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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Niu, Lan Bin, Jianxiong Wang, Junkongshuai Zhan, Gege Jiang, Haifeng Dong, Zhiyan Zhang, Lihua Kang, Xiaoyang Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Province Yiwu City China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

This article presented an experiment to determine the effect of dynamic 3D vision-evoked modules on the performance of the system we designed to steer a four-rotor unmanned aerial vehicle (UAV). Nowadays, UAV system is widely used in various industries, and UAV control has always been a focus of research. The Brain-computer Interface (BCI) system was based on the well-known steady-state visually evoked potentials (SSVEP). The specificity of the interface was the integration of dynamic 3D paradigms in virtual reality. In order to achieve a more immersive and interactive scene, VR scenes were moving synchronously with the UAV. Users can achieve remote control of four-rotor UAV through virtual scenarios. Ten subjects participated in the experiment and operated the UAV to complete a specific track. The dynamic paradigm based on the experimenter's feedback may indeed affect their attention, we proposed an effective solution to overcome this problem. The detailed results of these experiments were reported in this paper and we discussed the possible causes of performance loss under such conditions. © 2022 IEEE.

关键词： Brain computer interface

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Surgical Tool Occlusion Detection and Segmentation Using Deep Transfer Learning: A Machine Vision Approach

Surgical Tool Occlusion Detection and Segmentation Using Dee...

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2024 International Conference on Decision aid Sciences and Applications, DASA 2024

作者： Sevilla, Rovenson Alon, Alvin Sarraga Evangelista, Ryan S. Delos Santos Ligayo, Michael Angelo Arago, Ryan Flores Delos Santos, Ronnel Cabral Cunanan, Christopher Austria, Yolanda D. University of the Philippines Department of Electrical Engineering Technological Manila Philippines Center for AI Research Department of Trade and Industry Makati City Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Engineering Quezon City University Quezon City Philippines College of Computing Studies Don Honorio Ventura State University Pampanga Philippines Bulacan State University Computer Engineering Department Bulacan Philippines Adamson University Department of Computer Engineering Manila Philippines

ISBN: (纸本)9798350369106

The combination of deep transfer learning and mask region-based convolutional neural network (Mask-RCNN) technology offers a groundbreaking approach to enhancing machine vision for surgical robots. The objective of this work is to enhance the accuracy and precision of identifying tools in surgical environments by investigating the use of deep transfer learning for detecting and segmenting occluded surgical tools. Our investigation exhibits the model's robustness in successfully recognizing and segmenting obstructed surgical tools, as seen by an exceptional Mean Average Precision (mAP) score of 93.8%. By conducting thorough testing and inference studies, the model demonstrated an outstanding ability to accurately identify various instruments, such as 'mayo,' 'clamp,' and 'scalpel,' even in difficult situations when objects were partially occluded. The use of Mask-RCNN enhances our investigation, representing a crucial advancement in enhancing surgical robotics with sophisticated segmentation skills. The research underscores the potential of Mask-RCNN in the field of machine vision for surgical robots, underlining the necessity for ongoing progress to tackle complexities for improved real-world implementation. © 2024 IEEE.

关键词： Machine vision

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Self-Evolving Depth-Supervised 3D Gaussian Splatting from Rendered Stereo Pairs

arXiv

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arXiv 2024年

作者： Tosi, Fabio Güney, Fatma Safadoust, Sadra Poggi, Matteo Department of Computer Engineering KUIS AI Center Koç University Istanbul Turkey University of Bologna Italy

3D Gaussian Splatting (GS) significantly struggles to accurately represent the underlying 3D scene geometry, resulting in inaccuracies and floating artifacts when rendering depth maps. In this paper, we address this limitation, undertaking a comprehensive analysis of the integration of depth priors throughout the optimization process of Gaussian primitives, and present a novel strategy for this purpose. This latter dynamically exploits depth cues from a readily available stereo network, processing virtual stereo pairs rendered by the GS model itself during training and achieving consistent self-improvement of the scene representation. Experimental results on three popular datasets, breaking ground as the first to assess depth accuracy for these models, validate our findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Stereocenters

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Authentication using Biometric Data from Mobile Cloud Computing in Smart Cities

Authentication using Biometric Data from Mobile Cloud Comput...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Artificial Intelligence Engineering Dept AI and Robotics Institute Near Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and A1anagement(GLBITAM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

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Internet of Intelligent Things (IoT) – An Overview

Internet of Intelligent Things (IoT) – An Overview

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Manisha Aeri Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

