scholarly journals A photonic microwave and RF integrator based on a transversal filter

2020 ◽  
Author(s):  
David Moss ◽  
xingyuan xu ◽  
mengxi tan ◽  
Jiayang Wu ◽  
Roberto Morandotti ◽  
...  
Keyword(s):  
High Performance ◽  
Time Window ◽  
Integration Time ◽  
Signal Integration ◽  
Time Intervals ◽  
Diverse Range ◽  
Transversal Filter ◽  
Rf Signals ◽  
Rf Signal ◽  
Good Agreement

<p><b>We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.</b></p>

scholarly journals A photonic microwave and RF integrator based on a transversal filter

2020 ◽  
Author(s):  
David Moss ◽  
xingyuan xu ◽  
mengxi tan ◽  
Jiayang Wu ◽  
Roberto Morandotti ◽  
...  
Keyword(s):  
High Performance ◽  
Time Window ◽  
Integration Time ◽  
Signal Integration ◽  
Time Intervals ◽  
Diverse Range ◽  
Transversal Filter ◽  
Rf Signals ◽  
Rf Signal ◽  
Good Agreement

<p><b>We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.</b></p>

A photonic based microwave and RF integrator with a transversal filter

2021 ◽  
Author(s):  
david moss
Keyword(s):  
High Performance ◽  
Time Window ◽  
Integration Time ◽  
Signal Integration ◽  
Time Intervals ◽  
Diverse Range ◽  
Transversal Filter ◽  
Rf Signals ◽  
Rf Signal ◽  
Good Agreement

Abstract We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

scholarly journals IEEE Trans on Circuits and Systems Photonic Integrator with Microcombs

2020 ◽  
Author(s):  
David Moss
Keyword(s):  
High Performance ◽  
Time Window ◽  
Single Mode ◽  
Integration Time ◽  
Signal Integration ◽  
Diverse Range ◽  
Time Bandwidth Product ◽  
Rf Signals ◽  
High Degree ◽  
Rf Signal

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large time-bandwidth product of 81. Our approach also features a high degree of reconfigurability, by simply adjusting the value of dispersion (i.e., the length of dispersive fibre), the integration time window and resolution can be reconfigured to accommodate a diverse range of applications. We employed 13 km of standard single-mode fibre to achieve a large integration time window of ~6.8 ns, a time resolution as fast as ~84 ps, with a broad bandwidth of 11.9 GHz. In addition, we perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

scholarly journals New Results on the Motions of Asteroids in Resonances

1992 ◽  
Vol 152 ◽  
pp. 145-152 ◽  
Author(s):  
R. Dvorak
Keyword(s):  
Initial Conditions ◽  
Numerical Study ◽  
Equations Of Motion ◽  
Close Approach ◽  
Integration Time ◽  
Time Interval ◽  
Time Intervals ◽  
3 Dimensional ◽  
Special Cases ◽  
Good Agreement

In this article we present a numerical study of the motion of asteroids in the 2:1 and 3:1 resonance with Jupiter. We integrated the equations of motion of the elliptic restricted 3-body problem for a great number of initial conditions within this 2 resonances for a time interval of 104 periods and for special cases even longer (which corresponds in the the Sun-Jupiter system to time intervals up to 106 years). We present our results in the form of 3-dimensional diagrams (initial a versus initial e, and in the z-axes the highest value of the eccentricity during the whole integration time). In the 3:1 resonance an eccentricity higher than 0.3 can lead to a close approach to Mars and hence to an escape from the resonance. Asteroids in the 2:1 resonance with Jupiter with eccentricities higher than 0.5 suffer from possible close approaches to Jupiter itself and then again this leads in general to an escape from the resonance. In both resonances we found possible regions of escape (chaotic regions), but only for initial eccentricities e > 0.15. The comparison with recent results show quite a good agreement for the structure of the 3:1 resonance. For motions in the 2:1 resonance our numeric results are in contradiction to others: high eccentric orbits are also found which may lead to escapes and consequently to a depletion of this resonant regions.

scholarly journals For RF Signal-Based UAV States Recognition, Is Pre-processing Still Important At The Era Of Deep Learning?

2021 ◽  
Author(s):  
Changhao Ge ◽  
Shubo Yang ◽  
Wenjian Sun ◽  
Yang Luo ◽  
Chunbo Luo
Keyword(s):  
Performance Management ◽  
High Performance ◽  
Control Strategies ◽  
Classification Model ◽  
Emergency Control ◽  
Average Accuracy ◽  
Rf Signals ◽  
State Detection ◽  
Significant Research ◽  
Rf Signal

Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.

scholarly journals Ultra-high speed reconfigurable microwave and radio frequency photonic intensity differentiators using Kerr frequency optical microcombs

2021 ◽  
Author(s):  
David Moss
Keyword(s):  
Radio Frequency ◽  
High Speed ◽  
High Performance ◽  
Third Order ◽  
Transversal Filter ◽  
Multi Wavelength ◽  
Gaussian Input ◽  
On Chip ◽  
The Cost ◽  
Good Agreement

Abstract We propose and experimentally demonstrate a microwave photonic intensity differentiator based on a Kerr optical comb generated by a compact integrated micro-ring resonator (MRR). The on-chip Kerr optical comb, containing a large number of comb lines, serves as a high-performance multi-wavelength source for the transversal filter, which will greatly reduce the cost, size, and complexity of the system. Moreover, owing to the compactness of the integrated MRR, up to 200-GHz frequency spacing of the Kerr optical comb can be achieved, enabling a potential operation bandwidth of over 100 GHz. By programming and shaping individual comb lines according to the calculated tap weights, a reconfigurable intensity differentiator with variable differentiation orders can be realized. The operation principle is theoretically analyzed, and experimental demonstrations of first-order, second-order, and third-order differentiation functions based on the principle are presented. The radio frequency (RF) amplitude and phase responses of multi-order intensity differentiations are characterized, and system demonstrations of real-time differentiations for Gaussian input signal are also performed. The experimental results show good agreement with theory, confirming the effectiveness of our approach.

scholarly journals For RF Signal-Based UAV States Recognition, Is Pre-processing Still Important At The Era Of Deep Learning?

