scholarly journals Reduction of phase error on phase-only volume-holographic disc rotation with pre-processing by phase integral

2020 ◽  
Vol 28 (19) ◽  
pp. 28573
Author(s):  
Yeh-Wei Yu ◽  
Yuan-Cheng Chen ◽  
Kun-Hao Huang ◽  
Chih-Yuan Cheng ◽  
Tsung-Hsun Yang ◽  
...  
2008 ◽  
Vol 1 (4) ◽  
pp. 39-44
Author(s):  
Dallas Webster ◽  
Loi Phan ◽  
Oren Eliezer ◽  
Rick Hudgens ◽  
Donald Lie

2020 ◽  
Vol 96 (3s) ◽  
pp. 321-324
Author(s):  
Е.В. Ерофеев ◽  
Д.А. Шишкин ◽  
В.В. Курикалов ◽  
А.В. Когай ◽  
И.В. Федин

В данной работе представлены результаты разработки СВЧ монолитной интегральной схемы шестиразрядного фазовращателя и усилителя мощности диапазона частот 26-30 ГГц. СКО ошибки по фазе и амплитуде фазовращателя составили 1,2 град. и 0,13 дБ соответственно. Максимальная выходная мощность и КПД по добавленной мощности усилителя в точке сжатия Ку на 1 дБ составили 30 дБм и 20 % соответственно. This paper describes the design, layout, and performance of 6-bit phase shifter and power amplifier monolithic microwave integrated circuit (MMIC), 26-30 GHz band. Phase shifter MMIC has RMS phase error of 1.2 deg. And RMD amplitude error is 0.13 dB. MMIC power amplifier has output power capability of 30 dBm at 1 dB gain compression (P-1dB) and PAE of 20 %.


Author(s):  
Y. Deng ◽  
X. Guo ◽  
R. Wang ◽  
C. Hu ◽  
T. Zeng

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Chester Sungchung Park ◽  
Sunwoo Kim ◽  
Jooho Wang ◽  
Sungkyung Park

A digital front-end decimation chain based on both Farrow interpolator for fractional sample-rate conversion and a digital mixer is proposed in order to comply with the long-term evolution standards in radio receivers with ten frequency modes. Design requirement specifications with adjacent channel selectivity, inband blockers, and narrowband blockers are all satisfied so that the proposed digital front-end is 3GPP-compliant. Furthermore, the proposed digital front-end addresses carrier aggregation in the standards via appropriate frequency translations. The digital front-end has a cascaded integrator comb filter prior to Farrow interpolator and also has a per-carrier carrier aggregation filter and channel selection filter following the digital mixer. A Farrow interpolator with an integrate-and-dump circuitry controlled by a condition signal is proposed and also a digital mixer with periodic reset to prevent phase error accumulation is proposed. From the standpoint of design methodology, three models are all developed for the overall digital front-end, namely, functional models, cycle-accurate models, and bit-accurate models. Performance is verified by means of the cycle-accurate model and subsequently, by means of a special C++ class, the bitwidths are minimized in a methodic manner for area minimization. For system-level performance verification, the orthogonal frequency division multiplexing receiver is also modeled. The critical path delay of each building block is analyzed and the spectral-domain view is obtained for each building block of the digital front-end circuitry. The proposed digital front-end circuitry is simulated, designed, and both synthesized in a 180 nm CMOS application-specific integrated circuit technology and implemented in the Xilinx XC6VLX550T field-programmable gate array (Xilinx, San Jose, CA, USA).


2021 ◽  
Vol 187 ◽  
pp. 188-193
Author(s):  
Fang Liu ◽  
Ming Lyn ◽  
Haohao Hou

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.


Sign in / Sign up

Export Citation Format

Share Document