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Laser Beam Shaping XXIII 2023

作者： Zhang, Hao Hirschman, Jack Lemons, Randy Sun, Linshan Lu, Brittany Carbajo, Sergio Department of Electrical and Computer Engineering University of California 420 Westwood Los AngelesCA90095 United States SLAC National Accelerator Laboratory 2575 Sand Hill Rd Menlo ParkCA94025 United States Department of Applied Physics Stanford University 348 Via Pueblo StanfordCA94305 United States Physics and Astronomy Department UCLA 475 Portola Plaza Los AngelesCA90095 United States

ISBN: (纸本)9781510665484

The next generation of ultra-bright photoemission sources may offer opportunities to enhance our understanding of fundamental spatiotemporal scales. However, modeling photoemission and laser shaping systems precisely and efficiently is difficult due to the numerous interdependent linear and nonlinear processes involved and significant variations in modeling frameworks. Here, we present a new machine learning-based framework for photoemission laser systems and dynamic laser shaping. To showcase the effectiveness of our approach in system optimization, reverse engineering, and design. Our framework is designed to facilitate precise adaptive temporal shaping, including the generation of longitudinally flat-top or periodically-modulated pulses, through integration with four-wave mixing. © 2023 SPIE.

关键词： Photoemission

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Hierarchical Split Federated Learning: Convergence Analysis and System Optimization

arXiv

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arXiv 2024年

作者： Lin, Zheng Wei, Wei Chen, Zhe Lam, Chan-Tong Chen, Xianhao Gao, Yue Luo, Jun Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Institute of Space Internet Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Faculty of Applied Sciences Macao Polytechnic University China School of Computer Engineering Nanyang Technological University Singapore

As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting;it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloud-edge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA sub-problems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA for SFL within virtually any multi-tier system. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Predicting Death from Heart Disease: A Comparative Analysis of Machine Learning Algorithms

Predicting Death from Heart Disease: A Comparative Analysis ...

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International Conference on computer and Information Technology (ICCIT)

作者： Tahiya Hossain Raita Rahman Wasib Ul Navid Md Takbir Alam Abul Kalam Azad Mohammad Monir Uddin Department of Mechanical and Production Engineering Islamic University of Technology Gazipur Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Natural Sciences Islamic University of Technology Gazipur Bangladesh Department of Mathematics and Physics North South University Dhaka Bangladesh

The circulatory system's maintenance is a complex and essential function performed by the human heart. However, heart disease has been the leading cause of global mortality for many years, and it comprises various disorders that significantly impact this vital organ. Therefore, early detection of heart disease is crucial, and it requires a reliable, precise, and efficient method. In this regard, researchers and medical practitioners have employed several machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine, and Naive Bayes, to analyze a heart disease dataset from the UCI Machine Learning Repository. Hyperparameter tuning was also incorporated into the individual algorithms to improve model performance. The study aimed to assess the outcomes of various machine learning algorithms for predicting death from heart disease. Upon conducting a 10-fold cross-validation, the Random Forest algorithm performed the best, with an accuracy rate of 98%. This outcome demonstrates the potential of machine learning in developing accurate and reliable models to detect heart diseases at an early stage, ultimately saving lives. The results of this study are significant in terms of early detection of heart diseases and their prevention. The use of machine learning algorithms has shown promising results, and further development can improve the accuracy of the models. This information can be helpful to medical practitioners, policymakers, and the general public and can help save numerous lives globally.

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Ai Enabled Ant-Routing Protocol to Secure Communication in Flying Networks

SSRN

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SSRN 2022年

作者： Hussain, Sadoon Sami, Ahmed Thasin, Abida Saad, Redhwan M.A. Department of Physic College of Science University of Mosul Iraq Department of Computer Science College of Computer Science and Mathematics University of Mosul Iraq Department of Electrical Engineering Faculty of Engineering Ibb University Ibb70270 Yemen Department of Computer Engineering Faculty of Engineering Cairo University Giza12613 Egypt

Artificial intelligence has recently been used in FANET-based routing strategies for decision making, which is a unique paradigm. For effective communication in flying vehicles that use routing protocols to accomplish tasks collectively. Aerial vehicles are used in both civic and military applications. Aerial ad hoc networks are wirelessly connected, and designing routing schemes is difficult due to the rapid mobility. Ground base stations and satellites are frequently used to interconnect UAV ad hoc networks. This paper developed a novel routing protocol with a focus on ant behavior routing, which assists in end-to-end security. For the first time in flying networks, the column mobility model is used to evaluate the performance of routing protocols. While merging with aerial ad hoc networks, AI-based networking is a relatively new field. In simulation results, AntHocNet shows better results in comparison with other contemporary routing algorithms. Pheromone update process is used for data encryption in AntHocNet. This research study is performed on network simulator-2. © 2022, The Authors. All rights reserved.

