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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

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An Effective Implementation and Comparative Analysis of Mobile Robot Path Planning Algorithms

An Effective Implementation and Comparative Analysis of Mobi...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Vigenesh, M. Taneja, Madhur Jayasree, S. Samrat, Bhavuk Padmapriya, G. Kulkarni, Prutha P Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Chemistry and Biochemistry Karnataka Bangalore India Vishwakarma Institute of Technology Entc Pune India

ISBN: (纸本)9798350388602

Among the most pressing and intricate challenges in control and computer engineering today is the navigation of autonomous mobile robots. People are now more motivated than ever to use innovative methods, thanks to the development of cutting-edge computational engineering technology. In order to address complicated engineering challenges in the real world, researchers have mostly looked to natural occurrences and their methods for inspiration. In challenging and dense environments, path planning is the best way of defining a safe route in which a mobile robot can navigate to achieve a certain goal from another location. Mobile robots thus require smart and efficient route planning algorithms for proper navigation and self-execution capabilities. In this study, four well-known algorithms for route planning were investigated: Hybrid DA+CS algorithm: The proposed methods DA, CS and PSO using a non-holonomic mobile robot. To test these methods, a test environment has been created with static impediments which will remain constant throughout the experiment. This is because it is where the intended route of the mobile robot is followed and where the availability of the map acquired for path planning algorithms allows assessing and testing them. A comparative study of the following algorithms has been carried out in an effort to understand the trade off between them based on the conventional performance indicators being used. © 2024 IEEE.

关键词： Robot programming

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Improved YOLO framework for cell detection in bronchoalveolar lavage fluid 3

Improved YOLO framework for cell detection in bronchoalveola...

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3rd international conference on Control and Intelligent robotics, ICCIR 2023

作者： Zhang, Xin Li, Rui Wang, Chao Jia, Yujuan School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou730050 China School of Information Engineering East China Jiao Tong University Jiangxi Nanchang330010 China School of Information Science and Engineering Shandong Normal University Shandong Jinan250358 China

ISBN: (纸本)9781510671867

In response to the limited amount of cell detection data and the phenomenon of overlapping and adhesion in the existing bronchoalveolar lavage fluid (BALF), a YOLOv5s-based target detection algorithm (YOLOv5s-GAN) was proposed to optimize the model structure and loss function and effectively address the overlapping and adhesion of BALF cells. The algorithm enhances the accuracy of cell detection in BALF by using a lightweight convolution GSconv module and a normalization-based attention mechanism (NAS) that does not require additional calculations or parameters. Additionally, the algorithm improves detection accuracy by using D-IoU to mitigate false detections due to overlapping cells. Experimental results showed that the algorithm achieved an average accuracy of 88.9% in detecting four types of BALF cells, which demonstrated its high practicality for cell detection in BALF. © 2023 SPIE.

关键词： Adhesion

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A Hybrid Deep Learning Models: Optimization for Forecasting Wireless 6G IoT Network 1

A Hybrid Deep Learning Models: Optimization for Forecasting ...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, B.P. Venkadeshwaran, Kuthalingam Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar District India

ISBN: (纸本)9798331518578

New specifications for 6G mobile networks will surpass the performance targets of 5G networks. As more 5G networks are deployed, their limits become obvious, encouraging the exploration of networks using 6G as the future solution. This paper covers 6G technology's core privacy and security concerns. Monitoring real-time structures need secured wireless sensor networks (WSNs). Denial of service (DoS) outbreaks increase secure risks for WSNs and potentially undermine the whole system. This study presents a new approach to control and optimize the security of 6G wireless networks based on blockchain technology by means of a machine learning method. Utilizing a blockchain users datagram transit protocol in conjunction with reinforcement projection regression, this research manages security of organized 6G wireless sensor net. The network is then optimized via artificially democratized cuckoo glowworm remora optimizations. Numerous network characteristics pertaining to accuracy, end-to-end latency, packet delivery ratio, energy efficiency, and throughput have formed the basis of the simulation findings. Reduced network traffic may be achieved by figuring out which node and route are best for data transfer. 98% of Throughput, energy effectiveness of 96%, 97% accuracy, end to end latency of 51%, and packet delivery ratio of 95% were all achieved by the suggested method. © 2025 IEEE.

