Second harmonic generating (SHG) nanoprobes for in vivo imaging

Fluorescence microscopy has profoundly changed cell and molecular biology studies by permitting tagged gene products to be followed as they function and interact. The ability of a fluorescent dye to absorb and emit light of different wavelengths allows it to generate startling contrast that, in the best cases, can permit single molecule detection and tracking. However, in many experimental settings, fluorescent probes fall short of their potential due to dye bleaching, dye signal saturation, and tissue autofluorescence. Here, we demonstrate that second harmonic generating (SHG) nanoprobes can be used for in vivo imaging, circumventing many of the limitations of classical fluorescence probes. Under intense illumination, such as at the focus of a laser-scanning microscope, these SHG nanocrystals convert two photons into one photon of half the wavelength; thus, when imaged by conventional two-photon microscopy, SHG nanoprobes appear to generate a signal with an inverse Stokes shift like a fluorescent dye, but with a narrower emission. Unlike commonly used fluorescent probes, SHG nanoprobes neither bleach nor blink, and the signal they generate does not saturate with increasing illumination intensity. The resulting contrast and detectability of SHG nanoprobes provide unique advantages for molecular imaging of living cells and tissues

Magneto-optical trap for metastable helium at 389 nm

We have constructed a magneto-optical trap (MOT) for metastable triplet helium atoms utilizing the 2 3S1 -> 3 3P2 line at 389 nm as the trapping and cooling transition. The far-red-detuned MOT (detuning Delta = -41 MHz) typically contains few times 10^7 atoms at a relatively high (~10^9 cm^-3) density, which is a consequence of the large momentum transfer per photon at 389 nm and a small two-body loss rate coefficient (2 * 10^-10 cm^3/s < beta < 1.0 * 10^-9 cm^3/s). The two-body loss rate is more than five times smaller than in a MOT on the commonly used 2 3S1 -> 2 3P2 line at 1083 nm. Furthermore, we measure a temperature of 0.46(1) mK, a factor 2.5 lower as compared to the 1083 nm case. Decreasing the detuning to Delta= -9 MHz results in a cloud temperature as low as 0.25(1) mK, at small number of trapped atoms. The 389 nm MOT exhibits small losses due to two-photon ionization, which have been investigated as well.Comment: 11 page

A deep learning framework for quality assessment and restoration in video endoscopy

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. We propose a fully automatic framework that can: 1) detect and classify six different primary artifacts, 2) provide a quality score for each frame and 3) restore mildly corrupted frames. To detect different artifacts our framework exploits fast multi-scale, single stage convolutional neural network detector. We introduce a quality metric to assess frame quality and predict image restoration success. Generative adversarial networks with carefully chosen regularization are finally used to restore corrupted frames. Our detector yields the highest mean average precision (mAP at 5% threshold) of 49.0 and the lowest computational time of 88 ms allowing for accurate real-time processing. Our restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos we show that our approach preserves an average of 68.7% which is 25% more frames than that retained from the raw videos.Comment: 14 page

Fast traffic sign recognition using color segmentation and deep convolutional networks

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelli- gent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Ori- ented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a pre- processing step to reduce the search space, while classication is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traf- c Sign data set and on the novel Data set of Italian Trac Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the eectiveness of the proposed ap- proach in terms of both classication accuracy and computational speed

Laser Based Mid-Infrared Spectroscopic Imaging – Exploring a Novel Method for Application in Cancer Diagnosis

A number of biomedical studies have shown that mid-infrared spectroscopic images can provide both morphological and biochemical information that can be used for the diagnosis of cancer. Whilst this technique has shown great potential it has yet to be employed by the medical profession. By replacing the conventional broadband thermal source employed in modern FTIR spectrometers with high-brightness, broadly tuneable laser based sources (QCLs and OPGs) we aim to solve one of the main obstacles to the transfer of this technology to the medical arena; namely poor signal to noise ratios at high spatial resolutions and short image acquisition times. In this thesis we take the first steps towards developing the optimum experimental configuration, the data processing algorithms and the spectroscopic image contrast and enhancement methods needed to utilise these high intensity laser based sources. We show that a QCL system is better suited to providing numerical absorbance values (biochemical information) than an OPG system primarily due to the QCL pulse stability. We also discuss practical protocols for the application of spectroscopic imaging to cancer diagnosis and present our spectroscopic imaging results from our laser based spectroscopic imaging experiments of oesophageal cancer tissue

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events

Electromagnetic (EM) observations of gravitational-wave (GW) sources would bring unique insights into a source which are not available from either channel alone. However EM follow-up of GW events presents new challenges. GW events will have large sky error regions, on the order of 10-100 square degrees, which can be made up of many disjoint patches. When searching such large areas there is potential contamination by EM transients unrelated to the GW event. Furthermore, the characteristics of possible EM counterparts to GW events are also uncertain. It is therefore desirable to be able to assess the statistical significance of a candidate EM counterpart, which can only be done by performing background studies of large data sets. Current image processing pipelines such as that used by ROTSE are not usually optimised for large-scale processing. We have automated the ROTSE image analysis, and supplemented it with a post-processing unit for candidate validation and classification. We also propose a simple ad hoc statistic for ranking candidates as more likely to be associated with the GW trigger. We demonstrate the performance of the automated pipeline and ranking statistic using archival ROTSE data. EM candidates from a randomly selected set of images are compared to a background estimated from the analysis of 102 additional sets of archival images. The pipeline's detection efficiency is computed empirically by re-analysis of the images after adding simulated optical transients that follow typical light curves for gamma-ray burst afterglows and kilonovae. We show that the automated pipeline rejects most background events and is sensitive to simulated transients to limiting magnitudes consistent with the limiting magnitude of the images

Hybrid Coding Technique for Pulse Detection in an Optical Time Domain Reflectometer

The paper introduces a novel hybrid coding technique for improved pulse detection in an optical time domain reflectometer. The hybrid schemes combines Simplex codes with signal averaging to articulate a very sophisticated coding technique that considerably reduces the processing time to extract specified coding gains in comparison to the existing techniques. The paper quantifies the coding gain of the hybrid scheme mathematically and provide simulative results in direct agreement with the theoretical performance. Furthermore, the hybrid scheme has been tested on our self-developed OTDR