Integration of millimeter-wave and optical link for duplex transmission of hierarchically modulated signal over a single carrier and fiber for future 5G communication systems

Telecommunication Systems - Integration of optical and millimeter-wave systems provide a promising solution for future giga-bits per second wireless communication systems. We have proposed and...     

11Beta-hydroxysteroid dehydrogenase type 2 in human pregnancy and reduced expression in intrauterine growth restriction

The evolutionary theory of aging predicts that the equilibrium gene frequency for deleterious mutations should increase with age at onset of mutation action because of weaker (postponed) selection against later-acting mutations. According to this mutation accumulation hypothesis, one would expect the genetic variability for survival (additive genetic variance) to increase with age. The ratio of additive genetic variance to the observed phenotypic variance (the heritability of longevity) can be estimated most reliably as the doubled slope of the regression line for offspring life span on paternal age at death. Thus, if longevity is indeed determined by late-acting deleterious mutations, one would expect this slope to become steeper at higher paternal ages. To test this prediction of evolutionary theory of aging, we computerized and analyzed the most reliable and accurate genealogical data on longevity in European royal and noble families. Offspring longevity for each sex (8409 records for males and 3741 records for females) was considered as a dependent variable in the multiple regression model and as a function of three independent predictors: paternal age at death (for estimation of heritability of life span), paternal age at reproduction (control for parental age effects), and cohort life expectancy (control for cohort and secular trends and fluctuations). We found that the regression slope for offspring longevity as a function of paternal longevity increases with paternal longevity, as predicted by the evolutionary theory of aging and by the mutation accumulation hypothesis in particular.     

Stimuli-Responsive Dynamic Metaholographic Displays with Designer Liquid Crystal Modulators

Flat optics, realized by the artificially created 2D material platform called optical metasurfaces, is currently undergoing a science-to-technology transition. However, “real-time” active operations of such flat optical devices remain yet unresolved. Here, liquid crystals (LCs)-integrated metaholograms for ultracompact dynamic holographic displays are proposed. The anisotropic nature of the LCs allows facile and repeatable manipulation of the polarization of light. Specifically designed (“designer”) LCs and efficient helicity-encoded metaholograms are combined to realize stimuli-responsive dynamic displays. The designer LC modulators are used as switches that enable a variety of external stimuli (e.g., electric field, heat, surface pressure) to operate holographic images in real-time. Such a dynamic metaholographic platform will provide a path to external stimuli-driven “smart” sensing and display applications such as hologram labels for temperature/pressure/touch monitoring and interactive holographic displays with haptic motion recognition.     

Developing Iris Recognition System for Smartphone Security

Smartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.