identifier: Wen, Xin  (2021) How Does Aging Affect Housing Prices?—The Empirical Evidence from Asian Emerging Markets.  Masters thesis, Concordia University.

abstract: The 21st century is a century that the world is aging. As the engine machine of global world development, Asian emerging markets are going through the population aging process while rapidly developing economics. The aging will bring rather serious changes to economic and social development. Housing, an essential need of human beings, is controlled by market rules and inevitably influenced by economic fluctuation. Therefore, what kind of impact will population aging have on Asian real estate markets? How does it affect the house price? Those are the concentrations of this paper. Based on the literature research, this paper discovers a correlativity between aging and housing prices, then we deduce an empirical model according to supply and demand theory, life cycle hypothesis, and overlapping generations model; at last, we make transnational empirical tests taking 5 Asian emerging markets with the serious population aging issue, namely China, Hong Kong, South Korea, Singapore, and Thailand. The conclusions are that the rising old dependency rate will lead to a negative impact on housing prices while the decline of the youth dependency rate will bring a positive impact on them. GDP is one of the most important factors that push house prices. The tests in those 5 Asian markets verify the results of the theoretical model.


identifier: Poti, Kim  (2021) Les belgicismes emblématiques dans la retraduction de "A Tall Man In A Low Land : Some Time Among the Belgians".  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract: A Tall Man In A Low Land: Some Time Among the Belgians, récit satirique écrit par Harry Pearson relate son voyage en Belgique et les divers chocs culturels et langagiers auxquels il a fait face. La traduction de cet ouvrage réalisée par Laura Harmegnies et Sylvain Gilmont intitulée Un géant au plat pays appelle à la retraduction. Tout d’abord, une œuvre commence à réellement exister lorsqu’elle est retraduite. En effet, la retraduction est nécessaire à la pérennité d’une œuvre. En outre, les auteurs ont eu recours à un registre très soutenu, assez pompeux qui dénature la plume de Pearson, qui est, elle, très familière. Par ailleurs, en littérature, les belgicismes (terme utilisé en Belgique qui diffère du français de référence ou dont l’usage était également attesté en France autrefois, mais qui est désormais considéré comme archaïque), les rares fois où ils sont présents, se limitent souvent au cliché bruxellois (Voisin, 1986, p. 55). En effet, les auteurs ne tiennent que rarement compte des belgicismes dits emblématiques, c’est-à-dire propres à une région spécifique. Un géant au plat pays n’y échappe pas. Nous avons alors décidé de procéder, après critique de la traduction de Harmegnies et Gilmont, et ce, grâce à diverses tendances déformantes, à une retraduction commentée d’extraits qui témoignent des différences linguistiques en Belgique francophone. Nous avons eu recours à un corpus de wallonismes et de belgicismes non emblématiques lors de la traduction de l’introduction, du chapitre sept et neuf se déroulant respectivement à Bruxelles et à Liège.


identifier: Zhang, Shuying  (2021) Diffuseness quantification of a reverberation chamber and its uncertainty with fine-resolution measurements.  Masters thesis, Concordia University.

abstract: Insufficient diffuseness is the major cause of poor inter-laboratory reproducibility of acoustic measurements conducted in a reverberation chamber. Many previous studies have proposed new methods to quantify the diffuseness of a reverberation chamber more accurately, but there is no general agreement among researchers on the most reliable method. The number of measurement samples required for these diffuseness metrics is also unclear, even though it significantly impacts the robustness of the methods. This study, therefore, aims to quantify the diffuseness of a reverberation chamber by using the three widely used diffuseness metrics of spatial variation of sound pressure levels, the relative standard deviation of decay rates, and the degree of time-series fluctuations. The measurements were also carried out with fine resolution microphone positions and varied configurations of acoustic diffusers. With the measurement data, the minimum number of measurement samples to obtain an accurate diffuseness quantification was determined. It is shown that nine independent microphone positions are sufficient to provide the acceptable confidence interval for frequencies above 315 Hz for all three metrics. However, twenty or more microphone positions are needed for the same accuracy if lower frequencies are considered for the reverberation chamber under investigation.


identifier: Ghaderi, Mohsen  (2019) Cyber-Attack Detection and Mitigation in Networked Control Systems.  Masters thesis, Concordia University.

abstract: Cyber-Physical System (CPS) is the term used to describe the physical systems equipped with computation and communication capabilities. CPSs can be used in different applications e.g. autonomous vehicles, water distribution systems, smart grids, industry 4.0 and Internet of Things (IoT). CPSs have expectation of improving the capability of traditional engineering system but on the other hand, they arise several concerns about their security against cyber-attacks. In the last decade, several cyber-attacks targeting SCADA systems have been reported, see e.g. Maroochy water breach and the Stuxnet worm aimed Iran's nuclear facility. From a control point of view, a CPS can be interpreted as a Networked Control System (NCS) where the risk of cyber-attacks can be modeled as the possibility that malicious agents could compromise the communication channels. In order to benefit from CPSs, specially in safety critical systems, their vulnerabilities to cyber-attacks must be properly faced. In this thesis two control architectures for CPS are developed. In the first, starting from the analysis of active detection mechanisms available in the literature, we propose a novel architecture capable of detecting a broad class of False Data Injection (FDI) attacks. Such strategy has been contrasted with the well-known watermarking detection mechanism and it is shown that our solution is capable of detecting replay attacks without degrading the closed-loop performance of the system. Moreover, it is shown that compared to detection schemes resorting to auxiliary systems, the proposed strategy is less involved and of easier implementation. In particular, it can be installed on the existing NCS infrastructure without changing communications, controller or state estimator. In the second architecture, we propose another novel architecture capable of detecting and mitigating a broad class of FDI attacks. First, we propose a detection mechanism based on a coding scheme to limit the attacker's disclosure and disruptive resources and prevent the existence of stealthy attacks. Second, we propose an emergency local controller that is activated when an attack is detected or the plant's safety is in danger. It is proved that the proposed architecture always guarantees the safety of the system, regardless of the attack actions and detector performance. Moreover, plant's normal operation recovery is ensured once the attack is terminated.


identifier: Salarinezhad, Asefeh  (2021) Static and Dynamic Theoretical Studies on Improving Matching Design.  PhD thesis, Concordia University.

abstract:
This thesis consists of three independent papers on market design and matching theory. Each paper addresses a different matching model and environment, and together they represent a significant range of real-life matching problems which have not received enough attention.

In the first paper, we consider a new matching model to assign agents and objects on two sides of the market to each other. The new feature is that agents have consecutive acceptance intervals which are based on an exogenously given commonly known ranking of the objects. Each agent finds acceptable a consecutive set of objects with respect to this objective common ranking of the objects. Each agent has an individual preference ranking of the objects in her acceptance interval, which is determined independently of the common ranking of the objects. The main objective is to find new matching rules (algorithms) which are simpler and more efficient than the complicated conventional general algorithms for achieving a maximum matching which is Pareto-optimal, exploiting the special structure of consecutive acceptance intervals which are a common feature of many real-life matching problems.

Our main algorithm, the Block Serial Dictatorship Rule, starts with finding an ordering of agents based only on the acceptance interval structure and thus it is preference profile independent. This ordering is then used as a basis for a Serial Dictatorship which always finds a maximum Pareto-optimal matching, regardless of the agents’ preferences, for the solvable interval profiles that we characterize in the paper. These rules are also group strategy-proof.

In the second paper, I consider a matching model with minimum quotas for one side of the market. The main objective is to find algorithms which respect minimum quotas and find matchings which are both nonwasteful and fair if there exists such a matching. Otherwise, the algorithms find either fair or nonwasteful matchings. My algorithms, CNWF and FCNW (constrained nonwasteful fair and fair constrained nonwasteful), start with finding the range of possible matchings when there are minimum quotas. Then, using an innovative graph, they select the matchings which are both fair and nonwasteful, and if there do not exist such matchings, CNWF selects a constrained nonwasteful matching with a maximum degree of fairness, and FCNW selects a fair  matching with a maximum degree of nonwastefulness.

Furthermore, I show that my algorithms are applicable to the case where there are different types of agents, which is a key factor for matching markets that are concerned with diversity. Compared to the existing algorithms my algorithms are unified and more intuitive.

In the third paper, I consider a novel matching model in a dynamic environment. I define a dynamic environment in which the market is open for more than one period. At the beginning of each period new agents enter the market and the matched agents leave at the end. My model is motivated by couple match-making but the results apply to other similar matching markets as well. The main objective is to find an appropriate genderneutral algorithm with nice properties. I introduce a new algorithm which is based on the DA (Deferred Acceptance) algorithm and whose structure provides an opportunity to find two-sided optimal matchings, considering the requirements and characteristics of this dynamic marriage problem.

The novel structure of my algorithm, DM (Dynamic Marriage), allows both sides to make offers simultaneously and selects a matching which is optimal for both sides in a realistic dynamic setup whenever such a matching exists, and otherwise the algorithm finds a matching without favouring either side. This property makes the matching fair in the sense that it gives both sides a fair chance. I also study the dynamic strategy-proofness of the algorithm, as well as its stability and efficiency properties.

