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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Sun, Chen Faculty of Innovation Engineering School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9781450399586

Lunar domes have always been one of the important windows to understand lunar volcanic activity, however traditional identification methods for geological domes are expensive, so this study attempts to establish an automatic identification method for lunar volcanic domes. Given that no previous research in this area has attempted to automate the identification of lunar volcanic domes, our team attempted to automate the process for the first time. To achieve the purpose of this research, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data we needed from the corresponding coordinates on the CCD and DEM moon pictures. Subsequently, the researchers screened the data to find data with more obvious features and used these data to train 9 mainstream image recognition models and compared their accuracy rates to verify the feasibility of this study. Finally, the researchers counted the mAP and AP (IoU=0.5) of the nine models and found that the highest of them could reach 0.64 (mAP) and 0.74 (AP). Therefore, this study can conclude that an automated method for identifying lunar volcanic domes should be feasible. © 2023 ACM.

关键词： Domes

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Intelligent Transportation System for Sustainable and Efficient Urban Mobility Traffic Flow Prediction Using Optimized Deep Learning Approach 5

Intelligent Transportation System for Sustainable and Effici...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sreelekha, M. Janarthanan, Midhunchakkaravarthy Lincoln University College Faculty of Engineering Malaysia Lincoln University College Faculty of Computer Science and Multimedia Malaysia

ISBN: (纸本)9798350385649

Traffic blockage is described as the transport system's condition, characterized by the automobiles' low speed. It is also because of roads, climatic conditions, particular zones, temperature, the number of events on that day etc. It mainly happens in metropolitan cities. Due to the development of communication technology, most organizations have transferred their conventional methods to computing methods. The primary objective of this study is to investigate deep learning algorithms that can efficiently capture the inherent relationships present in network traffic flows, thereby facilitating precise predictions. To evaluate the precision and predictive potential of these approaches, two initial baseline models are constructed utilizing Recurrent Neural Network (RNN) models. These baseline models make predictions for target values' mean and mode, drawing from the training data. A model is considered accurate and proficient if it surpasses the basic, naive benchmarks established based on predefined performance metrics. The optimization process, relying on Red Fox (RF), is determined through a quantitative analysis employing well-established evaluation criteria commonly used in supervised deep learning, including accuracy, precision, recall, Mean Squared Error (MSE), and Root Mean Square Error (RMSE). © 2024 IEEE.

关键词： Forecasting

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Preliminary Study on Detection of Breasts 47

Preliminary Study on Detection of Breasts

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47th ICT and Electronics Convention, MIPRO 2024

作者： Pristavnik Vrešnjak, Matic Perušić, Ani Emeršič, Žiga Peer, Peter Batagelj, Borut University Of Ljubljana Faculty Of Computer And Information Science Ljubljana Slovenia University Of Rijeka Faculty Of Engineering Rijeka Croatia

ISBN: (纸本)9798350382495

In the realm of digital image manipulation, deep fakes, predominantly sourced from pornographic materials, present a significant challenge, especially prevalent in the form of face and body swapping techniques. This emerging issue involves substituting the faces or bodies of individuals in explicit content, using advanced methods like those demonstrated in the DeepNude application. In response, we present an approach which solves the first part of the pipeline - detection of breasts. There has been limited research regarding this biometric modality, with notable exceptions such as breast cancer identification. Due to the lack of research and the absence of open, freely available data, we developed our own dataset. Images with annotations were acquired from *** and curated by experts. Annotations include name, cup size, possible breast augmentations, ethnicity, among others. To demonstrate that dataset is challenging enough for future research we used images to train in class-agnostic way three CNN-based detection models. The results show the feasibility of not only the proposed detection approaches for the task, but also the dataset and hopefully pave the way for future applications such as supporting court decisions, enhancing virtual clothing fitting techniques, and more. © 2024 IEEE.

关键词： computer vision

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Green Dual Connectivity Selection Algorithm in 5G-NR/4G-LTE NSA HetNet 7

Green Dual Connectivity Selection Algorithm in 5G-NR/4G-LTE ...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Syukri, Nur Anisya Anis Binti Thiagarajah, Siva Priya Dwijaksara, Made Harta Alias, Mohamad Yusoff Lee, It Ee Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Indonesia Faculty of Computer Science Java Barat Indonesia

ISBN: (纸本)9798350392074

Dual connectivity (DC) can increase network capacity by allowing a user to simultaneously connect to more than one Radio Access Technology (RAT) at the same time. Although it offers higher network capacity, power management is a constraint in DC enabled heterogeneous networks (HetNet). This work aims to find the best trade-off between network capacity and power efficiency in DC deployed Fifth Generation New Radio (5G-NR) and Long Term Evolution (LTE) HetNet. Past works have not conducted a simultaneous optimization of power saving and capacity enhancement for DC in a Non Standalone(NSA) setup. A novel base station pairing technique is used to determine Next Generation Node B (gNB) sleep patterns. Two algorithms, the Super Greedy algorithm and the Integer Linear Programming (ILP)-Greedy algorithm are proposed to find the optimal way to preserve network power and enhance the user throughput. The ILP-Greedy uses ILP optimization for power minimizing through cell sleeping and a greedy algorithm for user association. Based on comparison with past baseline works, the ILP-Greedy algorithm is found to be superior for DC implementation in a 4G/5G NSA HetNet, saving 7.02 % of network power and still providing comparable network capacity improvement. © 2024 IEEE.

关键词： Integer linear programming

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A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

A Study on DDoS Attacks Detection on IoT Devices Using Machi...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Mwaura, Jedidah Araki, Shunsuke Kakizaki, Ken'Ichi Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering FuKuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350324136

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices. © 2024 IEEE.

