scholarly journals On some common compressive sensing recovery algorithms and applications

2017 ◽  
Vol 30 (4) ◽  
pp. 477-510 ◽  
Author(s):  
Andjela Draganic ◽  
Irena Orovic ◽  
Srdjan Stankovic
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Practical Applications ◽  
Sensing Applications ◽  
Different Types ◽  
Acquisition Strategy ◽  
Basic Ideas ◽  
Significant Attention ◽  
Intensive Growth

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals On full spark frames via Cauchy matrices

2021 ◽  
Author(s):  
Dongwei Li
Keyword(s):  
Signal Processing ◽  
Polynomial Time ◽  
Phase Retrieval ◽  
Signal Reconstruction ◽  
Sparse Signal ◽  
Tight Frames ◽  
Practical Applications ◽  
Vandermonde Matrices ◽  
Sparse Signal Processing ◽  
Processing Signal

Full spark frames have been widely applied in sparse signal processing, signal reconstruction with erasures and phase retrieval. Since testing whether a given frame is full spark is hard for NP under randomized polynomial-time reductions, hence the deterministic full spark (DFS) frames are particularly significant. However, the degree of freedom of choices of DFS frames is not enough in practical applications because the DFS frames are well known as Vandermonde frames and harmonic frames. In this paper, we focus on the deterministic constructions of full spark frames. We present a new and effective method to construct DFS frames by using Cauchy matrices. We also construct the DFS frames by using Cauchy-Vandermonde matrices. Finally, we show that full spark tight frames can be constructed from generalized Cauchy matrices.

scholarly journals Compressive Sensing Approach in the Hermite Transform Domain

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Srdjan Stanković ◽  
Ljubiša Stanković ◽  
Irena Orović
Keyword(s):  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Transform Domain ◽  
Numerical Examples ◽  
Hermite Transform ◽  
Sensing Applications ◽  
Random Samples ◽  
Significant Interest ◽  
Small Set ◽  
Short Time

Compressive sensing has attracted significant interest of researchers providing an alternative way to sample and reconstruct the signals. This approach allows us to recover the entire signal from just a small set of random samples, whenever the signal is sparse in certain transform domain. Therefore, exploring the possibilities of using different transform basis is an important task, needed to extend the field of compressive sensing applications. In this paper, a compressive sensing approach based on the Hermite transform is proposed. The Hermite transform by itself provides compressed signal representation based on a smaller number of Hermite coefficients compared to the signal length. Here, it is shown that, for a wide class of signals characterized by sparsity in the Hermite domain, accurate signal reconstruction can be achieved even if incomplete set of measurements is used. Advantages of the proposed method are demonstrated on numerical examples. The presented concept is generalized for the short-time Hermite transform and combined transform.

Essentially non-oscillatory and weighted essentially non-oscillatory schemes

Acta Numerica ◽  
2020 ◽  
Vol 29 ◽  
pp. 701-762
Author(s):  
Chi-Wang Shu
Keyword(s):  
Main Idea ◽  
High Order Accuracy ◽  
Finite Difference Schemes ◽  
Approximation Procedure ◽  
Discontinuous Solutions ◽  
Weno Schemes ◽  
Convection Diffusion ◽  
Different Types ◽  
Basic Ideas ◽  
Weighted Eno

Essentially non-oscillatory (ENO) and weighted ENO (WENO) schemes were designed for solving hyperbolic and convection–diffusion equations with possibly discontinuous solutions or solutions with sharp gradient regions. The main idea of ENO and WENO schemes is actually an approximation procedure, aimed at achieving arbitrarily high-order accuracy in smooth regions and resolving shocks or other discontinuities sharply and in an essentially non-oscillatory fashion. Both finite volume and finite difference schemes have been designed using the ENO or WENO procedure, and these schemes are very popular in applications, most noticeably in computational fluid dynamics but also in other areas of computational physics and engineering. Since the main idea of the ENO and WENO schemes is an approximation procedure not directly related to partial differential equations (PDEs), ENO and WENO schemes also have non-PDE applications. In this paper we will survey the basic ideas behind ENO and WENO schemes, discuss their properties, and present examples of their applications to different types of PDEs as well as to non-PDE problems.

scholarly journals A Novel Cobalt Metallopolymer with Redox-Matched Conjugated Organic Backbone via Electropolymerization of a Readily Available N4 Cobalt Complex

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1667
Author(s):  
Mikhail Karushev
Keyword(s):  
Redox Reactions ◽  
Repeat Unit ◽  
Cobalt Complex ◽  
Radical Cations ◽  
Electrochemical Quartz Crystal Microbalance ◽  
Practical Applications ◽  
Sensing Applications ◽  
Novel Approach ◽  
Redox Switching

Fast and reversible cobalt-centered redox reactions in metallopolymers are the key to using these materials in energy storage, electrocatalytic, and sensing applications. Metal-centered electrochemical activity can be enhanced via redox matching of the conjugated organic backbone and cobalt centers. In this study, we present a novel approach to redox matching via modification of the cobalt coordination site: a conductive electrochemically active polymer was electro-synthesized from [Co(Amben)] complex (Amben = N,N′-bis(o-aminobenzylidene)ethylenediamine) for the first time. The poly-[Co(Amben)] films were investigated by cyclic voltammetry, electrochemical quartz crystal microbalance (EQCM), in situ UV‑vis-NIR spectroelectrochemistry, and in situ conductance measurements between −0.9 and 1.3 V vs. Ag/Ag+. The polymer displayed multistep redox processes involving reversible transfer of the total of 1.25 electrons per repeat unit. The findings indicate consecutive formation of three redox states during reversible electrochemical oxidation of the polymer film, which were identified as benzidine radical cations, Co(III) ions, and benzidine di-cations. The Co(II)/Co(III) redox switching is retained in the thick polymer films because it occurs at potentials of high polymer conductivity due to the optimum redox matching of the Co(II)/Co(III) redox pair with the organic conjugated backbone. It makes poly-[Co(Amben)] suitable for various practical applications based on cobalt-mediated redox reactions.

scholarly journals Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 116 ◽  
Author(s):  
Alessandro Matese ◽  
Salvatore Di Gennaro
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Practical Applications ◽  
Water Stress Index ◽  
Sensing Applications ◽  
Wide Range ◽  
Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

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