Compared to previous algorithms that apply to the marriage problem in a static or dynamic environment, my algorithm is more realistic since it allows for realistic dynamic preferences and for real-life marriage considerations. Furthermore, it is more integrated regarding the optimality of the two sides than other algorithms and avoids some of the common issues of dynamic algorithms.


identifier: Amini, Sara  (2021) USING VISUALIZATION AND MODELLING TO IMPROVE PRE-SURGICAL DECISION-MAKING IN BREAST RECONSTRUCTION SURGERY.  Masters thesis, Concordia University.

abstract:
A mastectomy surgery is the removal of breast tissue in an operation, typically as a treatment or a method of prevention for breast cancer. The loss of breasts can have a negative impact on the patient's body image, therefore in some cases a reconstruction surgery is done in order to restore the lost shape and volume with the use of silicone breast implants. In cases where only one breast is removed, retrieving the lost symmetry is also of high importance. Unfortunately, over 20% of the cases do not end up with a satisfying result. [1]
In this dissertation, we first studied the use of augmented reality (AR) in surgery planning before reconstruction surgery. To do this we developed an app for Microsoft HoloLens that allows the users to go through implant models as AR objects. In a preliminary study we assessed the usefulness of AR for decision making, as well as users' abilities to adapt to the AR environment.
In addition, we investigated the potential of using FEM to model breast and implant deformation to improve the decision-making process in reconstruction surgery. In order to do this, we designed a system that can choose the best match in terms of shape for the patient based on 3D models generated from pre-surgery MR images. A user study with 7 users was performed to assess the choices made by the proposed system and determine if it is capable of making the same decisions as human users.
The results of our experiments show the e_ectiveness of AR, as well as the potential of our proposed system, in improving the e_ciency of the decision-making process. AR is capable of providing better visualization, while our system is able to decrease the number of options that need to be considered when planning for reconstruction surgery.
	The results of this thesis suggest that computerized decision-making tools in breast reconstruction surgery can improve clinical decision-making, as well as reduce costs associated with this surgery.


identifier: Hu, Xiaolei  (2021) Development of Imine-based pH-responsive Polymeric Drug Delivery Systems.  Masters thesis, Concordia University.

abstract:
Block copolymer-based nanoassemblies that can disassemble in response to endogenous stimuli found in tumor tissues and cancer cells are promising candidates for intracellular drug delivery exhibiting enhanced release of encapsulated drugs. Owning to excellent colloidal stability during blood circulation (pH = 7.4) and accelerated drug release ability in tumoral and endo/lysosomal acidic environments (pH = 4.0-6.8), acid-responsive degradable nanoassemblies have emerged as promising nanocarriers for advanced drug delivery with precisely controlled drug release. Benzoic imine bond, which is cleavable at acidic pH while stable under physiological pH, has been widely explored for the design of novel acid-degradable block copolymers. A general method to synthesize imine-containing block copolymers involves the post-polymerization modification. Functional copolymer precursors bearing primary amine or carbonyl groups are conjugated with hydrophobic moieties, drug molecules, and hydrophilic species through in situ imine formation reaction.
My master research explores a new approach to synthesize acid-degradable block copolymers bearing pendant imine groups (ImPs). The approach explores direct polymerization of a novel imine-containing methacrylate utilizing controlled radical polymerization techniques, enabling the synthesis of well-controlled ImPs with tunable functionalities. The resultant ImPs undergo self-assembly to form nanometer-sized micelles, composed of hydrophilic poly(ethylene glycol) (PEG) corona and acid-degradable hydrophobic core bearing imine linkages. In response to tumoral and endo/lysosomal acidic environments, they disassemble through a change in hydrophilic/hydrophobic balance upon the cleavage of imine linkages to the corresponding aldehyde and primary amine. As a consequence, such acid-catalyzed hydrolysis of imine linkages in hydrophobic cores leads to the enhanced release of encapsulated doxorubicin (Dox, a clinically used anticancer drug).
The proof-of-concept results suggest that this robust approach is versatile to further design advanced nanoassemblies responding to dual/multiple stimuli, thus being more effective to intracellular drug delivery.


identifier: Li, Steven  (2021) Flight Management Systems for Hybrid-Electric Aircraft.  Masters thesis, Concordia University.

abstract: Driven by the recent development in advanced air mobility and the need to transition to more sustainable aviation, this thesis provides a framework for the optimal flight management strategy that minimizes the direct operating cost (DOC) of a hybrid-electric aircraft in steady cruise flight. Specifically, this thesis solves the minimum DOC problem for a hybrid-electric aircraft flying in a constant wind field. Polynomial equations whose positive real roots are the solutions of the minimum DOC problem are obtained using Pontryagin’s minimum principle. The optimal solutions are simulated using the shooting method. The results of the shooting method are also validated using MATLAB's built-in boundary-value problem solver. Furthermore, this thesis studies the specific case of hydrogen hybrid-electric aircraft based on data found in the literature and compares it to kerosene-fuel hybrid-electric aircraft. Additionally, the optimal energy management strategy of the hybrid-electric aircraft is investigated through the simulations of multiple scenarios for two different aircraft.


identifier: Edmonds, Lorrie  (2021) Desertsoundlore: Deep Listening to Aura in the Mojave Desert.  Masters thesis, Concordia University.

format: text audio audio

abstract:
Desertsoundlore: Deep Listening to Aura in the Mojave Desert
By Lorrie Edmonds

This thesis engages with the notion of Walter Benjamin’s ‘aura’ through impressions of sonic aura as discovered within the soundscapes of a kilometre of rural desert situated in North Joshua Tree, California. By employing a methodology of immersive listening techniques and a Harawayan lens of ‘making-kin’, explorations involved focused aural attention to ephemeral soundscape moments, soundwalking into wild protected land and prioritization of respect-laden empathy in the reception of sound events onsite. This paper discusses overbearing constraints imposed by desert climates, the ubiquity of technological reproductions of subjective auratic experience in our contemporary world and the potential for the withering of sonic aura once its recorded atmospheric sounds have been decontextualized from their original site. This paper also discusses the struggle to represent the authenticity of an immersive listening experience at a site of aura through electroacoustic sound works when the recordist’s memory of perceived auratic moments become unstable through cracks in post-site information, understanding and reflection over time.

Keywords: research-creation, aura, sonic aura, immersive listening, authenticity, Mojave Desert.


identifier: Ge, Bingwei  (2021) Statistical Framework Based on the Weighted Generalized Gaussian Mixture Model : Application to Robust Point Clouds Registration and Single Target Tracking.  Masters thesis, Concordia University.

abstract:
Due to the introduction of the shape parameter, generalized Gaussian has better modelling capabilities than the Gaussian distribution. Therefore it is broadly used in various fields. Based on this, we established a statistical framework for the data-weighted multivariate generalized Gaussian mixture model (WMGGMM). By extending the traditional EM algorithm, we obtain the model parameters in non-closed forms and then apply an iterative method employing the fixed point equation to get the values of the components' mean and covariance. The Newton-Raphson approach is used to update the shape parameters. The complexity of the model is automatically determined by the minimum message length (MML) criterion. This thesis implements the proposed framework to two challenging tasks: point clouds registration and single-target visual tracking. The data weighting approach considers different techniques depending on the specific application's needs. In the first application, we adopt k-nearest neighbours to supply greater weight to points with high density, highlighting the main structure of the entity point cloud object while reducing noise. In the second application, the distance significance is based on the spatial kernel, which means pixels closer to the center of the target candidate ellipse have more contributions.

In the first application, WMGGMMs describe the target point cloud and the point cloud to be registered. Then the KL divergence between these two models will be used as the loss function for the stochastic optimization to obtain the best transformation model parameters, decreasing the probability of falling into a local optimum. The self-built point cloud is utilized to evaluate the performance of the algorithm on rigid registration. The results show that the algorithm significantly reduces the influence of outliers, enhances the robustness and accuracy of the algorithm, and effectively extracts the critical features of the object. In the second application, the preprocessing step of colour compression (image segmentation) improves the adaptability of the generalized Gaussian mixture model to the data while preserving the original image information as much as possible. According to the ratio of each pixel's responsiveness to the target and background model, segmentation weights of the pixels are obtained to guide the location and size update of the target. The performance of the proposed approach is experimentally verified on a public dataset and compared with other algorithms.


identifier: Jiang, Yuehao  (2021) The impact of corporate governance and state ownership on the default probabilities of Chinese firms.  Masters thesis, Concordia University.

abstract: Since 2014, the Chinese government has sanctioned corporate bond defaults of both state-owned and non-state-owned firms, which renders the country’s firms no longer immune to bankruptcy.  Actual defaults soared in 2015, and have again spiked since the onset of the global pandemic. This study investigates the impact of state ownership on corporate governance mechanisms on default risk of Chinese firms. There are some similarities observed: a) a non-linear relationship between inside ownership and default risk is observed for both state-owned and non-state-owned firms; b) Institutional ownership serves as a monitoring mechanism that reduces default risk, irrespective of state ownership.   There are also some striking differences:  non-state-owned ﬁrms with more independent boards are associated with higher default risk, while state-owned ﬁrms with larger boards and less independent boards have lower default risk. Pandemic effects are less severe for state-owned firms.


identifier: Madeline, Bogoch  (2021) Migratory Patterns: Media, Materiality, and Circulation in Moving Image Works.  Masters thesis, Concordia University.

abstract: Despite over a century of struggle against the myth of the artist as a singular genius, there persists a tendency to disregard the impact of circulation and viewership on the meaning of an artwork. This thesis proposes that the materiality of media and the methods by which it circulates are decisive factors in the way moving image works become lodged in the social memory. Drawing on an interdisciplinary framework established between the fields of film studies, media studies, cultural theory, and art history, these themes are developed through the analyses of three video works: Todd Haynes’ Superstar; The Karen Carpenter Story (1987), David Cronenberg’s Videodrome (1983), and Hito Steyerl’s Liquidity Inc. (2014). An exploration of the general relationship between social processes and technology leads to an analysis of the material characteristics of magnetic tape, and the impact this had on access and circulation. By tracking these historical developments through to the increasing dematerialization of digital video in the present day, this work elucidates the implications on, and entanglements between, image, economy, labour, and psyche in the contemporary moment. This thesis works to redistribute a portion of agency to those who bootleg, analyze, archive, view, and share artworks. It reveals how the materiality and circulation of images are important indicators for how these processes will be conducted, and ultimately dictate the way these films influence socio-historical memory.