关键词： 5G mobile communication systems

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SecureML: Machine Learning-Powered Malware Detection 2

SecureML: Machine Learning-Powered Malware Detection

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Awasthi, Animay Ahsan, Mohd Vanusha, D. Delsi Robinsha, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350383867

As malware threats become increasingly complex, the development of advanced detection systems is necessary to protect digital assets. SecureML Using Machine Learning presents an innovative approach to combat the escalating threat of malicious software by leveraging machine learning algorithms. This research paper outlines the design, implementation, and evaluation of SecureML, emphasizing its effectiveness in detecting both known and novel malware strains. Through comprehensive feature selection techniques and algorithmic comparison, SecureML identifies crucial features and selects the Random Forest algorithm as the most effective for malware detection, achieving an impressive accuracy rate of 99.40% on the test set. The system's low false positive and false negative rates validate its robustness and reliability in accurately distinguishing between benign and malicious files. SecureML demonstrates significant potential in bolstering cybersecurity defenses and safeguarding digital assets against evolving malware threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Monocular 3D Object Detection for robotics

Monocular 3D Object Detection for Robotics

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2024 Beyond technology Summit on Informatics international conference, BTS-I2C 2024

作者： Nurdin, Farid Rahman Sadida, Qaulan Ul Haq, Qazi Mazhar Ul Islam, Naeem Haa, Muhamad Amirul Universitas Muhammadiyah Surabaya Faculty of Engineering Surabaya Indonesia Universitas Gadjah Mada Faculty of Psychology Yogyakarta Indonesia Yuan Ze University Department of Computer Science and Engineering Taoyuan Taiwan

ISBN: (纸本)9798331508579

3D object detection is considered as computationally expensive problem as it heavily relies on processing extensive sensory data making it unsuitable for the real-time application. To address this problem, we propose a monocular 3D object detection method for robotics, particularly the autonomous vehicles, using the YOLOv3 algorithm, known for its efficiency and speed. To evaluate the performance of the proposed approach, we used KITTI dataset, one of the most robust 3D object detection datasets. The proposed approach is computationally less expensive, suitable for real-time applications, and highly accurate. Our approach utilizes the Darknet53 backbone network with 74 convolutional layers along with the YOLOv3. We have extensively evaluated the proposed approach where each object class has a custom mAP evaluation script 0.5 IoU for the evaluation process and mAP results. The best outcome, namely a mAP value of 0.60, was obtained after several training processes. The result showed that YOLOv3 is one of the key candidates for the monocular 3D object detection. © 2024 IEEE.

关键词： 3D object detection KITTI dataset monocular YOLOv3

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Adaptive Fusion Dual-Branch Convolutional Surface Reconstruction 5

Adaptive Fusion Dual-Branch Convolutional Surface Reconstruc...

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5th international conference on robotics and Computer Vision, ICRCV 2023

作者： Meng, Bo Shi, Xuhua Li, Shanzhi Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (纸本)9798350326369

Implicit neural networks based on point convolution and voxel convolution have been successfully used for point cloud surface reconstruction. This paper proposes an adaptive fusion dual-branch convolutional occupancy network, focusing on the inefficiency of searching local surface based on point convolution technology in reconstruction and the roughness of generated volumetric features based on voxel convolution technology. Dual-branch convolution is used during the encoding stage of the point cloud to extract informative volumetric features. Specifically, the point convolution branch calculates the features of each point in the input point cloud, and the voxel convolution branch calculates the features of each voxel in the regular space of the point cloud. The features of the two branches are then combined using an adaptive fusion module. We performed trilinear interpolation on the feature volume. Experiments on the scene-level Synthetic Rooms dataset and the object-level ShapeNet dataset show that our approach outperforms other methods on most classical metrics. © 2023 IEEE.

