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Asilomar conference on Signals, systems & Computers

作者： Divya Mohan Sameeksha Katoch Suren Jayasuriya Pavan Turaga Andreas Spanias Sensor Signal and Information Processing (SenSIP) Center School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

Energy-efficient computer vision is vitally important for embedded and mobile platforms where a longer battery life can allow increased deployment in the field. In image sensors, one of the primary causes of energy expenditure is the sampling and digitization process. Smart subsampling of the image-array in a manner that is task-specific, can result in significant savings of energy. We present an adaptive algorithm for video subsampling, which is aimed at enabling accurate object detection, while saving sampling energy. The approach utilizes objectness measures, which we show can be accurately estimated even from sub-sampled frames, and then uses that information to determine the adaptive sampling for the subsequent frame. We show energy savings of 18 - 67% with only a slight degradation in object detection accuracy in experiments. These results motivated us to further explore energy-efficient subsampling using advanced techniques such as, reinforcement learning and Kalman filtering. The experiments using these techniques are underway and provide ample support for adaptive subsampling as a promising avenue for embedded computer vision in the future.

关键词： Object detection Streaming media Computer vision Image sensors optical imaging Task analysis Adaptive algorithms

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Geological Feature Prediction Using Image-Based Machine Lear

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PETROPHYSICS 2018年第6期59卷 750-760页

作者： Jobe, T. D. vital-Brazil, E. Khait, M. Armco Res Ctr 16300 Pk Row Dr Houston TX 77469 USA IBM Res Rua Tutoia 1157 BR-04007005 Paraiso SP Brazil Delft Univ Technol Dept Petr Engn Bldg 23 Stevinweg 1 NL-2628 CN Delft Netherlands

Petrographic description is one of the primary methods by which geoscientists develop an understanding of geologic systems and constrain source, reservoir, and seal characteristics. Geologic facies or rock types are routinely interpreted from petrographic features in thin sections, hut these methods are time consuming and tedious. The interpretations can be heavily biased. Variations in experience base, microscope quality, and scale of observation can lead to inconsistent interpretations, absorbing costly time to achieve alignment. With advances in computing, millions of images can be analyzed in seconds through signal-processing technologies. With this capability, machine-learning methods can support geologic pattern identification to classify geologic features. Combined, automated image analysis and classification though machine learning can significantly reduce analysis time, interpreter bias and inconsistencies. These methods make it possible to share "expert knowledge" with nonexpert users. Image processing, segmentation and classification are key workflows for translating geologic features into discrete representations that can be used for computational modeling. Here, we present two examples where advanced image analysis and machine learning are used to predict geologic and petrophysical properties from optical microscopy thin-section images. The first example uses multiple machine-learning algorithms and pore geometries as a means for predicting and classifying rock properties, such as lithofacies, reservoir zone, porosity and permeability. The second example focuses on training and using convolutional neural networks to classify and predict Dunham textures (mudstone, wackestone, packstone and grainstone) from carbonate thin sections. Data for both machine-learning applications comes from a Jurassic bimodal carbonate reservoir. Key depositional lithofacies include micritic mudstones, bivalve-coated grain pack/grainstones, Cladocoropsis pack/grainstones, stro

关键词： Artificial Intelligence december 2018 aapg bulletin computer Reservoir Characterization geological feature prediction Symposium machine learning Upstream Oil & Gas computer neural network Artificial intelligence information Management and systems Reservoir Characterization

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optical phase retrieval using four rotated versions of a single binary mask - simulation results 2

Optical phase retrieval using four rotated versions of a sin...

