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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sadhukhan, Mrinmoy Bhattacharya, Indrajit Dutta, Paramartha Department of Computer and System Sciences Siksha Bhavana Visva Bharati Bolpur India Department of Computer Applications Kalyani Government Engineering College Kalyani India

ISBN: (纸本)9783031781711

Image-to-image translation has become a prominent trend in the field of computer vision. This innovative technique is widely employed for generating concealed facial features from given noise. It proves particularly useful in reconstructing obscured facial regions covered by masks. The underlying technology at the core of this method is the Generative Adversarial Network (GAN). The primary mechanism of GAN-based neural networks relies on an encoder-decoder structure combined with a discriminator network. The discriminator plays a crucial role in training the network to distinguish between authentic and generated images. FUGAN, a novel approach in this domain, incorporates a self-attention block and has demonstrated superior performance to other models. With an impressive SSIM and BRISQUE score of 0.928 and 27.990, respectively, FUGAN is at the forefront of achieving state-of-the-art results. The architecture of the FUGAN neural network is built upon the UNET framework, with modifications to enhance its effectiveness. These modifications involve increasing the network depth and incorporating batch instance normalization as a regularizer to achieve the desired outcomes. The resulting model attains a smaller size and exhibits high accuracy in image regeneration. This streamlined and efficient FUGAN model is well-suited for deploying low-resource Internet of Things (IoT) devices. Its capability to regenerate facial features from masked regions makes it instrumental in surveillance systems, detecting individuals wearing masks and reconstructing their facial details. Github link (FUGAN: https://***/mrinmoy-sadhukhan/FUGAN-model) of our work is provided. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gallium nitride

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A novel approach to identify parkinson’s disease and other similar neural stress by analysing keystrokes on modern active devices with ensemble classification

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Multimedia Tools and Applications 2025年 1-40页

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector - III Saltlake City Kolkata700106 India Department of Computer and System Sciences Visva-Bharati University Santiniketan731235 India

Motor skills (related to the motor nerve) and neurocognitive disorders affect humans’ typing ability to an extent that is noticeable while using a keyboard, smartphone, or other electronic gadgets. These two medical conditions are Parkinson’s disease (PD), caused by malfunctions of the motor nerve, and neurocognitive disorder, caused by a deficiency of organismic responses to stimuli. A mild symptom of PD, change in fine motor skills during typing, is reflected heavily in keystroke patterns during the early stages. Similarly, Emotional stress (ES) expresses a neurocognitive disorder that affects cognitive abilities as well. Early symptoms of this disorder are reflected in the keystroke patterns according to their severity. As there is no such pathological examination, it is challenging to perceive and measure the development of such disorders already developed in human behaviour as a disease. Furthermore, early screening of these diseases is essential for future diagnosis and preventing fatal consequences, since both are progressive illnesses. A modest attempt is made here to detect two such neurodegenerative disorders in humans using the way they type, formally known as Keystroke dynamics (KD). In this study, a bootstrapped-based homogeneous ensemble classification method has been proposed to address the uncertain performance and uneven distribution of classes for the detection of such medical conditions using users’ typing tendencies. For this purpose, two recent benchmark datasets were used for the validation and confirmation of operational improvements of the proposed method, which have been validated qualitatively and quantitatively. As a result, sensitivity/specificity of 0.82/0.78 in detecting PD and 0.98/0.98 in ES have been achieved, which is robust and accurate in a more realistic evaluation. The proposed framework explores the possibilities of implementing it in web-based systems, which has significant benefits. A better diagnosis, early detection at home

关键词： Neurodegenerative diseases

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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FlexIM: Efficient and Verifiable Index Management in Blockchain

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3399-3412页

作者： Li, Binhong Lin, Licheng Zhang, Shijie Xu, Jianliang Xiao, Jiang Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory The Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China Hong Kong Baptist University School of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets. © 1989-2012 IEEE.

关键词： Structured Query Language

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Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises

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IEEE Transactions on Wireless Communications 2025年第5期24卷 4055-4072页

作者： Pei, Jianhua Feng, Cheng Wang, Ping Tabassum, Hina Shi, Dongyuan Huazhong University of Science and Technology School of Electrical and Electronic Engineering Hubei Wuhan China Cornell University Energy Systems Engineering System Engineering IthacaNY United States York University Lassonde School of Engineering Department of Electrical Engineering and Computer Science TorontoON Canada

