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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Liu, Fengbei Jonmohamadi, Yaqub Maicas, Gabriel Pandey, Ajay K. Carneiro, Gustavo Australian Institute for Machine Learning School of Computer Science University of Adelaide Adelaide Australia School of Electrical Engineering and Robotics Science and Engineering Faculty Queensland University of Technology Brisbane Australia

ISBN: (纸本)9783030597092

Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging conditions (e.g., low texture, overexposure, etc.), automatic semantic segmentation is a challenging scenario, which justifies the scarce literature on this topic. In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy. To further regularise the depth estimation, we propose the use of clean training images captured by the stereo arthroscope of routine objects (presenting none of the poor imaging conditions and with rich texture information) to pre-train the model. We fine-tune such model to produce both the semantic segmentation and self-supervised monocular depth using stereo arthroscopic images taken from inside the knee. Using a data set containing 3868 arthroscopic images captured during cadaveric knee arthroscopy with semantic segmentation annotations, 2000 stereo image pairs of cadaveric knee arthroscopy, and 2150 stereo image pairs of routine objects, we show that our semantic segmentation regularised by self-supervised depth estimation produces a more accurate segmentation than a state-of-the-art semantic segmentation approach modeled exclusively with semantic segmentation annotation. © Springer Nature Switzerland AG 2020.

关键词： Semantic Segmentation

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Robofish as social partner for live guppies 9th

Robofish as social partner for live guppies

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9th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020

作者： Musiolek, Lea Hafner, Verena V. Krause, Jens Landgraf, Tim Bierbach, David Department of Computer Science Humboldt-Universität zu Berlin Berlin Germany Faculty of Life Sciences Division of Biology and Ecology of Fishes Humboldt Universität zu Berlin Berlin Germany Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin Germany

ISBN: (纸本)9783030643126

Biomimetic robots that are accepted as social partners by animals may help to gain insights into animals’ social interaction skills. Here, we present an experiment using the biomimetic Robofish which resembles live guppies (Poecilia reticulata) - a small tropical freshwater fish. Guppy females were given the opportunity to interact with different open-loop controlled Robofish replicas. We show that guppies interacting with a lifelike Robofish replica scored higher on social interaction variables than did those faced with a simple white cuboid performing the same movements, although this effect weakened with time. Our study exemplifies the use of Robofish as a research tool, providing highly standardized social cues for the study of fish social skills such as imitation and following. © Springer Nature Switzerland AG 2020.

关键词： Fish

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A Proposal of Speed Sensorless Control Based on IRM-ILQ Control for SPMSM Drive with Active LC Filter

A Proposal of Speed Sensorless Control Based on IRM-ILQ Cont...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Ryoki Mikami Kazuki Abe Hiroshi Takami Fuminori Ishibashi Graduate School of Electrical Eng. And Computer Science Shibaura Institute of Technology Koto-ku Tokyo Japan Dept. of Electrical Eng. and Robotics Course College of Engineering Shibaura Institute of Technology Koto-ku Tokyo Japan Dept. of Electrical Eng. Faculty of Eng. Shibaura Institute of Technology Koto-ku Tokyo Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Generally, a position detector has been used to drive the Surface Permanent Magnet Synchronous Motor (SPMSM), because the current and voltage must be controlled according to the magnetic pole position of the rotor. However, using a position detector causes problems such as more expensive and lower reliability due to position detector failure. Therefore, the speed sensor-less control, which estimates the magnetic pole position from voltage and current without using a position detector, is required. Harmonic components in the output voltage waveform of the PWM inverter used to drive the SPMSM can cause problems such as Electromagnetic Interference (EMI). Harmonic components can be suppressed by using an LC filter. In addition, IRM-ILQ control, which combines modern control theory's Inverse Linear Quadratic (ILQ) control and Inverse Reference Model (IRM), can achieve stable and robust SPMSM drive despite with LC filter. In this paper, robustness of speed sensorless control based on IRM-ILQ current control for SPMSM with active LC filter is described. In simulation, the proposed method allows stable control even when there are parameter errors.

