Ultrahigh spatiotemporal resolution beam signal reconstruction with bunch phase compensation

Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.

Equivalent sample theory of networked control systems and its application

The equivalent sample theory and its application in analysis of networked control system （NCS） are presented. After analyzing NCS＇s scheduling in master-slave mode, the characteristics of time delay and sample are summarized. Looking on master station visiting the slave station as a special sample process, the theory of equivalent sample is presented. And based on it, the stability of a kind of NCS is analyzed. The criterion to determine the upper bound of transmission delay is introduced, which guarantees the stability. Finally, an example with simulation shows the availability and usability of this analysis method.

Instantaneous frequency analysis of broadband LFM signals by photonics-assisted equivalent frequency sampling

We propose a photonics-assisted equivalent frequency sampling(EFS)method to analyze the instantaneous frequency of broadband linearly frequency modulated(LFM)microwave signals.The proposed EFS method is implemented by a photonic scanning receiver,which is operated with a frequency scanning rate slightly different from the repetition rate of the LFM signals.Compared with the broadband LFM signal analysis based on temporal sampling,the proposed method avoids the use of high-speed analog to digital converters,and the instantaneous frequency acquisition realized by frequency-to-time mapping is also simplified since real-time Fourier transformation is not required.Feasibility of the proposed method is verified through an experiment,in which frequency analysis of Kα-band LFM signals with a bandwidth up to 3 GHz is demonstrated with a moderate sampling rate of 100 MSa/s.The proposed method is highly demanded for analyzing the instantaneous frequency of broadband LFM signals used in radar and electronic warfare systems.

A simple error estimation method for linear-regression-based thermal sharpening techniques with the consideration of scale difference

Thermal remote sensing imagery is helpful for land cover classification and related analysis.Unfortunately,the spatial resolution of thermal infrared(TIR)band is generally coarser than that of visual near-infrared band,which limits its more precise applications.Various thermal sharpening(TSP)techniques have been developed for improving the spatial resolution of the imagery of TIR band or land surface temperature(LST).However,there is no research on the theoretical estimation of TSP error till now,which implies that the error in sharpened LST imagery is unknown and the further analysis might be not reliable.In this paper,an error estimation method based on classical linear regression theory for the linear-regression-based TSP techniques was firstly introduced.However,the scale difference between the coarse resolution and fine resolution is not considered in this method.Therefore,we further developed an improved error estimation method with the consideration of the scale difference,which employs a novel term named equivalent random sample size to reflect the scale difference.A simulation study of modified TsHARP(a typical TSP technique)shows that the improved method estimated the TSP error more accurately than classical regression theory.Especially,the phenomena that TSP error increases with the increasing resolution gap between the initial and target resolutions can be successfully predicted by the proposed method.