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Consumer Communications and Networking Conference, CCNC IEEE

作者： Tharindu D. Ponnimbaduge Perera Dushantha Nalin K. Jayakody Sahil Garg Neeraj Kumar Ling Cheng School of Computer Science and Robotics National Research Tomsk Polytechnic University RUSSIA School of Postgraduate Studies & Research Sri Lanka Technological Campus Padukka SRI LANKA Ecole de technologie supérieure Montreal QC CANADA Thapar Institute of Engineering and Technology INDIA School of Electrical and Information Engineering University of the Witwatersrand SOUTH AFRICA

ISBN: (数字)9781728138930

ISBN: (纸本)9781728138947

Recently, the use of unmanned aerial vehicles (UAVs) as a relay node has been envisaged as an enabling technology in the upcoming wireless communication era. Thus, in this paper, we consider a full-duplex (FD) cooperative communication system with a source and a destination, where UAV serves as a mobile relay. Here, the transmission power cost is debited to energy harvested using simultaneous wireless information and power transfer (SWIPT) and self-interference energy harvesting (EH) via power-splitting (PS) protocol. In poor channel conditions, UAV uses a soft angular modulation scheme to perceive the soft information. In this proposed system, we present the outage probability over the Nakagami-m fading channels. A closed-form solution for the outage probability is derived. In addition, we formulate an optimization problem to minimize end-to-end outage probability subject of the UAV's power profile. The KKT conditions have been used to obtain a closed-form solution of the proposed optimization problem. Finally, numerical results are provided to evaluate the proposed system under various setups.

关键词： autonomous aerial vehicles cooperative communication energy harvesting minimisation mobile radio Nakagami channels probability protocols radiofrequency interference relay networks (telecommunication) telecommunication network reliability telecommunication Nakagami channels cooperative communication Unmanned Aerial Vehicle telecommunication network reliability Radiofrequency interference Mobile radios energy harvesting Telecommunications drone honey bees Probability protocols COMMUNICATION SYSTEMS Trunk Node Fading channels

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Internal Model Control for Rudder Roll Stabilisation and Course Keeping of a Surface Marine Craft ⁎

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IFAC-PapersOnLine 2018年第29期51卷 457-462页

作者： Kazantzidou, Christina Perez, Tristan Donaire, Alejandro Valentinis, Francis Robotics and Autonomous Systems School of Electrical Engineering and Computer Science and Institute for Future Environments Queensland University of Technology BrisbaneQLD4000 Australia Maritime Division Defence Science and Technology Group Fishermans BendVIC3207 Australia

This paper presents a novel control design procedure for simultaneous course keeping and rudder roll stabilisation for a surface marine craft. We address the design using the Internal Model Principle. This approach has been used to analyse fundamental limitations of rudder roll stabilisation due to unstable zero dynamics, but it has not been exploited for full control system design. The design is based on roll sensitivity shaping and uses a simplified parametric model of the roll-induced wave motion. This design provides opportunities for adapting the performance due to changes in environmental and sailing conditions. This is a need driven by a well-known issue leading to performance degradation in this control problem, which is due to the presence of unstable zero dynamics and significant changes in the power spectrum of the disturbance. © 2018

关键词： Curricula Control systems Model predictive control Rudders Stabilization Fundamental limitations Internal model control Internal model principle Marine control systems Parametric modeling Performance degradation Sailing conditions Unstable zero dynamics

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Partial Caging: A Clearance-Based Definition and Deep Learning

Partial Caging: A Clearance-Based Definition and Deep Learni...

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

作者： Anastasiia Varava Michael C. Welle Jeffrey Mahler Ken Goldberg Danica Kragic Florian T. Pokomy Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of technology varavar AUTOLab Department of Electrical Engineenng and Computer Science University of California Berkeley

Caging grasps limit the mobility of an object to a bounded component of configuration space. We introduce a notion of partial cage quality based on maximal clearance of an escaping path. As this is a computationally demanding task even in a two-dimensional scenario, we propose a deep learning approach. We design two convolutional neural networks and construct a pipeline for real-time partial cage quality estimation directly from 2D images of object models and planar caging tools. One neural network, CageMaskNN, is used to identify caging tool locations that can support partial cages, while a second network that we call CageClearanceNN is trained to predict the quality of those configurations. A dataset of 3811 images of objects and more than 19 million caging tool configurations is used to train and evaluate these networks on previously unseen objects and caging tool configurations. Furthermore, the networks are trained jointly on configurations for both 3 and 4 caging tool configurations whose shape varies along a 1-parameter family of increasing elongation. In experiments, we study how the networks' performance depends on the size of the training dataset, as well as how to efficiently deal with unevenly distributed training data. In further analysis, we show that the evaluation pipeline can approximately identify connected regions of successful caging tool placements and we evaluate the continuity of the cage quality score evaluation along caging tool trajectories. Experiments show that evaluation of a given configuration on a GeForce GTX 1080 GPU takes less than 6 ms.

