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

Presents a dynamic fuzzy model (DFM) based fault detection and isolation (FDI) scheme for the nonlinear system. The dynamic behavior of a nonlinear process is represented by a fuzzy aggregation of a set of local linear models. The parameter variations of the DFM identified in online and the validity of the local linear models are used to generate a residual vector. The neural network classifier, which learned the relationship between the residual vector and fault type, detects and isolates the process faults. We apply the proposed FDI scheme to the design of an FDI system of a two-tank system and show the usefulness of the proposed scheme.

Fault diagnosis of nonlinear system based on fuzzy dynamic model

In an Intelligent Transport System (ITS), data dissemination based on inter-vehicle communication is effective for acquiring real-time traffic jams information. In this paper, we propose a novel method for traffic jams information dissemination in vehicular ad-hoc networks. In our proposed method, vehicles already trapped in a traffic jams elect leaders according to their locations from upstream and downstream respectively. Then, leaders generate traffic data which contains their position, velocity and counter respectively, and disseminate the information. Then, the data of the traffic jams can be disseminated to vehicles which do not trapped to the traffic jams.

Efficient dissemination method for traffic jams information sharing based on inter-vehicle communication

Real-Time Force Sensing in the Envelope of Zebrafish Egg during Micropipette Penetration

Robust Adaptive Fuzzy Controller for Nonlinear Systems with Unknown Nonlinearities

This paper describes a Vision-based Occupant Pose Recognition (VOPR) system, which can ensure a safe airbag deployment. Head detection and its tracking are necessary for occupant’s pose recognition in the car, since the position of occupant’s head provides valuable information, such as his pose, size, position, and so on. We use the stereo cameras to extract a disparity map. Against variable lighting conditions including the night drive, we adopt infrared illumination as well as normal one. Results suggest that VOPR system is reliable and performs reasonably well.

Head detection and tracking for the car occupant’s pose recognition

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