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检索条件"任意字段=Conference on Algorithms and Systems for Optical Information Processing VI"

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Matrix Profile vi: Meaningful Multidimensional Motif Discovery 17

Matrix Profile VI: Meaningful Multidimensional Motif Discove...

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17th IEEE International conference on Data Mining (ICDMW)

作者： Yeh, Chin-Chia Michael Kavantzas, Nickolas Keogh, Eamonn Univ Calif Riverside Riverside CA 92521 USA Oracle Corp Redwood City CA USA

ISBN: (纸本)9781538638354

Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made to generalize motifs to the multidimensional case. In this work, we show that these efforts, which typically attempt to find motifs on all dimensions, will not produce meaningful motifs except in the most contrived situations. We explain this finding and introduce mSTAMP, an algorithm that allows meaningful discovery of multidimensional motifs. Beyond producing objectively and subjectively meaningful results, our algorithm has a host of additional advantages, including being much faster, requiring fewer parameters and supporting streaming data. We demonstrate the utility of our mSTAMP-based motif discovery framework on domains as diverse as audio processing, industry, and sports analytics.

关键词： Time Series Motif Discovery Multidimensional Data

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Corona discharge classification based on UAV data acquisition 21

Corona discharge classification based on UAV data acquisitio...

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21st International conference on Control systems and Computer Science (CSCS)

作者： vidan, Cristian Maracine, Mihaela Mil Tech Acad Fac Mechatron & Integrated Armament Syst Aircraft Integrated Syst & Mech Dept Bucharest Romania Univ Politehn Bucuresti Fac Automat Control & Comp Comp Sci Dept Bucharest Romania

ISBN: (纸本)9781538618394

The Corona discharge represents one of the main concerns of the century in the field of electricity and powerline design, as it is both harmful for the environment as well as very costly to cover for the voltage losses on the transport. In this paper, we propose the design for a measurement and analysis platform for this phenomenon that can estimate the Corona discharge losses based on a video stream coming from an electro-optical sensor placed on board of an Unmanned Aerial Vehicle (UAV). After experimenting on a dataset generated from Corona discharge videos, our results suggest that of the supervised learning algorithms taken into consideration those based on Radial-Basis approaches are the most effective in classifying the information being fed to it. Before the classification task, we propose a starter set of filtering operation, composed of grayscale reduction, mean blurring and binary transformation.

关键词： Corona discharge UAV data classification data acquisition image processing machine learning

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MAvi: An Embedded Device to Assist Mobility of visually Impaired 30

MAVI: An Embedded Device to Assist Mobility of Visually Impa...

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30th International conference on VLSI Design / 16th International conference on Embedded systems (VLSID)

作者： Kedia, Rajesh Yoosuf, K. K. Dedeepya, Pappireddy Fazal, Munib Arora, Chetan Balakrishnan, M. Indian Inst Technol Delhi Delhi India Indraprastha Inst Informat Technol Delhi Delhi India

ISBN: (纸本)9781509057405

Mobility for visually impaired people in an outdoor environment has always been a challenge. Traditionally, white cane or guide dogs have been used to help in mobility, but they suffer from many limitations. Modern solutions like Smartcane or Ultracane can help detect obstacles in the path, but cannot differentiate among these obstacles. Moreover, they do not support navigation. Considering the limitations with existing aids for mobility of visually impaired, we propose a new device to help visually impaired in their outdoor mobility. This device uses an RGB camera and image processing techniques to recognize the surrounding objects and inform/alarm the user accordingly. Our prototype incorporates signboard detection, face detection and surface texture detection to address issues related to mobility of a visually impaired user. Localization module implemented using GPS and IMU helps to support navigation. In this paper, we explain our initial implementation of various image processing tasks. We focus on the exploration for the choice of algorithms, design decisions, and various implementation decisions. We explain the challenges and results of implementing these applications on an embedded platform (Zedboard) for prototyping.

关键词： Navigation optical character recognition software Feature extraction Support vector machines Cameras Engines

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The algorithm Study for Using the Back Propagation Neural Network in CT Image Segmentation

The algorithm Study for Using the Back Propagation Neural Ne...

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International conference on Innovative optical Health Science

作者： Zhang, Peng Liu, Jie Chen, Chen Li, YingQi Beijing Jiaotong Univ Sch Comp & Informat Technol Dept Biomed Engn Beijing 100044 Peoples R China

ISBN: (数字)9781510609921

ISBN: (纸本)9781510609914;9781510609921

Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can'tt accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.