Deep learning (DL)-based Semantic Communications (SemCom) is becoming critical to maximize overall efficiency of communication networks. Nevertheless, SemCom is sensitive to wireless channel uncertainties, source outliers, and suffer from poor generalization bottlenecks. To address the mentioned challenges, this paper develops a latent diffusion model-enabled SemCom system with three key contributions, i.e., i) to handle potential outliers in the source data, semantic errors obtained by projected gradient descent based on the vulnerabilities of DL models, are utilized to update the parameters and obtain an outlier-robust encoder, ii) a lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter and is placed before the decoder at the receiver, enabling adaptation for out-of-distribution data and enhancing human-perceptual quality, and iii) an end-to-end consistency distillation (EECD) strategy is used to distill the diffusion models trained in latent space, enabling deterministic single or few-step low-latency denoising in various noisy channels while maintaining high semantic quality. Extensive numerical experiments across different datasets demonstrate the superiority of the proposed SemCom system, consistently proving its robustness to outliers, the capability to transmit data with unknown distributions, and the ability to perform real-time channel denoising tasks while preserving high human perceptual quality, outperforming the existing denoising approaches in semantic metrics such as multiscale structural similarity index measure (MS-SSIM) and learned perceptual image path similarity (LPIPS). © 2002-2012 IEEE.

关键词： Image enhancement

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Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks 17

Drone-Assisted Load Distribution Framework for Traffic Optim...

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

作者： Soni, Priyanka Mense, Omkar Manohar Kanti Addya, Sourav National Institute of Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331531195

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms. © 2025 IEEE.

关键词： Drones

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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Hybrid signaling-droop dc-link voltage control for trains with multiple onboard power sources

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IEEE Access 2025年 13卷 75434-75447页

作者： Ahmad, Saad Saeed, Mariam Guerrero, Juan Manuel Muniategui, Iker Nuñez, Guillermo Larrazabal, Igor Briz, Fernando University of Oviedo Department of Electrical Computer & System Engineering Gijón33204 Spain Ingeteam Power Technology S.A. Traction R&D Zamudio48170 Spain

The increasing trend in efficiency and performance requirements of electric trains is leading to the deployment of more onboard energy storage elements. Hydrogen fuel cells along with battery systems are being considered by several train manufacturers. Control of the dc-link connecting all energy sources and loads can be challenging due to the need to guarantee reliable operation in all possible scenarios. This paper presents a dc-link control strategy for trains with multiple energy sources. The proposed method combines the advantages of dc bus signalling and droop methods, and allows smooth enabling and disabling of power sources by voltage command modification without any change in the overall control structure. Further, it is fault tolerant to communication loss between the battery and the central control. The proposed concepts are validated in multiple scenarios by means of simulation, as well as experimentally using a down-scaled test bench. © 2013 IEEE.

关键词： Electric traction

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Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data 24

Vision Transformer for Audio-Based Depression Detection on M...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Pratiwi, Monica Sanjaya, Samuel Ady Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798400709586

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed depression can result in deteriorating mental well-being, which includes a higher risk of experiencing suicidal ideation and engaging in self-destructive actions. Voice analysis can be employed to distinguish between individuals with depression and those without depression. However, the research that has been conducted on voice recognition to distinguish between depressed and non-depressed individuals keeps focusing on the use of a single dataset source as input for the classification model that is being developed. This work utilizes the Vision Transformer model and various pre-trained transformers, such as DeiT (Data-Efficient Image Transformer) and Swin Transformer, to detect depression based on voice. The objective was to create a more generic model not restricted to a specific language. Integrating Mel-spectrogram characteristics with a vision transformer-based model can enhance the efficacy of voice recognition models when dealing with multi-language data. The result shows 21% higher accuracy than the previous study that also implemented a cross-dataset test. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spectrographs

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An Affordable Sophisticated Frequency Control Ancillary Services for Australian National Electricity Market system Considering Industry and Infrastructure Challenges

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2025年第1期3卷 72-82页

作者： Alhelou, Hassan Haes Bahrani, Behrooz Ma, Jin School of Engineering CambridgeMA02139 United States Monash University Department of Electrical and Computer System Engineering ClaytonVIC3800 Australia University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia

This industry-oriented paper proposes a novel sophisticated frequency control and its ancillary services framework for the Australian National Electricity Market (NEM) power system considering the special design of its current automatic generation control system, which does not involve interconnectors between different power regions, resulting in technical challenges for both stability and security of the system. The proposed new method converts the NEM centralized frequency control system to a fully decentralized one with virtual consideration of interconnectors' power, thus the stability and security would be improved, and high fluctuations in interchanged power would be smoothed without extra costs of infrastructure and procurement of frequency control ancillary reserves (FCASs). The novel idea is to model the interconnectors' power deviation as unknown input to the operator in each power region, like load disturbances and renewable power variations, using dynamic observation techniques, for enabling the proposed method capabilities. The method is theoretically verified that it can enhance stability and security and stabilize both local frequencies and power exchanges. The experiment results confirm the capability of the proposed method for the NEM system and its superiority over the existing adopted approaches in industries. © 2024 IEEE.

关键词： Stability criteria

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