Analysis of frame-compatible subsampling structures for efficient 3DTV broadcast

The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences.

Data Processing Modeling in Decision Support Systems.

Due to the advancement of both, information technology in general, and databases in particular; data storage devices are becoming cheaper and data processing speed is increasing. As result of this, organizations tend to store large volumes of data holding great potential information. Decision Support Systems, DSS try to use the stored data to obtain valuable information for organizations. In this paper, we use both data models and use cases to represent the functionality of data processing in DSS following Software Engineering processes. We propose a methodology to develop DSS in the Analysis phase, respective of data processing modeling. We have used, as a starting point, a data model adapted to the semantics involved in multidimensional databases or data warehouses, DW. Also, we have taken an algorithm that provides us with all the possible ways to automatically cross check multidimensional model data. Using the aforementioned, we propose diagrams and descriptions of use cases, which can be considered as patterns representing the DSS functionality, in regard to DW data processing, DW on which DSS are based. We highlight the reusability and automation benefits that this can be achieved, and we think this study can serve as a guide in the development of DSS.

Global optimization of solar thermophotovoltaic systems

n this paper, we present a theoretical model based on the detailed balance theory of solar thermophotovoltaic systems comprising multijunction photovoltaic cells, a sunlight concentrator and spectrally selective surfaces. The full system has been defined by means of 2n + 8 variables (being n the number of sub-cells of the multijunction cell). These variables are as follows: the sunlight concentration factor, the absorber cut-off energy, the emitter-to-absorber area ratio, the emitter cut-off energy, the band-gap energy(ies) and voltage(s) of the sub-cells, the reflectivity of the cells' back-side reflector, the emitter-to-cell and cell-to-cell view factors and the emitter-to-cell area ratio. We have used this model for carrying out a multi-variable system optimization by means of a multidimensional direct-search algorithm. This analysis allows to find the set of system variables whose combined effects results in the maximum overall system efficiency. From this analysis, we have seen that multijunction cells are excellent candidates to enhance the system efficiency and the electrical power density. Particularly, multijunction cells report great benefits for systems with a notable presence of optical losses, which are unavoidable in practical systems. Also, we have seen that the use of spectrally selective absorbers, rather than black-body absorbers, allows to achieve higher system efficiencies for both lower concentration and lower emitter-to-absorber area ratio. Finally, we have seen that sun-to-electricity conversion efficiencies above 30% and electrical power densities above 50 W/cm2 are achievable for this kind of systems.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.