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Self-Supervised Reflective Learning Through Self-Distillation and Online Clustering for Speaker Representation Learning

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1535-1550页

作者： Danwei Cai Zexin Cai Ze Li Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Digital Innovation Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China

Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses these limitations by enabling continuous refinement of pseudo labels during training. Through a teacher-student architecture and online clustering mechanism, SSRL eliminates the need for iterative training rounds. To handle label noise, we incorporate noisy label modeling and pseudo label queues that maintain temporal consistency. Experiments on VoxCeleb show SSRL's superiority over current two-stage iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate self-supervised speaker representation learning through the novel reflective learning paradigm.

关键词： Training Representation learning Iterative methods Noise Adaptation models Noise measurement Predictive models Speech processing Robustness Convergence

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Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

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IEEE Control systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

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Effect of Ignition Voltage Amplitude on Breakdown Delay Time of Ring Laser Discharge Gap

Effect of Ignition Voltage Amplitude on Breakdown Delay Time...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Anton O. Sinelnikov Ilya A. Smetanin Bogdan A. Basov Egor A. Smetanin Uliana F. Bykanova Computer science and control systems department Laboratory of Technologies of Polymer Ferroelectrics Laboratory of Microelectronics and Nanotechnology Mechanical engineering technologies department Bauman Moscow State Technical University Moscow Russian Federation Department of Nanotechnology and Microsystems Engineering Department of Mechanics and Control Processes RUDN University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The modern space industry requires autonomous and high-precision control systems for scientific and commercial missions. Strapdown inertial navigation systems based on laser gyroscopes play a key role in spacecraft control, especially in environments where satellite navigation is unavailable. In this paper, ring lasers with an active medium made of a helium-neon mixture, their design and operating modes are investigated. The influence of voltage on the ignition electrodes (4.2-5 kV) on the breakdown time of the active medium has been analyzed. It was found that increasing the voltage reduces the breakdown time, providing reliable activation of gyroscopes. The experimental results allowed to determine the optimal ignition parameters that increase operation reliability and reduce production losses. This contributes to the development of highly efficient spacecraft control systems, opening up new prospects for deep space exploration and control of satellite systems.

关键词： Space vehicles Breakdown voltage Electric breakdown Power lasers Ignition Ring lasers Laser modes Control systems Gyroscopes Reliability

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Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

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Neural Computing and Applications 2025年 1-14页

作者： Kim, Tae Hoon Ojo, Stepehn Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia College of Computer Studies De La Salle University 2401 Taft Ave. Malate Manila1004 Philippines Faculty of Information and Communication Studies University of the Philippines Open University Los Baños4031 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

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An Evolutionary Ising Optimization Framework for Unconstrained Binary Quadratic Programming

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IEEE Transactions on Evolutionary Computation 2025年

作者： Fu, Wujie Trivedi, Anupam Chen, Guanyu Gao, Yuan Srinivasan, Dipti Danner, Aaron National University of Singapore NUS graduate school Singapore Singapore National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore Chongqing University Key Laboratory of Optoelectronic Technology and Systems College of Optoelectronic Engineering Chongqing China

An Ising machine (IM), as a type of analog computer tailored for tackling intractable combinatorial optimization problems, has attracted remarkable attention in recent years. In contrast to the blossoming field of bespoke IM hardware, developing metaheuristics from IMs remains largely uninvestigated. Here, we propose a physics-inspired evolutionary computation paradigm, termed the Ising optimization framework (IOF);it comprises a unique Ising algorithm and a hybrid annealing scheme, which together are well-suited for solving quadratic unconstrained binary optimization (QUBO) problems embedded in Ising system energy. The Ising algorithm leverages a set of iterated self-mapping functions to evolve an Ising-spin swarm, enabling efficient energy minimization in artificial Ising systems while mitigating detrimental chaos. Complementing the algorithm, a hybrid annealing scheme integrating singular value dropout, bifurcation control, and a nudging strategy, is devised to augment the overall optimization capacity. The effectiveness of the IOF is validated on various Ising and Max-cut problems with decision variables ranging from 625 to 5000 in number. In comparison to four major types of methods for solving QUBOs, including IM simulations, nature-inspired algorithms, a state-of-the-art heuristic, and the commercial solver Gurobi, the IOF consistently demonstrates notable optimization quality and computational efficiency. This paper provides a theoretical foundation and practical guidelines for bridging Ising-inspired approaches with evolutionary computation, offering an evolutionary perspective on Ising optimizations and suggesting a fertile avenue for future research and application. © 1997-2012 IEEE.

关键词： Ising model

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

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UAS Traffic Management: Integrating Weather Information using System Adaptability

UAS Traffic Management: Integrating Weather Information usin...

