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检索条件"机构=Dept. of Computer Systems and Telematics Engineering"

共 4624 条记录，以下是41-50 订阅

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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Driver Assistance System, based on the Convolutional Neural Network 16

Driver Assistance System, based on the Convolutional Neural ...

引用

16th Electrical engineering Faculty Conference, BulEF 2024

作者： Filipova-Petrakieva, Simona Yanev, Petar Dept. Fundamentals of Electrical Engineering Technical University of Sofia Sofia Bulgaria Dept. Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9798350391671

The continuously rising number of deaths due to car accidents on the roads is a critical problem nowadays. To reduce the number of risky situations on the road, it is necessary to increase the attention of drivers when driving motor vehicles. In this context, an advisory system to assist driving is proposed in this paper. The mathematical model uses a convolutional neural network to recognize traffic signs and detect real road situations. It is implemented using Python in the Jupyter Notebook environment. The following libraries were used: Tensorflow (Tensors and Keras libraries, for machine learning and deep learning, respectively), Tkinter (GUI library), Pillow (image processing library), matplotlib (data visualization library), NumPy (library for performing complex computations), and Pandas (data analysis and processing library). The developed application is installed on the onboard vehicle computer. The detected sign is displayed in an appropriate form on the automobile dashboard. Simulations of real traffic situations are implemented in the system. All this will facilitate driving and help drivers with less experience acquire the necessary driving reactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Movement Attentive Graph Embedding for Improving Next Point of Access Prediction 18

Movement Attentive Graph Embedding for Improving Next Point ...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Kim, Taesoo Yang, Huigyu Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Superintelligence Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Next Point-of-Attachment (PoA) prediction is a deep learning-based approach to provide seamless connectivity in the mobile networks from heterogenous and dense user access. Existing recurrent neural models utilize time series characteristics to extract and learn the sequential association changes occurs between all Base Stations (BSs). Since the user moves from certain BS to its adjacent BS, the deployments of these BSs are a crucial feature for improving prediction accuracy. To this end, we propose a Graph Embedding - Long Short-Term Memory (GE-LSTM) model using Recurrent Neural Network (RNN) and Graph Neural Network (GNN) jointly. The time-series pattern of the user movement is extracted by LSTM, and the movement between adjacent BSs is emphasized as GNN through GE. The joint property of the proposed model is implemented through mixture-of-experts approach from GNN and LSTM. The performance is evaluated along with the RNN families and Transformer models, furthermore, it explores the significance of the graph-based mobility embeddings. In our experiment, the proposed model outperforms other SOTA models by achieving the maximum accuracy 94%. © 2024 IEEE.

关键词： Forecasting

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Design of Array Microstrip Antenna with Circular Polarization for WBAN/WIMAX 26

Design of Array Microstrip Antenna with Circular Polarizatio...

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2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Ahumada, Cristian Kaschel, Hector Osorio-Comparan, Roman Cordero, Sergio Universidad Técnica Federico Santa María Dept. Electrotechnics and Computer Valparaiso Chile Dept. of Electrical Engieneering Santiago Chile Dept. of Computer Systems Engineering Ciudad de México Mexico

ISBN: (纸本)9798350378115

This article presents the design of a array microstrip antenna with circular polarization for the evaluation of the specific absorption rate (SAR) in human tissues. The analysis focuses on a single element and a two element microstrip patch, each designed at 2.5 GHz. Both antennas were crafted on a microstrip substrate, sporting a thickness of 1.6 mm and a dielectric constant of 4.3. An evaluation was conducted that contrasted the efficiency of the engineered antenna array with that of the standalone antenna, with particular emphasis on VSWR (Voltage Standing Wave Radio), gain, and directionality, was designed, simulated, and optimized using CST Microwave Studio (MWS) software. Furthermore, the SAR level was scrutinized under flat conditions within the human tissue representation. © 2024 IEEE.

