Artificial intelligence automates the characterization of reversibly actuating planar-flow-casted NiTi shape memory alloy foil.

Nickel-Titanium (NiTi) shape memory alloys (SMAs) are smart materials able to recover their original shape under thermal stimulus. Near-net-shape NiTi SMA foils of 2 meters in length and width of 30 mm have been successfully produced by a planar flow casting facility at CSIRO, opening possibilities of wider applications of SMA foils. The study also focuses on establishing a fully automated experimental system for the characterisation of their reversible actuation, significantly improving SMA foils adaptation into real applications.

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately.

Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body.

A Vertically Integrated CMOS Microsystem for Time-Resolved Fluorescence Analysis.

We describe a two-chip micro-scale time-resolved fluorescence analyzer integrating excitation, detection, and filtering. A new 8×8 array of drivers realized in standard low-voltage 0.35-μm complementary metal-oxide semiconductor is bump-bonded to AlInGaN blue micro-pixellated light-emitting diodes (micro-LEDs).

Hardware implementation and calibration of background noise for an integration-based fluorescence lifetime sensing algorithm.

A new integration based fluorescence lifetime imaging microscopy (FLIM) called IEM has been proposed to implement lifetime extraction [J. Opt. Soc.

A CMOS Time-Resolved Fluorescence Lifetime Analysis Micro-System.

We describe a CMOS-based micro-system for time-resolved fluorescence lifetime analysis. It comprises a 16 × 4 array of single-photon avalanche diodes (SPADs) fabricated in 0.35 μm high-voltage CMOS technology with in-pixel time-gated photon counting circuitry and a second device incorporating an 8 × 8 AlInGaN blue micro-pixellated light-emitting diode (micro-LED) array bump-bonded to an equivalent array of LED drivers realized in a standard low-voltage 0.

On-chip, time-correlated, fluorescence lifetime extraction algorithms and error analysis.

A new, simple, and hardware-only fluorescence-lifetime-imaging microscopy (FLIM) is proposed to implement on-chip lifetime extractions, and their signal-to-noise-ratio based on statistics theory is also deduced. The results are compared with Monte Carlo simulations, giving good agreement. Compared with the commonly used iterative least-squares method or the maximum-likelihood-estimation- (MLE-) based, general purpose FLIM analysis software, our algorithm offers direct calculation of fluorescence lifetime based on the collected photon counts stored in on-chip counters and therefore delivers faster analysis for real-time applications, such as clinical diagnosis.

Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic VLSI.

A transient-detecting very large scale integration (VLSI) pixel is described, suitable for use in a visual-processing, depth-recovery algorithm based upon spike timing. A small array of pixels is coupled to an adaptive system, based upon spike timing dependent plasticity (STDP), that aims to reduce the effect of VLSI process variations on the algorithm's performance. Results from 0.