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Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy

arXiv

引用

arXiv 2018年

作者： Varatharajah, Yogatheesan Berry, Brent Cimbalnik, Jan Kremen, Vaclav van Gompel, Jamie Stead, Matt Brinkmann, Benjamin Iyer, Ravishankar Worrell, Gregory Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic RochesterMN55905 United States Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6166 36 Czech Republic Department of Neurosurgery Mayo Clinic RochesterMN55905 United States Department of Physiology & Biomedical Engineering Mayo Clinic RochesterMN55905 United States

An ability to map seizure-generating brain tissue, i.e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between ninety minutes and two hours are sufficient to localize SOZs with accuracies that may prove clinically relevant. The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in

关键词： Support vector machines

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Algorithms for joint operation of service robotic platform and set of UAVs in agriculture tasks 5

Algorithms for joint operation of service robotic platform a...

引用

5th IEEE Workshop on Advances in Information, Electronic and electrical engineering, AIEEE 2017

作者： Vu, Quyen Nguyen, Vinh Solenaya, Oksana Ronzhin, Andrey Mehmet, H. Department of Electromechanics and Robotics Saint Petersburg State University of Aerospace Instrumentation SUAI St. Petersburg Russia Laboratory of Autonomous Robotic Systems St. Petersburg Institute for Informatics and Automation Russian Academy of Sciences SPIIRAS St. Petersburg Russia Guzey Department of Electrical and Electronics Engineering Erzurum Technical University Erzurum Turkey

ISBN: (纸本)9781538641378

The problems of designing a mobile robotic platform carrying a set of unmanned aerial vehicles and containing a container for a payload are considered. The issues of transportation of robots and joint activity of robots with various functions in solving agriculture problems are discussed. The conceptual model of a mobile platform design is given. The main stages of the algorithmic model of the joint operation of the mobile platform and the set of unmanned aerial vehicles brought to the working area are described. © 2017 IEEE.

关键词： Manipulators

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Outer-Linearization-Based Optimization Algorithm for Decentralized Control Design in Flexure-Linked H-Gantry

Outer-Linearization-Based Optimization Algorithm for Decentr...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Jun Ma Si-Lu Chen Wenyu Liang Xiaocong Li Chek Sing Teo Arthur Tay Abdullah Al Mamun Kok Kiong Tan Department of Electrical and Computer Engineering National University of Singapore Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Chinese Academy of Sciences Ningbo Zhejiang China Department of Electrical and Computer Engineering National Universit of Singapore Singapore Mechatronics Group Singapore Institute of Manufacturing Technology Singapore

The dual-drive H-gantry (DHG) is widely used for high-speed high-precision Cartesian motion control applications. Compared with the rigid-linked gantry design, the flexure-linked design is able to prevent the damage of the cross-arm because a small degree of rotation angle is allowed. Nevertheless, the flexure-linked design possibly induces the resonance of the gantry because of the chattering of control signals. To maintain the precision tracking of two carriages and minimize the chattering of control efforts, we aim to seek the most suitable flexure joints among the available ones and optimize the decentralized feedback controller parameters under parametric uncertainties. The mechatronic design problem is formulated as an H_2 guaranteed cost control problem. All the stabilizing feedback controller gains are parameterized over a convex set, from here, numerical procedures are developed to obtain the global optimum by means of an outer-linearization-based optimization algorithm. Experiments are carried out and the results successfully validate the optimality and the robustness of the proposed design approach.

关键词： Decentralized control Optimization Uncertainty Feedforward systems Adaptive control Robustness Actuators

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Robust visual tracking using multi-frame multi-feature joint modeling

arXiv

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arXiv 2018年

作者： Zhang, Peng Yu, Shujian Xu, Jiamiao You, Xinge Jiang, Xiubao Jing, Xiao-Yuan Tao, Dacheng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan Hubei China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China School of Automation Nanjing University of Posts and Telecommunications Nanjing210023 China UBTech Sydney Artificial Intelligence Institute and the School of Information Technologies Faculty of Engineering and Information Technologies at the University of Sydney DarlingtonNSW2008 Australia

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-Task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-The-Art trackers. MATLAB code is available from our project homepage http://***/ project/***. Copyright © 2018, The Authors. All rights reserved.

关键词： MATLAB

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Learning depth from single images with deep neural network embedding focal length

arXiv

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arXiv 2018年

作者： He, Lei Wang, Guanghui Hu, Zhanyi University of Chinese Academy of Sciences Beijing100049 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States

Learning depth from a single image, as an important issue in scene understanding, has attracted a lot of attention in the past decade. The accuracy of the depth estimation has been improved from conditional Markov random fields, non-parametric methods, to deep convolutional neural networks most recently. However, there exist inherent ambiguities in recovering 3D from a single 2D image. In this paper, we first prove the ambiguity between the focal length and monocular depth learning, and verify the result using experiments, showing that the focal length has a great influence on accurate depth recovery. In order to learn monocular depth by embedding the focal length, we propose a method to generate synthetic varying-focal-length dataset from fixed-focal-length datasets, and a simple and effective method is implemented to fill the holes in the newly generated images. For the sake of accurate depth recovery, we propose a novel deep neural network to infer depth through effectively fusing the middle-level information on the fixed-focal-length dataset, which outperforms the state-of-the-art methods built on pre-trained VGG. Furthermore, the newly generated varying-focal-length dataset is taken as input to the proposed network in both learning and inference phases. Extensive experiments on the fixed- and varying-focal-length datasets demonstrate that the learned monocular depth with embedded focal length is significantly improved compared to that without embedding the focal length information. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Waveband Selection Based Feature Extraction Using Genetic Algorithm 4

Waveband Selection Based Feature Extraction Using Genetic Al...

