INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Characterization of the Clear-PEM breast imaging scanner performance

Abstract: We present results on the characterization of the Clear-PEM breast imaging scanner Clear-PEM is a dual-head Positron Emission Mammography scanner using APD-based detector modules that are capable of measuring depth-of-interaction (DOI) with a resolution of 2 mm in LYSO:Ce crystals The full system comprises 192 detector modules in a total of 6144 LYSO:Ce crystals and 384 32-pixel APD arrays readout by ASICs with 192 input channels, which represents an unprecedented level of integration in APD-based PET systems The system includes Frontend and Data Acquisition electronics and a robotic gantry for detector placement and rotation The software implements calibration (energy, time and DOI), normalization and image reconstruction algorithms In this work, the scanner main technical characteristics, calibration strategies and the spectrometric performance in a clinical environment are presented Images obtained with point sources and extended uniform sources are also presented The first commissioning results show 997% active channels After calibration, the dispersion of the channels absolute gain is 153%, which demonstrate that despite the large number of channels the system is rather uniform The mean energy resolution at 511 keV is 159% for all channels, and the mean DOI constant is 59%/mm, which is consistent with a 2 mm DOI resolution, or better The coincidence time resolution at 511 keV, for a energy window between 400 and 600 keV, is 52 ns FWHM The image resolution measured with point sources was found to be of the order of 13 mm FWHM The DOI capability was found to have a strong impact on the image sharpness Images of extended uniform 68Ge sources, corrected for sensitivity and for the artifacts due detector dead spaces, have good uniformity First clinical breast images are presented

SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling

Abstract: This paper describes the data collection, annotation and sharing activities carried out within the FP7 EU-funded SAVAS project. The project aims to collect, share and reuse audiovisual language resources from broadcasters and subtitling companies to develop large vocabulary continuous speech recognisers in specific domains and new languages, with the purpose of solving the automated subtitling needs of the media industry.

Development and evaluation of scalable video motion estimators on GPU

Abstract: This work proposes a scalable parallelization approach for H.264/AVC motion estimation on multi-cores, and its efficient implementation on the most recent Graphical Processing Units (GPUs). Very efficient motion estimators are achieved by applying efficient data reusing techniques and exploiting the computational power of the most recent GPUs. The proposed motion estimators have been programmed on the GPU with the Tesla architecture and CUDA. Experimental results show that the proposed approach outperforms for more than 3 times motion estimators presented in the most recent publications. Moreover, real time motion estimation is achieved even for 720×576 resolution and 25 frames per second. The scalability of the solution is shown by implementing the motion estimators on two GPUs with the same architecture but different number of cores. Therefore, the proposed approach is useful for the more powerful future GPUs.

Duplicate detection through structure optimization

Abstract: Detecting and eliminating duplicates in databases is a task of critical importance in many applications. Although solutions for traditional models, such as relational data, have been widely studied, recently there has been some focus on solutions for more complex hierarchical structures as, for instance, XML data. Such data presents many different challenges, among which is the issue of how to exploit the schema structure to determine if two objects are duplicates. In this paper, we argue that structure can indeed have a significant impact on the process of duplicate detection. We propose a novel method that automatically restructures database objects in order to take full advantage of the relations between its attributes. This new structure reflects the relative importance of the attributes in the database and avoids the need to perform a manual selection. To test our approach we applied it to an existing duplicate detection system. Experiments performed on several datasets show that, using the new learned structure, we consistently outperform both the results obtained with the original database structure and those obtained by letting a knowledgeable user manually choose the attributes to compare.

A Single-Switch DC/DC Buck-Boost Converter with Extended Output Voltage

Abstract: DC-DC Buck-Boost power converters are extensively used in several applications, from renewable energy sources to most industrial equipment. Nevertheless, to achieve proper integration of certain devices it is often necessary to provide converters with extended output voltage. This paper presents a new DC-DC power converter topology with Buck- Boost operation and extended output voltage using a single power semiconductor. Additionally, this converter operates with reduced voltage stress in the power semiconductor. The performance of this power converter will be proven trough simulations and experimental tests.