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2022 IEEE International Conference and Expo on Real Time Communications at IIT, RTC 2022

作者： Agniel, Aurelien Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (数字)9781665475037

ISBN: (纸本)9781665475037

Continued adoption of the Internet of Things (IoT) redefines the paradigm of network architectures. Historically, network architectures relied on centralized resources and data centers. The introduction of the IoT challenges this notion by placing computing resources and observation at the edge of the network. As a result, decentralized approaches for information processing and gathering can be adopted and explored. However, this shift greatly expands the network footprint and shifts traffic away from the center of the network, where observation and cybersecurity monitoring tools are frequently located. Further, IoT devices are often computationally constrained, limiting their readiness to deal with cyber-threats. These security vulnerabilities make the IoT an easy target for hacking groups and lead to the proliferation of zombie networks of compromised devices. Frequently, zombie networks, otherwise known as botnets, are coordinated to attack targets and overload network resources through a Distributed Denial of Service (DDoS) attack. In order to crack down on these botnets, it is essential to develop new methods for quickly and efficiently detecting botnet activity. This study proposes a novel botnet detection technique that first pre-processes network data through computer vision and image processing. The processed dataset is then sent to a neural network for final classification. Two neural networks will be explored, a sequential model and an auto-encoder model. The application of image processing has two advantages over current methods. First, the image processing is simple enough to be completed at the edge of the network by the IoT devices. Second, preprocessing the data allows us to use a shallower network, decreasing detection time further. We will utilize the N-BaIoT dataset and compare our findings to their results. © 2022 IEEE.

关键词： Cybersecurity

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Design Flow of mmWave Radar and Machine vision Fusion for Pedestrian Collision Warning

Design Flow of mmWave Radar and Machine Vision Fusion for Pe...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Graves, Kaitlyn Kanwal, Madiha Yu, Xinrui Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper presents a millimeter wave (mmWave) radar and Machine vision fusion system to alert drivers of potential pedestrian collisions. The system is composed of two subsystems, the mmWave Pedestrian Localization subsystem and the Machine vision Pedestrian Classification subsystem. The mmWave Pedestrian Localization subsystem obtains the relative location of the pedestrians using a mmWave radar sensor while the Machine vision Pedestrian Classification subsystem uses Histogram of Oriented Gradients and Support Vector Machine algorithms to classify pedestrians in a camera's field-of-view. The two-layer pedestrian detection design protects the system from any miss detections within a single subsystem. By utilizing mmWave technology with Machine vision, the safety operation of cars and pedestrian safety can be increased. The proposed system utilizes Texas Instrument's AWR1642BOOST mmWave Radar with the high computing power of NVIDIA's Jetson Nano © 2022 IEEE.

关键词： computer vision

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Machine vision Surveillance System - Artificial Intelligence for Covid-19 Norms

Machine Vision Surveillance System - Artificial Intelligence...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Nigam, Ritika Kundu, Arpan Yu, Xinrui Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (数字)9781665480093

ISBN: (纸本)9781665480109

This paper presents the design and implementation of the Machine vision Surveillance System Artificial Intelligence (MaViSS-AI) for Covid-19 Norms using jetson nano. This system is designed to be cost-effective, accurate, efficient, and secure. The proposed system tracks and counts humans for monitoring social distancing and detects face masks using object detection methods. We used YOLO as an object detection method and neural network to detect a person and count them. And for social distancing monitoring the concept of the centroid is based on calculating the distance between pairs of centroids, and thus checking whether there are any violations of the threshold or not. To detect the face mask, a YOLO V4 deep learning model is used as the mask detection algorithm. The system also raises alerts when any suspicious event occurs. Given this alert, security personnel can take relevant actions. This research aims to provide a holistic approach to overcoming the real-time challenges encountered during the monitoring of Covid-19 norms. © 2022 IEEE.