关键词： Circular polarization

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Comparative analysis of OFDM in AWGN & Local Acoustic Channel 1

Comparative analysis of OFDM in AWGN & Local Acoustic Channe...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Ahmed, Areeb Siddqui, Aish Khan, Rehan Nabi, Ghulam Ali, Murtaza Mohammad Ali Jinnah University Dept. Electrical and Computer Systems Engineering Karachi Pakistan Islamabad Pakistan

ISBN: (纸本)9798350373042

In this research study, a comparative analysis is presented that analyze the Orthogonal Frequency Division Multiplexing (OFDM) in two distinct communication systems Additive White Gaussian Noise (AWGN) and a Local Acoustic Channel. This study incorporates insights from the BELLHOP model to enhance understanding of sound propagation in dept. of water and guide the adaptation of OFDM for optimized communication. From simulated experiments, evaluate the performance of OFDM is assessed in the presence of local acoustic channel and AWGN, the important parameters like signal-to-noise ratio (SNR) and bit error rate (BER). The system of BELLHOP provides valuable context in the comparative analysis, highlighting the result of environmental factors on the performance of OFDM. The study not only contributes to the ongoing efforts in advancing underwater communication technique but also emphasizes the significance of tailored approach's in different underwater scenarios, ultimately having the way for more robust and efficient communication systems beneath the waves. Through a comparative analysis between AWGN and a local acoustic channel, results show that the system achieves a BER of 10-3 at SNR of 10 in local acoustic channel, while in the AWGN, a similar BER is achieved at SNR of 16. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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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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2025 IEEE Texas Power and Energy Conference, TPEC 2025

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

ISBN: (纸本)9798331541125

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. © 2025 IEEE.

关键词： Lagrange multipliers

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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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2025 IEEE Texas Power and Energy Conference, TPEC 2025

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

ISBN: (纸本)9798331541125

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. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Efficient Hardware Implementation of Seizure Detector Using Machine Learning

Efficient Hardware Implementation of Seizure Detector Using ...

引用

2024 International Conference on Microelectronics, ICM 2024

作者： El-Tanbadawy, Mariam K. Salem, Mohammed A. Megeed Abd Elghany, Mohamed A. German University in Cairo Computer Engineering Dept. Egypt German University in Cairo Electronics Engineering Dept. Egypt Tu Darmstadt Integrated Electronic Systems Lab Germany

ISBN: (纸本)9798350379396

In the past few decades, Seizure Detection has been a vital cornerstone in biomedical research. Both hardware and software implementations have been explored to improve algorithms, detection performance and resources. In this paper, three handcrafted features composed of Coastline Function, Hurst Exponent and Fractal Dimension are used as training data for supervised learning models to build an efficient and sensitive seizure detector. Random Forest classifier exhibits the best accuracy at 98%. Hardware efficiency, too, is exploited through a modified Feature Extractor that combines the aforementioned features. The features are adjusted to reduce their hardware resources on Virtex-7 FPGA. The area utilization is improved by 10%, power utilization is improved by 20% and latency decreased by 32%. © 2024 IEEE.

关键词： Fractal dimension

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A Hybrid Flood Forecasting Approach Using Hidden Markov Model and Machine Learning Techniques 7

A Hybrid Flood Forecasting Approach Using Hidden Markov Mode...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sri Sakthi, L. Chitra, P. Harini Sree, M.S. Subbulakshmi, P. Umayal, C. Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350304268

Floods are one of the most common and adverse disasters that the world faces. In India, floods are seemed as the disaster that happens often and pose extreme risk to lives and belongings. Forecasting floods can greatly reduce the risk and effect of the forthcoming flood. A large quantity of parameters is answerable for flood occurrence. The amount of rainfall and temperature are the most vital parameters for inflicting floods. Hence rainfall and temperature are used as determinants inside the look at. Machine learning model such as logistic regression, Naive bayes, k-Nearest neighbours, support vector machines (SVM) and random forests generally generate a model or computation method to label the results. Further, in latest days, hybridization techniques are expected to yield better results, wherein multiple models are combined to provide a new model. The proposed work is to develop a hybrid model called HMM-ML which includes Hidden Markov model with the above cited ML models. as a result, HMM acts as a first-class-tuner to stimulate the model. The developed model is validated against the region Kozhikode located in the State of Kerala, India which gets the maximum amount of rainfall during the period between June - August. © 2023 IEEE.

关键词： Flood prediction Hidden Markov Model(HMM) Hybridization Machine Learning (ML) Rainfall Temperature

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Downlink Covariance Estimation in URA FDD Massive MIMO systems 48

Downlink Covariance Estimation in URA FDD Massive MIMO Syste...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bameri, Salime Almahrog, Khalid Gohary, Ramy H. El-Keyi, Amr Ahmed, Yahia Carleton University Dept. of Systems and Computer Engineering Canada Ericsson Canada Canada

ISBN: (纸本)9781728163277

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations. © 2023 IEEE.

关键词： angular power spectrum channel reciprocity estimation error Two-dimensional Fourier series

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