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Limited By:	Subject = learning (artificial intelligence);

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Displaying Results 1 - 3 of 3 on page 1 of 1

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A generic news story segmentation system and its evaluation (2004)

O'Hare, Neil; Smeaton, Alan F.; Czirjék, Csaba; O'Connor, Noel E.; Murphy, Noel

A generic news story segmentation system and its evaluation (2004)

O'Hare, Neil; Smeaton, Alan F.; Czirjék, Csaba; O'Connor, Noel E.; Murphy, Noel

Abstract:

The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters.

http://doras.dcu.ie/242/

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Inferential estimation of high frequency LNA gain performance using machine learning techniques (2007)

Hung, Peter C.; McLoone, Sean F.; Sainchez, Magdalena; Farrell, Ronan

Inferential estimation of high frequency LNA gain performance using machine learning techniques (2007)

Hung, Peter C.; McLoone, Sean F.; Sainchez, Magdalena; Farrell, Ronan

Abstract:

Functional testing of radio frequency integrated circuits is a challenging task and one that is becoming an increasingly expensive aspect of circuit manufacture. Due to the difficulties with bringing high frequency signals off-chip, current automated test equipment (ATE) technologies are approaching the limits of their operating capabilities as circuits are pushed to operate at higher and higher frequencies. This paper explores the possibility of extending the operating range of existing ATEs by using machine learning techniques to infer high frequency circuit performance from more accessible lower frequency and DC measurements. Results from a simulation study conducted on a low noise amplifier (LNA) circuit operating at 2.4 GHz demonstrate that the proposed approach has the potential to substantially increase the operating bandwidth ofATE.

http://mural.maynoothuniversity.ie/2321/

Marked Mark

Non-Linear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity (2007)

Reilly, Jane; Ghent, John; McDonald, John

Non-Linear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity (2007)

Reilly, Jane; Ghent, John; McDonald, John

Abstract:

The research discussed in this paper documents a comparative analysis of two nonlinear dimensionality reduction techniques for the classification of facial expressions at varying degrees of intensity. These nonlinear dimensionality reduction techniques are Kernel Principal Component Analysis (KPCA) and Locally Linear Embedding (LLE). The approaches presented in this paper employ psychological tools, computer vision techniques and machine learning algorithms. In this paper we concentrate on comparing the performance of these two techniques when combined with Support Vector Machines (SVMs) at the task of classifying facial expressions across the full expression intensity range from near-neutral to extreme facial expression. Receiver Operating Characteristic (ROC) curve analysis is employed as a means of comprehensively comparing the results of these techniques.

http://mural.maynoothuniversity.ie/8345/

Displaying Results 1 - 3 of 3 on page 1 of 1