Untitled

Funder

Organizational Unit

Publications

The solid solutions (Per)(2)[PtxAu(1-x)(mnt)(2)]; alloying para-and diamagnetic anions in two-chain compounds

Publication . Matos, Manuel; Bonfait, Gregoire; Santos, Isabel C.; Afonso, Monica L.; Henriques, Rui T.; Almeida, Manuel

The alpha-(Per)(2)[M(mnt)(2)] compounds with M = Pt and Au are isostructural two-chain solids that in addition to partially oxidized conducting perylene chains also contain anionic chains that can be either paramagnetic in the case of M = Pt or diamagnetic for M = Au. The electrical transport and magnetic properties of the solid solutions (Per)(2)[Pt-x-Au(1-x)(mnt)(2)] were investigated. The incorporation of paramagnetic [Pt(mnt)(2)] impurities in the diamagnetic chains, and the effect of breaking the paramagnetic chains with diamagnetic centers for the low and high Pt range of concentrations were respectively probed. In the low Pt concentration range, there is a fast decrease of the metal-to-insulator transition from 12.4 K in the pure Au compound to 9.7 K for x = 0.1 comparable to the 8.1 K in the pure Pt compound. In the range x = 0.5-0.95, only beta-phase crystals could be obtained. The spin-Peierls transition of the pure Pt compound, simultaneous with metal-to-insulator (Peierls) transition is still present for 2% of diamagnetic impurities (x = 0.98) with transition temperature barely affected. Single crystal X-ray diffraction data obtained a high-quality structural refinement of the alpha- phase of the Au and Pt compounds. The beta-phase structure was found to be composed of ordered layers with segregated donors and anion stacks, which alternate with disordered layers. The semiconducting properties of the beta-phase are due to the disorder localization effects.

2017-06Journal article

Restricted access

GPU implementation of the simplex identification via split augmented Lagrangian

Publication . Sevilla, Jorge; Nascimento, Jose

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.

2015Conference object

Restricted access

Parallel hyperspectral coded aperture for compressive sensing on GPUs

Publication . Bernabé, Sérgio; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, José M.; Plaza, António; Silva, Vítor

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.

2016-02Journal article

Restricted access

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

Publication . Sevilla, Jorge; Martin, Gabriel; Nascimento, Jose

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.

2016-05Journal article

Restricted access

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

Publication . Bernabé, Sergio; Martin, Gabriel; Nascimento, Jose; Bioucas-Dias, José M.; Plaza, Antonio; Botella, Guillermo; Prieto-Matias, Manuel

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.

2015Conference object

Restricted access