Non-invasive, near-field terahertz imaging of hidden objects using a single pixel detector

Terahertz (THz) imaging has the ability to see through otherwise opaque materials. However, due to the long wavelengths of THz radiation ({\lambda}=300{\mu}m at 1THz), far-field THz imaging techniques are heavily outperformed by optical imaging in regards to the obtained resolution. In this work we demonstrate near-field THz imaging with a single-pixel detector. We project a time-varying optical mask onto a silicon wafer which is used to spatially modulate a pulse of THz radiation. The far-field transmission corresponding to each mask is recorded by a single element detector and this data is used to reconstruct the image of an object placed on the far side of the silicon wafer. We demonstrate a proof of principal application where we image a printed circuit board on the underside of a 115{\mu}m thick silicon wafer with ~100{\mu}m ({\lambda}/4) resolution. With subwavelength resolution and the inherent sensitivity to local conductivity provided by the THz probe frequencies, we show that it is possible to detect fissures in the circuitry wiring of a few microns in size. Imaging systems of this type could have other uses where non-invasive measurement or imaging of concealed structures with high resolution is necessary, such as in semiconductor manufacturing or in bio-imaging

Recording and Analysis of Head Movements, Interaural Level and Time Differences in Rooms and Real-World Listening Scenarios

The science of how we use interaural differences to localise sounds has been studied for over a century and in many ways is well understood. But in many of these psychophysical experiments listeners are required to keep their head still, as head movements cause changes in interaural level and time differences (ILD and ITD respectively). But a fixed head is unrealistic. Here we report an analysis of the actual ILDs and ITDs that occur as people naturally move and relate them to gyroscope measurements of the actual motion. We used recordings of binaural signals in a number of rooms and listening scenarios (home, office, busy street etc). The listener's head movements were also recorded in synchrony with the audio, using a micro-electromechanical gyroscope. We calculated the instantaneous ILD and ITDs and analysed them over time and frequency, comparing them with measurements of head movements. The results showed that instantaneous ITDs were widely distributed across time and frequency in some multi-source environments while ILDs were less widely distributed. The type of listening environment affected head motion. These findings suggest a complex interaction between interaural cues, egocentric head movement and the identification of sound sources in real-world listening situations

Feature-domain super-resolution framework for Gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics

A new denoising technique for ultrasound images using morphological properties of speckle combined with tissue classifying parameters

In this paper we introduce a new speckle suppression technique for medical ultrasound images that incorporates morphological properties of speckle as well as tissue classifying parameters. Each individual speckles is located, and, exploiting our prior knowledge on the tissue classification, it is determined whether this speckle is noise or a medically relevant detail. We apply the technique on images of neonatal brains affected by White Matter Damage (leukomalacia). The results show that applying an active contour on a processed image, in order to segment the affected areas, yields a segmentation much closer to that of an expert