Recent Progress in Improving the Performance of Infrared Photodetectors via Optical Field Manipulations

Benefiting from the inherent capacity for detecting longer wavelengths inaccessible to human eyes, infrared photodetectors have found numerous applications in both military and daily life, such as individual combat weapons, automatic driving sensors and night-vision devices. However, the imperfect material growth and incomplete device manufacturing impose an inevitable restriction on the further improvement of infrared photodetectors. The advent of artificial microstructures, especially metasurfaces, featuring with strong light field enhancement and multifunctional properties in manipulating the light–matter interactions on subwavelength scale, have promised great potential in overcoming the bottlenecks faced by conventional infrared detectors. Additionally, metasurfaces exhibit versatile and flexible integration with existing detection semiconductors. In this paper, we start with a review of conventionally bulky and recently emerging two-dimensional material-based infrared photodetectors, i.e., InGaAs, HgCdTe, graphene, transition metal dichalcogenides and black phosphorus devices. As to the challenges the detectors are facing, we further discuss the recent progress on the metasurfaces integrated on the photodetectors and demonstrate their role in improving device performance. All information provided in this paper aims to open a new way to boost high-performance infrared photodetectors.

Deep-Red Electro-fluorescent based on an Excimer Emission with Hot-Exciton Channels

Red and deep-red fluorescent materials are broadly applied in many fields such as organic light emitting diodes (OLEDs), biological imaging and night-vision devices. In this work, we designed and synthesized...

Implementasi Metode Unsupervised Learning Pada Sistem Keamanan Dengan Optimalisasi Penyimpanan Kamera IP

Security monitoring systems using face recognition can be applied to CCTV or IP cameras. This is intended to improve the security system and make it easier for users to track criminals is theft. The experiment was carried out by detecting human faces for 24 hours using different cameras, namely an HD camera that was active during the day and a Night Vision camera that was active at night. The application of Unsupervised Learning method with the concept of an image cluster, aims to distinguish the faces of known or unknown people according to the dataset built in the Raspberry Pi 4. The user interface media of this system is a web-based application built with Python Flask and Python MySQL. This application can be accessed using the domain provided by the IP Forwarding device which can be accessed anywhere. According to the test results on optimization of storage, the system is able to save files only when a face is detected with an average file size of ± 2.28 MB for 1x24 hours of streaming. So that this storage process becomes more efficient and economical compared to the storage process for CCTV or IP cameras in general.

Dynamic and Full-Time Acquisition Technology and Method of Ice Data of Yellow River

Regarding the ice periods of the Yellow River, it is difficult to obtain ice data information. To effectively grasp the ice evolution process in the ice periods of the typical reach of the Yellow River, a fixed-point air-coupled radar remote monitoring device is proposed in this paper. The device is mainly composed of an air-coupled radar ice thickness measurement sensor, radar water level measurement sensor, temperature measurement sensor, high-definition infrared night vision instrument, remote switch control, telemetry communication machine, solar and wind power supply, lightning protection, and slewing arm steel tower. The integrated monitoring device can monitor ice thickness, water level, air temperature, ice surface temperature, and other related parameters in real time. At present, devices have obtained the ice change process of fixed points in ice periods from 2020 to 2021. Through a comparison with manual data, the mean error of the monitoring results of the water level and ice thickness was approximately 1 cm. The device realizes the real-time monitoring of ice thickness and water level change in the whole cycle at the fixed position. Through video monitoring, it can take pictures and videos regularly and realize the connection between the visual river and monitoring data. The research results provide a new model and new technology for hydrological monitoring in the ice periods of the Yellow River, which has broad application prospects.

Unsupervised Image-Generation Enhanced Adaptation for Object Detection in Thermal Images

Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance, and night vision. Deep learning-based detectors have achieved major progress, which usually need large amount of labelled training data. However, labelled data for object detection in thermal images is scarce and expensive to collect. How to take advantage of the large number labelled visible images and adapt them into thermal image domain is expected to solve. This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images and preserves the annotation information of the visible source domain. The image generation includes a CycleGAN-based image-to-image translation and an intensity inversion transformation. Generated fake thermal images are used as renewed source domain, and then the off-the-shelf domain adaptive faster RCNN is utilized to reduce the gap between the generated intermediate domain and the thermal target domain. Experiments demonstrate the effectiveness and superiority of the proposed method.

