An FPGA-based infant monitoring system

We have designed an automated visual surveillance system for monitoring sleeping infants. The low-level image processing is implemented on an embedded Xilinx’s Virtex II XC2v6000 FPGA and quantifies the level of scene activity using a specially designed background subtraction algorithm. We present our algorithm and show how we have optimised it for this platform

A Framework for Monetary and Banking Analysis

The paper sets out and analyzes a simple model of money, banking, and price level determination. The model is first used to illustrate recent developments in the theory and analysis of banking, particularly the distinction between the portfolio management services provided by banks and their provision of transactions services. The assumptions are then extended to analyze price level determination in an economy that becomes an inside money economy as high-powered money goes out of use. The paper concludes by discussing the major unresolved questions about banking, money, and price level determination.

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Is there too much certainty when measuring uncertainty

This paper criticises the econometric inflation uncertainty proxies found in the literature, which show an overly optimistic picture about our real ability to forecast, and highlights the sharp contrast between the evidence portrayed by that literature and the evidence conveyed by the literature on surveys of inflation expectations. While the latter shows that actual forecasts are usually biased and systematic forecast errors are pervasive the former shows a much more optimistic picture, in accordance with the rational expectations paradigm. Also, both literatures have historically shown conflicting evidence on the inflation level – inflation uncertainty link. Next, the performance of inflation forecasts from both the Central Bank of Brazil Inflation Report and the Focus Survey are analysed. The paper then pinpoints some simple measures that could be taken to improve the reliability of econometric inflation uncertainty proxies, and carries out a (pseudo) real-time forecasting simulation exercise to derive a set of such proxies for Brazil. The features of those forecasts are shown to be very similar to those found in surveys.inflation level, inflation uncertainty, in-sample forecasts, out-of-sample forecasts, temporal inconsistency, forecast failure, surveys of expectations, rationality

Safe Data Sharing and Data Dissemination on Smart Devices

The erosion of trust put in traditional database servers, the growing interest for different forms of data dissemination and the concern for protecting children from suspicious Internet content are different factors that lead to move the access control from servers to clients. Several encryption schemes can be used to serve this purpose but all suffer from a static way of sharing data. In a precedent paper, we devised smarter client-based access control managers exploiting hardware security elements on client devices. The goal pursued is being able to evaluate dynamic and personalized access control rules on a ciphered XML input document, with the benefit of dissociating access rights from encryption. In this demonstration, we validate our solution using a real smart card platform and explain how we deal with the constraints usually met on hardware security elements (small memory and low throughput). Finally, we illustrate the generality of the approach and the easiness of its deployment through two different applications: a collaborative application and a parental control application on video streams

Phoneme-based Video Indexing Using Phonetic Disparity Search

This dissertation presents and evaluates a method to the video indexing problem by investigating a categorization method that transcribes audio content through Automatic Speech Recognition (ASR) combined with Dynamic Contextualization (DC), Phonetic Disparity Search (PDS) and Metaphone indexation. The suggested approach applies genome pattern matching algorithms with computational summarization to build a database infrastructure that provides an indexed summary of the original audio content. PDS complements the contextual phoneme indexing approach by optimizing topic seek performance and accuracy in large video content structures. A prototype was established to translate news broadcast video into text and phonemes automatically by using ASR utterance conversions. Each phonetic utterance extraction was then categorized, converted to Metaphones, and stored in a repository with contextual topical information attached and indexed for posterior search analysis. Following the original design strategy, a custom parallel interface was built to measure the capabilities of dissimilar phonetic queries and provide an interface for result analysis. The postulated solution provides evidence of a superior topic matching when compared to traditional word and phoneme search methods. Experimental results demonstrate that PDS can be 3.7% better than the same phoneme query, Metaphone search proved to be 154.6% better than the same phoneme seek and 68.1 % better than the equivalent word search