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Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

Computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

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Towards online optimization for power grids

arXiv

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arXiv 2021年

作者： Yuan, Deming Bhardwaj, Abhishek Petersen, Ian Ratnam, Elizabeth L. Shi, Guodong School of Automation Nanjing University of Science and Technology Jiangsu210094 China School of Engineering Australian National University ACT2600 Australia Australian Centre for Field Robotics The University of Sydney NSW2006 Australia

In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offline and online distributed optimization, showcasing the suitability of such optimization techniques for power grid applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Electric power transmission networks

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When Vision Meets Touch: A Contemporary Review for Visuotactile Sensors from the Signal Processing Perspective

arXiv

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arXiv 2024年

作者： Li, Shoujie Wang, Zihan Wu, Changsheng Li, Xiang Luo, Shan Fang, Bin Sun, Fuchun Zhang, Xiao-Ping Ding, Wenbo Tsinghua Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Centre for Robotics Research King’s College London LondonWC2R 2LS United Kingdom Department of Automation Tsinghua University Beijing100084 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal processing

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Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects

arXiv

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arXiv 2024年

作者： Zhao, Zihang Li, Yuyang Li, Wanlin Qi, Zhenghao Ruan, Lecheng Zhu, Yixin Althoefer, Kaspar The Institute for Artificial Intelligence Peking University Beijing100871 China The Beijing Institute for General Artificial Intelligence Beijing100080 China The Department of Automation Tsinghua University Beijing100084 China The College of Engineering Peking University Beijing100871 China The PKU-Wuhan Institute for Artificial Intelligence Wuhan430075 China The Beijing Institute for General Artificial Intelligence China The Centre for Advanced Robotics @ Queen Mary United Kingdom The School of Engineering & Materials Science Queen Mary University of London LondonE1 4NS United Kingdom

Integrating robots into human-centric environments such as homes, necessitates advanced manipulation skills as robotic devices will need to engage with articulated objects like doors and drawers. Key challenges in robotic manipulation of articulated objects are the unpredictability and diversity of these objects’ internal structures, which render models based on object kinematics priors, both explicit and implicit, inadequate. Their reliability is significantly diminished by pre-interaction ambiguities, imperfect structural parameters, encounters with unknown objects, and unforeseen disturbances. Here, we present a prior-free strategy, Tac-Man, focusing on maintaining stable robot-object contact during manipulation. Without relying on object priors, Tac-Man leverages tactile feedback to enable robots to proficiently handle a variety of articulated objects, including those with complex joints, even when influenced by unexpected disturbances. Demonstrated in both real-world experiments and extensive simulations, it consistently achieves near-perfect success in dynamic and varied settings, outperforming existing methods. Our results indicate that tactile sensing alone suffices for managing diverse articulated objects, offering greater robustness and generalization than prior-based approaches. This underscores the importance of detailed contact modeling in complex manipulation tasks, especially with articulated objects. Advancements in tactile-informed approaches significantly expand the scope of robotic applications in human-centric environments, particularly where accurate models are difficult to obtain. See additional material at https://***. © 2024, CC BY.

关键词： Robot applications

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Applications of Machine Learning Techniques in Detecting Skin Cancer

Applications of Machine Learning Techniques in Detecting Ski...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Pankaj Kunekar Rounak Vaishnav E. Kalaivani Durgaprasad Gangodkar Chamandeep Kaur Joshuva Arockia Dhanraj Vishwakarma Institute of Technology Pune Department of Medical Oncology Sri Ram Cancer and Super specialty centre Clinical Research Manager Mahatma Gandhi University of medical sciences and technology Jaipur Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Information Technology Jazan University Saudi Arabia Department of Mechatronics Engineering Centre for Automation & Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai India

The most lethal kind of skin cancer is called malignant melanoma. Accurate diagnosis of skin malignancies are aided by dermoscopy, a non-invasive high-resolution imaging procedure. Melanoma is a very dangerous kind of skin cancer because of how quickly it spreads and how aggressive it is. For this reason, malignant melanoma maintains its place as one of the tumors with the highest rates of growth across the globe. Second-order texture feature data may be obtained by the GLCM (Grey Level Co-occurrence Matrix) technique. The reference pixel and neighboring pixels are considered by GLCM to ensure an accurate calculation. Grayscale pictures have a decent number of levels, which is straightforwardly relative to the number of segments and columns in the frameworks used to characterize the scale. Relative recurrence is addressed by the grid component P I j | Dx, Dy), where I and j are force values and Dx and Dy are pixel distances. In total, the co-occurrence matrix considers 14 different attributes, including energy, efficiency, latency, correlation, homogeneity, and contrast. Convolutional Neural networks (CNN) and Gaussian Filters (GF) classifiers were then used to sort these vectors. Comparisons of the results these classifiers provide are also made.

关键词： Force Medical treatment High-resolution imaging Melanoma Machine learning Gray-scale Skin

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Expression of Concern for: Applications of Machine Learning Techniques in Detecting Skin Cancer

Expression of Concern for: Applications of Machine Learning ...

