INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Detecting Repetitions in Spoken Dialogue Systems Using Phonetic Distances

Abstract: Repetitions in Spoken Dialogue Systems can be a symptom of problematic communication. Such repetitions are often due to speech recognition errors, which in turn makes it harder to use the output of ...

Combining flexibility with low power: Dataflow and wide-pipeline LDPC decoding engines in the Gbit/s era

Abstract: Power and flexibility are important constraints in the design of new chips. The efficiency extracted from a design is increasingly becoming a dominant question, and several techniques and technological advances can be used to optimize efficiency in its energy and functionality domains. These two characteristics are critical in digital communication systems that must work accordingly with multiple communication standards at different power, throughput and latency requirements. In this work, we focus on the physical layer Forward Error Correcting (FEC) system, due to the tight throughput and latency constraints they are required to meet, and develop specialized processing engines for Low-Density Parity-Check (LDPC) codes decoding, a class of widely standardized codes. The engines were developed for execution on Field-Programmable Gate Array (FPGA) devices by exploring dataflow and wide-pipeline design approaches, and have the design flexibility to target different LDPC codes, since they were implemented using recent High-Level Synthesis (HLS) tools. The generated engines and architectures allow achieving highly efficient decoders with decoding throughputs ranging from 16 Mbit/s to 1.2 Gbit/s at energy efficiencies of 42 to 908 Mbit/Joule/iteration, while the achieved clock frequencies of operation vary from 80 to 300 MHz. Furthermore, our bandwidth analysis shows that workload boundaries do not impose limitations on a system bus.

On the true Cramer-Rao lower bound for data-aided carrier-phase-independent frequency offset and symbol timing estimation

Abstract: In this letter we present new and simple closed form expressions for the true Cramer-Rao lower bound (CRB) for data-aided (DA) joint and individual carrier frequency offset and symbol timing estimation from a linearly modulated waveform transmitted over an AWGN channel. The bounds are derived under a carrier-phase-independent (CPI) estimation strategy wherein the carrier phase is viewed as a nuisance parameter and assumed to have a worst-case noninformative uniform distribution over [-?, ?]. The computation of these CRBs requires only a single numerical integration. In addition, computationally simpler yet highly accurate asymptotic lower bounds are presented. As particularizations, new bounds for individual CPI frequency estimation with known symbol timing from M-PSK and continuous wave (CW) signals are also reported.

Ontology Transformation of Enterprise Architecture Models

Abstract: Enterprise architecture supports the analysis and design of business-oriented systems through the creation of complementary perspectives from multiple viewpoints over the business, information systems and technological infrastructure, enabling communication between stakeholders. However, enterprise architecture modelling languages lack representation schemas that support the computable assessment of its models. This paper applies model transformation to address this issue. The proposed approach translates models specified using ArchiMate into OWL. The resulting ontological representation is therefore computable, allowing for the analysis of the consistency and completeness of the enterprise architecture models. The applicability of the approach is shown through a case study.

BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification

Abstract: We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain. The model assumes that lexical cohesion can be observed across documents, meaning that segments describing the same topic use a similar lexical distribution over the vocabulary. The model implements lexical cohesion in an unsupervised Bayesian setting by drawing from the same language model segments with the same topic. Contrary to previous approaches, we assume that language models are not independent, since the vocabulary changes in consecutive segments are expected to be smooth and not abrupt. We achieve this by using a dynamic Dirichlet prior that takes into account data contributions from other topics. BeamSeg also models segment length properties of documents based on modality (textbooks, slides, etc.). The evaluation is carried out in three datasets. In two of them, improvements of up to 4.8% and 7.3% are obtained in the segmentation and topic identifications tasks, indicating that both tasks should be jointly modeled.