To support and stimulate basic research, applied research and
advanced research at educational institutions, nonprofit organizations,
and commercial firms, which may have military or dual-use application.
This support may take the form of grants, cooperative agreements, or
technology investment agreements (TIAs). DARPA DoD
In this paper, we report a single-board integrated Doppler radar system (DRS) operating at 24 GHz. To verify the performance of the 24-GHz DRS, we make a comparison between the existing 5.8-GHz Doppler radar system (DRS) and the 24-GHz one about the ability to detect the bio-signals (heartbeats, respiration and human gait). In the experiment, DRSs are placed in the same experimental environment and working at the same time for the comparison purposes. Experimental results show that the 24-GHz DRS detects Doppler bio-signals of both the small-scale human cardiac motion and the large-scale human gait more accurately than the 5.8GHz one. The obtained results imply the great potential to implement the 24-GHz Doppler radar system for human bio-signals detection
Automated and autonomous biosensors integrated into public infrastructures (transportation hubs, schools, offices and so on) are envisaged to improve public safety by alerting the public about biological threats. These devices may also be connected through the ‘internet of things’ and generate large spatiotemporal datasets at the population scale14,15. Therefore, future biosensor technologies will inevitably need to harness artificial intelligence algorithms to handle the vast amount of information generated. Figure 1 illustrates this vision and gives a glimpse into the future of biosensors.
Resonance properties can be engineered by nanostructure design parameters, such as materials, geometry and arrangement, as well as by the nature of the optical phenomenon that leads to the resonances (for example, gap plasmons, Fano-like modes or bound state in the continuum (BIC))22,23,24,25. Most nanophotonic biosensors exploit resonances supported by metallic and dielectric nanostructures
Entropy search and its derivative methods are one class of Bayesian Optimization methods that achieve active exploration of black-box functions. They maximize the information gain about the position in the input space where the black-box function gets the global optimum. However, existing entropy search methods suffer from harassment caused by high dimensional optimization problems. On the one hand, the computation for estimating entropies increases exponentially as dimensions increase, which limits the applicability of entropy search to high dimensional problems. On the other hand, many high-dimensional problems have the property that a large number of dimensions have little influence on the objective function, but currently there is no compress mechanism to exclude these redundant dimensions. In this work, we propose Active Compact Entropy Search (AcCES) to fix the above two defects. Under the guidance of historical evaluation, we bring forward a novel acquisition function that considers the correlation between dimensions in entropy search, which is ignored by existing Bayesian Optimization methods. The correlation term added in the acquisition function will help discover the potential correlation between dimensions. In order to build a more compact input space, redundant dimensions are compressed by exploiting inter-dimensional correlations. We use Pearson Correlation Coefficient and curve fitting to represent the inter-dimensional correlations. Extensive experiments on several benchmarks demonstrate that AcCES achieves higher query efficiency as well as optimal results after convergence than existing entropy search methods.