In this paper, we present a low overhead beam management approach for near-field millimeter-wave multi-antenna communication systems enabled by Reconfigurable Intelligent Surfaces (RISs). We devise a novel variable-wi...
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Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are still faced with three challenging issues. First, they mostly lack the abili...
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This research explores the methods that Nonfungible Token (NFT)s can be recommended to people who interact with NFT-marketplaces to explore NFTs of preference and similarity to what they have been searching for. While...
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The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins an...
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Deep neural networks (DNNs) have demonstrated their efficacy in delivering accurate solutions to a range of optimization problems. However, in the context of wireless communications, the size of these problems may var...
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ISBN:
(数字)9798350362244
ISBN:
(纸本)9798350362251
Deep neural networks (DNNs) have demonstrated their efficacy in delivering accurate solutions to a range of optimization problems. However, in the context of wireless communications, the size of these problems may vary across adjacent time slots, due to fast changes in the networks’ architecture, e.g., the number of users. It is essential to note that this time-varying dimensionality of optimization problems in wireless networks necessitates adjustments in the DNN architecture, resulting in different numbers of input and output nodes. To address this challenge, in our paper, optimization problems of varying size are treated as distinct tasks. To tackle these tasks, a multi-task learning (MTL) approach based on modular sharing is proposed. The multi-task approach consists of a DNN, which is used to extract the solutions for all the optimization problems, and a router which manages which nodes and layers of the input and output layer of the DNN to be used during the forward propagation of each task. Consequently, all tasks share common parameters of the DNN, while the DNN dynamically adjusts to the number of nodes of its output and input layers. Numerical results demonstrate the superiority of the suggested approach over zero-padding, which is the current solution for handling resource allocation problems of varying size.
In this paper, we present a low overhead beam management approach for near-field millimeter-wave multi-antenna communication systems enabled by Reconfigurable Intelligent Surfaces (RISs). We devise a novel variable-wi...
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Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of t...
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Purpose: Localization and screening of the target tissue is a main prerequisite of numerous medical procedures, including capsule endoscopy, colonoscopy and histology. Convolutional Neural Networks (CNNs), by stacking...
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Purpose: Localization and screening of the target tissue is a main prerequisite of numerous medical procedures, including capsule endoscopy, colonoscopy and histology. Convolutional Neural Networks (CNNs), by stacking convolutional, down-sampling, and up-sampling operators in an encoder-decoder fashion, were the de-facto standard and has shown great promise in recent years. The main deficiency of these models is their local convolutional operators, which degrade accuracy, especially for targets with long-range dependencies. While CNNs excel at local feature extraction, Transformers are known for their ability to capture long-range dependencies. Also, CNN-Transformer models employ complex attention mechanisms for fusion that could increase model complexity and the potential for overfitting and underfitting in many datasets. Methods: In this paper, we propose an efficient context-aware CNN-Transformer fusion mechanism based on Semi-supervised Spatial and Global Attention mechanism (SSG-Att). Our model is designed to combine the strengths of both models and overcome their limitations. High-level features that are extracted from the two parallel branches are combined and fused using the proposed SSG-Att mechanism. A hybrid loss function is also employed, which is better adapted to the introduced fusion system. Results: We evaluated the performance of our proposed model on the Kvasir-SEG, a polyp segmentation and detection dataset, and the Gland segmentation dataset. The experimental results confirmed that the improvements yield a top-performing yet efficient deep fused CNN-Transformer architecture. The proposed model outperformed the best-reported accuracies, achieving improved dice scores of 92.11 ± 1.10 % and 91.16 ± 0.81 on the Kvasir-SEG and GlaS datasets, respectively. Conclusion: We concluded that the proposed context-aware fusion mechanism has the potential to be used in screening and localization applications in a more reliable and accurate operation compared to
The global energy situation highlights the critical need for sustainability-focused education and raising awareness for environmental issues among the younger generation. In the same context, the increased demand for ...
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ISBN:
(数字)9781665455053
ISBN:
(纸本)9781665455060
The global energy situation highlights the critical need for sustainability-focused education and raising awareness for environmental issues among the younger generation. In the same context, the increased demand for well-trained energy professionals is often emphasized as a key factor for a smooth and efficient energy transition. Despite this, there is a limited number of studies in the literature presenting examples of good practice in sustainability and energy efficiency implementation in higher education curricula. This study aims to fill this gap in the framework of an impact assessment survey concerning the implementation of a dedicated optional course in sustainability and energy efficiency field for undergraduate electrical engineering students. The results of the survey show that together with a better understanding of specific concepts, improvements in concern, willingness and behavior associated with sustainability and energy efficiency concepts have been reported by students, after they attended the proposed course during the semester.
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