Optical frequency-domain reflectometry and frequency-modulated continuous wave (FMCW)-based sensing technologies, such as LiDAR and distributed fiber sensors, fundamentally rely on the performance of frequency-swept laser sources. Specifically, frequency-sweep linearity, which determines the level of measurement distortion, is of paramount importance. Sweep-velocity-locked semiconductor lasers (SVLLs) controlled via phase-locked loops (PLLs) have been studied for many FMCW applications owing to its simplicity, low cost, and low power consumption. We demonstrate an alternative, self-adaptive laser control system that generates an optimized predistortion curve through PLL iterations. The described self-adaptive algorithm was successfully implemented in a digital circuit. The results show that the phase error of the SVLL improved by around 1 order of magnitude relative to the one without using this method, demonstrating that this self-adaptive algorithm is a viable method of linearizing the output of frequency-swept laser sources.
Distributed optical fiber sensors are increasingly utilized method of distributed strain and temperature sensing, and the swept laser source plays an significant role in these applications. However, there is dynamic frequency-noise as the laser sweeping. In this paper, we proposed and experimentally demonstrated a real-time in-situ phase noise detecting method in a field programmable gate array (FPGA) chip, which permits accurate and insightful investigation of laser stability. This method takes only 1 clock cycle to capture the phase noise.
Distributed optical fiber sensors are an increasingly utilized method of gathering distributed strain and temperature data. However, the large amount of data they generate present a challenge that limits their use in real-time, in-situ applications. This letter describes a parallel and pipelined computing architecture that accelerates the signal-processing speed of sub-terahertz fiber sensor (sub-THz-fs) arrays, maintaining high spatial resolution while allowing for expanded use for real-time sensing and control applications. The computing architecture described was successfully implemented in a field programmable gate array (FPGA) chip. The signal processing for the entire array takes only 12 system clock cycles. In addition, this design removes the necessity of storing any raw or intermediate data.
Sub-terahertz range fiber sensors have been well investigated for distributed stain sensing applications. Due to the use to sub-millimeter range structures, high accuracy measurement using relative small interrogation bandwidth (~ 100 GHz) can be achieved. The interrogation system is based on optical frequency domain reflectometry (OFDR), where the key component is the high-linear frequency sweep laser source. Previously the external cavity lasers have been employed as the frequency sweep sources. The external cavity lasers are capable to sweep over large interrogation bandwidth (>3 THz). However, compared with the 100 GHz resonation period of sub-terahertz range fiber sensors with a pitch length of 1mm, this broad sweep bandwidth is unnecessary. Besides, the external cavity lasers require the use of moving mechanical components, which limits the system update rate and increases the system complexity. This paper presents a design of a high linear sweep laser source suitable for sub-terahertz range fiber sensors. A distributed feedback laser is employed as the frequency sweep source based on the injection current modulation technique, and the sweep velocity is locked at a constant value (14.2 GHz/ms) using a semi-digital feedback control system. A high-linear sweep bandwidth of 117.69 GHz with a system update rate of 50 Hz has been demonstrated. In addition, a dynamic experiment was conducted to demonstrate the system distributed strain sensing capability. The proposed system holds the potential for dynamic structural health monitoring.
The recently proposed robust principal component analysis (RPCA) theory and its derived methods have attracted much attention in many computer vision and machine intelligence applications. From a wide view of these methods, independent motion objects are modeled as pixel-wised sparse or structurally sparse outliers from a highly correlated background signal, and all these methods are implemented under an ℓ1 -penalized optimization. Real data experiments reveal that even if ℓ1-penalty is convex, the optimization sometimes cannot be satisfactorily solved, especially when the signal-to-noise ratio is relatively high. In addition, the unexpected background motion (e.g., periodic or stochastic motion) may also be included. We propose a moving object detection method based on a proximal RPCA along with saliency detection. Convex penalties including low-rank and sparse regularizations are substituted with proximal norms to achieve robust regression. After the foreground candidates have been extracted, a motion saliency map using spatiotemporal filtering is constructed. The foreground objects are then filtered out by dynamically adjusting the penalty parameter according to the corresponding saliency values. Evaluations on challenging video clips and qualitative and quantitative comparisons with several state-of-the-art methods demonstrate that the proposed approach works efficiently and robustly.
As a novel method of 2D features extraction algorithm over the nonlinear scale space, KAZE provide a special method. However, the computation of nonlinear scale space and the construction of KAZE feature vectors are more expensive than the SIFT and SURF significantly. In this paper, the given image is used to build the nonlinear space up to a maximum evolution time through the efficient Additive Operator Splitting (AOS) techniques and the variable conductance diffusion. Changing the parameter can improve the construction of nonlinear scale space and simplify the image conductivities for each dimension space, with the predigest computation. Then, the detection for points of interest can exhibit a maxima of the scale-normalized determinant with the Hessian response in the nonlinear scale space. At the same time, the detection of feature vectors is optimized by the Wavelet Transform method, which can avoid the second Gaussian smoothing in the KAZE Features and cut down the complexity of the algorithm distinctly in the building and describing vectors steps. In this way, the dominant orientation is obtained, similar to SURF, by summing the responses within a sliding circle segment covering an angle of π/3 in the circular area of radius 6σ with a sampling step of size σ one by one. Finally, the extraction in the multidimensional patch at the given scale, centered over the points of interest and rotated to align its dominant orientation to a canonical direction, is able to simplify the description of feature by reducing the description dimensions, just as the PCA-SIFT method. Even though the features are somewhat more expensive to compute than SIFT due to the construction of nonlinear scale space, but compared to SURF, the result revels a step forward in performance in detection, description and application against the previous ways by the following contrast experiments.