The metrology field has been progressed with the appearance of the wireless intelligent sensor systems providing more capabilities such as signal processing, remote multi-sensing fusion etc. This kind of devices is rapidly making their way into medical and industrial monitoring, collision avoidance, traffic control, automotive and others applications. However, numerous design challenges for wireless intelligent sensors systems are imposed to overcome the physical limitations in data traffic, such as system noise, real time communication, signal attenuation, response dynamics, power consumption, and effective conversion rates etc, especially for applications requiring specific performances. This paper analyzes the performance metrics of the mentioned sensing devices systems which stands for superior measurement, more accuracy and reliability. Study findings prescribe researchers, developers/ engineers and users to realizing an optimal sensing motes design strategy that offers operational advantages which can offer cost-effective solutions for an application.

This paper presents a new model based on simulated annealing algorithm (ASA) and adaptive neuro-fuzzy inference system (ANFIS) for shape optimization and its applications to electromagnetic devices. The proposed model uses ANFIS system to evaluate the electromagnetic performance of the device. Both the ANFIS and ASA method are applied to the design/optimization of the electromagnetic actuator. The results of the proposed approach are compared with other techniques such as: method of moving asymptotes, penalty method, augmented lagrangian genetic algorithm and simulated annealing method (SA). Among the algorithms, the proposed ANFIS-ASA approach significantly outperforms the other methods.

For the nonlinear distortion problem of current power amplifiers (PAs) with memory effects, we use goal programming to present a memoryless predistorter matrix model based on limiting baseband predistortion technique, and the normalized mean squared error (NMSE) is limited in a satisfactory range while the output power is maximum. Then we propose a nonlinear power amplifier with memory effects based on back propagation neural network (BPNN) with three tapped delay nodes and six single hidden layer nodes, which is single input - dual output. Simulation results show that the method proposed in this paper makes the experimental precision higher. Further, the linearization effect of power amplifiers becomes better.

Wireless technology for communication and localization in train applications are widely used. Ultra wide band appears as a very suitable technology for this kind of application, due to its large bandwidth, also to its good resistance to the interference and to multipath. In this paper, a new system dedicated to railway transport, based on UWB technology is presented. The originality of this study is combination of the two main functionalities, localization and communication providing a high data rate. The sensor, in order to detect the position of vehicles, uses a matched digital correlation receiver. To allow a multi user access and to combine the two functionalities, two original multiplexing techniques called SSS2 (Sequential Spreading Spectrum technique) and CPM (Code Position Modulation) are performed, in addition to other parameters like used waveform and orthogonal codes.

The 0/1 Knapsack Problem is an optimization problem solved using various soft computing methods. The solution to the 0/1 Knapsack Problem (KP) can be viewed as the result of a sequence of decisions. Simple Genetic Algorithm (SGA) effectively solves knapsack problem for large data set. But it has problems like premature convergence and population diversity. Dual Population Genetic Algorithm (DPGA) is an improved version of Genetic Algorithm (GA) with the solution to above problems. This paper proposes Dual Population GA for solving 0/1 knapsack Problem. Experimental results of knapsack on SGA and DPGA are compared on standard as well as random data sets. The experimental result shows DPGA performs better than knapsack on SGA.

This paper mainly studies the effect of design patterns on the Software maintainability. Design patterns describe solutions for common design problems and they were introduced to improve software quality and accelerate software development. However, there are some difficulties to choose an optimal pattern adapted to a certain application and problem. So until now the results on the effect of design patterns on software quality are controversial. In this context, we propose a tool for design pattern guided that retrieves the appropriate pattern with respect to software maintainability from a repository of patterns. It measures the maintainability of design pattern by some metrics and candidate the more maintainable pattern to the designer or developer. It provides a support for decision making during system design and refactoring. As the results, the decision of applying a certain design pattern is usually a trade-off since the effect of design pattern on software maintainability is influenced by some factors such as the pattern size and the prior expertise of the developer.

In this paper, we discuss a Spread Spectrum based radar system for car detection in the road and autonomous guidance of vehicles. An autonomous intelligent vehicle has to perform a number of functionalities. Segmentation of the road, determining the boundaries to drive in and recognizing the vehicles and obstacles around are the main tasks for vision guided vehicle navigation. In this article we propose a set of algorithms which lead to the solution of road and vehicle collision using carrier recovery method from car velocity using DSSS RADAR. In such a spread spectrum system, the transmitted signal is spread over a larger bandwidth, which is much wider than the minimum bandwidth required to transmit the information. Automotive radar systems can take advantage of spread spectrum techniques because of their interference rejection, immunity from noise and multipath distortion, and high resolution ranging properties. In addition, there is no need for high-speed, fast-settling frequency synthesizers. Moreover, spread spectrum techniques can improve the reliability of automotive radars. The data from different sensors on different cars can be combined in order to observe the complete car environment. Thus a spread spectrum radar system will allow sharing the same bandwidth also for data link needed by car-to-car communication systems. The algorithm described here is to recover Doppler frequency using 2P power method from which we are able to detect the vehicle condition in road.

The main four objectives to design controllers are: stability, robust, minimum error and reliability. Linear PID controller is model-free controller and this controller is not reliable. One of the robust nonlinear controller to control of nonlinear systems is sliding mode controller (SMC). Sliding mode controller (SMC) is robust conventional nonlinear controller in a partly uncertain dynamic system’s parameters. Sliding mode controller is divided into two main sub parts: discontinues controller(τ_dis) and equivalent controller(τ_eq). Discontinues controller is used to design suitable tracking performance based on very fast switching. Fast switching or discontinuous part have essential role to achieve to good trajectory following, but it is caused system instability and chattering phenomenon. Chattering phenomenon is one of the main challenges in conventional sliding mode controller and it can causes some important mechanical problems such as saturation and heats the mechanical parts of robot manipulators or drivers. To reduce or eliminate the chattering two methods are used in many researches which these methods are: boundary layer saturation method and artificial intelligence based method. In this research fuzzy switching methodology is used to eliminate the chattering in presence of uncertainty to increase the robust of this controller with application to three dimensions of spherical motor.

Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

The optimized fertilizer usage for better yield of rice cultivation is influenced by key factors like soil fertility, crop variety, duration, season, nutrient content of the fertilizer, time of application etc., It is observed that 60 percent of yield gap in tamilnadu is due to farmers lack of knowledge on key factors and informal sources of information by pesticide dealers. In this study the major contributing factors for fertilizer requirement and optimum crop yield were analyzed based on rough set theory. In data analytics perspective the nutrient plan is sort of multiple attribute decision-making processes. To reduce the complexity of decision making, key factors that are indiscernible to conclusion are eliminated. Our rough set based approach improved the quality of agricultural data through removal of missing and redundant attributes. After pretreatment the data formed as target information, then attribute reduction algorithm was used to derive rules. The generated rules were used to structure the nutrition management decision-making. The precision was above 88% and experiments proved the feasibility of the developed decision support system for nutrient management.