Getting students to read and study before class, to be better prepared for lecture, or to enable a flipped classroom is a long-standing difficulty for teachers of introductory programming classes. Furthermore, getting students to do homework, consisting of small practice problems and questions, is also a long-standing difficulty without massive grading resources. And even then, preventing students from copying others' solutions is difficult as well. Today, the web enables new interactive learning material that is replacing past forms of textbooks and homework assignments, and students today commonly have access to needed devices and the internet. This paper provides data on student reading and homework completion rates for web-based interactive learning material we created that automatically records reading and homework activity by students. The data is for several thousand students at over 10 universities, for introductory programming classes in Java, Python, and C++. The data shows that, with an appropriate amount of awarded points, required-reading completion rate was 84%, and auto-graded homework completion rate was 75%, varying somewhat based on how many course grade points those items were worth. Students on average spent about 10 minutes reading each section, and about 3 minutes per homework problem, both appropriate amounts for those items. Furthermore, we developed measures of whether students were earnestly attempting the reading and homeworks, versus just "cheating the system" to get course grade points. We describe those earnestness measures in this paper. With proper design and amount of assigned work, 80%-90% of students earnestly did the reading and homework activities, even when no penalty existed for cheating the system, and fewer than 3% blatantly cheated the system to get their points.

The commercialization of sensor-based platforms is facilitating the realization of numerous sensor network applications with diverse application requirements. However, sensor network platforms are becoming increasingly complex to design and optimize due to the multitude of interdependent parameters that must be considered. To further complicate matters, application experts oftentimes are not trained engineers, but rather biologists, teachers, or agriculturists who wish to utilize the sensor-based platforms for various domain-specific tasks. To assist both platform developers and application experts, we present a centralized dynamic profiling and optimization platform for sensor-based systems that enables application experts to rapidly optimize a sensor network for a particular application without requiring extensive knowledge of, and experience with, the underlying physical hardware platform. In this article, we present an optimization framework that allows developers to characterize application requirements through high-level design metrics and fuzzy-logic-based optimization. We further analyze the benefits of utilizing dynamic profiling information to eliminate the guesswork of creating a “good” benchmark, present several reoptimization evaluation algorithms used to detect if re-optimization is necessary, and highlight the benefits of the proposed dynamic optimization framework compared to static optimization alternatives.

As capacity of the field-programmable gate arrays (FPGAs) continues to increase, power dissipated in the logic and routing resources has become a critical concern for FPGA architects. Recent studies have shown that static power is fast approaching the dynamic power in submicron devices. In this article, we propose an analytical model for relating homogeneous island-style-based FPGA architecture to static power. Current FPGA power models are tightly coupled with CAD tools. Our CAD-independent model captures the static power for a given FPGA architecture based on estimates of routing and logic resource utilizations from a pre-technology mapped netlist. We observe an average correlation ratio (C-Ratio) of 95% and a minimum absolute percentage error (MAPE) rate of 15% with respect to the experimental results generated by the Versatile Placement Routing (VPR) tool over the MCNC benchmarks. Our model offers application engineers and FPGA architects the capability to evaluate the impact of their design choices on static power without having to go through CAD-intensive investigations.

Technological advancements in wireless communications and embedded systems have led to the proliferation of wireless sensor network (WSN) applications, each with varying application requirements (i.e., lifetime, throughput, reliability, etc.). Sensor node tunable parameters enable WSN designers to specialize/tune a sensor node to meet application requirements, but however, parameter tuning is a challenging process that requires designer expertise to consider sensor node complexities and changing environmental stimuli. In this paper, we develop lightweight, online optimization algorithms for sensor node parameter tuning, which enables dynamic optimizations to meet application requirements and adapt to changing environmental stimuli. Results reveal that our online optimizations quickly converge to a near optimal solution using minimal computational and storage resources, and are thus amenable for implementation on resource and energy-constrained sensor nodes.

The integration of technology into autistic classrooms has shown promising results, including an increase in attention span, creativity, and social skills. We have introduced a low-cost learning technology composed by electronic modules called eBlocks to an autistic middle-school classroom. The participants of this study had the opportunity to learn concepts in the design and implementation of electronic systems by using the eBlocks. Our initial findings show that the integration of hands-on real world based projects, centered on the design of systems for a Smart House estimulated peer-to-peer interaction and teamwork, while promoting spontaneous creative thinking. We present our experiences with six students, including summaries of our overall experiences, teacher's pre-and post-surveys, and the examination of students' work.

Energy harvesting is becoming one of the most important issues today due to the fast depletion of conventional energy resources. Current research efforts focus on efficient and reliable use of renewable resources such as solar, hydro, wind, and thermal resources to provide environmentally friendly solutions while minimizing the cost. However, an efficient and effective delivery of green energy to individual households remains a challenging issue. One key roadblock is the high complexity of the smart grid system. In this paper, we propose a new interface theory based energy-timing model for smart homes and smart grid management and design. This model allows simple yet accurate computations for energy supply and consumptions involved with photovoltaic (PV) cells, storage units, and load appliances for each home. At community, and city or state level (or nationwide), this model also has the potential to facilitate fast and effective energy management, optimization, and load scheduling. The end result is the possibility of managing smart grid with millions of households to achieve low cost and high energy savings.

Technological advancements in embedded systems due to Moore's law have lead to the proliferation of wireless sensor networks (WSNs) in different application domains (e.g. defense, health care, surveillance systems) with different application requirements (e.g. lifetime, reliability). Many commercial-off-the-shelf (COTS) sensor nodes can be specialized to meet these requirements using tunable parameters (e.g. voltage, frequency) to specialize the operating state. Since a sensor node's performance depends greatly on environmental stimuli, dynamic optimizations enable sensor nodes to automatically determine their operating state in-situ. However, dynamic optimization methodology development given a large design space and resource constraints (memory and computational) is a very challenging task. In this paper, we propose a lightweight dynamic optimization methodology that intelligently selects initial tunable parameter values to produce a high-quality initial operating state in one-shot for time-critical or highly constrained applications. Further operating state improvements are made using an efficient greedy exploration algorithm, achieving optimal or near-optimal operating states while exploring only 0.04% of the design space on average.

Technology trends have enabled deployment of low-cost sensor-based systems, but designing customized sensor-based systems to carry out specific tasks still requires costly engineering by experts. We briefly summarize eBlocks, a technology enabling nonexperts to quickly construct basic customized sensor-based systems, without requiring electronics or knowledge of programming languages. We describe experiments illustrating successful construction of Boolean sensor-based systems by novice users, focusing on intuitive logic and state block design. Additionally, we present preliminary experiments demonstrating usability of integer-based blocks and introduce a programmable block and the corresponding configuration methodology intended for nonexpert users.

Application specific tuning has been shown to be beneficial for a variety of platforms, sensor-based systems are no exception. However, accurately capturing external stimuli or modeling application specific stimuli remains a challenge. Tuning a sensor-based system to erroneous or incomplete application information can limit the optimization achieved or even negatively impact the resulting system performance. We propose to obtain accurate application stimuli by dynamically observing the sensor-based system in the intended deployment location, storing application characteristics for later analysis or optimization.