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1st Workshop on Language Models for Low-Resource Languages, LoResLM 2025 - co-located at the 31st International Conference on Computational Linguistics, COLING 2025

作者： Mokhtarabadi, Hojjat Zamani, Ziba Maazallahi, Abbas Manshaei, Mohammad Hossein Department of Electrical and Computer Engineering Isfahan University of Technology Iran Department of Computer Engineering Shahid Bahonar University of Kerman Iran Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science Hunter College City University of New York NY United States

ISBN: (纸本)9798891762152

Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource languages. To address this challenge, we begin by introducing FarsInstruct: a comprehensive instruction dataset designed to enhance the instruction-following ability of large language models specifically for the Persian language—a significant yet underrepresented language globally. FarsInstruct encompasses a wide range of task types and datasets, each containing a mix of straightforward to complex manual written instructions, as well as translations from the Public Pool of Prompts, ensuring a rich linguistic and cultural representation. Furthermore, we introduce Co-CoLA, a framework designed to enhance the multi-task adaptability of LoRA-tuned models. Through extensive experimental analyses, our study showcases the effectiveness of the FarsInstruct dataset coupled with training by the Co-CoLA framework, in improving the performance of large language models within the Persian context. As of the current writing, FarsInstruct comprises 197 templates across 21 distinct datasets, and we intend to update it consistently, thus augmenting its applicability. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Smith Predictor Based Fractional LFC Strategy for Storage Supported Thermal Power System Amid Communication Dead Time and Cyber Threats

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IEEE Transactions on Industry Applications 2025年第3期61卷 4515-4529页

作者： Kumar, Deepak Raja, G. Lloyds Alkhatib, Mohamed Deep, Akash Kumar Muduli, Utkal Ranjan United Arab Emirates University Department of Electrical and Communication Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Institute of Technology Department of Electrical Engineering Patna800005 India Khalifa University APEC Dept. of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

Communication dead time (CDT) amid integration of renewable energy sources (RESs) and energy storage systems (ESSs) into modern power system networks can result in instabilities, phase lags and sluggish corrective action when dealing with frequency and tie-line power deviations. To compensate CDT, a Smith predictor based Fractional Order Proportional-Integral-Derivative with Integrator (FPIDI) controller is analytically designed for a dual-area thermal power system (TPS) with integrated RES, battery ESS, flywheel ESS and Ultra-capacitors. The FPIDI controller is designed using a modified internal model control (IMC) theory that is based on the frequency-relocated fractional-order model of the TPS. This modified IMC method eliminates the need of phase-lag inducing IMC filter. The performance-robustness tradeoff is enhanced by appropriate selection of the single tuning parameter (frequency-relocation factor (ψ)) by minimizing the integral of time-weighted absolute value of area control errors. The proposed controller is validated both by simulation and hardware-in-loop implementation to show its practical feasibility. © 1972-2012 IEEE.

关键词： Hardware-in-the-loop simulation

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Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks

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IEEE Access 2025年 13卷 81312-81325页

作者： Roozbehi, Zahra Narayanan, Ajit Mohaghegh, Mahsa Saeedinia, Samaneh-Alsadat Auckland University of Technology School of Electrical and Computer Engineering Auckland1010 New Zealand Auckland University of Technology Faculty of Design and Creative Technologies Auckland1010 New Zealand North Carolina Agricultural and Technical State University GreensboroNC27411 United States Iran University of Science and Technology Department of Electrical and Computer Engineering Tehran16846-13114 Iran

Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Our method uniquely leverages temporal data to detect and classify multiple sound sources simultaneously, integrating the physical concept of signal power matching with neuronal output power and employing a binaural strategy to enhance detection accuracy in real-world scenarios. The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). Statistical analysis indicates that these improvements are significant (p-value © 2013 IEEE.

关键词： Recurrent neural networks

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Spectral Estimation of Weak Signals with Strong Interference via Modulo Sampling 59

Spectral Estimation of Weak Signals with Strong Interference...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Ahmed, Shoaib Zeng, Cengcang Li, Hongbin Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

ISBN: (纸本)9798331513269

This paper introduces a framework for spectral analysis of weak signals in the presence of strong interference that can potentially exceed the dynamic range of the analog-to-digital converter (ADC) employed for data acquisition. The framework combines modulo sampling to avoid ADC saturation with interference cancellation (IC) and adaptive spectral estimators such as amplitude and phase estimation of a sinusoid (APES) and Capon for high-resolution spectral analysis. Numerical simulations validate the proposed approach's ability to accurately recover weak signals' spectrum with significantly lower mean-squared error (MSE) in amplitude estimation compared to conventional methods in the considered scenarios. Notably, it is shown that APES and Capon are able to effectively reject the out-of-range interference and reveal weak spectral content, without requiring the interference cancellation step. © 2025 IEEE.

