Blue-enhanced thin-film photodiode for dual-screen x-ray imaging

This article reports on a-Si:H-based low-leakage blue-enhanced photodiodes for dual-screen x-ray imaging detectors. Doped nanocrystalline silicon was incorporated in both the n- and p-type regions to reduce absorption losses for light incoming from the top and bottom screens. The photodiode exhibits a dark current density of 900 pA/cm(2) and an external quantum efficiency up to 90% at a reverse bias of 5 V. In the case of illumination through the tailored p-layer, the quantum efficiency of 60% at a 400 nm wavelength is almost double that for the conventional a-Si:H n-i-p photodiode.

Imposto pessoal sobre o rendimento: compatibilidade com um modelo dual de tributação

De entre os impostos que integram o nosso sistema fiscal, o imposto sobre o rendimento das pessoas singulares, ocupa um lugar de destaque na arrecadação de receitas. A sua im-portância coloca este imposto sobre pressão, pondo em confronto a tributação dos rendi-mentos de capitais e a tributação dos rendimentos do trabalho. O modelo de base compreensiva em que assenta o imposto pessoal está semi dualizado, dado tributar de forma diferente os rendimentos com origem em investimentos financeiros, subtraindo-os ao englobamento com os restantes rendimentos. Com a presente dissertação, pretende-se averiguar se o imposto pessoal, face ao recorte constitucional, pode adoptar um modelo de base semi-dual. Esta configuração permitiria simplificar o imposto, assumir duas bases e coloca-lo em linha com os modelos de tributação pessoal adoptados em alguns países europeus. O estudo realizado permitiu concluir que é possível a adopção de um modelo de base semi-dual, desde que se mantenha, por opção do contribuinte, o regime do englobamento com os restantes rendimentos. A dúvida que manifestamos relaciona-se com a oportuni-dade da concretização da reforma. O momento delicado de finanças públicas que o nosso país atravessa, traz tarefas acrescidas aos políticos, fruto dos compromissos internacionais assumidos, o que pode obstar ao agendamento da reforma do imposto pessoal que muitos reclamam. Daí que o caminho a seguir seria o do aperfeiçoamento do actual modelo.

Sub-micron structuring of silicon using femtosecond laser interferometry

We report the fabrication of planar sub-micron gratings in silicon with a period of 720 nm using a modified Michelson interferometer and femtosecond laser radiation. The gratings consist of alternated stripes of laser ablated and unmodified material. Ablated stripes are bordered by parallel ridges which protrude above the unmodified material. In the regions where ridges are formed, the laser radiation intensity is not sufficient to cause ablation. Nevertheless, melting and a significant temperature increase are expected, and ridges may be formed due to expansion of silicon during resolidification or silicon oxidation. These conclusions are consistent with the evolution of the stripes morphology as a function of the distance from the center of the grating. (C) 2013 Elsevier Ltd. All rights reserved.

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

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.

Laser surfasse patterning using a Michelson interferometer and a femtosecond laser radiation

We report on a simple method to obtain surface gratings using a Michelson interferometer and femtosecond laser radiation. In the optical setup used, two parallel laser beams are generated using a beam splitter and then focused using the same focusing lens. An interference pattern is created in the focal plane of the focusing lens, which can be used to pattern the surface of materials. The main advantage of this method is that the optical paths difference of the interfering beams is independent of the distance between the beams. As a result, the fringes period can be varied without a need for major realignment of the optical system and the time coincidence between the interfering beams can be easily monitored. The potential of the method was demonstrated by patterning surface gratings with different periods on titanium surfaces in air.

Sub-micron structuring of silicon using femtosecond laser interferometry

We report the fabrication of planar sub-micron gratings in silicon with a period of 720 nm using a modified Michelson interferometer and femtosecond laser radiation. The gratings consist of alternated stripes of laser ablated and unmodified material. Ablated stripes are bordered by parallel ridges which protrude above the unmodified material. In the regions where ridges are formed, the laser radiation intensity is not sufficient to cause ablation. Nevertheless, melting and a significant temperature increase are expected, and ridges may be formed due to expansion of silicon during resolidification or silicon oxidation. These conclusions are consistent with the evolution of the stripes morphology as a function of the distance from the center of the grating.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

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.

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.