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conference on Computational Optics II

作者： Karitans, Varis Nitiss, Edgars Tokmakovs, Andrejs Pudzs, Kaspars Univ Latvia Inst Solid State Phys Dept Ferroelect Kengaraga St 8 LV-1063 Riga Latvia Univ Latvia Inst Solid State Phys Dept Photon Kengaraga St 8 LV-1063 Riga Latvia Univ Latvia Fac Phys & Math Dept Optometry & Vis Sci Jelgavas St 1 LV-1004 Riga Latvia

ISBN: (数字)9781510619265

ISBN: (纸本)9781510619265

In signal processing one often faces the phase problem, i.e., when an image is formed information about the phase is lost so that only information about intensity is available. This is often an issue in astronomy, biology, crystallography, speckle imaging, diffractive imaging where the phase of the object must be known. While there have been many approaches how to find a solution to the phase problem, numerical algorithms recovering the phase from intensity measurements become more and more popular. One of such algorithms called PhaseLift has been recently proposed. In this study, we show that even 4 masks may be sufficient for reasonable recovery of the phase. The original wavefront and the recovered wavefront were visually indistinguishable and showed very high correlation. In addition, the four masks are essentially one and the same mask rotated around in steps of 90 degrees. By using just four rotated versions of a single mask, the PhaseLift could be easily implemented in real optical systems thus simplifying the wavefront sensing in astronomy, biology etc.

关键词： phase retrieval wavefront aberrations wavefront measurement adaptive optics diffractive imaging Fourier optics

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Reconfigurable integrated waveguide meshes for photonic signal processing and emerging applications 22

Reconfigurable integrated waveguide meshes for photonic sign...

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conference on Integrated Optics - Devices, Materials, and Technologies XXII

作者： Perez, Daniel Gasulla, Ivana Capmany, Jose Univ Politecn Valencia ITEAM Res Ist Camino Vera S-N E-46022 Valencia Spain

ISBN: (数字)9781510615564

ISBN: (纸本)9781510615564

We review the recent advances reported in the field of integrated photonic waveguide meshes, both from the theoretical as well as from the experimental point of view. We show how these devices can be programmed to implement both traditional signal processing structures, such as finite and infinite impulse response filters, delay lines, beamforming networks as well as more advanced linear matrix optics functionalities. Experimental results reported both in Silicon and Silicon Nitride material platforms will be presented. We will also discuss the main programming algorithms to implement these structures and discuss their applications either as standalone systems or as part of more elaborated subsystems in microwave photonics, quantum information and machine learning.

关键词： Integrated photonics programmable photonic processors optical signal processing

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Chebyshev Gradient Polynomials for High Resolution Surface and Wavefront Reconstruction 12

Chebyshev Gradient Polynomials for High Resolution Surface a...

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conference on optical Manufacturing and Testing XII

作者： Aftab, Maham Burge, James H. Smith, Greg A. Graves, Logan Oh, Chang-jin Kim, Dae Wook Univ Arizona Coll Opt Sci 1603 E Univ Blvd Tucson AZ 85721 USA Univ Arizona Steward Observ 933 N Cherry Ave Tucson AZ 85719 USA

ISBN: (数字)9781510620568

ISBN: (纸本)9781510620568

A new data processing method based on orthonormal rectangular gradient polynomials is introduced in this work. This methodology is capable of effectively reconstructing surfaces or wavefronts with data obtained from deflectometry systems, especially during fabrication and metrology of high resolution and freeform surfaces. First, we derived a complete and computationally efficient vector polynomial set, called G polynomials. These polynomials are obtained from gradients of Chebyshev polynomials of the first kind - a basis set with many qualities that are useful for modal fitting. In our approach both the scalar and vector polynomials, that are defined and manipulated easily, have a straightforward relationship due to which the polynomial coefficients of both sets are the same. This makes conversion between the two sets highly convenient. Another powerful attribute of this technique is the ability to quickly generate a very large number of polynomial terms, with high numerical efficiency. Since tens of thousands of polynomials can be generated, mid-to-high spatial frequencies of surfaces can be reconstructed from high-resolution metrology data. We will establish the strengths of our approach with examples involving simulations as well as real metrology data from the Daniel K. Inouye Solar Telescope (DKIST) primary mirror.

关键词： Surface reconstruction Surface measurements optical metrology information processing Deflectometry Testing Modal fitting Numerical approximation and analysis

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3rd International conference on Signal and information processing, Networking and Computers, ICSINC 2017

3rd International Conference on Signal and Information Proce...

