Thesis (M.Eng.)--Memorial University of Newfoundland, 2005. / Bibliography: leaves 110-113.

Efficient project planning and project management is crucial to complete the software projects in expected time and requirements. The most critical stage in project planning is estimation of the software size, time and budget. In this stage, effort data is used for benchmarking data sets, effort estimation, project monitoring and controlling. However, there are some problems related to effort data collection in the software industry. In this thesis, a pilot study and survey study are conducted to observe common practices and problems in effort data collection in the industry and results are analyzed. These problems are explained in terms of tool, process and people factors and solution suggestions are presented according to these problems. In accordance with the findings, a method and a tool which can facilitates to provide more accurate data are developed. A case study is performed in order to validate the method and applicability of the tool in the industry.

Despite the significant advances that have been made in traffic sensor technologies, there are only a few systems that provide measurements at the trip level and fewer yet that can do so for all travel modes. On the other hand, traditional methods of collecting individual travel behavior (i.e. manual or web-based travel diaries) are resource intensive and prone to a wide range of errors. Moreover, although dedicated GPS loggers provide the ability to collect detailed travel behavior data with less effort, their use still faces several challenges including the need to distribute and retrieve the logger; the potential need to have the survey participants upload data from the logger to a server; and the need for survey participants to carry another device with them on all their trips. The widespread adoption of smart phones provides an opportunity to acquire travel behavior data from individuals without the need for participants to record trips in a travel diary or to carry dedicated recording devices with them on their travels. The collected travel data can then be used by municipalities and regions for forecasting the travel demand or for analyzing the travel behavior of individuals. In the current research, a smart phone based travel behavior surveying system is designed, developed, and pilot tested. The custom software written for this study is capable of recording the travel characteristics of individuals over the course of any period of time (e.g. days or weeks) and across all travel modes. In this system, a custom application on the smart phone records the GPS data (using the onboard GPS unit) at a prescribed frequency and then automatically transmits the data to a dedicated server. In the server, the data are stored in a dedicated database to be then processed using trip characteristics inference algorithms. The main challenge with the implemented system is the need to reduce the amount of energy consumed by the device to calculate and transmit the GPS fixes. In order to reduce the power consumption from the travel behavior data acquisition software, several techniques are proposed in the current study. Finally, in order to evaluate the performance of the developed system, first the accuracy of the position information obtained from the data acquisition software is analyzed, and then the impact of the proposed methods for reducing the battery consumption is examined. As a conclusion, the results of implemented system shows that collecting individual travel behavior data through the use of GPS enabled smart phones is technically feasible and would address most of the limitations associated with other survey techniques. According to the results, the accuracy of the GPS positions and speed collected through the implemented system is comparable to GPS loggers. Moreover, proposed battery reduction techniques are able to reduce the battery consumption rate from 13.3% per hour to 5.75% per hour (i.e. 57% reduction) when the trip maker is non-stationary and from 5.75% per hour to 1.41% per hour (i.e. 75.5% reduction) when the trip maker is stationary.

In this thesis, a data collection application based on MapReduce programming is described. This application aims to collect tempera- ture data stream continuously from a specied set of sensors. Instead of collecting the temperature information of all the sensors by one machine, the sensors are divided into several subsets each of which is handled as a Map task. In each Map task, the temperature data stream of the assigned sensors is collected continuously and stored in a predened database. All the Map tasks can run simultaneously on several machines. This method can reduce the delay time and improve the eciency of the data collection service, especially in the case of having a huge number of sensors monitored remotely by a data center through Internet. We can use the value of remote sensors to predict the next value of remote sensors by some methods such as linear regres- sion and K-means. And, we can use it to predict the system alarm. Experimental results show that the proposed method is eective in temperature data collection,and eective in carbon reduction.

This thesis analyzes data collection tools and methods in domestic environments for smart product development processes. The aim of the study is to create a method for supporting data collection studies in user research of smart products at domestic environments. The study examines the utilization of information and communication technologies in ethnographic data collection methods at domestic environments with a qualitative approach. Two case studies are conducted to understand and analyze the effects of custom designed data collection tools in user research studies conducted at domestic environments. The results of the study revealed that utilization of data collection equipment and methods that are customized to the characteristics of aims and objectives of user research studies, product characteristic and study participants&rsquo / own environment might lead to the possibility to increase number of study participants and decrease researchers&rsquo / presence in domestic environments. Additionally, it was found that, sensor kits and internal device logs are capable of supporting user research studies for the evaluation of products, however due to technical complexity and unpredictable contextual factors, triangulation of data collection methods and redundancy of data collection equipment are necessary.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2009. / Härtill 4 uppsatser.

Diss. (sammanfattning) SLU : Sveriges lantbruksuniv., 2009. / Härtill 4 uppsatser.