identifier: Al makhadmeh, Wala'a  (2021) Shrinkage Of Alkali-Activated Slag: Mitigation Techniques.  PhD thesis, Concordia University.

abstract:
This thesis presents a simultaneous investigation of the autogenous and total drying shrinkage behavior of Alkali-activated slag (AAS) as a binder. AAS binders are receiving global interest as a potentially sustainable alternative binding material due to their environmental benefits, impressive mechanical properties performance, and non-heat curing requirement. However, high shrinkage and cracking tendencies are hindering the effectiveness of AAS binders and are the main obstacles that must be addressed before widespread use.
This dissertation addresses the substantial shrinkage in the AAS system by simultaneously investigating the autogenous and total drying shrinkage behavior of AAS. Applying three main mitigation strategies, this research presents novel findings to which show significant reductions in AAS’s shrinkage. Firstly, the effect of activator nature on shrinkage was studied, which highlighted the sensitivity of AAS systems to various levels of silicate modulus (Ms) and sodium oxide (Na2O%).
The findings highlighted the critical interactions between AAS pore solution compositions and shrinkage-reducing admixture (SRA) efficiencies as a shrinkage mitigation technique.  Overall, a 62% and 41% reduction in the measured autogenous and total drying shrinkage were acheived, respectively.
The second mitigation strategy is the internal curing of AAS by superabsorbent polymers (SAPs). Specifically, the influence of SAPs on reaction kinetics, pore structure, and microstructure of AAS was studied based upon the SAPs desorption kinetics (i.e., concentrations and release timing). SAPs significantly mitigated the autogenous and total drying shrinkage up to 90% and 30%, respectively.
Finally, the third strategy combines the benefits of both SRAs and SAPs to address the shrinkage behavior of AAS. At the time of submission, this combination approach has not been investigated to the best of our knowledge. This novel approach investigated the potential interactions between SRA molecules and SAPs networks, yielding enhancement in mitigation of the total drying shrinkage by around 69%. This effect is attributed to the comprehensive influence of both the surface tension and internal relative humidity.


identifier: Carbonneau, Alexandre ORCID: https://orcid.org/0000-0003-3292-3283 <https://orcid.org/0000-0003-3292-3283>  (2021) Pricing and hedging financial derivatives with reinforcement learning methods.  PhD thesis, Concordia University.

abstract: This thesis studies the problem of pricing and hedging financial derivatives with reinforcement learning. Throughout all four papers, the underlying global hedging problems are solved using the deep hedging algorithm with the representation of global hedging policies as neural networks. The first paper, "Equal Risk Pricing of Derivatives with Deep Hedging'', shows how the deep hedging algorithm can be applied to solve the two underlying global hedging problems of the equal risk pricing framework for the valuation of European financial derivatives. The second paper, "Deep Hedging of Long-Term Financial Derivatives'', studies the problem of global hedging very long-term financial derivatives which are analogous, under some assumptions, to options embedded in guarantees of variable annuities. The third paper, "Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments'', studies derivative prices generated by the equal risk pricing framework for long-term options when shorter-term options are used as hedging instruments. The fourth paper, "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures'', investigates the use of non-translation invariant risk measures within the equal risk pricing framework.


identifier: Occhiuto, Anna-Maria  (2021) The Invisible Shift: The Mental Load of Motherhood.  Masters thesis, Concordia University.

abstract: Mothers sometimes use the term “mental load” to describe the day-to-day experience of managing a home and family, an invisible workload that has received little attention in social research and in the discourses around the gendered division of labour. The aim of this study is to elaborate the experience of domestic cognitive work of a small sample of mothers from Quebec, Canada, using qualitative in-depth interviews. Through a critical grounded theory analysis, the main findings showed two overarching motivations for the mental load of mothers: (1) to manufacture household and family efficiency, and (2) to cultivate family well-being and happiness. Using social reproduction theory, I suggest a reflection of the mental load as an integral process to late capitalist reproduction, where mothers develop what I call an internalized manager to organize, plan, strategize, monitor, worry, forecast, negotiate, and remember information that mediate and protect the household against capital’s rising downward pressure on the time and resources available for workers’ reproduction.


identifier: Mohammad, Karamat  (2021) Elucidating the Effects of Different Aging-Delaying Interventions on the Cellular Lipidome and Metabolome of Budding Yeast.  PhD thesis, Concordia University.

abstract: In studies presented in this Thesis, I developed novel methods of liquid chromatography coupled with tandem mass spectrometry for the identification and quantitation of the cellular lipidomes and metabolomes of budding yeast. Both these methods are versatile, robust and sensitive. They provide several other advantages over the advanced methods currently employed for the mass spectrometry-based quantitative analyses of cellular lipids and water-soluble metabolites. Using the mass spectrometry-based methods of quantitative lipidomics and metabolomics, I investigated mechanisms through which several aging-delaying (geroprotective) interventions extend the longevity of budding yeast by specifically targeting the metabolism and transport of cellular lipids and water-soluble metabolites. My studies provided evidence that the extract from Salix alba’s bark, called PE21, slows the chronological aging of budding yeast because it lowers the intracellular concentrations of free fatty acids below a toxic threshold. The resulting weakening of an aging-associated liponecrotic form of regulated cell death increases the probability of cell survival during the entire process of chronological aging. My studies suggested two additional mechanisms by which a PE21-dependent reorganization of the cellular lipidome delays the chronological aging of budding yeast. In one of these additional mechanisms, the PE21-dependent rise in the concentrations of endoplasmic reticulum-generated phospholipids activates the pro-longevity process of unfolded protein response in the endoplasmic reticulum. In the other additional mechanism, the PE21-dependent changes in the composition of mitochondrial membrane lipids create a pro-longevity pattern of mitochondrial functionality. I also applied the mass spectrometry-based lipidomics to assess how three different geroprotectors (namely, caloric restriction, tor1Δ mutation and lithocholic acid) influence the lipidomes of quiescent and nonquiescent yeast cells purified using Percoll density gradient centrifugation. My data showed that caloric restriction reorganizes the lipidomes of these cells differently than two other geroprotectors. I proposed two mechanisms by which the caloric restriction-specific lipidome reorganization might slow the chronological aging of quiescent and nonquiescent yeast cells. Moreover, I used my non-targeted metabolomics method to investigate how three different geroprotective interventions (i.e., caloric restriction, tor1Δ mutation and lithocholic acid) affect the intracellular water-soluble metabolome of chronologically aging budding yeast. My findings provide evidence that the three different geroprotectors create distinct metabolic patterns throughout the budding yeast’s entire chronological lifespan. My study identified a distinct metabolic pattern created by the caloric restriction geroprotector. I found two characteristic features that distinguish the caloric restriction-specific metabolic pattern from the cellular metabolism patterns created by the tor1Δ and lithocholic acid geroprotectors. One characteristic feature that distinguishes the caloric restriction-specific metabolic design is its ability to suppress the biosynthesis of methionine, S-adenosylmethionine and cysteine from aspartate, sulfate and 5-methyltetrahydrofolate throughout the chronological lifespan. The other characteristic feature of the caloric restriction-specific metabolic pattern is a decline in the intracellular concentration of ATP, a rise in the intracellular concentrations of AMP and ADP, and an increase in the ADP:ATP and AMP:ATP ratios at various phases of chronological aging.


identifier: Khullar, Chirag  (2021) Development of a warehouse slotting model to improve picking performance.  Masters thesis, Concordia University.

abstract: Congestion during picking operations in warehouse with mixed aisles (narrow aisles and wide aisles) has rarely been studied in current literature in the context of warehouse slotting (i.e. arrangement of inventory in warehouse). This study aims at improving the picking efficiency of the Asmodee Canada Inc. warehouse. Using a combination of clustering slotting heuristics and popularity-based slotting heuristics, a re-slotting policy was developed. Furthermore, to provide a robust re-slotting with limited number of items moves, a healing technique based on urgency score was developed. Use of a process control chart to monitor the picking performance of Asmodee’s warehouse and hence to signal healing was suggested. Using picking simulation, we find that when the re-slotting heuristics is used, there is substantial reduction in distance travelled of up to 29% and waiting times due to congestion can be reduced by as much as 85%. The healing technique also decreased distance travelled and waiting times. However, as the number of items moved in healing constitute on average less than 5% of the items, such improvements are limited. The distance traveled was reduced by as high as 9.4% in some aggregated orders and waiting time reduction was as high as 29.1%. The techniques developed in this paper will help Asmodee Canada Inc. in improving their picking operations. It will also help to build better strategies for warehouses having mixed aisles, where in aisle congestion is an issue to consider while re-slotting.


identifier: Quan, Jianning  (2021) Pose Estimation and Object Detection using Deep Convolutional Networks.  Masters thesis, Concordia University.

