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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Gao, Xirong Binti Mohammad Noh, Liza Marziana Mu, Xian Wu, Chenhao Liu, Jiang University Teknologi Mara Faculty of Art and Design Selangor Malaysia School of Computer Science and Engineering Macau University of Science and Technology Taipa China Garduate School of Fundamental Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798350378214

Traditional opera character painting blends Chinese color ink painting with conventional opera, requiring both advanced opera art understanding and Chinese painting skills. However, creating these paintings demands specific artistic skills and the ability to integrate elements of opera art. Addressing these challenges is essential for the long-term development of opera character paintings and the promotion of traditional Chinese culture. Recent advancements in artificial intelligence (AI), particularly in image style transfer technology, offer new methods for generating opera character paintings. This paper uses our collected dataset of traditional opera character photographs and their paintings, employing an adversarial generative network (GAN) for implicit feature learning. Consequently, real opera character photographs are transformed into distinctive Chinese color ink paintings. To enhance the quality of the generated images, the encoding process incorporates feature mapping and attention module integration, which help reduce unimportant areas. Additionally, adaptive normalization techniques are used to improve neural network stability, accelerate convergence, and strengthen generalization capabilities. Simulation experiments demonstrate that the proposed method outperforms existing CNN and CycleGAN-based methods, indicates promising transfer performance and highlights the potential for the artistic creation of opera character paintings. © 2024 IEEE.

关键词： Generative adversarial networks

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Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

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Neural Computing and Applications 2024年第32期36卷 20067-20087页

作者： Saha, Mousumi Chakraborty, Mainak Maiti, Suchismita Das, Deepanwita Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India School of Innovation Design and Engineering Division of Intelligent Future Technologies Malardalen University Västerås Sweden Department of Information Technology Narula Institute of Technology West Bengal Kolkata700109 India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata700109 India

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Grading

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Fig Leaf Disease Prediction and Severity Estimation UsingDeep Learning

Fig Leaf Disease Prediction and Severity Estimation UsingDee...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Prasanna Kumar, K.R. Gunasekar, M. Logeswaran, K. Dhanya Sree, P. Malathi, P. Vignesh, V. Dept. of Computer Science and Design Kongu Engineering College Tamil Nadu Erode India School of Computer Science and Engineering VIT Tamil Nadu Vellore India Dept. of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331510299

Fig leaf diseases are among the major causes of crop loss. Better methods for detection and further increased accuracy in the estimation of the severity of the disease are demanded. In basis of what is previously researched, this project continues with not just the identification of the disease but also the identification of the level of infections. Having a large dataset of 1, 871 images that comprises 971 healthy samples and 900 infected samples, the analysis will be more elaborate to determine the disease in comparison with previous estimates. We have used two deep learning models - GoogLeNet and AlexNet with outstanding performance in preceding research to make reliable predictions on leaf diseases. The novel contribution of our work is estimating the disease severity with the help of Convolutional Neural Networks that could extract features and classify disease. For the estimation of the disease severity, we will use the VGG16 model to be able to categorize the infection severity into three classes- healthy, mild, and high. The diseases addressed in this paper are Cerotelium fici (fig rust) and Fig mosaic *** goal is to offer a comprehensive tool designed to identify diseases in fig crops and assess their severity for fig farmers and the researcher so that timely disease management strategies could be undertaken. This integrated approach not only aids in taking prompt intervention but also contributes significantly toward sustainable agriculture by reducing adverse impacts on fig leaf diseases. © 2024 IEEE.

关键词： AlexNet Deep Learning GoogLeNet Plant Disease prediction Severity Estimation VGG16

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A Converged Optical and mm-Wave, Dual-band, Multi-beam Rotman Lens Antenna System Enabling Simplified designs of 5G/mmW Base Stations and Network Densification

A Converged Optical and mm-Wave, Dual-band, Multi-beam Rotma...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Smith, Lauryn Lynch, Charles Kaylor, C. Alex Alberto Campos, L. Cheng, Lin Ralph, Stephen Tentzeris, Manos M. Georgia Institute of Technology ATHENA Lab School of Electrical and Computer Engineering United States Georgia Institute of Technology Georgia Electronic Design Center School of Electrical and Computer Engineering United States CableLabs United States

