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Texas Symposium on Wireless and Microwave Circuits and systems, (WMCS)

作者： Samuel Haug Austin Egbert Robert J Marks Charles Baylis Anthony Martone Department of Electrical and Computer Engineering Baylor University Waco TX DEVCOM Army Research Laboratory Adelphi MD

Modern radar must adapt to changing environments, and changepoint detection is a method to do so. Many radar systems employ a prediction action cycle to proactively determine transmission modes while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and subsequently transmits in predicted available bands. For these selection strategies to succeed, the assessment of the underlying environmental models must be robust to change. Changepoint detection increases this robustness. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard “garbage” data from a previous distribution, which has no relation to the current state of the environment. For spectrum sharing applications, these changepoints may represent interferers leaving and entering the spectral environment, or changing their spectrum access patterns. In this work, we demonstrate the effectiveness of the addition of changepoint detection to spectrum sharing algorithms in changing environments. Bayesian online changepoint detection (BOCD) is applied to the sense-and-predict algorithm to increase the accuracy of its models and improve its performance. The use of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change suddenly and dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to other predictive algorithms that create and maintain models in changing environments.

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Proactive and Automatic Underfrequency Load Shedding via PMUs and Particle Filters

arXiv

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arXiv 2023年

作者： Paramo, Gian Bretas, Arturo Bretas, Newton Electrical and Computer Engineering University of Florida GainesvilleFL United States Distributed Systems Group Pacifc Northwest National Laboratory RichlandWA United States Electrical and Computer Engineering University of Sao Paulo SP Sao Carlos Brazil

Underfrequency (UF) load shedding schemes are traditionally implemented in two ways: One approach is based on manual load shedding, with system operators requesting loads to be shed ahead of anticipated stressful operating conditions. Manual load shedding is usually done through phone calls. The second method is automatic load shedding via underfrequency relays. Using static settings, these schemes can be designed to operate in stages and drop previously identified loads. The main limitation of traditional load shedding schemes is that they are reactive and leave little room for optimized corrective actions. This work presents a proactive and automatic underfrequency load shedding solution for power systems. Measurements are captured via phasor measurement units (PMUs) at relatively low sampling rates of 30 Hz. These measurements are then processed by particle filters who predict the future state of the system’s frequency. Based on these predictions excess load is determined and shed. Comparative case studies are performed in simulated environments. Easy-to-implement models, without hard-to-derive parameters, highlight potential aspects for real-life implementation. © 2023, CC BY.

关键词： Phasor measurement units

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Automated Diagnosis of Breast Cancer Using Deep Learning-Based Whole Slide Image Analysis of Molecular Biomarkers

Automated Diagnosis of Breast Cancer Using Deep Learning-Bas...

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IEEE International Conference on Image Processing

作者： A. Aboudessouki Kh. M. Ali M. Elsharkawy A. Alksas A. Mahmoud F. Khalifa M. Ghazal J. Yousaf H. Abu Khalifeh A. El-Baz Bioengineering Department BioImaging Laboratory University of Louisville USA Pathology Department Faculty of Medicine Mansoura University Egypt Electrical and Computer Engineering Department Morgan State University USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE

Breast cancer is a prevalent and diverse type of cancer that exhibits unique clinicopathologic characteristics, making the correct identification of its subtype critical to providing targeted treatment and increasing survival rates. This identification process involves testing for the presence of four key molecular biomarkers, namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67. For accurate diagnosis ,the expertise of a pathologist and immunohistochemistry is required. To overcome this diagnostic challenge, we present a novel approach based on a deep learning pipeline for automated classification. Our approach can detect tumor and non-tumoral regions of the HER2 biomarker. Our deep learning framework comprises a Dense Convolutional Network (DenseNet), which process whole slide images (WSIs) of breast tissues, dividing them into patches for input into the network. Moreover, our approach provides both patchwise and pixelwise classification and analyzes ten WSIs of breast cancer histology. Our proposed approach generates an image map that classifies slide images on the pixel-level, detecting the status of hormone HER2 receptor as either positive or negative. The obtained results show that our deep learning-based approach has the potential to enhance the pathologist’s capabilities in diagnosing histopathological images with automated classification.

