Parallel hyperspectral unmixing on GPUs

This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. Both techniques are highly parallelizable, which significantly reduces the computing time. A design for the commodity graphics processing units of the two methods is presented and evaluated. Experimental results obtained for simulated and real hyperspectral data sets reveal speedups up to 100 times, which grants real-time response required by many remotely sensed hyperspectral applications.

Parallel hyperspectral compressive sensing method on GPU

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Parallel hyperspectral coded aperture for compressive sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.

Utilização de GPGPUs para a identificação de objectos em imagens tomográficas

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Algorithmic skeleton framework for the orchestration of GPU computations

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Studies on new processing aids and other compounding ingredients in special purpose rubbers

In the first part of the study we probed the effectiveness of rice bran oil as a multipurpose compounding ingredient for nitrile (NBR) and chloroprene (CR) rubbers. This oil has already been successfully employed in the compounding of NR and SBR in this laboratory.In this context we thought it worthwhile to try this oil in the polar rubbers viz, NBR and CR also. The principle of like dissolves like as applicable to solvents is equally applicable while selecting a plasticiser, elastomer combination. Because of the compatibility considerations polar plasticisers are preferred for polar rubbers like NBR and CR. Although plasticisation is a physical phenomenon and no chemical reaction is involved, the chemical structure of plasticisers determines how much physical attraction there is between the rubber and the plasticiser. In this context it is interesting to note that the various fatty acids present in rice bran oil have a long paraffinic chain, characteristic of waxes, with an acid group at the end of the molecule. The paraffinic end of the molecule contributes lubricating effects and limits compatibility whereas the acid end group contributes some polarity and is also chemically reactive. Because of absorption of acid group on the surface of pigments, these acids will have active pigment wetting characteristics also. These factors justifies the role of rice bran oil as a co-activator and lubricating agent for NBR and CR. In fact in our study we successfully replaced stearic acid as co-activator and aromatic oillDOP as processing aid for CR and NBR with rice bran oil.This part of the study has got special significance in the fact that rubber industry now heavily depends on petroleum industry for process oils. The conventional process oils like aromatic, naphthenic and paraffinic oils are increasingly becoming costlier, as its resources in nature are fast depleting. Moreover aromatic process oils are reported to be carcinogenic because of the presence of higher levels of polycyclic aromatic compounds in these oils.As a result of these factors, a great amount research is going on world over for newer processing aids which are cost effective, nontoxic and performanance wise at par with the conventional ones used in the rubber industry. Trials with vegetable oils in this direction is worth trying.Antioxidants are usually added to the rubber compound to minimise ageing effects from heat, light, oxygen etc. As rice bran oil contains significant amount of tocopherols and oryzanol which are natural antioxidants, we replaced a phenolic antioxidant like styrenated phenol (SP) from the compound recipe of both the rubbers with RBO and ascertained whether this oil could function in the role of antioxidant as well.Preparation and use of epoxidised rice bran oil as plasticiser has already been reported.The crude rice bran oil having an iodine value of 92 was epoxidised in this laboratory using peracetic acid in presence of sulphuric acid as catalyst. The epoxy content of the epoxidised oil was determined volumetrically by treating a known weight of the oil with excess HCI and back titrating the residual HCI with standard alkali solution. The epoxidised oil having an epoxy content of 3.4% was tried in the compounding of NBR and CR as processing aids. And results of these investigations are also included in this chapter. In the second part of the study we tried how RBO/ERBO could perform when used as a processing aid in place of aromatic oil in the compounding of black filled NRCR blends. Elastomers cannot have all the properties required for a particular applications, so it is common practice in rubber industry to blend two elastomers to have desired property for the resulting blend.In this RBO/ERBO was tried as a processing aid for plasticisation, dispersion of fillers, and vulcanisation of black filled NR-CR blends.Aromatic oil was used as a control. The results of our study indicate that these oils could function as a processing aid and when added together with carbon black function as a cure accelerator also.PVC is compatible with nitrile rubber in all proportions, provided NBR has an acrylonitrile content of 25 to 40%. Lower or higher ACN content in NBR makes it incompatible with PVC.PVC is usually blended with NBR at high temperatures. In order to reduce torque during mixing, additional amounts of plasticisers like DOP are added. The plasticiser should be compatible both with PVC and NBR so as to get a homogeneous blend. Epoxidised soyaben oil is reported to have been used in the compounding of PVC as it can perfonn both as an efficient plasticiser and heat stabilizer.At present DOP constitute the largest consumed plasticiser in the PVC compounding. The migration of this plasticiser from food packaging materials made of PVC poses great health hazards as this is harmful to human body. In such a scenario we also thought it worthwhile to see whether DOP could be replaced by rice bran oil in the compounding of NBR-PVC blends Different blends of NBR-PVC were prepared with RBO and were vulcanized using sulphur and conventional accelerators. The various physical and mechanical properties of the vulcanisates were evaluated and compared with those prepared with DOP as the control plasticiser. Epoxidised rice bran oil was also tried as plasticiser for the preparation of NBR-PVC blends. A comparison of the processability and cure characteristics of the different blends prepared with DOP and ERBO showed that ERBO based blends have better processability and lower cure time values. However the elastographic maximum torque values are higher for the DOP based blends. Almost all of the physical properties evaluated are found to be slightly better for the DOP based blends over the ERBO based ones. However a notable feature of the ERBO based blends is the better percentage retention of elongation at break values after ageing over the DOP based blends. The results of these studies using rice bran oil and its epoxidised variety indicated that they could be used as efficient plasticisers in place of DOP and justifies their role as novel, nontoxic, and cheap plasticisers for NBR-PVC blends.

