Parallel implementation of vertex component analysis for hyperspectral endmember extraction

International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July 2012, Munich, Germany

GPU implementation of a hyperspectral coded aperture algorithm for compressive sensing

This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.

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.

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.

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Surgical correction of scoliosis: Numerical analysis and optimization of the procedure

A previously developed model is used to numerically simulate real clinical cases of the surgical correction of scoliosis. This model consists of one-dimensional finite elements with spatial deformation in which (i) the column is represented by its axis; (ii) the vertebrae are assumed to be rigid; and (iii) the deformability of the column is concentrated in springs that connect the successive rigid elements. The metallic rods used for the surgical correction are modeled by beam elements with linear elastic behavior. To obtain the forces at the connections between the metallic rods and the vertebrae geometrically, non-linear finite element analyses are performed. The tightening sequence determines the magnitude of the forces applied to the patient column, and it is desirable to keep those forces as small as possible. In this study, a Genetic Algorithm optimization is applied to this model in order to determine the sequence that minimizes the corrective forces applied during the surgery. This amounts to find the optimal permutation of integers 1, ... , n, n being the number of vertebrae involved. As such, we are faced with a combinatorial optimization problem isomorph to the Traveling Salesman Problem. The fitness evaluation requires one computing intensive Finite Element Analysis per candidate solution and, thus, a parallel implementation of the Genetic Algorithm is developed.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. 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 proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Parallel sparse unmixing of hyperspectral data

In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. This method is based on the spectral unmixing by splitting and augmented Lagrangian (SUNSAL) that estimates the material's abundance fractions. The parallel method is performed in a pixel-by-pixel fashion and its implementation properly exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for simulated and real hyperspectral datasets reveal significant speedup factors, up to 1 64 times, with regards to optimized serial implementation.

A fast parallel hyperspectral coded aperture algorithm for compressive sensing using OpenCL

In this paper, we develop a fast implementation of an hyperspectral coded aperture (HYCA) algorithm on different platforms using OpenCL, an open standard for parallel programing on heterogeneous systems, which includes a wide variety of devices, from dense multicore systems from major manufactures such as Intel or ARM to new accelerators such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), the Intel Xeon Phi and other custom devices. Our proposed implementation of HYCA significantly reduces its computational cost. Our experiments have been conducted using simulated data and reveal considerable acceleration factors. This kind of implementations with the same descriptive language on different architectures are very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

Evaluation of the improvements on pharmacy technicians' work practices after implementation of operational procedures

Several antineoplasic drugs have been demonstrated to be carcinogenic or to have mutagenic and teratogenic effects. The greatest protection is achieved with the implementation of administrative and engineering controls and safety procedures. Objective: to evaluate the improvements on pharmacy technicians' work practices, after the implementation of operational procedures related to individual protection, biologic safety cabinet disinfection and cytotoxic drug preparation. Method: case-study in a hospital pharmacy undergoing a certification process. Six pharmacy technicians were observed during their daily activities. Characterization of the work practices was made using a checklist based on ISOPP and PIC guidelines. The variables studied concerning cleaning/disinfection procedures, personal protective equipment and procedures for preparing cytotoxic drugs. The same work practices were evaluated after four months of operational procedures implementation. Concordance between work practices and guidelines was considered to be a quality indicator (guidelines concordance practices number/total number of practices x 100). Results: improvements were observed after operational procedures implementation. An improvement of 6,25% in personal protective equipment practice was achieved by changing second pair of gloves every thirty minutes. The major progress, 10%, was obtained in disinfection procedure, where 80% of tasks are now realized according to guidelines.By now, we hot an improvement of only 1% at drug preparation procedure by placing one cytotoxic drug at a time inside the biological safety cabinet. Then, 85% of practices are according to guidelines. Conclusion: before operational procedures implementation 80,3% of practices were according to the guidelines, while now is 84,4%. This indicates that is necessary to review the procedures frequently in the benefit to reduce the risks associated with handling cytotoxic drugs and maintenance of drug specifications.

Implementation of proton therapy in the portuguese context of radiotherapy

Cancer is a national and international health care concern. It’s important to find strategies for early diagnosis as well as for the optimization of the various therapeutic options currently existing in Portugal. Cancer is the second leading cause of death in Portugal, the choice of this study, is due to the importance of radiotherapy approach in cancer treatment and because is the therapy used in 40% of oncology patients. Radiation therapy has evolve data technological level, that allows new treatment techniques that are more efficient and that also promotes greater professional satisfaction. The hadrons are charged particles, used in cancer therapy. These particles can bring a paradigm shift regarding the therapeutic approach in radiotherapy. The technique used is proton therapy, that reveal to be more accurate, efficacious and less toxic to surrounding tissue. Proton therapy may be a promising development in the field of oncology and how the treatment is given in radiotherapy. Although there is awareness of the benefits of proton therapy in oncology it’s also important to take in consideration the costs of these therapy, because they are considerably higher than conventional treatments of radiotherapy. Given the lack of a proton therapy service in Portugal, this study aims to be a documentary analysis of clinical records that will achieve the following objectives: to identify the number of cancer patients diagnosed in 2010 in Portugal and to calculate the estimated number of patients that could have been treated with proton therapy according to the Health Council of the Netherlands registration document.

A 802.11p prototype implementation

This paper presents an IEEE 802.11p full-stack prototype implementation to data exchange among vehicles and between vehicles and the roadway infrastructures. The prototype architecture is based on FPGAs for Intermediate Frequency (IF) and base band purposes, using 802.11a based transceivers for RF interfaces. Power amplifiers were also addressed, by using commercial and in-house solutions. This implementation aims to provide technical solutions for Intelligent Transportation Systems (ITS) field, namely for tolling and traffic management related services, in order to promote safety, mobility and driving comfort through the dynamic and real-time cooperation among vehicles and/or between vehicles and infrastructures. The performance of the proposed scheme is tested under realistic urban and suburban driving conditions. Preliminary results are promising, since they comply with most of the 802.11p standard requirements.

Implementation of thr INSPIRE Directive on road infrastructure in Portugal

Prémio - CEN/TC 287 AWARD FOR EXCELLENCE IN INSIRE 2012 Implementation of the INSPIRE Directive on Road Infrastructure in Portugal Inês Soares, aluna de Mestrado em engenharia civil do ISEL, Instituto Superior de Engenharia de Lisboa e Paulo Martins, e o seu orientador, receberam um prémio europeu, no dia 27 de junho, em Istambul na Turquia, numa conferência internacional organizada pela Comissão Europeia e pelo governo Turco.  A jovem portuguesa foi escolhida entre cerca de 20 candidatos de vários países. Paulo Matos Martins, professor no ISEL, além de mentor do trabalho premiado, foi orientador de mestrado da aluna e explica que se trata de um estudo sobre a aplicação da Diretiva comunitária INSPIRE à infraestrutura rodoviária nacional que contou com a estreita colaboração do InIR, Instituto da Infraestrutura Rodoviária através da coorientação da engenheira Adelaide Costa e colaboração técnica do engenheiro Rui Luso Soares.  O projeto-piloto correspondeu à criação de uma aplicação informática que permite aceder a informação geográfica harmonizada relativa à infraestrutura rodoviária nacional, de acordo com as disposições de execução INSPIRE, dando cumprimento aos requisitos impostos pela Diretiva às entidades responsáveis por este tipo de informação, entre as quais se incluem diversos organismos públicos (podendo no futuro vir a incluir as autarquias), permitindo aos decisores políticos e a todos os cidadãos o fácil acesso a informação de qualidade sobre as infraestruturas, o território e o ambiente.