Simulation results provide coding gain increases closer to of 0.5 dB in higher SNR and has good error floor at 10 −5 .

关键词： Wireless communication Quantization (signal) Simulation signal processing algorithms Dynamic range Encoding Iterative decoding Floors Gain signal to noise ratio

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advanced Comparative Analysis of Time-Frequency signal processing Techniques and Machine Learning Models for Optimized Biomedical Classification

Advanced Comparative Analysis of Time-Frequency Signal Proce...

引用

2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Reddy, Uma Lakhanpal, Sorabh Singh, Jay Meena, Talari Shakir, Sarah Abbas Sakthi, G. Mallireddy, Lohitha New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331527495

This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database. © 2025 IEEE.

关键词： Wavelet decomposition

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Speech Enhancement with Overlapped-Frame Information Fusion and Causal Self-Attention

Speech Enhancement with Overlapped-Frame Information Fusion ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yuewei Zhang Huanbin Zou Jie Zhu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Xiaohongshu Inc. Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For time-frequency (TF) domain speech enhancement (SE) methods, the overlap-and-add operation in the inverse TF transformation inevitably leads to an algorithmic delay equal to the window size. However, typical causal SE systems fail to utilize the future speech information within this inherent delay, thereby limiting SE performance. In this paper, we propose an overlapped-frame information fusion scheme. At each frame index, we construct several pseudo overlapped-frames, fuse them with the original speech frame, and then send the fused results to the SE model. Additionally, we introduce a causal time-frequency-channel attention (TFCA) block to boost the representation capability of the neural network. This block parallelly processes the intermediate feature maps through self-attention-based operations in the time, frequency, and channel dimensions. Experiments demonstrate the superiority of these improvements, and the proposed SE system outperforms the current advanced methods.

关键词： Time-frequency analysis Limiting Fuses Neural networks signal processing algorithms Speech enhancement Acoustics Delays Indexes

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CNNRF-Ensemble: A Multifaced Approach For Predicting White Spot Syndrome Virus In Shrimp Farming

CNNRF-Ensemble: A Multifaced Approach For Predicting White S...

引用

International conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Kazi Tanvir Sadi Al Huda Shaiful Ajam Opee Sayem Kabir Asraf Ali Touhid Bhuiyan Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Artificial Intelligence Research and Innovation Lab Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria VA USA

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries, filling the rapid growth demand, especially in Bangladesh. However, the White Spot Syndrome Virus(WSSV) creates and constantly threatens the global shrimp population, impacting the economic and ecological systems. Recently, researchers have tried to predict disease, but no studies have achieved significant accuracy using ML and DL algorithms. In this study, we have proposed an optimized CNNRF-Ensemble model for predicting WSSV using multifaceted data, including geographical features, environmental attributes, farm characteristics, operational practices, and socioeconomic indicators. Our proposed model achieved 95.714% accuracy in sequential model and afterwards, combining with RandomForestClassifier in Ensemble learning we obtained 99.99% accuracy. Our study suggested deep learning as an accurate, easy and low-cost method to predict shrimp WSSV, which helps to predict viruses and create awareness of shrimp firming.

关键词： Deep learning Accuracy Biological system modeling signal processing algorithms Ensemble learning Convolutional neural networks Socioeconomics Viruses (medical) Farming Diseases

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