scholarly journals Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

2019 ◽  
Vol 9 (21) ◽  
pp. 4596 ◽  
Author(s):  
Tongjing Sun ◽  
Ji Li ◽  
Philippe Blondel
Keyword(s):  
Compressive Sensing ◽  
A Priori ◽  
Original Data ◽  
Measurement Matrix ◽  
Critical Elements ◽  
Under Sampling ◽  
Information Sampling ◽  
Priori Information ◽  
Practical Field ◽  
Dedicated Hardware

Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

scholarly journals Influence of low spatial resolution a priori data on tropospheric NO<sub>2</sub> satellite retrievals

2011 ◽  
Vol 4 (9) ◽  
pp. 1805-1820 ◽  
Author(s):  
A. Heckel ◽  
S.-W. Kim ◽  
G. J. Frost ◽  
A. Richter ◽  
M. Trainer ◽  
...  
Keyword(s):  
High Resolution ◽  
San Francisco ◽  
Spatial Resolution ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Under Sampling ◽  
Priori Information

Abstract. The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2 if all a priori data used is at the coarsest resolution) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.

scholarly journals Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach

2021 ◽  
Vol 7 (11) ◽  
pp. 247
Author(s):  
Marco Salucci ◽  
Nicola Anselmi
Keyword(s):  
Compressive Sensing ◽  
Quantitative Imaging ◽  
A Priori ◽  
Wide Band ◽  
Frequency Diversity ◽  
A Priori Information ◽  
Spectral Components ◽  
Priori Information ◽  
The One ◽  
Ground Penetrating

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.

scholarly journals VISUAL ANALYSIS AND PROCESSING OF CLUSTERS STRUCTURES IN MULTIDIMENSIONAL DATASETS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 151-154 ◽  
Author(s):  
A. E. Bondarev
Keyword(s):  
Visual Analysis ◽  
A Priori ◽  
Cluster Structure ◽  
Original Data ◽  
Medical Data ◽  
A Priori Information ◽  
Multidimensional Datasets ◽  
Data Volume ◽  
Data Points ◽  
Priori Information

The article is devoted to problems of visual analysis of clusters structures for a multidimensional datasets. For visual analyzing an approach of elastic maps design [1,2] is applied. This approach is quite suitable for processing and visualizing of multidimensional datasets. To analyze clusters in original data volume the elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the cluster structure of multidimensional dataset. The approach of elastic maps does not require any a priori information about data in question and does not depend on data nature, data origin, etc. Elastic maps are usually combined with PCA approach. Being presented in the space based on three first principal components the elastic maps provide quite good results. The article describes the results of elastic maps approach application to visual analysis of clusters for different multidimensional datasets including medical data.

Forensic Watermarking for Secure Multimedia Distribution

2009 ◽  
pp. 261-280
Author(s):  
Farook Sattar ◽  
Dan Yu
Keyword(s):  
Digital Watermarking ◽  
Side Information ◽  
A Priori ◽  
Original Data ◽  
Watermark Embedding ◽  
Multimedia Distribution ◽  
Extraction Scheme ◽  
Priori Information ◽  
Common Signal ◽  
Ownership Verification

This chapter discusses on forensic tracking through digital watermarking for secure multimedia distribution. The existing watermarking schemes are elaborated and their assumptions as well as limitations for tracking are discussed. Especially, an Independent Component Analysis (ICA) based watermarking scheme is presented, which overcomes the problems of the existing watermarking schemes. Multiple watermarking techniques are used where one watermark is used for ownership verification and the other one is used to identify the legal user of the distributed content. In the absence of a priori information, i.e. the original data, original watermark, embedding locations as well as the strength, our ICA technique provides efficient watermark extraction scheme with the help of side information. The robustness against common signal processing attacks is presented. Lastly, the challenges in the forensic tracking through digital watermarking techniques are discussed.

scholarly journals Influence of under-sampled a priori data on tropospheric NO<sub>2</sub> satellite retrievals

2011 ◽  
Vol 4 (2) ◽  
pp. 1893-1934 ◽  
Author(s):  
A. Heckel ◽  
S.-W. Kim ◽  
G. J. Frost ◽  
A. Richter ◽  
M. Trainer ◽  
...  
Keyword(s):  
High Resolution ◽  
San Francisco ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Under Sampling ◽  
Global Chemical Transport Model ◽  
Priori Information

Abstract. The retrieval of tropospheric columns of NO2 and other trace gases from satellite observations of backscattered solar radiation relies on the use of accurate a priori information. The spatial resolution of current space sensors is often significantly higher than that of the a priori datasets used, introducing uncertainties from spatial misrepresentation. In this study, the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns was studied for a typical coastal area (around San Francisco). High-resolution (15 × 15 km2) NO2 a priori data from the WRF-Chem model in combination with high-resolution MODIS surface reflectance and aerosol data were used to investigate the uncertainty introduced by applying a priori data at typical global chemical transport model resolution. The results show that the relative uncertainties can be large (more than a factor of 2) for individual measurements, mainly due to spatial variations in NO2 profile and surface albedo, with smaller contributions from aerosols and surface height changes. Similar sensitivities are expected for other coastal regions and localised sources such as power plants, highlighting the need for high-resolution a priori data in quantitative analysis of the spatial patterns retrieved from satellite observations of tropospheric pollution.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

Sign in / Sign up

Export Citation Format

Share Document