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The design of an efficient digital circuit in term of low-power has become a very challenging issue. For this reason, low-power digital circuit design is a topic addressed in electrical and computer engineering curricula, but it also requires practical experiments in a laboratory. This PhD research investigates a novel approach, the low-power design laboratory system by developing a new technical and pedagogical system. The low-power design laboratory system is composed of two types of laboratories: the on-site (hands-on) laboratory and the remote laboratory. It has been developed at the Bonn-Rhine-Sieg University of Applied Sciences to teach low-power techniques in the laboratory. Additionally, this thesis contributes a suggestion on how the learning objectives can be complemented by developing a remote system in order to improve the teaching process of the low-power digital circuit design. This laboratory system enables online experiments that can be performed using physical instruments and obtaining real data via the internet. The laboratory experiments use a Field Programmable Gate Array (FPGA) as a design platform for circuit implementation by students and use image processing as an application for teaching low-power techniques.
This thesis presents the instructions for the low-power design experiments which use a top-down hierarchical design methodology. The engineering student designs his/her algorithm with a high level of abstraction and the experimental results are obtained and measured at a low level (hardware) so that more information is available to correctly estimate the power dissipation such as specification, latency, thermal effect, and technology used. Power dissipation of the digital system is influenced by specification, design, technology used, as well as operating temperature. Digital circuit designers can observe the most influential factors in power dissipation during the laboratory exercises in the on-site system and then use the remote system to supplement investigating the other factors. Furthermore, the remote system has obvious benefits such as developing learning outcomes, facilitating new teaching methods, reducing costs and maintenance, cost-saving by reducing the numbers of instructors, saving instructor time and simplifying their tasks, facilitating equipment sharing, improving reliability, and finally providing flexibility of usage the laboratories.
For many different applications, current information about the bandwidth-related metrics of the utilized connection is very useful as they directly impact the performance of throughput sensitive applications such as streaming servers, IPTV and VoIP applications. In literature, several tools have been proposed to estimate major bandwidth-related metrics such as capacity, available bandwidth and achievable throughput. The vast majority of these tools fall into one of Packet Pair (PP), Variable Packet Size (VPS), Self-Loading of Periodic Streams (SLoPS) or Throughput approaches. In this study, seven popular bandwidth estimation tools including nettimer, pathrate, pathchar, pchar, clink, pathload and iperf belonging to these four well-known estimation techniques are presented and experimentally evaluated in a controlled testbed environment. Differently from the rest of studies in literature, all tools have been uniformly classified and evaluated according to an objective and sophisticated classification and evaluation scheme. The performance comparison of the tools incorporates not only the estimation accuracy but also the probing time and overhead caused.
The nature of the program was an exchange program between Hochschule Bonn-Rhein-Sieg, University of Applied Sciences and the University of Cape Coast. The program was advertised and we applied. We were shortlisted for interview and we were selected as the candidates for the exchange program. The program took a period of five months. We set off from Accra, Ghana to Germany on 7th September 2015, and returned to Ghana on 25th January 2016.
Competency-Based Teaching Using Simulation Exercises: Evidence of the University of Cape Coast
(2018)
Tertiary institutions exist to train manpower to solve local, national, and international problems. Products from such institutions should not be a problem to countries as in the case of some Sub-Saharan African countries including Ghana which has a high level of graduate unemployment. Among the causes of the problem is the nature of teaching or the syllabus or the programs students pursue while in such institutions. The paper discusses one of the teaching strategies used to make a course relevant for a program and for the working world. In this course, students are introduced to practice-oriented learning through simulation exercises. The project activities specifically seek to assess the students’ understanding of business formation; examine students’ understanding of sustainability, creativity and innovation of business ideas; assess their understanding of the functional areas of business including marketing & sales, finance, human resource management, operations, and accounting, among others. Feedback from students who have participated indicates the exercise gave much more exposure and meaning to the concepts they learned in class. In this exercise, students build teams, develop a product, learn to set up a business, and design organogram, business vision, mission, and core values. The exercise empowers students to learn by doing. It accords students the opportunity to review their own knowledge and skills with respect to the concepts they have learned in the course. More than 3000 students have participated in this project since its inception in the academic year 2013/2014. It is estimated that 1000 students will participate in this project in the academic year 2017/2018.
Conclusion
(2018)
There is a paradigm shift from traditional content-based education and training to competencybased and practice-oriented training. This shift has occurred because practice-oriented teaching has been found to produce a training outcome that is industry focused, generating the relevant occupational standards. Competency-based training program often comprises of modules broken into segments called learning outcomes. These learning outcomes are based on criteria set by industry and assessment is designed to ensure students become competent in their respective areas of specialization.
Multidisciplinary, multicultural, and multitasking has taken center stage in the global educational debate. Globalization and improvement in communication have affected the way organisations operate and hence influenced whom they hire. Today, it is common practice to work with people from diverse backgrounds and it requires competencies that go beyond general project management. Intercultural awareness, networking in different global communities, and learning to develop specific communication strategies for different stakeholders is all part of the package of skills and competencies that are required in today's interconnected world. This has indirect implication on the nature of skills and competencies institutions/universities must equip their students with to enable them to compete successfully in the working world.
Estimation of Prediction Uncertainty for Semantic Scene Labeling Using Bayesian Approximation
(2018)
With the advancement in technology, autonomous and assisted driving are close to being reality. A key component of such systems is the understanding of the surrounding environment. This understanding about the environment can be attained by performing semantic labeling of the driving scenes. Existing deep learning based models have been developed over the years that outperform classical image processing algorithms for the task of semantic labeling. However, the existing models only produce semantic predictions and do not provide a measure of uncertainty about the predictions. Hence, this work focuses on developing a deep learning based semantic labeling model that can produce semantic predictions and their corresponding uncertainties. Autonomous driving needs a real-time operating model, however the Full Resolution Residual Network (FRRN) [4] architecture, which is found as the best performing architecture during literature search, is not able to satisfy this condition. Hence, a small network, similar to FRRN, has been developed and used in this work. Based on the work of [13], the developed network is then extended by adding dropout layers and the dropouts are used during testing to perform approximate Bayesian inference. The existing works on uncertainties, do not have quantitative metrics to evaluate the quality of uncertainties estimated by a model. Hence, the area under curve (AUC) of the receiver operating characteristic (ROC) curves is proposed and used as an evaluation metric in this work. Further, a comparative analysis about the influence of dropout layer position, drop probability and the number of samples, on the quality of uncertainty estimation is performed. Finally, based on the insights gained from the analysis, a model with optimal configuration of dropout is developed. It is then evaluated on the Cityscape dataset and shown to be outperforming the baseline model with an AUC-ROC of about 90%, while the latter having AUC-ROC of about 80%.
Sind kleinere und mittlere Unternehmen (KMU) bereits auf die Digitale Transformation vorbereitet?
(2018)
Eine von den Autoren durchgeführte Untersuchung konnte deutliche Indizien dafür ausmachen, dass viele kleinere und mittlere Unternehmen (KMU) aktuell noch keine ausreichende Reife zur Digitalen Transformation haben. Zur Lösung des Problems wird vorgeschlagen, ein agiles IT-Management-Konzept zu entwickeln, um den IT-Bereich dynamisch und ohne formalen Ballast des klassischen IT-Managements zu steuern.