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Digital Futures and Transformative Technologies (ICoDT2), International Conference on

作者： Talha Alam Mazhar Javed Abdur Rahman Qureshi Abdullah All Mamun Anik Electronic Engineering University of Engineering and Technology Peshawar Abbottabad Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Science and Technology Topi Pakistan Computer science The University of Haripur Haripur Pakistan Mechanical Engineering University of Huddersfield Queensgate United Kingdom

ISBN: (数字)9798350391862

ISBN: (纸本)9798350391879

Environmental monitoring and educational accessibility face significant challenges due to the complexity and high cost of traditional satellite systems. Addressing these issues requires a cost-effective solution that enables comprehensive study and intervention to improve environmental conditions. In this paper, we introduce CanSat, a compact satellite designed for educational and research purposes, utilizing off-the-shelf components and a 3D-printed frame to streamline operations and focus on critical processes such as modeling and communication. Equipped with a microcontroller (Esp32) and an RF communication (LoRa Module). CanSat efficiently processes and transmits data, including real-time video, temp, and %RH (% Relative humidity), to ground stations. The CanSat’s performance was observed through successful data transmission and global accessibility of data visualization and analysis. The results indicated reliable operation, with data visualization and analysis accessible worldwide through customized web pages and real-time tracking capabilities, demonstrating CanSat’s potential as a user-friendly and cost-effective platform for near-space exploration

关键词： Wireless communication Radio frequency Nanosensors Satellite broadcasting LoRa Data visualization Web pages Streaming media Reliability engineering Real-time systems

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A Novel Hybrid Coil Design and Implementation for Wireless Power Transfer Systems

SSRN

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SSRN 2023年

作者： Pashaei, Ali Aydin, Emrullah Ozdemir, Mehmet Akif Kosesoy, Yusuf Aydemir, Mehmet Timur Computer Engineering Faculty of Engineering and Natural Science Istanbul Rumeli University Istanbul Turkey Electric-Electronic Engineering Faculty of Engineering and Natural Science Malatya Turgut Ozal University Malatya Turkey Electric-Electronic Engineering Faculty of Engineering Gazi University Ankara Turkey Electrical Engineering Faculty of Engineering Eindhoven University of Technology Eindhoven Netherlands Electric-Electronic Engineering Faculty of Engineering and Natural Science Kadir Has University Istanbul Turkey

Wireless Power Transfer (WPT) has been drawing a lot attention in the last ten years in parallel with the market increase in electric vehicles. Although conductive charging methods are still the preferred ones, WPT based charging systems are eyed as clean and flexible alternatives of tomorrow. At the center of these systems are transmitting and receiving coils and there are different coil types proposed in the literature. This paper proposes a square-hexagonal hybrid coil structure to increase the magnetic coupling by shaping the magnetic field. In addition, this design aims to minimize the coupling coefficient variation for the misaligned coils which is one of the most important problems in WPT systems. 3D model of the coils is created and analyzed by ANSYS, Maxwell. Compared to the conventional square coil structure the coupling coefficient of the proposed structure is less affected from misalignment on the x and y axis, and as a result, it has a better efficiency. In addition, a WPT system operating at 5- W, 85-kHz was designed and tested in a laboratory environment. FEA analyzes and experimental application results largely overlap, and accordingly, the coil-to-coil efficiency of our WPT system was 93.5% and the overall efficiency of the system was 87%. © 2023, The Authors. All rights reserved.

关键词： Energy transfer

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Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity

arXiv

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arXiv 2025年

作者： Chen, Xiaolu Huang, Chenghao Zhang, Yanru Wang, Hao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Data Science and AI Faculty of IT Monash Energy Institute Monash University MelbourneVIC3800 Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Shenzhen Institute for Advanced Study of UESTC Shenzhen China

The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods. © 2025, CC BY.

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Abnormal wind speed detection and prediction: methodology and case study

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Intelligent Marine technology and Systems 2025年第1期3卷 1-10页

作者： Yang, Yuting Zhang, Cong Lam, Kin-Man Sun, Xin Xue, Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Faculty of Data Science City University of Macau Macao China College of Engineering Ocean University of China Qingdao China

Accurate wind speed prediction is crucial for conserving power resources and enhancing power utilization efficiency. However, deviations from typical wind patterns can introduce errors into predictions, potentially leading to imbalances between wind power supply and demand. Consequently, developing a model to forecast abnormal wind speeds is essential. To address this, we leverage the microcanonical multifractal formalism algorithm to detect abnormal wind speeds. In this paper, we integrate ensemble empirical mode decomposition, phase space reconstruction, and long short-term memory (LSTM) networks to predict these anomalies. Initially, wind speed data is meticulously pre-processed to generate datasets for one-hour, one-day, and non-zero wind speeds. Subsequently, LSTM networks are used to forecast abnormal wind speeds. Evaluations of our methodology across different datasets demonstrate its effectiveness, particularly excelling in one-hour forecasts.

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Surrogate-Assisted Particle Swarm Optimization with Dual-Subspace Search for Large-Scale Expensive Optimization

Surrogate-Assisted Particle Swarm Optimization with Dual-Sub...