ISBN: (纸本)9798350347647

The current state of the 5G/mmW (mmWave) wireless base stations makes network densification a challenging task. Its complex design, requires a large quantity of complex beam-forming systems, processing components, etc. which when integrating at the system level limits the bandwidth of operation of the beam-forming capabilities. In this effort, a converged optical and mmW system with modulation in the optical domain, simultaneous operation at multiple bands, and multiple beams at-a-time is presented for the simplification of the base station design, through the use of a dual-band Rotman lens antenna system. Error-free over-the-air transmission of 0.930 Gbit/s of total throughput is presented with the proposed dual-band Rotman lens antenna system and a receiver antenna for a proof-of-concept prototype system. © 2023 IEEE.

关键词： Millimeter waves

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

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IEEE Wireless Communications Letters 2025年第6期14卷 1748-1752页

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Linköping University Department of Computer and Information Science Linköping Sweden Singapore University of Technology and Design Information Systems Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. © 2012 IEEE.

关键词： Bandwidth

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STaaS: Cost Effective and Scalable Union Mounting in ICP for Aerospace Applications

STaaS: Cost Effective and Scalable Union Mounting in ICP for...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sakthivel, V. Li, Jueying Rama Abirami, K. Min, Dugki Lee, Jae Woo Choi, Eunmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Konkuk University Department of Computer Science and Engineering Seoul Korea Republic of Curtin University Department of Electrical and Computer Engineering Malaysia Konkuk Aerospace Design - Airworthiness Institute Konkuk University Seoul Korea Republic of Kookmin University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350357905

Cloud computing widely used in various specific areas leads to the emergence of 'Short-Time as a Service' (STaaS) as a cost-effective and scalable way for enterprises and organizations to access and utilize computing resources. Union mounting, a method that enables multiple file systems to be combined into one virtual file system, has been proposed as a strategy to optimize STaaS by allowing resources to be allocated dynamically based on user requirements. This research paper explores the feasibility and benefits of STaaS with the union mounting method, using a project providing a simulation environment on the Industry Cloud Platform (ICP) for the aerospace organization that requires an environment that can automatically and quickly create cluster groups of Cloud In the Loop Simulation (CILS) for Unmanned Aerial Vehicle (UAV) operations. The results demonstrate that STaaS with a union mounting method can provide significant benefits in terms of CILS cluster group creation, performance optimization, and flexibility. Specifically, our proposed research could reduce instance creation time costs by up to 90% compared to the traditional creation method. Overall, STaaS with union mounting method improves the creation time of the CILS cluster group for the aerospace organization requiring flexibility and cost-effectiveness. © 2024 IEEE.

关键词： Antennas

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Automated Test Generation: Taxonomy and Tool Applications 11th

Automated Test Generation: Taxonomy and Tool Applications

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11th IFIP WG 2.2 International Conference on Fundamentals of Software engineering, FSEN 2025

作者： Enoiu, Eduard Mehmood Minhas, Nasir Felderer, Michael Afzal, Wasif School of Innovation Design and Engineering Mälardalen University Västerås Sweden Oberpfaffenhofen Germany Department of Mathematics and Computer Science University of Cologne Cologne Germany

ISBN: (纸本)9783031870538

Automated test generation is an area that has seen a lot of research and development, resulting in many test automation methods and tools for test design. However, practitioners often face challenges in adopting these tools. This is not only due to the immaturity of some tools but also because of varying perspectives, confusing terminology, and, most importantly, the lack of a clear framework to guide the selection of the most suitable approach for their needs. We propose a taxonomy that characterizes the methods for automated test generation. The taxonomy was constructed using a process that involved analyzing secondary studies on automated test generation and existing taxonomies in the scientific literature. Direct observations and iterative refinements were included, followed by validation through conceptual evaluation and practitioner feedback. The resulting dimensions characterize automated test generation and its use in software testing. The taxonomy is organized into several dimensions: software artifact (i.e., type, notation, interfaces), test generation (i.e., objectives, methods, and monitoring), test execution, and test oracle. We demonstrate the taxonomy’s use by applying it to several automated test-generation tools. This paper provides the necessary concepts and a generic process for categorizing and assessing automated test generation approaches. © IFIP International Federation for Information Processing 2025.

