The absorption and emission properties of transition metal (TM)-doped zinc chalcogenides have been investigated to understand their potential application as room-temperature, mid-infrared tunable laser media. Crystals of ZnS, ZnSe, and ZnTe, individually doped with Cr/sup 2+/, Co/sup 2+/, Ni/sup 2+/, or Fe/sup 2+/ have been evaluated. The absorption and emission properties are presented and discussed in terms of the energy levels from which they arise. The absorption spectra of the crystals studied exhibit strong bands between 1.4 and 2.0 /spl mu/m which overlap with the output of strained-layer InGaAs diodes. The room-temperature emission spectra reveal wide-band emissions from 2-3 /spl mu/m for Cr and from 2.8-4.0 /spl mu/m for Co, Cr luminesces strongly at room temperature; Co exhibits significant losses from nonradiative decay at temperatures above 200 K, and Ni and Fe only luminesce at low temperatures, Cr/sup 2+/ is estimated to have the highest quantum yield at room temperature among the media investigated with values of /spl sim/75-100%. Laser demonstrations of Cr:ZnS and Cr:ZnSe have been performed in a laser-pumped laser cavity with a Co:MgF/sub 2/ pump laser. The output of both lasers were determined to peak at wavelengths near 2.35 /spl mu/m, and both lasers demonstrated a maximum slope efficiency of approximately 20%. Based on these initial results, the Cr/sup 2+/ ion is predicted to be a highly favorable laser ion for the mid-IR when doped into the zinc chalcogenides; Co/sup 2+/ may also serve usefully, but laser demonstrations yet remain to be performed.

Transition metal-doped zinc chalcogenides: Spectroscopy and laser demonstration of a new class of gain media

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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and category extremely imbalance. In this paper, we propose an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity. Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features. The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through pixel-wise feature interpolation to realize feature reconstruction and alignment. For more accurate rotation estimation, an approximate SkewIoU loss is proposed to solve the problem that the calculation of SkewIoU is not derivable. Experiments on three popular remote sensing public datasets DOTA, HRSC2016, UCAS-AOD as well as one scene text dataset ICDAR2015 show the effectiveness of our approach. Tensorflow and Pytorch version codes are available at this https URL and this https URL, and R3Det is also integrated in our open source rotation detection benchmark: this https URL.

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Arbitrary-oriented object detection has recently attracted increasing attention in vision for their importance in aerial imagery, scene text, and face etc. In this paper, we show that existing regression-based rotation detectors suffer the problem of discontinuous boundaries, which is directly caused by angular periodicity or corner ordering. By a careful study, we find the root cause is that the ideal predictions are beyond the defined range. We design a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection. We also propose a circular smooth label (CSL) technique to handle the periodicity of the angle and increase the error tolerance to adjacent angles. We further introduce four window functions in CSL and explore the effect of different window radius sizes on detection performance. Extensive experiments and visual analysis on two large-scale public datasets for aerial images i.e. DOTA, HRSC2016, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach. The code is public available at https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow.

Arbitrary-Oriented Object Detection with Circular Smooth Label

: The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or bird's eye imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.

Learning Deep Representations for Ground to Aerial Geolocalization (Open Access)

We present a real-time indoor visible light positioning system based on the optical camera communication, where the coordinate data in the ON–OFF keying format is transmitted via light-emitting diode-based lights and captured using a smartphone camera. The position of the camera is estimated using a novel perspective- $n$ -point problem algorithm, which determines the position of a calibrated camera from $n~3\text{D}$ -to-2D point correspondences. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 and 80 cm, respectively.

/pdf/a-vlc-smartphone-camera-based-indoor-positioning-system-285ej68wed.pdf

A VLC Smartphone Camera Based Indoor Positioning System

Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending fog computing to the conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a smart VFC system, by combining both PVA and smart parking. A VFC-aware parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services by monetary rewards to compensate for their service cost. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatibility, individual rationality, and budget balance. We further provide an observation stage with dynamic offload pricing update to improve the offload efficiency and the profit of the fog system. The simulation results confirm the win–win performance enhancement to the fog node controller, the smart vehicles, and the parking places from the proposed design.

Parking Reservation Auction for Parked Vehicle Assistance in Vehicular Fog Computing

Multiple-azimuth-channel (MAC) synthetic aperture radar (SAR) systems can overcome the minimum-antenna-area constraint. However, channel phase imbalance and range sampling time error can degrade the quality of the final image. To deal with these two problems, a calibration method based on the azimuth cross correlation is presented in this letter. The proposed method applies the azimuth cross-correlation operation after transforming the received signals of each channel into range frequency domain, where the phase of the cross-correlation function is linear with the range frequency. We obtain the range sampling time error by testing this line's slope, and then the phase mismatch can be derived. Simulation experiments and real data processing results validate the proposed method.

Phase Mismatch Calibration of the Multichannel SAR Based on Azimuth Cross Correlation

Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via using simpler object representation model and less regression parameters. In P-RSDet method, arbitrary-oriented object detection can be achieved by predicting the center point and regressing one polar radius and two polar angles. Besides, in order to express the geometric constraint relationship between the polar radius and the polar angle, a Polar Ring Area Loss function is proposed to improve the prediction accuracy of the corner position. Experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show that our P-RSDet achieves state-of-the-art performances with simpler model and less regression parameters.

/pdf/arbitrary-oriented-object-detection-in-remote-sensing-images-3nkg3tgl16.pdf

Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates

Unlike single geospatial objects extraction from high-resolution remote sensing images, the task of road extraction faces more challenges, including its narrowness, sparsity, diversity, multiscale characteristics, and class imbalance. Focusing on these challenges, this paper proposes an end-to-end framework called the multiple feature pyramid network (MFPN). In MFPN, we design an effective feature pyramid and a tailored pyramid pooling module, taking advantage of multilevel semantic features of high-resolution remote sensing images. In the optimization stage, a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads. The proposed novel loss function is more sensitive to the misclassified and the sparse real labeled pixels and helps to focus on the spare set of hard pixels in the training stage. Compared with the cross-entropy loss function, the weighted balance loss can reduce training time dramatically for the same precision. Experiments on two challenging datasets of high-resolution remote sensing images which illustrate the performance of the proposed algorithm have achieved significant improvements, especially for narrow rural roads.

Yi Zhang

Papers

A VLC Smartphone Camera Based Indoor Positioning System

Parking Reservation Auction for Parked Vehicle Assistance in Vehicular Fog Computing

Phase Mismatch Calibration of the Multichannel SAR Based on Azimuth Cross Correlation

Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates

An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network