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arXiv 2020年

作者： Agostini, Alejandro Lee, Dongheui Human-centered Assistive Robotics Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Intelligent and Interactive Systems Department of Computer Science University of Innsbruck Innsbruck6020 Austria Institute of Robotics and Mechatronics Wessling Germany

The definition of symbolic descriptions that consistently represent relevant geometrical aspects in manipulation tasks is a challenging problem that has received little attention in the robotic community. This definition is usually done from an observer perspective of a finite set of object relations and orientations that only satisfy geometrical constraints to execute experiments in laboratory conditions. This restricts the possible changes with manipulation actions in the object configuration space to those compatible with that particular external reference definitions, which greatly limits the spectrum of possible manipulations. To tackle these limitations we propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces than the traditional observer perspective counterpart. Based on this representation, we define universal planning operators for picking and placing actions that permits generating plans with geometric and force consistency in manipulation tasks. This object-centered description is directly obtained from the poses and bounding boxes of objects using a novel learning mechanisms that permits generating signal-symbols relations without the need of handcrafting these relations for each particular scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Wave functions

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Emergency Clinical Procedure Detection With Deep Learning

Emergency Clinical Procedure Detection With Deep Learning

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

作者： Lingfeng Li Richard A. Paris Conner Pinson Yan Wang Joseph Coco Jamison Heard Julie A. Adams Daniel V. Fabbri Bobby Bodenheimer Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee Vanderbilt University Medical Center Nashville Tennessee Department of Electrical and Microelectronic Engineering Rochester Institute of Technology Rochester NY Collaborative Robotics and Intelligent Systems Institute Oregon State University Oregon

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success.

关键词： Adaptation models Training Testing Machine learning Hospitals Cameras

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A DC-Ka-Band 7-Bit Passive Attenuator with Capacitive-Compensation-Based Bandwidth Extension Technique in 55-nm CMOS

A DC-Ka-Band 7-Bit Passive Attenuator with Capacitive-Compen...

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作者： Zhang, Zijiang Li, Nayu Gao, Huiyan Li, Min Wang, Shaogang Kuan, Yen-Cheng Song, Chunyi Yu, Xiaopeng Gu, Qun Jane Xu, Zhiwei Ocean College Institute of Marine Electronics and Intelligent Systems Zhejiang University Zhoushan China College of Information Science and Electronic Engineering Institute of VLSI Design Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of California at Davis DavisCA United States Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhoushan316021 China International College of Semiconductor Technology National Chiao Tung University Hsinchu30010 Taiwan

This article presents a 7-bit wideband passive attenuator with low insertion loss (IL) and high attenuation accuracy in 55-nm CMOS technology. The $\pi $ -type and bridge-T-type attenuation units utilize an effective capacitive compensation technique, whose bandwidth extension mechanism is detailed in a single-unit pole-zero analysis. The matching-induced performance deterioration is investigated to minimize amplitude and phase errors at the chip level. The fabricated attenuator demonstrates a 32.4-dB attenuation range with a 0.255-dB resolution and a 3.5-8.4-dB IL from dc to 32 GHz. The measured root-mean-square (rms) amplitude and phase errors are below 0.32 dB and 5.33°, respectively. The attenuator occupies a 0.054-mm2 core area and consumes negligible power. © 1963-2012 IEEE.

关键词： Insertion losses

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Adaptive RBF-Neural-Network Control with Force Observer for Teleoperation Robotic System 1st

Adaptive RBF-Neural-Network Control with Force Observer for...

