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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.
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.
Digitalisierung für kleinere und mittlere Unternehmen (KMU): Anforderungen an das IT-Management
(2018)
Renewable resources gain increasing interest as source for environmentally benign biomaterials, such as drug encapsulation/release compounds, and scaffolds for tissue engineering in regenerative medicine. Being the second largest naturally abundant polymer, the interest in lignin valorization for biomedical utilization is rapidly growing. Depending on resource and isolation procedure, lignin shows specific antioxidant and antimicrobial activity. Today, efforts in research and industry are directed toward lignin utilization as renewable macromolecular building block for the preparation of polymeric drug encapsulation and scaffold materials. Within the last five years, remarkable progress has been made in isolation, functionalization and modification of lignin and lignin-derived compounds. However, literature so far mainly focuses lignin-derived fuels, lubricants and resins. The purpose of this review is to summarize the current state of the art and to highlight the most important results in the field of lignin-based materials for potential use in biomedicine (reported in 2014–2018). Special focus is drawn on lignin-derived nanomaterials for drug encapsulation and release as well as lignin hybrid materials used as scaffolds for guided bone regeneration in stem cell-based therapies.