关键词： Maximum likelihood detection Magnetic separation Electromagnetic interference Sensorless control Detectors Nonlinear filters Drives Harmonic analysis Voltage control Power harmonic filters

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Attention state of children during Schulte tables task

Attention state of children during Schulte tables task

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Grubov, Vadim Maksimenko, Vladimir Kurkin, Semen Khramova, Marina Aleksandrova, Natalya Hramov, Alexander Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Lab Russia Saratov State University Faculty of Computer Science Saratov Russia

ISBN: (纸本)9781728187631

We have analyzed EEG signals of children during cognitive load of specific type in order to estimate level of attention during this task. EEG signals were recorded in accordance with proposed design of experiment. For obtained EEG data we have analyzed behavioral characteristics as well as EEG-related characteristics. We have found that behavioural characteristic changes through the task, so may the attention level. We have also found that cognitive task completion is accompanied by appearance of spatial structure-specific distribution of wavelet energy across cortex, which partially changes during completion of the task as well. We believe that changes in attention level are tied to changes in energy distribution, so we suggest this distribution to be used as the marker for attention state estimation. © 2020 IEEE.

关键词： Electroencephalography

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Detection of Algorithmically Generated Domain Names in Botnets 33rd

Detection of Algorithmically Generated Domain Names in Botne...

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33rd International Conference on Advanced Information Networking and Applications, AINA-2019

作者： Vishvakarma, Deepak Kumar Bhatia, Ashutosh Riha, Zdenek Center for Artificial Intelligence and Robotics CAIR Bangalore India Department of Computer Science BITS Pilani India Faculty of Informatics Masaryk University Brno Czech Republic

ISBN: (纸本)9783030150310

Botnets pose a major threat to the information security of organizations and individuals. The bots (malware infected hosts) receive commands and updates from the Command and Control (C&C) servers, and hence, contacting and communicating with these servers is an essential requirement of bots. However, once a malware is identified in the infected host, it is easy to find its C&C server and block it, if the domain names of the servers are hard-coded in the malware. To counter such detection, many malwares families use probabilistic algorithms known as domain generation algorithms (DGAs) to generate domain names for the C&C servers. This makes it difficult to track down the C&C servers of the Botnet even after the malware is identified. In this paper, we propose a probabilistic approach for the identification of domain names which are likely to be generated by a malware using DGA. The proposed solution is based on the hypothesis that human generated domain names are usually inspired by the words from a particular language (say English), whereas DGA generated domain names should contain random sub-strings in it. Results show that the percentage of false negatives in the detection of DGA generated domain names using the proposed method is less than 29% across 30 DGA families considered by us in our experimentation. © 2020, Springer Nature Switzerland AG.

关键词： Botnet

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Text-Guided Graph Temporal Modeling for Few-Shot Video Classification

SSRN

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SSRN 2024年

作者： Fuqin, Deng Jiaming, Zhon Li, Nannan Lanhui, Fu Jiang, Bingchun Yi, Ningbo Feng, Qi He, Xin Lun, Lam Tin School of Intelligent Manufacturing the Wuyi University Jiangmen China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Textile Materials and Engineering the Wuyi University Jiangmen China School of Applied Physics and Materials Science the Wuyi University Jiangmen China School of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China Shenzhen Vatop Semicon Tech Co. Ltd. Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Large-scale pre-trained models and graph neural networks have recently demonstrated remarkable success in few-shot video classification tasks. However, they generally suffer from two key limitations: i) the temporal relations between adjacent frames tends to be ambiguous due to the lack of explicit temporal modeling. ii) the absence of multi-modal semantic knowledge in query videos results in inaccurate prototypes construction and an inability to achieve multi-modal temporal alignment metrics. To address these issues, we develop a Text-guided Graph Temporal Modeling (TgGTM) method that consists of two crucial components: a text-guided feature refinement module and a learnable query text-token contrastive objective. Specifically, the former leverages the temporal masking layer to guide the model in learning temporal relationships between adjacent frames. Additionally, it utilizes multi-modal information to refine video prototypes for comprehensive few-shot video classification. The latter addresses the feature discrepancy between multi-modal support features and single-modal query features by aligning a learnable query text-token with corresponding base class text descriptions. Extensive experiments on three commonly used benchmarks demonstrate the effectiveness of our proposed method, which significantly outperforms existing state-of-the-art methods under various settings. A detailed ablation showcases the importance of learning temporal relationships between adjacent frames and obtaining query text-token. The source code and models will be publicly available at https://***/JiaMingZhong2621/TgGTM. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Distributed formation control of networked mechanical systems

