The rapid growth of connected sensing devices has resulted in enormous amounts of data being collected and processed. Air quality data collected from different monitoring stations is spatially and temporally correlated, and hence, collaborative …
Low-cost air quality monitoring devices can provide high-density spatiotemporal pollution data, thus offering a better opportunity to apply machine learning (ML). Low-cost sensor nodes usually utilize microcontrollers as the main processors, and …
Long-Short Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) in general, have demonstrated their suitability in many time series data applications, especially in Natural Language Pro- cessing (NLP). Computationally, LSTMs introduce …
Partial reconiguration (PR) is a key enabler to the design and development of adaptive systems on modern Field Programmable Gate Array (FPGA) Systems-on-Chip (SoCs), allowing hardware to be adapted dynamically at runtime. Vendor supported PR …
Coarse-grained FPGA overlays built around the runtime programmable DSP blocks in modern FPGAs can achieve high throughput and improved scalability compared to traditional overlays built without detailed consideration of FPGA architecture. These …
A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be …
The increasing size of modern FPGAs allows for ever more complex applications to be mapped onto them. However, long design implementation times for large designs can severely affect design productivity. A modular design methodology can improve design …
Battery-powered unmanned aerial vehicles (UAVs) have been widely used as enablers of wireless networks. In this letter, the optimal battery weight for UAV-enabled wireless sensor networks is studied. The energy available for communication by …
Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In …
The Internet of Things is manifested through a large number of low-capability connected devices. This means that for many applications, computation must be offloaded to more capable platforms. While this has typically been cloud datacenters accessed …