Innovative Projects Realized

Small unmanned aerial vehicles (UAVs) for high-resolution environmental remote sensing: a soil moisture case study.

Successful research in environmental remote sensing relies on multiple-view approaches to data collection. In multi-stage remote sensing, data are collected at different geographic scales. Low-altitude, high-resolution aerial observations bridge the gap between in situ and satellite-based observations. These can be achieved by unmanned aerial vehicles (UAVs), with minimum logistical support and lower operation/maintenance costs than manned aircrafts.

UAVs (also known as drones) are remotely-controlled or autonomous aircrafts without a pilot aboard. UAVs are categorized according to their range of action, flight altitude, endurance, maximum takeoff weight, and type of applications. They were initially designed for military purposes, to perform reconnaissance and attack missions while reducing casualties. However, they are now deployed in a small but growing number of civil, commercial, and scientific applications.

At Athabasca University, we have assembled several small UAVs, ranging from multicopters and EPP-foam airplanes with limited flight time (<1 hour) and payload weight (up to 2 kg) to larger fixed-wing aircrafts with extended endurance (6+ hours) and payload capability (up to 10 kg). We are using these UAVs mainly for tracking springtime snowmelt timing and spatial patterns and monitoring the impact of climate change on permafrost landscapes in Nordic communities. But, we are also looking into demonstrating the potential of UAV-based remote sensing for further applications, including soil and vegetation mapping, aerobiological sampling of pollens and pathogens, and the monitoring of populations of free-range mammals. The Summer 2015 research project will consist in equipping one of our small UAVs with a full-spectrum (UV, visible, and near-infrared) GoPro Hero 3 camera and a “coffee-can” L-band radar (QM-RDKIT Radar, Quonset Microwave) and deploy it for obtaining soil moisture maps in low vegetation areas and under forest over an experimental acreage located ?150 km west of Edmonton, Alberta. Ground-based observations of soil moisture, temperature and permittivity at 5 cm from the top soil surface will be collected with a portable, handheld device, concurrent with each flight. This information will be used to calibrate and validate a soil-moisture retrieval algorithm based on the UAV imagery. This project represent a unique opportunity for you to apply your knowledge of remote sensing while gaining new practical experience in the exciting rising field of unmanned aerial vehicles. We hope to welcome you to Athabasca University and beautiful Alberta in summer 2015!

Direct georeferencing of unmanned aerial vehicle photography and radar imagery with a low-cost real-time kinematic GPS.

The conventional (indirect) georeferencing of remote sensing imagery requires the use of control points that link known positions in the imagery to known positions in map coordinates. The number of control points depends on the amount of distortion in the imagery, method of transformation and desired level of accuracy, but it is often large. Overall, the collection of ground control points is a cumbersome and time-consuming operation, and almost an unrealistic one when it comes to the georeferencing and mosaicking of a set of images acquired from a small unmanned aerial vehicle (UAV). Indeed, small UAVs are typically flown below 400 feet above ground level and can rapidly collect an enormous amount of high-resolution images when mapping an area (i.e. over 500 images for every square kilometer).

The number of necessary ground control points can be substantially reduced using a direct georeferencing, an approach that is obviously better suited for UAV mapping projects. Direct georeferencing requires a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU) to directly measure the position and orientation of the imaging sensors on a remote sensing system in order to georeference their data. These are integral components of a UAV navigation system and the information they collect can, incidentally, be used for direct image georeferencing.

At Athabasca University, we have assembled several small UAVs, ranging from multicopters and EPP-foam airplanes with limited flight time (<1 hour) and payload weight (up to 2 kg) to larger fixed-wing aircrafts with extended endurance (6+ hours) and payload capability (up to 10 kg). We are using these UAVs for tracking springtime snowmelt timing and spatial patterns, monitoring the impact of climate change on permafrost landscapes in Nordic communities, and soil moisture mapping. The Summer 2015 research project will consist in equipping our UAVs with a low-cost (<$150), lightweight real-time kinematic (RTK) GPS system, the NavSpark-Raw, and assessing its performance in achieving highly accurate direct georeferencing of UAV photography and radar imagery. The Navspark-Raw is an-Arduino-compatible board with onboard GNSS that can send out carrier phase raw measurements to a host computer to be applied RTK corrections in order to generate positions with centimeter-level accuracy. On several occasions throughout the summer, our smallest UAVs will be deployed over an experimental acreage located approximately 150 km west of Edmonton (Alberta), to test the RTK GPS/INS system and verify the positioning accuracy with and without ground control points.

