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The study of effectiveness of a high-performance crystal lattice parametric identification algorithm based on CUDA technology

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Journal of Physics: Conference Series 2019年第5期1368卷

作者： Shirokanev, A S Kirsh, D v Kupriyanov, A v Samara National Research University Moskovskoe Shosse 34A Samara Russia 443086 Image Processing Systems Institute of RAS - Branch of the FSRC “Crystallography and Photonics” RAS Molodogvardejskaya street 151 Samara Russia 443001

The study of substances with a crystal structure is a complex multi-step process. The key step in the crystalline substance analysis is the unit cell parameter estimation. The estimation of the crystal lattice unit cell parameters is a particular problem that involves the search of the crystal lattice model’s parameters according to the information which can be extracted from the substance. In these recent times, the most accurate information about the substance structure can be obtained with the electron microscope whose linear resolution is high enough to observe the atomic structure of a substance. The problem of parameter estimation in this case means the reconstruction of the three-dimensional crystal lattice with 2-dimentional images received by an electron microscope, and the estimation of the crystal lattice unit cell parameters by reconstructed lattice. In the previous papers the crystal lattice parametric identification algorithms based on solving the local optimization problem were presented. However, the analysis of a large crystal lattice database requires a lot of computations. In this paper, a high-performance crystal lattices parametric identification algorithm using the CUDA technology is proposed. The investigation of the algorithm effectiveness is carried out on the GPU GeForce Nvidia GTX 1070 Ti. With data dimension more than 32 translations the acceleration is higher than 70. The algorithm runs more efficiently at the use of a large number of CUDA-blocks.

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algorithms of aerospace photo processing in a quasi-two-dimensional non-trygonometrical spectral space

Algorithms of aerospace photo processing in a quasi-two-dime...

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International Conference on Mechanical, System and Control Engineering (ICMSC)

作者： B. v. Kostrov A. N. Kolesenkov E. v. Ruchkina v. N. Ruchkin D. v. Grigorenko Computer Dept. Ryazan State Radio Engineering University Ryazan Russian Federation Computer Science and Computer Engineering Dept. Ryazan State University Ryazan Russian Federation

ISBN: (纸本)9781509065318

The paper employs theoretic and methodological generalization in order to develop new algorithms of orthogonal transformation in the field of aerospace photo processing. Employing systems of vilenkin-Chrestenson functions (vCF) in a non-trigonometrical space, we obtain a minimally possible shape of their construction. The paper describes algorithms and filtration samples (quasi-two- dimensional filtering and correlation analysis of aerospace photos when applied to distortions and image noise.

关键词： Filtration Distortion Algorithm design and analysis Thermal sensors Aerospace engineering Charge-coupled image sensors Sensor systems

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visual servoing for autonomous landing of a multi-rotor UAS on a moving platform

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JOURNAL OF UNMANNED vEHICLE systems 2017年第1期5卷 13-26页

作者： Borshchova, Iryna O'Young, Siu Mem Univ Newfoundland Engn & Appl Sci St John NF A1B 3X5 Canada

In this paper, a method to control a small multi-rotor unmanned aerial system (UAS) while landing on a moving platform using image-based visual servoing is described. The landing scheme is based on positioning visual markers on a landing platform in the form of a detectable pattern. When the onboard camera detects the object pattern, the flight control algorithm will send visual-based servo-commands to align the multi-rotor with the targets. The main contribution is that the proposed method is less computationally expensive as it uses color-based object detection applied to a geometric pattern instead of feature tracking algorithms. This method has the advantage that it does not demand calculating the distance to the objects (depth). The proposed method was tested in simulation using a quadcopter model in v-REP (virtual robotics experimental platform) working in parallel with robot operating system (ROS). Finally, this method was validated in a series of real-time experiments with a quadcopter.

关键词： UASs autonomous landing image-processing algorithm visual servoing ROS

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Analysis and comparison of multichannel SAR imaging algorithms

Analysis and comparison of multichannel SAR imaging algorith...

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IEEE National Conference on Radar

作者： Risto vehmas Juha Jylhä Minna väilä Ari visa Tampere University of Technology Laboratory of Signal Processing Tampere Finland

Multichannel synthetic aperture radar (MSAR) systems are essential for applications such as ground moving target indication (GMTI), interferometric SAR (InSAR), and high-resolution wide-swath (HRWS) imaging. In this paper, we analyze and compare MSAR image reconstruction algorithms. Previously, image reconstruction for MSAR has relied heavily on frequency domain matched filtering. Time domain image reconstruction algorithms have several attractive qualities, but their use has been limited due to a high computational burden. In this paper, we utilize digital beamforming and the phase center approximation to develop a fast time domain (fast factorized back-projection, FFBP) algorithm for MSAR. We present two FFBP implementations for MSAR and perform a comparative study between MSAR imaging algorithms. The numerical results confirm the feasibility of the proposed FFBP algorithms for MSAR.

关键词： image reconstruction Receivers Signal processing algorithms Synthetic aperture radar Time-domain analysis Transmitters Radar imaging

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Optimization of interval type-2 fuzzy systems for image edge detection

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APPLIED SOFT COMPUTING 2016年第0期47卷 631-643页

作者： Gonzalez, Claudia I. Melin, Patricia Castro, Juan R. Castillo, Oscar Mendoza, Olivia Univ Autonoma Baja California Tijuana Mexico Tijuana Inst Technol Tijuana Mexico

This paper presents the optimization of a fuzzy edge detector based on the traditional Sobel technique combined with interval type-2 fuzzy logic. The goal of using interval type-2 fuzzy logic in edge detection methods is to provide them with the ability to handle uncertainty in processing real world images. However, the optimal design of fuzzy systems is a difficult task and for this reason the use of meta heuristic optimization techniques is also considered in this paper. For the optimization of the fuzzy inference systems, the Cuckoo Search (CS) and Genetic algorithms (GAs) are applied. Simulation results show that using an optimal interval type-2 fuzzy system in conjunction with the Sobel technique provides a powerful edge detection method that outperforms its type-1 counterparts and the pure original Sobel technique. (C) 2014 Elsevier B.v. All rights reserved.

