Spontaneous parametric down-conversion (SPDC) in a laser pumped optical nonlinear medium can produce heralded single photons with a high purity but a very low yield. Improving the yield by increasing the pump power in SPDC inevitably reduces the purity due to excitation of multiphoton events. We propose a scheme to overcome this purity-yield trade-off by suppressing multiphoton events in a cavity-enhanced SPDC via the photon-blockade effect. By introducing a strong photon-photon interaction into the intracavity medium and increasing the pump power, we can improve the available single-photon yield to larger than 90%, while keeping the purity high, towards on-demand generation of single photons through the SPDC process. Our quasi-on-demand SPDC sources may boost single-photon-based quantum-information technology.

To solve the problem of low accuracy and poor robustness of vehicle tracking in complex traffic scenes, scale-adaptive vehicle tracking based on background information is therefore proposed to this paper. The traditional correlation filter tracking algorithm is less dependent on background information. This easily leads to tracking error. We propose to use the background information of the vehicle and the surrounding as a sample set to establish a position classifier. It transforms the target tracking problem into the classification of the target and the background. This also improves the position accuracy of the tracking target response point when the background is complex. The dimensions of the vehicle change as the relative distance between the vehicle and the camera changes, affecting the tracking reliability. This algorithm crops Histogram of Oriented Gradient (HOG) features of the different-scale vehicle images and establishes a scale classifier. It determines the best scale of the target built on the output response peak of the scale classifier. This improves the adaptability of classifier against vehicle scale change. Extensive experimental results demonstrate that the method improves the accuracy and robustness of vehicle tracking significantly.

It is difficult with traditional methods to realise real-time and robust detection of moving vehicles under complex traffic scenes. In this paper, a moving vehicle video detection method that combines ViBe and inter-frame difference is proposed. The proposed method improves the background update efficiency of the traditional ViBe method by adding a multi-threshold comparison step to the inter-frame difference method. The improved background update strategy can judge whether the detected pixel point belongs to the foreground or background, and dynamically adjusts the background update rate according to the inter-frame difference results. Experimental results showed the proposed method can effectively remove of "ghosting" phenomenon that occurs in traditional ViBe method and realise accurate and complete detection of the moving vehicle in video.

Niobium nitride (NbN) nanowires have a high repetition rate and efficiency, making them ideal for superconducting nanowire single photon detectors (SNSPDs). However, it is difficult to fabricate NbN arrays over large areas, which is critical for various applications. This paper describes a 4 × 4 NbN SNSPD array (16 pixels) and optical coupling with a 300-μm-diameter multimode fiber using beam compression technology. This is the first NbN SNSPD coupled with such large-diameter fibers. The designed pixels are positioned as closely as possible (pixel filling factor about 98.5%), almost without dead area between them. This results in a system efficiency of 46% and a quantum efficiency of 94.5% for photons (λ = 1064 nm) coupled from multimode fibers. An intrinsic time resolution of less than 69 ps can be obtained. The proposed high-performance single photon detector is suitable for satellite laser ranging. Furthermore, the proposed system is feasible for large SNSPD arrays with NbN, paving the way for the development of efficient photon cameras with NbN nanowires.

LiDAR point registration is a key procedure for the acquisition of complete point cloud datasets. It has great significance for the fusion of multisource LiDAR data. In general, the widely used methods for LiDAR point registration can be categorized into three types: auxiliary methods, direct methods, and feature methods. However, for the registration of complex objects (e.g., stadium and tower), such methods may face varying degrees of technical problems owing to the unavailability of auxiliary data or targets, requirement of sufficient overlapping areas, and difficulty in feature extraction and matching. In the real world, numerous objects with extremely complicated geometric shapes have the characteristic of symmetry. This study focuses on complex objects with symmetry and tries to exploit their intrinsic symmetry characteristic in order to facilitate their point cloud registration. A symmetry-based method for LiDAR point registration is proposed, in which the general idea is to derive 3-D central axes from multisource point clouds, based on the symmetry of objects. The proposed method consists of six main steps: detection of rotational symmetry, adaptive point cloud slicing, central point extraction, central axis fitting, central axis matching, and orientation and positioning. Comparative experiments and quantitative evaluations are conducted. The experimental results indicate that the proposed framework can achieve satisfactory registration of objects with rotational symmetry.