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3rd International conference on Signal and information processing, Networking and Computers, ICSINC 2017

ISBN: (纸本)9789811075209

The proceedings contain 61 papers. The special focus in this conference is on Signal, information processing, Networking and Computers. The topics include: An Energy Detection Based on Coefficient of Variation for Spectrum Sensing in Cognitive Radio;performance Analysis for User-Centric Cloud Radio Access Network in Millimeter Wave;millimeter Wave Cloud Radio Access Network Coverage and Capacity;Optimal Downtilts for 3D Beamforming Based on Greedy Algorithm in Massive MIMO Networks with Imperfect CSI;Dynamic User Scheduling algorithms for Massive MIMO Multicast System;a Bat Algorithm Based on Centroid Strategy;an Optimized Daisy-chain Topology for Multi-load Interconnection in High–speed and High–density Electronic systems;Realization and Optimization of Pulse Compression Algorithm on OpenCL-Based FPGA Heterogeneous Computing Platform;The Research of SAR processing Performance Based on Multi-core GPU;A PHY-Based Secret Sharing Scheme in MIMO systems;ship Detection in optical Satellite Images Based on Sparse Representation;application of Back Propagation Neural Network with Simulated Annealing Algorithm in Network Intrusion Detection systems;an Improved Binary Bee Colony Algorithm for Satellite Resource Scheduling Method;Automatic Liver Segmentation on CT Images;An Improved Blind Spectrum Sensing Algorithm Based on QR Decomposition and SVM;Sentiment Analysis Using Modified LDA;co-training Based on Multi-type Text Features;GNSS Spoofing Jamming Recognition Based on Machine Learning;TinyPEP: Tiny Pairwise-Key Establishment Protocol for Wireless Sensor Networks;An Innovative Indoor Location Algorithm Based on Supervised Learning and WIFI Fingerprint Classification;long Short-Term Memory Network for Wireless Channel Prediction;deep Learning and Machine Learning for Object Detection in Remote Sensing Images.

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Machine learning and digital image processing for non-contact modal parameters identification of structures

Machine learning and digital image processing for non-contac...

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conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace systems

作者： Torbol, M. Park, K. T. Ulsan Natl Inst Sci & Technol 50 UNIST Gil Ulsan 44919 South Korea

ISBN: (数字)9781510616936

ISBN: (纸本)9781510616936

This study introduces an innovative non-contact sensing technique for vision-based displacement measurement. Existing vision-based displacement measurement techniques utilizes physical target panels or physical features to compute relative displacement between the target and the observation point. Instead, the proposed method exploits the optical reference of a speckle pattern. A coherent light that is diffusely reflected on the surface of the target structure creates the speckle pattern. In this study, a camera records the changes in the speckle pattern in real time. Because the speckle pattern is sensitive to small changes of surface, the ambient vibration is enough to affect it. To estimate the displacement of the target from the raw speckle images, speckle contrast imaging (SCI), speckle flow imaging (SFI), and k-means clustering algorithm were used. After SCI and SFI quantifies the blurring effect in each image, the k-means clustering algorithm creates virtual sensing node from each image. The connection of virtual nodes from frame to frame highlights the displacements of the surface in time domain. Because the algorithms are time-consuming and computationally intensive, a GPU executes the entire post-processing operation in parallel and identifies the natural frequencies of the structure.

关键词： GPGPU computing CUDA C speckle pattern modal identification

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Time-of-flight calibration of an MCT-APD sensor for a flash imaging lidar system

Time-of-flight calibration of an MCT-APD sensor for a flash ...

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International conference on Space Optics, ICSO 2018

作者： Parahyba, victor E. S. De Borniol, Eric Perrier, Regis Nowicki-Bringuier, Yoanna-Reine R. Ciapponi, Alessandra Chanussot, Jocelyn CEA-LETI MINATEC Campus Grenoble38054 France European Space Agency ESA-ESTEC Noordwijk Netherlands Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble38000 France