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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Human-collective visualization transparency

arXiv

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arXiv 2020年

作者： Roundtree, Karina A. Cody, Jason R. Leaf, Jennifer Onan Demirel, H. Adams, Julie A. School of Mechanical Industrial and Manufacturing Engineering Oregon State University Department of Electrical Engineering and Computer Science United States Military Academy Collaborative Robotics and Intelligent Systems Institute Oregon State University

Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective transparency research has focused on how the design of the algorithms, visualizations, and control mechanisms influence human-collective behavior. Traditional collective visualizations have shown all of the individual entities composing a collective, which may become problematic as collectives scale in size and heterogeneity, and tasks become more demanding. Human operators can become overloaded with information, which will negatively affect their understanding of the collectives current state and overall behaviors, which can cause poor teaming performance. An analysis of visualization transparency and the derived visualization design guidance, based on remote supervision of collectives, are the primary contributions of this manuscript. The individual agent and abstract visualizations were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. The abstract visualization provided better transparency by enabling operators with different individual differences and capabilities to perform relatively the same and promoted higher human-collective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Transparency

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An Approach of CA with M-RIPPER for Heart Disease Prediction

Research Square

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Research Square 2021年

作者： Manimurugan, S. Almutairi, Saad Aborokbah, Majed Mohammed Narmatha, C. Ganesan, Subramaniam AlzahebHani, Riyadh A. Almoamari, Hani Industrial Innovation & Robotics Center Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Electrical and Computer Engineering Oakland university Rochester United States Faculty of Applied Medical Sciences University of Tabuk Tabuk Saudi Arabia Faculty of Computer and Information Systems Islamic University of Madinah Saudi Arabia

In the present world, mobile computing devices are popular and are identified in each aspect of life. This combination among computing and the present world is not restricted to the everyday life. The medical field was similarly concerned, where care is given in a wide scope of areas and conditions. The medical domain is continually being immersed with new kinds of innovations, including context-aware system and application. In this research, a context aware healthcare model based on IoT application is proposed. The smart medical devices are used to measure the data from the patients and store it in database. From the database, the patient’s information and medical records are considered as context aware data. For analyzing and classifying the data, the MRIPPER (Modified Repeated Incremental Pruning to Produce ErroR) algorithm is used. This algorithm is a rule-based machine learning algorithm. By using this algorithm, the rules are framed for the analysis of dataset for the prediction of heart disease. The performance analysis of the proposed model is experimented in MATLAB simulation tool. Further, the performance of the proposed model is compared with other existing models like J48, random forest, CART, OneR, and JRip algorithms. The proposed algorithm has achieved 98.89% accuracy, Precision is 96.76%, recall or sensitivity is 99.05%, specificity is 94.35%, and f-score is 97.60%. Overall, the proposed model has obtained 97.38% accuracy in predicting normal class and 97.93% in predicting abnormal class subjects. © 2021, CC BY.

关键词： MATLAB

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Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

Decoding of single-trial EEG reveals unique states of functi...

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作者： Al-Fahad, Rakib Yeasin, Mohammed Bidelman, Gavin M. Department of Electrical and Computer Engineering University of Memphis MemphisTN United States Institute for Intelligent Systems University of Memphis MemphisTN United States School of Communication Sciences and Disorders University of Memphis MemphisTN United States University of Tennessee Health Sciences Center Department of Anatomy and Neurobiology MemphisTN United States

Objective. Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions. Approach. We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. Main results. We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Significance. Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC = 0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach. © 2020 IOP Publishing Ltd.

关键词： Speech processing

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Synthesizing skeletal motion and physiological signals as a function of a virtual human's actions and emotions

arXiv

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arXiv 2021年

作者： Banerjee, Bonny Kapourchali, Masoumeh Heidari Baruah, Murchana Deb, Mousumi Sakauye, Kenneth Olufsen, Mette Institute for Intelligent Systems Department of Electrical & Computer Engineering University of Memphis MemphisTN38152 United States Department of Electrical & Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Texas A&M AgriLife Research & Extension Center TempleTX76502 United States Department of Psychiatry University of Tennessee Health Science Center MemphisTN38163 United States Department of Mathematics North Carolina State University RaleighNC27695 United States

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals. © 2021, CC BY.

关键词： Clocks

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