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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies 15

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science & Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350370249

The major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. The proposed model is fast, compact, and efficient and requires less storage. The objective of this work, is to compress the original model using structured pruning strategy. This strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy. © 2024 IEEE.

关键词： Channel Attention LLR Pruning Road Extraction RSI RSRP

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Adaptive Scale Selection Network for Crowd Counting

Adaptive Scale Selection Network for Crowd Counting

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lai, Ting-Hsu Liu, Tsung-Jung Liu, Kuan-Hsien National Chung Hsing University Dept. of Electrical Engineering Taiwan National Taichung University of Science and Technology Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Crowd counting is a computer vision task that focuses on accurately estimating the number of people present in a given scene. In the past few years, convolutional neural network-based deep learning techniques have achieved remarkable success in many computer vision tasks, including crowd counting. In the field of crowd counting, large-scale changes have always been a great challenge. To resolve this problem, previous work used multiple branches to obtain information at different scales and combined it. However, purely combining multi-branch features cannot effectively utilize multi-scale information. In this work, we modify the previous multi-branch architecture, which can reasonably select the appropriate scale information. Furthermore, we test our model on the ShanghaiTech dataset and demonstrate the competitive performance of our method. © 2023 IEEE.

关键词： computer vision

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Lakkadwala, Aayushya Lakkadwala, Prashant Acropolis Institute of Technology and Research Dept. of Computer Science and Engineering Indore India Acropolis Institute of Technology and Research Dept. of Information Technology Indore India

ISBN: (纸本)9798350388800

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field. © 2024 IEEE.

关键词： Cloud platforms

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The journey of computer science and software engineering in Israeli schools

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ACM Inroads 2022年第3期13卷 29-37页

作者： Cohen, Avi Dolev, Shlomi Gal-Ezer, Judith Computer Science & Information Technology Ministry of Education State of Israel Israel Dept. of Computer Science Ben-Gurion University of the Negev Beer-Sheva84105 Israel Dept. of Mathematics and Computer Science The Open University of Israel Raanana43107 Israel

We present a historical overview of how computer science (CS) was introduced in schools in Israel. We analyze the importance of the inclusion of CS in school curricula, for the individual development of pupils’ capabilities in all facets, and for Israel’s society and economy. We stress the need to expose all pupils to CS in a seamless learning gamified approach, in a spiral deepening of the two essential complementing aspects of CS—algorithms and programming. We start with perceptible physical moving robots that demonstrate an unbiased reaction of algorithms and programs, and gradually train for developing capabilities to abstract data structures, algorithms, and programs. We detail the journey of the introduction of computer science to the Israel Education system. The CS Curricula are described starting with elementary school, into middle school and then high school. Implementation issues are also briefly discussed. Finally, we discuss our vision for future developments in Israel’s CS education. © 2022 Association for Computing Machinery.

关键词： Curricula

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Optimizing mmWave UAV Networks: Mobility-Aware Deployment and Resource Allocation 100

Optimizing mmWave UAV Networks: Mobility-Aware Deployment an...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Li, Sicong Yoshii, Kazutoshi Shimamoto, Shigeru Ho, Tu Dac Waseda University Dept. of Communications and Computer Engineering Tokyo Japan Norwegian University of Science and Technology Dept. of Information Security and Communication Technology Trondheim Norway

ISBN: (纸本)9798331517786

Application of Unmanned Aerial Vehicles (UAVs) as small airborne base stations is gradually becoming a research hotspot in the field of wireless communications. With the flexible deployment, wide coverage and low cost, UAV communications show great potential in the fields of emergency rescue, environmental monitoring and military operations. In particular, the easier availability of line-of-sight (LOS) links for UAVs makes its combination with millimetre wave (mmWave) technology significantly enhance the communication bandwidth and data transfer rate. However, the very limited coverage of mmWave communications greatly affects the actual data rates provided by small aerial base stations of mmWave UAVs, especially considering user movement on the ground. In this paper, we study the joint optimisation problem of UAV deployment, user association, spectrum resource allocation in millimetre-wave heterogeneous networks, and explore the impact of different user movement models on system performance. Given that this problem is NP-hard, our paper proposes an MCR-SAC method to maximize the network's total throughput. Simulation results demonstrate an average 18% improvement in total transmission rate compared to the conventional. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Autoencoded Image Compression for Secure and Fast Transmission

