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Secure speaker identification in open and closed environments modeled with symmetric comb filters

Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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Boost DC-DC Converter with Non-dissipative Snubber

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IEEJ Transactions on electrical and Electronic engineering 2024年第11期19卷 1858-1878页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung No.57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung41170 Taiwan

To provide a hold-up time function in DC-DC supplies for cell site stations or data centers, using a boost converter with a bulk output capacitor as a front-end converter stage is a simple and highly cost-effective solution. However, conventional boost converters with a hard-switching operation result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a boost converter with a new non-dissipative snubber is proposed that only requires two additional diodes, an inductor, and a multilayer ceramic capacitor. The main feature of the proposed snubber is that the components are not located on the main power processing path. This means they only require low ratings, improving cost-effectiveness. A prototype converter is analyzed, realized, and applied to a 300 W redundant DC-DC power supply module for a 5G cell base station. © 2024 The Author(s). IEEJ Transactions on electrical and Electronic engineering published by Institute of electrical Engineers of Japan and Wiley Periodicals LLC. © 2024 The Author(s). IEEJ Transactions on electrical and Electronic engineering published by Institute of electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： Boost converter

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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A deep decentralized privacy-preservation framework for online social networks

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Blockchain(Research and Applications) 2024年第4期5卷 163-174页

作者： Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China Department of Computer Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Electrical&Electronic Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Computer Science and Information Systems Ashesi UniversityAccra3042Ghana Department of Telecommunication Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana

This paper addresses the critical challenge of privacy in Online Social Networks(OSNs),where centralized designs compromise user *** propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these *** methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly ***,a blockchain network secures users’data via smart contracts,ensuring robust data *** tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing *** integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

关键词： Preprocessing Privacy-preservation Blockchain Deep learning Online social network

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Feature-oriented Digital Twins for Life Cycle Phases Using the Example of Reliable Museum Analytics 8th

Feature-oriented Digital Twins for Life Cycle Phases Using t...

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8th International Symposium on Reliability engineering and Risk Management, ISRERM 2022

作者： Luther, W. Auer, E. Sacher, D. Baloian, N. Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Germany Electrical Engineering and Computer Science Wismar University of Applied Sciences Germany Department of Computer Science University of Chile Santiago Chile

ISBN: (纸本)9789811851841

In this paper, we consider partial, feature-oriented digital twins of several virtual museums and formulate an approach to assessing them from the viewpoint of their reliability. Although the formulation specifically concerns the chosen application, it can be extended to any kind of feature-oriented digital twin. The features of a virtual museum that define its scope and focus fit into three broad categories: content, communication and collaboration. Moreover, any virtual museum needs to be risk-informed, that is, seek to support the communication and perception of different types of risks from a variety of threat categories. At the same time, appropriate algorithms should identify and analyze different types of risks for museums’ buildings, visitors, exhibitions, and pieces of art with the help of sensor data, operating rules and standards, in this way supporting stakeholders’ decision making. Using comprehensible quality criteria and their metrics, formal assessment can be carried out with the help of formulated requirements, ground truth data and related standards from the involved professional associations. Additionally, empirical assessment based on user surveys can be conducted during the museum’s lifetime. Aside from various realizations or interactive learning systems, the use cases we base our analysis on are a joint project with the Museo de Arte Contemporâneo (MAC, Santiago de Chile);a digital representation of largely destroyed works by German-Jewish sculptor Leopold Fleischhacker, a joint project initiated by the Salomon Ludwig Steinheim-Institute in Essen, presented onsite at the Düsseldorf Memorial to the Victims of Persecution;and the virtual Khachkar Museum dedicated to endangered Armenian cross stones in cooperation with the American University of Armenia. © 2022 ISRERM Organizers. Published by Research Publishing, Singapore.

关键词： Risk assessment

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Space systems safety and mission assurance case study-based graduate education

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Journal of Space Safety engineering 2025年第1期12卷 12-16页

作者： Bosch, Liz Babiceanu, Radu Department of Aerospace Engineering Embry-Riddle Aeronautical University United States Department of Electrical Engineering and Computer Science Western Michigan University United States

There is a significant number of discussions lately, at both government agencies and private industry, about sending crewed missions further into space. Sending astronauts back to the Moon and, for the first time, to Mars seems to make space enthusiasts around the world excited given the noteworthy tests and preparations going on in the last few years. The decision to launch crewed missions into space depends primarily on three aspects: technology, budget, and mission risk. Even when the first two aspects are addressed, the third question still remains: is it safe enough? Generally, Safety and Mission Assurance (S&MA) for space systems is taught in space systems programs from an operations standpoint. There are very few to no courses across the USA that address space S&MA from the design engineering perspective. There is also limited teaching of ethics-based safety culture in the engineering programs offered across the country. The resultant gap between graduates’ knowledge of space S&MA and the needed skills to conduct design engineering of space systems is, at the moment, mostly filled by the space agencies and private space industry. The graduate course framework presented in this paper is built on a student-centered approach through customized case-study experiences that promotes understanding and motivation, which are significant aspects of making risk-based decisions with considerations to safeguarding human lives. By exploring the root causes of human space flight close calls, incidents, and mishaps, students can envision themselves in the space operational environment and can become aware of how design engineering, safety culture, and ethics act through a combined feedforward and feedback mechanism to ensure reliable and safe operations. Then, connecting back to the theoretical course material creates the student's understanding and motivation once they act as decision-makers after graduation. The proposed case study-based course framework also promotes crit

关键词： Case study-based instruction Design engineering of space systems Graduate engineering education Human spaceflight Safety and mission assurance

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Right Coprime Factorization for Nonlinear System with Uncertainty Based on Time-Varying Bezout Identity

Right Coprime Factorization for Nonlinear System with Uncert...

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2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： An, Zizhen Deng, Mingcong Tokyo University of Agriculture and Technology Graduate School of Engineering Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350366204

In this note, a new structure of Right Coprime Factorization (RCF) for nonlinear systems with uncertainty has been proposed based on a time-varying Bezout identity. This is inspired from the concept of dilation from homogeneous theory. However, this time-varying dilation in right coprime factorized uncertain nonlinear systems is not limited to homogeneous plants but is integrated into Bezout identity so as to ensure the bounded input bounded output (BIBO) stability of uncertain systems. Additionally, simulation results of two numerical examples are exhibited to validate the proposition. © 2024 IEEE.

关键词： Nonlinear simulations

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An Integrated Control Framework for Safe and Ergonomic Human-Drone Interaction in Industrial Warehouses

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3570-3585页

作者： Proia, Silvia Cavone, Graziana Scarabaggio, Paolo Carli, Raffaele Dotoli, Mariagrazia University of Modena and Reggio Emilia Department of Sciences and Methods for Engineering Modena41121 Italy University Roma Tre Department of Civil Computer Science and Aeronautical Technologies Engineering Rome00154 Italy Polytechnic of Bari Department of Electrical and Information Engineering Bari70126 Italy

This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the speed and separation monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots' ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. In addition, we employ the rapid upper limb assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates toward the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant artificial potential field (APF) technique for trajectory planning and the iterative linear quadratic regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator's minimum RULA. The presented control strategy facilitates drone management by adapting the drone's position to changes in the operator's position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and er

关键词： ISO Standards

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