We propose a deep learning computational ghost imaging (CGI) scheme to achieve sub-Nyquist and high-quality image reconstruction. Unlike the second-order-correlation CGI and compressive-sensing CGI, which use lots of illumination patterns and a one-dimensional (1-D) light intensity sequence (LIS) for image reconstruction, a deep neural network (DAttNet) is proposed to restore the target image only using the 1-D LIS. The DAttNet is trained with simulation data and retrieves the target image from experimental data. The experimental results indicate that the proposed scheme can provide high-quality images with a sub-Nyquist sampling ratio and performs better than the conventional and compressive-sensing CGI methods in sub-Nyquist sampling ratio conditions (e.g., 5.45%). The proposed scheme has potential practical applications in underwater, real-time and dynamic CGI.

The reliability of MEMS inertial devices applied in complex environments involves interdisciplinary fields, such as structural mechanics, material mechanics and multi-physics field coupling. Nowadays, MEMS inertial devices are widely used in the fields of automotive industry, consumer electronics, aerospace and missile guidance, and a variety of reliability issues induced by complex environments arise subsequently. Hence, reliability analysis and design of MEMS inertial devices are becoming increasingly significant. Since the reliability issues of MEMS inertial devices are mainly caused by complex mechanical and thermal environments with intricate failure mechanisms, there are fewer reviews of related research in this field. Therefore, this paper provides an extensive review of the research on the reliability of typical failure modes and mechanisms in MEMS inertial devices under high temperature, temperature cycling, vibration, shock, and multi-physical field coupling environments in the last five to six years. It is found that though multiple studies exist examining the reliability of MEMS inertial devices under single stress, there is a dearth of research conducted under composite stress and a lack of systematic investigation. Through analyzing and summarizing the current research progress in reliability design, it is concluded that multi-physical field coupling simulation, theoretical modeling, composite stress experiments, and special test standards are important directions for future reliability research on MEMS inertial devices.

Infrared thermal imaging technology that has unique advantages in obtaining target information at night and in harsh weather conditions has been widely used in the military, security, environmental monitoring, and other fields. Existing visible light object detection methods often face challenges of high false alarm rates and low accuracy when detecting small infrared objects. Moreover, current small infrared object detection methods mostly require much detailed annotation data, and it is challenging to visually depict the positions of small targets on the original complex background images. To address these issues, we propose a multi-branch backbone and adaptive spatial feature fusion detection head-based YOLO network called MBFormer-YOLO to achieve the high-accuracy detection of small infrared targets. We design a multi-branch backbone with structural re-parameterization capability called MBFormer, which has strong capabilities to fit object features. In the neck part, MBFormer-YOLO utilizes convolutional block attention modules and C3-SwinTransformer modules to suppress noise and redundant information. We develop a 4-level adaptive spatial feature fusion detection head to improve the detection accuracy of the proposed network. Many experiments are conducted on the SIRST-V2 dataset to validate the effectiveness of MBFormer-YOLO. The results show that MBFormer-YOLO achieves an 11.3% increase in AP50 and 2% in AP50-95 over the baseline model YOLOv8n and surpasses other advanced object detection models.

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F₁-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.