关键词： DDoS IoT Machine Learning TinyML

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Thermal Model of a Soldering Iron 33rd

Thermal Model of a Soldering Iron

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Barić, Tomislav Glavaš, Hrvoje Dubravac, Anto Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Osijek Croatia

ISBN: (纸本)9783031805967

Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with complex temperature control. The implementation of temperature control is based on the knowledge of the thermal models of the soldering iron. Thermal models of soldering irons are based either on the theory of thermal fields or on the theory of equivalent Kirchhoff thermal circuits. The theory of thermal fields is applied for very accurate modeling. In doing so, the finite element method (FEM) is used. For the above, it is necessary to use software packages based on FEM. Unlike FEM-based modeling, thermal circuit modeling does not require specialized software tools. It is also possible to derive analytical expressions that describe the thermal process of the soldering iron. In this paper, a thermal model of a soldering iron based on equivalent Kirchhoff thermal circuits is presented. Analytical expressions were derived to describe the thermal transient. The validity of the theoretical model is supported by measurement. Measurements were carried out on two soldering irons from different manufacturers. The measurement results were analyzed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hand tools

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Traffic Sign Recognition using Convolutional Neural Networks YOLOv3 Algorithm

Traffic Sign Recognition using Convolutional Neural Networks...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Gnanabaskaran, A. Pranav Ram, K.S. Vaishnavi, N. Shabari Prasannaa, D. K S Rangasamy College of Technology Faculty of Computer Science and Engineering Department of Computer Science and Engineering Tiruchengode India K S Rangasamy College of Technology Computer Science and Engineering Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350300857

Nowadays, traffic sign recognition is disrupted through various external factors such as chromatic aberration, geographical separation, and brightness of lights. This eventually poses possible safety hazards during navigation in real life. To overcome this issue, a deeplearning based method is proposed in this research using YOLOv3 with DenseNet as the backbone design. This approach uses attention mechanism to accelerate the feature extraction process by minimizing the overhead due to high computations to optimize the latency problem. Secondly, it incorporates location-specific hierarchical network to increase the traffic sign detection performance in challenging circumstances such as magnitude and illumination variations while maintaining adaptability and flexibility. In addition to this Cross Stage Partial Network is fused with DenseNet to enhance the recognition accuracy and effectiveness of the proposed system. The experimental findings on the Chinese Traffic sign dataset, CCTSDB dataset, showed that the YOLOv3-DenseNet model exhibited significant detection efficiency of 97.6% than the other deep learning models considered for performance comparison. It is observed that the proposed model is capable enough to detect the traffic signs in complicated scenarios as well as in places where the traffic signs are placed in smaller dimensions. © 2023 IEEE.

关键词： Traffic signs

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Lunar Dome Detection Method Based on Few-Shot Object Detection

Lunar Dome Detection Method Based on Few-Shot Object Detecti...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Sun, Chen Tian, Xiaolin Macau University of Science and Technology Faculty of Innovation Engineering School of Computer Science and Engineering China

Lunar domes have always been one of the important windows to understand lunar volcanic activities, but traditional geological dome identification methods are costly. This study attempts to establish an automatic identification method for lunar volcanic domes through FSOD (Few-Shot Object Detection). Since our previous research has been trying to automatically identify lunar volcanic hills using traditional recognition algorithms, our team will try Few-Shot Object Detection this time. In this study, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data this research need from the corresponding coordinates on the DEM moon images. Subsequently, this research used the existing data to train six traditional recognition algorithms and FSOD and compared their performance to verify the feasibility of this study. This research use AP50 and AP75 to evaluate the performance of the model. Finally, this research found that the AP50 of FSOD can reach 0.76, and the AP75 can reach 0.43. However, although FSOD does perform better than other traditional identification methods, this research believe its accuracy is still far from our expectations. Admittedly, compared with YOLO v5, the performance of FSOD has not been significantly improved, but this study still verifies that it is feasible to apply FSOD to the identification of lunar volcanic domes. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Quantum spin systems: Toroidal classification and geometric duality

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Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

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Neural Computing and Applications 2024年第36期36卷 23275-23293页

作者： Waqas, Muhammad Ahmed, Amr Maul, Tomas Liao, Iman Yi School of Computer Science Faculty of Science and Engineering University of Nottingham Malaysia Campus Selangor Dar ul Ehsan Semenyih43500 Malaysia Department of Software Engineering University of Malakand Khyber Pakhtunkhwa Lower Dir Chakdara18800 Pakistan Department of Computer Science Edge Hill University Lancashire L39 4QP United Kingdom School of Computer Science Faculty of Science University of Nottingham NottinghamNG8 1BB United Kingdom

Breast cancer, the most common cancer affecting female patients, presents serious challenges for proper detection. Although computer-aided diagnostic techniques have progressed, their accuracy and efficacy remain limited. To overcome these challenges, we introduce DHA-Net, a new deep learning system that combines an effective attention module (EAM) and a high-order pooling layer with a ResNet-18 backbone. DHA-Net is tested using three well-known breast cancer histopathology image datasets: BreakHis, BACH2018, and a closely related Kaggle-Breast cancer histopathology dataset. Our experiments show that DHA-Net not only improves on existing state-of-the-art approaches, but significantly outperforms them in classifying breast cancer images. This work emphasizes the novel combination of an EAM with high-order pooling, demonstrating DHA-Net’s potential to improve diagnostic accuracy and serve as a more effective tool for medical imaging applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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