Imaging live humans through smoke and flames using far-infrared digital holography

The ability to see behind flames is a key challenge for the industrial field and particularly for the safety field. Development of new technologies to detect live people through smoke and flames in fire scenes is an extremely desirable goal since it can save human lives. The latest technologies, including equipment adopted by fire departments, use infrared bolometers for infrared digital cameras that allow users to see through smoke. However, such detectors are blinded by flame-emitted radiation. Here we show a completely different approach that makes use of lensless digital holography technology in the infrared range for successful imaging through smoke and flames. Notably, we demonstrate that digital holography with a cw laser allows the recording of dynamic human-size targets. In this work, easy detection of live, moving people is achieved through both smoke and flames, thus demonstrating the capability of digital holography at 10.6 μm.

Visible reconstruction by a circular holographic display from digital holograms recorded under infrared illumination

A circular holographic display that consists of phase-only spatial light modulators is used to reconstruct images in visible light from digital holograms recorded under infrared (10.6 μm) illumination. The reconstruction yields a holographic digital video display of a three-dimensional ghostlike image of an object floating in space where observers can move and rotate around it.

An automatic method for assembling a large synthetic aperture digital hologram

A major issue so far for digital holography is the low spatial resolution generally achieved. The numerical aperture is limited by the area of currently available detectors, such as CCD sensors, which is significantly lower than that of a holographic plate. This is an even more severe constraint when IR sensors such as microbolometers are taken into account. In order to increase the numerical aperture of such systems, we developed an automatic technique which is capable of recording several holograms and of stitching them together, obtaining a digital hologram with a synthetic but larger numerical aperture. In this way we show that more detail can be resolved and a wider parallax angle can be achieved. The method is demonstrated for visible as well IR digital holography, recording and displaying large size objects.

Function approximation--a fast-convergence neural approach based on spectral analysis

We propose a constructive approach to building single-hidden-layer neural networks for nonlinear function approximation using frequency domain analysis. We introduce a spectrum-based learning procedure that minimizes the difference between the spectrum of the training data and the spectrum of the network's estimates. The network is built up incrementally during training and automatically determines the appropriate number of hidden units. This technique achieves similar or better approximation with faster convergence times than traditional techniques such as backpropagation.

Entropic analysis and incremental synthesis of multilayered feedforward neural networks

Neural network architecture optimization is often a critical issue, particularly when VLSI implementation is considered. This paper proposes a new minimization method for multilayered feedforward ANNs and an original approach to their synthesis, both based on the analysis of the information quantity (entropy) flowing through the network. A layer is described as an information filter which selects the relevant characteristics until the complete classification is performed. The basic incremental synthesis method, including the supervised training procedure, is derived to design application-tailored neural paradigms with good generalization capability.