According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient′s blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert–Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG′s first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.

Compared with traditional cameras, event cameras (ECs) have the significant advantages of high temporal resolution, low data redundancy, and microsecond delay, which are beneficial in structural monitoring to extract the dense response of structures in both spatial and temporal dimensions. In this paper, the vibration frequency detection method based on ECs is studied. This study investigates vibration frequency detection methods based on ECs, and proposes two algorithms for vibration frequency detection based on event streams: marker tracking and event count. Experimental verification is conducted through forced vibration experiments. The results indicate that the event count method achieves high-precision measurement of vibration frequencies in the range of 10–190 Hz for different vibration scales, with a maximum relative error of 1% and an average relative error of 0.673%. The marker tracking method demonstrates a maximum relative error of 1.43% and an average relative error of 0.575% in frequency measurement for large-amplitude vibrations. However, as the amplitude decreases, the frequency measurement error increases. When the amplitude is less than 3 pixels, the frequency measurement error exceeds 30%, rendering the measurement results unreliable. This research provides technical support for high-precision structural vibration frequency monitoring and further expands the application of ECs in structural monitoring.

The temperature characteristics of space point targets are essential indicators of their operational status and performance. To address the issue of significant temperature measurement errors in space point targets caused by low temperatures and a low imaging signal-to-noise ratio (SNR), we propose a mathematical model for multi-spectral radiation thermometry, derived from the principles of dual-band radiation thermometry. Furthermore, a multi-spectral image pixel binning method is introduced to enhance the SNR and minimize measurement errors. The experimental results indicate that the proposed multi-spectral radiation thermometry outperforms dual-band radiation thermometry. After merging 2 to 20 pixels, multi-spectral radiation thermometry in the 3.75–4.1 and 4.3–4.62 µm bands demonstrates an enhanced SNR and reduced temperature measurement errors. For a 378.15 K blackbody, the relative errors decrease from 1.52% and 2.19% to 0.26% and 0.74%, respectively, after merging six and eight pixels in the two different bands, compared to unmerged images. This method provides a valuable reference for developing techniques to enhance the SNR and improve temperature measurement accuracy for space point targets.

An event camera (EC) is a bioinspired vision sensor with the advantages of a high temporal resolution, high dynamic range, and low latency. Due to the inherent sparsity of space target imaging data, EC becomes an ideal imaging sensor for space target detection. In this work, we conduct detection of small space targets using a CeleX-V camera with a megapixel resolution. We propose a target detection method based on field segmentation, utilizing the event output characteristics of an EC. This method enables real-time monitoring of the spatial positions of space targets within the camera’s field of view. The effectiveness of this approach is validated through experiments involving real-world observations of space targets. Using the proposed method, real-time observation of space targets with a megapixel resolution EC becomes feasible, demonstrating substantial practical potential in the field of space target detection.

波前编码系统采用在传统光学系统中加入相位板来扩大光学系统的景深而避免传统景深延拓技术的不利影响. 由于相位板的参数不可调, 整个系统的景深延拓扩展率也不能动态可调. 采用两相位板组合的方法可以有效克服这一点. 本文首先从光线差的角度提出了两三次相位板组合下的光线像差分布以及点扩散函数尺寸的具体关系表达式, 直观体现了系统的光线结构, 指出了光线结构和点扩散函数尺寸受两三次相位板的面型和相对位移量的影响. 其次采用稳相法从空间域给出了系统点扩散函数表达式, 依据点扩散函数的振荡性质给出了有效带宽表达式, 提出了点扩散函数在像面的位置会随两相位板面型参数以及相对于光瞳中心的位移量而发生平移. 最后利用菲涅耳积分给出两三次相位板任意面型参数和相对位移组合下的准确光学传递函数. 在得到的调制传递函数中直观体现出了面型参数和相对位移量对调制传递函数和相位传递函数以及有效带宽的影响, 并说明了此系统相位传递函数的非线性性质. 通过空间域与频率域相结合的方法分析验证了传统的两三次相位板组合具有景深可调和带宽可调的性质, 为设计可调谐波前编码系统提供了理论依据.