This dissertation describes a research study that investigated cooperative inquiry as a strategy for professional learning of inner-city school principals in a large urban centre in Western Canada. The study attempted to identify the central issues of concern and means of redress for school leaders in high-poverty communities, many of which focused on educational leadership, school management, the context of their schools within impoverished communities, and the challenges of personal well-being. The findings suggest that cooperative inquiry was an effective strategy in that the approach was participatory, democratic, empowering, life-enhancing, and fostered community-building among participants. The findings also suggest that the approach was effective in that it was grounded in the action research cycle of planning, action, observation, and reflection. The study further examined the use of dialogue as a means of constructing knowledge regarding these issues, and identified the ways in which such knowledge impacts upon the professional practice of these principals. Findings suggest that participants gained knowledge from each other, offered knowledge from others, constructed knowledge together as a group, and developed deeper understandings of their own perspectives. Findings also suggest that meaning is lost when dialogic interactions are transcribed into print. Thus, dialogue is a form of communication in and of itself, one that cannot simply be transformed into the written word without losing part of that dialogic essence. Further, this study posits that dialogue has unique power to be both a process for meaning making, as well as an ontological means of clarifying one’s own sense of reality. / October 2008

The purpose of the study was to determine how the Business Research program at Biola University can meet the needs of small business. The Business Research program originated at Biola University was a capstone course for all business seniors in which student consulting teams used previous classroom and book knowledge to analyze and make recommendations to small business firms selected by the faculty.An eighty-one item survey was administered by mail to forty-seven business firms which had participated in the business research course at any time during its seven year history. Responses from the thirty-eight firm administrators who returned the completed survey were analyzed with frequency distributions, measures of central tendency, Chi-square, Kendall's tau, a contingency table, and a summary of narrative statements.Conclusions(1) While Business Research students appear to benefit more from working with a corporation which contains a well developed management team, the client does not find student recommendations to be as valuable as a smaller, less sophisticated firm might.(2) Special emphasis should be placed on market research, relating to others, and ethical and moral values because clients appeared to value these skills and related them directly to their evaluation of the overall project.(3) As the program became more refined over time, businesses perceived faculty as more aware of business problems and found students better able to express themselves on paper.(4) Evaluation of teaching techniques in sales promotion, inventory control, accounts receivable and payable, computer usage, and information on competitors should be considered since clients tended to find student performance in these areas less than satisfactory.

