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2019 IEEE East-West Design and Test Symposium, EWDTS 2019

作者： Adamov, Alexander Carlsson, Anders Surmacz, Tomasz NioGuard Security Lab Kharkiv National University of Radio Electronics Kharkiv Ukraine Dep. of Computer Science Blekinge Institute of Technology Karlskrona Sweden Institute of Computer Engineering Control and Robotics Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9781728110035

This paper contains an analysis of the LockerGoga ransomware that was used in the range of targeted cyberattacks in the first half of 2019 against Norsk Hydra-A world top 5 aluminum manufacturer, as well as the US chemical enterprises Hexion, and Momentive-Those companies are only the tip of the iceberg that reported the attack to the public. The ransomware was executed by attackers from inside a corporate network to encrypt the data on enterprise servers and, thus, taking down the information control systems. The intruders asked for a ransom to release a master key and decryption tool that can be used to decrypt the affected files. The purpose of the analysis is to find out tactics and techniques used by the LockerGoga ransomware during the cryptolocker attack as well as an encryption model to answer the question if the encrypted files can be decrypted with or without paying a ransom. The scientific novelty of the paper lies in an analysis methodology that is based on various reverse engineering techniques such as multi-process debugging and using open source code of a cryptographic library to find out a ransomware encryption model. © 2019 IEEE.

关键词： Reverse engineering

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Implementation of IoT Concept for Early Diagnostic of Subacute Rumen Acidosis in Cows

Implementation of IoT Concept for Early Diagnostic of Subacu...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Anatolijs Zabasta Nadezda Kunicina Uldis Grunde Janis Judvaitis Ilga Sematovica Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia Faculty of Veterinary Medicine of Latvia University of Life Sciences and Technologies Jelgava Latvia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

Highly productive dairy cow' ration under intensive production conditions causes the development of subacute rumen acidosis (SARA). In this paper we discuss a reticulo-ruminal long-acting cyber-physical diagnostic system' prototype, which applies Internet of Things (IoT) for monitoring rumen parameters of cows. The new diagnostic system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes gateways for data collection from boluses, an MQTT (Message Queuing Telemetry Transport) broker, a web server and a database. The diagnostic system' prototype provides timely data on cow health status to the users, so they can evaluate the cow' health status and implement further actions. Therefore, the use of diagnostic system provides opportunity to increase cow productivity, longevity and to save maintenance cost of milk farms. The results of the first stage of the research are discussed in this paper.

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Robust lane marking detection algorithm using drivable area segmentation and extended SLT

Robust lane marking detection algorithm using drivable area ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ozgunalp, Umar Liu, Ming Fan, Rui Cheng, Shanshan Sun, Yuxiang Zuo, Weixun Zhu, Yilong Xue, Bohuan Zheng, Linwei Liang, Qing Cyprus International University Electrical and Electronics Engineering North Cyprus Turkey Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hangzhou ATG Intelligent Technology Co. Ltd. Hangzhou China Guolu Gaoke Engineering Technology Institute Co. Ltd. Beijing China Robotics Institute Hong Kong University of Science and Technology Robotics and Multiperception Laboratory Hong Kong

ISBN: (纸本)9781728163215

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset. © 2019 IEEE.

关键词： Hough transforms

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Intrinsic Complexity of Sympathetic and Parasympathetic Dynamics from HRV series: a Preliminary Study on Postural Changes

Intrinsic Complexity of Sympathetic and Parasympathetic Dyna...

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

作者： Mimma Nardelli Luca Citi Riccardo Barbieri Gaetano Valenza Bioengineering and Robotics Research Centre E. Piaggio University of Pisa Pisa Italy School of Computer Science and Electronic Engineering University of Essex UK Department of Electronics Informatics and Bioengineering Politecnico di Milano Milano Italy

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The analysis of complex heartbeat dynamics has been widely used to characterize heartbeat autonomic control in healthy and pathological conditions. However, underlying physiological correlates of complexity measurements from heart rate variability (HRV) series have not been identified yet. To this extent, we investigated intrinsic irregularity and complexity of cardiac sympathetic and vagal activity time series during postural changes. We exploited our recently proposed HRV-based, time-varying Sympathetic and Parasympathetic Activity Indices (SAI and PAI) and performed Sample Entropy, Fuzzy Entropy, and Distribution Entropy calculations on publicly-available heartbeat series gathered from 10 healthy subjects undergoing resting state and passive slow tilt sessions. Results show significantly higher entropy values during the upright position than resting state in both SAI and PAI series. We conclude that an increase in HRV complexity resulting from postural changes may derive from sympathetic and vagal activities with higher complex dynamics.

