Non-intrusive water flow rate measurement: a TEG-powered ultrasonic sensing approach

This paper proposes a thermoelectric generator (TEG)-powered ultrasonic sensing system for non-intrusive water flow rate measurement. The limited power provided by the TEGs is handled by a dedicated energy management unit (EMU), allowing reliable sensing, computation, and transmission tasks. First, we introduce the delta time-of-flight (ΔToF)-based ultrasonic sensing and thermoelectric energy generation theory. Then, the design is given, followed by the system evaluation under different harvesting conditions to show their impact on average sensing and transmission times. The results revealed that our method could achieve high measurement accuracy (±1.4%), comparable to intrusive and battery-powered counterparts, thereby offering a 'plug&play+deploy&forget' hybrid solution

The experiences of grade 6 Science and Technology learners of experiential learning as method of instruction

The purpose of this study was to explore and describe grade 6 learners’ experiences of experiential learning in the context of the TekkiKids Programme. The TekkiKids Programme followed a constructivist approach to learning and emphasis was placed on a learnercentred approach. Documents that were written by a consultant, who was involved with the TekkiKids Program, were selected as data sources. These documents included: A feedback report to the facilitators regarding the sessions; general field notes of the consultant pertaining to observations made during lessons; notes of individual unstructured interviews; a questionnaire that explored learners’ experiences of TekkiKids; and notes of a focus group discussion. A qualitative, documentary research design was implemented, and the documents were analysed according to guidelines pertaining to a process of inductive analysis. This study found that learners experienced experiential learning as a method of instruction to be but only partially supportive and encouraging. They furthermore experienced a need for more structure pertaining to problem-solving. Multicultural differences and group conflict had a negative influence on their learning experiences. Learners experienced cognitive load distribution as positive . English as the language of instruction was experienced as a barrier to learners from other language groups CopyrightDissertation (MEd)--University of Pretoria, 2010.Educational Psychologyunrestricte

Ultra-Low Power and Non-intrusive Wireless Monitoring for Smart Buildings

Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances

Non-intrusive Zigbee power meter for load monitoring in smart buildings

Energy efficiency in smart buildings requires distributed sensing infrastructure to monitor the power consumption of appliances, machines and lighting sources. The analysis of current and voltage waveforms is fundamental for gathering diagnostic information about the power quality and for reducing power wastage. Moreover, it enables Non-intrusive Load Monitoring (NILM), which is the process of disaggregating a household's total electricity consumption into its contributing appliances, by analysing the voltage and current changes. In this paper, an innovative full Energy-neutral (i.e. battery free) and Non-intrusive Wireless Energy Meter (NIWEM) is presented to measure current, voltage and power factor. As key features, the NIWEM is completely non-invasive and it can self-sustain its operations by harvesting energy from the monitored load. It also features a standard (Zigbee) wireless interface for communication with the smart-building system. Experimental results have confirmed that complete energy sustainability can be achieved also with very low-power loads

Hibernus: sustaining computation during intermittent supply for energy-harvesting systems

A key challenge to the future of energy-harvesting systems is the discontinuous power supply that is often generated. We propose a new approach, Hibernus, which enables computation to be sustained during intermittent supply. The approach has a low energy and time overhead which is achieved by reactively hibernating: saving system state only once, when power is about to be lost, and then sleeping until the supply recovers. We validate the approach experimentally on a processor with FRAM nonvolatile memory, allowing it to reactively hibernate using only energy stored in its decoupling capacitance. When compared to a recently proposed technique, the approach reduces processor time and energy overheads by 76-100% and 49-79% respectively

Graceful performance modulation for power-neutral transient computing systems

Transient computing systems do not have energy storage, and operate directly from energy harvesting. These systems are often faced with the inherent challenge of low-current or transient power supply. In this paper, we propose “power-neutral” operation, a new paradigm for such systems, whereby the instantaneous power consumption of the system must match the instantaneous harvested power. Power neutrality is achieved using a control algorithm for dynamic frequency scaling (DFS), modulating system performance gracefully in response to the incoming power. Detailed system model is used to determine design parameters for selecting the system voltage thresholds where the operating frequency will be raised or lowered, or the system will be hibernated. The proposed control algorithm for power-neutral operation is experimentally validated using a microcontroller incorporating voltage threshold-based interrupts for frequency scaling. The microcontroller is powered directly from real energy harvesters; results demonstrate that a power-neutral system sustains operation for 4–88% longer with up to 21% speedup in application execution

Thermally-aware composite run-time CPU power models

Accurate and stable CPU power modelling is fundamental in modern system-on-chips (SoCs) for two main reasons: 1) they enable significant online energy savings by providing a run-time manager with reliable power consumption data for controlling CPU energy-saving techniques; 2) they can be used as accurate and trusted reference models for system design and exploration. We begin by showing the limitations in typical performance monitoring counter (PMC) based power modelling approaches and illustrate how an improved model formulation results in a more stable model that efficiently captures relationships between the input variables and the power consumption. Using this as a solid foundation, we present a methodology for adding thermal-awareness and analytically decomposing the power into its constituting parts. We develop and validate our methodology using data recorded from a quad-core ARM Cortex-A15 mobile CPU and we achieve an average prediction error of 3.7% across 39 diverse workloads, 8 Dynamic Voltage-Frequency Scaling (DVFS) levels and with a CPU temperature ranging from 31 degrees C to 91 degrees C. Moreover, we measure the effect of switching cores offline and decompose the existing power model to estimate the static power of each CPU and L2 cache, the dynamic power due to constant background (BG) switching, and the dynamic power caused by the activity of each CPU individually. Finally, we provide our model equations and software tools for implementing in a run-time manager or for using with an architectural simulator, such as gem5

Hibernus++: a self-calibrating and adaptive system for transiently-powered embedded devices

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This work proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over stateof- the-art approaches