Search

This paper discusses about different MPPT techniques used in PV systems. The techniques include various conventional methods, artificial intelligence based methods and predictive type methods. A brief comparison of all these methods based on their performance is also discussed.

Monozygotic twins, commonly known as identical twins, share extreme facial resemblance that they deceive the majority of the prevailing face recognition systems. Hence, the facial recognition systems, which are turning out to be exploited in almost all real-life biometric authentication-needing applications, require significant upgradation to cope with this great challenge. This paper pays special attention towards categorizing and investigating the existing research on the facial recognition of monozygotic twins in an exhaustive sense. Subsequently, the research gap is analyzed. It is then followed by the description of the possible refinements, like the choice of feature and other methodological advancements, which may aid in augmenting the twin recognition accuracy of the face recognition systems. It is believed that the outcomes of the review can well-support and pave the way for further research in the future. Consequently, the “Double Trouble” posed on the commercial face recognition systems can be effectively evaded.

The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter (qZS-CMI)-dependent 3-phase grid-tie Photovoltaic (PV) power system is considered in this paper. The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models. However, such processes are limited to known system models, which are uncertain in future systems. Here, the controller for 3-phase qZS-CMI is modeled based on two phases, and the source PV voltage and output grid current are controlled. In Phase I, optimized Proportional Integral (PI) controller is used for finding out the total PV voltage, and Phase II utilizes the optimized Proportional Resonant (PR) controller enabled with the Artificial Neural Network (ANN) for controlling the grid current. For two phases, the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm (FE-ROA) is used. Moreover, in Phase II, ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA, and it guarantees the control of grid current. The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm. The main objective of phase I is to lessen the error among the reference PV voltage, and measured voltage, and phase II is to lessen the error among the reference and measured grid current. Hence, the grid-tie current injection is achieved by the developed module, and system-level control offers independent Maximum Power Point Tracking (MPPT). Lastly, the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.

Display Omitted We propose a new denoising algorithm using wavelet and quincunx diamond filter bank.The subband coefficients are modeled with Gaussian scale mixture model.Bayes least means square is used to obtain the denoised coefficients.The new method does spatial averaging without smoothing the edges. The main challenge in image denoising is, how to preserve the information such as edges and textures to get satisfactory visual quality when improving the signal to noise ratio. In this paper, we propose a hybrid filter bank for denoising based on wavelet filter bank and quincunx diamond filter bank. The noisy image is decomposed into different subbands of frequency and orientation using DMeyer wavelet. The quincunx diamond filter bank is designed from finite impulse response (FIR) filters using Kaiser window, which is applied on the detail subband of wavelet filter bank. The directional subband coefficients are modeled with Gaussian scale mixture model (GSM). The Bayes least squares estimator is used to obtain the denoised detail coefficients from the noisy image decomposition. Experimental results show that the new method performs spatial averaging without smoothing edges, and thereby enhances the visual quality and peak signal-to-noise-ratio.