Autoencoded Image Compression for Secure and Fast Transmissi...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Naveen, Aryan Kashyap Thunga, Sunil Murki, Anuhya Kalale, Mahati Anil, Shriya National Institute of Technology Karnataka Dept. of Information Technology Surathkal India National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798350376876

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer. © 2024 IEEE.

关键词： Image compression

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Optimized Battery Controlling for Smart Homes with Dynamic Insensitive-Zone Adjustment 13

Optimized Battery Controlling for Smart Homes with Dynamic I...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Ting, Lo Pang-Yun Chiang, Yu-Hao Chang, Yu-Hsiang Konagai, Michihiro Chuang, Kun-Ta National Cheng Kung University Dept. of Computer Science and Information Engineering Taiwan NextDrive K.K. Japan

ISBN: (纸本)9798350355079

Renewable energy sources have become increasingly important, highlighting the significance of battery energy storage systems. However, existing methods for controlling battery storage systems typically require a substantial amount of historical data to develop accurate charging and discharging strategies. This becomes challenging when there is insufficient past charging information for a household. In our paper, we introduce the DIA framework, which aims to address data scarcity in early-stage household operations. The core of DIA is to augment household electrical data by dynamically adjusting the insensitivity of the model, thereby reducing bias and accommodating variations in data quality and quantity. Our experiments on a real-world dataset reveal that our method can effectively reduce household costs compared to baseline approaches. © 2024 IEEE.

关键词： Smart homes

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Leveraging Support Vector Machine for Sports Injury Classification 15

Leveraging Support Vector Machine for Sports Injury Classifi...

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15th International Conference on information Technology and Electrical engineering, ICITEE 2023

作者： Surasak, Thattapon Praking, Phornnatee Kitchat, Kotcharat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Dept. of Computer and Information Science Bangkok Thailand National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350304466

This study explores the utility of Support Vector Machines (SVM) for the classification of injuries in Athletes including tennis, table tennis, and badminton. The primary objective of this research was to develop an automated method to detect between normal and injury pose, providing a poten-tially powerful tool for early detection. In other words, this research is the pioneer study to represent one another solution for sports injury classification using the image data apart from the current approaches that might be focused on the sensor-based. Therefore, we collect a dataset from various social media across the three sports, labeled into two categories: 'Normal' and 'Injury'. The SVM model was trained on this dataset, and tuned to optimize its performance in distinguishing the two classes. Achieving an accuracy of 96.63%, the result demonstrates that the SVM model can effectively identify injury patterns in the considered sports. The implications of this study are sig-nificant, presenting the initial steps towards applying computer vision tasks in sports topics. By demonstrating the feasibility of an SVM-based approach to injury detection in these racket sports. © 2023 IEEE.

关键词： Sports

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A Construction of Three Party Post Quantum Secure Authenticated Key Exchange for Mobile Users 14

A Construction of Three Party Post Quantum Secure Authentica...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Uddeshaya Pal, Yogesh Nikhil, Perla Sai Garg, Manish Kumar, M. Ranjith Chaudhary, Dharminder The Lnm Institute of Information Technology Dept. of Mathematics Jaipur India Noida India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Chennai India

ISBN: (纸本)9798350335095

A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server through an insecure channel. Lattice based cryptography plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. However, it is not easy to design quantum resistant password based three-party protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a new post quantum three party key exchange based on a variant of lattice assumption, the ring learning errors. The protocol ensures security against impersonation attack, stolen smartcard attack, password guessing attack, and other existing attacks. In authentication phase, the protocol have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications. © 2023 IEEE.

关键词： Authentication

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