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International Conference on Contemporary Computing and Informatics (IC3I)

ISBN: (数字)9798350398267

ISBN: (纸本)9798350398274

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Gesture Control Method for Oral Blood Mixed Liquid Removal Robot*

Gesture Control Method for Oral Blood Mixed Liquid Removal R...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Xudong Ma Ziao Kuang Han Li Anqi Chen Changsheng Li Wenyong Liu Junchen Wang Yida David Hu Li Gao Bai-Quan Su Medical Robotics Laboratory School of Automation Beijing University of Posts and Tele-communications Beijing China School of Computer Beijing University of Posts and Tele-communications Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Periodontology National Stomatological Center Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases Beijing China

All of oral surgeries are accompanied by bleeding. Manual removal of blood is carried out by a nurse and dozens of repetitive blood suction operations are implemented by the nurse during a single oral surgery. In our previous work, a robot for removal of blood-water mixed liquid is designed. However, the robot requires an initial order to autonomous removal of blood mixed liquid. In this paper, a hand gesture control method for typical operations of the robot is developed. Gesture dataset are developed at first and a deep learning neural network is trained for detection of hand gesture. The gesture control procedures for four typical operations are provided in detail. In a group of experiments, it’s demonstrated that an oral surgeon controls the robot’s motion by presenting prescribed hand gesture. The proposed method could be an alternative manner for blood mixed liquid removal.

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Speech-Vision Based Multi-Modal AI Control of a Magnetic Anchored and Actuated Endoscope

Speech-Vision Based Multi-Modal AI Control of a Magnetic Anc...

引用

IEEE International Conference on robotics and Biomimetics

作者： Jixiu Li Yisen Huang Wing Yin Ng Truman Cheng Xixin Wu Qi Dou Helen Meng Pheng Ann Heng Yunhui Liu Shannon Melissa Chan David Navarro-Alarcon Calvin Sze Hang Ng Philip Wai Yan Chiu Zheng Li Department of Surgery The Chinese University of Hong Kong Hong Kong Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Department of Mechanical and Automation Engineering T stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Department of Surgery Chow Yuk Ho Technology Centre of Innovative Medicine Li Ka Shing Institute of Health Science and Multi-scale Medical Robotics Center Ltd. The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781665481106

In minimally invasive surgery (MIS), controlling the endoscope view is crucial for the operation. Many robotic endoscope holders were developed aiming to address this prob-lem,. These systems rely on joystick, foot pedal, simple voice command, etc. to control the robot. These methods requires surgeons extra effort and are not intuitive enough. In this paper, we propose a speech-vision based multi-modal AI approach, which integrates deep learning based instrument detection, automatic speech recognition and robot visual servo control. Surgeons could communicate with the endoscope by speech to indicate their view preference, such as the instrument to be tracked. The instrument is detected by the deep learning neural network. Then the endoscope takes the detected instrument as the target and follows it with the visual servo controller. This method is applied to a magnetic anchored and guided endoscope and evaluated experimentally. Preliminary results demonstrated this approach is effective and requires little efforts for the surgeon to control the endoscope view intuitively.

关键词： Deep learning Visualization Target tracking Endoscopes Instruments Magnetic resonance imaging Surgery

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Design and evaluation of various knee assistive mechanisms for rehabilitation

引用

AIP Conference Proceedings 2023年第1期2946卷

作者： Devashish Sharma Sahil Shaik Hemang Parab R. Saran Manju Mohan Pavan Lingampally Kuppan Ramanathan Arockia Selvakumar Hindustan Institute of Technology and Science Chennai India Centre for Automation and Robotics Department of Mechatronics Engineering Hindustan Institute of Technology and Science Chennai India School of Mechanical Engineering VIT Chennai Campus Chennai-600127 Tamil Nadu India

Lower limb motor or knee joints are one the most vulnerable joints among the human joints. These knee joints play a major role in supporting the entire weight of human body. Dysfunction of these knee joints could lead to gait and posture imbalance while performing activities of daily living (ADLs) such as standing, walking, running and etc. The main reason for knee dysfunction is due to ligament, spinal cord injuries, accidents, surgeries and stroke. The individuals suffering with knee dysfunction/pain post-surgery opt for manual physiotherapy (i.e., medically approved rehabilitation process). Due to the demerits of the manual rehabilitation process, knee assistive devices for rehabilitation purposes are being developed to aid the individuals to provide an exercise platform to relive the pain and improve their gait while performing ADLs. These assistive/wearable devices are also called exoskeletons, which comprises of mechanical and mechatronics systems, to mimic human knee movement. This article focusses on developing a simple and effective design for the knee rehabilitation application. As a part of this research, various mechanisms for the knee rehabilitation purpose are studied, designed and a comparison was conducted to find the suitable mechanism for knee rehabilitation. Based on the design study, it is observed that the slider mechanism using rack and pinion is appropriate for the knee rehabilitation. Due, to this proposed design is easy to maintain, compact, portable due to a smaller number of mechanical devices, and decreases complexity compared to other designs.

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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