Deploying clustered wireless sensor network by multi-robot system

Deployment strategy is vital for efficiency in applications of wireless sensor network. Deploying wireless sensor network in vast area is very hard and time consuming job. In many applications there is also need to deploy sink other than wireless sensor nodes which sink deployment is another issue. In this paper, we use spanning tree for autonomous deploying wireless nodes and sinks. Deploying actor in this method is multi-robot system. In this method robots act as dispenser for wireless nodes. Using spanning tree as exploration algorithm helps robot to full coverage deployment of nodes. For analyzing the algorithm using multi-robot a simulation test bed is developed with a parameterized environment with obstacle. The result shows performance of sensing coverage in different environments

A review of computer assisted detection/diagnosis (CAD) in breast thermography for breast cancer detection

Breast cancer is the leading type of cancer diagnosed in women. For years human limitations in interpreting the thermograms possessed a considerable challenge, but with the introduction of computer assisted detection/diagnosis (CAD), this problem has been addressed. This review paper compares different approaches based on neural networks and fuzzy systems which have been implemented in different CAD designs. The greatest improvement in CAD systems was achieved with a combination of fuzzy logic and artificial neural networks in the form of FALCON-AART complementary learning fuzzy neural network (CLFNN). With a CAD design based on FALCON-AART, it was possible to achieve an overall accuracy of near 90%. This confirms that CAD systems are indeed a valuable addition to the efforts for the diagnosis of breast cancer. Lower cost and high performance of new infrared systems combined with accurate CAD designs can promote the use of thermography in many breast cancer centres worldwide

A real-time Malaysian automatic license plate recognition (M-ALPR) using hybrid fuzzy

The Road Transport Department of Malaysia has endorsed a specification for car plates that includes the font and size of characters that must be followed by car owners. However, there are cases where this specification is not followed. This paper proposes a new methodology to segment and recognize Malaysian car license plates automatically. The proposed methodology solves the problem of segmenting different length licenses such as license with different number of character and number. There are two main objectives for this paper: first is to develop fuzzy rules to recognize the segmented characters and numbers from the same input-sets, which is the same size without overlapping between the characters and numbers sets. Secondly, this paper proposes a method to recognize non-standard plates by Template Matching theorem. Finally, the hybrid method of Fuzzy and Template matching is tested on 300 samples of car images captured in outdoor environment. The results yield 90.4% recognition accuracy, the Fuzzy based required 1.7 seconds and Template matching based took 0.75 seconds to perform the recognition. The adaptability factor of the hybrid method is also discussed

Computer aided detection/diagnisis for breast cancer detection in computed tomography laser mammography (CTLM)

Breast cancer is the leading cancer killer among women. Early detection and new treatments have improved survival rates. Although mammography is the gold standard for breast cancer screening, increasing awareness indicate that there is some limitation for part of women whom mammography reduce sensitivity based on their breast density. Other modalities such as ultrasound and magnetic resonance imaging and recently computed tomography laser mammography (CTLM) are often suggested as an adjunct to mammography to achieve additional information and increase sensitivity. The angiogenesis is known a critical for tumor growth and spread of breast cancers. Computed tomography laser mammography (CTLM) CTLM has been introduced to verify angiogenesis at early stage. In this modality, there are no restriction factors such as age or breast density. Main difficulty for radiologists is closeness of color shade to interpret CTLM images. Computer-aided detection /diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) pre-processing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. The aim of this research is to develop a CAD system in computed tomography laser mammography (CTLM) to detect and classify benign and malignant lesions in the breast

Robust committee machine for water saturation prediction.

Water saturation is one of the important physical properties of the petroleum reservoir which are usually determined by core analysis. An accurate determination of this parameter is significant to execute a realistic evaluation of hydrocarbon reserves in the formation and also decreasing the economic risk. In this study, a robust technique is proposed to determine an accurate value of this parameter from well log data in un-cored well or at un-cored interval of the same well by combining different types of machine learning techniques. The final results (sub-CM outputs) demonstrated that integrating these techniques using proposed method provides an accurate, fast and cost-effective method for estimating the target value

Robust combining methods in committee neural networks

Combining a set of suitable experts can improve the generalization performance of the group when compared to single experts alone. The classical problem in this area is to answer the question about how to combine the ensemble members or the individuals. Different methods for combining the outputs of the experts in a committee machine (ensemble) are reported in the literature. The popular method to determine the error in every prediction is Mean Square Error (MSE), which is heavily influenced by outliers that can be found in many real data such as geosciences data. In this paper we introduce Robust Committee Neural Networks (RCNNs). Our proposed approach is the Huber and Bisquare function to determine the error between measured and predicted value which is less influenced by outliers. Therefore, we have used a Genetic Algorithm (GA) method to combine the individuals with the Huber and Bisquare as the fitness functions. The results show that the Root Mean Square Error (RMSE) and R-square values for these two functions are improved compared to the MSE as the fitness function and the proposed combiner outperformed other five existing training algorithms

Committee neural networks with fuzzy genetic algorithm.

Combining numerous appropriate experts can improve the generalization performance of the group when compared to a single network alone. There are different ways of combining the intelligent systems' outputs in the combiner in the committee neural network, such as simple averaging, gating network, stacking, support vector machine, and genetic algorithm. Premature convergence is a classical problem in finding optimal solution in genetic algorithms. In this paper, we propose a new technique for choosing the female chromosome during sexual selection to avoid the premature convergence in a genetic algorithm. A bi-linear allocation lifetime approach is used to label the chromosomes based on their fitness value, which will then be used to characterize the diversity of the population. The label of the selected male chromosome and the population diversity of the previous generation are then applied within a set of fuzzy rules to select a suitable female chromosome for recombination. Finally, we use fuzzy genetic algorithm methods for combining the output of experts to predict a reservoir parameter in petroleum industry. The results show that the proposed method (fuzzy genetic algorithm) gives the smallest error and highest correlation coefficient compared to five members and genetic algorithm and produces significant information on the reliability of the permeability predictions

Active and passive compliance mechanisms in legged robot locomotion

Legged robot locomotion is a challenging field. Problems can occur during locomotion such as morphology, controller, and ambience factor, to name a few. However, there are always trade-offs in designing legged robots, for example, speed against stability, number of limbs against complexity of controller, and mass of the robot against energy consumption of the actuators. Therefore, the problems can be minimized when the hardware and software complement each other. Active compliance mechanism describes a closed-loop system which actively sense-and-act according to the surroundings. Passive compliance mechanism, as its name suggests, is a regulatory mechanism in which it does not rely on the controller to actively respond in order to achieve adaptability. The composition materials of a legged robot provide the advantages during locomotion. In this review, we are going to investigate the differences of the mechanisms and how they can be complemented to diminish problems during locomotion

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