Multi-frequency point supported LLRF front-end for CiADS wide-bandwidth application

The China initiative Accelerator Driven System,CiADS,physics design adopts 162.5 MHz,325 MHz,and 650 MHz cavities,which are driven by the corresponding radio frequency(RF)power system,requiring frequency translation front-end for the RF station.For that application,a general-purpose design front-end prototype has been developed to evaluate the multi-frequency point supported design feasibility.The difficult parts to achieve the requirements of the general-purpose design are reasonable device selection and balanced design.With a carefully selected low-noise wide-band RF mixer and amplifier to balance the performance of multi-frequency supported down-conversion,specially designed LO distribution net to increase isolation between adjacent channels,and external band-pass filter to realize expected up-conversion frequencies,high maintenance and modular front-end generalpurpose design has been implemented.Results of standard parameters show an R2 value of at least 99.991%in the range of-60-10 dBm for linearity,up to 18 dBm for P1dB,and up to 89 dBc for cross talk between adjacent channels.The phase noise spectrum is lower than 80 dBc in the range of 0-1 MHz;cumulative phase noise is 0.006°;and amplitude and phase stability are 0.022%and 0.034°,respectively.

Application of AI and IoT in Clinical Medicine:Summary and Challenges

The application of artificial intelligence(AI)technology in the medical field has experienced a long history of development.In turn,some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth.With the development of advanced technologies such as the Internet of Things(IoT),cloud computing,big data,and 5G mobile networks,AI technology has been more widely adopted in the medical field.In addition,the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way.In this work,we examine the technical basis of IoT,cloud computing,big data analysis and machine learning involved in clinical medicine,combined with concepts of specific algorithms such as activity recognition,behavior recognition,anomaly detection,assistant decision-making system,to describe the scenario-based applications of remote diagnosis and treatment collaboration,neonatal intensive care unit,cardiology intensive care unit,emergency first aid,venous thromboembolism,monitoring nursing,image-assisted diagnosis,etc.We also systematically summarize the application of AI and IoT in clinical medicine,analyze the main challenges thereof,and comment on the trends and future developments in this field.

Precise calibration of cavity forward and reflected signals using low-level radio-frequency system

Precise measurements of the cavity forward(Vf)and reflected signals(Vr)are essential for characterizing other key parameters such as the cavity detuning and forward power.In practice,it is challenging to measure V_(f) and V_(r) precisely because of cross talk between the forward and reflected channels(e.g.,coupling between the cavity reflected and forward signals in a directional coupler with limited directivity).For DESY,a method based on the cavity differential equation was proposed to precisely calibrate the actual V_(f) and V_(r).In this study,we verified the validity and practicability of this approach for the Chinese ADS front-end demo superconducting linac(CAFe)facility at the Institute of Modern Physics and a compact energy recovery linac(cERL)test machine at KEK.At the CAFe facility,we successfully calibrated the actual V_(f) signal using this method.The result demonstrated that the directivity of directional couplers might seriously affect the accuracy of V_(f) measurement.At the cERL facility,we calibrated the Lorentz force detuning(LFD)using the actual Vf.Our study confirmed that the precise calibration of V_(f) significantly improves the accuracy of the cavity LFD measurement.

Measurement of the cavity-loaded quality factor in superconducting radio-frequency systems with mismatched source impedance

The accurate measurement of parameters such as the cavity-loaded quality factor(Q_(L))and half bandwidth(f_(0.5))is essential for monitoring the performance of superconducting radio-frequency cavities.However,the conventional"field decay method"employed to calibrate these values requires the cavity to satisfy a"zero-input"condition.This can be challenging when the source impedance is mismatched and produce nonzero forward signals(V_(f))that significantly affect the measurement accuracy.To address this limitation,we developed a modified version of the"field decay method"based on the cavity differential equation.The proposed approach enables the precise calibration of f_(0.5) even under mismatch conditions.We tested the proposed approach on the SRF cavities of the Chinese Accelerator-Driven System Front-End Demo Superconducting Linac and compared the results with those obtained from a network analyzer.The two sets of results were consistent,indicating the usefulness of the proposed approach.