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Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming

arXiv

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arXiv 2023年

作者： Huang, Xinyu Wu, Wen Shen, Xuemin Department of Electrical & Computer Engineering University of Waterloo Canada Frontier Research Center Peng Cheng Laboratory China

In this paper, we propose a digital twin (DT)assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collecting real-time user status, including channel condition, location, watching duration, and preference. A reinforcement learning-empowered K-means++ algorithm is developed to cluster users based on the collected user status in UDTs. We then analyze users' watching duration and preferences in each multicast group to obtain the swiping probability distribution and recommended videos, respectively. The obtained information is utilized to predict radio and computing resource demand of each multicast group. Initial simulation results demonstrate that the proposed scheme can accurately predict resource demand. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Using Factor Analysis to Increase Accuracy of Neural Networks for Predicting Student Test Scores

Using Factor Analysis to Increase Accuracy of Neural Network...

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Frontiers in Education (FIE) Conference

作者： Michael Scott Brown Maliha Momtaz Aaron Mai Dhruvil Modi Information Systems Department University of Maryland Baltimore County Baltimore MD USA Computer Science and IT Department Hood College Frederick MD USA Computer Science and Engineering Department Texas A&M College Station College Station TX USA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education greatly by improving advising, scheduling, tutoring assignment and other educational processes. This research extends previous research by using a domain space reduction technique to improve accuracy. Factor Analysis is used to reduce the number of domain attributes for improving the accuracy of a Neural Network to predict student test scores. In this research datasets for Mathematics and Language of high school student test scores were used. Test scores were predicted using a Neural Network computing the Mean Absolute Error as a measurement of accuracy. The datasets have 30 domain attributes each. Factor Analysis was used to reduce the domain size from between 1 to 29, each time using it to train the Neural Network. Because the Mean Absolute Error may vary depending upon which records in the dataset are used for training versus testing, 50 trials of each dataset size were executed producing an Average Mean Absolute Error for each domain size. A statistical test was used to show statistical significance between the Neural Network without Factor Analysis and the Neural Network with varying domain sizes using Factor Analysis. Results were very promising and correspond to previous research that used Principal Component Analysis. Numerous domain sizes had significantly better Average Mean Absolute Errors than the accuracy of the Neural Network without Factor Analysis. This research shows that reducing the domain size using Factor Analysis can greatly improve the accuracy of Neural Networks when predicting student test scores. The best improvements occurred when domain sizes were very small ranging from 2 to 6. Domain reduction techniques, such as Factor Analysis, have been shown to improve predictive models for student test score prediction. Future research

关键词： Training Support vector machines Accuracy Neural networks Measurement uncertainty Predictive models Nearest neighbor methods Mathematical models Testing Principal component analysis

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Value of Information Optimization for Object Detection-Driven Joint Video Transmission and Processing in UAV-Enabled Wireless Networks

IEEE Journal on Miniaturization for Air and Space Systems

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IEEE Journal on Miniaturization for Air and Space systems 2025年第2期6卷 59-69页

作者： Cheng, Nan Chen, Haoran Sun, Ruijin Ma, Longfei Zhou, Conghao Zhang, Yuan Hui, Yilong Xidian University State Key Laboratory of ISN School of Telecommunications Engineering Xi’an710071 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Huzhou313001 China

In the wake of disasters, rapid and efficient search and rescue operations are essential. Unmanned aerial vehicles (UAVs) have become instrumental in such scenarios, providing real-time video streaming that can be used for object detection to locate survivors. This technology, however, faces significant challenges due to the limited communication and onboard computational resources, which are critical for processing and transmitting high-quality video data. To address these issues, this paper proposes a novel approach that leverages the concept of the value of information (VoI) to optimize the trade-off between the accuracy of object detection and the associated communication costs. By dynamically adjusting the video stream’s quality, the proposed system aims to ensure that the most valuable information is transmitted within the constraints of bandwidth and computational power. To operationalize this concept, we introduce a Deep reinforcement learning (DRL) algorithm that employs the soft actor-critic (SAC) method. The algorithm benefits from the integration of object features and contextual information extracted by ResNet50, which is then processed through a cross-attention structure within the critic network. Our simulation results indicate that our approach significantly enhances the VoI, achieving higher accuracy in object detection with better resource management compared to traditional strategies. © 2019 IEEE.