Deep learning for natural language text and image analysis

This research project focuses on natural language text and image analysis based on statistical and deep learning techniques, for recognizing semantics or meaningful relations from unstructured big data, for better understanding of the data and serving the users.
This project aims to develop a mixed natural language text and image analysis system for an online social network environment, where plenty of articles and comments including both text and images are increasingly produced by its users. Several predefined events and semantic relations useful for the stakeholders will be extracted, identified and recognized against the mixed data by using statistical and deep learning algorithms, and then visualized in an interface as real time report. The key research problems include how to choose suitable language and image processing models, how to design effective statistical and deep learning algorithms and programs for analyzing the unstructured data, and how to make use of higher level semantic information in the analysis. Moreover, some additional information associated with multiple modals, will ideally be jointly modeled in the learning process and further leveraged for analysis. The prototype system of the project should be able to perform effective text and image analysis with expected outputs for the purpose of proof of concept.

Traffic Flow Optimization

In 2000, road traffic congestion in USA alone caused 3.6 billon vehicle-hours of delay, 21.6 billion liters of wasted fuel, and US$67.5 billion in lost productivity. Yearly estimates on economic, health, and environmental cost of traffic congestion in New Zealand is in excess of NZ$1 billion [Hazelton, 2010]. Traditionally, traffic modelling has concentrated on simulating traffic behaviour. The science of traffic analysis, modelling, and optimization aims to estimate traffic load, to detect and prevent traffic congestion, and to optimize the flow of traffic. Optimization of traffic flow not only reduces drivers’ stress levels, but also reduces air pollution [Angleno, 1999] and controls fuel consumption with respect to the environment and the economy. This proposal directly addresses the later – to optimise vehicular gasoline consumption in urban centers by regulating the flow of traffic using smart traffic lights.
Classical traffic models are mostly based on the treatment of vehicles on the road, their statistical distribution, or their density and average velocity as a function of space and time. Most models employ techniques ranging from cellular automata, particle-hopping, car-following, gas-kinetics, through to fluid dynamics present a passive approach to traffic optimization. That is, traffic data is collated apriori and the models are validated posthoc. In a compelling argument for the need to change the manual adjustments to traffic signals, Thorpe [1997] showed, using limited simulation models, that the best traffic signal performance could be achieved using Reinforcement Learning.
Thorpe [1997] reports the re-timing of a major artery in Denver, CO, USA, from 90 seconds to 100 seconds, in the heavy-flow direction, to yield 87% reduction in times vehicles stopped at light. Many urban centers now employ traffic lights that respond to real-time data obtained from devices such as road loops, video cameras, and other traffic detectors [Olsson, 1996]. In contemporary models, traffic situations are represented by statistical or mathematical abstractions and traffic control is exerted by methods that utilize information gleaned from these abstractions. This proposal focuses primarily on loosely modelling the causality of traffic. The causal model then drives the state changes in traffic control. Such a causal model approaches a fully-informed solution; that is, the more we know at real-time about vehicles on the road the smoother the flow of traffic and the better the gasoline usage. The proposed method will be accurate enough to capture the exact nature of undesirable traffic outcomes (e.g., traffic jams, longer wait period, higher number of stops) as well as to model the causality of these undesirable traffic outcomes. It is also possible to direct the traffic to enact a desirable traffic outcome, such as one that clears a pathway for an ambulance or a VIP’s convoy. Further, the causal model is updated at real-time and hence the state changes are real-time responses to the dynamics of the causal model.

Can educational systems help in motivating students?

This undergraduate project is part of a larger project. The aim of this larger project is to design, develop and evaluate a mechanism that identifies motivational preferences of learners and then accommodates these preferences by providing each learner with motivational techniques that best support his/her learning process.

