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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Manogna, B. Sai Rao, R. Gowri Shankar Chythanya, K. Ravi Navamani, C. Raghuram, K.S. Isaac, J. Samson Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Physics Chennai India Sr University School of Computer Science and Artificial Intelligence Warangal India Department of Computer Science and Engineering Erode India Department of Mechanical Engineering Visakhapatnam India School of Engineering and Technology Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798331529246

The paper highlights a Wireless Sensor Network (WSN) implementation for environmental monitoring, emphasizing cost-effectiveness and adaptability. APIs designed to handle values instead of voltage levels increase system resilience, while open standards promote security, cost efficiency, and auditability. The data shows a diurnal temperature range from 20°C (night) to 38°C (day), stable humidity at 38%, and UV radiation peaking at 6 (day) and dropping to 1.5 (night). Smoke levels are consistently high at 490 μg/m3, while toxic gas concentrations slightly rise to 0.5 ppm during the day from 0.45 ppm at night. Fire metrics remain low and stable. These findings highlight the need for UV protection, air quality controls, and consistent fire safety measures. The system achieves effective environmental monitoring while remaining adaptable for future needs, underscoring its value in scalable, cost-efficient, and sustainable public management solutions. © 2025 IEEE.

关键词： Environmental monitoring

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and Information Organization). All rights

关键词： Ant colony optimization

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Subwavelength 3D terahertz imaging with a single-pixel laser transceiver

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Optics Express 2025年第10期33卷 21938-21950页

作者： Silva, Jorge Plöschner, Martin Bertling, Karl Ghantala, Mukund Gillespie, Tim Torniainen, Jari Herbert, Jeremy Lim, Yah Leng Taimre, Thomas Qi, Xiaoqiong Donose, Bogdan C. Zhou, Tao Lui, Hoi-Shun Indjin, Dragan Han, Yingjun Li, Lianhe Valavanis, Alexander Linfield, Edmund H. Davies, A. Giles Dean, Paul Rakić, Aleksandar D. School of Electrical Engineering and Computer Science The University of Queensland Brisbane 4072 QLD Australia School of Mathematics and Physics The University of Queensland Brisbane 4072 QLD Australia School of Electronic and Electrical Engineering University of Leeds Leeds LS2 9JT United Kingdom

Terahertz imaging holds great potential for non-destructive material inspection, but practical implementation has been limited by resolution constraints. In this study, we present a single-pixel THz imaging system based on a confocal microscope architecture, utilizing a quantum cascade laser as both transmitter and phase-sensitive receiver. We demonstrate, for the first time to the best of our knowledge, that laser feedback interferometry-based imaging systems achieve enhanced lateral and axial resolution compared to conventional confocal imaging. Specifically, our approach yields a twofold improvement in lateral resolution, reaching λ/2, and a two-order-of-magnitude enhancement in axial resolution, from 25λ to beyond λ/5, through interferometric phase detection. The system can produce a 0.5 megapixel image in under three minutes, surpassing both raster-scanning single-pixel and multipixel focal-plane array-based imagers. Coherent operation enables simultaneous amplitude and phase image acquisition, and a custom visualization method links amplitude to image saturation and phase to hue, enhancing material characterization. A 3D tomographic analysis of a silicon chip reveals subwavelength features, demonstrating the system’s potential for high-resolution THz imaging and material analysis. This work overcomes the resolution limits of conventional lens-based imaging systems, by enabling rapid, high-fidelity imaging of subwavelength features beyond the diffraction limit. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Imaging systems Laser imaging Organic solar cells Single pixel imaging Spatial resolution Terahertz imaging

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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU) corresponding to changed areas in RSI. The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multi-level features, leading to challenges in extracting LCLU features in complex changing scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multi-Level Feature Interaction module (TML-FI), and a Multi-Level Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multi-level features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multi-scale features, thus improving the model's capability to interpret complex changing scenes. Finally, ML-FFD integrates the multi-level features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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Research on transient response optimization of fiber optic seismic sensor targeting oil and gas field exploration 4

Research on transient response optimization of fiber optic s...

