Innovative Projects Realized

Novel 3-D User Interfaces for improved situation awareness and mobile robot control

In an alien or possibly hostile environment, the situation awareness of a remote robot operator will be limited. Map information may not be known beforehand. The site may also be in a dynamic state where changes occur in the surrounding in any moment. The main objective of this project is to develop novel technologies to increase situation awareness of remote robot operators and their ability to intuitively interact with the robots for more efficient operations. It will involve development of the use of commercially available stereoscopic display and motion sensing device for robot-control user interface, as well as an assessment on the development. MDA expects this project will develop proof-of-concept user interfaces based on novel ideas and technologies and test them in several simple scenarios. In the future some of these concepts may be adopted in prototypes and products.

Assessing and improving the analysis of migration count data for population monitoring

Counts of birds passing a geographic location during migration to or from their breeding grounds are often used to estimate long-term population change. However, birds often stop at count sites for several days to fatten for their next migratory flight, affecting probability of detection. The migratory path followed might also vary among years. The influence of such factors on population estimates are unknown. We will use simulated migration count data to test the influence of birds stopping at a site and of variation in migratory route among years on our ability to estimate population size and change. We will also assess whether population estimates can be improved by alternative sampling or analysis techniques that mitigate the effects of stopover or shifts in migration route – for example, by combining data from multiple sites to better distinguish real population change from variation in counts from alternative factors. 

Maneuvering Simulation of Planing Hull

With dramatic improvements in vessel performance and tactical systems of high speed crafts in recent years, naval, coast guard and law enforcement agencies increasingly task them to complete a growing range of operational objectives. The combination of faster vessels, more sophisticated systems and extended responsibilities has driven fleet operators to re-examine how their boat crews are trained. Simulation has been used for decades to enhance personnel training however the quantity and distribution of crews combined with variations in vessel design and equipment have limited the use of simulation for high speed crafts training. To overcome these limitations and address HSC training requirements, an attempt will be made in this project to develop a maneuvering simulation model for high speed crafts with planing hull. Virtual Marine Technology (VMT), the partner organization, develops simulators for survival craft, fast response craft and high speed electronic navigation training. Participating in this internship will be very beneficial for VMT because by developing planing hull maneuvering simulation program, this proposed project aims to solve one the critical problems faced during the development of any high speed craft simulator.

This research project was undertaken and completed with a grant from and the financial assistance of Petroleum Research Newfoundland & Labrador.

Appropriate sensor signal analysis and abstraction in physical activity game design

The industrial partner, Digido, is interested in developing and marketing exercise games targeted at children that leverage the increased prevalence of  smartphones and their sensing and computational capacity, including: their ability to detect activity levels; their increasing use as a gaming platform; and their integration with social media and online communities. However, current activity sensing and classification techniques are too limiting for use in smartphone-based exergames for children. This project will combine sophisticated signal analysis and classification techniques with game design to produce pairs of robust, accurate and reliable sensor-input-game-mechanic dyads to produce an activity sensing input library for children’s smartphone exergames.

Parameter Uncertainty and Model Adequacy for GLMs Applied to Property/Casualty Insurance Data

Accurate forecasting is of crucial importance in managing insurance risks and ensuring a solvent and profitable operation. In recent years the property/Casualty insurance industry has adopted generalized linear models (GLMs) to improve the fit and prediction accuracy of their insurance portfolio models. Yet, the interdependence between the different insurance covers included in packaged products, such as car insurance, need to be explained in the GLM in order to include them in the predictive process. This objective of this project is the implementation and fine tuning of the model derived in a first internship last summer. 

Reflections of a successful career

Management scholars and practitioners have long recognized that in today’s knowledge economy, an organization’s competitive advantage is determined by how quickly and effectively knowledge can be transferred and shared. The knowledge economy, however, is driven by an aging workforce that is anticipated to retire in record numbers in the coming decade. Despite the impending retirement of valued employees who have amassed critical knowledge, know-how, and experience over their careers, few organizations have actively implemented techniques whereby the knowledge that resides in the  minds of their employees is captured, stored and available for later re-use  and retrieval. The objective of the current study is to contribute to the knowledge continuity literature by developing a methodology in conjunction with SF Marketing, to elicit and capture the tacit knowledge of employees nearing retirement and evaluate whether or not such knowledge can be easily documented. 

Modulation of nerve growth factor as a target in arthritic pain

Arthritis is a debilitating disorder characterized by chronic inflammation and persistent pain. This project aims to uncover ways of reducing the pain and discomfort felt by people afflicted by this disorder.  A number of key points will be addressed through this project. Firstly, we will gain a better understanding of the possible neurological component in arthritis. Secondly, we will determine if modulating nerve growth factor effects pain in an animal model of arthritis. If it does, we will determine if nerve growth factor can be manipulated in such a way as to be well tolerated. 

