scholarly journals On Bandlimited Signal Reconstruction From the Distribution of Unknown Sampling Locations

2015 ◽  
Vol 63 (5) ◽  
pp. 1259-1267 ◽  
Author(s):  
Animesh Kumar
Keyword(s):  
Signal Reconstruction ◽  
Sampling Locations ◽  
Bandlimited Signal

Distribution and Contamination Status of Heavy Metals in the Surface Sediments along Western Coast of Bali Strait, Banyuwangi

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Defri Yona ◽  
Syarifah Hikmah Julinda Sari ◽  
Anedathama Kretarta ◽  
Citra Ravena Putri Effendy ◽  
Misba Nur Aini ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Contamination Factor ◽  
Pollution Index ◽  
Western Coast ◽  
Sampling Locations ◽  
Accumulation Index ◽  
Geo Accumulation Index ◽  
Geo Accumulation

This study attempted to analyze the distribution and contamination status of heavy metals (Cu, Fe and Zn) along western coast of Bali Strait in Banyuwangi, East Java. Bali Strait is one of the many straits in Indonesia with high fisheries activities that could potentially contributed to high heavy metal pollution. There were five sampling areas from the north to south: Pantai Watu Dodol, Pantai Kalipuro, Ketapang Port, Pantai Boom and Muncar as the fish landing area. Heavy metal pollution in these locations comes from many different activities such as tourism, fish capture and fish industry and also domestic activities. Contamination factor (CF), geo-accumulation index (Igeo) and enrichment factor (EF) of each heavy metal were calculated to obtain contamination status of the research area. The concentrations of Fe were observed the highest (1.5-129.9 mg/kg) followed by Zn (13.2-23.5 mg/kg) and Cu (2.2-7.8 mg/kg). The distribution of Cu, Fe and Zn showed variability among the sampling locations in which high concentrations of Cu and Zn were higher in Ketapang Port, whereas high concentration of Fe was high in almost all sampling locations. According to the pollution index, contamination factors of Cu, Fe and Zn were low (CF < 1 and Igeo < 1). However, high index of EF (> 50) showed high influence of the anthropogenic activities to the contribution of the metals to the environment. This could also because of the high background value used in the calculation of the index due to the difficulties in finding background value from the sampling areas.Keywords: heavy metals, pollution index, contamination factor, geo-accumulation index, Bali Strait

An assessment of toxic metals content in the marine sediments of the Shuaiba Industrial Area, Kuwait, after the oil spill during the Gulf War

1996 ◽  
Vol 34 (7-8) ◽  
pp. 203-210 ◽  
Author(s):  
S. Al-Muzaini ◽  
P. G. Jacob
Keyword(s):  
Oil Spill ◽  
Marine Sediment ◽  
Oil Spills ◽  
Industrial Area ◽  
Gulf War ◽  
Oil Wells ◽  
Environmental Damage ◽  
Base Line ◽  
Sampling Locations ◽  
Very High

A field study was carried out involving seven fixed sampling stations. The sampling locations were selected to cover the distribution of pollutants in the Shuaiba Industrial Area (SIA), which was contaminated with oil released from oil wells and broken pipelines and with a vast amount of burnt and unburnt crude oil from the burning and gushing oil wells. The samples were collected biweekly between July 1993 and July 1994. The concentrations of V, Ni, Cr, Cd and Pb were determined and compared with the previously collected baseline data to assess the degree of environmental damage caused due to the oil spills during the Gulf war. The average concentrations (mg/kg) of various elements in the marine sediment were 17.3 for V, 30.8 for Ni, 55.5 for Cr, 0.02 for Cd and 1.95 for Pb. Our results show that even after the heavy spillage of oil, associated metal concentrations were not very high compared with previously reported base line values.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

2019 ◽  
Vol 15 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Mei Sun ◽  
Jinxu Tao ◽  
Zhongfu Ye ◽  
Bensheng Qiu ◽  
Jinzhang Xu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Low Rank ◽  
Mr Images ◽  
Scanning Time ◽  
Previous State ◽  
Rank Approximation ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Rank Constraints

Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction. </P><P> Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction. Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation. </P><P> Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

Virtual array interpolation for coprime arrays: a signal reconstruction perspective

2020 ◽  
Vol 56 (22) ◽  
pp. 1213-1215
Author(s):  
Zhen Chen ◽  
Chongyi Fan ◽  
Xiaotao Huang
Keyword(s):  
Signal Reconstruction ◽  
Virtual Array ◽  
Coprime Arrays
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