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4th International Computational Imaging Conference, CITA 2024

作者： Tang, Guitao Li, Dongqing Zhou, Xinghong Yi, Duo Liu, Fei College of Physics and Optoelectronic Engineering Shenzhen Key Laboratory of Sensor Technology Shenzhen University Shenzhen518060 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781510688834

The current research relating to the fiber optic seismic sensor (FOSS) mainly focuses on optimizing its steady-state characteristic parameters including sensitivity, cross-sensitivity and operating bandwidth etc., while there are few studies on its transient response characteristics. In this study, we discuss the transient response improvement of the FOSS in both laboratory platform and oilfield. In the laboratory platform, the ball-falling process is used to simulate the perforation shot signal during down-hole seismic monitoring, and the transient response between the FOSS and the commercial piezoelectric sensor has been compared in both time and frequency domain. Besides, two different types of balls (steel and cotton) are used to verify the transient response performance of the presented FOSS when retrieving different types of signals. The experimental results demonstrate that correlation coefficients as high as 90% are obtained within the frequency range 0~3000 Hz for two ball-falling processes. Finally, the presented FOSS has been used for detecting the perforation shot signal in the oilfield, the comparative analyses based on the key parameters including taking off time and attenuation constant also confirm the transient response optimization of the presented sensor, which is beneficial to improve the acquisition accuracy of seismic signal. © 2025 SPIE.

关键词： Gas industry

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Superconductivity in Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics with cobalt ferrite (CoFe2O4) nanoparticle addition

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Ceramics International 2025年第12期51卷 16134-16142页

作者： Tran, Duc H. Pham, An T. Binh, Nguyen Thanh Anh, Do Thi Kim Nam, Nguyen Hoang Hong, Nguyen T.M. Cuong, Le Viet Pham, Phong V. Huy, Nguyen Quang Huong, Tran Thi Man, Nguyen K. Lim, Kean Pah Kechik, Mohd Mustafa Awang Chen, Soo Kien Le, Tien Faculty of Physics University of Science Vietnam National University Hanoi100000 Viet Nam Nano and Energy Center University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Engineering Physics and Nanotechnology VNU University of Engineering and Technology Hanoi100000 Viet Nam University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Electronics and Computer Engineering University of Economics - Technology for Industries Hanoi100000 Viet Nam Faculty of Electronic Materials and Devices School of Materials Science and Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Serdang43400 Malaysia University of Science and Technology of Hanoi Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi100000 Viet Nam

This study explores the use of cobalt ferrite (CoFe2O4) nanoparticles as artificial pinning centers to investigate flux pinning characteristics in the Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics. Polycrystalline samples with the composition (Bi1.6Pb0.4Sr2Ca2Cu3O10+δ)1−x(CoFe2O4)x, where x ranges from 0.000 to 0.010, are synthesized based on the solid-state formation. Structural and surface analyses reveal that the samples predominantly comprise superconducting phases, with volume fractions varying with x. An increase in x leads to a drop in the critical temperature (Tc). However, an improvement in the critical current density (Jc) is observed at x = 0.002, owing to the addition of CoFe2O4. The collective pinning theory indicates a shift from a single-vortex to small bundle vortex pinning at the crossover field (Bsb) in all samples. The dependence of Bsb on temperatures is consistent with δl pinning, attributed to changes in the mean free path of the charge carrier. Geometries of artificial pinning centers are identified as 2D using the Dew–Hughes model. CoFe2O4 nanoparticles are primarily located at the grain boundaries, filling voids and supporting pinning through grain boundaries. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Flux pinning

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Takei, Gaku Obara, Hidemine Haneda, Ryo Nozaki, Takahiro Graduate School of Science and Technology Keio University Yokohama Japan Yokohama National University Department of Physics Electrical and Computer Engineering Yokohama Japan Keio University Department of System Design Engineering Yokohama Japan

ISBN: (纸本)9798331533892

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths. © 2025 IEEE.