With the recent development of smart-well technology, the reservoir community now faces the challenge of developing robust and efficient techniques for reservoir characterization by means of data inversion. Unfortunately, classical history-matching methodologies do not possess computational efficiency and robustness needed to assimilate data measured almost in real time. Therefore, the reservoir community has started to explore techniques previously applied in other disciplines. Such is the case of the representer method, a variational data assimilation technique that was first applied in physical oceanography. The representer method is an efficient technique for solving linear inverse problems when a finite number of measurements are available. To the best of our knowledge, a general representer-based methodology for nonlinear inverse problems has not been fully developed. We fill this gap by presenting a novel implementation of the representer method applied to the nonlinear inverse problem of identifying petrophysical properties in reservoir models. Given production data from wells and prior knowledge of the petrophysical properties, the goal of our formulation is to find improved parameters so that the reservoir model prediction fits the data within some error given a priori. We first define an abstract framework for parameter identification in nonlinear reservoir models. Then, we propose an iterative representer-based scheme (IRBS) to find a solution of the inverse problem. Sufficient conditions for convergence of the proposed algorithm are established. We apply the IRBS to the estimation of absolute permeability in single-phase Darcy flow through porous media. Additionally, we study an extension of the IRBS with Karhunen-Loeve (IRBS-KL) expansions to address the identification of petrophysical properties subject to linear geological constraints. The IRBS-KL approach is compared with a standard variational technique for history matching. Furthermore, we apply the IRBS-KL to the identification of porosity, absolute and relative permeabilities given production data from an oil-water reservoir. The general derivation of the IRBS-KL is provided for a reservoir whose dynamics are modeled by slightly compressible immiscible displacement of two-phase flow through porous media. Finally, we present an ad-hoc sequential implementation of the IRBS-KL and compare its performance with the ensemble Kalman filter. / text

