In this brief, the implementation of efficient image encryption and decryption scheme in Residue Number System (RNS) is analyzed. The RNS domain is mainly chosen for its carry free parallel and fast arithmetic operati...
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ISBN:
(纸本)9781509010677
In this brief, the implementation of efficient image encryption and decryption scheme in Residue Number System (RNS) is analyzed. The RNS domain is mainly chosen for its carry free parallel and fast arithmetic operations. But, the converters make RNS inefficient by having large delays. Hence, to use the RNS for our applications we must have the efficient converters. Here, we designed pure combinational logic based forward and reverse converters without using any preloaded ROM's. We used most efficient moduli set {2 n ,2 2n+1 -1,2 n +1,2 n -1} for realizing these converters. New Chinese Reminder theorem-ii is used for the implementation of reverse converter. The image is encrypted by doing modular conversion using forward converted and the original image is decrypted using reverse converter.
A large portion of imageprocessingapplications often come with stringent requirements regarding performance, energy efficiency, and power. FPGAs have proven to be among the most suitable architectures for algorithms...
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A large portion of imageprocessingapplications often come with stringent requirements regarding performance, energy efficiency, and power. FPGAs have proven to be among the most suitable architectures for algorithms that can be processed in a streaming pipeline. Yet, designing imagingsystems for FPGAs remains a very time consuming task. High-Level Synthesis, which has significantly improved due to recent advancements, promises to overcome this obstacle. In particular, Altera OpenCL is a handy solution for employing an FPGA in a heterogeneous system as it covers all device communication. However, to obtain efficient hardware implementations, extreme code modifications, contradicting OpenCL's data-parallel programming paradigm, are necessary. In this work, we explore the programming methodology that yields significantly better hardware implementations for the Altera Offline Compiler. We furthermore designed a compiler back end for a domain-specific source-to-source compiler to leverage the algorithm description to a higher level and generate highly optimized OpenCL code. Moreover, we advanced the compiler to support arbitrary bit width operations, which are fundamental to hardware designs. We evaluate our approach by discussing the resulting implementations throughout an extensive application set and comparing them with example designs, provided by Altera. In addition, as we can derive multiple implementations for completely different target platforms from the same domain-specific language source code, we present a comparison of the achieved implementations in contrast to GPU implementations.
The Consultative Committee for Space Data systems (CCSDS) 121.0-B-2 lossless data compression standard defines a lossless adaptive source coding algorithm which is applicable to a wide range of imaging and nonimaging ...
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The Consultative Committee for Space Data systems (CCSDS) 121.0-B-2 lossless data compression standard defines a lossless adaptive source coding algorithm which is applicable to a wide range of imaging and nonimaging data. We introduce a field-programmable gate array (FPGA) implementation of CCSDS 121.0-B-2 as an intellectual property (IP) core with the following features: (a) it is enhanced with a two-dimensional (2-D) second-order predictor making it more suitable for image compression, (b) it is enhanced with near-lossless compression functionality, (c) its parallel, pipelined architecture provides high data-rate performance with a maximum achievable throughput of 205 Msamples/s (3.2 Gbps at 16 bit) when targeting the xilinx Virtex-5QV FPGA, and (d) it requires very low FPGA resources. When mission requirements impose lossless image compression, the CCSDS 121.0-B-2 IP core provides a very low implementation cost solution. According to European Space Agency PROBA-3 Bridging Phase, the CCSDS 121.0-B-2 IP core will be implemented in a Microsemi RTAx2000 FPGA, hosted in the data processing unit of the Coronagraph Control Box, of the Association of Spacecraft for Polarimetric and imaging Investigation of the Corona of the Sun Coronagraph System Payload. To the best of our knowledge, it is the fastest FPGA implementation of CCSDS 121.0-B-2 to date, also including a 2-D second-order predictor making it more suitable for image compression. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
The embedded and high-performance computing (HPC) sectors, that in the past were completely separated, are now somehow converging under the pressure of two driving forces: the release of less power consuming server pr...
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The embedded and high-performance computing (HPC) sectors, that in the past were completely separated, are now somehow converging under the pressure of two driving forces: the release of less power consuming server processors and the increased performance of the new low power systems-on-Chip (SoCs) developed to meet the requirements of the demanding mobile market. This convergence allows the porting to low power embedded architectures of applications that were originally confined to traditional HPC systems. In this paper, we present our experience of porting the Filtered Back-projection Algorithm to a low power, low cost system-on-chip, the NVIDIA Tegra K1, which is based on a quad core ARM CPU and on a NVIDIA Kepler GPU. This Filtered Back-projection Algorithm is heavily used in 3D Tomography reconstruction software. The porting has been done exploiting various programming languages (i.e. OpenMP, CUDA) and multiple versions of the application have been developed to exploit both the SoC CPU and GPU. The performances have been measured in terms of 2D slices (of a 3D volume) reconstructed per time unit and per energy unit. The results obtained with all the developed versions are reported and compared with those obtained on a typical x86 HPC node accelerated with a recent NVIDIA GPU. The best performances are achieved combining the OpenMP version and the CUDA version of the algorithm. In particular, we discovered that only three Jetson TK1 boards, equipped with Giga Ethernet interconnections, allow to reconstruct as many images per time unit as a traditional server, using one order of magnitude less energy. The results of this work can be applied for instance to the construction of an energy-efficient computing system of a portable tomographic apparatus.