The first step towards providing personalization based on motivation in learning systems is to develop a framework of motivational techniques that can be easily integrated into learning systems. Such a framework has been developed and suggests a set of motivational techniques that facilitate the enhancement of motivation in online courses. These techniques are domain and course independent, making the framework easily applicable to different systems and courses.

The next steps in this larger project is the implementation of these techniques as well as the design, implementation and evaluation of an approach to observe the log data and performance data of students related to the respective techniques. In addition, a mechanism will be developed that intelligently assists with the analysis of the usage of these techniques by learners with different characteristics, learning progress and performance. Based on the results of such analysis, an algorithm will be developed that dynamically identifies individual preferences for motivational techniques and a mechanism will be designed that presents learners with the respective techniques.
The undergraduate student will be working in the larger project on a particular project task, helping to achieve the goals of the larger project. For example, tasks can include the design and implementation of a motivational technique or the creation of an algorithm that identifies individual preferences for motivational techniques. Another task could be the development of an approach to identify learning strategies and investigating the relation between learning strategies and the use of motivational techniques.

Quest Design for Web-based Multiplayer Online Game

In the past, we have developed a chessboard like Web-based Multiplayer Game (http://megaworld.is-very-good.org) for learning. The game allows teachers to create their own game world maps, NPCs, quests and items. The students can pickup quests from NPCs in vary places. This research project focuses on the design of RPG quests for the chosen disciplines and learning subjects include English, Math (elementary to high school), high school science, or social studies. Due to the reward of a quest usually include experience points and receiving experience points can make student’s character level up, this research project also needs to consider the experience points required for leveling as well as the experience point range that different quest types should have.

An integrated solid state fermentation approach for production of enzymes from agro-wastes / Une approche solide de fermentation à l’état intégré pour la production d’enzymes à partir de déchets agricoles (Nouveau)

One third of the total apple production in Canada (447,035 ton/year) is processed to produce juices, flavours and concentrates and Quebec has 32 % share (2007 statistics) in it. The end result is a solid residue containing high moisture content (70%–75%) and biodegradable organic load (high BOD and COD values). These wastes have low nutritional value and their high biodegradability causes environmental problems. A typical apple processing industry generates 25%–30% apple pomace and 5%–11% sludge (liquid waste obtained after clarification). Apple pomace consists mainly of apple skin/flesh (95%), seeds (2%–4%) and stems (1%). Apple pomace, including seeds, contains polyphenolics. These natural anti-oxidants are high in demand owing to their role as a free radical scavenger, in a number of degenerative diseases, such as cancer and atherosclerosis. The recovered antioxidants from apple pomace could be used as a nutraceutical and a food supplement.
In this context, the proposed project investigates the use of apple pomace as a raw material for production of high value added products such as, polyphenols along with other products, namely lignolytic enzymes (principally laccases) and left over fermented residue as animal feed in aquaculture farms. Phanerochaete chrysosporium, a white rot fungus, will be utilized for the solid state fermentation of apple pomace. The principal objective is as follows:
* Solid state fermentation of apple pomace to produce lignolytic enzymes, namely laccases: this will involve optimization of moisture content, fermentation time, pH and temperature for the growth of lignolytic enzymes using statistical techniques. The produced enzymes will be extracted by using buffer and concentrated by ultrafiltration and later transformed into liquid or solid formulations for use in pulp and paper industry and also for degradation of toxic organic compounds, so finding use in seafood processing wastewater treatment, municipal wastewater treatment and bioremediation of soils as well as the contaminants from apple sprays, etc.

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Au Canada, un tiers de la production mondiale de pomme (447,035 tonnes/an) est traitée pour produire des jus de fruits, des arômes et des concentrés. Le Québec possède 32% de part (statistiques 2007) en celle-ci. Le résultat final est un résidu solide contenant un fort taux d’humidité (70% -75%) et une charge organique biodégradable (DBO et DCO élevé). Ces déchets ont une faible valeur nutritive et leur biodégradabilité élevée cause des problèmes environnementaux. Une industrie classique de transformation de pommes génère 25% à 30% de déchets solides de jus de pomme et 5% à 11% des boues (déchets liquides obtenus après clarification). Les déchets solides de jus de pommes sont principalement constitués de peau/chair de pommes (95%), de pépins (2% -4%) et de tiges (1%). Les déchets solides de jus de pomme, y compris les pépins, contiennent des polyphénols. Ces antioxydants naturels sont très demandés en raison de leur rôle en tant que piégeur de radicaux libres, dans un certain nombre de maladies dégénératives, telles que le cancer et l’athérosclérose. Les antioxydants récupérés à partir de déchets solides de jus de pomme pourraient être utilisés comme nutraceutique et comme complément alimentaire.

