scholarly journals A Single-Step Method for Over-the-Horizon Geolocation Using Importance Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiexin Yin ◽  
Ding Wang ◽  
Bin Yang ◽  
Xin Yang
Keyword(s):  
Importance Sampling ◽  
Low Complexity ◽  
Single Step ◽  
Step Method ◽  
Position Information ◽  
Importance Function ◽  
Localization Performance ◽  
Monte Carlo Importance Sampling ◽  
Simulation Results ◽  
Cramer Rao Bound

This paper investigates the geolocation for an over-the-horizon (OTH) transmitter observed by widely separated arrays. We propose a maximum likelihood (ML) based direct position determination (DPD) method to directly locate the transmitter in a single step by exploiting the position information embedded in azimuth angles. The Monte Carlo importance sampling (IS) technique is employed to find an approximate global solution to this DPD problem, where the importance function analogous to Gaussian distribution is derived. This enables the transmitter to be precisely located with low complexity in a noniterative manner. Additionally, we derive the Cramér–Rao bound (CRB) expression for the investigated problem. The simulation results corroborate the superior localization performance of the proposed method with respect to the conventional two-step approaches and the iterative DPD method.

scholarly journals Reflector-aided direct locations of multiple signals in the presence of small reflector position biases

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877130
Author(s):  
Jiexin Yin ◽  
Ding Wang ◽  
Ying Wu ◽  
Zhidong Wu
Keyword(s):  
Signal To Noise Ratio ◽  
Single Step ◽  
Multiple Sources ◽  
Step Method ◽  
Signal Model ◽  
Multiple Signals ◽  
First Order ◽  
Localization Performance ◽  
Cramer Rao Bound ◽  
Theoretical Results

Direct position determination (DPD) is a single-step method that localizes transmitters from sensor outputs without computing intermediate parameters. It outperforms conventional two-step localization methods, especially under low signal-to-noise ratio conditions. This article proposes a reflector-aided DPD algorithm for multiple signals of known waveforms received by an array observer. In previous studies, reflector-aided localization has always required very precise locations of reflectors. Therefore, the localization performance depends sensitively on accurately knowing each reflector position. This study considers the presence of small biases in reflector locations. To make the problem tractable, we simplify the signal model through an approximation using the first-order Taylor expansion and then directly localize multiple sources in a decoupled manner. Unlike most DPDs that presume noise is spatially uncorrelated, our study imposes no restriction on the correlation structure of noise, allowing this algorithm to be used in more general scenarios. In addition, we derive the Cramér–Rao bound expression and perform an analysis of the direct locations of multiple signals when the reflector positions are assumed accurate but in fact have small biases. Simulation results corroborate the theoretical results and a good localization performance of the proposed algorithm in the presence of small reflector position biases.

Purification of Urokinase from Complex Mixtures Using Immobilized Monoclonal Antibody Against Urokinase Light Chain

1983 ◽  
Vol 49 (01) ◽  
pp. 024-027 ◽  
Author(s):  
David Vetterlein ◽  
Gary J Calton
Keyword(s):  
Monoclonal Antibody ◽  
Light Chain ◽  
Culture Media ◽  
Biological Fluids ◽  
Single Step ◽  
High Yield ◽  
Step Method ◽  
Commercial Preparation ◽  
E Coli ◽  
Tissue Culture Media

SummaryThe preparation of a monoclonal antibody (MAB) against high molecular weight (HMW) urokinase light chain (20,000 Mr) is described. This MAB was immobilized and the resulting immunosorbent was used to isolate urokinase starting with an impure commercial preparation, fresh urine, spent tissue culture media, or E. coli broth without preliminary dialysis or concentration steps. Monospecific antibodies appear to provide a rapid single step method of purifying urokinase, in high yield, from a variety of biological fluids.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

scholarly journals High-resolution limited-angle phase tomography of dense layered objects using deep neural networks

2019 ◽  
Vol 116 (40) ◽  
pp. 19848-19856 ◽  
Author(s):  
Alexandre Goy ◽  
Girish Rughoobur ◽  
Shuai Li ◽  
Kwabena Arthur ◽  
Akintunde I. Akinwande ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Single Step ◽  
Training Set ◽  
Step Method ◽  
3 Dimensional ◽  
Dimensional Reconstruction ◽  
Optical Domain

We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

A Single-Step Method for the Isolation of Antithrombin IIP

Vox Sanguinis ◽  
1984 ◽  
Vol 47 (6) ◽  
pp. 397-405
Author(s):  
Milan Wickerhauser ◽  
Craigenne Williams
Keyword(s):  
Single Step ◽  
Step Method

Single step method for the accurate concentration determination of polysorbate 80

1997 ◽  
Vol 786 (1) ◽  
pp. 99-106 ◽  
Author(s):  
Travis H. Tani ◽  
Jamie M. Moore ◽  
Thomas W. Patapoff
Keyword(s):  
Single Step ◽  
Polysorbate 80 ◽  
Step Method ◽  
Concentration Determination

A unified framework for the design of low-complexity wavelet filters

2017 ◽  
Vol 15 (06) ◽  
pp. 1750054 ◽  
Author(s):  
Ameya K. Naik ◽  
Raghunath S. Holambe
Keyword(s):  
Feature Extraction ◽  
Compact Support ◽  
Low Complexity ◽  
Design Parameters ◽  
Unified Framework ◽  
Vanishing Moments ◽  
Wavelet Filters ◽  
Design Variables ◽  
Optimum Filter ◽  
Simulation Results

An outline is presented for construction of wavelet filters with compact support. Our approach does not require any extensive simulations for obtaining the values of design variables like other methods. A unified framework is proposed for designing halfband polynomials with varying vanishing moments. Optimum filter pairs can then be generated by factorization of the halfband polynomial. Although these optimum wavelets have characteristics close to that of CDF 9/7 (Cohen-Daubechies-Feauveau), a compact support may not be guaranteed. Subsequently, we show that by proper choice of design parameters finite wordlength wavelet construction can be achieved. These hardware friendly wavelets are analyzed for their possible applications in image compression and feature extraction. Simulation results show that the designed wavelets give better performances as compared to standard wavelets. Moreover, the designed wavelets can be implemented with significantly reduced hardware as compared to the existing wavelets.

Sign in / Sign up

Export Citation Format

Share Document