This paper discusses how GigE Vision (R) video interfaces - the technology used to transfer data from a camera or image sensor to a mission computer or display - help designers reduce the cost and complexity of milita...
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ISBN:
(纸本)9781628415971
This paper discusses how GigE Vision (R) video interfaces - the technology used to transfer data from a camera or image sensor to a mission computer or display - help designers reduce the cost and complexity of military imagingsystems, while also improving usability and increasing intelligence for end-users. The paper begins with a detailed review of video connectivity approaches commonly used in military imagingsystems, followed by an overview on the GigE Vision standard. With this background, the design, cost, and performance benefits that can be achieved when employing GigE Vision-compliant video interfaces in a vetronics retrofit upgrade project are outlined.
Arabic optical character recognition (OCR) has been an active field of research for decades. Yet, it has limited application domains constrained to printed text. This is partly due to high time complexity of Arabic OC...
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Arabic optical character recognition (OCR) has been an active field of research for decades. Yet, it has limited application domains constrained to printed text. This is partly due to high time complexity of Arabic OCR algorithms. Accelerating these algorithms allows for applications in real-time assistive technologies, office automation, and portable devices. Modern programmable hardware devices like FPGAs have numerous logic resources that allow parallel implementations of many algorithms. In this paper, we investigate implementing the feature extraction and classification stages of handwritten Arabic words on FPGAs. We study the performance and cost of four commonly-used feature extraction techniques and neural network classifiers on images from the IFN/ENIT database of handwritten Arabic words. The most efficient feature extraction technique and the best neural network found are implemented. Multiple FPGA implementations with varying cost and performance are evaluated. An implementation that only consumes about one quarter of the FPGA resources is 20 times faster than the software implementation and is less accurate by only 2.8 %.
An automated alignment optical system will greatly simplify alignment tasks, increase the flexibility and utility of reconfigurable optical systems, and allow for the quick and efficient set up distributed optical sys...
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ISBN:
(纸本)9781628415971
An automated alignment optical system will greatly simplify alignment tasks, increase the flexibility and utility of reconfigurable optical systems, and allow for the quick and efficient set up distributed optical systems. In this work, we demonstrate automated alignment of a tilted and decentered focal lens using only focal plane imaging by exploiting the aberration effects caused by the misalignment. A Gaussian beam is passed through the lens with 4 degrees of freedom and onto a science camera. The deformation of the spot image is analyzed to determine the tilt and shift misalignments on the lens. Corrections based on these measurements are applied in closed loop to align the system. We discuss various techniques for mitigating measurement errors, characterizing the system and operating the control loop and present results from the experiment.
Pattern recognition problem is outlined in the context of textural and spectral analysis of remote sensing imagery processing. Main attention is paid to Bayesian classifier that can be used to realize the processing p...
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Pattern recognition problem is outlined in the context of textural and spectral analysis of remote sensing imagery processing. Main attention is paid to Bayesian classifier that can be used to realize the processing procedures based on parallel machine-learning algorithms and high-productive computers. We consider the maximum of the posterior probability principle and the formalism of Markov random fields for the neighborhood description of the pixels for the related classes of objects with the emphasis on forests of different species and ages. The energy category of the selected classes serves to account for the likelihood measure between the registered radiances and the theoretical distribution functions approximating remotely sensed data. Optimization procedures are undertaken to solve the pattern recognition problem of the texture description for the forest classes together with finding thin nuances of their spectral distribution in the feature space. As a result, possible redundancy of the channels for imaging spectrometer due to their correlations is removed. Difficulties are revealed due to different sampling data while separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images. Such separation of pixels for the forest classes is maintained to enhance the recognition accuracy, but learning ensembles of data need to be agreed for these categories of pixels. We present some results of the Bayesian classifier applicability for recognizing airborne hyperspectral images using the relevant improvements in separating such pixels for the forest classes on a test area of the 4 x 10 km size encompassed by 13 airborne tracks, each forming the images by 500 pixels across the track and from 10,000 to 14,000 pixels along the track. The spatial resolution of each image is near to 1 m from the altitude near to 2 km above the ground level. The results of the hyperspectral imagery pr
This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray le...
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This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and timepixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical inve
The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. T...
ISBN:
(数字)9783319193687;9783319193694
ISBN:
(纸本)9783319193687;9783319193694
The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised full papers presented in the volumes, were carefully reviewed and selected from 322 submissions. These proceedings present both traditional artificial intelligence methods and soft computing techniques. The goal is to bring together scientists representing both areas of research. The first volume covers topics as follows neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification and estimation, computer vision, image and speech analysis and the workshop: large-scale visual recognition and machine learning. The second volume has the focus on the following subjects: data mining, bioinformatics, biometrics and medical applications, concurrent and parallelprocessing, agent systems, robotics and control, artificial intelligence in modeling and simulation and various problems of artificial intelligence.
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