An Implementation of Mermera: A Shared Memory System that Mixes Coherence with Non-coherence

Coherent shared memory is a convenient, but inefficient, method of inter-process communication for parallel programs. By contrast, message passing can be less convenient, but more efficient. To get the benefits of both models, several non-coherent memory behaviors have recently been proposed in the literature. We present an implementation of Mermera, a shared memory system that supports both coherent and non-coherent behaviors in a manner that enables programmers to mix multiple behaviors in the same program[HS93]. A programmer can debug a Mermera program using coherent memory, and then improve its performance by selectively reducing the level of coherence in the parts that are critical to performance. Mermera permits a trade-off of coherence for performance. We analyze this trade-off through measurements of our implementation, and by an example that illustrates the style of programming needed to exploit non-coherence. We find that, even on a small network of workstations, the performance advantage of non-coherence is compelling. Raw non-coherent memory operations perform 20-40~times better than non-coherent memory operations. An example application program is shown to run 5-11~times faster when permitted to exploit non-coherence. We conclude by commenting on our use of the Isis Toolkit of multicast protocols in implementing Mermera.

A simulation-based design method to transfer surface mount RF system to flip-chip die implementation

The flip-chip technology is a high chip density solution to meet the demand for very large scale integration design. For wireless sensor node or some similar RF applications, due to the growing requirements for the wearable and implantable implementations, flip-chip appears to be a leading technology to realize the integration and miniaturization. In this paper, flip-chip is considered as part of the whole system to affect the RF performance. A simulation based design is presented to transfer the surface mount PCB board to the flip-chip die package for the RF applications. Models are built by Q3D Extractor to extract the equivalent circuit based on the parasitic parameters of the interconnections, for both bare die and wire-bonding technologies. All the parameters and the PCB layout and stack-up are then modeled in the essential parts' design of the flip-chip RF circuit. By implementing simulation and optimization, a flip-chip package is re-designed by the parameters given by simulation sweep. Experimental results fit the simulation well for the comparison between pre-optimization and post-optimization of the bare die package's return loss performance. This design method could generally be used to transfer any surface mount PCB to flip-chip package for the RF systems or to predict the RF specifications of a RF system using the flip-chip technology.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Design and implementation of a real-time data acquisition system for the identification of dynamic temperature models in a hydroponic greenhouse

This text describes a real data acquisition and identification system implemented in a soilless greenhouse located at the University of Algarve (south of Portugal). Using the Real Time Workshop, Simulink, Matlab and the C programming language a system was developed to perform real-time data acquisition from a set of sensors.

Implementation of a quality system in joint master programmes

Joint master programmes are systems which by default demand a proper quality system in order to sustain and improve. Objective of this thesis is analysing and proposing solutions to difficulties associated with the implementation of a quality management system to joint master programmes, with the focus on international joint master programmes. The application of the analysis to the Erasmus Mundus joint master programme European Master in Quality in Analytical Laboratories (EMQAL) is discussed. QA systems implementation in HEIs in Europe is an ongoing process, and implementation of such systems in JPs is one step further to enhancing quality in higher education in Europe. The issue closely discussed in this thesis is: should QMS be developed independently from the institutions, or should the institutions, when developing their quality management systems, take into account the (future) development of joint courses and prepare their quality procedures accordingly? A quality management system is normally developed for one organization, and different aspects of cooperation are considered within. A joint master programme is a result of successful cooperation of two or more organizations; therefore a development of its quality management system must be approached in a different manner. This thesis proposes a QMS with emphasis both on the HEI and the consortium. Different processes in the QMS can be managed independently at the level of the HEI or at the level of the consortium. Most processes in joint master programmes should be designed in programmes’ and institutions’ QMSs. Quality of a joint master programme cannot be analyzed separately from the higher education institutions which are organizing it. Comparative analysis of organization of one Erasmus Mundus Master programme to the solutions proposed in discussion showed that from all of the aspects considered, processes in EMQAL are organized in harmony with the proposed delegation of processes of the QMS for a joint master programme. The solutions proposed in the discussion are based on theoretical application of the quality principles and concepts. Comparison to the quality processes and procedures in an existing EM programme showed that analysis is applicable in practice.