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17th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2024

作者： Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331506421

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures. © 2024 IEEE.

关键词： Federated learning

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Lightweight image super-resolution reconstruction based on mixed attention and global inductive bias network

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Multimedia Tools and Applications 2024年 1-17页

作者： Cai, Yuxi Wen, Xiaopeng Wang, Tongguan College of Computer Science Sichuan University Chengdu China Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China College of Informatics Huazhong Agricultural University Wuhan China

Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability to model global information of hierarchical features, incomplete attention to information, and excessive parameters make some based on convolutional methods face challenges. To address these issues, this paper designs the lightweight image super-resolution reconstruction based on Mixed Attention and Global Inductive bias Network(MAGIN), which directly captures the global information of phased features from the horizontal and vertical directions respectively to produce global inductive bias information. Furthermore, the global spatial attention block is proposed, which can realize the perception of global feature space without redundant operation. To realize more comprehensive attention for features, a mixed attention block arises, which enables the network to attend to features across different dimensions: channel, spatial, and pixel levels. Extensive experimental results demonstrate that the MAGIN delivers more satisfactory results than some other advanced lightweight reconstruction algorithms on several standard test datasets under the condition of considering the network’s performance, parameter and computational complexity. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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Evaluation of RIS-Enabled B5G/6G Indoor Positioning and Mapping using Ray Tracing Models 11

Evaluation of RIS-Enabled B5G/6G Indoor Positioning and Mapp...

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11th Workshop on Satellite Navigation Technology, NAVITEC 2024

作者： Kompostiotis, Dimitris Vordonis, Dimitris Paliouras, Vassilis Alexandropoulos, George C. Grec, Florin University of Patras Electrical and Computer Engineering Department Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Noordwijk Netherlands

ISBN: (纸本)9798350356854

A Reconfigurable Intelligent Surface (RIS) can significantly enhance network positioning and mapping, acting as an additional anchor point in the reference system and improving signal strength and measurement diversity through the generation of favorable scattering conditions and virtual line-of-sight paths. In this paper, we present a comprehensive framework aimed at user localization and scatterer position estimation in an indoor environment with multipath effects. Our approach leverages beam sweeping through codebook-based beamforming at an 1-bit RIS to scan the environment, applies signal component extraction mechanisms, and utilizes a super-resolution algorithm for angle-based positioning of both connected users and scatterers. To validate the system's effectiveness, accurate 3D ray tracing models are employed, ensuring the robustness and effectiveness of the proposed approach in practical scenarios. © 2024 IEEE.

关键词： Mapping

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Preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference ISPRS Annals

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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2024年第4/W4-2024期10卷 1-4页

作者： Pantazis, Dimos N. Karanikolas, Nikitas N. Vassilakopoulos, Michael Coors, Volker Voyiatzis, Ioannis Ellul, Claire Department of Surveying and Geoinformatics Engineering Research laboratory S.O.C.R.A.T.E.S. University of West Attica Greece Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Computer Engineering University of Thessaly Greece Germany Department of Civil Environmental & Geomatic Engineering University College London United Kingdom

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Preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference - ISPRS Archives

International Archives of the Photogrammetry, Remote Sensing...

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 2024年第4/W10-2024期48卷 1-4页

作者： Pantazis, Dimos N. Karanikolas, Nikitas N. Vassilakopoulos, Michael Coors, Volker Voyiatzis, Ioannis Ellul, Claire Department of Surveying and Geoinformatics Engineering Research Laboratory S.O.C.R.A.T.E.S. University of West Attica Greece Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Computer Engineering University of Thessaly Greece Germany Department of Civil Environmental & Geomatic Engineering University College London United Kingdom

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Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT 23

Text Analysis and Recognition of Emotional Content Using Dee...

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23rd IEEE/ACIS International Conference on computer and Information Science, ICIS 2023

作者： Andrikakis, Efthymios Perikos, Isidoros Paraskevas, Michael Hatzilygeroudis, Ioannis University of Patras Department of Computer Engineering & Informatics Patras Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering & Informatics Patras Greece Department of Electrical and Computer Engineering University of Peloponnese Computer Technology Institute and Press Diophantus Patras Greece

ISBN: (纸本)9798350345841

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively. © 2023 IEEE.

关键词： Deep neural networks

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval 19

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Kim, Ju-Chan Van Nguyen, Pham Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

This paper presents NDAS (Noise-Decomposed Abnormal Segmentation), an innovative framework for robust medical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS’s superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability. © 2025 IEEE.

关键词： Image segmentation

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

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Multimedia Tools and Applications 2025年 1-22页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans presents notable challenges. This particularly in differentiating tumors from surrounding tissues with similar intensity. This study utilizes the DeepLabV3Plus model with an Xception encoder to address these challenges. We evaluated the model on a dataset of 3064 MR images, which included meningioma, glioma, and pituitary tumors. The model achieved high segmentation performance, with an accuracy of 0.9991, Dice Similarity Coefficient (DSC) of 0.9529, and Jaccard Index (JI) of 0.9133. It also demonstrated substantial boundary precision, recording an Average Symmetric Surface Distance (ASSD) of 0.84 mm and a Hausdorff Distance (HD) of 2.3 mm. These metrics surpass those of existing state-of-the-art models. While the model performed well with well-defined tumor boundaries, it faced challenges segmenting tumors with ambiuous edges. These findings suggest significant potential for enhancing diagnostic accuracy in neuro-oncology. Future work will focus on enhancing the model’s sensitivity to subtle tumor features and expanding the dataset to include a broader range of tumor characteristics. Additionally, incorporating multimodal imaging data could further improve segmentation precision. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification 17th

Cross Feature Fusion of Fundus Image and Generated Lesion ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Mok, Dahyun Bum, Junghyun Tai, Le Duc Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819609000

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model’s ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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CEGSet: Collection of standardized cause-effect graph specifications 12

CEGSet: Collection of standardized cause-effect graph specif...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Pozderac, Damir Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322910

Cause-effect graphs are a commonly used black-box testing method, and many different algorithms for converting system requirements to cause-effect graph specifications and deriving test case suites have been proposed. However, in order to test the efficiency of black-box testing algorithms on a variety of cause-effect graphs containing different numbers of nodes, logical relations and dependency constraints, a dataset containing a collection of cause-effect graph specifications created by authors of existing papers is necessary. This paper presents CEGSet, the first collection of existing cause-effect graph specifications. The dataset contains a total of 65 graphs collected from the available relevant literature. The specifications were created by using the ETF-RI-CEG graphical software tool and can be used by future authors of papers focusing on the cause-effect graphing technique. The collected graphs can be re-imported in the tool and used for the desired purposes. The collection also includes the specification of system requirements in the form of natural language from which the cause-effect graphs were derived where possible. This will encourage future work on automatizing the process of converting system requirements to cause-effect graph specifications. © 2023 IEEE.

关键词： Black-box testing

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