Data Compression, Communications and Processing 

Abstract: At present, data centers consume a considerable percentage of the worldwide produced electrical energy, equivalent to the electrical production of 26 nuclear power plants, and such energy demand is growing at fast pace due to the ever increasing data volumes to be processed, stored and accessed every day in the modern grid and cloud infrastructures. Such energy consumption growth scenario is clearly not sustainable and it is necessary to limit the data center power budget by controlling the absorbed energy while keeping the desired level of service. In this paper, we describe Energy Farm, a data center energy manager that exploits load fluctuations to save as much energy as possible while satisfying quality of service requirements. Energy Farm achieves energy savings by aggregating traffic during low load periods and temporary turning off a subset of computing resources. Energy Farm respects the logical and physical dependencies of the interconnected devices in the data center and performs automatic shut down even in emergency cases such as temperature peaks and power leakages. Results show that high resource utilization efficiency is possible in data center infrastructures and that huge savings in terms of energy (MWh), emissions (tons of CO2) and costs (k€) are achievable.

Abstract: A new standard for image coding is being developed by the MHDC working group of the CCSDS, targeting onboard compression of multi- and hyper-spectral imagery captured by aircraft and satellites. The proposed standard is based on the "Fast Lossless" adaptive linear predictive compressor, and is adapted to better overcome issues of on-board scenarios. In this paper, we present a review of the state of the art in this field, and provide an experimental comparison of the coding performance of the emerging standard in relation to other state-of-the-art coding techniques. Our own independent implementation of the MHDC Recommended Standard, as well as of some of the other techniques, has been used to provide extensive results over the vast corpus of test images from the CCSDS-MHDC.

Abstract: Grammar-based compression is a well-studied technique for constructing a small context-free grammar (CFG) uniquely deriving a given text. In this paper, we present an online algorithm for lightweight grammar-based compression. Our algorithm is based on the LCA algorithm [Sakamoto et al. 2004]which guarantees nearly optimum compression ratio and space. LCA, however, is an offline algorithm and requires external space to save space consumption. Therefore, we present its online version which inherits most characteristics of the original LCA. Our algorithm guarantees $O(\log^2 n)$-approximation ratio for an optimum grammar size, and all work is carried out on a main memory space which is bounded by the output size. In addition, we propose more practical encoding based on parentheses representation of a binary tree. Experimental results for repetitive texts demonstrate that our algorithm achieves effective compression compared to other practical compressors and the space consumption of our algorithm is smaller than the input text size.

Abstract: In Hyperspectral imaging the sensors measure the light refelcted by the earth surface in differents wavelenghts, usually the number of measures is between one and several hundreds per pixel. This generates huge data ammounts that must be transmitted to the earth and for subsequent processing. The real-time requirements of some applications make that the bandwidth required between the sensor and the earth station is very large. The Compressive Sensing (CS) framework tries to solve this problem. Althougth the hyperspectral images have thousands of bands usually most of the bands are highly correlated. The CS exploit this feature of the hyperspectral images and allow to represent most of the information in few bands instead of hundreds. This compressed version of the data can be sent to a earth station that will recover the original image using the corresponding algorithm. In this paper we describe an Compressive Sensing algorithm called Hyperspectral Coded Aperture (HYCA) that was developed in previous works. This algorithm has a parameter that need to be optimized empirically in order to get the better results. In this work we present a novel way to reconstruct the compressed images under the HYCA framework in which we do not need to optimize any parameter due to all parameters can be estimated automatically. The results show that this new way to reconstruct the images without the parameter provides similar results with respect to the best parameter setting for the old algorithm. The proposed approach have been tested using synthetic data and also we have used the dataset obtained by the AVIRIS sensor of NJPL over the Cuprite mining district in Nevada.