Surface plasmon resonance (SPR) is an optical sensing technique that is capable of performing real-time, label-free and high-sensitivity monitoring of molecular interactions. SPR biosensors can be divided according to their operating principles into angle-, wavelength-, intensity- and phase-interrogated devices. With their complex optical configurations, phase-interrogated SPR sensors generally provide higher sensitivity and throughput, and have thus recently emerged as prominent biosensing devices. To date, several methods have been developed for SPR phase interrogation, including heterodyne detection, polarimetry, shear interferometry, spatial phase modulation interferometry and temporal phase modulation interferometry. This paper summarizes the fundamentals of phase-sensitive SPR sensing, reviews the available methods for phase interrogation of these sensors, and discusses the future prospects for and trends in the development of this technology.

Developing effective automated visual surface defect detection of metal parts is a challenge due to highly reflective surfaces and miscellaneous defect patterns. In this work, an effective framework consisting of an image registration module and a defect detection module is proposed to cope with this challenge. First, an algorithm based on dual weighted principal component analysis is designed for the image registration module. In this algorithm, the visual feature and spatial feature are integrated into a unified principal component to obtain highly accurate image registration results. Then, a defect detection module is constructed using an image difference algorithm with prior constraints. In this module, two categories of constraint bounding boxes are designed to reduce the misdetection due to the susceptible edges and improve the accuracy of surface defect detection of metal parts. Three applications, including a magnetic resonance imaging dataset, a printed circuit board dataset, and an industrial case of the stainless-steel positioning pins are provided to illustrate the superiority of the proposed framework.

The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).