Dans ce contexte, le projet proposé étudie l’utilisation de déchets solides de jus de pomme comme matière première dans la production de produits à forte valeur ajoutée tels que les polyphénols avec d’autres produits, à savoir les enzymes lignolytiques (principalement des laccases), et le restant de résidus fermentés comme alimentation animale dans les fermes d’aquaculture. Phanerochaete chrysosporium, un champignon de pourriture blanche, sera utilisé pour la fermentation à l’état solide des déchets solides de jus de pomme. L’objectif principal est le suivant:

* La fermentation à l’état solide de déchets solides de jus de pomme pour produire des enzymes lignolytiques, à savoir laccases: il s’agira d’optimiser la teneur en humidité, le temps de fermentation, le pH et la température pour la croissance des enzymes lignolytiques à l’aide de techniques statistiques. Les enzymes produites seront extraites à l’aide de tampon, concentrées par ultrafiltration et seront ensuite transformées en formulations liquides ou solides pour être utiliser en pulpe, ainsi que dans l’industrie du papier. Elles seront également utilisées pour la dégradation des composés organiques toxiques, afin d’y trouver une utilité dans le traitement des fruits de mers provenant des eaux usées, dans le traitement des eaux usées municipales et dans la bioremédiation des sols ainsi que des contaminants provenant des goutelettes fines de pomme, etc.

Chemical modification of biopolymers and cellulose nanocrystals for the development of bioactive films

The proposed project is aligned in the chemical modification of polymers and cellulose nanocrystals (CNC) to improve the nanoparticle-polymer interactions and maximize i) the reinforcing effect of nanoparticles (polymer filling) as well as ii) interactions nanoparticles bioactive-agent in the manufacture of bioactive packaging films.

Hypothesis: The chemical modification of polymers and CNC optimizes interactions bioactive polymer-CNC-agent and significantly improve the functional properties of bioactive films.
Objectives:
1) Optimize four methods of functionalization of bioactive movies: TEMPO reaction (carboxylation CNC) with the application of chitosan films, crosslinking by gamma irradiation nanocomposites starch CNC crosslinking of chitosan by gamma irradiation in the presence of CNC, CNC grafting polycaprolactone films on the isocyanate to compatibilize CNC (hydrophobic) with insoluble polyesters.
2) characterize the progress of chemical reactions carried out.
3) Determine the in vitro antioxidant capacity of the films and their ability to release bioactive agents during storage.
Methodology
The films will be synthesized according to procedures developed in our laboratories. Bioactive agents will be selected on the basis of previous studies. The molecular structure of the films will be analyzed by ATR-FTIR spectroscopy. SEM analysis may also be performed.
The mechanical properties of the films, barriers and water resistance of the films will be analyzed to verify the effect of functionalization. The antioxidant properties of the films will be determined according to the method of Salmieri and Lacroix (2006). The release of bioactive agents from film to food (meat or vegetables) during storage will be assessed on the best formulation of bioactive film on a 2-week period by the Folin-Ciocalteu.
Expected Results
Targeted functionalization of CNC and polymers can significantly improve the rheological properties of the films by increasing the CNC-polymer molecular interactions. These results allow to consider a technology transfer with the help of the industrial company that works in this project with our laboratory. “

Language Technologies for Health Informatics (New)

We seek to develop an Integrated and Distributed Software Platform for Text Mining, Semantic Web, Machine Learning. Students are invited to carry out applied research in optimizing existing Natural Language Processing (NLP) platforms such as Unstructured Information Management Architecture (UIMA) and other tools to be integrated for real-time big data processing. We apply this technology to High-Frequency, Real-Time Complex Event Processing (CEP), including in Finance, Healthcare, and Cybersecurity. The applications are not industrial grade but will serve as demo to recruit external partners. No funding is available but work can continue into a masters or doctoral thesis afterwards.

