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The wireless charging technology (WPT) is a hot research topic at present. Because of its plug-in-free feature, it is very suitable for patrol robots. In order to reduce the influence of wireless charging coupling mechanism on electromagnetic environment in non-working area, a shielding device was designed and its finite element model (FEM) was established. The feasibility of the method was verified by finite element calculation. The simulation results show that the horizontal active shielding coil can significantly reduce the vertical electromagnetic leakage and eddy current loss of the magnetic coupling mechanism, and the vertical active shielding coil can effectively reduce the horizontal electromagnetic leakage, thereby reducing the radiation loss. It is proved that the active shielding coil can enhance the uniformity of space magnetic field and reduce the electromagnetic interference from the wireless charging system of inspection robot.

Generalized Frequency Division Multiplex (GFDM) is a new multiple subcarrier modulation technology. The problem of inter-carrier interference (ICI) caused by non-orthogonality using GFDM modulation is analyzed in this paper. For fading channel, a new double-band self-interference cancellation algorithm based on channel status information is proposed. Simulation results show that the bit error rate (BER) level of the GFDM system can be controlled to a lower level by the proposed method.

To face the challenge of spectral efficiency for the conventional binary chirp-BOK modulation, an M-ary chirp modulation (MCM) is proposed. Different from the traditional quadrature amplitude modulation technology, M-ary chirp modulation scheme with a constant envelope uses up-chirp and down-chirp as orthogonal components. The expression for its symbol error rate (SER) under AWGN channel is given. MCM has much better performance than MPSK under the multipath fading channels which are selected from COST207 standard.

Due to the relative movement between the transmitter and receiver in wireless communication systems, Doppler shift and time delay are introduced results in the probability of symbol synchronization decreases. Therefore, fast and steady synchronization method is pretty important. In this paper, a new symbol synchronization scheme based on chirp signals has been proposed. The proposed coarse synchronization sequence chosen five chirp signals with same chirp rates which time and bandwidth (TB) product is higher, while the fine synchronization sequence is composed of chirp signals with different chirp rates which TB product is small. The performance of the proposed scheme is evaluated via simulations. The simulation results show that the performance of proposed scheme is superior, and achieves a clearly performance gain with a high estimation accuracy both in timing and frequency offset.

Resonant wireless charging technology can overcome many shortcomings of traditional charging methods and is favored by universities and research institutions in the world. However, the inevitable leakage flux during transmission may cause electromagnetic interference problems. First of all, this paper designs a set of coils for resonant wireless charging system with the background of wireless charging of slow-moving inspection robots. Secondly, the magnetic field distribution around the coil is analyzed, and some indicators are established to evaluate its shielding performance. Some shielding designs are simulated where the magnetic induction intensity exceeds the standard. Finally, the shielding method is validated by experiments, and some guiding suggestions are put forward for the safe development of wireless charging system.

Machine learning algorithms depend heavily on the data representation, which dominates its success in experiment accuracy. Autoencoder model structure is proposed to learn from data a good representation with the least possible amount of distortion. Furthermore, it has been proven that boosting sparsity when learning representation can significantly improve performance on classification tasks and also make the feature vector easy to interpret. One straightforward approach for autoencoder to obtain sparse representation is to impose sparse penalty on its overall cost function. Nevertheless, few comparative analysis has been conducted to evaluate which sparse penalty term works better. In this paper, we adopt L1 norm, L2 norm, Student-t penalties, which are rarely deployed to penalise the hidden unit outputs, and commonly used penalty KL-divergence in the literature. Then, we present a detailed analysis to evaluate which penalty achieves better result in terms of reconstruction error, sparseness of representation and classification performance on test datasets. Experimental study on MNIST, CIFAR-10, SVHN, OPTDIGITS and NORB datasets reveals that all these penalties achieve sparse representation and outperforms representations learned by pure autoencoder on classification performance and sparseness of feature vectors. Moreover, we hope this topics and the practices would provide insights for future research.