Esta tesis contribuye tanto a la literatura teórica como a la literatura empírica sobre proyectos conjuntos de investigación.En primer lugar analizo el problema que los proyectos conjuntos de investigación no garantizan siempre una cooperación beneficiosa porque las empresas que participan no actuan siempre como seria de esperar. También hay vezes que proyectos conjuntos de investigación que parecen beneficiosos no se realizan. Este fenomeno se puede explicar por la existencia de información asimetrica entre los participantes y el hecho que ellos no pueden firmar contratos sobre la transferencía de conocimiento. Este problema es especialmente importante quando las empresas compitenen el mercado de producto o en otras actividades de I+D y consecuentemente no tienen incentivos propios para transferir su conocimiento. En mi tesis propongo una solución para este problema: las empresas se pueden comprometer con su nivel de deuda para transferir su conocimiento. Demuestro que elnivel de deuda tiene influencia sobre la transferencia de información y que existen unas condiciones sobre la función de benefício que guarantizan que las empresas tengan deuda positiva en equilibrio. Gracias a la posibilidad de financiación por deuda el nivel de trancferencia de información en equilibrio es más alto que en caso de financiación interna. Es decir que la deuda funciona como instrumento de compromiso para compartir información. Por eso contratos sobre el nivel de la deuda sustituyen parcialmente los contratos sobre la transferencia de conocimiento y esta posibilidad aumenta el nivel de bienestar. Tambien presento una prueba empirica de mi modelo y concluyo que empresas con mas deuda participan en proyectos conjuntos de investigación con una probabilidad más alta.En la segunda parte de mi disertación utilizo tecnicas de microeconometria para investigar la relación entre participación en proyectos conjuntos de investigación y productividad. Hay que tener mucho cuidado con la evaluación de beneficios en productividad de cooperación en I+D porque la cooperación también tiene un impacto sobre los gastos de investigación y la estructura de competencia en la industría. Teniendo en cuenta estos efectos utilizo un panel muy grande de empresas de los EEUU, Japón y la Unión Europea. Encuentro que los proyectos conjuntos de investigación aumentan la productividad de los participantes. También presento resultados que indican indirectamente que empresas en cooperación horizontal de I+D comparten los gastosde la investigación.En la tercera parte de mi tesis analizo los incentivos para iniciar proyectos conjuntos de investigación. Ademas de investigar los incentivos generales de empresas presto atención a la cooperación horizontal de I+D. Encuentro que empresas en este tipo de cooperación comparten los gastos de de investigación. Este resultado confirma los resultados de la literatura teorica. / The work presented in this dissertation contributes both to the theoretical and the empirical literature on research joint ventures.Firstly, I analyse the problem that in spite of the advantages mentioned above, research joint ventures do not always guarantee fruitful cooperation as partners may not deliver what is expected from them. Also, there are cases when firms do not start potentially very profitable RJVs. These failures can be due to the problem that firms cannot contract the transfer of the know-how and without the required amount of information disclosure the RJV is not profitable. This problem arises especially when firms are competitors either in the product market or in other R&D activities and therefore do not have the right incentives to share their knowledge. I propose a novel way to alleviate this problem. I show that firms can use their debt level as a commitment to disclose know-how. I find that there is a direct relationship between the debt of a firm and the incentives to disclose its know-how in a RJV. Moreover, I show conditions on the profit functions under which firms, in equilibrium, finance at least partially with debt. Due to the possibility of debt financing, the equilibrium level of disclosure is higher than in case of equity/internal financing. That is, the leverage acts as a commitment device to share knowledge. Hence, contracting on debt levels is sometimes a partial substitute of contracting on disclosure of know-how. Therefore, the possibility of debt financing is likely is improve welfare. I also present empirical evidence that firms with more leveraged financial structure are more likely to participate in horizontal research joint ventures.In the second section of the thesis I provide a microeconometric analysis of the impact of RJV participation on productivity. Evaluating the overall benefits of cooperative research is very difficult because the cooperation may have an impact both on R&D spending and the competitive structure of the industry. Controling for this effects, I study the productivity implications of research joint venture participation using a large panel of European, Japanese and US companies. I find evidence that joint R&D increases productivity. I also find indirect evidence for cost sharing in horizontal research consortia.Finally, I analyse the firms' incentives to engage in cooperative research. After conducting a simple investigation into general firm characteristics that are associated with RJV participation, the analysis mainly focuses on horizontal research joint ventures, i.e. when firms engage in cooperative research with their direct competitors. I find evidence for cost sharing in horizontal research joint ventures, which is consistent with the results of the theoretical literature.

Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution to all datasets of different properties, sizes, structures, and distributions. Challenges in data clustering include, identifying proper number of clusters, scalability of the clustering approach, robustness to noise, tackling distributed datasets, and handling clusters of different configurations. This thesis addresses some of these challenges through cooperation between multiple clustering approaches. We introduce a Cooperative Clustering (CC) model that involves multiple clustering techniques; the goal of the cooperative model is to increase the homogeneity of objects within clusters through cooperation by developing two data structures, cooperative contingency graph and histogram representation of pair-wise similarities. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of cooperative clusters through a merging process. Obtaining the co-occurred objects from the different clusterings enables the cooperative model to group objects based on a multiple agreement between the invoked clustering techniques. In addition, merging this set of sub-clusters using histograms poses a new trend of grouping objects into more homogenous clusters. The cooperative model is consistent, reusable, and scalable in terms of the number of the adopted clustering approaches. In order to deal with noisy data, a novel Cooperative Clustering Outliers Detection (CCOD) algorithm is implemented through the implication of the cooperation methodology for better detection of outliers in data. The new detection approach is designed in four phases, (1) Global non-cooperative Clustering, (2) Cooperative Clustering, (3) Possible outlier’s Detection, and finally (4) Candidate Outliers Detection. The detection of outliers is established in a bottom-up scenario. The thesis also addresses cooperative clustering in distributed Peer-to-Peer (P2P) networks. Mining large and inherently distributed datasets poses many challenges, one of which is the extraction of a global model as a global summary of the clustering solutions generated from all nodes for the purpose of interpreting the clustering quality of the distributed dataset as if it was located at one node. We developed distributed cooperative model and architecture that work on a two-tier super-peer P2P network. The model is called Distributed Cooperative Clustering in Super-peer P2P Networks (DCCP2P). This model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as two layers of peer neighborhoods and super-peers. Summarization of the global distributed clusters is achieved through a distributed version of the cooperative clustering model. Three clustering algorithms, k-means (KM), Bisecting k-means (BKM) and Partitioning Around Medoids (PAM) are invoked in the cooperative model. Results on various gene expression and text documents datasets with different properties, configurations and different degree of outliers reveal that: (i) the cooperative clustering model achieves significant improvement in the quality of the clustering solutions compared to that of the non-cooperative individual approaches; (ii) the cooperative detection algorithm discovers the nonconforming objects in data with better accuracy than the contemporary approaches, and (iii) the distributed cooperative model attains the same quality or even better as the centralized approach and achieves decent speedup by increasing number of nodes. The distributed model offers high degree of flexibility, scalability, and interpretability of large distributed repositories. Achieving the same results using current methodologies requires polling the data first to one center location, which is sometimes not feasible.

Cooperative communication is a new class of wireless communication techniques in which wireless nodes help each other relay information and realize spatial diversity advantages in a distributed manner. This new transmission technique promises significant performance gains in terms of link reliability, spectral efficiency, system capacity, and transmission range. Analysis and design of cooperative communication wireless systems have been extensively studied over the last few years. The introduction and integration of cooperative communication in next generation wireless standards will lead to the design of an efficient and reliable fully-distributed wireless network. However, there are various technical challenges and open issues to be resolved before this promising concept becomes an integral part of the modern wireless communication devices. A common assumption in the literature on cooperative communications is the equal distribution of power among the cooperating nodes. Optimum power allocation is a key technique to realize the full potentials of relay-assisted transmission promised by the recent information-theoretic results. In this dissertation, we present a comprehensive framework for power allocation problem. We investigate the error rate performance of cooperative communication systems and further devise open-loop optimum power allocation schemes to optimize the performance. By exploiting the information about the location of cooperating nodes, we are able to demonstrate significant improvements in the system performance. In the first part of this dissertation, we consider single-relay systems with amplify-and-forward relaying. We derive upper bounds for bit error rate performance assuming various cooperation protocols and minimize them under total power constraint. In the second part, we consider a multi-relay network with decode-and-forward relaying. We propose a simple relay selection scheme for this multi-relay system to improve the throughput of the system, further optimize its performance through power allocation. Finally, we consider a multi-source multi-relay broadband cooperative network. We derive and optimize approximate symbol error rate of this OFDMA (orthogonal frequency division multiple access) system.

