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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Aarhus University Dept. Electrical and Computer Engineering Aarhus8000 Denmark Mirpur Univ. Science and Technology Dept. Computer Systems Engineering AJK Mirpur10250 Pakistan North Carolina State University Dept. Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9798350368369

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025 IEEE.

关键词： Markov chains

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Synergizing CXL with Unified Memory for Scalable GPU Memory Expansion

Synergizing CXL with Unified Memory for Scalable GPU Memory ...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Junseung Lee Jungrae Kim Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon Republic of Korea

Heterogeneous computing, which combines the power of CPUs and GPUs, is increasingly important in today's data-centric era. However, it also presents significant challenges, particularly in terms of GPU memory management. Compute eXpress Link (CXL) technology, which is supported by industry leaders, offers enhanced memory expansion capabilities. Alongside this, NVIDIA's Unified Memory (UM) system simplifies the process of sharing data between CPUs and GPUs. We explore the role of CXL memory in various scenarios within the UM framework while simultaneously highlighting issues such as the handling of page faults at the software level.

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Attention-Based Improvement on 3D Point Cloud Classification 2

Attention-Based Improvement on 3D Point Cloud Classification

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2nd International Conference on Artificial Intelligence and Education, ICAIE 2021

作者： Jin, Jiaoyang Zhao, Jiabao Nanjing University Dept. of Control and Systems Engineering Nanjing China

ISBN: (纸本)9781665424929

Recently, the researches on 3D model analysis begin to come into focus. With the development of 3D data capturing devices, people can obtain 3D data much easier than before. At present, many existing models for 3D classification are constructed with convolution operator which can benefit from improvements on convolution. Meanwhile, attention mechanisms have demonstrated their utility across quite a few tasks including sequence learning, understanding in images. Some lightweight and general modules based on the attention mechanism are designed to improve the performance of network based on CNN architectures. Inspired by the research on image representations based on the attention mechanism, this paper applies the mechanism to address the problem of 3D shape classification. In this work, a channel attention module named 3DSE block is used to improve the performance of some representative models for 3D point cloud classification. The dependency relationship between the different channels is explicitly modeled to obtain the channel descriptor of the feature map, and it is used to refine the original feature map, so as to sift through the information from the feature and enhance the ability to feature extraction. This paper uses 3DSE module to improve the performance of the existing typical 3D classification networks. Then some experiments are performed on popular shape classification benchmark to test our ideas. The result demonstrates the effectiveness of our method. © 2021 IEEE.

关键词： Convolution

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EMDA-Net: Earth Mover’s Distance (EMD) influenced Attention-aided Neural Network for Medical Image Classification

EMDA-Net: Earth Mover’s Distance (EMD) influenced Attention...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Surya Majumder Kushaj Mallick Wrick Pal Somenath Chakraborty Ram Sarkar Dept. of Computer Science & Engineering Heritage Institute of Technology Kolkata India Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science & Information Systems West Virginia University Institute of Technology West Virginia USA Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

Medical image analysis methods need to be more generalized in order to meet the substantial problems associated with cancer detection and treatment, including skin, cervical, and breast cancer. In this work, we propose a novel lightweight convolutional neural network (CNN) architecture, named EMDA-Net (Earth Mover’s Distance (EMD) influenced Attention-aided Neural Network), specifically designed to meet the requirements of cancer subtype classification. It is built on the MobileNetV2 backbone and is equipped with hybrid statistical mechanism-aided attention mechanisms. Our model ensures high-performance while maintaining a lightweight design by leveraging the capabilities of the MobileNetV2 architecture and the attention mechanism. To improve similarity detection across data channels, we present a novel EMD influenced attention technique. We have examined datasets related to skin, cervical, and breast cancer for experimentation. Our model especially outperforms the state-of-the-art techniques, demonstrating its effectiveness in maximizing diagnosis precision for various cancer types across the datasets. The code of the proposed model will be made public at: https://***/q5DlN

关键词： Earth Attention mechanisms Codes Neural networks Skin Breast cancer Convolutional neural networks Medical diagnostic imaging Machine intelligence Image classification

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Mingze Li Weihang Ren Lei Fan Yongpei Guan Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA Dept. of Industrial and Systems Engineering University of Florida FL USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation.

关键词： Heuristic algorithms Clustering methods Clustering algorithms Search problems Boundary conditions Power grids Complexity theory Optimization AC power transmission

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Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification

Time-Stretched Imaging Flow Cytometry and Photonic Neuromorp...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Tsirigotis, Aris Deligiannidis, Stavros Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. 83200 Karlovasi Samos Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

ISBN: (纸本)9798350345995

In this work, we investigate the compression capabilities of a photonic neuromorphic accelerator relying on an optical spectrum slicing technique [1], following a high-flow 1D imaging cytometry setup, able to image 62000 particles/sec. The utilized image scheme relies on a technique, where spatial information is transfered to the spectrum and subsequently through dispersion to the temporal domain. Therefore, it can replace high speed spectrometers with a single photodetector [2]. On the other hand, the elevated image acquisition rate, trigger the generation of a staggering amount of data, which can cause a processing backlog at the digital backend. In this context, we explore, for the first time, the use of a photonic neuromorphic pre-processing unit, following the cytometer that can reduce the number of trainable parameters at a lightweight digital backend by a factor of 2.5, while at the same time, it can project incoming data to a high dimensional space boosting accuracy. © 2023 IEEE.

关键词： Photonics

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Data-driven modeling of heat pumps and thermal storage units for MPC

Data-driven modeling of heat pumps and thermal storage units...

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2023 International Conference on the Built Environment in Transition, CISBAT 2023

作者： Brandes, M. Cai, H. Vivian, J. Croci, L. Heer, P. Smith, R. Urban Energy Systems Laboratory Empa Dübendorf Switzerland Dept. of Industrial Engineering University of Padova Padua Italy Dept. of Power System Development Ricerca Sistema Energetico SpA Milan Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Switzerland

Heat pumps can play a crucial role in the European energy strategy 2050, which aims to achieve net-zero greenhouse gas emissions. When coupled with thermal energy storage and integrated with advanced control strategies, heat pump operation can be optimized to reduce carbon footprint and respond to the needs of system operators. However, to scale in a multitude of buildings, the transferability of the modeling into heterogeneous systems is crucial. In this paper, two different interpretable linear models, a hybrid (grey-box) and a fully data-driven (black-box) model are investigated. Specifically, two regression-based identification methods (SINDYc and DMDc) are used for dynamic models and the LASSO regression is used for static models. The transferability of the approach is evaluated using two real-world facilities with heterogeneous sizing and configuration. The results show a similar simulation performance for both cases with a maximum normalized RMSE of 0.41 and 0.60, respectively. This confirms the transferability of the approach that is necessary for large-scale implementation. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Carbon footprint

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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Deligiannidis, Stavros Tsirigotis, Aris Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach. © 2023 IEEE.

关键词： Cameras

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Weihang Ren Mingze Li Lei Fan Yongpei Guan Dept. of Industrial and Systems Engineering University of Florida FL USA Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks.

关键词： Runtime Scalability Power systems Planning Security Optimization AC power transmission Load flow

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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