Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion

2021 ◽  
Vol 36 (2) ◽  
pp. 190-198
Author(s):  
Samuel Kingston ◽  
Hunter Ellis ◽  
Mashad Saleh ◽  
Evan Benoit ◽  
Ayobami Edun ◽  
...  

In this paper, we present a method for estimating complex impedances using reflectometry and a modified steepest descent inversion algorithm. We simulate spread spectrum time domain reflectometry (SSTDR), which can measure complex impedances on energized systems for an experimental setup with resistive and capacitive loads. A parametric function, which includes both a misfit function and stabilizer function, is created. The misfit function is a least squares estimate of how close the model data matches observed data. The stabilizer function prevents the steepest descent algorithm from becoming unstable and diverging. Steepest descent iteratively identifies the model parameters that minimize the parametric function. We validate the algorithm by correctly identifying the model parameters (capacitance and resistance) associated with simulated SSTDR data, with added 3 dB white Gaussian noise. With the stabilizer function, the steepest descent algorithm estimates of the model parameters are bounded within a specified range. The errors for capacitance (220pF to 820pF) and resistance (50 Ω to 270 Ω) are < 10%, corresponding to a complex impedance magnitude |R +1/jωC| of 53 Ω to 510 Ω.

2019 ◽  
Vol 3 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Naveen Kumar Tumkur Jayakumar ◽  
Evan Benoit ◽  
Samuel Kingston ◽  
Mashad Uddin Saleh ◽  
Michael Scarpulla ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guillermo Cabrera-Guerrero ◽  
Nibaldo Rodriguez ◽  
Carolina Lagos ◽  
Enrique Cabrera ◽  
Franklin Johnson

One important problem in radiation therapy for cancer treatment is the selection of the set of beam angles radiation will be delivered from. A primary goal in this problem is to find a beam angle configuration (BAC) that leads to a clinically acceptable treatment plan. Further, this process must be done within clinically acceptable times. Since the problem of selecting beam angles in radiation therapy is known to be extremely hard to solve as well as time-consuming, both exact algorithms and population-based heuristics might not be suitable to solve this problem. In this paper, we compare two matheuristic methods based on local search algorithms, to approximately solve the beam angle optimisation problem (BAO). Although the steepest descent algorithm is able to find locally optimal BACs for the BAO problem, it takes too long before convergence, which is not acceptable in clinical practice. Thus, we propose to use a next descent algorithm that converges quickly to good quality solutions although no (local) optimality guarantee is given. We apply our two matheuristic methods on a prostate case which considers two organs at risk, namely, the rectum and the bladder. Results show that the matheuristic algorithm based on the next descent local search is able to quickly find solutions as good as the ones found by the steepest descent algorithm.


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