ALGORITHM FOR PRECISE LANDING OF MULTIROTOR UNMANNED AIRCRAFT SYSTEMS AT AN AUTONOMOUS CHARGING STATION

In this article, we propose an algorithm for accurately landing multirotor (quadcopters, hexacopters, etc.) unmanned aerial vehicles (UAVs) at an autonomous charging station. This article also presents methods for locating the charging station and landing the UAV at night. Section 1 describes the general sequential landing procedures. Section 2 describes methods for detecting the ArUco marker and evaluating its position and orientation using the OpenCV computer vision library and shows the recognition result. In section 3, the precise landing algorithm is analyzed in detail, and a block diagram of the algorithm is given. Section 4 discusses the integration of the night vision camera into the landing algorithm.

The genome of the Balearic shearwater (Puffinus mauretanicus), a Critically Endangered seabird: a valuable resource for evolutionary and conservation genomics

The Balearic shearwater (Puffinus mauretanicus) is the most threatened seabird in Europe. The fossil record suggests that human colonisation of the Balearic Islands resulted in a sharp decrease of the population size. Currently, populations continue to be decimated mainly due to predation by introduced mammals and bycatch in longline fisheries, and some studies predict their extinction by 2070. We present the first high-quality reference genome for the species which was obtained by a combination of short and long-read sequencing. Our hybrid assembly includes 4,169 scaffolds, with a scaffold N50 of 2.1 Mbp, a genome length of 1.2 Gbp, and BUSCO completeness of 96%, which is amongst the highest across sequenced avian species. This reference genome allowed us to study critical aspects relevant to the conservation status of the species, such as an evaluation of overall heterozygosity levels and the reconstruction of its historical demography. Our phylogenetic analysis using whole-genome information resolves current uncertainties in the order Procellariiformes systematics. Comparative genomics analyses uncover a set of candidate genes that may have played an important role into the adaptation to a pelagic lifestyle of Procellariiformes, including those for the enhancement of fishing capabilities, night vision and the development of natriuresis. This reference genome will be the keystone for future developments of genetic tools in conservation efforts for this Critically Endangered species.

Retinoic acid signaling mediates peripheral cone photoreceptor survival in a mouse model of retina degeneration

Retinitis Pigmentosa (RP) is a wide array of progressive, debilitating visual disorders caused by mutations in a diverse set of genes. In both human patients and mouse models of RP, rod photoreceptor dysfunction leads to loss of night vision, and is followed by secondary cone photoreceptor dysfunction and degeneration, leading to loss of daylight color vision. A strategy to prevent secondary cone death could provide a generalized RP therapy to preserve daylight color vision regardless of the underlying mutation. In mouse models of RP, cones in the far peripheral retina survive long-term, despite complete rod loss. The mechanism for such peripheral cone survival had not been explored. Here, we found that active retinoic acid (RA) signaling in peripheral Muller glia is both sufficient and necessary for the extended cone survival. RA depletion by conditional knockout of RA synthesis enzymes, or overexpression of an RA degradation enzyme, abrogated peripheral cone survival. Conversely, constitutive activation of RA signaling in the central retina promoted long-term cone survival. These results indicate that RA signaling mediates the prolonged peripheral cone survival in the rd1 mouse model of retinal degeneration, and provide a basis for a generic strategy for cone survival in the many diseases that lead to loss of cone-mediated vision.

Memory-Efficient AI Algorithm for Infant Sleeping Death Syndrome Detection in Smart Buildings

Artificial intelligence (AI) is fundamentally transforming smart buildings by increasing energy efficiency and operational productivity, improving life experience, and providing better healthcare services. Sudden Infant Death Syndrome (SIDS) is an unexpected and unexplained death of infants under one year old. Previous research reports that sleeping on the back can significantly reduce the risk of SIDS. Existing sensor-based wearable or touchable monitors have serious drawbacks such as inconvenience and false alarm, so they are not attractive in monitoring infant sleeping postures. Several recent studies use a camera, portable electronics, and AI algorithm to monitor the sleep postures of infants. However, there are two major bottlenecks that prevent AI from detecting potential baby sleeping hazards in smart buildings. In order to overcome these bottlenecks, in this work, we create a complete dataset containing 10,240 day and night vision samples, and use post-training weight quantization to solve the huge memory demand problem. Experimental results verify the effectiveness and benefits of our proposed idea. Compared with the state-of-the-art AI algorithms in the literature, the proposed method reduces memory footprint by at least 89%, while achieving a similar high detection accuracy of about 90%. Our proposed AI algorithm only requires 6.4 MB of memory space, while other existing AI algorithms for sleep posture detection require 58.2 MB to 275 MB of memory space. This comparison shows that the memory is reduced by at least 9 times without sacrificing the detection accuracy. Therefore, our proposed memory-efficient AI algorithm has great potential to be deployed and to run on edge devices, such as micro-controllers and Raspberry Pi, which have low memory footprint, limited power budget, and constrained computing resources.