关键词： Neural networks Biomedical optics Evolutionary algorithms Image processing algorithms and systems Image segmentation optical imaging

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Black Phosphorus Avalanche Photodetector 75

Black Phosphorus Avalanche Photodetector

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75th Annual Device Research conference (DRC)

作者： Atalla, Mahmoud R. M. Koester, Steven J. Univ Minnesota Twin Cities ECE Dept 200 Union St SE Minneapolis MN 55455 USA

ISBN: (纸本)9781509063284

The strong light-matter interaction of atomically-thin layered black phosphorus (BP) has attracted great attention recently. Exfoliated few-layer and monolayer BP flakes have a layer-tunable direct bandgap that varies from 2.0 eV for monolayer to 0.3 eV for bulk, which makes BP a very promising optoelectronic material in the visible and telecom wavelengths [1-3]. The high mobility of BP devices suggested its utilization as an active material of an avalanche photodetector (APD). Recently, APDs utilizing two-dimensional (2D) materials such as transition-metal dichalcogenides (e.g. MoS₂) and layered III-vi semiconductors (e.g. InSe) have been demonstrated [4,5]. Both of these 2D materials possess wide bandgaps that discourage their usage in the infrared [6,7]. The successful demonstration of waveguide-integrated BP photodetector working at 1.55 μm wavelength [1] has stimulated the pursuit of high-performance BP APDs. In this work, we report on the first 2D material-based BP APD featuring high external quantum efficiency (EQE) and avalanche gain. These results provide strong evidence that BP is a promising material for high-performance optical communication and imaging systems.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Lane Detection and Prediction under Hazy Situations for Autonomous Vehicle Navigation

Lane Detection and Prediction under Hazy Situations for Auto...

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International conference on Advances in ICT for Emerging Regions

作者： Bandarage Shehani Sanketha Rathnayake Lochandaka Ranathunga Department of Information Technology University of Moratuwa Katubedda.Moratuwa Sri Lanka

ISBN: (纸本)9781538673539;9781538673522

Lane detection and tracking is essential concern in vision based autonomous vehicle navigation. This paper proposes a novel method based on probabilistic Hough transform and motion vector based analysis for detecting and tracking lane and lane departures. It addresses drawbacks of current systems under hazy situations with learning method based on previous tracking records of the system. In this method relevant lane mark features are extracted based on color variations. Therefore used HSL color model which gives higher color contrast of road surface and the markings. In order to reduce computation, processing algorithms are applied only to the region of interest (ROI). Edges of the selected regions are extracted with Canny Edge Detection. Since lane markings are made with set of straight lines, straight lines are extracted with Probabilistic Hough Transform. Extracted lines are analyzed to detect lane. To monitor lane departures vehicle's motion is detected and tracked based on motion vector analysis with Lucas Kanade optical Flow algorithm. Previous detection records are used to track the lane continuously in hazy situations and provide prediction on approximate lane for the roads without lane markings. Experiments were performed on set of videos taken from vehicles' front camera mounted on dashboard. According to the experiments the proposed algorithm has 81.9% of sensitivity in lane detection and 63.5% in approximate lane predictions for the roads without lane markings. Lane detection algorithm has 83% precision, 68% recall and 0.75 F1 score with threshold brightness value 140. Algorithm can further improved to guide the autonomous vehicles to take accurate decisions on selecting safety lane to drive ahead in different situations.

关键词： Feature extraction Roads Image color analysis Prediction algorithms Tracking Image edge detection Transforms

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Image based method for aberration measurement of lithographic tools

Image based method for aberration measurement of lithographi...

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International conference on optical Instruments and Technology (OIT) at the Topical Meeting on Optoelectronic Imaging/Spectroscopy and Signal processing Technology

作者： Xu, Shuang Tao, Bo Guo, Yongxing Li, Gongfa Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Hubei Peoples R China

ISBN: (数字)9781510617520

ISBN: (纸本)9781510617520;9781510617513

information of lens aberration of lithographic tools is important as it directly affects the intensity distribution in the image plane. Zernike polynomials are commonly used for a mathematical description of lens aberrations. Due to the advantage of lower cost and easier implementation of tools, image based measurement techniques have been widely used. Lithographic tools are typically partially coherent systems that can be described by a bilinear model, which entails time-consuming calculations and does not lend a simple and intuitive relationship between lens aberrations and the resulted images. Previous methods for retrieving lens aberrations in such partially coherent systems involve through-focus image measurements and time-consuming iterative algorithms. In this work, we propose a method for aberration measurement in lithographic tools, which only requires measuring two images of intensity distribution. Two linear formulations are derived in matrix forms that directly relate the measured images to the unknown Zernike coefficients. Consequently, an efficient non-iterative solution is obtained.

关键词： optical lithography lens aberration partially coherent imaging aberration measurement non-iterative method

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Experimental evaluation of PICAS: An electro-optical array for non-cooperative collision sensing on unmanned aircraft systems

Experimental evaluation of PICAS: An electro-optical array f...