关键词： Convolution

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Conditional Autoregressive-Salp Swarm Algorithm with Differential Pulse Code Modulation for Efficient Block-based Compressed Sensing

Conditional Autoregressive-Salp Swarm Algorithm with Differe...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Dhabliya, Dharmesh Mukuntharaj, C. Ghosal, Shreya Kalanandhini, G. Khanna, Lalit Shankar Prasd, S. Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Karnataka Bangalore India

ISBN: (纸本)9798350388602

If a signal is compressible or sparse in a particular base representation, one of the most effective ways to minimize its dimensions is to use compressed sensing in a video streaming application. The sparse representation is a good approximation to the signal since the amplitude of the coefficient's decays according to the power law. Transmitting signals into a space with few dimensions allows them to be transformed into measurement vectors. It is possible to reassemble a sparse signal with high precision using the measurement vector. Imprecise or sparse yet compressible signal or noise that could result in an approximation. Waveletor frequency-domain images are well-suited for Compressed Sensing because of their inherent compressibility. With a smaller compression ratio - the ratio of an original picture's pixel size to its measurement vector - and an development in the superiority of the rebuilt image, the modernization is now more accurate. In order to improve the compressed video streaming system, this technique proposes an optimum mechanism that integrates the use of DPCM with the Provisional Autoregressive-Salp Swarm system. Through careful frame segmentation, the approach investigates the spatial redundancy of individual video frames. At this stage, we quantify the resulting vectors by applying DPCM after measuring each block using a dimension matrix. © 2024 IEEE.

关键词： Video streaming

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Design and Implementation of Light Weight Cryptography Using Spartan-6 FPGA 2

Design and Implementation of Light Weight Cryptography Using...

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2nd IEEE international conference on Recent advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Poojari, Asmita Kiran Kumar, V.G. NMAM Institute of Technology Department of Computer Science and Engineerng Karnataka Nitte574110 India A J Institute of Engineering and Technology Department of Electronics and Communication Engineering Kottara Mangaluru575006 India

ISBN: (纸本)9798350354461

In the current scenario where our lives are totally obsessed with the internet, data security (confidentiality, authenticity, integrity, non-repudiation) plays a cardinal role. Therefore, there's constant demand of stronger encryption methods. Because of these reasons, information systems need to be secured. The foundation of these systems are cryptographic algorithms, which guard data and resources against unauthorized access. In recent years, the Internet of Things and the cyber-physical systems have expanded rapidly and significantly. This expansion has fuelled the development and broad adoption of low-cost devices across widespread IoT *** of the initial lack of security features in resource-constrained devices, many are being used to meet the broad application needs of the IoT and the cyber physical systems, where privacy and security have become key concerns and thus need for lightweight cryptography for these resource constrained devices. This study presents a lightweight cipher that combines the BRIGHT and SIMON structures into a single Feistel framework using ARX (Addition-Modulo, Rotation, and XOR) operations. The cipher passes the Avalanche test, the key sensitivity test, correlation coefficient, entropy, and histogram requirements, encrypting 32-bit plaintext with a 64-bit key. The software implementation is done with the Xilinx toole or Abstract. © 2024 IEEE.

关键词： Ciphertext

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE international conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Fake Profile Detection in Online Social Networks Using Machine Learning Models 3

Fake Profile Detection in Online Social Networks Using Machi...

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3rd IEEE international conference on Recent advances in Systems science and engineering, RASSE 2023

作者： Joseph, Jyothis Vineetha, S. Rajiv Gandhi Institute of Technology Kottayam APJ Abdul Kalam Technological University Dept of Computer Science and Engg Kerala India Rajiv Gandhi Institute of Technology Kottayam APJ Abdul Kalam Technological University Dept of Computer Applications Kerala India

ISBN: (纸本)9798350341676

A new era of networking has emerged due to the easy access of social networks. People can share a lot of information using online social networks. Some of the widely used social networks are Twitter, Facebook, Google+, Instagram, Pinterest and LinkedIn. The majority of social network users are ignorant of the security threats that OSNs pose. There are several issues with modern online social networks, such as fraudulent profiles and online impersonation. False profiles spread inaccurate information about a certain individual or make fraudulent attempts with bad intentions. Fake profiles can be identified using machine learning techniques. Various machine learning models, such as Support Vector Machine, Decision Tree, Neural Networks, Random Forest, Naive Bayes, Logistic Regression, and K-nearest Neighbor can be employed for the detection of fake profiles. The different machine learning techniques for identifying fake profiles are compared in this paper. © 2023 IEEE.

关键词： Social networking (online)

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