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.
The event camera is a novel type of bio-inspired vision sensor inspired by the biological retina. Compared to traditional frame-based cameras, it offers high temporal resolution, high dynamic range, reduced redundancy, and lower transmission bandwidth. These unique features pave the way for innovative solutions in the field of computer vision. However, the heightened sensitivity of event cameras to fluctuations in brightness, along with their susceptibility to environmental factors and hardware limitations, presents a significant challenge. It involves capturing spatiotemporal information from the target signal simultaneously with the generation of a substantial volume of noise events. In applications relying on event cameras, this noise compromises target detection precision. Therefore, event stream denoising is essential before further applications can be pursued. Unfortunately, conventional frame-based algorithms are ill-suited for processing event data due to the distinct format of event cameras. In response to the challenges of event stream denoising, using the event stream generated by Celex-V as an example, this paper categorizes noise events and conducts an analysis of the event noise distribution model. Leveraging the characteristics of noise events, such as randomness and isolation, the paper proposes an event-based cascaded noise processing method. This method involves analyzing events in the spatiotemporal vicinity of arriving events and removing noise events from the event stream data. While ensuring the integrity of data flow information, it achieves rapid and efficient noise removal. The denoised event stream is advantageous for subsequent processing in various applications based on event cameras.
In this paper, we use imaging photoplethysmography (IPPG) to realize non-contact measurement of blood volume change of human fingertip, which can avoid distortion of blood vessel wall caused by pressure applied to fingertip. We use CMOS color camera to collect signals and white LED as light source. In the process of signal processing, we abandon the traditional morphological filtering algorithm in the form of double-layer cascade, and use single-layer morphological filtering algorithm. Experiments show that the single-layer morphological filtering algorithm has a good effect of eliminating baseline drift of signals, and can perfectly retain the detail components of signals without shifting the transverse components. We proposed a peak-to-valley value detection algorithm to calculate the heart rate by detecting the time interval between the adjacent peaks value. The experiment compared the accuracy of calculating the heart rate by using the traditional fast Fourier transform and the heart rate based on peak-to-valley value detection. The respiration rate was detected by using the third-order Butterworth filter. The accuracy of heart rate monitoring can be achieved at 97.86% and the accuracy of respiration monitoring can be achieved at 95.02%.
In recent years, the number of patients with hypertension has increased. Hypertension is an invisible killer. Long-term hypertension can cause a series of cardiovascular diseases such as angina pectoris, stroke, and heart failure. Therefore, early evaluation and grade assessment of blood pressure (BP) are essential to human health. The seventh report of the National Joint Committee for the Prevention, Detection, Evaluation, and Treatment of Hypertension in the United States (JNC7) classified BP levels into normotension (NT), prehypertension (PHT) and hypertension (HT). In this paper, we adopted a deep learning model (ResNet18) based on the ensemble empirical mode decomposition (EEMD) and the Hilbert Transform (HT) to predict the risk level of BP only using photoplethysmography (PPG) signals. We collected 582 data records from the Multiparameter Intelligent Monitoring in Intensive Care database (MIMIC), and each file contained arterial BP signals as the labels for inputs and the corresponding PPG signals as the inputs. Besides, the last fully connected layer of the model was initialized. We conducted three classification experiments: HT vs. NT, HT vs. PHT, and (HT + PHT) vs. NT, the F1 score of these three classification experiments is 88.03%, 70.94%, and 84.88%, respectively. A quick and accessible noninvasive BP evaluation method was offered to low- and middle-income countries.
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