Technical Workshop: Advanced Helicopter Cockpit Design

Information processing demands on both civilian and military aircrews have increased enormously as rotorcraft have come to be used for adverse weather, day/night, and remote area missions. Applied psychology, engineering, or operational research for future helicopter cockpit design criteria were identified. Three areas were addressed: (1) operational requirements, (2) advanced avionics, and (3) man-system integration

Options for a new integrated natural resources monitoring framework for Wales. Phase 1 project report

Healthy natural resources underpin significant economic sectors in Wales including agriculture, fisheries, tourism and forestry, they also make a significant contribution across Cabinet policies including the health and well-being agenda. In order to develop policies that build social, economic and environmental resilience and to evaluate policy implementation, a robust natural resources monitoring framework is required. Current monitoring activities are of varying quality, not sufficiently aligned to the new legislative and policy landscape, disjointed and when considered as a whole, potentially not as cost-effective as they could be. This project was tasked with identifying options and developing recommendations for an integrated natural resources monitoring framework for Wales reflecting the ambitions and integrating principles of the Environment Act and Well Being of Future Generations Act. The monitoring community, the Welsh Government and Natural Resources Wales Core Evidence Group, the project team, stakeholders and partners, have agreed on a set of recommendations

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017