Niobium Nitride (NbN) nanowire is the most popular detection material of superconducting nanowire single photon detectors (SNSPDs) for high repetition rate and high efficiency. However, it has been assumed to be difficult for fabricating SNSPDs with arrays over large area, which are critical components of quantum imaging, linear optical quantum computing, satellite laser ranging, and high-speed quantum key distribution. This paper reported a 4*4 pixel NbN SNSPDs array with an equivalent receiving aperture of 300 micrometer associated with beam compression technology, which is the first NbN SNSPD coupled by 300 micrometer fiber according to our knowledge. The designed pixel was compact with a filling factor of 98.5%, resulting in a high quantum efficiency of 94.5%, a system efficiency of 46% for photons coupled from 300 micrometer fiber without optimizing polarization, and a system time resolution of 92 ps. This work demonstrates the feasibility of a large SNSPDs array, and paves the way for developing efficient photon camera with NbN nanowires.

We propose and examine theoretically a new type of photodetector that senses THz single photons by the wavefunction change of a single electron confined in a quantum dot. A possible readout scheme is also presented.

Niobium nitride (NbN) nanowires are commonly used as photosensitive materials for superconducting nanowire single-photon detectors (SNSPDs). Their optical properties are the key factors influencing the performance of SNSPD. According to the experimental data and simulation results, in this paper we systematically study the optical properties of various niobium nitride nanowire detector device structures, and characterize the reflection spectra and transmission spectra of the following four device structures: 1) Backside optical devices with SiO2 as the antireflection layer, 2) backside optical devices with SiN as the antireflection layer, 3) front-facing optical devices with Au+SiN as a mirror, and 4) front-facing optical devices with distributed Bragg reflector (DBR) as a mirror. The NbN films with different thickness are grown on the basis of the four device structures, and the absorption efficiencies of the NbN films with different thickness are observed. The relationships between the optimal NbN thickness and the optical absorption rate for different device structures are found as follows: The maximum absorption rate of NbN on the SiO2 antireflection layer is 91.7% at 1606 nm, while the absorption rates of the remaining structures at the optimal thickness of NbN can reach 99% or more. The absorption rate of the SiN device, the Au+SiN device and the DBR device are 99.3%, 99.8% and 99.9%, respectively. The measured results and simulation structure of DBR device are analyzed. These results are of significance for guiding the design and development of high efficiency SNSPD.

Capturing single photons through light matter interactions is a fascinating and important topic for both fundamental research and practical applications. The light matter interaction enables the transfer of the energy of a single photon (similar to 1 eV) to a bound electron, making it free to move either in the crystal lattice or in the vacuum. In conventional single photon detectors (e.g., avalanche photodiodes), this free electron triggers a carrier multiplication process which amplifies the ultraweak signal to a detectable level. Despite their popularity, the timing jitter of these conventional detectors is limited to tens of picoseconds, mainly attributed to a finite velocity of carriers drifting through the detectors. Here we propose a new type of single photon detector where a quantum dot, embedded in a single-electron transistor like device structure, traps a photogenerated charge and gives rise to a sizable voltage signal (similar to 7 mV per electron or hole by simulation) on a nearby sense probe through capacitive coupling (with a capacitance similar to aF). Possible working modes of the proposed detector are theoretically examined. Owing to a small lateral dimension of the quantum dot, detailed analyses reveal that the intrinsic timing jitter of the proposed detector is in the femtosecond to subpicosecond range, and the intrinsic dark count rate is negligible up to moderately high temperatures. These figures of merit are orders of magnitude superior to those of the state-of-the-art single photon detectors work in the same spectral range, making the proposed detector promising for timing-sensitive and quantum information applications.

