Qianwei Zhou

Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity.

An Efficient Clustering Protocol for Wireless Sensor Networks Based on Localized Game Theoretical Approach

Game theory has emerged as a brand new approach to model and analyse several problems of wireless sensor networks, such as routing, data collection, and topology control. Recently, a novel clustering mechanism called clustered routing for selfish sensors (CROSS) has been proposed based on game theory. The sensor nodes, which are modelled as players, join in a clustering game to campaign for cluster heads with an equilibrium probability. However, the CROSS algorithm needs the global information of how many nodes participate in the game at every round. Considering that this global way introduces much more packets exchange and energy consumption, we present a Localized game theoretical clustering algorithm (LGCA). In our protocol, each node selfishly plays a localized clustering game only with its neighbours within a communication radius R c . Moreover, exactly one node can successfully bid for a position of the cluster head in one district, thus achieving an optimal payoff. Simulation results show that our method achieves a better result compared with CROSS and LEACH in terms of network lifetime.

A Practicable Method for Ferromagnetic Object Moving Direction Identification

The direction of a moving target is an important piece of information in many wireless sensor network (WSN) applications, such as in boundary security, traffic flow control, etc. Due to its robustness, the magnetic sensor can be used to detect a passing ferromagnetic object. By using the orthogonality of two perpendicularly placed sensing units in a monomer magnetic sensor, a linear algorithm based on a magnetic dipole model to identify the ferromagnetic objects moving direction is introduced in this paper. It has been successfully applied in real WSN applications to reduce the numbers of nodes, and prolong the lifetime of the network. Both simulation and field experiments show it has strong noise immunity and more than 95% correction rate in direction detecting.

A Novel Energy-Efficient Cluster Formation Strategy: From the Perspective of Cluster Members

Traditional clustering methods mostly concentrate on how to choose nodes to serve as cluster heads. As for cluster formation, most papers assume that a normal node joins a nearest cluster head. However, this is not an optimal solution to form a good cluster. It is shown in this paper that a cluster member may not response to the advertisement of the closest cluster head but join a farther cluster head in order to achieve better energy efficiency or longer co-alive lifespan. Based on our new observation, a novel cluster formation strategy is proposed. Besides, simulation results also verify the correctness of our analysis.

A Practical Fundamental Frequency Extraction Algorithm for Motion Parameters Estimation of Moving Targets

In this paper, a practical method is proposed for a moving targets fundamental frequency (MTFF) extraction from its acoustic signal. This method is developed for the application of motion parameters estimation. Starting from the analysis of the target frequency model and the acoustic Doppler model, the characteristics of moving targets signal are discussed. Based on the signatures of targets acoustic signal, a new approximate greatest common divisor (AGCD) method is developed to obtain an initial fundamental frequency (IFF). Then, the corresponding harmonic number associated with the IFF is determined by maximizing an objective function formulated as an impulse-train-weighted symmetric average magnitude sum function (SAMSF) of the observed signal. The frequency of the SAMSF is determined by targets acoustic signal, the period of the impulse train is controlled by the estimated IFF harmonic, and the maximization of the objective function is carried out through a time-domain matching of periodicity of the impulse train with that of the SAMSF. Finally, a precise fundamental frequency is achieved based on the obtained IFF and its harmonic number. In order to demonstrate the effectiveness of the proposed method, experiments are conducted on wheeled vehicles, tracked vehicles, and propeller-driven aircrafts. Evaluation of the algorithm performance in comparison with other traditional methods indicates that the proposed MTFF is practical for the fundamental frequency extraction of moving targets.

Magnetic Properties and Microstructure of Melt-Spun Ce–Fe–B Magnets

A series of Ce_xFe_balB₆ (x = 12, 14, 17, 19, and 23 wt%) ternary ribbons was prepared by melt-spinning. Magnetic properties and the microstructure of the Ce-Fe-B ribbons with different Ce contents were investigated. The X-ray diffraction results indicated that multiphase coexisted in the as-spun Ce-Fe-B ribbons, which contained Ce₂Fe₁₇, CeFe₂, Ce-rich phase, Fe-rich phase, cerium oxide, and iron oxide. The magnetic properties, microstructure, and phase composition of the ribbons were directly affected by the cerium content. The magnetic properties could be obtained by exchange coupling between the hard and soft magnetic phases in the pure ternary Ce-Fe-B ribbons. Furthermore, by heat treatment, the magnetic properties of the as-spun Ce-Fe-B ribbons could be optimized. The highest magnetic properties of H_cj = 6.2 kOe, B_r = 6.9 kGs, and (BH)_m = 8.6 MGOe were obtained in Ce₁₇FebalB₆ magnets.

A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

Seismic signal is widely used in ground target classification due to its inherent characteristics. However, its propagation is highly dependent on local underlying geology. It means that nearly every one geographical environment requires a unique classifier. To resolve the problem, this paper presents a robust feature extraction method Log-Sigmoid Frequency Cepstral Coefficients (LSFCC) which evolves from Mel frequency cepstral coefficients (MFCC) for ground target classification by means of geophone. With the LSFCCs, the average classification accuracy of tracked and wheeled vehicle is more than 89% in three different geographical environments by only one classifier which is trained in one of the three environments.

A Chain-Based Data Gathering Protocol Under Compressive Sensing Framework for Wireless Sensor Networks

This paper proposes an efficient data gathering protocol for large scale wireless sensor networks by using the compressive sensing technology. All sensor nodes construct a chain by a greedy algorithm. Data packets are transmitted through this chain from one node to another and till the end node of the chain. This end node, namely chain leader, then relays all packets to the base station. By using compressive sensing technology, all sensing data can be simultaneously transmitted and aggregated to a few packets by relaying nodes. The base station is responsible for recovering every nodes data according to these few packets. Simulation results show that this method can reduce the energy consumption of large scale wireless sensor network.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

A Feature Extraction Method for Wheeled and Tracked Vehicle Classification Based on Geologic Model

Seismic signal is widely used in ground vehicle classification due to its inherent characteristics. But the generalization accuracy of classifier is heavily degraded due to different underlying geologies. To overcome the weakness of the seismic signal, a feature extraction method is proposed in this paper. The extracted feature is the cepstrum of the seismic signal whose logarithmic power spectrum density will be preprocessed to suppress the geology related components, which is based on the special characteristics of the employed geologic model, before further calculations. The efficiency of the proposed feature is verified with a mixed database taking from our field experiments and SensIT project.