A testbed for embedded systems

Testing and Debugging are often the most difficult phase of software development. This is especially true of embedded systems which are usually concurrent, have real-time performance and correctness constraints and which execute in the field in an environment which may not permit internal scrutiny of the software behaviour. Although good software engineering practices help, they will never eliminate the need for testing and debugging. This is because failings in the specification and design are often only discovered through testing and understanding these failings and how to correct them comes from debugging. These observations suggest that embedded software should be designed in a way which makes testing and debugging easier and that tools which support these activities are required. Due to the often hostile environment in which the finished embedded system will function, it is necessary to have a platform which allows the software to be developed and tested "in vitro". The Testbed system achieves these goals by providing dynamic modification and process migration facilities for use during development as well as powerful monitoring and background debugging support. These facilities are built on a basic run-time harness supporting an event-driven programming model with a global communication mechanism. This programming model is well suited to the reactive nature of embedded systems. The main research contributions of this work are in the areas of finding deadlock-free, path-optimal routings for networks and of dynamic modification with automated conversion of data which may include pointers

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Efficient process migration in the EMPS multiprocessor system

The process migration facility in the Eindhoven multiprocessor system (EMPS) is presented. In the EMPS system, mailboxes are used for interprocess communication. These mailboxes provide transparency of location for communicating processes. The major advantages of mailbox communication in the EMPS system are: (1) interprocess communication can proceed without losing messages; and (2) the communication paths can be updated very efficiently when a process moves to another processor. By redirecting the mailbox connection of the migrating process, the communication paths of all processes connected to the same mailbox are updated