ISBN: (纸本)9781510630772

Flash Imaging LiDARs (Light Detection and Ranging) are active systems that resolves depth in a scene by time-of-flight measurements and are being seen as a competitive technological alternative that presents great advantages in the space scenario. The system records full 3D-images with a single laser pulse, thus eliminating the need for a scanning device. A first step to improve the overall quality of the measurements is the calibration of the camera. There is a vast literature and well-stablished techniques concerning radiometric calibration (2D information). However, for time-of-flight (3D information) the subject remains open to improvement and dependent upon the specific characteristics of the detector. In this article we propose a calibration scheme for CEA-LETI's LiDAR that combines both intensity, range accuracy and range precision calibration and presents the first enhanced results based on data acquired under laboratory conditions. This project is a partnership between ESA and CEA-LETI aiming the design of a LiDAR system based on a custom MCTAPD FPA (Avalanche PhotoDiode Focal Plane Array) detector developed by CEA-LETI and the formulation of a set of imaging processing algorithms. The target is to demonstrate the potential of such detector technology and to evaluate the performances of the full system chain in the frame of the targeted application. In the future, a validation campaign on a real terrain, at ESA's campsite, will be performed to demonstrate the system in a close-to-real configuration. © COPYRIGHT SPIE.

关键词： optical radar

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Signal processing for optical Communication System Assisted by Computer vision Techniques

Signal Processing for Optical Communication System Assisted ...

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International conference on Advanced Infocomm Technology (ICAIT)

作者： Tianwai Bo Calvin Chun-Kit Chan Hoon Kim KAIST School of Electrical Engineering Yuseong-gu Daejeon Korea Department of Information Engineering The Chinese University of Hong Kong N.T. Hong Kong SAR

ISBN: (纸本)9781538679371;9781538679364

We review the recent progress on the application of image processing techniques to optical communication systems. The focus is placed mainly on the implementation complexity and performance of the techniques for optical performance monitoring and the compensation of common phase error. We also briefly introduce several applications where machine learning algorithms could be beneficial to fiber-optic transmission system.

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optical d-Level Frequency-Time-Based Cluster States

Optical d-Level Frequency-Time-Based Cluster States

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conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Michael Kues Christian Reimer Stefania Sciara Piotr Roztocki Mehedi Islam Luis Romero Cortés Yanbing Zhang Bennet Fischer Sébastien Loranger Raman Kashyap Alfonso Cino Sai T. Chu Brent E. Little David J. Moss Lucia Caspani William J. Munro José Azaña Roberto Morandotti School of Engineering University of Glasgow Glasgow UK Institut National de la Recherche Scientifique (INRS-EMT) Varennes Canada Department of Energy Information Engineering and Mathematical Models University of Palermo Palermo Italy Engineering Physics Department Polytechnique Montreal Montreal Canada Electrical Engineering Department Polytechnique Montreal Montreal Canada Department of Physics and Material Science City University of Hong Kong Hong Kong China State Key Laboratory of Transient Optics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Science Xi'an China Centre for Micro Photonics Swinburne University of Technology Hawthorn Victoria Australia Institute of Photonics Department of Physics University of Strathclyde Glasgow UK National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781728104690

Cluster states, a specific class of multi-partite entangled states, are of particular importance for quantum science, as such systems are equivalent to the realization of one-way (or measurement-based) quantum computers [1]. In this scheme, algorithms are implemented through high-fidelity measurements on the parties of the state [2]. While two-level (i.e. qubit) cluster states have been realized so far, increasing the number of particles to boost the computational resource comes at the price of significantly reduced coherence time and detection rates, as well as increased sensitivity to noise, restricting the realization of discrete cluster states to a record of eight qubits. In contrast, the demonstration of d-level (i.e. qudit) cluster states has the potential to i) increase quantum resources without modifying the number of particles; ii) enable the implementation of highly efficient computational protocols; iii) reduce the noise sensitivity of the states. Up till now, the realization of discrete d-level cluster states has not been shown in any quantum platform. We here demonstrate the realization of d-level cluster states, perform d-level one-way quantum processing operations on the states, and show that higher-dimensional forms of cluster states are more noise tolerant than lower dimensional realizations.

关键词： Quantum computing Quantum entanglement Photonics Frequency-domain analysis Atmospheric measurements Particle measurements Computers

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