Maximizing Plant Production using Light Emitting Diode Arrays

This proposal is for the development and optimization of light emitting diodes (LEDs) for plant growth. The development of LEDs over the complete range (and beyond) of photosynthetically active radiation (PAR: 400 – 700nm) now allows focused research into the effect of wavelength on specific plant responses. The LEDs produce light in a narrow spectrum (+/- 10nm) at intensities approaching full sunlight. This project consists of characterizing LED arrays developed by our organization sponsor and other sources to determine the arrays light intensity, spectral quality, energy usage, and lifespan which will be used to optimize the design of future LED systems. These arrays will be used to grow plants under both greenhouse and growth chamber conditions (primary lighting and intercanopy lighting) to maximize production. LEDs have the potential to maximum plant production with reduced energy costs over existing lighting systems. The ability to understand the impact that light has on plant growth and development will be fundamental to allow improved designs of LED lighting systems for greenhouse and growth chamber systems.

Analysis-synthesis strategies for simple and robust transformations of complex sounds

This project will explore the control of sound transformations applied to complex sounds such as: explosions with debris, glass breaking, various object impacts and bounces, etc. These complex sounds will be decomposed into multiple individual collisions components. Based on a database of simple collisions gathered from the analysis of one or several complex impact sounds, we will derive new techniques to control the transformation of model parameters in order to synthesize a multitude of sounds showing some characteristics of the originals with modified duration, density of collision, spectral and temporal domain evolution.

To achieve this, the project will build on well established signal segmentation techniques to perform a robust decomposition of complex sounds into its constituting elements. Improvements to traditional techniques for spectral envelope estimation will be applied to individual components identified and allow further transformation possibilities. Finally, common signal processing limitation to parametric modeling transformations will be addressed in order to provide transformation controls that can alter physical attributes of the collisions (e.g. material of the object, the point of impact, etc.). 

Thousand Protein Multiplexed Mass Spectrometry Assay for Biomarker Discovery

In recent years, the interest given to disease biomarkers has boomed. Biotechnology and pharmaceutical companies are exploring ways to use biomarkers to speed up the drug development process, as well as to rapidly assess a diseases state, staging, progression and response to therapy. Multiple reaction monitoring (MRM) Mass Spectrometry (MS) has been shown to be well suited for the selective and sensitive quantification of proteins in plasma and has recently emerged as the technology of choice for disease biomarker study. The focus of this research project is to develop a quantitative multiplexed MRM assay as a biomarker discovery tool to directly detect and quantify proteins secreted from various tissues into plasma from the rat. This assay will then be used to screen and quantify protein biomarkers associated with various disease states, such as cardiovascular disease, in the rat plasma. The internship will be very beneficial to Caprion Proteomics, the partner organization, as the developed method will be implemented into their existing biomarker discovery and verification platform and offered to their clients as a biomarker discovery service for a variety of clinical diseases.

Topic Segmentation for Text Mining on Legal Documents

Text mining is the process of automatically extracting knowledge from unstructured, natural language documents. It aims to support users in dealing with large amount of textual information. Examples for specific text mining tasks are entity detection, summarization, and opinion mining. Due to the complexity and ambiguity of natural language, this analysis is broken down into individual processing steps, which are based on the techniques from the fields of machine learning, natural language processing, and semantic computing.

In this project, the goal is to enrich the text mining pipelines developed at KeaText for the processing of legal documents. Specifically, the analysis is to be enriched with a topic segmentation module that is tailored to the specific domain and application requirements. Automatic topic segmentation, also known as text tiling, structures documents into individual parts, each representing a distinct theme. It is well-known that topic segmentation can improve several information retrieval and text analysis tasks. In this project, the following tasks are to be completed: (1) Survey of existing research literature to identify suitable methods and tools; (2) Design of a new topic segmentation algorithm specifically for legal documents; and (3) Implementation and evaluation of this algorithm based on the General Architecture for Text Engineering (GATE) framework. 

Detection and recognition of crisis using Markov models and Case-based reasoning

This project pertains to the modeling, detection, and monitoring of crises in geopolitical dynamic environments. As risks are inherent to crises, we need tools to cope with the uncertainty factors involved in these situations. The objective of this project is to conduct research activities to support the understanding of crisis situations and to model their potential evolution. Our main goal is to explore how to find patterns from episodes of conflict that can be reused as templates by human operators in their analysis process. The technologies targeted for this project are Markov models and Case-Based reasoning (CBR). Markov models are efficient tools for modeling sequences of events as they capture the uncertainty inherent to a process. Various extensions, such as Hidden Markov models (HMM), are available for modeling complex stochastic processes with partial information.  CBR is a framework, originating from artificial intelligence, which can be used to find similarities in situations and to help the interpretation of these situations. The CBR community has proposed various algorithms for acquiring cases from previous experiences and for exploiting them when facing new situations. We will study how each of these two technologies can contribute to the modeling of crisis situations. We will adapt some of these algorithms for complex situations described by sequential and uncertain events. And we aim to propose hybrid approaches for finding similar sequences of events from geopolitical conflict data sets currently available on the Web.