With limited road infrastructure and increasing number of vehicles on the road, an intelligent transport system is needed to increase the throughput in traffic and minimize traffic jams in highly populated areas. The purpose of this project is to design and implement a control system that is capable of driving and following the preceding vehicle autonomously in the longitude direction only. The vehicle is also equipped with a vehicle to vehicle communication unit. With this information, all vehicles on the road can communicate with each other and are able to achieve shorter distances between vehicles and damp any disturbance caused by upstream traffic. A general structure on Cooperative Adaptive Cruise Control (CACC) is created by studying the research from The Netherlands Organization for Applied Scientific Research (TNO). A string stability criterion is used to determine if the system is suitable of driving in a platoon, where a string of vehicles are following a lead vehicle. This system is then implemented in a Volvo S60 and has participated in the 2011 Grand Cooperative Driving Challenge hosted in The Netherlands. The results show that the system has ability to increase throughput and damp disturbance on the upstream traffic by communicating with the other vehicles ahead. The system is also robust and simple enough to earn the 2nd place in the competition. / Grand Cooperative Driving Challenge

Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution to all datasets of different properties, sizes, structures, and distributions. Challenges in data clustering include, identifying proper number of clusters, scalability of the clustering approach, robustness to noise, tackling distributed datasets, and handling clusters of different configurations. This thesis addresses some of these challenges through cooperation between multiple clustering approaches. We introduce a Cooperative Clustering (CC) model that involves multiple clustering techniques; the goal of the cooperative model is to increase the homogeneity of objects within clusters through cooperation by developing two data structures, cooperative contingency graph and histogram representation of pair-wise similarities. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of cooperative clusters through a merging process. Obtaining the co-occurred objects from the different clusterings enables the cooperative model to group objects based on a multiple agreement between the invoked clustering techniques. In addition, merging this set of sub-clusters using histograms poses a new trend of grouping objects into more homogenous clusters. The cooperative model is consistent, reusable, and scalable in terms of the number of the adopted clustering approaches. In order to deal with noisy data, a novel Cooperative Clustering Outliers Detection (CCOD) algorithm is implemented through the implication of the cooperation methodology for better detection of outliers in data. The new detection approach is designed in four phases, (1) Global non-cooperative Clustering, (2) Cooperative Clustering, (3) Possible outlier’s Detection, and finally (4) Candidate Outliers Detection. The detection of outliers is established in a bottom-up scenario. The thesis also addresses cooperative clustering in distributed Peer-to-Peer (P2P) networks. Mining large and inherently distributed datasets poses many challenges, one of which is the extraction of a global model as a global summary of the clustering solutions generated from all nodes for the purpose of interpreting the clustering quality of the distributed dataset as if it was located at one node. We developed distributed cooperative model and architecture that work on a two-tier super-peer P2P network. The model is called Distributed Cooperative Clustering in Super-peer P2P Networks (DCCP2P). This model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as two layers of peer neighborhoods and super-peers. Summarization of the global distributed clusters is achieved through a distributed version of the cooperative clustering model. Three clustering algorithms, k-means (KM), Bisecting k-means (BKM) and Partitioning Around Medoids (PAM) are invoked in the cooperative model. Results on various gene expression and text documents datasets with different properties, configurations and different degree of outliers reveal that: (i) the cooperative clustering model achieves significant improvement in the quality of the clustering solutions compared to that of the non-cooperative individual approaches; (ii) the cooperative detection algorithm discovers the nonconforming objects in data with better accuracy than the contemporary approaches, and (iii) the distributed cooperative model attains the same quality or even better as the centralized approach and achieves decent speedup by increasing number of nodes. The distributed model offers high degree of flexibility, scalability, and interpretability of large distributed repositories. Achieving the same results using current methodologies requires polling the data first to one center location, which is sometimes not feasible.

