The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

The method commonly used to produce a field-programmable gate array (FPGA)-based time-to-digital converter (TDC) creates a tapped delay line (TDL) for time interpolation to yield high time precision. We conduct timing adjustment and bin realignment to implement a TDC in the Altera Cyclone-IV FPGA. The former tunes the carry look-up table (LUT) cell delay by changing the LUT’s function through low-level primitives according to timing analysis results, while the latter realigns bins according to the timing result obtained by timing adjustment so as to create a uniform TDL with bins of equivalent width. The differential nonlinearity and time resolution can be improved by realigning the bins. After calibration, the TDC has a 18 ps root-mean-square timing resolution and a 45 ps least-significant bit resolution.

The defect detection of printed circuit board (PCB) images faces challenges such as limited sample number, imbalanced sample types, and varying detection reliability. To address these issues, this article proposes an uncertainty-aware unsupervised detection model on PCB images, short for $\rm { U^{2}D^{2}}$ PCB. The proposed method uses two U-Net networks to serve as the reconstructive subnetwork and the discriminative subnetwork, respectively. The former one reconstructs defect-free PCB images from defective PCB images, while the latter segments the defects and evaluates the defects uncertainty with the concatenated inputs of the defective and reconstructed images. The $\mathbf { U^{2}D^{2}}$ PCB model is trained in an unsupervised manner with only defect-free images embedding with multiscale artificial defects. Experimental results on the public PCB defect dataset and DeepPCB dataset demonstrate the effectiveness of the proposed method. The mean average precision (mAP) is 99.29% on the PCB defect dataset, while it reaches 95.78% on the DeepPCB dataset. These results are competitive to those of state-of-the-art (SOTA) fully supervised methods. The findings of $\mathrm { U^{2}D^{2}}$ PCB highlight the potential significance of using unsupervised learning techniques for PCB defect detection.

Recently, advancements have been made in the design, implementation, and application of time-to-digital converters (TDCs) based on field-programmable gate array (FPGA) technology. The progress can be attributed to the low cost, short development cycle, and easy integration offered by FPGA platforms, which continuously provide TDCs with state-of-the-art high-performance hardware featuring low latency, low jitter, and multiple interfaces. Moreover, the progress has been driven by the demand for high-performance TDCs tailored to specific applications. The demand has driven the improvement, upgrading, and iterative optimization of various modules within TDC systems. Therefore, we present a comprehensive review and summarize the research reports on FPGA-based TDCs. The aim of the review is to clarify the ongoing efforts to improve measurement resolution and precision, reduce nonlinearity, and enhance the reliability of TDCs in specific applications. This review summarizes and analyzes the development of FPGA-based TDCs from both technical and application perspectives to consolidate past research findings and outline the directions for future research. This paper reviews the latest literature on the implementation architecture and performance of FPGA-based TDCs, refining the classification methods for implementation architectures and summarizing the performance evaluation metrics. Through comparisons and analysis, this review identifies the limitations faced by FPGA-based TDC research, highlights pressing issues that need to be addressed, and explores potential challenges that may arise.

Recently, deep learning (DL) based remote photoplethysmography (rPPG) methods have made great progress in non-contact heart rate (HR) measurements. However, the reliability of rPPG measurements is still a bottleneck for realistic applications due to its susceptibility to motion and illumination noise. It is increasingly important to develop quantitative assessment methods for rPPG measurements. In this paper, we propose to quantify the uncertainty of a convolutional neural network (CNN)-based rPPG model as the reference-free quantitative quality assessment metric. Specifically, we evaluate and compare both the Bayesian neural network (BNN) method and the deep ensemble method to estimate the uncertainty of HR measurements from the CNN model. The effectiveness of the predicted uncertainty from both methods is verified on two public databases, where the uncertainty is found to be highly correlated with the true absolute error, and the advantages and limitations of BNN and ensemble methods are also analyzed. This study is instructive for the development of uncertainty-aware DL-based rPPG models for realistic applications.

In this paper, a new kinematic parameter error calibration method based on the Levenberg–Marquard and the beetle antennae search algorithm is proposed to enhance the positioning accuracy of the industrial robot. Firstly, the Modified Denavit–Hartenberg model is chosen to establish the kinematic model for the industrial robot. Secondly, the kinematic parameter errors are calibrated by Levenberg–Marquard algorithm and then obtain the kinematic parameter errors of the industrial robot. Thirdly, these kinematic parameter errors are taken as the center values for the initial individual of the beetle antennae search algorithm. The kinematic parameter errors are accurately calibrated using the beetle antennae search algorithm to obtain the best value for the kinematic parameter errors. Finally, experimental verification results demonstrate that the positioning error of the industrial robot is decreased from 0.7332 mm to 0.1392 mm by using the proposed Levenberg–Marquard and beetle antennae search algorithm. The results also demonstrate that the proposed Levenberg–Marquard and beetle antennae search algorithm calibrate the kinematic parameter errors of the industrial robot effectively and enhance the positioning accuracy of the industrial robot significantly.