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

It is a challenge to grow perovskite films with large crystalline grains and a full coverage on the surface of an organic compound film by a solution process. Herein, we for the first time introduce environmental‐friendly urea into PbI2 precursor to mediate the perovskite film growth and crystallization on the top of PEODT:PSS for efficient inverted solar cells. This method attains high‐quality perovskite films with large‐size grains of over 2 μm and a full coverage. This enables us to fabricate the inverted perovskite solar cells showing a maximum efficiency of 18.01% with a fill factor of 82.29% and a stabilized efficiency of 17.56%. Infrared spectroscopy and scanning electron microscopy images demonstrate that urea is coordinated with PbI2 to form PbI2 · OC(NH2)2 complex, resulting in large PbI2 flakes by a slow crystallization. The large PbI2 flake with fewer nucleation sites is conducive to forming monolithic perovskite grains. Further investigation indicates that CH3NH3I · PbI2 · OC(NH2)2 complex is formed as an intermediate phase, which retards the crystallization process of the perovskite films. These factors lead to a high‐quality perovskite film with large grain size and excellent crystallinity.

The monitor of sea fogs become more important with the rapid development of marine activities. Remote sensing through laser is an effective tool for monitoring sea fogs, but still challengeable for large distance. We demonstrated a Long-distance Lidar for sea fog with superconducting nanowire single-photon detector (SNSPD), which extended the ranging area to a 180-km diameter area. The system, which was verified by using a benchmark distance measurement of a known island, is applied to the Mie scattering weather prediction Lidar system. The fog echo signal distribution in the range of 42.3∼63.5 km and 53.2∼74.2 km was obtained by the Lidar system. Then the fog concentration and the velocity of the fog were deduced from the distribution, which is consistent with the weather prediction. The height of the sea fog is about two hundred meter while the visibility at this height is about 90 km due to the Earth’s radius of curvature. Therefore, the capability of this SNSPD-based Lidar was close to the theoretical limit for sea fog measurements for extremely high signal-to-noise ratio of SNSPD.

This research investigates the fundamental limits and trade-space of quantum semiconductor photodetectors using the Schrödinger equation and the laws of thermodynamics.We envision that, to optimize the metrics of single photon detection, it is critical to maximize the optical absorption in the minimal volume and minimize the carrier transit process simultaneously. Integration of photon management with quantum charge transport/redistribution upon optical excitation can be engineered to maximize the quantum efficiency (QE) and data rate and minimize timing jitter at the same time. Due to the ultra-low capacitance of these quantum devices, even a single photoelectron transfer can induce a notable change in the voltage, enabling non-avalanche single photon detection at room temperature as has been recently demonstrated in Si quanta image sensors (QIS). In this research, uniform III-V quantum dots (QDs) and Si QIS are used as model systems to test the theory experimentally. Based on the fundamental understanding, we also propose proof-of-concept, photon-managed quantum capacitance photodetectors. Built upon the concepts of QIS and single electron transistor (SET), this novel device structure provides a model system to synergistically test the fundamental limits and tradespace predicted by the theory for semiconductor detectors. This project is sponsored under DARPA/ARO's DETECT Program: Fundamental Limits of Quantum Semiconductor Photodetectors.

Searching for innovative approaches to detect single photons remains at the center of science and technology for decades. This paper proposes a zero transit-time, non-avalanche quantum capacitive photodetector to register single photons. In this detector, the absorption of a single photon changes the wave function of a single electron trapped in a quantum dot (QD), leading to a charge density redistribution nearby. This redistribution translates into a voltage signal through capacitive coupling between the QD and the measurement probe. Using InAs QD/AlAs barrier as a model system, the simulation shows that the output signal reaches ~4 mV per absorbed photon, promising for high-sensitivity, ps single-photon detection.

Three-dimensional (3D) model data of overpasses is significant for traffic navigation, landscape design, and many other applications. In this study, we explore the potential of using airborne light detection and ranging (LiDAR) data for the 3D recon-struction of large multilayer overpasses. To reduce the technical difficulty of this 3D reconstruction process, we propose a concept of“structure unit”. The“structure unit”represents a contiguous object with a consistent width, but does not include the bifurcation and/or intersection structures. A new technical framework, based on the structure units, is proposed to reconstruct the 3D models of large multi-layer overpass using the airborne LiDAR data. First, the overpass points are extracted from the raw LiDAR data by us-ing a Reversed Iterative Mathematic Morphological (RIMM) method and inputting the area of overpass. Then, the hierarchal seg-mentation strategy, including the connectivity-based segmentation and the determination of structure units, is used to determinate the structure units from the overpass points. The central line of each structure is derived by the binarization and vectorization opera-tions. And the obscured structures are detected and restored based on the central lines of the overpass. Finally, the complete 3D mod-el of the overpass can be obtained by using the complete central line and the corresponding width value. Experiments were carried out to evaluate the validities of the proposed method on two different overpasses. The completeness rates of the 3D models of over-passes A and B are 92.77%and 94.58%, respectively. And the correctness rates of the 3D models of overpasses A and B are 98.84%and 98.97%, respectively. The experimental results indicate that the proposed method can provide satisfactory 3D models for large complex overpasses, and is capable to restore the occluded structures with high quality result.