关键词： Drones

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Recent Monte Carlo-Based Simulations for Modeling Disease Spread

Recent Monte Carlo-Based Simulations for Modeling Disease Sp...

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International Conference on computer and Applications (ICCA)

作者： Mohamad Chakroun Samir Haddad Wafaa M.R. Shakir Jinane Sayah Chadi Kallab Salah Falou Computer Science and Electrical Engineering Universität Rostock Rostock Germany Computer Science Department University of Balamand Koura Lebanon Department of Computer Systems Al-Furat Al-Awsat Technical University Babil Iraq Telecom and Networks Department University of Balamand Koura Lebanon Computer Science and Mathematics Lebanese American University Jbeil Lebanon Department of Computer Science Lebanese University Tripoli Lebanon

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The COVID-19 outbreak demonstrated the significance of computer models in comprehending the complex dynamics of disease transmission. Epidemiological models have played a crucial role in understanding the complexities of COVID-19, developing effective strategies for its containment, and supporting theories regarding its transmission. The SIR (Susceptible-Infectious-Recovered) paradigm classifies people into 3 separate categories: risky, infectious, and healed. Agent-Based Models (ABM) are advanced frameworks to depict actions and relation-ships of people within a community including their various de-mographics and habits. The models have been utilized to analyze the impact of COVID-19 on specific locations, healthcare systems, and the efficacy of various treatments. However, they may face difficulties in specific situations because to the need for significant computing power and information for optimization. computer models have been useful in studying disease transmission and creating effective antiviral therapies.

关键词： COVID-19 Monte Carlo methods Pandemics Computational modeling computer simulation Collaboration Vaccines Public healthcare Streams Diseases

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IMPROVED VARIATIONAL BAYESIAN PHYLOGENETIC INFERENCE USING MIXTURES

arXiv

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arXiv 2023年

作者： Kviman, Oskar Molén, Ricky Lagergren, Jens Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Science for Life Laboratory Stockholm Sweden

We present VBPI-Mixtures, an algorithm designed to enhance the accuracy of phylogenetic posterior distributions, particularly for tree-topology and branch-length approximations. Despite the Variational Bayesian Phylogenetic Inference (VBPI), a leading-edge black-box variational inference (BBVI) framework, achieving remarkable approximations of these distributions, the multimodality of the tree-topology posterior presents a formidable challenge to sampling-based learning techniques such as BBVI. Advanced deep learning methodologies such as normalizing flows and graph neural networks have been explored to refine the branch-length posterior approximation, yet efforts to ameliorate the posterior approximation over tree topologies have been lacking. Our novel VBPI-Mixtures algorithm bridges this gap by harnessing the latest breakthroughs in mixture learning within the BBVI domain. As a result, VBPI-Mixtures is capable of capturing distributions over tree-topologies that VBPI fails to model. We deliver state-of-the-art performance on difficult density estimation tasks across numerous real phylogenetic datasets. © 2023, CC BY.

关键词： Phylogenetic

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Power-Optimal HARQ Protocol for Reliable Free Space Optical Communication

Power-Optimal HARQ Protocol for Reliable Free Space Optical ...

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IEEE International Conference on Communications Workshops, ICC

作者： Georgios D. Chondrogiannis Nikos A. Mitsiou Nestor D. Chatzidiamantis Alexandros-Apostolos A. Boulogeorgos George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

This paper investigates the usage of hybrid automatic repeat request (HARQ) protocols for power-efficient and reliable communications over free space optical (FSO) links. By exploiting the large coherence time of the FSO channel, the proposed transmission schemes combat turbulence-induced fading by retransmitting the failed packets in the same coherence interval. To assess the performance of the presented HARQ technique, we extract a theoretical framework for the outage performance. In more detail, a closed-form expression for the outage probability (OP) is reported and an approximation for the high signal-to-noise ratio (SNR) region is extracted. Building upon the theoretical framework, we formulate a transmission power allocation problem throughout the retransmission rounds. This optimization problem is solved numerically through the use of an iterative algorithm. In addition, the average throughput of the HARQ schemes under consideration is examined. Simulation results validate the theoretical analysis under different turbulence conditions and demonstrate the performance improvement, in terms of both OP and throughput, of the proposed HARQ schemes compared to fixed transmit power HARQ benchmarks.

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