In recent years， a wireless platform for speech recognition using multiple customized and stretchable Radio Frequency Identification （RFID） tags has been proposed， however， it is difficult for the tags to accurately capture large frequency shifts caused by stretching， and multiple tags need to be detected and recalibrated when the tags fall off or wear out naturally. In response to the above problems， a lip language recognition algorithm based on single-tag RFID was proposed， in which a flexible， easily concealable and non-invasive single universal RFID tag was attached to the face， allowing lip language recognition even if the user does not make a sound and relies only on facial micro-actions. Firstly， a model was established to process the Received Signal Strength （RSS） and phase changes of individual tags received by an RFID reader responding over time and frequency. Then the Gaussian function was used to preprocess the noise of the original data by smoothing and denoising， and the Dynamic Time Warping （DTW） algorithm was used to evaluate and analyze the collected signal characteristics to solve the problem of pronunciation length mismatch. Finally， a wireless speech recognition system was created to recognize and distinguish the facial expressions corresponding to the voice， thus achieving the purpose of lip language recognition. Experimental results show that the accuracy of RSS can reach more than 86.5% by the proposed algorithm for identifying 200 groups of digital signal characteristics of different users.

In brain functional Magnetic Resonance Imaging (fMRI) experiment, motion correction is an important step in data preprocessing. Results of motion correction affect the follow up analysis such as detecting the functional activation area and functional connectivity. There are some simplified hypotheses for head motion in the common analysis software packages. Due to large volume of data, the correction error is also large. In order to reduce the correction error, a novel motion correction method was proposed based on local rigid transform. This method first used adjacent weighted slices to construct local volumetric data for each slice in a multi-slice echo planar imaging volume data, and then estimated the space position of each slice by the registration of local volumetric data using the modified Gauss-Newton optimization algorithm. Finally the image stack was re-sliced using Delaunay triangulation method. Results of implementation based on this method during phantom data and human vision experiments reveal that it is effective to reduce the correction error, which leads to accurate realignment.