Automatic Analysis of Digital Retinal Images for Glaucoma Detection

In this paper we propose a novel automated glaucoma detection framework for mass-screening that operates on inexpensive retinal cameras. The proposed methodology is based on the assumption that discriminative features for glaucoma diagnosis can be extracted from the optical nerve head structures,
such as the cup-to-disc ratio or the neuro-retinal rim variation. After automatically segmenting the cup and optical disc, these features are feed into a machine learning classifier. Experiments were performed using two different datasets and from the obtained results the proposed technique provides
better performance than approaches based on appearance. A main advantage of our approach is that it only requires a few training samples to provide high accuracy over several different glaucoma stages.

Automatic extruder for processing recycled polymers with ultrasound and temperature control system

Melt viscosity is one of the main factors affecting product quality in extrusion processes particularly with regard to recycled polymers. However, due to wide variability in the physical properties of recycled feedstock, it is difficult to maintain the melt viscosity during extrusion of polymer blends and obtain good quality product without generating scrap. This research investigates the application of ultrasound and temperature control in an automatic extruder controller, which has ability to maintain constant melt viscosity from variable recycled polymer feedstock during extrusion processing. An ultrasonic modulation system has been developed and fitted to the extruder prior to the die to convey ultrasonic energy from a high power ultrasonic generator to the polymer melt. Two separate control loops have been developed to run simultaneously in one controller: the first loop controls the ultrasonic energy or temperature to maintain constant die pressure, the second loop is used to control extruder screw speed to maintain constant throughput at the extruder die. Time response and energy consumption of the control methods in real-time experiments are also investigated and reported this paper.

Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

Automatic extraction of Reduced order models from CFD simulations for building energy modeling

Thermal comfort is defined as “that condition of mind which expresses satisfaction with the thermal environment’ [1] [2]. Field studies have been completed in order to establish the governing conditions for thermal comfort [3]. These studies showed that the internal climate of a room was the strongest factor in establishing thermal comfort. Direct manipulation of the internal climate is necessary to retain an acceptable level of thermal comfort. In order for Building Energy Management Systems (BEMS) strategies to be efficiently utilised it is necessary to have the ability to predict the effect that activating a heating/cooling source (radiators, windows and doors) will have on the room. The numerical modelling of the domain can be challenging due to necessity to capture temperature stratification and/or different heat sources (radiators, computers and human beings). Computational Fluid Dynamic (CFD) models are usually utilised for this function because they provide the level of details required. Although they provide the necessary level of accuracy these models tend to be highly computationally expensive especially when transient behaviour needs to be analysed. Consequently they cannot be integrated in BEMS. This paper presents and describes validation of a CFD-ROM method for real-time simulations of building thermal performance. The CFD-ROM method involves the automatic extraction and solution of reduced order models (ROMs) from validated CFD simulations. The test case used in this work is a room of the Environmental Research Institute (ERI) Building at the University College Cork (UCC). ROMs have shown that they are sufficiently accurate with a total error of less than 1% and successfully retain a satisfactory representation of the phenomena modelled. The number of zones in a ROM defines the size and complexity of that ROM. It has been observed that ROMs with a higher number of zones produce more accurate results. As each ROM has a time to solution of less than 20 seconds they can be integrated into the BEMS of a building which opens the potential to real time physics based building energy modelling.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

Simulation using novel equipment designed to explain spirometric abnormalities in respiratory disease enhances learning in higher cognitive domains

Simulation of disorders of respiratory mechanics shown by spirometry provides insight into the pathophysiology of disease but some clinically important disorders have not been simulated and none have been formally evaluated for education. We have designed simple mechanical devices which, along with existing simulators, enable all the main dysfunctions which have diagnostic value in spirometry to be simulated and clearly explained with visual and haptic feedback. We modelled the airways as Starling resistors by a clearly visible mechanical action to simulate intra- and extra-thoracic obstruction. A narrow tube was used to simulate fixed large airway obstruction and inelastic bands to simulate restriction. We hypothesized that using simulators whose action explains disease promotes learning especially in higher domain educational objectives. The main features of obstruction and restriction were correctly simulated. Simulation of variable extra-thoracic obstruction caused blunting and plateauing of inspiratory flow, and simulation of intra-thoracic obstruction caused limitation of expiratory flow with marked dynamic compression. Multiple choice tests were created with questions allocated to lower (remember and understand) or higher cognitive domains (apply, analyse and evaluate). In a cross-over design, overall mean scores increased after 1½ h simulation spirometry (43-68 %, effect size 1.06, P < 0.0001). In higher cognitive domains the mean score was lower before and increased further than lower domains (Δ 30 vs 20 %, higher vs lower effect size 0.22, P < 0.05). In conclusion, the devices successfully simulate various patterns of obstruction and restriction. Using these devices medical students achieved marked enhancement of learning especially in higher cognitive domains.