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Annual IEEE systems Conference

作者： William Brooks V. Chandrasekar Rob Cifelli Systems Engineering Department Colorado State University Ft. Collins CO Electrical and Computer Engineering Department Colorado State University Ft. Collins CO NOAA Physical Sciences Laboratory Boulder CO

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly prevalent in the commercial and defense sectors, operating throughout the continental United States airspace. UAVs have wing sizes ranging from 0.25 centimeters to 61 meters, and they perform a myriad of roles from job site surveillance to 3D mapping of agricultural fields. However, a UAV is not a standalone system, but joins a ground control system, pilot and communications network to form an Unmanned Aircraft System (UAS) platform. With the various UAS platforms of different UAV sizes and missions, the Federal Aviation Administration (FAA) is developing a UAS Traffic Management (UTM) system to safely integrate them into the airspace. UAS ground systems require ingestion, processing, compilation and dissemination of weather data specific to a local area. The NOAA Advanced Quantitative Precipitation Information (AQPI) system has proven to reliably perform these functions. Expanding the mission for a system changes the requirements originally used to design the system. The ability of a system to adjust is the foundation of System Adaptability. Using the concept of System Adaptability and leveraging the existing Digital Twin of the Bay Area AQPI, this paper presents the theoretical application of adapting the San Francisco Bay Area AQPI system to provide meteorological information to UAS platforms operating under the Bay Area UTM.

关键词： Meters Three-dimensional displays Precipitation Surveillance FAA Autonomous aerial vehicles systems engineering and theory Reliability engineering Digital twins National Oceanic and Atmospheric Administration

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A Multi-layer Non-Terrestrial Networks Architecture for 6G and Beyond under Realistic Conditions and with Practical Limitations

arXiv

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arXiv 2025年

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled LGEERE Laboratory Department of Electrical Engineering Echahid Hamma Lakhdar University El-Oued Algeria Laboratory Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey The Department of Electrical and Electronic Engineering University of Tripoli Libya The Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Division of Communications Systems KTH Royal Institute of Technology Sweden The Department of Computer Engineering King Fahd University of Petroleum and Minerals Saudi Arabia

In order to bolster the next generation of wireless networks, there has been a great deal of interest in non-terrestrial networks (NTN), including satellites, high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAV). To unlock their full potential, these platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and next-generation multiple access (NGMA). However, in practical applications, transceivers often suffer from radio frequency (RF) impairments, which limit system performance. In this regard, this paper explores the potential of multi-layer NTN architecture to mitigate path propagation loss and improve network performance under hardware impairment limitations. First, we present current research activities in the NTN framework, including RIS, multiple access technologies, and hardware impairments. Next, we introduce a multi-layer NTN architecture with hardware limitations. This architecture includes HAPS super-macro base stations (HAPS-SMBS), UAVs–equipped with passive or active transmissive RIS–, and NGMA techniques, like non-orthogonal multiple access (NOMA), as the multiple access techniques to serve terrestrial devices. Additionally, we present and discuss potential use cases of the proposed multi-layer architecture considering hardware impairments. The multi-layer NTN architecture combined with advanced technologies, such as RIS and NGMA, demonstrates promising results;however, the performance degradation is attributed to RF impairments. Finally, we identify future research directions, including RF impairment mitigation, UAV power management, and antenna designs. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Amorphous boron nitride: synthesis, properties and device application

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Nano Convergence 2025年第1期12卷 1-22页

作者： Sattari-Esfahlan, Seyed Mehdi Mirzaei, Saeed Josline, Mukkath Joseph Moon, Ji-Yun Hyun, Sang-Hwa Jang, Houk Lee, Jae-Hyun Institute for Microelectronics TU Vienna 1040 Austria CEITEC BUT Brno University of Technology Purkynova 123 Brno 61200 Czech Republic Fraunhofer Institute for Material and Beam Technology WinterbergstraBe 28 Dresden E01277 Germany Department of Energy Systems Research Ajou University Suwon 16499 South Korea Department of Mechanical Engineering and Materials Science Washington University in St. Louis St. Louis 63130 MO United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 South Korea Center for Functional Nanomaterials Brookhaven National Laboratory Upton New York 11973 United States Institute for Functional Intelligent Materials National University of Singapore Singapore 117575 Singapore

Amorphous boron nitride (a-BN) exhibits remarkable electrical, optical, and chemical properties, alongside robust mechanical stability, making it a compelling material for advanced applications in nanoelectronics and photonics. This review comprehensively examines the unique characteristics of a-BN, emphasizing its electrical and optical attributes, state-of-the-art synthesis techniques, and device applications. Key advancements in low-temperature growth methods for a-BN are highlighted, offering insights into their potential for integration into scalable, CMOS-compatible platforms. Additionally, the review discusses the emerging role of a-BN as a dielectric material in electronic and photonic devices, serving as substrates, encapsulation layers, and gate insulators. Finally, perspectives on future challenges, including defect control, interface engineering, and scalability, are presented, providing a roadmap for realizing the full potential of a-BN in next-generation device technologies. © The Author(s) 2025.

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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

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IEEE Transactions on Wireless Communications 2025年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen Da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai200234 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece Artificial Intelligence and Cyber Systems Research Center Lebanon

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennas M or RIS elements N, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero as M or N tends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart. © 2002-2012 IEEE.

关键词： Genetic algorithms

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