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4th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE International Conference of Scalable and Smart Cloud, SSC 2017

作者： Li, Yujun Liang, Kun Tang, Xiaojun Gai, Keke School of Automation and Information Engineering Xian University of Technology Xian Shaanxi710048 China School of Computer Science and Engineering Xian University of Technology Xian Shaanxi710048 China State Key Laboratory of Electrical Insulation and Power Equipment Xian Jiaotong University Xian Shaanxi710049 China Department of Computer Science Pace University New YorkNY10038 United States

ISBN: (纸本)9781509066438

In order to explain the geological structure accurately and quickly, we analyze the gas mixture gathered from the well by Infrared Spectroscopy Fourier Transform Spectrometer instead of gas chromatograph. In the process of the spectrum analysis, the reduction of the spectrum data dimention is very neccessary to perform. In this paper, we propose a feature extraction method is based on waveband selections using genetic algorithm, which is named FEWSGA. This approach can directly selecte eigenvalues from the limited waveband spectrum data instead of using mathematical transformation, such as the PCA (principal component analysis) and PLS (partial least squares) algorithm. Experiments results show that our method can reduce the spectrum data dimention from 1866 to 317, and the mean relative error (MRE) of the analysis model decrease from 34.68% to 26.59%. Moreover, the feature extraction from the whole waveband spectrum data using GA only reduce the data dimention from 1866 to 937. The MRE of the analysis model only reduces from 34.68% to 32.97%. Our approach has a better performance. © 2017 IEEE.

关键词： Genetic algorithms

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Fairness and transmission-aware caching and delivery policies in OFDMA-based HetNets

arXiv

引用

arXiv 2017年

作者： Rezvani, Sepehr Mokari, Nader Javan, Mohammad R. Jorswieck, Eduard A. Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Robotics Engineering Shahrood University of Technology Shahrood Iran Dresden University of Technology Communications Laboratory Chair of Communication Theory Dresden Germany

Recently, wireless edge caching has been emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)- based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, In the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27% compared to the conventional baseline popular *** Codes 90C11 Copyright © 2017, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Single image super resolution - When model adaptation matters

arXiv

引用

arXiv 2017年

作者： Liang, Yudong Timofte, Radu Wang, Jinjun Gong, Yihong Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zürich Switzerland

In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external priors modeling are the key ingredients. The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors. In this paper we propose a novel deep convolutional neural network carefully designed for robustness and efficiency at both learning and testing. Moreover, we propose a couple of model adaptation strategies to the internal contents of the low resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors we achieve 0.1 up to 0.3dB PSNR improvements over best reported results on standard datasets. Our adaptation especially favors images with repetitive structures or under large resolutions. Moreover, it can be combined with other simple techniques, such as back-projection or enhanced prediction, for further improvements. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Multiple Model based Unscented Particle Filter Algorithm for a SINS/CNS Integrated Navigation System

Multiple Model based Unscented Particle Filter Algorithm for...

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American Control Conference

作者： Fangfang Zhao Shuzhi Sam Ge Wei He School of Computer Science and Engineering Center for Robotics University of Electronic Science and Technology of China Chengdu 611731 China the Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore and Center for Robotics University of Electronic Science and Technology of China Chengdu 611731 China School of Automation and Electrical Engineering University of Science and Technology of Beijing Beijing 100083 China

ISBN: (纸本)9781509045839

The single model filter has poor adaptability under the uncertain or unknown system parameters, multiple model filters can be used to resolve this problem. This paper investigates the multiple model adaptive estimation (MMAE) algorithm and proposes a multiple model unscented particle filter (MMUPF) algorithm. Forward the traditional MMAE method which uses extend Kalman filter (EKF) or unscented Kalman filter (UKF) as each filter to execute the state estimation process. In this method, we combine MMAE with unscented particle filter (UPF), and replace UKF with UPF which shows higher accuracy than UKF for the nonlinear system. At the same time, this algorithm is applied to the integrated navigation system which combines strap-down inertial navigation with celestial navigation by a ballistic missile. Simulations are provided to demonstrate that MMUPF algorithm has the highest navigation precision, but at the cost of heavier computational burden.

关键词： resolve problem paper Resolve hybrid navigation systems Navigation systems Particle filtering algorithm State estimation Celestial navigation INERTIAL NAVIGATION paper Particle filters Filters multiples Nonlinear systems system parameter

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Gone with the Wind: Nonlinear Guidance for Small Fixed-Wing Aircraft in Arbitrarily Strong Windfields

Gone with the Wind: Nonlinear Guidance for Small Fixed-Wing ...

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American Control Conference

作者： Luca Furieri Thomas Stastny Lorenzo Marconi Roland Siegwart Igor Gilitschenski Laboratory of Automation and Robotics Department of Electronics Computer Science and Systems University of Bologna Italy Autonomous Systems Lab Department of Mechanical & Processing Engineering Swiss Federal Institute of Technology (ETH Zurich) Switzerland

ISBN: (纸本)9781509045839

The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this paper we present a principled nonlinear guidance strategy, addressing this problem. More broadly, we propose a methodology for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.

关键词： Arbitrarily Strong Windfields Fixed wing aircraft Guidance Wind Wings Unmanned Aerial Vehicle low speed level control vehicle visual tracking wall coping nonlinearity Platform

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