关键词： Object detection

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Design Flow and Implementation of a vision-Based Gesture-Controlled Drone

Design Flow and Implementation of a Vision-Based Gesture-Con...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Findelair, Arthur Yu, Xinrui Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

The increasing efficiency of complex Neural Network architecture and the continuous improvement of embedded edge computing has reached a point that allows the deployment of advanced computer vision tasks on some of the most critical embedded applications, such as aerial drones. While similar applications were already possible by moving heavy processing on a ground station, an autonomous and centralized system significantly improves usability and security. The machine is thus self-sufficient and less prone to network attacks. Three main challenges stand-out during the deployment of our complex gesture recognition pipeline: (1) allowing user-defined controls, (2) ensuring robustness, and (3) on-board deployment. These challenges are tackled through handcrafted features to avoid the curse of dimensionality, neural network optimization on GPUbased companion computers, and data augmentation to cover reallife edges cases such as partial inputs. © 2022 IEEE.

关键词： Drones

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset

M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dat...

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Chaney, Kenneth Cladera, Fernando Wang, Ziyun Bisulco, Anthony Hsieh, M. Ani Korpela, Christopher Kumar, Vijay Taylor, Camillo J. Daniilidis, Kostas University of Pennsylvania GRASP Laboratory PA19104 United States Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

ISBN: (纸本)9798350302493

We present M3ED, the first multi-sensor event camera dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized and labeled data from multiple platforms, including ground vehicles, legged robots, and aerial robots, operating in challenging conditions such as driving along off-road trails, navigating through dense forests, and performing aggressive flight maneuvers. Our dataset also covers demanding operational scenarios for event cameras, such as scenes with high egomotion and multiple independently moving objects. The sensor suite used to collect M3ED includes high-resolution stereo event cameras (1280×720), grayscale imagers, an RGB imager, a high-quality IMU, a 64-beam LiDAR, and RTK localization. This dataset aims to accelerate the development of event-based algorithms and methods for edge cases encountered by autonomous systems in dynamic environments. © 2023 IEEE.

关键词： Cameras

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A Rigid-Flexible Coupling Recursive Formulation for Dynamic Modeling of Biped Robots 16th

A Rigid-Flexible Coupling Recursive Formulation for Dynamic...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Tang, Lingling Liang, Dingkun Wang, Xin Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9789819964796

Biped robots are complex rigid-flexible coupling multibody systems that achieve the desired stable locomotion through foot-ground interactions. In the recent bio-inspired structure designs for biped robots, flexible parts are usually adopted to absorb the foot-ground impact and store the contact energy. However, most existing modeling formulations can only treat biped robots as rigid multibody systems, and the flexible parts are simply replaced by some linear or torsion springs. Since the deformations of the flexible parts have significant effects on the dynamic responses of the biped robot, the rigid multibody modeling method inevitably leads to a gap between the simulation model and the real prototype, and makes the sim-to-real transfer more difficult. To address this problem, a rigid-flexible coupling recursive modeling formulation is presented in this paper. In addition, the foot-ground interaction is modeled via a smooth normal contact model and a velocity-based Coulomb friction model. Finally, several numerical examples including inverse dynamics, kinematic closed-loop and frictional contact are given to verify the rigid-flexible coupling formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine design

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vision-Based Categorical Object Pose Estimation and Manipulation 16th

Vision-Based Categorical Object Pose Estimation and Manipul...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Meng, Qiwei Liao, Jianfeng Jun, Shao Xu, Nuo Xu, Zeming Sun, Yinan Sun, Yao Zhu, Shiqiang Gu, Jason Song, Wei Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China School of Mechanical Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964826

Object manipulation and environment interaction are of great significance for intelligent robots, especially service robots working under unstructured household and office scenarios. This paper proposes a novel approach for categorical unseen object grasping and manipulation. Different from recently popular end-to-end reinforcement learning methods, we develop models for geometric primitive abstraction of target objects, and accordingly estimate their pose as well as generate task-orientated grasp points. Such design emphasizes visual perception in guiding robotic manipulation, thereby enhancing model interpretability and reliability during implementation. In addition, we also conduct object grasping experiments both under simulation and real-world settings, which further verify the effectiveness and superiority of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intelligent robots

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A Joint Tracking System: Robot is Online to Access Surveillance Views

A Joint Tracking System: Robot is Online to Access Surveilla...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Lin, Zheyuan Ji, Shanshan Wang, Wen Qin, Mengjie Yang, Rong Wan, Minhong Gu, Jason Li, Te Zhang, Chunlong Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot's field of vision and heavy occlusion by people or objects. © 2023 IEEE.

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