Highly-sensitive NO,NO2,and NH3 measurements with an open-multipass cell based on mid-infrared wavelength modulation spectroscopy

A compact prototype based on mid-infrared wavelength modulation spectroscopy（WMS）is developed for the simul-taneous monitoring of NO,NO2,and NH3 in the urban area.Three quantum cascade lasers（QCLs）with central frequencies around 1900.0 cm^-1,1600.0 cm^-1,and 1103.4 cm^-1are used for NO,NO2,and NH3detections,respectively,by timedivision multiplex.An open-path multi-pass cell of 60-m optical path length is applied to the instrument for high sensitivity and reducing the response time to less than 1 s.The prototype achieves a sub-ppb detection limit for all the three target gases with an average time of about 100 s.The instrument is installed in the Jiangsu environmental monitoring center to conduct performance tests on ambient air.Continuous 24-hour measurements show good agreement with the results of a reference instrument based on the chemiluminescence technique.

Research on Transfer Learning of Vision-based Gesture Recognition

Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.

A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence

In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box.The other part is applied to remove the temporal redundancy of the 3D point cloud data.The temporal redundancy between point clouds is removed by using the motion vector,i.e.,the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame.The hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two frames.The second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame.The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames.Finally,the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the curren t and the motion vectors are transmit ted into the rem ote end.In order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT descriptor.In particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two clusters.Experimen tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression ratio.The proposed met hod achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.