A 3-D Polarization Quadrature Amplitude Modulation Method and Constellation Mapping

As a new three-dimensional(3-D)modulation,Polarization Quadrature Amplitude Modulation(PQAM) can be regarded as the combination of Pulse amplitude modulation(PAM) and Quadrature Amplitude Modulation(QAM) Modulation.It can better improve the digital communication efficiency and reduce the Symbol error rate(SER) of the system than one-dimensional or two-dimensional modulation scheme.How to design a feasible constellation is the most concerned problem of PQAM currently.This paper first studies the relationship between the SER theoretical value of PQAM and the distribution of M and N,proposes a new M,N allocation scheme.Secondly,a new and straightforward design method of constructing higher-level 3-D signal constellations,which can be matched with the PQAM,and the constellation can divided into three different structures according to the ary for PQAM.Finally,the simulation results show that:in PQAM system,the modulation scheme and the constellation mapping scheme are proposed in this paper which can effectively reduce the system SER and improve the anti-noise performance of the system.

Robust Lane Detection and Tracking Based on Machine Vision

Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(ADAS).However,gradient information varies with illumination changes.In the complex scenes of urban roads,highlight and shadow have effects on the detection,and non-lane objects also lead to false positives.In order to improve the accuracy of detection and meet the robustness requirement,this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects.And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting.Finally,Kalman filter is used to correct lane lines which are wrong detected or missed.The experimental results show that computation times meet the real-time requirements,and the overall detection rate of the proposed method is 95.63%.

YOLO-CORE: Contour Regression for Efficient Instance Segmentation

Instance segmentation has drawn mounting attention due to its significant utility.However,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level labeling.In this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance segmentation.The mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector loss.Our proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and speed.It achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO dataset.The superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding field.Moreover,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).