237 research outputs found
Dataset for "Practical Implementation of Memristor-Based Threshold Logic Gates"
No full textThis dataset supports the publication:
Georgios Papandroulidakis, Alexander Serb, Ali Khiat, Geoff V. Merrett, Themis Prodromakis
Practical Implementation of Memristor-Based Threshold Logic Gates
Transactions on Circuits and Systems I: Regular Papers
DOI: 10.1109/TCSI.2019.2902475
For more information see the readme file.</span
Dasaset supporting the publication "Late Breaking Results: Adaptive Ensembles of Dynamic DNNs for Collaborative Edge Inference"
No full textThis dataset supports the publication: "Late Breaking Results: Adaptive Ensembles of Dynamic DNNs for Collaborative Edge Inference" by Mingyu Hu, Amit Kumar Singh, Jonathon Hare, Geoff V. Merrett.
CONFERENCE: Design, Automation and Test in Europe Conference 2026
This dataset includes the experimental results for:
Figure 3: Results of Ensemble accuracy from different combinations of 4 model instance of Dynamic ResNet-18.
Figure 4: Comparison of inference latency with different methods under different deadlines.
Figure 5: Comparison of inference accuracy with different methods under different deadlines.</span
Water quality monitoring, control and management (WQMCM) framework using collaborative wireless sensor networks
No full textImproving water quality is of global concern, with agricultural practices being the major contributors to reduced water quality. The reuse of nutrient-rich drainage water can be a valuable strategy to gain economic-environmental benefits. However, currently the tools and techniques to allow this do not exist. Therefore, we have proposed a framework, WQMCM, which utilises increasingly common local farm-scale networks across a catchment, adding provision for collaborative information sharing. Using this framework, individual sub-networks can learn their environment and predict the impact of catchment events on their locality, allowing dynamic decision making for local irrigation strategies. Since resource constraints of network nodes (e.g. power consumption, computing power etc.) require a simplified predictive model for discharges, therefore low-dimensional model parameters are derived from the existing National Resource Conservation Method (NRCS), utilising real-time field values. Evaluation of the predictive models, developed using M5 decision trees, demonstrates accuracy of 84-94% compared with the traditional NRCS curve number model. The discharge volume and response time model was tested to perform with 6% relative root mean square error (RRMSE), even for a small training set of around 100 samples; however the discharge response time model required a minimum of 300 training samples to show reasonable performance with 16% RRMS
Supercapacitor leakage in energy-harvesting sensor nodes: fact or fiction?
No full textAs interest in energy-harvesting sensor nodes continues to grow, the use of supercapacitors as energy stores or buffers is gaining popularity. The reasons for their use are numerous, and include their high power density, simple interfacing requirements, simpler measurement of state-of-charge, and a greater number of charging cycles than secondary batteries. However, supercapacitor energy densities are orders of magnitude lower. Furthermore, they have been reported to exhibit significant leakage, and this has been shown to increase exponentially with terminal voltage (and hence stored energy). This observation has resulted in a number of algorithms, designs and methods being proposed for effective operation of supercapacitor-based energy-harvesting sensor nodes. In this paper, it is argued that traditional ‘leakage’ is not as significant as has commonly been suggested. Instead, what is observed as leakage is in fact predominantly due to internal charge redistribution. As a result, it is suggested that different approaches are required in order to effectively utilize supercapacitors in energy-harvesting sensor nodes
An explicit linearized state-space technique for accelerated simulation of electromagnetic vibration energy harvesters
No full textVibration energy harvesting systems pose significant modeling and design challenges due to their mixed-technology nature, extremely low levels of available energy and disparate time scales between different parts of a complete harvester. An energy harvester is a complex system of tightly coupled components modeled in the mechanical, magnetic as well as electrical analog and digital domains. Currently available design tools are inadequate for simulating such systems due to prohibitive CPU times. This paper proposes a new technique to accelerate simulations of complete vibration energy harvesters by approximately two orders of magnitude. The proposed technique is to linearize the state equations of the system's analog components to obtain a fast estimate of the maximum step-size to guarantee the numerical stability of explicit integration based on the Adams-Bashforth formula. We show that the energy harvester's analog electronics can be efficiently and reliably simulated in this way with CPU times two orders of magnitude lower than those obtained from two state-of-the art tools, VHDL-AMS and SystemC-A. As a case study, a practical, complex microgenerator with magnetic tuning and two types of power processing circuits have been simulated using the proposed technique and verified experimentally
Photovoltaic sample-and-hold circuit enabling MPPT indoors for low-power systems