Τα Δίκτυα Αισθητήρων αποτελούνται από ένα μεγάλο αριθμό μικρών αυτόνομων συσκευών, που αλληλεπιδρούν με το άμεσο περιβάλλον τους μέσω αισθητήρων, επικοινωνούν μεταξύ τους ασύρματα και συνεργάζονται φέροντας εις πέρας εργασίες που δε θα μπορούσε να ολοκληρώσει μία μόνο συσκευή. Κάθε συσκευή του δικτύου διαθέτει περιορισμένη υπολογιστική δύναμη και ενεργειακούς πόρους, επομένως η όσο το δυνατόν λιγότερη κατανάλωση ενέργειας είναι βασικό πρόβλημα των δικτύων αισθητήρων για τη μεγιστοποίηση του χρόνου ζωής τους. Συνήθως τα δίκτυα αισθητήρων αναπτύσσονται σε μεγάλες περιοχές ενδιαφέροντος για την υποστήριξη σημαντικών εφαρμογών του πραγματικού κόσμου. Η πληροφορία που ανιχνεύεται από τους κόμβους αισθητήρων προωθείται προς ένας σταθερό, συνήθως, κέντρο ελέγχου, με αναμεταδόσεις των δεδομένων στους ενδιάμεσους κόμβους. Η διαδικασία αυτή έχει ως αποτέλεσμα τη μεγάλη κατανάλωση ενέργειας στις συσκευές, ιδιαίτερα σε αυτές που βρίσκονται κοντά στο κέντρο ελέγχου, αφού πρέπει να αναμεταδίδουν και τα δεδομένα που φτάνουν από το υπόλοιπο δίκτυο προς το κέντρο ελέγχου. Για την επίτευξη μιας πιο ισορροπημένης και αποδοτικής διαδικασίας συλλογής δεδομένων, τα τελευταία χρόνια έχει υιοθετηθεί μια νέα προσέγγιση, όπου το κέντρο ελέγχου είναι κινητό. Η βασική ιδέα είναι ότι το κέντρο ελέγχου διαθέτει σημαντικά και εύκολα ανανεώσιμα αποθέματα ενέργειας, επομένως μπορεί να κινείται στην περιοχή όπου έχει αναπτυχθεί το δίκτυο αισθητήρων, αναλαμβάνοντας να συλλέξει τα δεδομένα από τους κόμβους με πολύ μικρό κόστος. Ωστόσο, η μετάδοση των δεδομένων μπορεί να παρουσιάζει σημαντικές καθυστερήσεις. Στην παρούσα εργασία αναπτύχθηκαν πρωτόκολλα ελέγχου της κίνησης ενός κέντρου ελέγχου σε δίκτυο αισθητήρων με ανομοιογενή ανάπτυξη των κόμβων αισθητήρων, με στόχο την αποδοτική, ως προς την ενέργεια και τον χρόνο παράδοσης, συλλογή των δεδομένων. Συγκεκριμένα, το κέντρο ελέγχου διαιρεί νοητά το δίκτυο σε περιοχές τις οποίες και επισκέπτεται διαδοχικά, σταματώντας σε κάθε περιοχή για ένα συγκεκριμένο χρονικό διάστημα, ώστε να συλλέξει τα δεδομένα. Προτείνουμε δύο τρόπους κίνησης του κέντρου ελέγχου, ντετερμινιστικό και τυχαίο. Στην τυχαία κίνηση, η επιλογή της επόμενης περιοχής την οποία θα επισκεφτεί το κέντρο ελέγχου γίνεται με τυχαίο τρόπο, εισάγοντας όμως ένα όρο μεροληψίας, έτσι ώστε να προτιμούνται περιοχές που έχουν δεχτεί λιγότερες επισκέψεις. Επιπλέον η μέθοδός μας αποφασίζει το χρόνο παύσης σε κάθε περιοχή λαμβάνοντας υπόψιν κάποιες βασικές παραμέτρους του δικτύου, όπως τα αρχικά αποθέματα ενέργειας των κόμβων αισθητήρων και την πυκνότητα της κάθε περιοχής, έτσι ώστε να παραμένει περισσότερο χρόνο σε περιοχές με μεγαλύτερη πυκνότητα, άρα και μεγαλύτερη ποσότητα πληροφορίας. Με τον τρόπο αυτό επιτυγχάνεται η γρήγορη κάλυψη όλου του δικτύου, καθώς επίσης και η δίκαιη εξυπηρέτηση των επιμέρους περιοχών του δικτύου. Τα προτεινόμενα πρωτόκολλα αξιολογήθηκαν πειραματικά μέσω προσομοίωσης, χρησιμοποιώντας ποικίλες τιμές για βασικές παραμέτρους του δικτύου και σύγκρινοντάς τα με σχετικές υπάρχουσες ευρέως αποδεκτές μεθόδους. Τα αποτελέσματα που πήραμε δείχνουν ότι τόσο ο χρόνος παράδοσης των μηνυμάτων, όσο και η ενέργεια που καταλώθηκε διατηρούνται σε χαμηλά επίπεδα, βελτιώνοντας σημαντικά την προηγούμενη σχετική έρευνα. / Wireless Sensor Networks consist of a large number of small, autonomous devices, that are able to interact with their inveronment by sensing and collaborate to fulfill their tasks, as, usually, a single node is incapable of doing so; and they use wireless communication to enable this collaboration. Each device has limited computational and energy resources, thus a basic issue in the applicastions of wireless sensor networks is the low energy consumption and hence, the maximization of the network lifetime. The collected data is disseminated to a static control point – data sink in the network, using node to node - multi-hop data propagation. However, sensor devices consume significant amounts of energy in addition to increased implementation complexity, since a routing protocol is executed. Also, a point of failure emerges in the area near the control center where nodes relay the data from nodes that are farther away. Recently, a new approach has been developed that shifts the burden from the sensor nodes to the sink. The main idea is that the sink has significant and easily replenishable energy reserves and can move inside the area the sensor network is deployed, in order to acquire the data collected by the sensor nodes at very low energy cost. However, the need to visit all the regions of the network may result in large delivery delays. In this work we have developed protocols that control the movement of the sink in wireless sensor networks with non-uniform deployment of the sensor nodes, in order to succeed an efficient (with respect to both energy and latency) data collection. More specifically, a graph formation phase is executed by the sink during the initialization: the network area is partitioned in equal square regions, where the sink, pauses for a certain amount of time, during the network traversal, in order to collect data. We propose two network traversal methods, a deterministic and a random one. When the sink moves in a random manner, the selection of the next area to visit is done in a biased random manner depending on the frequency of visits of its neighbor areas. Thus, less frequently visited areas are favored. Moreover, our method locally determines the stop time needed to serve each region with respect to some global network resources, such as the initial energy reserves of the nodes and the density of the region, stopping for a greater time interval at regions with higher density, and hence more traffic load. In this way, we achieve accelerated coverage of the network as well as fairness in the service time of each region.Besides randomized mobility, we also propose an optimized deterministic trajectory without visit overlaps, including direct (one-hop) sensor-to-sink data transmissions only. We evaluate our methods via simulation, in diverse network settings and comparatively to related state of the art solutions. Our findings demonstrate significant latency and energy consumption improvements, compared to previous research.

Defining health and economic burden of Invasive Meningococcal Disease (IMD) in Canada is critical to inform Public Health Policy around immunization programs. A comprehensive literature review was conducted to assess available studies, a lack of comprehensive Canadian data to allow evaluation of total economic burden of IMD was identified in Canada. Thus, this dissertation proposes a prospective cost collection methodology tailored to Canadian data and healthcare utilization (HCU). All patient-related HCU is considered and outlined. HCU is then categorized as direct or indirect and relevant direct and indirect healthcare costs are detailed. Intangible costs are described and methodology for capturing these costs using validated quality of life instruments is proposed. As all published economic evaluations of this disease lack prospective collection of data, this study proposes the use of a patient diary to serve as a memory aid during patient cost-collection interviews.