Contemporary surveillance systems mainly use video cameras as their primary sensor. However, video cameras possess fundamental deficiencies, such as the inability to handle low-light environments, poor weather conditions, and concealing clothing. In contrast, radar devices are able to sense in pitch-dark environments and to see through walls. In this paper, we investigate the use of micro-Doppler (MD) signatures retrieved from a low-power radar device to identify a set of persons based on their gait characteristics. To that end, we propose a robust feature learning approach based on deep convolutional neural networks. Given that we aim at providing a solution for a real-world problem, people are allowed to walk around freely in two different rooms. In this setting, the IDentification with Radar data data set is constructed and published, consisting of 150 min of annotated MD data equally spread over five targets. Through experiments, we investigate the effectiveness of both the Doppler and time dimension, showing that our approach achieves a classification error rate of 24.70% on the validation set and 21.54% on the test set for the five targets used. When experimenting with larger time windows, we are able to further lower the error rate.

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.

Simulations are often used for the design of complex systems as they allow one to explore the design space without the need to build several prototypes. Over the years, the simulation accuracy, as well as the associated computational cost, has increased significantly, limiting the overall number of simulations during the design process. Therefore, metamodeling aims to approximate the simulation response with a cheap to evaluate mathematical approximation, learned from a limited set of simulator evaluations. Kernel-based methods using stationary kernels are nowadays widely used. In many problems, the smoothness of the function varies in space, which we call nonstationary behavior [20]. However, using stationary kernels for nonstationary responses can be inappropriate and result in poor models when combined with sequential design. We present the application of two recent techniques: Deep Gaussian Processes and Gaussian Processes with nonstationary kernel, which are better able to cope with these difficulties. We evaluate the method for nonstationary regression on a series of real-world problems, showing that these recent approaches outperform the standard Gaussian Processes with stationary kernels. Results show that these techniques are suitable for the simulation community, and we outline the variational inference method for the Gaussian Process with nonstationary kernel.

Integrated polymer photonics is efficient and cheap solution for many applications, such as optical communications and sensors. Waveguides are basic elements of photonic integrated circuits, and their performance directly influences optical circuit efficiency. However, it is a challenge to develop small footprint polymer waveguides with reasonable losses due to low refractive index comparing to cladding. In this letter, we present free-standing SU-8 waveguides with high refractive index contrast. SiO ₂ cladding is selectively etched, leaving only air as a cladding material; waveguides are supported by thin pillars. Removal of SiO ₂ cladding eliminates light leakage to substrate and provides better light confinement. Straight and bending SU-8 waveguides are numerically simulated and experimentally verified at λ = 1550 nm. We also report reduced propagation and bending losses in discussed waveguides.