Novel Nanodevices for Spectroscopy and Nonlinear Optics

One of the next frontiers of integrated photonics is surely represented by the challenge of extending the use of optical techniques to nanometer length scales, overcoming the limit imposed by diffraction, which does not allow focusing light on dimensions smaller than roughly half a wavelength. Metallic nanostructures have proven to be an efficient way to “squeeze” light on such dimensions, significantly enhancing the local field at the same time. This has brought to a
myriad of applications in many fields, including subwavelength optical imaging of nanomaterials and DNA, efficient generation of deep ultraviolet light and the very recent perspective of significantly improving the efficiency of thin-film solar cells. Given this unquestionable interest, we want to develop at INRS-EMT a vigorous research program regarding the exploitation of these concepts in the mid-infrared (mid-IR ~2 – 20 ?m) and terahertz (THz ~20 – 1000 ?m) regions of the electromagnetic spectrum. These spectral ranges are of great interest for spectroscopy, since many molecules exhibit specific vibrational/rotational transitions at these frequencies. In particular, mid-IR spectroscopy gives full access to a rich region of vibrations of molecules that are relevant for both inorganic and organic chemistry, and can as well give information regarding protein structure and folding. THz spectroscopy, instead, is sensitive to rotational transitions of light molecules and vibrations of molecules composed by large functional groups, as it is the case for biological molecules.
In the framework of the present Project, we intend to shed some light on the use of metallic nanostructures for assisting long-wavelength spectroscopies and nonlinear optics. More specifically, we will study: (i) the possibility of employing arrays of “nanoantennas” for enhancing terahertz spectroscopy of biomolecules, in view of some exciting bio-sensing applications; (ii) localized terahertz nonlinear optics mediated by nanoplasmonics, for its fundamental interest and for its possible use in next-generation terahertz nanoelectronic devices; (iii) a novel kind of mid-infrared nanoscope, capable of acquiring chemical maps of surfaces with nanometric resolutions; and (iv) new nanoplasmonic tools for boosting mid-infrared nonlinear optical processes for applications in silicon photonics.
The outcomes of the proposed investigations may have an important impact in biological studies and biosensing, offering a crucial contribution to few-molecule absorption spectroscopy, a field of paramount relevance, for example, in the early diagnosis of diseases. Besides this, the information offered by localized nonlinear experiments at mid-IR and THz frequencies may help understanding unexplored aspects of radiation-matter interaction and could bring to the development of novel nanophotonic devices capable of processing, shaping, and frequency-converting long-wavelength pulses and to deliver them to the nanoscale.

Bio-hybrid material chemistry

A certain number of key challenges exist for the rational design of bio-hybrids, which limit their innovative potential. More specifically: (1) bio-conjugation approaches for grafting synthetic molecules to proteins are limited and can be inefficient; (2) a poor physical-chemical understanding of how synthetic molecules can alter the bio-activity of proteins exists; and methods for controlling the function (3) and structure (4) of bio-hybrids are at a crude stage of development and thus limit their specificity and bio-activity compared to e.g., proteins.

Our research program aims to address these challenges and thus design new functional bio-hybrid materials for applications in biotechnology. Awaiting more information from the professor. Please check back soon. Do not contact Globalink Research Internships.

Development of (doped-)Graphene for Clean Energy Applications

The Nobel Prize in Physics for 2010 was awarded to two researchers “for groundbreaking experiments regarding the graphene”. Since then, graphene, a new-type and two-dimensional (one-atom-thickness) allotrope of carbon with a planar honeycomb lattice, has become one of the most exciting topics of research due to their remarkable properties including ultra-high surface area (calculated value, 2,630 m2g-1), high conductivity (resistivity: 10-6 ? cm, the lowest resistivity substance known at room temperature) and high chemical stability. Accordingly, graphene has many potential applications in nanotechnology, electronic, optics, and other fields of materials science, as well in architectural fields. It is also expected that graphene research will offer a new type of carbon-metal nanocomposite for the next generation of catalysts.

The student will be actively engaged in working on cutting-edge topics in a multi-disciplinary environment, and will receive significant training on the synthesis of graphene and doped graphene (such as N-doping), and their characterization.