关键词： Interval type-2 fuzzy logic Edge detection image processing Cuckoo Search algorithms

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A novel method for the extraction of primary visual features from an image through intelligent feature descriptors

A novel method for the extraction of primary visual features...

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International Conference on Recent Innovations in Signal processing and Embedded systems (RISE)

作者： Abhinav v. Deshpande M. Monica Subashini School of Electronics Engineering V.I.T University Vellore India School of Electrical Engineering V. I. T. University Vellore India

ISBN: (纸本)9781509047611;9781509047604

As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only there cognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as conversion of the original RGB images into HSv Color Space, Adjustment of the Contrast of the Color images as well as applying the Histogram of Oriented Gradients (HOG) algorithm in which the process of extraction and plotting of the HOG features from a given image is performed that is most popular amongst the feature extraction algorithms. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board.

关键词： Feature extraction vehicles image color analysis image recognition image databases Classification algorithms Shape

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Can surgical simulation be used to train detection and classification of neural networks?

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HEALTHCARE TECHNOLOGY LETTERS 2017年第5期4卷 216-222页

作者： Zisimopoulos, Odysseas Flouty, Evangello Stacey, Mark Muscroft, Sam Giataganas, Petros Nehme, Jean Chow, Andre Stoyanov, Danail Kinosis Ltd Touch Surg 230 City Rd London EC1V 2QY England UCL Comp Sci London WC1E 6BT England

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

关键词： surgery neural nets learning (artificial intelligence) image recognition image classification image segmentation biomedical optical imaging video signal processing medical image processing surgical simulation computer-assisted interventions CAI surgical phase recognition algorithms vision20 based tool detection deep learning image recognition image classification tool detection tool segmentation deep convolutional neural networks generative adversarial networks image segmentation cataract surgery

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High-Level Design using Intel FPGA OpenCL: a Hyperspectral Imaging Spatial-Spectral Classifier 12

High-Level Design using Intel FPGA OpenCL: a Hyperspectral I...

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12th International Symposium on Reconfigurable Communication-Centric systems-on-Chip (ReCoSoC)

作者： Domingo, R. Salvador, R. Fabelo, H. Madronal, D. Ortega, S. Lazcano, R. Juarez, E. Callico, G. Sanz, C. Univ Politecn Madrid UPM Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain Univ Las Palmas Gran de Canaria ULPGC Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain

ISBN: (纸本)9781538633441

Current computational demands require increasing designer's efficiency and system performance per watt. A broadly accepted solution for efficient accelerators implementation is reconfigurable computing. However, typical HDL methodologies require very specific skills and a considerable amount of designer's time. Despite the new approaches to high-level synthesis like OpenCL, given the large heterogeneity in today's devices (manycore, CPUs, GPUs, FPGAs), there is no one-fits-all solution, so to maximize performance, platform-driven optimization is needed. This paper reviews some latest works using Intel FPGA SDK for OpenCL and the strategies for optimization, evaluating the framework for the design of a hyperspectral image spatial-spectral classifier accelerator. Results are reported for a Cyclone v SoC using Intel FPGA OpenCL Offline Compiler 16.0 out-of-the-box. From a common baseline C implementation running on the embedded ARM (R) Cortex (R)-A9, OpenCL-based synthesis is evaluated applying different generic and vendor specific optimizations. Results show how reasonable speedups are obtained in a device with scarce computing and embedded memory resources. It seems a great step has been given to effectively raise the abstraction level, but still, a considerable amount of HW design skills is needed.

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

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Realization of the FPGA-based reconfigurable computing environment by the example of morphological processing of a grayscale image

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IOP Conference Series: Materials Science and Engineering 2018年第1期363卷

作者： v Shatravin D v Shashev National Research Tomsk State University Tomsk Russia

Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove the efficiency of vision systems in autonomous robotic devices. However, the use of these systems is limited because of the computational and energy resources available in the robot device. The paper describes the results of applying the original approach for image processing on reconfigurable computing environments by the example of morphological operations over grayscale images. This approach is prospective for realizing complex image processing algorithms and real-time image analysis in autonomous robotic devices.

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image processing of globular clusters - Simulation for deconvolution tests (GlencoeSim)

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NEW ASTRONOMY 2016年 48卷 24-29页

作者： Blazek, Martin Pata, Petr Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 6 Czech Republic

This paper presents an algorithmic approach for efficiency tests of deconvolution algorithms in astronomic image processing. Due to the existence of noise in astronomical data there is no certainty that a mathematically exact result of stellar deconvolution exists and iterative or other methods such as aperture or PSF fitting photometry are commonly used. Iterative methods are important namely in the case of crowded fields (e.g., globular clusters). For tests of the efficiency of these iterative methods on various stellar fields, information about the real fluxes of the sources is essential. For this purpose a simulator of artificial images with crowded stellar fields provides initial information on source fluxes for a robust statistical comparison of various deconvolution methods. The "GlencoeSim" simulator and the algorithms presented in this paper consider various settings of Point-Spread Functions, noise types and spatial distributions, with the aim of producing as realistic an astronomical optical stellar image as possible. (C) 2016 Elsevier B.v. All rights reserved.

关键词： image processing Globular clusters Deconvolution Simulation

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