Signal Processing and Renewable Energy
Volume:3 Issue: 1, Winter 2019

Liver cancer emerges as a mass in the right upper of the abdomen with general symptoms such as jaundice and ‎weakness. In recent years, the liver cancer has been responsible for increasing the rate of deaths. Due to some discrepancies in the ‎analytical results of CT images and the disagreement among specialists about different parts of the liver, ‎accurate diagnosis of possible conditions requires skill, experience, and precision. In this paper, a new ‎integrative model based on image processing techniques and machine learning is provided, which is used for ‎segmentation of damages caused by the liver disease on CT images. The implementation process consists of three ‎steps: (1) using discrete wavelet transform to remove noise and separate the region of interest (ROI) in the image; (2) ‎creating the recognition pattern based on feature extraction by Gray-Level Co-occurrence matrix and ‎hierarchical visual HMAX model; reducing the feature dimensions is also optimized by principle ‎component analysis and support vector machine (SVM) classification, and finally (3) evaluating the algorithm performance by using K-fold method. The results of implementation were satisfactory both in performance evaluation and use of ‎features selection. The mean recognition accuracy on test images was 91.7%. The implementation was in the ‎presence of both descriptors irrespective of feature dimension ‎reduction; with unique HMAX model and feature ‎dimension reduction and application of both ‎descriptors and reduction of feature dimensions and their effect ‎on recognition were measured.‎

A Doubly Fed Induction Generator (DFIG)-based wind power generation system in microgrid significantly affects the power system operation. This paper describes the behavior of a microgrid with DFIG’s by use of control strategies under voltage sag conditions. Superconducting Magnetic Energy Storage (SMES) unit and a Static Var Compensator (SVC) are employed to increase the operation of a wind power generation system based on DFIG during a voltage sag. Comprehensive simulation with the relevant details is performed using MatLab/Simulink software to define the effect of the SMES and SVC units by increasing the efficiency and performance of the system during voltage sag condition in a microgrid and the results are compared. For optimal use of the SMES and SVC units, economic considerations are applied.

This paper presents a hardware implementation of both Hodgkin-Huxley (HH) and Leaky Integrate and Fire (LIF) spiking neuronal models. FPGA is used as digital platform due to flexibility and reconfigureability. The proposed neural models are simulated by MatLab and the results are compared with the HDL software’s output in order to evaluate the design. Simple architecture uses two counters and a comparator used as the main part of leaky Integrate and Fire model. For the Hodgkin and Huxley model a Look Up Table based structure is utilized. Although it consumes large amount of area, it results more reasonable propagation delay time hence higher operating frequency. The proposed architectures are evaluated on Stratix III device using Quartus II simulator. Maximum operating frequency of 583 MHz (limited to 500 MHz due to the device port rate) and 76 MHz are achieved for the LIF and HH architectures respectively.

As opposed to the existing approaches which recognize communication network time delays, when they are introduced into the feedback signals, as a main cause of instability or poor performance in power system wide-area damping controller (WADC), this paper shows that if time delay in feedback loops is properly determined, the WADC performance to damp out inter-area oscillations will be improved. In other words, in situations where it is not easy to design and implement the WADC for Flexible AC Transmission Systems (FACTS) devices without delay, in order to effectively compensate the delay, in this paper, a new Wide-Area Damping Controller Delay Effect (WADCDE) is designed. First the model of power system with delay as a design parameter is established. Then, the WADCDE based on objective function of the rightmost real part of eigenvalues is designed and the sufficient condition about stability of the closed-loop system is given. A four-machine power system for numerical simulations has been used to evaluate the accuracy of the proposed control function and the feasibility study. The simulation results showed that the controller designed in a wide range of delay feedback reduces the oscillation of power system without restricting TCSC operation.

VANETs are a subset of MANETs in which vehicles are considered as network nodes. These networks have been created to communicate between vehicle and traffic control on the roads. VANETs have similar features to MANETs, and their main special property is the high-speed node mobility which makes a quick-change topology in the network. The rapid change of network topology is a major challenge in routing. One of the well-known routing protocols in VANETs is the AODV routing protocol. In this inquiry, nature-inspired algorithms such as GOA and GA are used to improve routing in VANETs to search the optimal configuration of the AODV routing protocol, and its impact on network evaluation criteria has been investigated. The rating measures applied in this research are the packet delivery ratio, end-to-end delays, and normalized routing load.

In this paper optimal allocation of electric vehicles parking and distributed generations (DGs) with objective of minimizing energy costs is proposed by using a hybrid water cycle-moth flame optimizer (WCMFO) algorithm. The purpose of the study is reduction the losses of distribution system, improvement the voltage profile, minimization the distribution system voltage deviations, and reduction the energy received from the main feeder. The optimization problem is implemented on a 33 IEEE bus distribution system. In this study, due to the optimal combination of WCA and MFO methods, WCMFO method is used to solve the problem with high convergence rate. In this study, the proposed method is compared with WCA, MFO and particle swarm optimization (PSO) methods. The simulation results show superiority of the proposed hybrid method in achieving lower cost and high convergence rate. The results show that with the optimal use of electrical parking and also DGs, the system capacity can be released and the level of upstream system dependency can be reduced. It also reduced the amount of system energy not supplied.