Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

Quantum Fluctuations of a Single Trapped Atom: Transient Rabi Oscillations and Magnetic Bistability

Isolation of a single atomic particle and monitoring its resonance fluorescence is a powerful tool for studies of quantum effects in radiation-matter interaction. Here we present observations of quantum dynamics of an isolated neutral atom stored in a magneto-optical trap. By means of photon correlations in the atom's resonance fluorescence we demonstrate the well-known phenomenon of photon antibunching which corresponds to transient Rabi oscillations in the atom. Through polarization-sensitive photon correlations we show a novel example of resolved quantum fluctuations: spontaneous magnetic orientation of an atom. These effects can only be observed with a single atom.Comment: LaTeX 2e, 14 pages, 7 Postscript figure

Antineoplastische Wirkung von PARP-Inhibitoren in Kombination mit ATR- oder ATM-Inhibitoren auf Ewing-Sarkomzellen

In dieser Dissertation wurde die Kombination von PARP- und ATR- oder ATM-Inhibitoren auf Ewing-Sarkomzellen in vitro untersucht

Event-related potential correlates of sound organization: Early sensory and late cognitive effects

We tested whether incoming sounds are processed differently depending on how the preceding sound sequence has been interpreted by the brain. Sequences of a regularly repeating three-tone pattern, the perceived organization of which spontaneously switched back and forth between two alternative interpretations, were delivered to listeners. Occasionally, a regular tone was exchanged for a slightly or moderately lower one (deviants). The electroencephalogram (EEG) was recorded while listeners continuously marked their perception of the sound sequence. We found that for both the regular and the deviant tones, the early exogenous P1 and N1 amplitudes varied together with the perceived sound organization. Percept dependent effects on the late endogenous N2 and P3a amplitudes were only found for deviant tones. These results suggest that the perceived sound organization affects sound processing both by modulating what information is extracted from incoming sounds as well as by influencing how deviant sound events are evaluated for further processing

Computational Models of Auditory Scene Analysis: A Review

Auditory scene analysis (ASA) refers to the process(es) of parsing the complex acoustic input into auditory perceptual objects representing either physical sources or temporal sound patterns, such as melodies, which contributed to the sound waves reaching the ears. A number of new computational models accounting for some of the perceptual phenomena of ASA have been published recently. Here we provide a theoretically motivated review of these computational models, aiming to relate their guiding principles to the central issues of the theoretical framework of ASA. Specifically, we ask how they achieve the grouping and separation of sound elements and whether they implement some form of competition between alternative interpretations of the sound input. We consider the extent to which they include predictive processes, as important current theories suggest that perception is inherently predictive, and also how they have been evaluated. We conclude that current computational models of ASA are fragmentary in the sense that rather than providing general competing interpretations of ASA, they focus on assessing the utility of specific processes (or algorithms) for finding the causes of the complex acoustic signal. This leaves open the possibility for integrating complementary aspects of the models into a more comprehensive theory of ASA

„Es gibt keine Lücke“

Professor Thomas Roeb kennt den Discount. Er lehrt Handelsbetriebslehre an der Hochschule Bonn-Rhein-Sieg und berät Unternehmen aus Handel und Konsumgüterbranche