abstract:
Human pose estimation and object detection are fundamental problems in computer vision and autonomous systems with applications ranging from healthcare and sports to surveillance, autonomous driving and traffic monitoring. The task of 3D human pose estimation is to predict the positions of a person’s joints, while the goal of object detection is to identify the object category and locate the position using a bounding box for every known object within an image or video. The contributions in this thesis are two-fold. One is to tackle the 3D human pose estimation problem in the graph-theoretic setting. More specifically, we introduce a higher-order graph convolutional framework with initial residual connections for 3D-to-2D pose estimation. The proposed approach is derived from implicit fairing on graphs using a scale-dependent graph Laplacian filtering scheme. Using multi-hop neighborhoods for node feature aggregation, our model is able to capture the long-range dependencies between body joints. Moreover, our approach alleviates the oversmoothing problem caused by repeated graph convolutions, preventing the learned feature
representations from converging to similar values thanks in part to residual connections with the first layer of the network. These residual connections are integrated by design in our network architecture, and help ensure that the learned feature representations retain important information from the initial features of the input layer as the network depth increases. Experiments and ablations studies conducted on a standard benchmark demonstrate the effectiveness of our model, achieving superior performance over strong baseline methods for 3D human pose estimation.
The other contribution consists of designing a single-stage object detection model for aerial imagery using a class-balanced loss function in conjunction with a feature pyramid network in an effort to mitigate the data imbalance problem without the need to rely on data augmentation. The
key benefit of using the class-balanced focal loss is the ability to adjust the contributions of minority classes to the loss function with the aim to tackle the class imbalance problem, allowing our model to detect different classes evenly. The performance of our proposed object detection model is demonstrated through extensive experiments on a standard aerial image benchmark, achieving comparable or better object detection results in comparison with competing baselines.


identifier: Kroft, Emily  (2021) The effects of urban density on the provision of multiple health related ecosystem services.  Masters thesis, Concordia University.

abstract: North American cities are currently expanding at an unprecedented rate. Rapid growth in urban development has sparked debate about how to grow cities in a way that minimizes environmental impact and provides ecological benefits to people. Part of this conversation has involved the idea that urban areas should be densely built, so as to minimize their environmental impact, and promote sustainable development goals. However, there is minimal research available on whether there may be a point at which urban areas become too dense for ecosystem service provision (the provision of benefits to humans by nature). Our research explores the relationship between urban density and ecosystem service provision by measuring indicators of health related ecosystem services (temperature regulation, air pollution regulation, and green space accessibility) at 250 study sites in Montreal across a range of building densities (ranging from 0-100%) and population densities (number of households). Using data derived from Landsat-8 and SENTINEL 5P images as well as GIS based analyses, this study addresses the questions: 1) How does building density and its associated landscape features affect multiple health-based ecosystem service indicators? And 2) Is population density related to the provision of ecosystem services at the scale of investigation once building density is accounted for? Results indicate that higher building densities lead to decreases in temperature regulation, but do not impact air quality regulation. High population density sites tend to be more exposed to high temperatures, but are not more exposed to high levels of air pollution. We did not find a statistically significant relationship between household density and distance to public green space, and although marginally significant, the relationship between building density and distance to public green space was weak. Area of private green space per household is negatively correlated with both building density and population density. These results indicate that while some ecosystem services are unaffected by densification, maintenance of multiple services may require creative solutions at the interface of ecology, planning and design.


identifier: BERBERIAN, GARABED  (2021) Shareholder Litigation and Anti-Takeover Provisions.  Masters thesis, Concordia University.

abstract: In this paper, we analyze the impact of anti-takeover provisions (ATPs) on shareholder litigation. In theory, ATPs could result in an increased propensity for litigation if they block the market for corporate control and leave aggrieved shareholders looking for an alternative for redress in the form of litigation, or in a decreased propensity for litigation if they directly block the ability to litigate. Our results indicate that the net effect of an index of ATPs on litigation is unclear. However, individual ATPs have an impact on litigation. Some that directly block specific avenues of litigation appear to reduce the probability of litigation, while others that do not do so increase the probability of litigation. Our results indicate that the overall lack of a relationship between an ATP index and litigation is driven by these two opposing forces. We conclude that the relationship between ATPs and takeovers and the concomitant effect on firm governance is not limited to a simple reduction in the probability of takeovers but is more complex, including litigation and possibly other aspects of corporate governance.


identifier: Castonguay, Tristan  (2021) Evaluating a dynamic moment of inertia bat in baseball: the influence on swing speed, training, and infrared technology.  Masters thesis, Concordia University.

abstract: This dissertation reports the results of multiple quantitative investigations looking at the effect of using a dynamic moment of inertia bat prototype. This dissertation includes four (4) experimental research projects. The four (4) experimental research projects look at different ways to measure the efficacy of a dynamic moment of inertia bat to improve swing speed in baseball and softball. The findings suggest that a dynamic moment of inertia bat can be used as an effective warm-up tool and an effective training tool to improve bat swing speed in competitive baseball and softball players. The findings suggest that a dynamic moment of inertia bat is superior to a weighted bat as a warm-up tool because it negates the kinesthetic illusion. When athletes combine strength training and swing training using a dynamic moment of inertia bat over a 6-week training period it can lead to a significant improvement in bat swing speed that is superior to the improvement following a normal warm-up. The last experiment confirmed that using the dynamic moment of inertia bat can replicate a high-intensity warm-up and be effective as a warm-up tool. Baseball and softball players should consider using a dynamic moment of inertia to improve their bat swing speed during their training and as an on-deck warm-up tool before their at-bat performance.


identifier: Hasannezhad, Mojtaba  (2021) Speech Enhancement with Improved Deep Learning Methods.  PhD thesis, Concordia University.

abstract:
In real-world environments, speech signals are often corrupted by ambient noises during their acquisition, leading to degradation of quality and intelligibility of the speech for a listener. As one of the central topics in the speech processing area, speech enhancement aims to recover clean speech from such a noisy mixture. Many traditional speech enhancement methods designed based on statistical signal processing have been proposed and widely used in the past. However, the performance of these methods was limited and thus failed in sophisticated acoustic scenarios. Over the last decade, deep learning as a primary tool to develop data-driven information systems has led to revolutionary advances in speech enhancement. In this context, speech enhancement is treated as a supervised learning problem, which does not suffer from issues faced by traditional methods. This supervised learning problem has three main components: input features, learning machine, and training target. In this thesis, various deep learning architectures and methods are developed to deal with the current limitations of these three components.

First, we propose a serial hybrid neural network model integrating a new low-complexity fully-convolutional convolutional neural network (CNN) and a long short-term memory (LSTM) network to estimate a phase-sensitive mask for speech enhancement. Instead of using traditional acoustic features as the input of the model, a CNN is employed to automatically extract sophisticated speech features that can maximize the performance of a model. Then, an LSTM network is chosen as the learning machine to model strong temporal dynamics of speech. The model is designed to take full advantage of the temporal dependencies and spectral correlations present in the input speech signal while keeping the model complexity low. Also, an attention technique is embedded to recalibrate the useful CNN-extracted features adaptively. Through extensive comparative experiments, we show that the proposed model significantly outperforms some known neural network-based speech enhancement methods in the presence of highly non-stationary noises, while it exhibits a relatively small number of model parameters compared to some commonly employed DNN-based methods.

Most of the available approaches for speech enhancement using deep neural networks face a number of limitations: they do not exploit the information contained in the phase spectrum, while their high computational complexity and memory requirements make them unsuited for real-time applications. Hence, a new phase-aware composite deep neural network is proposed to address these challenges. Specifically, magnitude processing with spectral mask and phase reconstruction using phase derivative are proposed as key subtasks of the new network to simultaneously enhance the magnitude and phase spectra. Besides, the neural network is meticulously designed to take advantage of strong temporal and spectral dependencies of speech, while its components perform independently and in parallel to speed up the computation. The advantages of the proposed PACDNN model over some well-known DNN-based SE methods are demonstrated through extensive comparative experiments.

Considering that some acoustic scenarios could be better handled using a number of low-complexity sub-DNNs, each specifically designed to perform a particular task, we propose another very low complexity and fully convolutional framework, performing speech enhancement in short-time modified discrete cosine transform  (STMDCT) domain. This framework is made up of two main stages: classification and mapping. In the former stage, a CNN-based network is proposed to classify the input speech based on its utterance-level attributes, i.e., signal-to-noise ratio and gender. In the latter stage, four well-trained CNNs specialized for different specific and simple tasks transform the STMDCT of noisy input speech to the clean one. Since this framework is designed to perform in the STMDCT domain, there is no need to deal with the phase information, i.e., no phase-related computation is required. Moreover, the training target length is only one-half of those in the previous chapters, leading to lower computational complexity and less demand for the mapping CNNs. Although there are multiple branches in the model, only one of the expert CNNs is active for each time, i.e., the computational burden is related only to a single branch at anytime. Also, the mapping CNNs are fully convolutional, and their computations are performed in parallel, thus reducing the computational time. Moreover, this proposed framework reduces the latency by %55 compared to the models in the previous chapters. Through extensive experimental studies, it is shown that the MBSE framework not only gives a superior speech enhancement performance but also has a lower complexity compared to some existing deep learning-based methods.


identifier: Vieira, Henrique ORCID: https://orcid.org/0000-0002-4928-3668 <https://orcid.org/0000-0002-4928-3668>  (2022) Measuring, modeling and assessing evaporative fluxes over an integrated lake-wetland system in Southern Quebec.  Masters thesis, Concordia University.

