Video Compressed Sensing Reconstruction Based on Multi-Dimensional Reference Frame Multi Hypothesis Rediction

In this paper,a video compressed sensing reconstruction algorithm based on multidimensional reference frames is proposed using the sparse characteristics of video signals in different sparse representation domains.First,the overall structure of the proposed video compressed sensing algorithm is introduced in this paper.The paper adopts a multi-reference frame bidirectional prediction hypothesis optimization algorithm.Then,the paper proposes a reconstruction method for CS frames at the re-decoding end.In addition to using key frames of each GOP reconstructed in the time domain as reference frames for reconstructing CS frames,half-pixel reference frames and scaled reference frames in the pixel domain are also used as CS frames.Reference frames of CS frames are used to obtain higher quality assumptions.Themethod of obtaining reference frames in the pixel domain is also discussed in detail in this paper.Finally,the reconstruction algorithm proposed in this paper is compared with video compression algorithms in the literature that have better reconstruction results.Experiments show that the algorithm has better performance than the best multi-reference frame video compression sensing algorithm and can effectively improve the quality of slowmotion video reconstruction.

Study on Environmental Multi-parameter Sensor Based on MEMS

A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results illustrate that the heat-release unit in micro-system intercross greatly affects other sensing units on the temperature.We study the method of etching process,which formed cavity to reduce the heat exchange efficiency and decrease temperature intercross effect.

High-Performance Distributed Brillouin Optical Fiber Sensing

This paper reviews the recent advances on the high-performance distributed Brillouin optical fiber sensing, which include the conventional distributed Brillouin optical fiber sensing based on backward stimulated Brillouin scattering and two other novel distributed sensing mechanisms based on Brillouin dynamic grating and forward stimulated Brillouin scattering, respectively. As for the conventional distributed Brillouin optical fiber sensing, the spatial resolution has been improved from meter to centimeter in the time-domain scheme and to millimeter in the correlation-domain scheme, respectively;the measurement time has been reduced from minute to millisecond and even to microsecond;the sensing range has reached more than 100 km. Brillouin dynamic grating can be used to measure the birefringence of a polarization-maintaining fiber, which has been explored to realize distributed measurement of temperature, strain, salinity, static pressure, and transverse pressure. More recently, forward stimulated Brillouin scattering has gained considerable interest because of its capacity to detect mechanical features of materials surrounding the optical fiber, and remarkable works using ingenious schemes have managed to realize distributed measurement, which opens a brand-new way to achieve position-resolved substance identification.

GroupNet:Learning to group corner for object detection in remote sensing imagery

Due to the attractive potential in avoiding the elaborate definition of anchor attributes,anchor-free-based deep learning approaches are promising for object detection in remote sensing imagery.Corner Net is one of the most representative methods in anchor-free-based deep learning approaches.However,it can be observed distinctly from the visual inspection that the Corner Net is limited in grouping keypoints,which significantly impacts the detection performance.To address the above problem,a novel and effective approach,called Group Net,is presented in this paper,which adaptively groups corner specific to the objects based on corner embedding vector and corner grouping network.Compared with the Corner Net,the proposed approach is more effective in learning the semantic relationship between corners and improving remarkably the detection performance.On NWPU dataset,experiments demonstrate that our Group Net not only outperforms the Corner Net with an AP of 12.8%,but also achieves comparable performance to considerable approaches with 83.4%AP.

Single-shot spectral-volumetric compressed ultrafast photography

In ultrafast optical imaging,it is critical to obtain the spatial structure,temporal evolution,and spectral composition of the object with snapshots in order to better observe and understand unrepeatable or irreversible dynamic scenes.However,so far,there are no ultrafast optical imaging techniques that can simultaneously capture the spatial–temporal–spectral five-dimensional(5D)information of dynamic scenes.To break the limitation of the existing techniques in imaging dimensions,we develop a spectral-volumetric compressed ultrafast photography(SV-CUP)technique.In our SV-CUP,the spatial resolutions in the x,y and z directions are,respectively,0.39,0.35,and 3 mm with an 8.8 mm×6.3 mm field of view,the temporal frame interval is 2 ps,and the spectral frame interval is 1.72 nm.To demonstrate the excellent performance of our SV-CUP in spatial–temporal–spectral 5D imaging,we successfully measure the spectrally resolved photoluminescent dynamics of a 3D mannequin coated with CdSe quantum dots.Our SV-CUP brings unprecedented detection capabilities to dynamic scenes,which has important application prospects in fundamental research and applied science.