关键词： Routing protocols

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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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Research, Invention, and Innovation Congress (RI2C)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331506018

ISBN: (纸本)9798331506025

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields.

关键词： Deep learning Technological innovation Accuracy Training data Sensor phenomena and characterization Logic gates Data augmentation Human activity recognition Reliability Monitoring

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Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition

Leveraging Residual Deep Neural Networks and Multi-Device Se...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection.

关键词： Deep learning Accuracy Machine learning algorithms Multimodal sensors Signal processing Telecommunications Gyroscopes

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IceCache: Recommendation-Based Edge Caching for Life Cycle of Video

IceCache: Recommendation-Based Edge Caching for Life Cycle o...

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IEEE Conference on Wireless Communications and Networking

作者： Shaohua Cao Wanli Xing Quancheng Zheng Huaqi Lv Xuyang Yuan Zijun Zhan Kai Fang Mohammad Kamrul Hasan Weishan Zhang College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Electrical and Computer Engineering Department University of Houston Houston TX USA College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Selangor Malaysia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The surge in Internet video traffic driven by 5G advancement strains network infrastructure. Edge computing emerges as a solution for video distribution, yet faces challenges from limited cache capacity and dynamic user requests. To address these challenges, we propose IceCache - a recommendationdriven edge Caching architecture for the life cycle of video streaming. IceCache enhances Quality of Experience (QoE) while reducing backhaul traffic through two-stage caching: cache placement before playback, dynamic prefetching and cache admission during playback. A user behavior simulation integrating recommender systems was developed to evaluate the proposed caching strategy. Experiments on real-world MovieLens and synthetic datasets validated the strategy's performance.

关键词： Prefetching computer architecture Streaming media Internet Quality of experience Surges Recommender systems Backhaul networks Synthetic data Strain

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A Novel Wavelet-base Algorithm for Reconstruction of the Time-Domain Impulse Response from Band-limited Scattering Parameters with Applications

arXiv

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arXiv 2024年

作者： Yarahmadian, Shantia Rahmani, Maryam Mazzola, Michael The Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIN46202 United States University of North Carolina Charlotte United States EPIC NC28223 United States Department of Mathematics Mississippi State University MS39762 United States

In this paper, we introduce a novel wavelet-based algorithm for reconstructing time-domain impulse responses from band-limited scattering parameters (frequency-domain data) with a particular focus on ship hull applications. We establish the algorithm and demonstrate its convergence, as well as its efficiency for a class of functions that can be expanded as exponential functions. We provide simulation results to validate our numerical results. © 2024, CC BY.

关键词： Frequency domain analysis

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Diversity-Driven Selection Operator for Combinatorial Optimization 11th

Diversity-Driven Selection Operator for Combinatorial Optimi...

引用

11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Carrano, Eduardo G. Campelo, Felipe Takahashi, Ricardo H. C. Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil School of Engineering and Applied Science Aston University Birmingham United Kingdom Department of Mathematics Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (纸本)9783030720612

A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Numerical experiments were performed considering four different algorithms in 24 instances of seven combinatorial optimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives. © 2021, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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A Sensor-Based Deep Learning Approach for Recognizing Daily and Work Activities in Open Environments for Sanitation Workers

A Sensor-Based Deep Learning Approach for Recognizing Daily ...

引用

International ECTI Northern Section Conference on electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Sanitation workers play a crucial role in maintaining public hygiene and cleanliness. Understanding their daily routines and job responsibilities is essential for improving their work environment, task distribution, and overall supervision. However, tracking sanitation workers in open environments remains a persistent challenge. This paper introduces a deep learning model named CNN-BiGRU-CBAM, designed to recognize sanitation workers’ daily and working activities using smartwatch sensors. The model leverages advanced convolutional neural networks to extract highly informative features from multi-modal sensor data, including acceleration information, collected from smartwatches. The dataset used in this study is carefully curated and collected from sanitation workers during their daily work routines, encompassing activities such as walking, running, sweeping, lifting, and driving vehicles. These activities are systematically categorized to cover both work-related tasks and non-work activities. Experimental results demonstrate that the proposed model consistently achieves an average F1-score of over 94.50% across seven activity groups, outperforming baseline deep learning methods by an impressive margin of 5.62%. The model excels in accurately identifying operations in diverse and dynamic work settings. This research highlights the potential of smartwatches and deep learning in continuously analyzing sanitation workers’ duties and developing improved working regulations that consider their well-being and daily tasks.

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