Knowledge Maps and Their Application to Student and Faculty Assessment

This paper discusses the development of knowledge maps for enhancing engineering learning. These maps are somewhat similar to concept maps, which have been widely used and developed in various areas of study. Knowledge maps, however, extend concept maps in that they not only illustrate the underlying concepts of a discipline, but they actually embed the knowledge in each of those concepts through various multimedia attachments. Knowledge maps also allow reverse mapping so that students can be assessed based upon how many concepts they know and whether they have understood the proper relationships between the concepts. A reverse map can be used to evaluate students and act as a record of student learning. Aggregate course maps may be used to gain an average understanding of the gains of entire classes of students and may be used to evaluate faculty effectiveness and provide valuable insight into the gains and weaknesses of students matriculating from one course to the next. The work contained herein presents the strategies implemented to allow for the design of custom knowledge maps. Reverse mapping techniques are discussed to indicate the method for evaluation of students

Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems

Dynamic power management (DPM) refers to the strategies employed at system level to reduce energy expenditure (i.e. to prolong battery life) in embedded systems. The trade-off involved in DPM techniques is between the reductions of energy consumption and latency suffered by the tasks. Such trade-offs need to be decided at runtime, making DPM an on-line problem. We formulate DPM as a hybrid automaton control problem and integrate stochastic control. The control strategy is learnt dynamically using stochastic learning hybrid automata (SLHA) with feedback learning algorithms. Simulation-based experiments show the expediency of the feedback systems in stationary environments. Further experiments reveal that SLHA attains better trade-offs than several former predictive algorithms under certain trace data

Utilizing Hands-on Learning to Facilitate Progression through Bloom\u27s Taxonomy within the First Semester

Hands-on learning has been utilized in engineering curriculums for several years in order to illustrate theory in a physical way. This paper presents the use of two hands-on learning activities in a first semester, freshman year engineering course designed to introduce basic concepts from mechanical engineering, electrical engineering, and computer engineering. In previous offerings of the course, several disjoint activities have been provided in order to introduce the fundamentals of these disciplines. This paper presents how several weeks worth of material are synthesized in a hands-on activity in order to allow deeper levels of student understanding and to showcase how engineering knowledge from a variety of disciplines can be synthesized in a meaningful way. Through these exercises students are able to understand how computer programs can be used to collect data from sensors, determine the appropriate response to this sensor data, and control circuits that are used to drive mechanical systems based on the sensor data. Through this activity, students are able to escalate through several levels of Bloom\u27s taxonomy by drawing connections between theory and practice from a variety of fields

Stochastic Learning Feedback Hybrid Automata for Power Management in Embedded Systems

In this paper we show that stochastic learning automata based feedback control switching strategy can be used for dynamic power management (DPM) employed at the system level. DPM strategies are usually incorporated at the operating systems of embedded devices to exploit multiple power states available in today\u27s ACPI compliant devices. The idea is to switch between power states depending on the device usage, and since device usage times are not deterministic, probabilistic techniques are often used to create stochastic strategies, or strategies that make decisions based on probabilities of device usage spans. Previous work (Irani et al., 2001) has shown how to approximate the probability distribution of device idle times and dynamically update them, and then use such knowledge in controlling power states. Here, we use stochastic learning automata (SLA) which interacts with the environment to update such probabilities, and then apply techniques similar to (Irani et al., 2001) to optimize power usage with minimal effect on response time for the devices

Confinement enhances the diversity of microbial flow fields

Despite their importance in many biological, ecological and physical processes, microorganismal fluid flows under tight confinement have not been investigated experimentally. Strong screening of Stokelets in this geometry suggests that the flow fields of different microorganisms should be universally dominated by the 2D source dipole from the swimmer's finite-size body. Confinement therefore is poised to collapse differences across microorganisms, that are instead well-established in bulk. Here we combine experiments and theoretical modelling to show that, in general, this is not correct. Our results demonstrate that potentially minute details like microswimmers' spinning and the physical arrangement of the propulsion appendages have in fact a leading role in setting qualitative topological properties of the hydrodynamic flow fields of micro-swimmers under confinement. This is well captured by an effective 2D model, even under relatively weak confinement. These results imply that active confined hydrodynamics is much richer than in bulk, and depends in a subtle manner on size, shape and propulsion mechanisms of the active components.Comment: Accepted for publication in Physical Review Letters. 5 pages and 4 figures, plus Supplementary Materia

The DRIFT Dark Matter Experiments

The current status of the DRIFT (Directional Recoil Identification From Tracks) experiment at Boulby Mine is presented, including the latest limits on the WIMP spin-dependent cross-section from 1.5 kg days of running with a mixture of CS2 and CF4. Planned upgrades to DRIFT IId are detailed, along with ongoing work towards DRIFT III, which aims to be the world's first 10 m3-scale directional Dark Matter detector.Comment: Proceedings of the 3rd International conference on Directional Detection of Dark Matter (CYGNUS 2011), Aussois, France, 8-10 June 201