Volume 14, Issue 2

Content-Based Image Retrieval (CBIR) is an automatic process of retrieving images that are the most similar to a query image based on their visual content such as colour and texture features. However, CBIR faces the technical challenge known as the semantic gap between high level conceptual meaning and the low-level image based features. This paper presents a new method that addresses the semantic gap issue by exploiting cluster shapes. The method first extracts local colours and textures using Discrete Cosine Transform (DCT) coefficients. The Expectation-Maximization Gaussian Mixture Model (EM/GMM) clustering algorithm is then applied to the local feature vectors to obtain clusters of various shapes. To compare dissimilarity between two images, the method uses a dissimilarity measure based on the principle of Kullback-Leibler divergence to compare pair-wise dissimilarity of cluster shapes. The paper further investigates two respective scenarios when the number of clusters is fixed and adaptively determined according to cluster quality. Experiments are conducted on publicly available WANG and Caltech6 databases. The results demonstrate that the proposed retrieval mechanism based on cluster shapes increases the image discrimination, and when the number of clusters is fixed to a large number, the precision of image retrieval is better than that when the relatively small number of clusters is adaptively determined.

Although the advanced technology in satellites and optical fiber communication systems exists now a day, but the researches in HF sky wave propagation for Mesopotamia (Iraq) area is suffered from shortage. In this paper, the novelty is that the communication path from Baghdad to any distance out of Iraqi border had been predicted, calculated and measured experimentally by using real data (Ionogram) supplemented by Nicosia Ionosound station 1000Km from Baghdad and a radio station model TS-130SE as a transmitter. The Predicted results generated by using MATLAB and NTIA/ITS software package like VOACAP. Radio communication using TS-130SE with 36 countries had been done experimentally. A comparison between the theoretical and experimental results was done. The experimental results were in the range of the predicated results which emphasis proposed method Presented in this paper.

The Fast Fourier Transform (FFT) and Inverse FFT(IFFT) are used in most of the digital signal processing applications. Real time implementation of FFT/IFFT is required in many of these applications. In this paper, an FPGA reconfigurable fixed point implementation of FFT/IFFT is presented. A manually VHDL codes are written to model the proposed FFT/IFFT processor. Two CORDIC-based FFT/IFFT processors based on radix-2and radix-4 architecture are designed. They have one butterfly processing unit. An efficient In-place memory assignment and addressing for the shared memory of FFT/IFFT processors are proposed to reduce the complexity of memory scheme. With “in-place” strategy, the outputs of butterfly operation are stored back to the same memory location of the inputs. Because of using DIF FFT, the output was to be in reverse order. To solve this issue, we have re-use the block RAM that used for storing the input sample as reordering unit to reduce hardware cost of the proposed processor. The Spartan-3E FPGA of 500,000 gates is employed to synthesize and implement the proposed architecture. The CORDIC based processors can save 40% of power consumption as compared with Xilinx logic core architectures of system generator.

Load Frequency Control (LFC) is a basic control strategy for proper operation of the power system. It ensures the ability of each generator in regulating its output power in such way to maintain system frequency and tie-line power of the interconnected system at prescribed levels. This article introduces comprehensive comparative study between Chaos Optimization Algorithm (COA) and optimal control approaches, such as Linear Quadratic Regulator (LQR), and Optimal Pole Shifting (OPS) regarding the tuning of LFC controller. The comparison is extended to the control approaches that result in zero steady-state frequency error such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Ziegler-Nicholas method is widely adopted for tuning such controllers. The article then compares between PI and PID controllers tuned via Ziegler-Nicholas and COA. The optimal control approaches as LQR and OPS have the characteristic of steady-state error. Moreover, they require the access for full state variables. This limits their applicability. Whereas, Ziegler-Nicholas PI and PID controllers have relatively long settling time and high overshoot. The controllers tuned via COA remedy the defects of optimal and zero steady-state controllers. The performance adequacy of the proposed controllers is assessed for different operating scenarios. Matlab and its dynamic platform, Simulink, are used for stimulating the system under concern and the investigated control techniques. The simulation results revealed that COA results in the smallest settling time and overshoot compared with traditional controllers and zero steady-state error controllers. In the overshoot, COA produces around 80% less than LQR and 98.5% less than OPS, while in the settling time, COA produces around 81% less than LQR and 95% less than OPS. Moreover, COA produces the lowest steady-state frequency error. For Ziegler-Nicholas controllers, COA produces around 53% less in the overshoot and 42% less in the settling time.

The advancements in modern day computing and architectures focus on harnessing parallelism and achieve high performance computing resulting in generation of massive amounts of data. The information produced needs to be represented and analyzed to address various challenges in technology and business domains. Radical expansion and integration of digital devices, networking, data storage and computation systems are generating more data than ever. Data sets are massive and complex, hence traditional learning methods fail to rescue the researchers and have in
turn resulted in adoption of machine learning techniques to provide possible solutions to mine the information hidden in unseen data. Interestingly, deep learning finds its place in big data applications. One of major advantages of deep learning is that it is not human engineered. In this paper, we look at various machine learning algorithms that have already been applied to big data related problems and have shown promising results. We also look at deep learning as a rescue and solution to big data issues that are not efficiently addressed using traditional methods. Deep learning is finding its place in most applications where we come across critical and dominating 5Vs of big data and is expected to perform better.

Chaotic Sine-Cosine Algorithms (CSCAs) are new metaheuristic optimization algorithms. However, Chaotic Sine-Cosine Algorithm (CSCAs) are able to manipulate the problems in the standard Sine-Cosine Algorithm (SCA) like, slow convergence rate and falling into local solutions. This manipulation is done by changing the random parameters in the standard Sine-Cosine Algorithm (SCA) with the chaotic sequences. To verify the ability of the Chaotic Sine-Cosine Algorithms (CSCAs) for solving problems with large scale problems. The behaviors of the Chaotic Sine-Cosine Algorithms (CSCAs) were studied under different dimensions 10, 30, 100, and 200. The results show the high quality solutions and the superiority of all Chaotic Sine-Cosine Algorithms (CSCAs) on the standard SCA algorithm for all selecting dimensions. Additionally, different initial values of the chaotic maps are used to study the sensitivity of Chaotic Sine-Cosine Algorithms (CSCAs). The sensitivity test reveals that the initial value 0.7 is the best option for all Chaotic Sine-Cosine Algorithms (CSCAs).

This paper presents new device to simulate and inject a 4-20 mA current signal to PLC and control on this signal wirelessly. The proposed simulator device has been designed and implemented by a PIC 18f4520 microcontroller and an Ethernet click. This device is connected to Wireless Local Area Network (WLAN) via Wi-Fi router using TCP/IP protocol. The simulator has two channels for 4-20 mA current output signals with two channels for digital output signals, controlled by a laptop or a smart mobile. The purpose of this work is to demonstrate the usefulness of the Wi-Fi wireless technology for remote controlling on the 4-20 mA output current signal and the digital output signal in the designed simulator device. The experiments indicate that the proposed wireless simulator outputs the 4-20 mA current with high accuracy and very fast response. The experiments also indicate that the proposed wireless simulator is easy, comfortable and convenient practically to use in the test operations of protections, interlocks and integrity of analog input channels for PLC compared to the wired simulator.