abstract: Evapotranspiration (ET) is the second largest component of the hydrological cycle globally and a major factor in surface energy balance. Despite its importance, quantifying evapotranspiration presents high uncertainties due to limitations in available data and modeling approaches. This thesis provides – for the first time – a temporally high-resolution ET dataset (30 minutes) using Eddy Covariance method for a lake-wetland duo in Southern Québec during 2020’s growing season, during the Covid-19 pandemic lockdown. In addition, this thesis benchmarks the performance of 40 existing ET models – the largest number of empirical ET models intercompared to date – across different time scales, times of the day and times of the season. The benchmarking effort uses a non-dominated sorting framework with multiple goodness-of-fit measures to rank models. In general, the most non-falsified models in the marsh are Carpenter (aerodynamic), McMillan (aerodynamic), Kimberley-Penman (combination) and Stephens-Stewart (temperature-hybrid). In the lake, Hamon’s (temperature) equation remains non-falsified across most scenarios. Comparing continuous simulations in the two landscapes, the expected Nash-Sutcliffe Efficiency of non-falsified models is consistently higher in the marsh across all timescales from half-hour to one month and different times of the season. Considering different diurnal segments, the performance of non-falsified models becomes comparable in daytime and strictly better in the lake during nighttime. ET was better estimated during daytime and nighttime separately than full days. Overestimation of ET was observed during local temperature peaks preceded by prolonged net radiation peaks without precipitation, which potentially points at models’ inability to capture the effect of stomata closure of the canopy during heatwaves. Capturing evapotranspiration in wetlands and lakes requires more physically-based parameterizations to represent thermal and biological dynamics at weekly and finer scales. This study also provides evidence for the necessity of using multi-objective ranking to benchmark evapotranspiration and points at strategic directions for future developments.


identifier: Afshar, Parnian  (2021) Capsule Network-based Radiomics: From Diagnosis to Treatment.  PhD thesis, Concordia University.

abstract: Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals have resulted in a surge of significant interest in ``radiomics". Radiomics is an emerging and relatively new research field, which refers to semi-quantitative and/or quantitative features extracted from medical images with the goal of developing predictive and/or prognostic models.  Radiomics is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional radiomics workflow is, typically, based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Clinical application of hand-crafted radiomics is, however, limited by the fact that features are pre-defined and extracted without taking the desired outcome into account. The aforementioned drawback has motivated trends towards development of deep learning-based radiomics (also referred to as discovery radiomics). Discovery radiomics has the advantage of learning the desired features on its own in an end-to-end fashion. Discovery radiomics has several applications in disease prediction/ diagnosis. Through this Ph.D. thesis, we develop deep learning-based architectures to address the following critical challenges identified within the radiomics domain. First, we cover the tumor type classification problem, which is of high importance for treatment selection. We address this problem, by designing a Capsule network-based architecture that has several advantages over existing solutions such as eliminating the need for access to a huge amount of training data, and its capability to learn input transformations on its own. We apply different modifications to the Capsule network architecture to make it more suitable for radiomics. At one hand, we equip the proposed architecture with access to the tumor boundary box, and on the other hand, a multi-scale Capsule network architecture is designed. Furthermore, capitalizing on the advantages of ensemble learning paradigms, we design a boosting and also a mixture of experts capsule network. A Bayesian capsule network is also developed to capture the uncertainty of the tumor classification. Beside knowing the tumor type (through classification), predicting the patient's response to treatment plays an important role in treatment design. Predicting patient's response, including survival and tumor recurrence, is another goal of this thesis, which we address by designing a deep learning-based model that takes not only the medical images, but also different clinical factors (such as age and gender) as inputs. Finally, COVID-19 diagnosis, another challenging and crucial problem within the radiomics domain, is dealt with using both X-ray and Computed Tomography (CT) images (in particular low-dose ones), where two in-house datasets are collected for the latter and different capsule network-based models are developed for COVID-19 diagnosis.


identifier: Hindle, Meaghan, Dover, Geoffrey, Dakin, Christopher and Forbes, Patrick  (2021) The Adaptation of Lower Leg Muscles during Gait to Galvanic Vestibular Stimulation.  Masters thesis, Concordia University.

creator:
Hindle, Meaghan
Dover, Geoffrey
Dakin, Christopher
Forbes, Patrick

abstract:
Fall prevention is a growing concern as our elderly population reflects a greater proportion of our population. Some muscles have been shown to exhibit a decrease in response as they become accustomed to certain types of stimulation and perturbations, dependant on the task and the muscle observed. This is possible by analyzing the relationship between muscle activity and stimulation in human participants.

We measured the response to galvanic vestibular stimulation in the medial gastrocnemius and soleus muscles. Galvanic vestibular stimulation is when electric current is sent through pads placed over the mastoid processes on each side, which are found behind the ears. This induces a nerve impulse to be generated and sent from the vestibular nerve to the appropriate processing centers in the brain. This results in the brain thinking the body is losing its balance, since they head is being stimulated to change its orientation, causing a sway in the posture.

15 healthy participants were recruited from the local university, and screened for any neurological disease. The goal was to determine if the medial gastrocnemius and soleus muscles also experience a decrease in response, over one hour of walking with the stimulation on a treadmill. Through the analysis of the relationship between muscle activity and stimulation, we did not find any significant decrease in muscle activity over time. The results cannot however be widespread to other muscles or other tasks, and so more research is needed to better understand how muscle response to dynamic tasks is organized.


identifier: El Tanab, Manal  (2021) Congestion Control for Massive Machine-Type Communications: Distributed and Learning-Based Approaches.  PhD thesis, Concordia University.

abstract:
The Internet of things (IoT) is going to shape the future of wireless communications by allowing seamless connections among wide range of everyday objects. Machine-to-machine (M2M) communication is known to be the enabling technology for the development of IoT. With M2M, the devices are allowed to interact and exchange data without or with little human intervention. Recently, M2M communication, also referred to as machine-type communication (MTC), has received increased attention due to its potential to support diverse applications including eHealth, industrial automation, intelligent transportation systems, and smart grids.

M2M communication is known to have specific features and requirements that differ from that of the traditional human-to-human (H2H) communication. As specified by the Third Generation Partnership Project (3GPP), MTC devices are inexpensive, low power, and mostly low mobility devices. Furthermore, MTC devices are usually characterized by infrequent, small amount of data, and mainly uplink traffic. Most importantly, the number of MTC devices is expected to highly surpass that of H2H devices. Smart cities are an example of such a mass-scale deployment. These features impose various challenges related to efficient energy management, enhanced coverage and diverse quality of service (QoS) provisioning, among others.

The diverse applications of M2M are going to lead to exponential growth in M2M traffic. Associating with M2M deployment, a massive number of devices are expected to access the wireless network concurrently. Hence, a network congestion is likely to occur. Cellular networks have been recognized as excellent candidates for M2M support. Indeed, cellular networks are mature, well-established networks with ubiquitous coverage and reliability which allows cost-effective deployment of M2M communications. However, cellular networks were originally designed for human-centric services with high-cost devices and ever-increasing rate requirements. Additionally, the conventional random access (RA) mechanism used in Long Term Evolution-Advanced (LTE-A) networks lacks the capability of handling such an enormous number of access attempts expected from massive MTC. Particularly, this RA technique acts as a performance bottleneck due to the frequent collisions that lead to excessive delay and resource wastage. Also, the lengthy handshaking process of the conventional RA technique results in highly expensive signaling, specifically for M2M devices with small payloads. Therefore, designing an efficient medium access schemes is critical for the survival of M2M networks.

In this thesis, we study the uplink access of M2M devices with a focus on overload control and congestion handling. In this regard, we mainly provide two different access techniques keeping in mind the distinct features and requirements of MTC including massive connectivity, latency reduction, and energy management. In fact, full information gathering is known to be impractical for such massive networks of tremendous number of devices. Hence, we assure to preserve the low complexity, and limited information exchange among different network entities by introducing distributed techniques. Furthermore, machine learning is also employed to enhance the performance with no or limited information exchange at the decision maker. The proposed techniques are assessed via extensive simulations as well as rigorous analytical frameworks.

First, we propose an efficient distributed overload control algorithm for M2M with massive access, referred to as M2M-OSA. The proposed algorithm can efficiently allocate the available network resources to massive number of devices within relatively small, and bounded contention time and with reduced overhead. By resolving collisions, the proposed algorithm is capable of achieving full resources utilization along with reduced average access delay and energy saving. For Beta-distributed traffic, we provide analytical evaluation for the performance of the proposed algorithm in terms of the access delay, total service time, energy consumption, and blocking probability. This performance assessment accounted for various scenarios including slightly, and seriously congested cases, in addition to finite and infinite retransmission limits for the devices. Moreover, we provide a discussion of the non-ideal situations that could be encountered in real-life deployment of the proposed algorithm supported by possible solutions. For further energy saving, we introduced a modified version of M2M-OSA with traffic regulation mechanism.

In the second part of the thesis, we adopt a promising alternative for the conventional random access mechanism, namely fast uplink grant. Fast uplink grant was first proposed by the 3GPP for latency reduction where it allows the base station (BS) to directly schedule the MTC devices (MTDs) without receiving any scheduling requests. In our work, to handle the major challenges associated to fast uplink grant namely, active set prediction and optimal scheduling, both non-orthogonal multiple access (NOMA) and learning techniques are utilized. Particularly, we propose a two-stage NOMA-based fast uplink grant scheme that first employs multi-armed bandit (MAB) learning to schedule the fast grant devices with no prior information about their QoS requirements or channel conditions at the BS. Afterwards, NOMA facilitates the grant sharing where pairing is done in a distributed manner to reduce signaling overhead. In the proposed scheme, NOMA plays a major role in decoupling the two major challenges of fast grant schemes by permitting pairing with only active MTDs. Consequently, the wastage of the resources due to traffic prediction errors can be significantly reduced. We devise an abstraction model for the source traffic predictor needed for fast grant such that the prediction error can be evaluated. Accordingly, the performance of the proposed scheme is analyzed in terms of average resource wastage, and outage probability. The simulation results show the effectiveness of the proposed method in saving the scarce resources while verifying the analysis accuracy. In addition, the ability of the proposed scheme to pick quality MTDs with strict latency is depicted.


identifier: Rezasoltani, Shirin  (2021) Towards Semantic-Empowered Communication.  Masters thesis, Concordia University.

abstract:
The issues of timeliness and accuracy are becoming increasingly important with the advent of heterogeneous applications/networks and standardization of new services in areas of Cyber-Physical systems where the popular performance measures such as the throughput, error rate and packet loss lose their intuitive interpretation and new formal metrics must be defined on which the intuition will be built. To solve this problem, Semantic of Information has been recently introduced: the purpose and the final utility of the delivered data should be considered, and the performance is to be related to the semantics.

