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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Chong Chee Soon Rozaimi Ghazali Muhamad Fadli Ghani Siaw-Hong Liew Howe Cheng Teng Nur Farhanah Zulkipli Chong You Beh Mohd Saidi Hanaffi Department of Engineering and Built Environment Tunku Abdul Rahman University of Management and Technology (TAR UMT) Penang Branch Campus Pulau Pinang Malaysia Centre for Robotics and Industrial Automation Fakulti Teknologi dan Kejuruteraan Elektrik Universiti Teknikal Malaysia Melaka (UTeM) Melaka Malaysia Malaysian Institute of Marine Engineering Technology (MIMET) Universiti Kuala Lumpur Perak Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak (UNIMAS) Kota Samarahan Sarawak Malaysia

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

engineering systems heavily rely on mathematical modeling, significantly influencing both system performance and the design of control systems. This paper thoroughly investigates the establishment of a third-order mathematical model for heavy machinery systems, with a specific emphasis on the ElectroHydraulic Actuator (EHA) system. The formulation of a double-acting EHA system model using a grey box system identification approach is outlined, encompassing the system’s dynamics and behavior. In addition, the details of the EHA system experimental setup that is used for system identification are described. The identification process demonstrates promising performance, achieving an impressive accuracy of 89.1%, as highlighted in the results and discussion sections.

关键词： Actuators System performance Transfer functions Mathematical models Data models System identification Object recognition

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A Robotized Soft Endoscope with Stereo Vision for Upper Gastrointestinal Endoscopic Submucosal Dissection (ESD)

A Robotized Soft Endoscope with Stereo Vision for Upper Gast...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jian Chen Shuai Wang Qingxiang Zhao Mingcong Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Centre of AI and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong School of Biomedical Engineering and Imaging Sciences Kings College London London UK

This work presents a novel dual-segment flexible robotic endoscope designed to enhance reachability and dexterity during ESD surgery. The proposed system is capable of executing multi-angle cutting operations at a small angle relative to the lesion surface, allowing for efficient en-bloc resection. Additionally, the system incorporates two calibrated RGB cameras and a depth estimation algorithm to provide detailed 3D information of the tumour, which is used to guide the control framework. A stereo visual servoing controller is also implemented to improve path-following performance during surgery. Experiments results indicate that the proposed system improves motion stability and precision. The root means square error (RMSE) of circle path following is 1.1991mm with a maximum of 1.4751mm. Ex-vivo testing demonstrates its significant potential for use in endoscopic surgery.

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An Appraisal in to the Effects of Partial Shading on an Urban Photovoltaic System Using the Internet of Things

An Appraisal in to the Effects of Partial Shading on an Urba...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Lakshmana Phaneendra Maguluri S Farook Saravanan R Ashutosh Dixit K.P. Aishwarya JoshuvaArockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhra Pradesh India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Electrical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Partial shadowing of a solar array is known to decrease the array's output power. Unfortunately, it is not always possible to calculate the exact degree of decrease in energy output from the darkened region alone. In this study, the process of partial PV shadowing on multiple PV cells by using IoT wired in series and/or parallel, with and without bypass diodes, is elucidated. A layperson interested in learning how a certain shading geometry affects a PV system may benefit from this study, which is provided in clear language. Commercial 100 W panel and 10 kW PV array data are used to show the study.

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Hair Direction Detection*

Hair Direction Detection*

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Peng Ba Pengyi Wang Hongde Wu Qian Yang Yongqiang Feng Junchen Wang Yida David Hu Changsheng Li Wenyong Liu Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Plastic Surgery Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems 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 Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

关键词： Hair Image segmentation Accuracy Three-dimensional displays Merging Prediction algorithms Stability analysis

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Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing

Modeling Spoof Noise by De-spoofing Diffusion and its Applic...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Bin Zhang Xiangyu Zhu Xiaoyu Zhang Zhen Lei Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

Face anti-spoofing is crucial for ensuring the security and reliability of face recognition systems. Several existing face anti-spoofing methods utilize GAN-like networks to detect presentation attacks by estimating the noise pattern of a spoof image and recovering the corresponding genuine image. But GAN’s limited face appearance space results in the denoised faces cannot cover the full data distribution of genuine faces, thereby undermining the generalization performance of such methods. In this work, we present a pioneering attempt to employ diffusion models to denoise a spoof image and restore the genuine image. The difference between these two images is considered as the spoof noise, which can serve as a discriminative cue for face anti-spoofing. We evaluate our proposed method on several intra-testing and inter-testing protocols, where the experimental results showcase the effectiveness of our method in achieving competitive performance in terms of both accuracy and generalization.

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Cuifeng Shen Zhaokun Deng Jinzhi Wang Shuangyi Wang Chen Chen Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing P. R. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong China

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer’s skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation.