Information extraction and three-dimensional (3D) reconstruction of buildings using the vehicle-borne laser scanning (VLS) system is significant for many applications. Extracting LiDAR points, from VLS, belonging to various types of building in large-scale complex urban environments still retains some problems. In this paper, a new technical framework for automatic and efficient building point extraction is proposed, including three main steps: (1) voxel group-based shape recognition; (2) category-oriented merging; and (3) building point identification by horizontal hollow ratio analysis. This article proposes a concept of "voxel group" based on the voxelization of VLS points: each voxel group is composed of several voxels that belong to one single real-world object. Then the shapes of point clouds in each voxel group are recognized and this shape information is utilized to merge voxel group. This article puts forward a characteristic nature of vehicle-borne LiDAR building points, called "horizontal hollow ratio", for efficient extraction. Experiments are analyzed from two aspects: (1) building-based evaluation for overall experimental area; and (2) point-based evaluation for individual building using the completeness and correctness. The experimental results indicate that the proposed framework is effective for the extraction of LiDAR points belonging to various types of buildings in large-scale complex urban environments.

针对航空和地面IiDAR数据配准中点云数据的共轭特征较少且精度差异较大的问题,提出了一种基于可移动角点的航空和地面LiDAR数据配准方法:从航空和地面LiDAR数据中分别提取相应的建筑物角点,采用6参数模型对角点进行初始配准;以地面角点为参照,利用迭代移动方法对误差较大的航空角点进行修正;最后根据移动后的航空和地面角点计算获得点云配准关系.实验结果表明,该文方法可取得较好的点云配准效果,角点修正后能有效提升点云配准精度,适合于含有角点特征的航空和地面LiDAR数据配准.

Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) data can be used to obtain abundant and precise side information of trees. Therefore, it can enable extracting individual tree parameters, such as the tree height, crown size, crown base height, and diameter at breast height, and it can provide basic data for forest research and management. This study proposes a technical framework for segmenting individual trees from TLS and MLS data. This framework contains six steps: 1) data preprocessing, 2) octree construction, 3) spatial clustering, 4) stem detection, 5) initial segmentation, and 6) overlapped canopy segmentation. This framework makes two main contributions: 1) a top-down hierarchical segmentation approach, including connectivity-based spatial clustering (regional scale), stem-based initial segmentation (individual tree scale), and fine segmentation of overlapped canopy (canopy scale), is proposed to reduce technical difficulties and improve process efficiency; and 2) a modified node similarity calculation for normalized cut method aiming at segmenting overlapped canopy, which can effectively separate neighboring trees even if their canopies are overlapped, is proposed. The proposed framework was tested on a leaves-off terrestrial LiDAR dataset and a leaves-on mobile LiDAR dataset. For terrestrial LiDAR data, our framework achieved completeness of 92.4%, correctness of 95.4%, and F-score of 0.94. For mobile LiDAR data, the corresponding values were 94.0%, 93.7%, and 0.94.

A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy.

A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an initial matching relationship between the terrestrial corners and airborne corners is automatically derived using a matching technique based on maximum matching corner pairs with minimum errors (MTMM). Finally, a set of leading points are generated from matched airborne corners, and a shiftable leading point method is proposed. The key feature of this approach is the implementation of the concept of shiftable leading points in the final step. Since the geometric accuracy of terrestrial LiDAR data is much better than that of airborne LiDAR data, leading points corresponding to anomalous airborne corners could be modified for the improvement of the geometric accuracy of registration. The experiment demonstrates that the proposed approach can advance the geometric accuracy of two-platform LiDAR data registration effectively.