The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

A review: On path planning strategies for navigation of mobile robot

A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems

Artificial intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics and motor drives. Neural networks have created a new and advancing frontier in power electronics, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. This paper gives a comprehensive introduction and perspective of neural network applications in the intelligent control and estimation for power electronics and motor drives area. The principal topologies of neural networks that are currently most relevant for applications in power electronics have been reviewed including the detailed description of their properties. Both feedforward and feedback or recurrent architectures have been covered in the description. The application examples that are discussed in this paper include nonlinear function generation, delayless filtering and waveform processing, feedback signal processing of vector drive, space vector PWM of two-level and multilevel inverters, adaptive flux vector estimation, and some of their combination for vector-controlled ac drive. Additional selected applications in the literature are included in the references. From the current trend of the technology, it appears that neural networks will find widespread applications in power electronics and motor drives in future

Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective

This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.

Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems

In this paper, we present the modeling of time series data which are corrupted by noise via nonsingleton fuzzy logic system. Nonsingleton fuzzy logic system (NFLS) is useful in cases where the available data are corrupted by noise. NFLS is a fuzzy system whose inputs are modeled as fuzzy number. The abilities of NFLS to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze corrupted time series data. In the simulation results, we compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system.

/pdf/uncertain-rule-based-fuzzy-technique-nonsingleton-fuzzy-vjk0cjei67.pdf

Uncertain Rule-based Fuzzy Technique : Nonsingleton Fuzzy Logic System for Corrupted Time Series Analysis

In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.

Performance comparison between backpropagation, neuro-fuzzy network, and SVM

In this paper, we propose a practical and robust localization algorithm over the obstructed interferences in the wireless sensor networks (WSNs). The proposed algorithm uses the Maximum Likelihood Estimation (MLE) method on the position probability grid. In addition, the proposed algorithm detects and compensates the large RSS measurement errors by using the Min-Max algorithm in obstructed environments. Simulation results show that the proposed algorithm takes the advantages of the range-based and the range-free algorithms simultaneously to handle the obstructed interferences in the wireless sensor networks.

Practical robust localization over obstructed interferences in wireless sensor networks

In optical disk drives, which support various speeds, it is not avoidable to have a varying periodic disturbance. However, it is still possible to control optical disk drives by using a controller which repeatedly control the drive to change sampling frequency to follow the change of reference period. This paper introduces an adaptive repetitive control method to attenuate the periodic disturbances. The proposed adaptive repetitive control is built with two parts, the repetitive controller and the frequency multiplier. The repetitive controller uses a varying sampler operating at a variable sampling rate maintained at fixed multiple times of the disturbance frequencies and the frequency multiplier generates the varying sampling frequencies based on the disturbance frequency.

/pdf/a-novel-method-of-adaptive-repetitive-control-for-optical-1d917zlt3r.pdf

A novel method of adaptive repetitive control for optical disk drivers

A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma-etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high-prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.

A Plasma-Etching Process Modeling Via a Polynomial Neural Network

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