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Speech signal’s phase information based Alzheimer’s disease detection using deep learning

International Journal of Speech Technology 2025年 1-14页

作者： Kumar, Mohit Sushant Yadav, Arun Kumar Department of Computer Science & Engineering NIT Hamirpur India

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that affects millions worldwide. Early and accurate diagnosis is crucial for timely intervention and management, as it can significantly improve patient outcomes and quality of life. This study explores a novel deep learning approach for AD detection using speech analysis, a promising non-invasive technique. We analysed the performance of various feature types, including magnitude spectrograms, raw phase, modulated group delay (MGD), and instantaneous frequency (IF) phase, on custom Convolutional Neural Network (CNN) and pre-trained ResNet50 models. The findings demonstrate that the magnitude spectrogram achieved the highest accuracy among single-input models. However, a dual-input model that combines magnitude and instantaneous frequency information from pre-trained ResNet50 models achieved superior performance, reaching an accuracy of 90%, precision of 95%, recall of 90%, and F1-score of 92.4%. This suggests that integrating magnitude and phase features effectively captures the nuances of speech patterns, leading to improved classification for AD detection. Moreover, the proposed approach outperforms previous state-of-the-art methods, highlighting its potential as a valuable tool for early AD diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech analysis

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Cumulative Capacity Credit Estimation for Renewable Energy Projects

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1643-1651页

作者： Arif S.Malik Majid A.Al Umairi the Electrical and Computer Engineering Department Sultan Qaboos UniversityAl-Khod 123Oman Oman Electricity Transmission Company MuscatOman Electrical and Computer Engineering Department Sultan Qaboos UniversityAl-Khod 123Oman

This paper presents a novel method for accurately estimating the cumulative capacity credit(CCC)of renewable energy(RE)*** data from the main interconnected system(MIS)of Oman for 2028,where a substantial increase in RE generation is anticipated,the method is introduced alongside the traditional effective load carrying capability(ELCC)*** ensure its robustness,we compare CCC results with ELCC calculations using two distinct standards of reliability criteria:loss of load hours(LOLH)at 24 hour/year and 2.4 hour/*** method consistently gives accurate results,emphasizing its exceptional accuracy,efficiency,and simplicity.A notable feature of the method is its independence from loss of load probability(LOLP)calculations and the iterative procedures associated with analytic-based reliability ***,it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE *** innovation is of particular significance to prospective independent power producers(IPPs)in the RE sector,offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data,often restricted by privacy concerns.

关键词： Effective load carrying capability renewable energy resource capacity credit generation system reliability loss of load probability

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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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Improved Subsynchronous Oscillation Parameter Identification with Synchrophasor Based on Matrix Pencil Method in Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 22-33页

作者： Xiaoxue Zhang Fang Zhang Wenzhong Gao Jinghan He the School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China the Department of Electrical and Computer Engineering University of DenverDenverUSA

The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos es an improved MPM-based parameter identification with syn *** MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coeffi cients of the matrix pencil to ensure the accuracy of the identi fied parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation ***,the identification and recovery of bad data are proposed by uti lizing the difference in temporal continuity of the synchropha sors before and after noise *** proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)*** with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.

关键词： Subsynchronous oscillations(SSOs) synchrophasor parameter identification matrix pencil method bad data

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Word Sense Disambiguation for Indic Language using Bi-LSTM

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Multimedia Tools and Applications 2024年第16期84卷 1-26页

作者： Mishra, Binod Kumar Jain, Suresh Department of Computer Science and Engineering Chandigarh University Punjab Mohali India Department of Computer Science and Engineering IPS Academy Institute of Engineering and Science Indore India

In the enormous field of Natural Language Processing (NLP), deciphering the intended significance of a word among a multitude of possibilities is referred to as word sense disambiguation. This process is essential for accurately understanding the refinements of language within a specific context. The various collection of government services supports the everyday lives of Indian citizens. Some of the initiatives that are available for use are the National e-Governance Plan, UMANG, Bharat Bill Payment System, e-Panchayat, police clearance, and e-Sign. However, the effectiveness of these services depends on NLP tasks that are customized for Indian languages, requiring skilled management of linguistic uncertainties. Hindi and its equivalents lack the linguistic resources that are abundant in English, Mandarin, Spanish, French, and Russian. To tackle this challenge, this article introduces a fresh approach to resolving the ambiguity of Hindi words. By combining the capabilities of word embedding Word2Vec or FastText with the Bi-Directional LSTM neural network, it carefully analyzes Hindi sentences to determine the most contextually appropriate meaning. The effectiveness of this approach is highlighted by the remarkable F1 score of 70.03% achieved in experimental findings. This significant improvement over previous models confirms the effectiveness of the proposed methodology in addressing the intrinsic language ambiguities found in Indic languages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Meta-Learning Online Control for Linear Dynamical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4163-4169页

作者： Muthirayan, Deepan Kalathil, Dileep Khargonekar, Pramod P. University of California Irvine Department of Electrical Engineering and Computer Sciences IrvineCA United States Texas A&M University Department of Electrical and Computer Engineering United States

In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a linear dynamical system for a finite horizon of T time steps. The cost function and system noise at each time step are adversarial and unknown to the controller before taking the control action. Meta-learning is a broad approach where the goal is to prescribe an online policy for any new unseen task exploiting the information from other tasks and the similarity between the tasks. We propose a meta-learning online control algorithm for the control setting and characterize its performance by meta-regret, the average cumulative regret across the tasks. We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor D/D∗ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where D is a problem constant and D∗ is a scalar that decreases with increase in the similarity between tasks. Thus, when the sequence of tasks are similar the regret of the proposed meta-learning online control is significantly lower than that of the naive approaches without meta-learning. We also present experiment results to demonstrate the superior performance achieved by our meta-learning algorithm. © 1963-2012 IEEE.