关键词： Mean square error

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Machine Learning-Driven MPPT Techniques for Optimal Solar Energy Utilization

Machine Learning-Driven MPPT Techniques for Optimal Solar En...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Khanam, Jobeda J. Issapour, Marjaneh Foo, Simon Y Castillo, Paulo Farmingdale State College Department of Electrical and Computer Engineering Technology NY United States FAMU-FSU College of Engineering Department of Electrical and Computer Engineering FL United States

ISBN: (纸本)9798331504847

In this paper, we utilized machine learning (ML) algorithms to optimize Maximum Power Point Tracking (MPPT) in photovoltaic systems. Predicting the optimal voltage is important as, at that voltage, the system gains maximum energy output and efficiency. We predicted optimal voltage from irradiance and temperature datasets by applying ML algorithm. We evaluated several ML models, finding Linear Regression to perform best with an MSE (mean square error) of 0.0024 and RMSE (root mean square error) of 0.0489. The proposed solution integrates an ML (linear regression)-driven voltage predictor, a PID controller, and a DC-DC buck-boost converter in a MATLAB simulation environment. In MATLAB Simulink, we observed that the buck-boost converter produced the same voltage as the predicted value across the load. © 2025 IEEE.

关键词： MATLAB

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Right-Half-Plane Zeros Free Buck-Boost DC-DC Converter with Wide Voltage Range 12

Right-Half-Plane Zeros Free Buck-Boost DC-DC Converter with ...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Salehi, Seyed Mohsen Hasanzadeh, Saeed Yazdian, Ali Sezavar, Hamid Reza Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran Iran Qom University of Technology Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

This document presents a novel right-half-plane zeros (RHPZ)-free buck-boost DC-DC converter designed for a broad voltage range. It employs diode capacitor (DC) and diode inductor (DL) cells, featuring a positively regulated output voltage. The design of the proposed converter facilitates the achievement of substantial voltage gain. Furthermore, due to its dependence on a continuous input current, it is suitable for applications utilizing renewable energy sources. The fundamental operations of buck and boost modes are thoroughly analyzed, including steady-state analysis, voltage gain, and design methodology. The proposed converter is simulated in PSIM software to confirm the theoretical analyses. The results of the simulation are documented for both buck and boost modes. The input voltage operates within a range of 12V to 36V, and the output voltage is regulated to a stable 24V. The maximum output power of the converter is 100 W. © 2025 IEEE.

关键词： Buck-Boost converter

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Best Linear Controllers for Decentralized Linear Quadratic Systems

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Journal of Systems Science & Complexity 2025年第1期38卷 210-237页

作者： AFSHARI Mohammad MAHAJAN Aditya Institute for Robotics and Inteligent Machines Georgia Institute of TechnologyAtlanta 30332-0530USA Department of Electrical and Computer Engineering McGill UniversityMontreal H3A 0G4Canada

In this paper,the authors revisit decentralized control of linear quadratic(LQ)*** of imposing an assumption that the process and observation noises are Gaussian,the authors assume that the controllers are restricted to be *** authors show that the multiple decentralized control models,the form of the best linear controllers is identical to the optimal controllers obtained under the Gaussian noise *** main contribution of the paper is the solution ***,optimal controllers for decentralized LQ systems are identified using dynamic programming,maximum principle,or spectral *** authors present an alternative approach which is based by combining elementary building blocks from linear systems,namely,completion of squares,state splitting,static reduction,orthogonal projection,(conditional)independence of state processes,and decentralized estimation.

关键词： Decentralized estimation decentralized stochastic control linear quadratic systems team theory

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A Speech Enhancement Algorithm Combining Wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines Wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with Wavelet Transform significantly improves noise reduction, outperforming the use of Wavelet Transform alone. © 2025 IEEE.

关键词： Adaptive filtering

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Fast alternating minimization algorithm for coded aperture snapshot spectral imaging based on sparsity and deep image priors

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Applied Optics 2025年第14期64卷 4121-4131页

作者： Zhao, Qile Zhao, Xianhong Ma, Xu Chen, Xudong Arce, Gonzalo R. Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Coded aperture snapshot spectral imaging is a computational imaging technique used to reconstruct three-dimensional hyperspectral images from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels lead to a severely ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors of natural images. By integrating deep image prior into the principle of compressive sensing reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that the Fama-SDIP method significantly outperforms prevailing methods on simulation and real HSI datasets. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Computational Imaging

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Adaptive Numerology-based Rate Optimization for enhanced-Vehicle-to-Everything Use Cases in Beyond 5G Networks 17

Adaptive Numerology-based Rate Optimization for enhanced-Veh...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Hasan, Nadine Kumar, Abhinav Kataoka, Kotaro Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad India Indian Institute of Technology Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331531195

The existing 5G new radio (NR) numerology supports certain values of parameters like subcarrier spacing, symbol duration, and guard interval for vehicle-to-everything (V2X) communications. However, with ever evolving use cases for enhanced-V2X in beyond 5G networks, these recommended values are not sufficient to provide the required quality of service (QoS). Motivated by this, we propose an adaptive numerology scheme (ANS) that dynamically selects these parameters to mitigate the effect of Doppler and multi-path. We present exhaustive simulation results demonstrating the efficiency of the proposed scheme for the dynamic nature of V2X communication networks. © 2025 IEEE.

关键词： Vehicle to Everything

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