In this thesis, we explore two major issues in the area of semantic communications. First, we investigate the issue of wireless sensing in non-real time systems when the time delivery constraints are not restricted to be in real domain and the system tolerates delay in accessing the measurement results. To illustrate the application of this principle, we use the Markovian Gaussian models, and assuming the information semantic, we study the optimal estimation methods to non-causally reconstruct the source signal. We derive the explicit expressions and optimal buffer management policies for the proposed information accuracy metric.

Second, we evaluate the impact of the erroneous wireless control feedback channel using the Age of Information, one particular metric for semantic communications to capture the freshness and timeliness of information in real-time applications. To mitigate the impact of the imperfect feedback channel on the system performance, we adopt a  Binary Asymmetric Channel model to control the detection accuracy of the control signals. We then compute the explicit expressions for the average Age of Information. Further, we show the optimum parameter design for the control channel model in order to minimize the average Age of Information.


identifier: Uludag, Pakize  (2021) Developing, Validating, and Incorporating a Rubric for Assessing the Construct of Integration into an EAP Program.  PhD thesis, Concordia University.

abstract:
Due to their authenticity as an academic writing task, integrated writing tests are widely used for evaluating English for Academic Purposes (EAP) students’ writing ability. However, apart from validation studies focusing on a common set of standardized test rubrics, little research has explored the construct of integration or how to assess it effectively in second language (L2) writing classrooms. Thus, this dissertation designed and validated an analytic rubric for assessing the construct of integration in L2 writing while also examining students’ conceptualizations of integrated writing assessment in an EAP writing course.
Study 1 investigated which sub-constructs EAP instructors orient to when assessing L2 writers' integrated essays. Triangulation of data sources from instructor ratings, stimulated recall interviews, and textual analysis of the integrated essays informed the development of an analytic rubric with four categories (i.e., content, organization, source use and language use) for assessing classroom-based integrated writing tasks.
Study 2 focused on the validation of the rubric by employing a mixed-methods design, which involved a many-facet Rasch measurement analysis, semi-structured interviews with EAP instructors, and linguistic analysis of student essays for fluency, syntactic and lexical complexity, cohesion, and lexical diversity measures. Results from the Rasch model and textual analysis suggest the rubric is of good quality in terms of assessing one single construct and differentiating the students’ task performance across four levels. The instructor comments during the follow-up interviews contributed to the reformulation of descriptors.
Study 3 adopted a case study methodology and investigated L2 learners’ conceptualizations of integrated writing assessment and their use of the analytic rubric for self-assessment in an EAP writing course. Data sources included integrated writing samples that were evaluated by the students and their instructor, a writing self-efficacy questionnaire, individual retrospective interviews, and course materials. Qualitative analysis revealed themes related to three aspects of classroom-based integrated writing assessment: task requirements, task conditions, and instructor feedback. There was an overlap between students’ self-assessment and instructor evaluation of their integrated essays, suggesting that students could use the rubric effectively.


identifier: Yousefzadeh, Zeynab ORCID: https://orcid.org/0000-0002-9594-0753 <https://orcid.org/0000-0002-9594-0753>  (2021) Prospective Life Cycle Assessment in Surface Engineering: Case Studies on a Novel Thermal Spray Coating System and a Novel Coating Removal Method.  Masters thesis, Concordia University.

abstract: Life Cycle Assessment (LCA) is a framework for quantifying the potential environmental impact of products from raw material extraction through materials processing, manufacturing, distribution, use, and end-of-life. Prospective LCA estimates the future environmental impacts of emerging technologies. It can be used to support eco-design, such as green surface engineering. Prospective LCA is used in this thesis to compare emerging surface engineering and incumbent technologies. The first case study compares a novel thermal sprayed multi-layered alumina-nickel chromium resistive heating coating to heat tracing cables for pipe freeze protection. The coating system’s impacts are higher for fabrication but lower for use, making it environmentally preferable in areas with colder climates and non-renewable electricity mixes. Specific life expectancy and efficiency improvements were identified to achieve environmentally preferability in most locations. Alternate strategies include reducing the environmental impact of fabrication by using alternate materials or deposition processes and developing strategies for recovering and recycling coating materials. The second case study compared a novel pulse water jet (PWJ) technology with alkaline electrochemical cleaning for removing hard chromium from aircraft landing gear. If the PWJ system can be designed to remove the coating from workpieces with complex geometry, its environmental impact is expected to be lower due to its lower electricity consumption, chemical use, and waste management. The case studies demonstrate the value of using prospective LCA during early development, adopting a range of techniques for addressing uncertainty, and breaking down the results to provide developers with strategies for reducing environmental impact.


identifier: Nasr, Tony  (2021) Large-Scale Study of Internet-Connected Electric Vehicle Charging Station Management Systems: Discovery, Security Analysis and Mitigation.  Masters thesis, Concordia University.

abstract: The demand for Electric Vehicles (EVs) has been exponentially increasing, and to achieve sustainable growth, the industry dictated rapid development of the supporting infrastructure. This resulted in a subsequent increase in the number of deployed EV charging stations (EVCS) to fulfill charging demands. Moreover, while these Internet-connected EVCS are equipped with management systems (EVCSMS) to enable extended remote operations, the insecurity of their EVCSMS can open doors for various cyber attacks, threatening the availability, privacy and resiliency of EVCS users and the connected critical infrastructure. This requires building a reliable EV charging ecosystem that serves customer demands while ensuring the security of the Internet-enabled systems and the connected critical infrastructure against possible cyber attacks. Therefore, in this thesis, we propose a multi-stage framework for investigating the EVCS threat landscape by fingerprinting online EVCSMS and evaluating their (in)security from an adversary (external) point of view without having the privilege and level of access that the respective system developers have, thus providing a realistic perspective of the attack surface. The framework relies on extracting features from a small sample of EVCSMS to perform an iterative and extended discovery/fingerprinting process by leveraging existing device search engines and a sequence of classification/clustering approaches. Consequently, the security of the identified EVCSMS is assessed through in-depth vulnerability analysis. Specifically, we leverage reverse engineering and penetration testing techniques to perform a novel and comprehensive security and vulnerability analysis of the identified EVCSMS and their software/firmware implementations. Our systematic analysis unveils an array of vulnerabilities, which demonstrate the insecurity of the EVCSMS against remote cyber attacks. Considering the feasibility of such attacks, we discuss attack implications against the various stakeholders (i.e., the EVCS, users/operators, and the power grid). More importantly, we simulate the impact of practical cyber attack scenarios against the power grid, which result in possible service disruption and failure in the grid. Indeed, we leverage the framework to identify 27,439 EVCS hosts that are instrumented by 44 different EVCSMS products. Our in-depth analysis demonstrates the insecurity of EVCSMS at scale by identifying 120 vulnerabilities across the majority of the hosts (92%), representing mainly critical and/or high risk vulnerabilities (e.g., SQL injection) that lead to remote exploitation. While recommending countermeasures to mitigate future threats, our discoveries raise concerns about the lack of adequate security considerations in the design of the deployed EVCS, which will motivate vendors to take immediate action to patch their developed systems. Finally, our communication with the concerned parties resulted in positive responses from vendors such as Schneider Electric, who acknowledged our findings by reserving more than 20 CVEs, respectively. Moreover, we contribute towards the security of the EVCS ecosystem by providing our framework and knowledge to motivate vendors/developers towards evaluating and improving the security of their EVCSMS. We conclude this thesis by summarizing the main takeaways and discussing research gaps that pave the way for future work.


identifier: Petersen, Berkley  (2021) The Impact of Multisensory and Cognitive Load on Younger and Older Adults’ Cognitive-Motor Dual-Task Performance.  Masters thesis, Concordia University.

abstract: Maintaining postural control efficiently is dependent upon the coordination of motor, sensory and cognitive systems, all of which are subject to decline with aging. Evidence suggests that increased cognitive load, sensory loss and cognitive impairments alone reduce postural control, but rarely are these factors considered in conjunction. We therefore investigated how younger and older adults’ postural control was impacted by increased cognitive load, simulated vision impairment, and hearing loss. Using a Nintendo Wii Balance Board, 32 younger (M = 23.03 SD = 3.53), and 27 older adults, 16 with hearing loss, (M = 77.13 SD = 7.53) and 11 without hearing loss (M = 71.27 SD = 11.30), underwent five balance conditions (i.e., eyes closed, normal and low vision single- and dual-tasks). We found that as task complexity increased (i.e., presence of a visual and/or backwards counting task), postural control decreased. Younger adults outperformed older adults on all tests of postural control, whereas minimal variations in postural performance existed between older adults with and without hearing loss. Older adults with hearing loss had greater medial-lateral sway in single-task normal and low vision conditions. Positive dual-task postural costs were evident among all three groups, but no group differences existed. Under normal and low vision conditions, older adults without hearing loss displayed positive dual-task cognitive costs, while those with hearing loss experienced no costs, suggesting differences in task prioritization. Taken together, our results illustrate that aging impacts how increased cognitive load and the presence of vision impairment challenge can affect postural control.