关键词： motion control Multi-level inverter voice coil motor

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Refining Pseudo Labels with a Teacher-Student Paradigm for Accurate Brain Lesion Segmentation

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IEEE Access 2025年 13卷 84474-84487页

作者： Chatterjee, Pubali Sharma, Kaushik Das Chakrabarti, Amlan Department of Computer Science and Engineering ITER Bhubaneswar751030 India University of Calcutta Electrical Engineering Department of Applied Physics Kolkata700073 India University of Calcutta A. K. Choudhury School of Information Technology Kolkata700073 India

In medical image segmentation, obtaining large volume of high quality labeled data is a persistent challenge, especially for intricate tasks like brain lesion segmentation, where annotations are time-consuming, costly, and require expert knowledge. This work introduces a novel learning framework where the abundance of labeled data is scarce, and the model learns from both limited number of annotated data as well as from unlabeled data. The framework initially leverages superpixel segmentation to group pixels with similar properties into compact, perceptually coherent regions while preserving critical structural details, and produce coarse segmentation mask on unlabeled data. In the next phase, a teacher-student model is employed, where the teacher module generates pseudo-labels for unlabeled data, and the student model learns from both of these pseudo-labels and minimal amount of annotated data. In addition, a feature refinement module is introduced to further strengthen the teacher-student framework, where the student module also learns from the mutual agreement between superpixel prediction and the pseudo label generated by the teacher module. The overall framework helps in efficiently using the unlabeled data with the help of handful amount of labeled data. Experimental results across multiple datasets demonstrate the effectiveness of the proposed framework in achieving precise, computationally efficient brain lesion segmentation. The integration of superpixel-based grouping and the student-teacher architecture not only improves segmentation accuracy but also offers a scalable solution for scenarios with limited annotated data. This method provides a promising pathway for advancing medical imaging applications, enabling accurate and efficient segmentation while addressing the challenges of data scarcity and annotation constraints. © 2013 IEEE.

关键词： Image segmentation

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The Internet of Things in Elderly Healthcare Applications: A Systematic Review and Future Directions

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IEEE Access 2025年 13卷 71335-71373页

作者： Shirvanian, Naser Shams, Maryam Masoud Rahmani, Amir Lansky, Jan Mildeova, Stanislava Hosseinzadeh, Mehdi Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran14778-93855 Iran Kurdistan University Department of Physics Faculty of Basic Science Sanandaj66177-15175 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan University of Finance and Administration Department of Computer Science and Mathematics Faculty of Economic Studies Prague101 00 Czech Republic Duy Tan University School of Computer Science Da Nang550000 Viet Nam Jadara University Jadara Research Center Irbid21110 Jordan

The increase in the elderly population globally and the prevalence of chronic diseases among the elderly have resulted in rising healthcare costs worldwide. Utilizing the Internet of Things (IoT) and smart sensors to reduce healthcare costs and enhance the quality of life for the elderly is a highly effective and valuable solution. Effective monitoring can improve resource management in IoT-based healthcare systems. We conducted a systematic literature review (SLR) for elderly healthcare on the IoT to evaluate its various facets and key areas. The taxonomy of elderly healthcare studies in the IoT comprises six categories: smart homes, security and privacy, remote healthcare system, wearable devices, smartphone, healthcare and healthcare costs. Management of chronic diseases has the highest percentage of evaluation statistics in the field of smart homes, at 61%. In addition, 26% of smart homes in healthcare are devoted to behavioral characteristics and 13% to biological characteristics. Finally, this study highlights the visions and challenges of elderly healthcare articles and future work. © 2013 IEEE.

关键词： Medical services Internet of Things Older adults Security Monitoring Diseases Costs Privacy Biomedical monitoring Smart homes

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