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Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images

arXiv

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arXiv 2024年

作者： Geng, Yimeng Meng, Gaofeng Chen, Mingcong Cao, Guanglin Zhao, Mingyang Zhao, Jianbo Liu, Hongbin State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences Hong Kong City University of Hong Kong Hong Kong School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom

Accurate identification of arteries and veins in ultrasound images is crucial for vascular examinations and interventions in robotics-assisted surgeries. However, current methods for ultrasound vessel segmentation face challenges in distinguishing between arteries and veins due to their morphological similarities. To address this challenge, this study introduces a novel force sensing guided segmentation approach to enhance artery-vein segmentation accuracy by leveraging their distinct deformability. Our proposed method utilizes force magnitude to identify key frames with the most significant vascular deformation in a sequence of ultrasound images. These key frames are then integrated with the current frame through attention mechanisms, with weights assigned in accordance with force magnitude. Our proposed force sensing guided framework can be seamlessly integrated into various segmentation networks and achieves significant performance improvements in multiple U-shaped networks such as U-Net, Swin-unet and Transunet. Furthermore, we contribute the first multimodal ultrasound artery-vein segmentation dataset, Mus-V, which encompasses both force and image data simultaneously. The dataset comprises 3114 ultrasound images of carotid and femoral vessels extracted from 105 videos, with corresponding force data recorded by the force sensor mounted on the US probe. Our code and dataset will be publicly available. © 2024, CC BY-NC-SA.

关键词： Ultrasonic imaging

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Record Framework Utilization for the Collection of Wireless Device Static Users

Record Framework Utilization for the Collection of Wireless ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Maganti Syamala Annaji Kuthe V. Selvakumar Alankrita Joshi K. Baranidharan Joshuva Arockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering K. D. K. College of Engineering Nagpur Maharashtra India Department of Master of Business Administration Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Master of Business Administration Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article, we look at a few ongoing improvements in the examination of mobile phone data. With the developing openness of enormous, anonymised datasets, this field of concentrate previously seemed decade prior and has since formed into an independent subject. The estimating investigation of record framework utilization for a gathering of mobile gadget users in different spots is introduced in this work. The purpose of this research is to refute the notion that mobile system behaviour can be modelled as being uniform across different environments. The research demonstrates that when participants travel between places, they obtain statistically substantially diverse sets of data. These findings call into question how mobile computing systems are normally evaluated, which often models a user's behaviour as being comparable to her behaviour at a single place over a longer length of time.

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Mitigation Methods of Reducing DDOS Attack Frequency on Blockchain

Mitigation Methods of Reducing DDOS Attack Frequency on Bloc...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： R. Sathees Kumar Phanikanth Chintamaneni Madarapu Naresh Kumar Ankita Joshi Bajila Swathi Joshuva Arockia Dhanraj Department of CSE Narasaraopeta Institute of Technology JNTUK University Narasaraopet Andhra Pradesh Department Of CSE Koneru Lakshmaiah Education Foundation Scientist B National Informatics Centre Ministry of Electronics and Information Technology (MEITY) New Delhi National Informatics Centre Ministry of Electronics and Information Technology (MEITY) New Delhi Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai India

A serious risk to genuine organization demands is a distributed denial of service (DDoS) attack. The main DDoS attack was archived in 1996, however, from that point forward, their intricacy and refinement have consistently expanded. A 2 TBps attack on a crucial foundation, including the monetary area, was accounted for in August 2020 during the Coronavirus pandemic. In the accompanying two years, it is anticipated that the number of attacks would twofold, stretching around 15 million. Since its beginning, many relief plans have been made, however, the intricacy is rising, requiring modern arrangements in view of new innovation. A potential and common-sense method for DDoS relief is blockchain. The point of the review is to give an exhaustive examination of the fundamental and intrinsic properties of blockchain, including their decentralization, inside and outer trust lessness, unchanging nature, honesty, obscurity, and evidence, which have demonstrated to be a fantastic contender for battling this destructive cyber threat.

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Human Action Classification using seismic sensor and machine learning techniques

Human Action Classification using seismic sensor and machine...

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International Conference on Information Systems and Computer Networks (ISCON)

作者： Mainak Chakraborty Srinivasan A Srinivasa Reddy Sanjib Kumar Mandal Subhasis Bhaumik School of Mechatronics and Robotics Indian Institute of Engineering Science And Technology Shibpur India CSIR- CMERI Robotics and Automation Group Durgapur India Jagadish Chandra Bose Centre of Advanced Technology Defence R and D Organization (DRDO) Kolkata India Aerospace Engineering and Applied Mechanics Indian Institute of Engineering Science and Technology Durgapur India

ISBN: (纸本)9781665447874

In this work, we propose a novel method for identifying human activities like walking and running, utilising ground vibration obtained from seismic sensor and using machine learning techniques. The proposed methodology is based on statistical feature extraction. It is grounded on the idea, that each of the activity generate distinctly unique seismic signatures. We curated a unique dataset of various activites using off-the-shelve geophones and 16 bit analog to digital converter. The seismic data were sampled at 10000Hz. The datasets we recorded is 2 min long each. Single and 6-channels sensors are used to record the dataset. The data recorded is denoised using bandpass filter. The denoised data is used to detect peaks by comparing with neighbouring values. Peak based segementation is done and various statistical features are extracted from the dataset. The effectiveness of feature extraction is increased by converting the data in spectral domain, by extracting the power spectrum. The feature extracted are labelled and is used to train machine learning models. We have explored different machine learning algorithms and Random Forest algorithm gives the accuracy of 91.62%.

关键词： Vibrations Training Machine learning algorithms Surveillance Feature extraction Data models Real-time systems

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