identifier: Tafreshi Motlagh, Maryam  (2021) THE APPLICATION OF MULTIPLE IMPUTATION TO EXPLORE ALCOHOL CONSUMPTION AMONG YOUNG ADULTS AND ITS IMPACT ON BODY MASS INDEX.  Masters thesis, Concordia University.

abstract:
ABSTRACT
“The Application of Multiple Imputation to Explore Alcohol Consumption Among Young Adults and Its Impact on Body Mass Index”

Maryam Tafreshi Motlagh

Obesity is associated with health issues such as high blood pressure, cardiovascular disease, and cancer. Concurrently, excessive alcohol consumption and its negative health effects among young adults are growing global public health concerns. However, the studies on the association between alcohol consumption and weight gain are inconsistent. In particular, analyzing weight gain and alcohol consumption over time is complicated by data missingness. Indeed, one potential reason for the mixed findings could be how those studies addressed missing data. Missing data can occur due to a variety of reasons including loss to follow-up and various non-response cases. As a result, missing data can lead to loss of efficacy in data analysis and is one of the main challenges of longitudinal studies and large surveys. Therefore this thesis attempted to address missing data challenges by utilizing multiple imputation (MI). Generalized linear models were then conducted to assess the relationship between alcohol consumption with BMI, adjusting for age at baseline, sex, race, smoking, depression status, relationship status, employment status, student status and physical activities. Analyses were conducted separately for a complete-case dataset and the imputed dataset.

Data were from the Nicotine in Dependence in Teens (NDIT) cohort, a 20-year prospective cohort initiated in 1999 (n=1294, 52% female). Data were collected every three months when participants were in high school (grade 7 to grade 11; 1999-2005, 20 cycles). After high school, follow-up assessments were conducted approximately every four years (2007-08 and 2011-12 for cycles 21 and 22). The GLM results demonstrated that there was no association between alcohol consumption and BMI at cycle 21, but at cycle 22 a negative association was detected. Many similarities between the complete-case and MI general linear models were observed. However, estimates and standard errors were different between the two models, and were generally smaller in the MI models compared with the complete-case.

Although counterintuitive, the negative association is consistent with the existing literature. Preliminary evidence from the literature further suggests there is an interaction with age on the alcohol and obesity relationship. Indeed, it seems that alcohol consumption does not have an immediate impact on body mass among young adults, or it may have an inverse relationship. However, as alcohol remains one of the main contributing factors to chronic diseases such as high blood pressure, cardiovascular disease, and cancer, it is perhaps a latent effect that does not show any immediate positive relationship with symptoms of these diseases at early stages of young adulthood. Specific details about the alcohol use (such as type of beverage or calorie count) were not available in this study. While this is similar to other cross-sectional study limitations, they are important confounding variables that should be addressed in future studies. Although the statistical methodology is not consistently utilized in the alcohol and obesity risk literature, this thesis demonstrated the use of MI to produce unbiased estimates and smaller standard errors compared to the complete cases analysis. Further research is needed on the same cohort to further track the weight changes and other possible health problems due to alcohol use in the long-term.


identifier: Zhao, Wanting  (2021) Connection between Influencer Characteristics and Purchase Intention: The Mediating Role of Consumer Engagement.  Masters thesis, Concordia University.

abstract: With the growth of various social media websites in the past decade, a novel profession, online influencer, emerged and bloomed from numerous social media platforms. The question of could specific characteristics of online influencers further trigger consumer purchase intention arose. This research aims to clarify the connection between specific influencer characteristics and consumer purchase intention with the help of consumer engagement. In addition, consumer susceptibility to interpersonal influence, congruence between the brand image and the influencer’s image, and Hofstede’s six dimensions of cultural values were also incorporated as moderated variables further to test the link between influencer characteristics and purchase intention. Two conducted studies revealed that influencer expertise, trustworthiness, and likability positively influence purchase intention and are mediated by online consumer engagement. Moreover, we concluded from the results that people who are highly susceptible to both normative and informative inﬂuence were more likely to exhibit consumer engagement behavior with online influencers based on their expertise and trustworthiness than those who are not as influenced by others. The findings may provide some new insights for both consumer behavior research and brands and influencers who needed to bring their career to the next level.


identifier: Jamshidi, Hossein  (2021) Design and Implementation of a Drone-based Forest Fire Monitoring System Including an Exclusive Hardware-in-the-Loop Simulator.  Masters thesis, Concordia University.

abstract: The purpose of this study is to design a fire detection drone system with a unique hardware-in-the-loop (HIL) simulation architecture, mainly focusing on the search and localization algorithms and simulating thermal cameras to test computer vision-based detection algorithms. The autopilot hardware has been designed exclusively for this research work. The basic flight algorithm has been implemented in the autopilot firmware. To communicate and configure the autopilot, a ground control station (GCS) is developed. The GCS exchanges data with autopilot hardware using a serial port for both telemetry and HIL data links. A game engine (Unity3D) is used for implementing the simulator’s 3D graphics. To solve the rigid-body equations, the Unity3D built-in Nvidia PhysX system is utilized. The simulator exchanges data with the GCS using a UDP port. The GCS acts as a bridge between autopilot and simulator. To achieve real-time simulation performance, in most of the simulation systems and the GCS, multitasking is implemented. Furthermore, a simulated thermal camera with a raw image provider (similar to the actual hardware output) and a fire-making system in a forest-like environment has been developed to set fire to the simulated forest either at a specific location or randomly. The system consistency has been tested by performing some simulation tests and furthermore by testing the system in a real flying platform and testing the drone outdoor. Finally, the outcome of the system exhibited a good agreement with the autopilot as well as the guidance and navigation system in terms of the fire detection and positioning algorithms.


identifier: Cardoso de Sousa, Wanessa  (2021) Domestic Spaces of Affection: The Role of Vernacular Architecture, Material Culture and Memory in the Cases of two Brazilian Households.  Masters thesis, Concordia University.

abstract: This thesis explores the role of vernacular architecture, material culture and the establishment of spatial memories in the context of domestic environments. By critically analyzing the case of two Brazilian families, one living in Brazil and one living in Canada, I argue that Brazilian domestic vernacular architecture, objects used in interior decoration and memories related to household spaces can influence affection for a place. They perform as symbols of identity and belonging, demonstrating that the sense of affection can be associated with people, objects and buildings. The houses examined in this thesis have different backgrounds: the first family lives in northeastern Brazil in a home they started to build in the 1970s. The second family immigrated to Greater Montreal in the 2010s, where they moved to a house they did not construct. Through oral interviews, I investigated the collection of emplaced memories in specific domestic environments, the entwined sense of identity and space, and how people create their domestic settings through spatial practices. In addition to interviews, the participants drew memory maps and shared vernacular photographs and personal objects that illustrate their memories related to the house. This methodology helped me in analyzing examples of spatial practice concerning both self-built domestic vernacular architecture and found domestic spaces occupied and reshaped by the family, besides tracing the affective spatial relationships in each case.


identifier: Ronaghi, Farnoush  (2021) Deep Learning-based Information Fusion Frameworks for Stock Price Movement Prediction.  Masters thesis, Concordia University.

abstract: The challenges of modeling behaviour of financial markets, such as its high volatility, poor predictive behaviour, and its non-stationary nature, have continuously attracted attention of the researchers to employ advanced engineering methods. Within the context of financial econometrics, stock market movement prediction is a key and challenging problem. The research works reported in this thesis are motivated by the potentials of Artificial Intelligence (AI) and Machine Learning (ML)-based models, especially Deep Neural Network (DNN) architectures, for stock movement prediction. Considering recent progress in design and implementation of advanced DNN-based models, there has been a surge of interest in their application for predicting stock trends. In particular, the focus of the thesis is on utilization of information fusion to combine Twitter data with extended horizon market historical data for the task of price movement prediction. In this regard, the thesis made a number of contributions, first, the Noisy Deep Stock Movement Prediction Fusion (ND-SMPF) framework is proposed to extract news level temporal information; identify relevant words with highest correlation and effects on the stock trends, and; perform information fusion with historical price data. A real dataset is incorporated to evaluate performance of the proposed ND-SMPF framework. In addition, given that recent COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe, a unique COVID-19 related PRIce MOvement prediction (\CDATA) dataset is constructed. The objective is to incorporate effects of social media trends related to COVID-19 on stock market price movements. A novel hybrid and parallel DNN-based framework is then designed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (\SMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical market data and perform accurate price movement prediction during a pandemic crisis.


identifier: Bawa, Kamaljeet Kaur  (2021) Development of Dual Stimuli-responsive Degradable Polylactide-based Multifunctional Materials for Drug Delivery.  PhD thesis, Concordia University.