The basic goal of the IoT is to permit the online connection and communication of everyday physical objects like appliances, vehicles, and other gadgets. This paves the way for effortless and automated data collection and exchange, as well as interaction between the objects themselves and with humans. The Internet of Everything (IoE) is an expansion of the Internet of Things (IoT) that aims to maximize and improve human life by connecting and making the most of all available resources (humans, information, and technology). The transportation, healthcare, agricultural, energy, and manufacturing sectors are all examples. The IoIT aims to incorporate ai and ML within the IoT’s interconnected network of physical items. These objects can then independently behave in response to the information they have gathered and the instructions they have been given. This has the potential to lead to smarter, more efficient systems that can react to new information and human input in real time. The Internet of Things, the Internet of Everything, and the Industrial Internet of Things all have the same overarching goal: to pave the way for the development of more efficient and intelligent systems and processes that can enhance a wide range of human endeavors. However, in order to make sure that these technologies are used in a responsible and ethical way, it will be necessary to carefully analyze their consequences and the issues they provide.

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, ai, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated.

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NGP-RT: Fusing Multi-Level Hash Features with Lightweight Attention for Real-Time Novel View Synthesis

arXiv

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arXiv 2024年

作者： Hu, Yubin Guo, Xiaoyang Xiao, Yang Huang, Jingwei Liu, Yong-Jin BNRist Department of Computer Science and Technology Tsinghua University China Horizon Robotics China Huawei Technologies China Game AI Center Tencent Games

This paper presents NGP-RT, a novel approach for enhancing the rendering speed of Instant-NGP to achieve real-time novel view synthesis. As a classic NeRF-based method, Instant-NGP stores implicit features in multi-level grids or hash tables and applies a shallow MLP to convert the implicit features into explicit colors and densities. Although it achieves fast training speed, there is still a lot of room for improvement in its rendering speed due to the per-point MLP executions for implicit multi-level feature aggregation, especially for real-time applications. To address this challenge, our proposed NGP-RT explicitly stores colors and densities as hash features, and leverages a lightweight attention mechanism to disambiguate the hash collisions instead of using computationally intensive MLP. At the rendering stage, NGP-RT incorporates a pre-computed occupancy distance grid into the ray marching strategy to inform the distance to the nearest occupied voxel, thereby reducing the number of marching points and global memory access. Experimental results show that on the challenging Mip-NeRF 360 dataset, NGP-RT achieves better rendering quality than previous NeRF-based methods, achieving 108 fps at 1080p resolution on a single Nvidia RTX 3090 GPU. Our approach is promising for NeRF-based real-time applications that require efficient and high-quality rendering. © 2024, CC BY-NC-ND.

关键词： Rendering (computer graphics)

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Locate N’ Rotate: Two-Stage Openable Part Detection with Foundation Model Priors 17th

Locate N’ Rotate: Two-Stage Openable Part Detection with F...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Institute for AI Industry Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819609628

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqi-zju/MOPD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent robots

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Design and Implementation of a Conference Attendance Monitoring System Using Blockchain and ai Technologies 2

Design and Implementation of a Conference Attendance Monitor...

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2nd IEEE International Conference on Artificial Intelligence in Everything, aiE 2022

作者： Vubangsi, Mercel Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin 10 Nicosia Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin 10 Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we design and implement a conference attendance monitoring system based on blockchain and Artificial Intelligence (ai) technologies. This system is built with *** for front-end and makes use of Google's real-time database for storage. The system is designed to auto-track the users device location and screen behavior during a time slot allocated to a conference or conference session for which the user has subscribed. ai powered analytics of the data using custom mathematical models and JavaScript Machine Learning Libraries permits decision making on the data, facilitating the task on the conference organizers with regards to rating attendee performances. © 2022 IEEE.

关键词： Smart contract

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Applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects 2

Applications of Transformer Attention Mechanisms in Informat...

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, aioTCs 2022

作者： Vubangsi, M. Abidemi, Sarumi Usman Akanni, Olukayode Mubarak, Auwalu Saleh Al-Turjman, Fadi Artificial Intelligence Engineering Dept. Ai and Robotics Institute Near East University Mersin 10 Nicosia Cyprus Research Center for Ai and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Cyprus Computational Materials Science Lab Department of Computer Science Htttc Bambili University of Bamenda NWR Cameroon

ISBN: (纸本)9798350334104

In this work, we present a comprehensive survey on applications of the most recent transformer architecture based on attention in information security. Our review reveals three primary areas of application: Intrusion detection, Anomaly Detection and Malware Detection. We have presented an overview of attention-based mechanisms and their application in each cybersecurity use case, and discussed open grounds for future trends in Artificial Intelligence enabled information security. © 2022 IEEE.

关键词： Anomaly detection

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