A simple approach to determine the best threshold value for automatic image thresholding

Image thresholding is a powerful yet simple method to highlight the subject from its background in image scene analysis. Lots of methods have been proposed around the globe while some researchers regard this matter as a non-trivial problem. This paper proposes a simple approach for fast calculation of the threshold value for automatic image thresholding based on gradient analysis of the image histogram. The method manages to successfully differentiate the subject from the background. The proposed approach is validated by illustrative examples. Satisfactory results were acquired with other methods that use more complex algorithms

Estimation of K-distributed clutter by using characteristic function method

Detection performance of the maritime radars is often limited by the unwanted sea echo or clutter. K-distribution is one of the long-tailed densities which is known in the signal processing community for fitting the radar sea clutter accurately. In this paper, a novel approach for estimating the parameter of K-distribution is presented. The method is derived from the empirical characteristic function of the quadrature components. Simulation results show a great improvement in term of estimated bias and variance, compared with any existing non-maximum likelihood method

Application of image processing and adaptive neuro-fuzzy system for estimation of the metallurgical parameters of a flotation process

It is a well-known fact in the literature and practice that flotation froth features are closely related to process conditions and performance. The authors have already developed some reliable algorithms for measurement of the froth surface visual parameters such as bubble size distribution, froth color, velocity and stability. Furthermore, the metallurgical parameters of a laboratory flotation cell were successfully predicted from the extracted froth features. In this research study, the fuzzy c-mean clustering technique is utilized to classify the froth images (collected under different process conditions) based on the extracted visual characteristics. The classification of the images is actually necessary to determine the ideal froth structure and the target set-points for a machine vision control system. The results show that the captured froth images are well-classified into five categorizes on the basis of the extracted features. The correlation between the visual properties of froth (in different classes) and the metallurgical parameters is discussed and modeled by the adaptive neuro-fuzzy inference system (ANFIS). The promising results illustrate that the performance of the existing batch flotation system can be satisfactorily estimated from the measured froth characteristics. Therefore, the outputs from the current machine vision system can be inputted to a process control system

Development of a machine vision system for real-time monitoring and control of batch flotation process

Substantial progresses have been made over the past decade in using machine vision for automatic control of the froth flotation process. A machine vision system is able to extract the visual features from the captured froth images and present the results to process control systems. The current research work is concerned with the development and implementation of a machine vision system for real time monitoring and control of a batch flotation system. The proposed model-based control system comprises two in-series models connecting the process variables to the froth features and the metallurgical parameters along with a stabilizing fuzzy controller. The results indicate the developed machine vision based control system is able to accurately predict the metallurgical parameters of the existing batch flotation system from the extracted froth features and efficiently maintain them at their set-points despite step disturbances in the process variables. Furthermore, the proposed control system leads to higher target values for the metallurgical parameters than the previously developed system (RCu = 91.1 % ; GCu = 11.2% vs. RCu = 87.6 % ; GCu = 8.1%)

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy

A self-organizing quasi-linear ARX RBFN model for nonlinear dynamical systems identification

The quasi-linear ARX radial basis function network (RBFN) model has shown good approximation ability and usefulness in nonlinear system identification and control. It has an easy-to-use structure, good generalization and strong tolerance to input noise. In this paper, we propose a self-organizing quasi-linear ARX RBFN (QARX-RBFN) model by introducing a self-organizing scheme to the quasi-linear ARX RBFN model. Based on the active firing rate and the mutual information of RBF nodes, the RBF nodes in the quasi-linear ARX RBFN model can be added or removed, so as to automatically optimize the structure of the quasi-linear ARX RBFN model for a given system. This significantly improves the performance of the model. Numerical simulations on both identification and control of nonlinear dynamical system confirm the effectiveness of the proposed self-organizing QARX-RBFN model

Stability study of model predictive control in presence of undesirable factors.

In this study, the stability behavior of Generalized Predictive Controllers (GPC) as one of model predictive control algorithm is studied and effects of noise, error in delay estimation, input disturbances, unstable system and non-minimum phase system is analyzed. The results showed that GP-controller can be achieved stability and resist against wrong parameterization. This stability studies is completed by means of a numerical example. The results show that the GP algorithm can guarantee the stability of this system

Wireless energy monitoring in biped robot based on Xbee RF module

For mobile and humanoid robots, in the field of analysis, conservation, data monitoring,and management of the energy, the need of real time wireless communication for data to access and control is important. Wireless technology, which is widely used not only in close range but also in long range, is a technique which uses electronic components that can interface to the other devices. In this work, the energy sensors, such as voltage and current analogue sensors are connected to the XBee RF module directly to reduce the power consumption and the complexity. The wireless XBee RF module together with the sensors were utilized in such a way that direct coupling and data processing have been used to send the energy values wirelessly for monitoring or managing station which may be considered as a computer. A significant clear signals for the current and voltage rates were harvested from the developed circuits and to compare with the direct oscilloscope measuring method. The suitable setting up of the components selection and configuration were the main reason for this met hod to success. The contribution of this proposed method is the utilizing of the XBee built-in microcontroller at the remote and base XBee’s modes to dispense about any other microcontroller or extra components

Switch-ladder: reliable and efficient multilevel inverter

A DC-AC-AC multilevel inverter based on a multi-winding transformer is presented. The most important advantage of this inverter is that only four switches conduct during each cycle, which means that it exhibits lower on-state power dissipation. High reliability when switches fail in open-circuit behaviour is another advantage of this inverter structure. Finally, practical results are presented

Analyzing the on-state power dissipation analysis in staircase diode-clamped multi-level inverter

In renewable power generators, because of high initial cost and duty cycle of systems, efficiency parameter has an important place. For this reason, line frequency controlled multilevel inverters are one of most proper choices for renewable power converters. Among these, diode-clamped multilevel inverter structures are one of most important and best efficient inverters. In this paper, a simple diode-clamped equivalent circuit for exploring the efficiency under resistive loads is proposed, and based on this simple circuit, the on-state power dissipation in improved and original diode-clamped multilevel inverter under resistive loads is analyzed. Then, comparative efficiency equations are extracted for inverters that use metal oxide semiconductor field-effect transistors (MOSFETs) and other p-n junction as switches. These equations enable us to have a better idea of conducting power dissipation in diode-clamped and help us to choose appropriate switches for having a lower on-state power dissipation. Some cases are studied and in the end it is proven that the calculated efficiency under resistive load is a boundary for inductive load with the same impedance in diode-clamped inverter with p-n junction switches. This means that calculating the efficiency under resistive loads enables us to approximately predict efficiency under inductive loads