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Quantitative susceptibility atlas construction in montreal neurological institute space: Towards histological-consistent Iron-rich deep brain nucleus sub-region identification

Research Square

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Research Square 2021年

作者： He, Chenyu Guan, Xiaojun Zhang, Weimin Li, Jun Liu, Chunlei Wei, Hongjiang Xu, Xiaojun Zhang, Yuyao School of Information Science and Technology ShanghaiTech University 393 Huaxia Road Shanghai201210 China Department of Radiology Second Affiliated Hospital Zhejiang University School of Medicine 88 Jiefang Road Hangzhou Zhejiang 310009 China Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States School of Biomedical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200030 China Institute of Medical Robotics Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200030 China Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University 393 Huaxia Road Shanghai201210 China

Iron-rich deep brain nuclei (DBN) of the human brain are involved in various motoric, emotional and cognitive brain functions. The abnormal iron alterations in the DBN are closely associated with multiple neurological and psychiatric diseases. Quantitative susceptibility mapping (QSM) provides the spatial distribution of tissue magnetic susceptibility in the human brain. Compared to traditional structural imaging, QSM has superiority for imaging the iron-rich DBN owing to the susceptibility difference existing between brain tissues. In this study, we construct a Montreal Neurological Institute (MNI) space unbiased QSM human brain atlas via group-wise registration from 100 healthy subjects aged 19-29 years. The atlas construction process is guided by hybrid images that fused from multi-modal Magnetic Resonance Images (MRI), thus named as Multi-modal-fused magnetic Susceptibility (MuSus-100) atlas. The high-quality susceptibility atlas provides extraordinary image contrast between iron-rich DBN with their surroundings. Parcellation maps of DBN and their sub-regions that are highly related to neurological and psychiatric pathology are then manually labeled based on the atlas set with the assistance of an image border-enhancement process. Especially, the bilateral thalamus is delineated into 64 detailed sub-regions referring to the Schaltenbrand and Wahren stereotactic atlas. To our best knowledge, the histological-consistent thalamic nucleus parcellation map is well defined for the first time in MNI space. Comparing with existing atlases emphasized on DBN parcellation, the newly proposed atlas outperforms on atlas-guided individual brain image DBN segmentation accuracy and robustness. Moreover, we apply the proposed DBN parcellation map to conduct detailed identification of the pathology-related iron content alterations in subcortical nuclei for Parkinson Disease (PD) patients. We envision that the MuSus-100 atlas could play a crucial role in improving the accuracy of

关键词： Brain

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TTPP: Temporal transformer with progressive prediction for efficient action anticipation

arXiv

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arXiv 2020年

作者： Wang, Wen Peng, Xiaojiang Su, Yanzhou Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeline is inefficient in capturing long-term information which may limit its performance in predication task. To address this problem, this paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction (TTPP) framework, which repurposes a Transformer-style architecture to aggregate observed features, and then leverages a light-weight network to progressively predict future features and actions. Specifically, predicted features along with predicted probabilities are accumulated into the inputs of subsequent prediction. We evaluate our approach on three action datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also conduct a comprehensive study for several popular aggregation and prediction strategies. Extensive results show that TTPP not only outperforms the state-of-the-art methods but also more efficient. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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ParsBERT: Transformer-based model for Persian language understanding

arXiv

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arXiv 2020年

作者： Farahani, Mehrdad Gharachorloo, Mohammad Farahani, Marzieh Manthouri, Mohammad Department of Computer Engineering Islamic Azad University North Tehran Branch Tehran Iran School of Electrical Engineering Robotics Queensland University of Technology Brisbane Australia Department of Computing Science Umeå University Umeå Sweden Department of Electrical and Electronic Engineering Shahed Univerisity Tehran Iran

