Signal Processing and Renewable Energy
Volume:6 Issue: 3, Summer 2022

Image mosaicing refers to stitching two or more images that have regions overlapping with a larger and more comprehensive image. The Scale Invariant Feature Transform (SIFT) is one of the most common matching methods previously used in image mosaicing. The de-fects of SIFT are lots of mismatches, that reduce the efficiency of this algorithm. In this article, to solve this problem, a novel approach to image mosaicing is suggested.  At first, the features of both images are matched based on SIFT to improve the mosaicing process. Then, the A-RANSAC algorithm suggested in [1] is employed to eliminate mismatches based on an adaptive threshold. This algorithm is used to delete incorrect matches and to improve the accuracy of images mosaicing. Image blending is the final step of mosaicing to blend the intensity of the pixels in the overlapped region to avoid the seams. The sug-gested approach of blending is based on the absolute Gaussian weighting function. The mean and variance of this function are considered as the average and variance of the data of the range of two images common to each other, respectively. The suggested blending method reduces border line in the combined images while preserving the information of the original images as much as possible, performing the mosaicing process better. The simula-tion results of the suggested image mosaicing technique, which includes the use of SIFT algorithm, A-RANSAC, and suggested image blending algorithm on the standard image databases and the created image database, show the superiority of the suggested approach according to median error criteria, precision.

With the development of industry and the demand for electricity, energy supply is economically important with respect to environmental issues. The use of small, distributed products has spread near the subscribers' locations. Determining the location and capacity of products at the distribution network level has a great impact on managing financial resources and improving network parameters. In this paper, the optimal model for determining the location and capacity of distributed generation and static synchronous compensator (STATCOM) is presented, which is economically and technically multi-objective. In the economic part, the reduction of the installation cost of distributed products and STATCOM has been considered, and in the technical part, the reduction of losses and the reduction of the voltage droop of the network bus have been considered. This problem is solved using a genetic algorithm. The simulation results are determined using MATLAB software. The results show the effect of location on voltage reduction

In some telecommunication systems, the signals received or sent by different antennas are combined with each other in a way that has the most benefit in a specific spatial direction. The signal sent or received by all antennas is the same and differs only in amplitude and phase. On the other hand, in some array systems, the transmitted signal is different from different antennas. Similarly, the receiver has a separate processor for each antenna. In MIMO systems where the transmitted signals from various antennas are different and each antenna has a separate receiver in reception, the physical distance between the different antennas must be greater than a certain value so that the paths of each antenna to the destination are independent. In this paper, considering this distance in practical systems, a number of auxiliary antennas are placed between the main antennas of the MIMO system. The main antenna together with its adjacent auxiliary antennas forms antenna assemblies. The function of auxiliary antennas is to eliminate co-channel interference by performing appropriate irradiation. In this paper, the considered model is a channel with a feed rail that has a full order. For this channel model, different radiators such as MVDR, LCMV, GSC, ZF and maxSNR were investigated. And two different modes were considered for the transmitter. One of them is when the desired transmitter has one antenna and the other is when the desired transmitter has two antennas and uses Alamouti coding. In addition, both the state that the receiver is aware of the interference channel and the state that is not aware were examined. For each of these radiators, the output signal of the radiator was presented in order to decode the Alamouti coding. In addition, the LCMV radiator was examined under different conditions. In all these cases, the effect of the presence or absence of auxiliary antennas in eliminating all-channel interference was investigated. The simulations performed show the superiority of the performance of the methods proposed in this paper in the face of inter-channel interference

This paper constructs a novel 4D system with nonlinear complex dynamic behaviors. By analyzing the hyperchaotic attractors, bifurcation diagram, equilibrium points, Poincare map, Kaplan–Yorke dimension, and Lyapunov exponent behaviors, we prove that the introduced system has complex and nonlinear behavior. Next, the work describes a finite-time terminal sliding mode controller scheme for the synchronization and stability of the novel hyperchaotic system. All the results obtained from the proposed control are verified using Lyapunov stability theory. For synchronization, both systems designed with different parameters and model uncertainties are disturbed. Both stages of the finite-time terminal sliding mode controller have been shown to have fast convergence properties. Simply put, it has been shown that the state paths of both master and slave systems can reach each other in a finite–time. The new controller feature is that the terminal sliding surface designed with a high–order power function of error and derivative of error, is stable in finite–time. At last, using the MATLAB simulation, the results are confirmed for the new hyperchaotic system

Stepping motors are normally operated without feedback and may suffer from loss of synchroni-zation. The permanent magnet stepper motor (PMSM) is generally two-phase. In this paper, the nonlinear dynamic equations governing the performance of the permanent magnet stepper motor are linearized at an operating point for small signal stability studies. Small signal stability is the system ability to maintain synchronism when a small disturbance occurs. It is based on the state space averaging approach. A detailed description of the method, results, and conclusions are also presented. Finally, simulation results for three motors have been reported and compared

This paper describes ANFIS introduced by R. Jang et al. ANFIS actually is an offline method in fuzzy control systems. First, a fuzzy file called FIS (Fuzzy Inference System) is designed that relates the input and output of the system by membership functions that are optimized during the learning process. Input and output learning data are given to the ANFIS (MATLAB command line or ANFSI utility) and the output file is used to test or predict new input data. We can then construct a SIMULINK file to simulate the control system. This simulation is not real-time and if the environmental or input conditions are changed, the output will be altered because the FIS file is fixed and not adapted to input variations. The library of online ANFIS and CANFIS introduced does not have that problem and easily learns the online training data and then can mitigate the output in real-time. To avoid the unsuitable patient data itself as training data, we should use a healthy person ECG (heart rate) data in memory to train our fuzzy system and then switch the input data from healthy data to the patient original heart rate as input data. If the heartbeat falls below (60 bpm that is called Bradycardia) or exceeds (100bpm that is called Tachycardia) from a predetermined value. The online controller will switch the controller to healthy data and will stimulate heart muscles at a right beat rate (70-75 bpm). To distinguish tachycardia from body natural states like running, practicing, walking, sleeping and resting, MEMS accelerometer and in some situations, gyros are used. The Bode diagram stability shows gain and phase margin as follows: GM (dB)= 42.1 and PM (deg) = 100. FIS file is saved after an acceptable rms error (0.38). The simulation results of unity step input response (Rise time, settling time, overshoot) will be demonstrated in chapter 4. The overshoot was less than 2 percent and rise time of 2 seconds with settling time of less than 2 seconds. The parameters have been shown for 60 and 72 and 85 bpm.