Abstract-In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.Index Terms-Dead reckoning, pedestrian navigation system, wireless sensor network.

Recommended by Kanishka PereraWe obtain a sufficient condition for the existence of periodic and subharmonic solutions of second-order p-Laplacian difference equations using the critical point theory.

This paper focuses on the adaptive control of a class of nonlinear systems with unknown deadzone using neural networks. By constructing a deadzone precompensator, a neural adaptive control scheme is developed using backstepping design techniques. Transient performance is guaranteed and semi-globally uniformly ultimately bounded stability is obtained. Another feature of this scheme is that the neural networks reconstruction error bound is assumed to be unknown and can be estimated online. Simulation results are given to demonstrate the effectiveness of the proposed controller.

In this paper, a proportional integral (PI) controller that optimized with the modified different evolutional (DE) algorithm is proposed for speed control of brushless direct-current (BLDC) motor. The parameters of PI controller are tuned by the modified DE algorithm which based on adaptive mutation factor, multivariable fitness function and the starting rule for the modified algorithm. The performances of proposed controller, the conventional PI controller and the PI controller optimized with standard DE controller (PI-SDE controller) are investigated and compared in simulation. Also, the proposed controller is compared with other optimization controller in this study. The simulation results and the experimental verification show that the proposed controller leads to the smaller overshoot, less setting time and rising time compared to other controllers in this study. The results also show that the proposed controller can accelerate the response speed of BLDC motor, strengthen the robustness and guarantee motor runs smoothly as well as precisely. This work indicates the distinguished performance of proposed controller for the speed control of BLDC motor.

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In this paper, some sufficient conditions for the global exponential stability and the existence of periodic solutions of cellular neural networks with delay (DCNN) model are obtained by means of a Lyapunov functional approach. These conditions can be used to design globally stable DCNN's and periodic oscillatory DCNN's and thus have important significance in both theory and applications.

In this paper, we investigate the following nonlinear and non-homogeneous elliptic system:where φ i (t) = a i (|t|)t(i = 1, 2) are two increasing homeomorphisms from R onto R, functions V i (i = 1, 2) and F are 1-periodic in x, and F satisfies some (φ 1 , φ 2 )-superlinear Orlicz-Sobolev conditions. By using a variant mountain pass lemma, we obtain that the system has a ground state. MSC: 35J20; 35J50; 35J55; 35A15