Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range underwater acoustic applications such as the long-term monitoring of the sea, the PA works in full consumption mode. Thus, it becomes a challenging task to minimize power consumption and unnecessary distortion. To mitigate this problem, a receiver-based nonlinearity distortion mitigation method is proposed, assuming that the transmitting side has enough computation power. We propose a novel approach to identify the nonlinear power model using a modern deep learning algorithm named frequentative decision feedback (FFB); PAPR performance is verified by the clipping method. The simulation results prove the better performance of the PA model with a BER with the shortest learning time.
Electrochemical water splitting is one of the promising way to enhance energy with less outflow. In this regard different electrocatalysts have been reported for Oxygen evolution reaction (OER) to get alternative of noble metal based electrocatalysts. In this work, we have introduced Cadmium-oxide/Cobalt-oxide (CdO/Co3O4) nanocomposite by co-precipitation chemical strategy with impressive OER performance in alkaline medium. Almost 310 mV overpotential value is required to achieve 10 mA/cm2 current density with Tafel slope value of 62 mV/Dec. The as synthesized nanocomposite has stability of 6h as its longer electrochemical performance
Direct Interfacing Technique (DIT) eradicates additional circuit requirements for sensorembedded system interface and digitization of analog signals. This technique provides advantages in designing an efficient, portable, and low-cost sensor system. Pulsed Eddy Current Testing (PECT) systems are used for thickness and defect measurements of conductive materials. Circuitous sensor interfacing methods and tedious data interpretation processes make PECT systems inapt for miniaturization and portable applications. In this
work, DIT is used in conjunction with PECT for thickness estimation of conductive material. Change in the de-energizing time of a single coil probe with sample thickness with respect to air is used as a signal. The curve fitting method yields a maximum relative error of ≈ 2% in the thickness estimation. Effects of temperature and liftoff on system accuracy are also investigated. A liftoff compensation method using a 3-signal data group is proposed. It is shown that for thicknesses in the range of 0.508 mm – 3.175 mm and liftoffs up to 3.000 mm (step size: 0.500 mm), the proposed scheme produces a maximum relative error of 5.2 % only, which otherwise can go up to 50 %. The DIT and PECT combination can be applied for different structural ECT in the future.
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