Once the shovel hits the ground : Evaluating the management of complex implementation processes of public-private partnership infrastructure projects with qualitative comparative analysis

Much attention is being paid to the planning of public-private partnership (PPP) infrastructure projects. The subsequent implementation phase – when the contract has been signed and the project ‘starts rolling’ – has received less attention. However, sound agreements and good intentions in project planning can easily fail in project implementation. Implementing PPP infrastructure projects is complex, but what does this complexity entail? How are projects managed, and how do public and private partners cooperate in implementation? What are effective management strategies to achieve satisfactory outcomes? This is the fi rst set of questions addressed in this thesis. Importantly, the complexity of PPP infrastructure development imposes requirements on the evaluation methods that can be applied for studying these questions. Evaluation methods that ignore complexity do not create a realistic understanding of PPP implementation processes, with the consequence that evaluations tell us little about what works and what does not, in which contexts, and why. This hampers learning from evaluations. What are the requirements for a complexity-informed evaluation method? And how does qualitative comparative analysis (QCA) meet these requirements? This is the second set of questions addressed in this thesis

Embedded biometric verification system for access control

The main objectives of this research project are: 1. Develop an automatic face detection and location technique and combine it with LEM for face verification. 2. Build a prototype embedded system using face verification and evaluate the effectiveness of our techniques under realistic situations. Secondary objectives include: 1. Develop a fingerprint recognition technique based on the LEM. 2. Develop a decision fusion technique that integrates fingerprint and face recognition for high accuracy user verification. 3. Build a prototype embedded system using both fingerprint and face verification for high security access control