No full textPhotovoltaic (PV) energy harvesting is commonly used to power autonomous devices, and maximum power point tracking (MPPT) is often used to optimize its efficiency. This paper describes an ultra low-power MPPT circuit with a novel sample-and-hold and cold-start arrangement, enabling MPPT across the range of light intensities found indoors, which has not been reported before. The circuit has been validated in practice and found to cold-start and operate from 100 lux (typical of dim indoor lighting) up to 5000 lux with a 55cm2 amorphous silicon PV module. It is more efficient than non-MPPT circuits, which are the state-of-the-art for indoor PV systems. The proposed circuit maximizes the active time of the PV module by carrying out samples only once per minute. The MPPT control arrangement draws a quiescent current draw of only 8µA, and does not require an additional light sensor as has been required by previously-reported low-power MPPT circuits
Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment
No full textOur natural environment is complex and sensitive, and is home to a number of species on the verge of extinction. Surveying is one approach to their preservation, and can be supported by technology. This paper presents the deployment of a smartphone-based citizen science biodiversity application. Our findings from interviews with members of the biodiversity community revealed a tension between the technology and their established working practices. From our experience, we present a series of general guidelines for those designing citizen science apps
Full Citation
Moran, Stuart, Pantidi, Nadia, Rodden, Tom, Chamberlain, Alan, Griffiths, Chloe, Zilli, Davide, Merrett, Geoff V. and Rogers, Alex (2014) Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment. In, ACM CHI Conference on Human Factors in Computing Systems, Toronto, CA, 26 Apr - 01 May 2014. (doi:10.1145/2556288.255702)
Empirical Evaluation of OI-MAC: Direct Interconnection between Wireless Sensor Networks for Collaborative Monitoring
No full textCooperation between co-located Wireless Sensor Networks (WSNs) has the potential to present new opportunities for novel applications and provide network performance improvements. The traditional interconnection approach for WSNs is based on a backbone network such as the Internet, but this may have intermittent or unavailable connectivity in remote locations. To address this, Opportunistic Direct Interconnection (ODI) has been proposed to allow distinct and independent WSNs to communicate directly with neighbouring networks, and OIMAC is a link-layer protocol which implements this functionality. However, OI-MAC has not been experimentally validated, instead with analysis performed through simulation. In this paper, we present a practical implementation of OI-MAC using two separate multi-hop networks with 6 sensor nodes in each. We validate its effective operation through experimentally obtained timing diagrams, sensor data output, and energy consumption. Results show successful cross-network packet communication, while networks remain independent by maintaining individual configurations and communication channels. Furthermore, we show that the process of discovering neighbouring networks has an insignificant impact on energy consumption
Design of a linearized magnetic spring for body-worn inertial energy harvesters
No full textA potential method for powering body-worn sensors is that of inertial energy harvesting; extracting energy from the movement of the human body. However, the frequencies typically present are <5 Hz, hence requiring physically large devices. A promising solution utilizes a magnetic spring, but these exhibit a non-linear relationship between force (and hence resonant frequency) and displacement. This paper describes a design for implementing a linearized magnetic spring. Finite element analysis is used to model this device and compare against those reported in the literature. Simulation results indicate that, compared to the state-of-the-art, this design exhibits improved linearity (2%) across a wider displacement range (±25 mm). A prototype has been fabricated, and the simulation results experimentally validated
Energy-driven computing for energy-harvesting embedded systems
No full textThere has been increasing interest over the last decade in the powering of embedded systems from ‘harvested’ energy, and this has been further fuelled by the promise and vision of IoT. Energy harvesting systems present numerous challenges, although some of these are also posed by their battery-powered counterparts: e.g. ultra-low power consumption. However, a significant challenge not witnessed in battery-powered systems is a requirement to manage the combination of a highly unpredictable and variable (spatially and temporally) power supply with a highly dynamic (across many orders of magnitude) and often event-driven system power consumption. This problem is typically rectified through the addition of energy storage (e.g. a supercapacitor) to provide energy buffering to smooth out the dynamics of supply and consumption. This has the significant advantage of making the system ‘look like’ a battery-powered system, yet usually adds volume, mass and cost to the resultant system – something that is counterproductive in future flexible, wearable and implantable IoT systems. Such systems can, alternatively, include only a very small amount (or even zero) energy-storage. Now, instead of the system’s operation being dictated solely by the application, operation starts to become ‘energy-driven’, with execution being highly intertwined with power and energy availability. In this presentation, I will first introduce the landscape of energy-harvesting computing systems, and articulate how energy-driven computing presents a different class of computing to conventional approaches. A significant issue in the successful operation of these systems is their ability to operate from an intermittent, constrained and variable supply, and I will show how transient operation and power-neutrality can be used to achieve the vision for these systems, and hence enable the proliferation of tiny self-powered systems that will underpin much of the IoT
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