Hybrid intelligent techniques for MRI brain images classification

This paper presents a hybrid technique for the classification of the magnetic resonance images (MRI). The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 97% and 98% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.     

Robust control of T‐S fuzzy systems with time‐varying delay using new approach

This paper aims to design a controller to robustly stabilize uncertain nonlinear systems with time‐varying delay and norm bounded uncertainties via Takagi–Sugeno (T‐S) fuzzy model. The stabilization conditions are given in the form of linear matrix inequalities using a single Lyapunov–Krasovskii functional (LKF) combining the introduction of some relaxation matrices and only one tuning parameter. In comparison with the existing techniques in the literature, the proposed approach has two major advantages. The first is the reduction of computational complexity when the number of IF‐THEN rules, r, is big. The second concerns the conservatism reduction. Several examples are given to show the effectiveness and the merits of the design procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

Dynamic Compensation for Control of a Rotary wing UAV Using Positive Position Feedback

This paper presents a novel compensation method for the coupled fuselage-rotor mode of a Rotary wing Unmanned Aerial Vehicle (RUAV). The presence of stabilizer bar limits the performance of attitude control of an RUAV. In this paper, a Positive Position Feedback (PPF) is introduced to increase the stability margins and allow higher control bandwidth. The identified model is used to design a PPF controller to mitigate the presence of stabilizer bar. Parameters for the linear RUAV model are obtained by performing linear system identification about a few selected points. This identification process gives complete RUAV dynamics and is suitable for designing a Stability Augmentation System (SAS) of an RUAV. The identified parameters of an RUAV model are verified using experimental flight data and can be used to obtain the nonlinear model of an RUAV. The performance of the proposed algorithm is tested using a high-fidelity RUAV simulation model, which is validated through experimental flight data. The closed-loop response of the rotorcraft indicates that the desired attitude performance is achieved while ensuring that the coupled fuselage-rotor mode is effectively compensated without penalizing the phase response.     

Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

This paper presents a method for multi-scale segmentation of surface data using scale-adaptive region growing. The proposed segmentation algorithm is initiated by an unsupervised learning of optimal seed positions through the surface attribute clustering with a two-criterion score function. The seeds are selected as consecutive local maxima of the clustering map, which is computed by an aggregation of the local isotropic contrast and local variance maps. The proposed method avoids typical segmentation errors caused by an inappropriate choice of seed points and thresholds used in the region-growing algorithm. The scale-adaptive threshold estimate is based on the image local statistics in the neighborhoods of seed points. The performance of this method was evaluated on LiDAR surface images.     

Perimeter discovery in wireless sensor networks

In this paper, we focus on the perimeter detection problem using wireless sensor networks, as perimeter detection has a wide range of uses in several areas. We present a decentralized localized algorithm where sensor nodes determine if they are located along the perimeter of a wireless sensor network. Our proposed algorithm uses the location neighborhood information in conjunction with the Barycentric technique to determine if the sensor node enclosed by neighboring nodes, and consequently, if it is located within the interior of the wireless sensor network. We define performance metrics to analyze the performance of our approach and the simulation shows that the algorithm gives fairly accurate results.     

Copula and s-map on a quantum logic

A quantum logic is one of possible mathematical models for non-compatible random events. In this paper, we introduce and study functions called QL-copula and QL-co-copula on a quantum logic and we compare them with the classical definition of copula and co-copula, respectively. Finally, we show several examples of these functions by means of an s-map.     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

Palladium nanoclusters-coated polyfuran as a novel sensor for catecholamine neurotransmitters and paracetamol

A promising electrochemical biosensor was developed by electrodeposition of palladium nanoclusters on polyfuran film modified platinum electrode. This biosensor electrode was used to determine some catecholamines, namely dopamine, epinephrine and norepinephrine, ascorbic acid and paracetamol. The method of formation of the polymer film and deposition of Pd particles plays a key role in the electroactivity of the resulting hybrid material. This sensor effectively resolved the overlapping anodic peaks of ascorbic acid (AA), dopamine (DA) and paracetamol (ACOP) into three well-defined voltammetric peaks in differential pulse voltammetry analysis. The detection limit of DA in the absence and presence of AA and ACOP are eventually the same which indicates that the oxidation processes of DA, AA and ACOP are independent and that the simultaneous measurements of the three analytes are possible without interference. The electrodeposition of Pd on polyfuran improved exceptionally the detection limit about four decades. Moreover, diffusion coefficient measurements confirmed the fast electron transfer kinetics of the electrochemical oxidation of the analyte molecules at the sensor/solution interface. It is very interesting to note that the electrocatalytic effect of PF/Pd composite has been increased to be sometimes 21 times that of the pristine PF which has been considered for a long time to be of low conductivity and attracted low attention as a result of the difficulty of its formation and poor conductivity.     

The effect of the microfluidic diodicity on the efficiency of valve-less rectification micropumps using Lattice Boltzmann Method

The efficiency of the valve-less rectification micropump depends primarily on the microfluidic diodicity (the ratio of the backward pressure drop to the forward pressure drop). In this study, different rectifying structures, including the conventional structures (nozzle/diffuser and Tesla structures), were investigated at very low Reynolds numbers (between 0.2 and 60). The rectifying structures were characterized with respect to their design, and a numerical approach was illustrated to calculate the diodicity for the rectifying structures. In this study, the microfluidic diodicity was evaluated numerically for different rectifying structures including half circle, semicircle, heart, triangle, bifurcation, nozzle/diffuser, and Tesla structures. The Lattice Boltzmann Method (LBM) was utilized as a numerical method to simulate the fluid flow in the microscale. The results suggest that at very low Reynolds number flow, rectification and multifunction micropumping may be achievable by using a number of the presented structures. The results for the conventional structures agree with the reported results.     

A multi-agent system model of a diesel engine using rule induction

The proliferation of a multi-agent system (MAS) and ideas from Artificial Intelligence (AI)/distributed AI have changed the way systems, in general are controlled, and operation of a system (diesel engine) in particular is automated. In this paper a distributed multi-agent architecture for a diesel engine and the knowledge sources that handle electricity generation is developed. Electronic devices and components used for data handling are described. The sensed data are presented in fuzzy logic and calculated in entropy values and depicted in a decision hierarchy. A comparative performance assessment of the proposed multi-agent based system with an existing system is presented and discussed.