关键词： Software testing

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Wireless sequential dual light delivery for programmed PDT in vivo

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Light(Science & Applications) 2024年第6期13卷 1047-1061页

作者： Jiayi Liu Bowen Sun Wenkai Li Han-Joon Kim Shu Uin Gan John S.Ho Juwita Norasmara Bte Rahmat Yong Zhang Department of Oncology The Second Xiangya HospitalCentral South UniversityChangshaHunan410011China Department of Biomedical Engineering College of Design and EngineeringNational University of SingaporeSingapore117585Singapore Department of Mechanical Engineering College of Design and EngineeringNational University of SingaporeSingapore117576Singapore Department of Electrical and Computer Engineering College of Design and EngineeringNational University of SingaporeSingapore117583Singapore Department of Medical IT Convergence Engineering Kumoh National Institute of TechnologyGumi39253Republic of Korea Department of Surgery Yong Loo Lin School of MedicineNational University of SingaporeSingapore119228Singapore The N.1 Institute for Health National University of SingaporeSingapore117456Singapore Institute for Health Innovation and Technology National University of SingaporeSingapore119276Singapore Department of Biomedical Engineering College of EngineeringCity University of Hong KongKowloonHong Kong SARChina

Using photodynamic therapy(PDT)to treat deep-seated cancers is limited due to inefficient delivery of photosensitizers and low tissue penetration of *** nanocarriers are widely used for photosensitizer delivery,while the self-quenching of the encapsulated photosensitizers would impair the PDT ***,the generated short-lived reactive oxygen spieces(ROS)can hardly diffuse out of nanocarriers,resulting in low PDT ***,a smart nanocarrier system which can be degraded by light,followed by photosensitizer activation can potentially overcome these limitations and enhance the PDT efficacy.A light-sensitive polymer nanocarrier encapsulating photosensitizer(RB-M)was *** implantable wireless dual wavelength microLED device which delivers the two light wavelengths sequentially was developed to programmatically control the release and activation of the loaded *** transmitter coils with matching resonant frequencies allow activation of the connected LEDs to emit different wavelengths *** irradiation time,dose,and RB-M concentration were determined using an agent-based digital simulation *** vitro and in vivo validation experiments in an orthotopic rat liver hepatocellular carcinoma disease model confirmed that the nanocarrier rupture and sequential low dose light irradiation strategy resulted in successful PDT at reduced photosensitizer and irradiation dose,which is a clinically significant event that enhances treatment safety.

关键词： PDT overcome hardly

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A Deep Learning Approach for Multimodal Deception Detection 19th

A Deep Learning Approach for Multimodal Deception Detection

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Krishnamurthy, Gangeshwar Majumder, Navonil Poria, Soujanya Cambria, Erik Singapore Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031237928

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multimodal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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A Novel Split Learning-Based Consumer Electronics Network Traffic Anomaly Detection Framework for Smart City Environment

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4197-4204页

作者： Kim, Dong-Jo Amma, N. G. Bhuvaneswari Sarveshwaran, Velliangiri Sunchon National University Department of Image Design Sunchon57922 Korea Republic of Vellore Institute of Technology School of Computer Science and Engineering Chennai600 127 India Srm Institute of Science and Technology Department of Computational Intelligence Chennai603 203 India National Chung Cheng University Department of Computer Science and Information Engineering Chiayi621301 Taiwan

Due to technology advancement, connectivity and intelligence have revolutionized the consumer electronics (CE) world and IoT systems pose a new cybersecurity threat because smart devices are constantly connected to sensors, which are connected directly to enormous cloud servers. To address the cybersecurity vulnerabilities associated with IoT in smart cities, we provide a Software Defined Network (SDN) orchestrated anomaly detection system based on Split Learning (SL). The traffic generated by the CE is pre-processed and the relevant traffic features are selected. Then the model is split into at least two sub-models using SL to detach model training from the necessity for direct access to raw data. Client-side sub-models are trained on local training data on client devices, whereas server-side sub-models are trained on servers. Experiments show that the proposed SL-based CE network anomaly detection framework decreases computation needs on the client side by just computing a sub-model rather than the complete computationally costly model. The proposed solution has the potential to detect compromised CE network traffic in smart city environments. As evidenced by our results, the proposed framework performed significantly better than the existing state-of-the-art anomaly detection system in terms of accuracy. © 1975-2011 IEEE.

关键词： Smart city

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