An acoustic–seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic–seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic–seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

A novel reconfigurable filter antenna with three ports for three dependent switchable states for impulse radio-ultrawideband (IR-UWB)/wireless local area network (WLAN)/worldwide interoperability for microwave access (WiMAX) applications is presented in this paper. Three positive-intrinsic-negative diodes, controlled by direct current, are employed to realize frequency reconfiguration of one ultra-wideband state and two narrowband states (2.4 GHz and 3.5 GHz). The time domain characteristic of the proposed antenna in the ultra-wideband state is studied, because of the features of the IR-UWB system. The time domain analysis shows that the reconfigurable filtering antenna in the wideband state performs similarly to the original UWB antenna. The compact size, low cost, and expanded reconfigurable filtering features make it suitable for IR-UWB systems that are integrated with WLAN/WiMAX communications.

A reconfigurable bandwidth antenna for an impulse radio-UWB (IR/UWB) system design is illustrated in this paper. By adopting a continuously tunable low-pass filter by varactor at the feed of the antenna, the proposed antenna obtains a continuous tunable bandwidth from 1.02 GHz to 2.42 GHz. To ensure the identifiability of transmitted pulses in (IR-UWB) system, the antenna is analyzed in both frequency domain and time domain. The proposed antenna is valid with a system fidelity factor (SFF) above 0.8 while the bandwidth is tuning. The compact size, low cost, and tunable bandwidth with the identifiability of the transmitted pulse makes it suitable for UWB impulse radars to improve the utility ratio of frequency, and dynamic adjustment avoids interference of the IR-UWB in other communication frequency bands.

The giant magneto-impedance (GMI) magnetic sensor based on the amorphous wire has been believed to be tiny dimensions, high sensitivity, quick response, and small power consumption. This kind of sensor is usually working under a bias magnetic field that is called the sensor’s operating point. However, the changes in direction and intensity of the external magnetic field, or the changes in sensing direction and position of the sensor, will lead to fluctuations in operating point when the sensor is working without any magnetic shield. In this work, a GMI sensor based on the operating point self-regulator is designed to overcome the problem. The regulator is based on the compensated feedback control that can maintain the operating point of a GMI sensor in a uniform position. With the regulator, the GMI sensor exhibits a stable sensitivity regardless of the external magnetic field. In comparison with the former work, the developed operating point regulator can improve the accuracy and stability of the operating point and therefore decrease the noise and disturbances that are introduced into the GMI sensor by the previous self-regulation system.

A novel multi-class classification method named the voting-cross support vector machine (SVM) method was proposed in this study, for classifying vehicle targets in wireless sensor networks. The advantages and disadvantages of available methods were summarized, after a comparative analysis of commonly used multi-objective classification algorithms. To improve the classification accuracy of multi-class classification and ensure the low complexity of the algorithm for engineering implementation on wireless sensor network (WSN) nodes, a framework was proposed for cross-matching and voting on the category to which the vehicle belongs after combining the advantages of the directed acyclic graph SVM (DAGSVM) method and binary-tree SVM method. The SVM classifier was selected as the basis two-class classifier in the framework, after comparing the classification performance of several commonly used methods. We utilized datasets acquired from a real-world experiment to validate the proposed method. The calculated results demonstrated that the cross-voting SVM method could effectively increase the classification accuracy for the classification of multiple vehicle targets, with a limited increase in the algorithm complexity. The application of the cross-voting SVM method effectively improved the target classification accuracy (by approximately 7%), compared with the DAGSVM method and the binary-tree SVM method, whereas time consumption decreased by approximately 70% compared to the DAGSVM method.