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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Krisha R. Patel Vishva B. Brahmbhatt Nilesh Dubey Hardik Jayswal Department of Computer Engineering Devang Patel Institute of Advanced Technology and Research Vadodara India Department of Computer Science and Engineering Devang Patel Institute of Advanced Technology and Research Vadodara India Department of Infromation Technology Devang Patel Institute of Advanced Technology and Research Vadodara India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Augmented Reality (AR) and Virtual Reality (VR) are emerging technologies that combine digital and physical worlds to provide immersive and interactive experiences. Their use in education has been revolutionary, particularly in terms of increasing student engagement, grasp of complicated concepts, and encouraging experiential learning. AR superimposes virtual things on real-world surroundings, whereas VR immerses viewers in a completely digital environment. These tools gained traction during the COVID-19 epidemic, demonstrating their potential for remote learning and increasing student enthusiasm and information retention. While AR and VR are commonly employed in higher education, particularly in sectors such as healthcare and engineering, there are still barriers to their complete adoption, including cost, accessibility, and ethical concerns. This study explores the impactful uses of AR and VR in education, shedding light on their potential to modernize traditional teaching methods and improve student outcomes.

关键词： Visualization Ethics Epidemics Costs Distance learning Education Memory management Medical services Machine learning Immersive learning

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Constructing Rectilinear Steiner Minimum Tree with Conditional Generative Adversarial Network

Constructing Rectilinear Steiner Minimum Tree with Condition...

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International Conference on VLSI Design

作者： Kritanta Saha Pritha Banerjee Susmita Sur-Kolay Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering University of Calcutta Kolkata India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Given n points in the 2D plane which are the pins of a net, a Rectilinear Steiner Minimum Tree (RSMT) which consists only of vertical and horizontal line segments is the shortest network to interconnect the pins along with a few extra (Steiner) points. An RSMT for a net has a minimum total wirelength in Manhattan distance and acts as a backbone to many state-of-the-art global routers. However, finding an RSMT is NPcomplete. In this work, we propose a method for constructing an RSMT with an image-to-image translation using a conditional generative adversarial network (cGAN) based model. Images of RSMTs with optimal wirelength for randomly generated nets form our training dataset. Experimental results show that, after training, our model generates RSMTs with $\leq 0.348\%$ wirelength error on average compared with the optimal solution for nets with degrees up to 50. This is the first successful attempt to solve the RSMT construction problem as an image-to-image translation problem using a cGAN based model.

关键词： Training Steiner trees Translation Computational modeling Machine learning Very large scale integration Generative adversarial networks Generators Pins Testing

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On the Cycle Structure of Some Nonlinear Feedback Shift Registers

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Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia computer science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： Data handling

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Enhancing Building Safety Through Machine Learning-Based Smoke Detection

Enhancing Building Safety Through Machine Learning-Based Smo...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Sulaiman Syed Mohamed Judeson Antony Kovilpillai J Pragya Mahmood Hussain Mir Golda Brunet R Tawseef Ahmad Mir Alliance School of Advanced Computing Alliance University Bangalore India Department of Computer Science & Engineering Government College of Engineering Thanjavur India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Smoke detection in surveillance systems plays a crucial role in ensuring the safety and security of various environments, including buildings, forests, and industrial sites. In this paper, a comprehensive analysis of machine learning algorithms applied to smoke detection is presented. The proposed technique is validated using the Smoke Detection Dataset from Kaggle. The dataset comprises a diverse collection of images captured in different scenarios, including both smoke and non-smoke instances. The performance of various machine learning classifiers was evaluated. This included ensemble methods Random Forest (RF), Gradient Boosting (GB), AdaBoost (AB), linear models Logistic Regression (LR), kernel methods Support Vector Machine (SVM), decision-based methods Decision Tree (DT), and nearest neighbor methods K-Nearest Neighbors (KNN). Their precision, recall, AUC-ROC score, and Intersection over Union (IoU) are measured to assess their effectiveness in smoke detection. The results highlight that classifiers such as RF, DT, KNN, GB and AdaBoost achieve outstanding performance, with perfect scores in multiple metrics.

关键词： Support vector machines Radio frequency Smart buildings Surveillance Buildings Urban areas Nearest neighbor methods Feature extraction Safety Smoke detectors

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Hybrid Modulation for a SHB-FB Converter Employed in An Integrated On-board Charger and Auxiliary Power Module

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IEEE Transactions on Transportation Electrification 2025年

作者： Liang, Ziwei Bijukchhe, Rajib Zhu, Liyan Bai, Hua Forouzesh, Mojtaba Samila, Myron Cebanov, Evgheni Phillips, Headley University of Tennessee department of electrical engineering and computer science KnoxvilleTN37923 United States Magna Powertrain Inc Advanced Engineering & e-Propulsion ON Vaughan Canada

This paper presents a highly integrated EV charger that offers multiple charging modes, including a three-phase input 11kW on-board charger (OBC), an 11kW vehicle-toload (V2L) and a 2.6kW auxiliary power module (APM). The proposed multilevel, three-port topology simultaneously achieves magnetic and circuit integration while accommodating a three-phase (3P) 400-V AC input and a 700V DC bus using 650V GaN HEMTs. A mathematical model of the zero voltage switching (ZVS) boundary of the stacked half-bridge-full-bridge (SHB-FB) converter in the proposed integrated charger is built. Based on this, one hybrid modulation (HM) method is further proposed to improve the full-range efficiency of the SHB-FB converter in the OBC and V2L modes. A prototype is built to validate the proposed integration method and the HM method. Experimental results validate the OBC, V2L and APM modes of the proposed integrated charger and the effectiveness of the HM in improving the medium- and light-load efficiency by achieving full-range ZVS or quasi-ZVS (QZVS). © 2015 IEEE.

关键词： Gallium nitride

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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

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Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fangmin Xie Zhan Xiong Zhuoyue Wang Kun Yang Guoqiang Xiao College of Computer and Information Science Southwest University China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues— namely, deep layer semantic information and shallow layer spatial details—and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet.

关键词： Adaptive systems Source coding Semantics Lighting Object detection Detectors Signal processing Feature extraction Speech processing Drones

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