The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance. However, these models are usually focused on English, leaving other languages to multilingual models with limited resources. This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-of-the-art performance compared to other architectures and multilingual models. Also, since the amount of data available for NLP tasks in Persian is very restricted, a massive dataset for different NLP tasks as well as pretraining the model is composed. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones and improves the state-of-the-art performance by outperforming both multilingual BERT and other prior works in Sentiment Analysis, Text Classification and Named Entity Recognition tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Sentiment analysis

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A Step Towards a Reinforcement Learning De Novo Genome Assembler

Research Square

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Research Square 2021年

作者： Padovani, Kleber Xavier, Roberto Carvalho, André Reali, Anna Chateau, Annie Alves, Ronnie Federal University of Pará Computer Science Graduate Program Belém66.075-110 Brazil University of the State of Amazonas Itacoatiara-AM 69.101-416 Brazil Vale Technology Institute Sustainable Development Belém66.055-090 Brazil Institute of Mathematics and Computer Sciences University of São Paulo São Carlos-SP 13566-590 Brazil Polytechnic School University of São Paulo São Paulo-SP 05508-010 Brazil LIRMM-Montpellier Laboratory of Informatics Robotics and Microelectronics Univ Montpellier CNRS Montpellier France

Genome assembly is one of the most relevant and computationally complex tasks in genomics projects. It aims to reconstruct a genome through the analysis of several small textual fragments of such genome — named reads. Ideally, besides ignoring any errors contained in reads, the reconstructed genome should also optimally combine these reads, thus reaching the original genome. The quality of the genome assembly is relevant because the more reliable the genomes, the more accurate the understanding of the characteristics and functions of living beings, and it allows generating many positive impacts on society, including the prevention and treatment of diseases. The assembly becomes even more complex (and it is termed de novo in this case) when the assembler software is not supplied with a similar genome to be used as a reference. Current assemblers have predominantly used heuristic strategies on computational graphs. Despite being widely used in genomics projects, there is still no irrefutably best assembler for any genome, and the proper choice of these assemblers and their configurations depends on Bioinformatics experts. The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems - such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including stat

关键词： Reinforcement learning

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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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Enabling AI in future wireless networks: A data life cycle perspective

arXiv

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arXiv 2020年

作者： Nguyen, Dinh C. Cheng, Peng Ding, Ming Lopez-Perez, David Pathirana, Pubudu N. Li, Jun Seneviratne, Aruna Li, Yonghui Poor, H. Vincent School of Engineering Deakin University Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Data61 CSIRO Australia Huawei France R&D Boulogne-Billancourt France School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk634050 Russia NSW Australia School of Electrical and Information Engineering University of Sydney Australia Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area. © 2020, CC BY.

关键词： Wireless networks

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Design of a Sextuple Cross-Coupled SRAM Cell with Optimized Access Operations for Highly Reliable Terrestrial Applications

Design of a Sextuple Cross-Coupled SRAM Cell with Optimized ...

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Asian Test Symposium (ATS)

作者： Aibin Yan Zhen Wu Jun Zhou Yuanjie Hu Yan Chen Zuobin Ying Xiaoqing Wen Patrick Girard School of Computer Science and Technology Anhui Engineering Laboratory of IoT Security Technologies Hefei China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Laboratory of Informatics Robotics and Microelectronics of Montpellier CNRS Montpellier France

ISBN: (数字)9781728126951

ISBN: (纸本)9781728126968

The Aggressive technology scaling makes modern advanced SRAMs more and more sensitive to soft errors that include single-node upsets (SNUs) and double-node upsets (DNUs). This paper presents a novel Sextuple Cross-Coupled SRAM cell, namely SCCS cell, which can tolerate both SNUs and DNUs. The cell mainly consists of six cross-coupled input-split inverters, constructing a large error-interceptive feedback loop to robustly retain stored values. Since the cell has many redundant storage nodes, the cell achieves the following robustness: (1) the cell can self-recover from all possible SNU; (2) the cell can self-recover from partial DNUs; (3) the cell can avoid the occurrence of other DNUs due to node-separation. Simulation results validate the excellent robustness of the proposed cell. Moreover, compared with the state-of-the-art typical existing hardened cells, the proposed cell achieves an approximate 61% read access time as well as 12% write access time reduction at the costs of 47% power dissipation as well as 44% silicon area on average.

关键词： Transistors SRAM cells Simulation Integrated circuit reliability Feedback loop

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