引用

Congress on Evolutionary Computation

作者： Jiajia Zhang Chaoli Sun Guochen Zhang Jing Li Hui Shi Zongchao Xie School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan China School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China Faculty of Science University of Melbourne Melbourne Australia

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Several meta-heuristic algorithms have gained attention for addressing large-scale optimization problems (LSOPs). However, their effectiveness is limited when dealing with LSOPs that involve expensive objective functions because the significant number of objective evaluations required before identifying the optimal solution. In this paper, two sets of subproblems are op-timized, assisted by surrogate models, to collaboratively explore the optimal solution space for large-scale expensive optimization problems (LSEOPs). A variant of particle swarm optimization (PSO), engineered to accelerate convergence, is proposed to search for the optimal solution for each subproblem. Moreover, a novel infill criterion is introduced, where the solution with the maximum expected improvement in the first set of subproblems and the solution with the minimum objective value discovered so far in the second set of subproblems are selected. Then, their mean position will be evaluated using the real objective function. Experimental findings on 15 CEC2013 benchmark problems show that our proposed approach outperforms four state-of-the-art algorithms in solving expensive large-scale optimization problems with up to 1000 decision variables.

关键词： Sociology Metaheuristics Focusing Linear programming Search problems Resource management Object recognition

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Transformer-based Single-Cell Language Model: A Survey

arXiv

引用

arXiv 2024年

作者： Lan, Wei He, Guohang Liu, Mingyang Chen, Qingfeng Cao, Junyue Peng, Wei The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning China The School of Computer and Electronic Information Guangxi University Nanning China The School of Computer Electronic and Information Guangxi University Nanning China The College of Life Science and Technology Guangxi University Nanning China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structure and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks such as batch correction, cell clustering, cell type annotation, gene regulatory network inference and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers interested in the direction of single-cell language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Cytology

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Mako:A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 205-218页

作者： Jiadong Lin Xiaofei Yang Walter Kosters Tun Xu Yanyan Jia Songbo Wang Qihui Zhu Mallory Ryan Li Guo Chengsheng Zhang The Human Genome Structural Variation Consortium Charles Lee Scott E.Devine Evan E.Eichler Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Leiden Institute of Advanced Computer Science Faculty of ScienceLeiden UniversityLeiden 2311EZNetherland School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Institute for Genome Sciences University of Maryland School of MedicineBaltimoreMD 21201USA Department of Genome Sciences University of Washington School of MedicineSeattleWA 98119USA Howard Hughes Medical Institute University of WashingtonSeattleWA 98195USA The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China 不详

Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural ***,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match *** a result,there has been limited progress for CSV discovery compared with simple structural ***,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read ***,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined *** evaluations on both simulated and real datasets revealed that Mako outperformed other ***,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,***,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed *** analysis of these CSVs also revealed the impact of sequence homology on the formation of *** is publicly available at https://***/xjtu-omics/Mako.

关键词： Next-generation sequencing Complex structural variant Pattern growth Graph mining Formation mechanism

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Somatic Mutations and Alzheimer's Disease

引用

JOURNAL OF ALZHEIMERS DISEASE 2022年第2期90卷 475-493页

作者： [Anonymous] Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong ChinaDepartment of Computer Science and Technology University of Cambridge UK The Elton Laboratory for Molecular Neuroendocrinology Department of Human Molecular Genetics and Biochemistry Sackler Faculty of Medicine Adams Super Center for Brain Studies and Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel

Alzheimer's disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-beta plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer's disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerations, with the aim to bring hope for the vulnerable aging population.

关键词： Alzheimer's disease amyloid-beta peptide artificial intelligence big data late onset Alzheimer's disease somatic mutations tau

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Privacy-Preserved Incentive Mechanism for Split Learning in Edge Computing System

Privacy-Preserved Incentive Mechanism for Split Learning in ...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Lu Yu Zheng Chang Timo Hämäläinen Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Computer Science University of Exeter Exeter U.K.

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the edge computing system, split learning (SL) is an emerging distributed learning approach that allows mobile users (MUs) and edge nodes (ENs) to train the model together without sharing the raw data of the MU. Although MU can preserve its privacy in SL, attacks on the intermediate data at the cut layer for model training can still lead to privacy leakage, which prevents privacy-sensitive MUs from participating in training. Therefore, it is important to implement an effective incentive mechanism to motivate MUs to join SL while preserving their privacy. In this work, from the perspective of maximizing the utility of edge service provider (ESP) while considering the privacy-sensitivity of different MUs, the incentive problem of ESP and MUs is transformed into the utility optimization problem, and an incentive mechanism based on the differential privacy (DP) and contract theory is established to model the interactions between the MUs and EN. The obtained convex optimization problem is obtained through the mathematical derivations, and the optimal contract is presented by solving this problem. The simulation results demonstrate the effectiveness of our proposed privacy preserved incentive mechanism.

关键词： Training Privacy Computational modeling Simulation Distributed databases Data models Convex functions Contracts Optimization Edge computing

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