关键词： Heart rate variability Complexity theory Entropy Physiology Measurement Standards Time series analysis

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Communication and networking technologies for UAVs: A survey

arXiv

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arXiv 2020年

作者： Sharma, Abhishek Vanjani, Pankhuri Paliwal, Nikhil Wijerathna Basnayaka, Chathuranga M. Jayakody, Dushantha Nalin K. Wang, Hwang-Cheng Muthuchidambaranathan, P. Dept. of Electronics and Communication Engineering LNM Institute of Information and Technology Jaipur India Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Department of Electronic Engineering National Ilan University Yilan Taiwan Department of Electronic and Communications Engineering National Institute of Technology Trichy India

With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication *** and optimization techniques for ensuring long−lasting and secure communications, as well as for power management, are ***, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra-reliable and low−latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications. Our literature review reveals the need for more research work on drone−to−drone and drone−to−device communications. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Communication and networking technologies for UAVs: A survey

TechRxiv

引用

TechRxiv 2020年

作者： Sharma, Abhishek Vanjani, Pankhuri Paliwal, Nikhil Wijerathna Basnayaka, Chathuranga M. Jayakody, Dushantha Nalin K. Wang, Hwang-Cheng Muthuchidambaranathan, P. Dept. of Electronics and Communication Engineering The LNM Institute of Information and Technology Jaipur India Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Department of Electronic Engineering National Ilan University Yilan Taiwan Department of Electronic and Communications Engineering National Institute of Technology Trichy India

关键词： Wireless networks

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Reconstructing Endovascular Catheter Interaction Forces in 3D using Multicore Optical Shape Sensors

Reconstructing Endovascular Catheter Interaction Forces in 3...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Christoff M. Heunis Vincenza Belfiore Marilena Vendittelli Sarthak Misra Surgical Robotics Laboratory University of Twente The Netherlands Department of Information Engineering Electronics and Telecommunications and with the Robotics Laboratory at the Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Catheterization instruments are increasingly being improved to accurately diagnose and treat cardiovascular conditions. However, current catheter systems provide limited information about the shape of the catheter and tissue-instrument interaction forces during an intervention. Furthermore, relying on inconsistent feedback of such interaction forces during an intervention may result in tissue injury. This paper presents the first steps to estimate the interaction forces between a catheter and a mock-up arterial environment. We base the proposed method on a Pseudo-Rigid Body approximation of the catheter and integrate three-dimensional shape information provided by Fiber Bragg Grating sensors inside the catheter. The reconstructed forces along the catheter body can be fed back to the surgeon in visual and/or haptic form. In this work, the estimated forces are displayed in real-time in a graphical user interface with the reconstructed catheter shape. Experimental validation demonstrates a root mean square error of 0.03 N and a mean reconstruction error of 0.02 N.

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MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation

arXiv

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arXiv 2020年

作者： Liu, Jun Li, Qing Cao, Rui Tang, Wenming Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham United Kingdom

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently, deep learning has emerged as an effective approach to monocular depth estimation. As obtaining labeled data is costly, there is a recent trend to move from supervised learning to unsupervised learning to obtain monocular depth. However, most unsupervised learning methods capable of achieving high depth prediction accuracy will require a deep network architecture which will be too heavy and complex to run on embedded devices with limited storage and memory spaces. To address this issue, we propose a new powerful network with a recurrent module to achieve the capability of a deep network while at the same time maintaining an extremely lightweight size for real-time high performance unsupervised monocular depth prediction from video sequences. Besides, a novel efficient upsample block is proposed to fuse the features from the associated encoder layer and recover the spatial size of features with the small number of model parameters. We validate the effectiveness of our approach via extensive experiments on the KITTI dataset. Our new model can run at a speed of about 110 frames per second (fps) on a single GPU, 37 fps on a single CPU, and 2 fps on a Raspberry Pi 3. Moreover, it achieves higher depth accuracy with nearly 33 times fewer model parameters than state-of-the-art models. To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Unsupervised learning

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Correction to: SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification

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Medical & biological engineering & computing 2024年第9期62卷 2785页

作者： Akib Zaman Shiu Kumar Swakkhar Shatabda Iman Dehzangi Alok Sharma Computer Science and Artificial Intelligence Laboratory (CSAIL) Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge MA USA. School of Electrical & Electronics Engineering Fiji National University Suva Fiji. shiu.kumar@fnu.ac.fj. Centre for Artificial Intelligence and Robotics (CAIR) United International University Dhaka Bangladesh. Department of Computer Science Rutgers University Camden NJ USA. Center for Computational and Integrative Biology Rutgers University Camden USA. Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230‑0045 Japan. Institute for Integrated and Intelligent Systems Griffith University Nathan Brisbane QLD Australia.

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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