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AIAA information systems-Infotech At Aerospace conference, 2017

作者： Minwalla, Cyrus Ellis, Kristopher White, Johnathan Flight Research Laboratory - Aerospace Portfolio National Research Council Canada 1920 Research Rd. OttawaON Canada

ISBN: (数字)9781624104497

ISBN: (纸本)9781624104497

This paper describes the initial flight test evaluation of the Passive Intelligent Collision Avoidance Sensor (PICAS) developed at the National Research Council of Canada (NRC). PICAS represents the latest iteration of a non-cooperative electro-optical (EO) airborne collision sensing instrument designed to explore technology appropriate for under-25 kg Unmanned Aircraft systems (UAS). PICAS is a prototype, selectively-sampled, multi-camera array mated to a computing platform capable of simultaneously recording and processing images in real-time. A selective sampling approach tailored the sensor to the performance requirement by varying the angular resolution and field of view as a function of azimuth. PICAS was designed to detect a a Cessna 172-sized target at 10 km in the head-on direction. The sensor was flight-tested on a Bell 205 rotorcraft acting as a surrogate UAS and flying collision-course intercepts against a Harvard Mark IV intruder. An NRC developed Collision Intercept Display was utilized to provide beyond visual line of sight guidance for both aircraft to conduct the intercepts. Once the collision geometry was coordinated, the Bell 205 switched to automatic operation, controlling altitude, groundspeed and ground-track for the duration of the run. PICAS was operated in pure recording mode, with each camera recording synchronized and time-stamped images at 15 frames per second. The detection performance was evaluated by simulating the real-time processing algorithms against pre-collected imagery and associated aircraft data. Analysis results indicated that PICAS exceeded the minimum detection requirement throughout its field of view for an under-25 kg UAS operating at an airspeed of 80 knots encountering typical (Cessna 172) intruders on co-altitude collision course geometries in Canadian Class G airspace. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Iterative methods

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802.11ac WLAN MIMO Radio-over-Fiber Distributed-Antenna System for In-Building Networks based on Multicore Fiber 11

802.11ac WLAN MIMO Radio-over-Fiber Distributed-Antenna Syst...

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Photonics West OPTO conference on Broadband Access Communication Technologies XI

作者： Morant, Maria Llorente, Roberto Univ Politecn Valencia Nanophoton Technol Ctr Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9781510606975;9781510606982

In this work we propose and evaluate experimentally the performance of IEEE 802.11ac WLAN standard signals in radio-over-fiber (RoF) distributed-antenna systems based on multicore fiber (MCF) for in-building WLAN connectivity. The RoF performance of WLAN signals with different bandwidth is investigated considering up to IEEE 802.11ac maximum of 160 MHz per user. We evaluate experimentally the performance of WLAN signals employing different modulation and coding schemes achieving bitrates from 78 Mbps to 1404 Mbps per user in distances up to 300 m in a 4-core MCF. The performance of the wireless standard multiple-input multiple-output (MIMO) processing algorithms included in WLAN signals applied to the RoF transmission in MCF optical systems is also evaluated. The impact on the quality of the signal from one of the cores in the MIMO processing is investigated and compared with the results achieved with single-input single-output (SISO) transmission in each core. We measured the error vector magnitude (EVM) and the OFDM data burst information of the received WLAN signals after RoF transmission for different distributed-antenna systems with uni-and bi-directional MCF communication. Finally, we compare the received EVM of a single-antenna system (SISO arrangement) with WLAN systems using two antennas (2x2 MIMO) and four antennas (4x4 MIMO).

关键词： Radio-over-fiber multi-core fiber multiple-input multiple-output MIMO processing orthogonal frequency division multiplexing (OFDM) wireless local area network (WLAN)

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Online Sparse Gaussian Process Based Human Motion Intent Learning for an Electrically Actuated Lower Extremity Exoskeleton

Online Sparse Gaussian Process Based Human Motion Intent Lea...

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International conference on Rehabilitation Robotics (ICORR)

作者： Long, Yi Du, Zhi-jiang Chen, Chao-feng Dong, Wei Wang, Wei-dong Harbin Inst Technol State Key Lab Robot & Syst Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538622964

The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems. GP regression is restrictive for large data sets due to its computation complexity. In this paper, an online sparse GP algorithm is constructed to learn the HMI. The original training dataset is collected when the user wears the exoskeleton system with friction compensation to perform unconstrained movement as far as possible. The dataset has two kinds of data, i.e., (1) physical HRI, which is collected by torque sensors placed at the interaction cuffs for the active joints, i.e., knee joints;(2) joint angular position, which is measured by optical position sensors. To reduce the computation complexity of GP, grey relational analysis (GRA) is utilized to specify the original dataset and provide the final training dataset. Those hyper-parameters are optimized offline by maximizing marginal likelihood and will be applied into online GP regression algorithm. The HMI, i.e., angular position of human joints, will be regarded as the reference trajectory for the mechanical legs. To verify the effectiveness of the proposed algorithm, experiments are performed on a subject at a natural speed. The experimental results show the HMI can be obtained in real time, which can be extended and employed in the similar exoskeleton systems.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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