关键词： Cost functions

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Analysis of the effect of different scattering mechanisms on the excess noise behavior of Sb-based avalanche photodiodes

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Optics Express 2025年第4期33卷 7337-7344页

作者： Karimi, Hannaneh Herrera, Daniel J. Dadey, Adam A. Wei, Dongxia Mcarthur, Andrew Bank, Seth R. Campbell, Joe. C. Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Carlo (MC) model described in this work reveals the effect of high alloy scattering on the excess noise factor in these material systems. A comparison between the excess noise factor of different combinations of scattering rates is investigated. Furthermore, the ionization path length probability distribution is presented for AlInAsSb APDs with different scattering mechanisms. Good agreement is achieved between simulation results and experimental measurements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Weighted rank aggregation based on ranker accuracies for feature selection

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Soft Computing 2025年第4期29卷 1981-2001页

作者： Abdolrazzagh-Nezhad, Majid Kherad, Mahdi Faculty of Engineering Department of Computer Engineering Bozorgmehr University of Qaenat Qaen Iran Department of Computer Science Faculty of Computer and Industrial Engineering Birjand University of Technology Birjand Iran Faculty of Engineering Department of Computer Engineering University of Qom Qom Iran

Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity of methods, there are some base rankers (Filter-based methods) that are of diverse quality and usually the ground truth of ratings is not available. Existing rank aggregation methods that take the diverse quality of base rankers into account do not have any explicit approach for appropriate weighting, require prior assumptions, and suffers from high computational complexity. In this paper, to overcome these challenges, an efficient unsupervised method is introduced for estimating the base rankers’ qualities and aggregating the rankers based on the estimated weights. We first compute the ratio of disagreement between base rankers in ordering different element pairs and then estimate the accuracies in a way that to minimize the discrepancy between these computed ratios and their analytical counterparts. We use the weighted majority voting method for obtaining the aggregated results. To resolve the probable inconsistencies in the final aggregation, the result is formed as a graph, and a greedy algorithm is used to find an acyclic subgraph with the highest weigh. To demonstrate the performance of the proposed method, nine standard UCI datasets are used. The obtained results by the proposed method have higher values of classifier measures than the existing baseline Feature Selection methods and rank aggregation-based multi-filter methods in the most datasets. The experiments show that rank aggregation-based Feature Selection methods outperform individual methods. The proposed method also shows the weight of each Filter-based Feature Selection method, in which the MRMR method has a higher weight than other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wiener filtering

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A Deep Learning based Ultra-Lightweight Architecture for Atrial Fibrillation Detection Using Single-Lead ECG Recordings

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IEEE Access 2025年 13卷 86474-86486页

作者： Khan, Abbas Mughal, Muhammad Rizwan Irtaza, Syed Ali Khan, Mohsin Tahir, Madiha Ali, Ahtisham Saeed, Zoraize Ghulam Ishaq Khan Institute of Engineering Science and Technology Pakistan Sultan Qaboos University Electrical and Computer Engineering Department Muscat Oman Institute of Space Technology Electrical Engineering and Computer Science Department Islamabad Pakistan FAST National University of Computer and Emerging Sciences Islamabad Pakistan

In this paper, we have proposed a novel deep-learning model to process electrocardiogram (ECG) signals from single-lead ECG device. This is achieved by using a hybrid of CNN (convolutional neural network) and LSTM (long short-term memory) neural netwrok, where CNN extracts localized features from ECG signals, while the LSTM network extract temporal relationships. Moreover, a filtering-based method is proposed to generate synthetic data to overcome the data imbalance posed by the publicly available PhysioNet/CinC Challenge 2017 dataset. Specifically, three distinct ECG filters: Neurokit, BioSPPy, and EngZeeMod are used to perform data augmentation. As depicted by the simulation results, the proposed model exhibits superior performance with an F1 score of 99.56% providing a gain of 102.25 dB in terms of model size and performance gain of 1.06% outperforming existing state-of-the-art method enabling cost-effective real-time diagnosis with an inference time of 0.48×10-3 secs. Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a 0.48×10-3 second inference time. These simulation results confirm the superior performance of the proposed model. © 2013 IEEE.

关键词： Electrocardiography

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Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro-Waveform Measurements

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IEEE Transactions on Industry Applications 2025年第3期61卷 4290-4300页

作者： Qiu, Wei Yin, He Wu, Yuru Dong, Yuqing Zheng, Yao Yao, Wenxuan Liu, Yilu Hunan University Department of Electrical Engineering and Computer Science Changsha410082 China University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. The outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements. © 1972-2012 IEEE.

关键词： Synchros

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