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IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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A Hybrid NOMA-PLNC Wireless Relay Scheme

A Hybrid NOMA-PLNC Wireless Relay Scheme

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Samikkannu Rajkumar Dushantha Nalin K. Jayakody Zheng Chang School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Faculty of Information Technology University of Jyvaskykä Jyvskylä Finland

ISBN: (数字)9781728197944

ISBN: (纸本)9781728197951

In this paper, a novel system model for the non orthogonal multiple access (NOMA) based wireless network is proposed using physical layer network coding (PLNC) scheme. The outage performance of a two pairs hybrid NOMA-PLNC relay network is analyzed. Proposed network is designed with three time slots and half-duplex decode-and-forward (DF) relay. A closed form expression for the end-to-end outage probability of the proposed NOMA-PLNC relay network is derived. It is noted that the performance of the proposed hybrid NOMA-PLNC relay network is significantly improved when compare to the conventional orthogonal multiple access (OMA)-PLNC relay network. Finally, numerical results of the proposed relay network is validated using the simulated experimental results.

关键词： NOMA Closed-form solutions Wireless networks Relay networks (telecommunication) Probability Physical layer Power system reliability

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3D semantic mapping from arthroscopy using out-of-distribution pose and depth and in-distribution segmentation training

arXiv

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arXiv 2021年

作者： Jonmohamadi, Yaqub Ali, Shahnewaz Liu, Fengbei Roberts, Jonathan Crawford, Ross Carneiro, Gustavo Pandey, Ajay K. School of Electrical Engineering and Robotics Science and Engineering Faculty Queensland University of Technology United Kingdom Australian Institute for Machine Learning School of Computer Science University of Adelaide

Minimally invasive surgery (MIS) has many documented advantages, but the surgeon's limited visual contact with the scene can be problematic. Hence, systems that can help surgeons navigate, such as a method that can produce a 3D semantic map, can compensate for the limitation above. In theory, we can borrow 3D semantic mapping techniques developed for robotics, but this requires finding solutions to the following challenges in MIS: 1) semantic segmentation, 2) depth estimation, and 3) pose estimation. In this paper, we propose the first 3D semantic mapping system from knee arthroscopy that solves the three challenges above. Using out-of-distribution non-human datasets, where pose could be labeled, we jointly train depth+pose estimators using self-supervised and supervised losses. Using an in-distribution human knee dataset, we train a fully-supervised semantic segmentation system to label arthroscopic image pixels into femur, ACL, and meniscus. Taking testing images from human knees, we combine the results from these two systems to automatically create 3D semantic maps of the human knee. The result of this work opens the pathway to the generation of intra-operative 3D semantic mapping, registration with pre-operative data, and robotic-assisted arthroscopy. Copyright © 2021, The Authors. All rights reserved.

关键词： Endoscopy

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Lateralized Learning for Multi-Class Visual Classification Tasks

arXiv

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arXiv 2023年

作者： Siddique, Abubakar Browne, Will N. Grimshaw, Gina M. The School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand The Faculty of Engineering School of Electrical Engineering & Robotics Queensland University of Technology Brisbane4000 Australia The Cognitive the School of Psychology Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

The majority of computer vision algorithms fail to find higher-order (abstract) patterns in an image so are not robust against adversarial attacks, unlike human lateralized vision. Deep learning considers each input pixel in a homogeneous manner such that different parts of a "locality-sensitive hashing table" are often not connected, meaning higher-order patterns are not discovered. Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high confidence. Conversely, vertebrate brains afford heterogeneous knowledge representation through lateralization, enabling modular learning at different levels of abstraction. This work aims to verify the effectiveness, scalability, and robustness of a lateralized approach to real-world problems that contain noisy, irrelevant, and redundant data. The experimental results of multi-class (200 classes) image classification show that the novel system effectively learns knowledge representation at multiple levels of abstraction making it more robust than other state-of-the-art techniques. Crucially, the novel lateralized system outperformed all the state-of-the-art deep learning-based systems for the classification of normal and adversarial images by 19.05% − 41.02% and 1.36% − 49.22%, respectively. Findings demonstrate the value of heterogeneous and lateralized learning for computer vision applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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