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O. Ozdemir

Publications and source records attributed to O. Ozdemir.

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Comprehensive Optical, Electrical and Humidity Sensing Properties of Bifidobacterium infantis 35624 Thin Films

In this study, we present a comprehensive investigation of the structural, optical, and electrical properties of Bifidobacterium longum subsp. longum 35624 (BB35) thin films, and demonstrate their application as a novel relative humidity sensor. UV-Visible spectroscopy revealed that BB35 exhibits two distinct optical absorption regions, corresponding to direct band gaps of 2.1 \pm 0.05 eV and 2.8 \pm 0.05 eV, as confirmed by Tauc plot analysis, establishing BB35 as a genuine wide-bandgap semiconductor material. Photoluminescence measurements under 280 nm excitation exhibited a broad emission spectrum, which was deconvoluted into four Gaussian peaks centered at 434 nm (2.86 eV), 499 nm (2.48 eV), 543 nm (2.3 eV), and 620 nm (2.0 eV), indicating the presence of multiple radiative recombination centers characteristic of semiconducting materials. Electrical characterization revealed dispersive charge transport with current decay following a power-law I \propto t^{-α} (α\approx 0.3), suggesting Poole-Frenkel conduction mechanism typically observed in disordered organic semiconductors. The relative humidity (RH) sensing performance of BB35 films was evaluated using gold interdigital electrodes across 15-90% RH range. The sensor exhibited reversible response with sensitivity increasing linearly from 0.85 to 4.80 as RH increased from 15% to 90%. The devices demonstrated excellent stability over two months with less than 5% degradation in baseline current. These results establish BB35 thin films as a promising eco-friendly semiconducting material for humidity sensing applications and open new avenues for integrating biological materials into electronic and optoelectronic devices.

physics.app-ph

Asynchronous Linear Modulation Classification with Multiple Sensors via Generalized EM Algorithm

In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the computational efficiency and effectiveness of the proposed algorithm.

cs.OH