Measurement of stress build-up of ion-exchange strengthened lithium aluminosilicate glass.

A recently developed non-destructive method was used to investigate the stress build-up in chemically strengthened lithium aluminosilicate glass. We utilized an updated version of the gradient scattered light method, which now enables more precise determination of the depth coordinates, recovering a more detailed stress profile around the knee. The main motivation of the work was to characterize and optimize the development of the knee-shaped breaking point in stress profile in lithium aluminosilicate glass using Saunders-Kubichan method of 1-step strengthening in a mixture of KNO3+NaNO3 molten salt bath. In the industry, a 2-step process is still commonly used to build such a stress profile. 1-step strengthening will simplify the process as well as save the cost. Compared to previous studies, which used a destructive method based on transmitted light photoelasticity, we found that in the samples ion-exchanged for 24h the knee-shaped breaking points were situated two times deeper whereas the case-depths were 28% shallower. The measured stress profiles were validated by stress equilibrium and by comparison to Na+ ion concentration profiles.

Action Recognition Using Single-Pixel Time-of-Flight Detection.

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject's privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.

Realization of laterally nondispersing ultrabroadband Airy pulses.

We present the measurements of the spatiotemporal impulse response of a system creating nondispersing Airy pulses, i.e., ultrabroadband Airy beams whose main lobe size remains constant over propagation. A custom refractive element with a continuous surface profile was used to impose the cubic phase on the input beam. The impulse response of the Airy pulse generator was spatiotemporally characterized by applying a white-light spatial-spectral interferometry setup based on the SEA TADPOLE technique. The results were compared with the theoretical model and previously spatiotemporally characterized Airy pulses generated by a spatial light modulator.

Spatiotemporal characterization of ultrabroadband Airy pulses.

We present experimental results of a full spatiotemporal characterization of an optical system for ultrabroadband Airy pulse generation with a liquid-crystal-on-silicon spatial light modulator. Measurements with a few micrometer spatial and almost one-wave-cycle temporal resolution were performed using a white light spatial spectral interferometry setup based on the SEA TADPOLE ultrashort pulse characterization technique. The results were compared with the theoretical model for Airy pulse propagation.