In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent.
Miniaturized artificial compound eyes with a large field of view (FOV) have potential application in the area of micro-optical-electro-mechanical-system (MOEMS). A new non-uniform microlens array (MLA) on a negative meniscus substrate, fabricated by the melting photoresist method, was proposed in this paper. The multi-focusing MLA reduced the defocus effectively, which was caused by the uniform array on a spherical substrate. Moreover, like most ommatidia in compound eyes, each microlens of the multi-focusing MLA was arranged in one of the eleven concentric circles. In order to match with the multi-focusing MLA and avoid the total reflection, the negative meniscus substrate was fabricated by a homebuilt mold with a micro-hole array and polydimethylsiloxane coelomic compartment attached. The coelomic compartment is capable of offering an excellent injection environment without bubbles and impurities. Due to the direct 3D implementation of the MLA, rich available materials can be used by this method without substrate reshaping. As the molding material, the ultraviolet curing adhesive NOA81 can be cured within ten few seconds under ultraviolet which relieve intensive labor and protect the stereolithography apparatus effectively. The experimental results show that this new MLA has a better imaging performance, higher light usage efficiency and larger FOV because of the negative meniscus and multi-focusing MLA. Moreover, due to the homebuilt mold, more accurate geometrical parameters and shorter processing cycle were realized. Accordingly, together with an appropriate hardware, this MLA has diverse potential applications in medical imaging, military and machine vision.
Microwave-absorbing materials have attracted increased research interest in recent years because of their core roles in the fields of electromagnetic (EM) pollution precaution and information security. In this paper, microwave-absorbing material NiFe-layered double hydroxide (NiFe-LDH) was synthesized by a simple co-precipitation method and calcined for the fabrication of NiFe-mixed metal oxide (NiFe-MMO). The phase structure and micromorphology of the NiFe-LDH and NiFe-MMO were analyzed, and their microwave-absorbing properties were investigated with a vector network analyzer in 2–18 GHz. Both NiFe-LDH and NiFe-MMO possessed abundant interfaces and a low dielectric constant, which were beneficial to electromagnetic wave absorption, owing to the synergistic effect of multi-relaxation and impedance matching. The optimum reflection loss (RL) of NiFe-LDH and NiFe-MMO was −58.8 dB and −64.4 dB, respectively, with the thickness of 4.0 mm in the C band. This work demonstrates that LDH-based materials have a potential application in electromagnetic wave absorption.
Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization states in atmospheric remote sensing images. In the extracted polarization feature map, the tilting suggestion box was obtained based on the multi-direction window extraction network (similarity-based region proposal networks, SRPN) and the rotation of the suggestion box (Rotation Region of interests, RRoIs). Fast Region Convolutional Neural Networks (RCNN) was used to screen the suggestion boxes, and the Non-maximum suppression (NMS) method was used to select the angle, corresponding to the label of the suggestion box with the highest score, as the solar meridian azimuth in the vehicle coordinate system. The azimuth angle of the solar meridian in the atmospheric coordinate system can be calculated by the astronomical formula. Finally, the final heading angle can be obtained according to the conversion relationship between the coordinate systems. By fitting the measured data based on the least Square method, the slope K value is −1.062, RMSE (Root Mean Square Error) is 6.984, and the determination coefficient R-Square is 0.9968. Experimental results prove the effectiveness of the proposed algorithm, and this study can construct an autonomous navigation algorithm with high concealment and precision, providing a new research idea for the research of autonomous navigation technology.
Heart rate variability (HRV) is a specific quantitative indicator of autonomic nerve regulation of the heart. The research of HRV can quantify the changes of human mental state. In this paper, an improved differential threshold method was proposed for R wave detection and recognition of ECG signals. The recognition rate was improved by improving the starting position of R wave and the time window function of the traditional differential threshold method. The experimental platform in this paper is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Experimental results showed that the recognition rate of R wave of real-time 5min ECG data collected by this algorithm was more than 99%. Then, analytic hierarchy Process (AHP) was used to construct the mental stress assessment model, and the weight judgment matrix was constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check was carried out to obtain the weight value of the corresponding HRV analysis parameters. Finally, comparative experiment proved that the model can describe the mental stress of the body quantitatively.
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