2021 ◽  
Author(s):  
Changhao Ge ◽  
Shubo Yang ◽  
Wenjian Sun ◽  
Yang Luo ◽  
Chunbo Luo
Keyword(s):  
Performance Management ◽  
High Performance ◽  
Control Strategies ◽  
Classification Model ◽  
Emergency Control ◽  
Average Accuracy ◽  
Rf Signals ◽  
State Detection ◽  
Significant Research ◽  
Rf Signal

Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.

The Energy Response of Calorimeters

2018 ◽  
Author(s):  
Richard Wigmans
Keyword(s):  
Sampling Frequency ◽  
Integration Time ◽  
Signal Integration ◽  
Energy Response ◽  
Absorption Process ◽  
Active Materials ◽  
Unit Energy ◽  
Sampling Fraction ◽  
Signal Integration Time ◽  
Calorimeter Response

This chapter deals with the signals produced by particles that are being absorbed in a calorimeter. The calorimeter response is defined as the average signal produced per unit energy deposited in this absorption process, for example in terms of picoCoulombs per GeV. Defined in this way, a linear calorimeter has a constant response. Typically, the response of the calorimeter depends on the type of particle absorbed in it. Also, most calorimeters are non-linear for hadronic shower detection. This is the essence of the so-called non-compensation problem, which has in practice major consequences for the performance of calorimeters. The origins of this problem, and its possible solutions are described. The roles of the sampling fraction, the sampling frequency, the signal integration time and the choice of the absorber and active materials are examined in detail. Important parameters, such as the e/mip and e/h values, are defined and methods to determine their value are described.

scholarly journals Calculating the optimal hematocrit under the constraint of constant cardiac power

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michal Sitina ◽  
Heiko Stark ◽  
Stefan Schuster
Keyword(s):  
Blood Flow ◽  
High Performance ◽  
Animal Species ◽  
Normal Values ◽  
Mechanical Load ◽  
The Body ◽  
Trade Off ◽  
Cardiac Power ◽  
Optimal Value ◽  
Good Agreement

AbstractIn humans and higher animals, a trade-off between sufficiently high erythrocyte concentrations to bind oxygen and sufficiently low blood viscosity to allow rapid blood flow has been achieved during evolution. Optimal hematocrit theory has been successful in predicting hematocrit (HCT) values of about 0.3–0.5, in very good agreement with the normal values observed for humans and many animal species. However, according to those calculations, the optimal value should be independent of the mechanical load of the body. This is in contradiction to the exertional increase in HCT observed in some animals called natural blood dopers and to the illegal practice of blood boosting in high-performance sports. Here, we present a novel calculation to predict the optimal HCT value under the constraint of constant cardiac power and compare it to the optimal value obtained for constant driving pressure. We show that the optimal HCT under constant power ranges from 0.5 to 0.7, in agreement with observed values in natural blood dopers at exertion. We use this result to explain the tendency to better exertional performance at an increased HCT.

scholarly journals A high-fidelity realization of the Euclid code comparison N-body simulation with Abacus

2019 ◽  
Vol 485 (3) ◽  
pp. 3370-3377 ◽  
Author(s):  
Lehman H Garrison ◽  
Daniel J Eisenstein ◽  
Philip A Pinto
Keyword(s):  
Power Spectrum ◽  
High Performance ◽  
High Fidelity ◽  
Step Size ◽  
Time Step ◽  
Code Comparison ◽  
Force Accuracy ◽  
Integration Errors ◽  
Better Than ◽  
Good Agreement

Abstract We present a high-fidelity realization of the cosmological N-body simulation from the Schneider et al. code comparison project. The simulation was performed with our AbacusN-body code, which offers high-force accuracy, high performance, and minimal particle integration errors. The simulation consists of 20483 particles in a $500\ h^{-1}\, \mathrm{Mpc}$ box for a particle mass of $1.2\times 10^9\ h^{-1}\, \mathrm{M}_\odot$ with $10\ h^{-1}\, \mathrm{kpc}$ spline softening. Abacus executed 1052 global time-steps to z = 0 in 107 h on one dual-Xeon, dual-GPU node, for a mean rate of 23 million particles per second per step. We find Abacus is in good agreement with Ramses and Pkdgrav3 and less so with Gadget3. We validate our choice of time-step by halving the step size and find sub-percent differences in the power spectrum and 2PCF at nearly all measured scales, with ${\lt }0.3{{\ \rm per\ cent}}$ errors at $k\lt 10\ \mathrm{Mpc}^{-1}\, h$. On large scales, Abacus reproduces linear theory better than 0.01 per cent. Simulation snapshots are available at http://nbody.rc.fas.harvard.edu/public/S2016.

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