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.

A New Efficient VLSI Architecture for Full Search Block Matching Motion Estimation

Abstract: A new efficient type I architecture for motion estimation in video sequences based on the Full-Search Block-Matching (FSBM) algorithm is proposed in this paper. This architecture presents minimum latency, maximum throughput and full utilization of the hardware resources, combining both pipelining and parallel processing techniques. The implementation of an array processor for motion estimation in a single-chip using 0.25 µm CMOS technology is presented. Experimental results show that this processor is able to estimate motion vectors in 4CIF video sequences at a rate of 16 frames/s.

Wireless Sensor Networks for the Protection of an Electrical Energy Distribution Infrastructure

Abstract: From a safety and security point of view, the electrical energy distribution infrastructure needs to be protected.. In this paper solutions to increase the safety aspects of substation components, power lines and power transformers are discussed. Also security solutions related to perimeter intrusion detection in substations and remote surveillance of power transformer installations are introduced. All the solutions are based on the deployment of wireless sensor and actuator networks in the substation, power lines and power transformers, which perform remote monitoring and provide alarms when required. The sensor network interacts with the SCADA system of the electricity provider to allow for centralised control of the protection system.

Loosely-coupled, mobile replication of objects with transactions

Abstract: Advances in wireless technology and affordable info-appliances are making mobile computing a reality. However, programmers do have a real hard task while developing mobile distributed applications in which sharing is needed. Such difficulty arises, mainly because programmers are forced to deal with system level issues such as consistency, durability, availability, etc. We designed, implemented and evaluated an object-based platform called M-OBIWAN that releases the programmer from the above mentioned system-level issues. It supports mobile transactions and replication in an integrated way. In contrast with other approaches, M-OBIWAN provides an automatic replication mechanism allowing the creation of dynamic clusters of objects which are accessed within transactions. In addition, the transactional model is adapted to mobile environments. A prototype implementation has been developed. Its performance has been measured with PDAs and desktop machines, linked via Bluetooth.

Uncertainty decoding for DNN-HMM hybrid systems based on numerical sampling.

Abstract: In this article, we propose an uncertainty decoding scheme for DNN-HMM hybrid systems based on numerical sampling. A finite set of samples is drawn from the estimated probability distribution of the acoustic features and subsequently passed through feature transformations/extensions and the deep neural network (DNN). Then, the nonlinearly-transformed feature samples are averaged at the output of the DNN in order to approximate the posterior distribution of the context-dependent Hidden Markov Model (HMM) states. This concept is experimentally verified for the REVERB challenge task using a reverberation-robust DNN-HMM hybrid system: The numerical sampling is performed in the logmelspec domain, where we estimate the posterior distribution of the acoustic features by combining coherence-based Wiener filtering and uncertainty propagation. The experimental results highlight the good performance of the proposed uncertainty decoding scheme with significantly increased recognition accuracy even for a small number of feature samples.

Quality-of-data for consistency levels in geo-replicated cloud data stores

Abstract: Cloud computing has recently emerged as a key technology to provide individuals and companies with access to remote computing and storage infrastructures. In order to achieve highly-available yet high-performing services, cloud data stores rely on data replication. However, providing replication brings with it the issue of consistency. Given that data are replicated in multiple geo-graphically distributed data centers, and to meet the increasing requirements of distributed applications, many cloud data stores adopt eventual consistency and therefore allow to run data intensive operations under low latency. This comes at the cost of data staleness. In this paper, we prioritize data replication based on a set of flexible data semantics that can best suit all types of Big Data applications, avoiding overloading both network and systems during large periods of disconnection or partitions in the network. Therefore we integrated these data semantics into the core architecture of a well-known NoSQL data store (e.g., HBase), which leverages a three-dimensional vector-field model (i.e., regarding timeliness, number of pending updates and divergence bounds) to provision data selectively in an on-demand fashion to applications. This enhances the former consistency model by providing a number of required levels of consistency to different applications such as, social networks or ecommerce sites, where priority of updates also differ. In addition, our implementation of the model into HBase allows updates to be tagged and grouped atomically in logical batches, akin to transactions, ensuring atomic changes and correctness of updates as they are propagated.