abstract:
Progress in the synthesis of amphiphilic block copolymers has contributed to promising advances in the construction of smart nanocarriers for anticancer drug delivery systems. Their excellent features such as biodegradability through enzymatic hydrolysis and biocompatibility have led to tremendous applications in the biomedical field. Polylactide (PLA) is a biocompatible and FDA-approved polymer which has been used as hydrophobic core-forming block in drug delivery vehicles. However, hydrophobicity and slow degradation are the two main drawbacks that limits its usage in drug delivery systems. An introduction of stimuli-responsive degradable (SRD) platform into the design of PLA-based polymeric drug delivery systems can overcome these challenges as they can precisely tune drug release kinetics to fit the therapeutic window of the encapsulated drug. However, most of the smart PLA-based block copolymers are designed to respond to a single stimulus and therefore, multi-stimuli responsive degradable systems in which the location, number and type of degradable linkages can be varied needs to be developed.
My PhD research was focused on the studies of dual stimuli-responsive degradation (DSRD) platform to synthesize advanced PLA-based nanomaterials. They were featured with different types and location of labile linkages, that cleaved in response to biological stimuli found in cancer cells and tumor tissues. In this thesis, I had developed robust strategies that allowed for the synthesis of three novel PLA-based SRD copolymers designed with a) reduction-responsive disulfide linkage and b) an acid-labile linkage. These synthetic strategies utilized a combination of ring opening polymerization (ROP), atom transfer radical polymerization (ATRP) and facile coupling reactions. The results demonstrated the feasibility to design and synthesize smart DSRD-based block copolymers which self-assemble to form colloidally-stable micellar aggregates and encapsulate hydrophobic dyes or drugs in the hydrophobic cores. Furthermore, the SRD-driven enhanced/controlled release of loaded cargoes suggested that DSRD strategy can offer the versatility and hold great potential in the development of intracellular drug delivery nanocarriers.


identifier: Locke, Steven ORCID: https://orcid.org/0000-0001-7401-2993 <https://orcid.org/0000-0001-7401-2993>  (2021) LogAssist: Assisting Log Analysis Through Log Summarization.  Masters thesis, Concordia University.

abstract: Logs contain valuable information about the runtime behaviors of software systems. Thus, practitioners rely on logs for various tasks such as debugging, system comprehension, and anomaly detection. However, logs are difficult to analyze due to their unstructured nature and large size. In this thesis, we propose a novel approach called LogAssist that assists practitioners with log analysis. LogAssist provides an organized and concise view of logs by first grouping logs into event sequences (i.e., workflows), which better illustrate the system runtime execution paths. Then, LogAssist compresses the log events in workflows by hiding consecutive events and applying n-gram modeling to identify common event sequences. We evaluated LogAssist on logs generated by one enterprise and two open source systems. We find that LogAssist can reduce the number of log events that practitioners need to investigate by up to 99%. Through a user study with 19 participants, we find that LogAssist can assist practitioners by reducing the time required for log analysis tasks by an average of 40%. The participants also rated LogAssist an average of 4.53 out of 5 for improving their experiences of performing log analysis. Finally, we document our experiences and lessons learned from developing and adopting LogAssist in practice. We believe that LogAssist and our reported experiences may lay the basis for future analysis and interactive exploration on logs.


identifier: Khademi, Hamidreza  (2021) Learning Pronunciation with Google Translate: Focus on English Past -ed.  Masters thesis, Concordia University.

abstract:
The limited amount of time is one of the most common problems that teachers and second language (L2) students face in language classrooms, particularly for pronunciation instruction. To address the problem, teachers often ask students to engage in self-directed and out-of-classroom activities (e.g., homework) by using technologies such as Text-to-Speech Synthesis (TTS) and Automatic-Speech-Recognition (ASR). However, little is known about the feasibility of using TTS and ASR combined (as found in Google Translate - GT) as pedagogical tools, nor do we understand what happens when the learner is asked to use the technology to learn on their own, outside of their classrooms.
Following a pre-test-post-test design, this mixed-methods one-shot study investigates the pedagogical affordances of GT’s TTS and ASR speech capabilities and how their use can help learners acquire L2 pronunciation. More specifically, the present study focuses on the acquisition of English past -ed pronunciation, a challenging learning target (in terms of phonological awareness, listening discrimination or perception, and production) in a teacher-guided semi-autonomous out-of-class context. Emulating the completion of a homework assignment, 20 Farsi-speaking English learners used GT’s TTS and ASR functions to learn the pronunciation of past -ed allomorphy (/d/, /t/, /id/) through various listening (via TTS) and speaking activities (via ASR).
Findings indicate that there were significant improvements in both the participants’ awareness and perception of the English past -ed allomorphy, thus confirming that GT and its speech capabilities can help learners acquire the target pronunciation feature in these two initial stages of L2 pronunciation development. However, in production (the subsequent stage), only one of the allomorphs (/id/) had improved by the end of the experiment.


identifier: Khorasani Ghassab, Vahid ORCID: https://orcid.org/0000-0001-7697-9364 <https://orcid.org/0000-0001-7697-9364>  (2021) Multi-frame reconstruction using super-resolution, inpainting, segmentation and codecs.  PhD thesis, Concordia University.

abstract:
In this thesis, different aspects of video and light field reconstruction are considered such as super-resolution, inpainting, segmentation and codecs. For this purpose, each of these strategies are analyzed based on a specific goal and a specific database. Accordingly, databases which are relevant to film industry, sport videos, light fields and hyperspectral videos are used for the sake of improvement.

This thesis is constructed around six related manuscripts, in which several approaches are proposed for multi-frame reconstruction. Initially, a novel multi-frame reconstruction strategy is proposed for lightfield super-resolution in which graph-based regularization is applied along with edge preserving filtering for improving the spatio-angular quality of lightfield. Second, a novel video reconstruction is proposed which is built based on compressive sensing (CS), Gaussian mixture models (GMM) and sparse 3D transform-domain block matching. The motivation of the proposed technique is the improvement in visual quality performance of the video frames and decreasing the reconstruction error in comparison with the former video reconstruction methods. In the next approach, student-t mixture models and edge preserving filtering are applied for the purpose of video super-resolution. Student-t mixture model has a heavy tail which makes it robust and suitable as a video frame patch prior and rich in terms of log likelihood for information retrieval. In another approach, a hyperspectral video database is considered, and a Bayesian dictionary learning process is used for hyperspectral video super-resolution. To that end, Beta process is used in Bayesian dictionary learning and a sparse coding is generated regarding the hyperspectral video super-resolution. The spatial super-resolution is followed by a spectral video restoration strategy, and the whole process leveraged two different dictionary learnings, in which the first one is trained for spatial super-resolution and the second one is trained for the spectral restoration.

Furthermore, in another approach, a novel framework is proposed for replacing advertisement contents in soccer videos in an automatic way by using deep learning strategies. For this purpose, a UNET architecture is applied (an image segmentation convolutional neural network technique) for content segmentation and detection. Subsequently, after reconstructing the segmented content in the video frames (considering the apparent loss in detection), the unwanted content is replaced by new one using a homography mapping procedure.

In addition, in another research work, a novel video compression framework is presented using autoencoder networks that encode and decode videos by using less chroma information than luma information. For this purpose, instead of converting Y'CbCr 4:2:2/4:2:0 videos to and from RGB 4:4:4, the video is kept in Y'CbCr 4:2:2/4:2:0 and merged the luma and chroma channels after the luma is downsampled to match the chroma size. An inverse function is performed for the decoder. The performance of these models is evaluated by using CPSNR, MS-SSIM, and VMAF metrics. The experiments reveal that, as compared to video compression involving conversion to and from RGB 4:4:4, the proposed method increases the video quality by about 5.5% for Y'CbCr 4:2:2 and 8.3% for Y'CbCr 4:2:0 while reducing the amount of computation by nearly 37% for Y'CbCr 4:2:2 and 40% for Y'CbCr 4:2:0.

The thread that ties these approaches together is reconstruction of the video and light field frames based on different aspects of problems such as having loss of information, blur in the frames, existing noise after reconstruction, existing unpleasant content, excessive size of information and high computational overhead. In three of the proposed approaches, we have used Plug-and-Play ADMM model for the first time regarding reconstruction of videos and light fields in order to address both information retrieval in the frames and tackling noise/blur at the same time. In two of the proposed models, we applied sparse dictionary learning to reduce the data dimension and demonstrate them as an efficient linear combination of basis frame patches. Two of the proposed approaches are developed in collaboration with industry, in which deep learning frameworks are used to handle large set of features and to learn high-level features from the data.


identifier: Alishahi, Nastaran ORCID: https://orcid.org/0000-0003-1738-1172 <https://orcid.org/0000-0003-1738-1172>  (2021) Analyzing WiFi connection counts in commercial/institutional buildings to estimate/predict occupancy patterns for optimizing buildings’ systems operation.  Masters thesis, Concordia University.

abstract:
Accurate occupancy information can help in optimizing the operation of building systems. To obtain this information, previous studies suggested using WiFi connection counts due to their strong correlation with occupancy counts. However, validating this correlation and investigating its variation have remained limited due to challenges regarding collecting ground-truth data. Moreover, the difficulty of integrating real-time WiFi traffic data in building automation systems hinders wide-scale deployment of this approach. This research addressed these gaps by proposing a methodology, including two modules focused on developing frameworks, for (i) validating the correlation between WiFi connection counts and actual building occupancy counts by using continuous ground-truth data collected from camera-based occupancy counters; and (ii) extracting occupancy indicators from WiFi connection count data which can then be used for updating control sequences.
The proposed research was implemented in two institutional buildings to validate the proposed methods in two case studies. Results of the first case study showed Hour of the day, Day of the week, as well as occupancy level, affect the correlation between WiFi and occupancy counts. Furthermore, the proposed models could successfully estimate real-time occupancy counts and predict day-ahead occupancy counts with an average accuracy (R2) of 0.97 and 0.87, respectively. Moreover, the results of the second case study revealed that the proposed models could successfully predict weekly building occupancy patterns, with an average accuracy (RD2) of 0.90. Furthermore, the analysis identified peak occupancy timing, as well as arrival/departure times variations between different zones. These findings provided a proof-of-concept for the proposed methodology and demonstrated the potential of using WiFi connection count for estimating/forecasting occupancy counts at a large scale and extracting actionable information to optimize buildings’ system operation based on buildings’ unique occupancy patterns.