Studies on the treatment of effluents from natural rubber processing units

The present study was undertaken to evaluate the effectiveness of a few physico-chemical and biological methods for the treatment of effluents from natural rubber processing units. The overall objective of this study is to evaluate the effectiveness of certain physico-chemical and biological methods for the treatment of effluents from natural rubber processing units. survey of the chemical characteristics of the effluents discharged from rubber processing units showed that the effluents from latex concentration units were the most polluting

Implementação do algoritmo de treinamento do classificador Floresta de Caminhos Ótimos em GPU

Pós-graduação em Ciência da Computação - IBILCE

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

Methodology based on registration techniques for representing subjects and their deformations acquired from general purpose 3D sensors

In this thesis a methodology for representing 3D subjects and their deformations in adverse situations is studied. The study is focused in providing methods based on registration techniques to improve the data in situations where the sensor is working in the limit of its sensitivity. In order to do this, it is proposed two methods to overcome the problems which can difficult the process in these conditions. First a rigid registration based on model registration is presented, where the model of 3D planar markers is used. This model is estimated using a proposed method which improves its quality by taking into account prior knowledge of the marker. To study the deformations, it is proposed a framework to combine multiple spaces in a non-rigid registration technique. This proposal improves the quality of the alignment with a more robust matching process that makes use of all available input data. Moreover, this framework allows the registration of multiple spaces simultaneously providing a more general technique. Concretely, it is instantiated using colour and location in the matching process for 3D location registration.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

General-purpose autonomic computing

The success of mainstream computing is largely due to the widespread availability of general-purpose architectures and of generic approaches that can be used to solve real-world problems cost-effectively and across a broad range of application domains. In this chapter, we propose that a similar generic framework is used to make the development of autonomic solutions cost effective, and to establish autonomic computing as a major approach to managing the complexity of today’s large-scale systems and systems of systems. To demonstrate the feasibility of general-purpose autonomic computing, we introduce a generic autonomic computing framework comprising a policy-based autonomic architecture and a novel four-step method for the effective development of self-managing systems. A prototype implementation of the reconfigurable policy engine at the core of our architecture is then used to develop autonomic solutions for case studies from several application domains. Looking into the future, we describe a methodology for the engineering of self-managing systems that extends and generalises our autonomic computing framework further.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.