A coarse-grain reconfigurable architecture for multimedia applications supporting subword and floating-point calculations

Signal processors exploiting ASIC acceleration suffer from sky-rocketing manufacturing costs and long design cycles. FPGA-based systems provide a programmable alternative for exploiting computation parallelism, but the flexibility they provide is not as high as in processor-oriented architectures: HDL or C-to-HDL flows still require specific expertise and a hardware knowledge background. On the other hand, the large size of the configuration bitstream and the inherent complexity of FPGA devices make their dynamic reconfiguration not a very viable approach. Coarse-grained reconfigurable architectures (CGRAs) are an appealing solution but they pose implementation problems and tend to be application specific. This paper presents a scalable CGRA which eases the implementation of algorithms on field programmable gate array (FPGA) platforms. This design option is based on two levels of programmability: it takes advantage of performance and reliability provided by state-of-the-art FPGA technology, and at the same time it provides the user with flexibility, performance and ease of reconfiguration typical of standard CGRAs. The basic cell template provides advanced features such as sub-word SIMD integer and floating-point computation capabilities, as well as saturating arithmetic. Multiple reconfiguration contexts and partial run-time reconfiguration capabilities are provided, tackling this way the problem of high reconfiguration overhead typical of FPGAs. Selected instances of the proposed architecture have been implemented on an Altera Stratix II EP2S180 FPGA. On this system, we mapped some common DSP, image processing, 3D graphics and audio compression algorithms in order to validate our approach and to demonstrate its effectiveness by benchmarking the benefits achieved.     

A bounded distance metric for comparing tree structure

Comparing tree-structured data for structural similarity is a recurring theme and one on which much effort has been spent. Most approaches so far are grounded, implicitly or explicitly, in algorithmic information theory, being approximations to an information distance derived from Kolmogorov complexity. In this paper we propose a novel complexity metric, also grounded in information theory, but calculated via Shannon's entropy equations. This is used to formulate a directly and efficiently computable metric for the structural difference between unordered trees. The paper explains the derivation of the metric in terms of information theory, and proves the essential property that it is a distance metric. The property of boundedness means that the metric can be used in contexts such as clustering, where second-order comparisons are required. The distance metric property means that the metric can be used in the context of similarity search and metric spaces in general, allowing trees to be indexed and stored within this domain. We are not aware of any other tree similarity metric with these properties.     

The MORPHEUS Heterogeneous Dynamically Reconfigurable Platform

Reconfigurable computing offers a wide range of low cost and efficient solutions for embedded systems. The proper choice of the reconfigurable device, the granularity of its processing elements and its memory architecture highly depend on the type of application and their data flow. Existing solutions either offer fine grain FPGAs, which rely on a hardware synthesis flow and offer the maximum degree of flexibility, or coarser grain solutions, which are usually more suitable for a particular type of data flow and applications. In this paper, we present the MORPHEUS architecture, a versatile reconfigurable heterogeneous System-on-Chip targeting streaming applications. The presented architecture exploits different reconfigurable technologies at several computation granularities that efficiently address the different applications needs. In order to efficiently exploit the presented architecture, we implemented a complete software solution to map C applications to the reconfigurable architecture. In this paper, we describe the complete toolset and provide concrete use cases of the architecture.     

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

Using frameworks to develop a distributed conferencing system: an experience report

Application frameworks are a powerful means to reduce software development costs while improving quality. However, at the same time they are difficult to select and understand, as well as hard to learn, use, and debug effectively and efficiently. In this paper we report the story of eConference, a distributed conferencing system that was developed as part of a broader research effort. Here we discuss the lessons learned from the evolution of our conferencing tool over four generations, which have been necessary to find good frameworks and build a flexible distributed tool. Copyright © 2009 John Wiley & Sons, Ltd.     

Space and time localization for the estimation of distributed parameters in a finite element model

In this paper we study the problem of estimating the possibly non-homogeneous material coefficients inside a physical system, from transient excitations and measurements made in a few points on the boundary. We assume there is available an adequate Finite Element (FEM) model of the system, whose distributed physical parameters must be estimated from the experimental data.We propose a space–time localization approach that gives a better conditioned estimation problem, without the need of an expensive regularization. Some experimental results obtained on an elastic system with random coefficients are given.     

The subsequence composition of a string

Words that appear as constrained subsequences in a text-string are considered as possible indicators of the host string structure, hence also as a possible means of sequence comparison and classification. The constraint consists of imposing a bound on the number ω$\omega$ of positions in the text that may intervene between any two consecutive characters of a subsequence. A subset of such ω$\omega$-sequences is then characterized that consists, in intuitive terms, of sequences that could not be enriched with more characters without losing some occurrence in the text. A compact spatial representation is then proposed for these representative sequences, within which a number of parameters can be defined and measured. In the final part of the paper, such parameters are empirically analyzed on a small collection of text-strings endowed with various degrees of structure.     

A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data

The selection of the optimal band combination for the estimation of specific crop variables is a key aspect in order to obtain reliable estimation of in-field variability from multi- and hyperspectral remote-sensing data. The selection of the bands is strongly influenced by the phenological stage of the crop at the acquisition time. In this work, the influence of the growing stage on the combination of spectral bands related to grain nitrogen (N) uptake in wheat was evaluated using multispectral (Satellite Pour l’Observation de la Terre – SPOT) and hyperspectral (Compact High Resolution Imaging Spectrometer – CHRIS-PROBA) satellite images at different growth stages over two wheat growth seasons in central Italy. In order to identify the more appropriate covariates (spectral bands) for each phenological stage, stepwise regression with backward selection was combined with stepwise variance inflation factors (VIFs) analysis and linear mixed effect model (LMEM). The results obtained in this study suggest that the spectral region most related to N uptake varies over the growing season of the wheat crop. For SPOT data, near-infrared (NIR) region was selected at all the phenological stages in both growing seasons, except for the latest stage, with low chlorophyll content due to the onset of senescence, in which the red band was selected. At stem elongation, the shortwave infrared (SWIR) band of SPOT data was also selected. At this stage, the best N estimation accuracy was obtained using an LMEM (root mean square error, RMSE = 0.012 t ha^?1). The inclusion of a spatial component in the estimation model by means of LMEMs provided a more accurate estimation than ordinary least square (OLS) models at all growth stages. The test carried out with CHRIS-PROBA data at the fourth stage confirmed the importance of NIR and in particular of the red-edge region for N uptake prediction. A novel methodology is proposed, which involves two crucial aspects in the context of the use of remote-sensing data in precision agriculture: i) the standardization of the spatial resolution for in-field and satellite data by a geostatistical data technique (data fusion); and ii) the selection of the most appropriate spectral bands for each phenological stage, taking into account both correlation with the target variable and collinearity.     

Many-Objective Virtual Machine Placement

The process of selecting which virtual machines (VMs) should be executed at each physical machine (PM) of a virtualized infrastructure is commonly known as Virtual Machine Placement (VMP). This work presents a general many-objective optimization framework that is able to consider as many objective functions as needed when solving a VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, a formulation of a many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated MaVMP problem, an interactive memetic algorithm is proposed. Experimental results prove the correctness of the proposed algorithm, its effectiveness converging to a manageable number of solutions and its capabilities to solve problem instances with large numbers of PMs and VMs.