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 Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time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.

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 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.

Parallel hyperspectral unmixing method on GPU

Many Hyperspectral imagery applications require a response in real time or near-real time. To meet this requirement this paper proposes a parallel unmixing method developed for graphics processing units (GPU). This method is based on the vertex component analysis (VCA), which is a geometrical based method highly parallelizable. VCA is a very fast and accurate method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Experimental results obtained for simulated and real hyperspectral datasets reveal considerable acceleration factors, up to 24 times.

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.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Anthropocentric-based robotic and autonomous systems: assessment for new organisational options

Text based on the paper presented at the Conference "Autonomous systems: inter-relations of technical and societal issues" held at Monte de Caparica (Portugal), Universidade Nova de Lisboa, November, 5th and 6th 2009 and organized by IET-Research Centre on Enterprise and Work Innovation

A study on parallel versus sequential relational fuzzy clustering methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

Bridging environmental conflicts with social metabolism : forestry expansion and socioeconomic change

Dissertação apresentada para obtenção do Grau de Doutor em Ciências do Ambiente, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Endemic Pemphigus foliaceus ("Fogo selvagem"): a series from the Northeastern region of the State of São Paulo, Brazil, 1973-1998

Endemic Pemphigus Foliaceus (EPF) is a bullous autoimmune skin disease whose incidence used to be high in the State of São Paulo (SP), Brazil, during the forties, but has declined thereafter. OBJECTIVES: to report a series of EPF patients from the northeastern region of SP. METHODS: a retrospective study concerning demographic and epidemiological data of patients seen from 1973 to 1998 was conducted at the University Hospital, Faculty of Medicine of Ribeirão Preto, SP. RESULTS: bullous disease was diagnosed in 340 patients, 245 with EPF (72.1%), 9.4 cases per year, 60.4% females, and 70.2% white, 7 to 82 year-old (29.4% in their teens); 46.9% lived in the rural zone. Concerning profession, housewives predominated among women (67.6%) and agricultural workers among men (40.2%). The time of disease was less than 1 year in 62.0% of cases, followed by 1 and 5 years (27%), and more than 5 years for the remaining patients (11%). 36.7% of patients were referred by the Direção Regional de Saúde (DIR) XVIII of Ribeirão Preto, with the largest number of cases being from Ribeirão Preto and Batatais: 33.3% and 23.3%, respectively; 22% from DIR XIII (Franca); 13.5% from DIR VII (Araraquara); 2.9% from DIR IX (Barretos); 4.1% from other DIRs of SP, and 20.8% from other States (16.7% from Minas Gerais). Thirteen (5.3%) patients reported occurrence of the disease in some relative, and 4 (1.6%) in neighbors. CONCLUSIONS: the present data characterize the northeastern region of the state of São Paulo as a remaining endemic focus of EPF.

Accidental tetanus: prognosis evaluation in a historical series at a hospital in Salvador, Bahia, Brazil

A total of 868 (84.89%) patients diagnosed with tetanus were studied, out of the 1,024 tetanus patients hospitalized at Couto Maia Hospital (Salvador, Bahia, Brazil), during the period between 1986 and 1997. Of this group (n = 868), 63.5% (n = 551) were discharged, 35.4% (n = 307) died, and 1.1% (n = 10) were transferred. The average age of the deceased patients (38.73 ± 23.31 years) was significantly greater (p < 0.0001) than the age of those who survived (29.21 ± 20.05 years). Analyzing the variables of the logistic regression model with statistic significance (p £ 0.25) for univariate analysis, we observed a greater association of risk for worst prognosis (death) in patients aged ³ 51 years; time of illness < 48 hours; time of incubation < 168 hours; neck rigidity; spasms; opisthotonos; body temperature ³ 37.7 ºC; heart beat ³ 111 beats/minute; sympathetic hyperactivity and association with pneumonia. Among the group of those who survived, patients with 1 to 5 of those variables (n = 398; 76.8%) were more frequent, while among patients of the group of the deceased, 70.3% (n = 206) presented 6 to 10 of those variables, with a highly significant difference (p < 10-8). In conclusion, the indicators described provide early information that may guide the prognosis and medical and nurse care.

Methodologies for the WCET Analysis of Parallel Applications on Many-core Architectures

Euromicro Conference on Digital System Design (DSD 2015), Funchal, Portugal.

Online Admission of Parallel Real-Time Tasks

6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.

A Multi-DAG Model for Real-Time Parallel Applications with Conditional Execution

The 30th ACM/SIGAPP Symposium On Applied Computing (SAC 2015). 13 to 17, Apr, 2015, Embedded Systems. Salamanca, Spain.