Cooperative communication is a new class of wireless communication techniques in which wireless nodes help each other relay information and realize spatial diversity advantages in a distributed manner. This new transmission technique promises significant performance gains in terms of link reliability, spectral efficiency, system capacity, and transmission range. Analysis and design of cooperative communication wireless systems have been extensively studied over the last few years. The introduction and integration of cooperative communication in next generation wireless standards will lead to the design of an efficient and reliable fully-distributed wireless network. However, there are various technical challenges and open issues to be resolved before this promising concept becomes an integral part of the modern wireless communication devices. A common assumption in the literature on cooperative communications is the equal distribution of power among the cooperating nodes. Optimum power allocation is a key technique to realize the full potentials of relay-assisted transmission promised by the recent information-theoretic results. In this dissertation, we present a comprehensive framework for power allocation problem. We investigate the error rate performance of cooperative communication systems and further devise open-loop optimum power allocation schemes to optimize the performance. By exploiting the information about the location of cooperating nodes, we are able to demonstrate significant improvements in the system performance. In the first part of this dissertation, we consider single-relay systems with amplify-and-forward relaying. We derive upper bounds for bit error rate performance assuming various cooperation protocols and minimize them under total power constraint. In the second part, we consider a multi-relay network with decode-and-forward relaying. We propose a simple relay selection scheme for this multi-relay system to improve the throughput of the system, further optimize its performance through power allocation. Finally, we consider a multi-source multi-relay broadband cooperative network. We derive and optimize approximate symbol error rate of this OFDMA (orthogonal frequency division multiple access) system.

Cooperative communication is a promising technique for wireless communication systems where wireless nodes cooperate together in transmitting their information. Such communication transmission technique, which realizes the multiple antenna arrays in a distributed manner over multiple wireless nodes, succeeds in extending the network coverage, increasing throughput, improving both link reliability and spectral efficiency. Available channel state information at the transmitting nodes can be used to design adaptive transmission schemes for improving the overall system performance. Throughout our work, we adaptively change loaded power and/or bit to the Orthogonal Frequency Division Multiplexing (OFDM) symbol in order to minimize bit error rate or maximize the throughput. In the first part of this dissertation, we consider single-relay OFDM system with amplify-and-forward relaying. We propose three algorithms to minimize the bit error rate under total power constraint and fixed transmission rate. These algorithms are optimal power loading, optimal bit loading and optimal bit and power loading. Through Monte Carlo simulations we study the proposed system performance and discuss the effect of relay location and channel estimation. This study shows that the proposed algorithms result in exploiting the multi-path diversity and achieving extra coding gain. In the second part, we extend the problem to a multi-relay OFDM network but with decode-and-forward relaying. We propose an adaptive power loading algorithm to minimize the bit error rate under total power constraint based on two relay selection strategies. The proposed system leads to achieve both multi-path and cooperative spatial diversity using maximal-ratio combiner for the detection. In the last part, we consider also multi-relay network but with amplify and forward relaying. We optimize the bit loading coefficients to maximize the throughput under target bit error rate constraint. The proposed algorithm is considered more practical since it takes into consideration the channel estimation quality. The considered adaptive system has less complexity compared with other adaptive systems through reducing the feedback amount. Furthermore, the full network channel state information is needed only at the destination.

It is expected that Wireless Body Area Network (WBAN) will greatly improve the quality of our life because of its myriad applications for our human beings. However, one of the challenges is to design energy efficient communication protocols to support the reliable communication as well as to prolong the network lifetime. Cooperative communications have the advantage of spatial diversity to combat multipath fading, thus improving the link reliability and boosting energy efficiency. In this thesis, we investigate the energy efficient cooperative communications for WBAN. We first analyze the outage performance of three transmission schemes, namely direct transmission, single relay cooperation, and multi-relay cooperation. To minimize the energy consumption, we then study the problem of optimal power allocation with the constraint of targeted outage probability. Two strategies of power allocation are considered: power allocation with and without posture state information. Simulation results verify the accuracy of the analysis and demonstrate that: 1) power allocation making use of the posture information can reduce the energy consumption; 2) within a possible range of the channel quality in WBAN, cooperative communication is more energy efficient than direct transmission only when the path loss between the transmission pair is higher than a threshold; and 3) for most of the typical channel quality due to the fixed transceiver locations on human body, cooperative communication is effective in reducing energy consumption.