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.

Gammaretroviral and lentiviral vectors for gene therapy: stability and inactivation mechanisms

Dissertation presented to obtain a Ph.D degree in Engineering and Technology Sciences, Gene Therapy at the Instituto de Tecnologia Quimica e Biológica, Universidade Nova de Lisboa

Domestic vectors of Chagas' disease in three rural communities of Nicaragua

A triatomine survey was conducted in three rural settlements of Nicaragua (Santa Rosa, Quebrada Honda and Poneloya) where Chagas' disease is endemic, to determine rates of house infestation, evaluate the housing condition and to asess the performance of the María sensor box in detection of domestic vectors. A total of 184 households were selected and vectors were sought by the methods of timed manual capture and by sensor boxes. The sole vectors species found in this study was Triatoma dimidiata. Of the examined bugs 50, 60 and 33%, in the respective communities, were infected with T. cruzi. The rates of house infestation as determined by manual capture and sensor boxes were respectively, 48.3% and 54.2% in Santa Rosa, 29.8% and 51.2% in Quebrada Honda and in Poneloya 3.8 and 5.9% with significant difference between the methods in Quebrada Honda. When compared with the manual capture, the Maria sensor box detected vectors in 71.4% of positive houses in two of the communities but also was able to detect bugs in 39.3% and 41.1% of houses where manual capture had been negative. Housing condition was evaluated according to three structural parameters, in this way, in the first community 79.2% of houses were classified as bad, 20.8% as regular; in the second one 42.5% were bad and 57.5% regular, whereas in the third 62.5% of the houses were regular. Rates of infestation did not differ greatly between the different housing conditions. Our results show that the sensor box is as efficient as manual capture and could be implemented in our country.

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.

POSSIBLE ROLE OF LUTZOMYIA MARANONENSIS AND LUTZOMYIA ROBUSTA (DIPTERA: PSYCHODIDAE) AS VECTORS OF HUMAN BARTONELLOSIS IN THREE PROVINCES OF REGION NOR ORIENTAL DEL MARAÑON, PERU

Human bartonellosis is found predominantly in Perú2, 6, 8, 12, 15, as well as in Ecuador3, 7, 10 and Colombia13, 15. In Peru, the disease is restricted to the valleys of the western-side and a few inter-andean and eastern-slopes of the andean valleys6, 15, 18 at altitudes between 1000 and 3200 masl. Most human cases are reported from the regions of Chavin, Nor Oriental del Marañon and Lima16. Lutzomyia verrucarum is presumed to be the only vector of human bartonellosis in the valleys of Peru1, 2, 8, 11, 17, 19/ Our research objetive was to detect the presence of Lu. verrucarum in various localities known to be endemic for human bartonellosis in three provinces of Region Nor Oriental del Marañon. Sandfly collections were made between 1987 and 1992 during four visits to bartonellosis-endemic provinces: San Ignacio (districts of San José de Lourdes: 1020-1260 m and La Coipa: 1200-1560 m), Jaén (districts of Santa Rosa: 1300-1680 m and Jaén: 1220-1680 m) and Utcubamba (districts of Lonya Grande: 1200 m and El Milagro: 1200-1540 m)

A study on parallel versus sequential relational fuzzy clustering methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Malaria vectors in the Brazilian Amazon: Anopheles of the subgenus Nyssorhynchus

Various species of Anopheles (Nyssorhynchus) were studied in the Amazon with the objective of determining their importance as malaria vectors. Of the 33 known Anopheles species occurring in the Amazon, only 9 were found to be infected with Plasmodium. The different species of this subgenus varied both in diversity and density in the collection areas. The populations showed a tendency towards lower density and diversity in virgin forest than in areas modified by human intervention. The principal vector, An. darlingi, is anthropophilic with a continuous activity cycle lasting the entire night but peaking at sunset and sunrise. These species (Nyssorhynchus) are peridomiciliary, entering houses to feed on blood and immediately leaving to settle on nearby vegetation. Anopheles nuneztovari proved to be zoophilic, crepuscular and peridomiciliary. These habits may change depending on a series of external factors, especially those related to human activity. There is a possibility that sibling species exist in the study area and they are being studied with reference to An. darlingi, An. albitarsis and An. nuneztovari. The present results do not suggest the existence of subpopulations of An. darlingi in the Brazilian Amazon.

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.

Allocation of Parallel Real-Time Tasks in Distributed Multi-core Architectures supported by an FTT-SE Network

Distributed real-time systems such as automotive applications are becoming larger and more complex, thus, requiring the use of more powerful hardware and software architectures. Furthermore, those distributed applications commonly have stringent real-time constraints. This implies that such applications would gain in flexibility if they were parallelized and distributed over the system. In this paper, we consider the problem of allocating fixed-priority fork-join Parallel/Distributed real-time tasks onto distributed multi-core nodes connected through a Flexible Time Triggered Switched Ethernet network. We analyze the system requirements and present a set of formulations based on a constraint programming approach. Constraint programming allows us to express the relations between variables in the form of constraints. Our approach is guaranteed to find a feasible solution, if one exists, in contrast to other approaches based on heuristics. Furthermore, approaches based on constraint programming have shown to obtain solutions for these type of formulations in reasonable time.