Terms Of Signal Recovery Research Articles

The 2020 Hpakant Jade Mine Disaster, Myanmar: A multi-sensor investigation for slope failure

A quarry failure along the slopes of the Wai Khar open-pit jade mine in Hpakant, Myanmar has led to the deaths of at least 172 jade miners on 2 July 2020. This paper conducts a systematic investigation of the incident by integrating data from multiple sensors, including high-resolution optical imagery, Sentinel-1 synthetic aperture radar (SAR) images, unmanned aerial system (UAS) footage, SRTM and ALOS digital elevation models (DEMs), soil moisture product from multi-spectral Landsat-8 satellite and precipitation records from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Optical imagery, UAS footage and DEMs allow us to build a comprehensive mapping of tailing areas and quarry scarps from 2010 and reconstruct the 2D pit geometry prior to failure. Deformation signals from multi-temporal SAR interferometry (MTInSAR), soil moisture variations and precipitation trends further allow us to identify possible failure causes. To evaluate the quality of deformation obtained from different distributed-scatterer phase estimators, we develop an empirical mapping function based on areal fraction values to facilitate the comparison of temporal coherence values that are differently formulated in each phase estimator. The comparison shows that phase linking algorithm outperforms the small baseline subset method in terms of signal recovery and phase reliability. Our investigation points out that the mining site is under aggressive mining cycles that are exacerbated by frequent, uncontrolled landslides. Seepage failure, which involves the expulsion of water from rapidly compacting tailings, may be a critical factor in the 2020 incident. Instead of extreme weather, the failure had occurred under normal to drier conditions. This means that the sliding planes were already in a critical state, which is evident from the accelerated deformation around the collapse area since the beginning of 2020. Based on these findings, we provide recommendations to improve mining site regulations and management practices for safer open-pit mining in Myanmar and probably in similar contexts outside Myanmar.

Pleiotropic mapping for genome-wide association studies using group variable selection

Introduction Results from genome-wide association studies (GWAS) suggest that complex diseases are often affected by many variants with small effects, known as polygenicity. Bayesian methods provide attractive tools for identifying signal in data where the effects are small but clustered. For example, by incorporating biological pathway membership in the prior they are able to integrate the ideas of gene set enrichment to identify groups of biologically significant genetic variants. Accumulating evidence suggests that genetic variants may affect multiple different complex diseases, a phenomenon known as pleiotropy. Method In this work we propose frequenstist and Bayesian statistical method to leverage pleiotropic effects and incorporate prior pathway knowledge to increase statistical power and identify important risk variants. We offer novel feature selection methods for the group variable selection in multi-task regression problem. We develop methods using both penalised likelihood methods and Bayesian spike and slab priors to induce structured sparsity at a gene and SNP level. We implement Gibbs sampling algorithms for the Bayesian analysis and an alternating direction method of multipliers (ADMM) algorithm for the penalised regression methods. The performances of the proposed approaches are compared to state-of-the-art variable selection strategies on simulated data sets. Results The penalised likelihood approaches are computationally efficient using alternating direction method of multipliers algorithm. These approaches perform reasonably well in variable selection but the reconstructed signal is underestimated. The multivariate Bayesian sparse group selection with spike and slab prior performed the best in terms of signal recovery. The Bayesian method provides a natural method for quantifying the variability of the estimated coefficients. Simulation results suggest that when computationally possible the Bayesian estimators should be used. Conclusion The developed statistical approaches is applied for enriching our insights about the genetic mechanisms of thyroid and breast cancer types. The analysed data come from case-control studies including 6677 SNPs from 618 genes from 10 non-overlapping gene pathways. The thyroid cancer data set includes 482 cases and 463 controls. The breast cancer data set includes 1172 cases and 1125 controls.

WINDOW: wideband demodulator for optical waveforms.

Optical communication systems, which operate at very high rates, are often limited by the sampling rate bottleneck. The optical wideband regime may exceed analog to digital converters (ADCs) front-end bandwidth. Multi-channel sampling approaches, such as interleaved ADCs, also known as multicoset sampling in some contexts, have been proposed to sample the wideband signal using several channels. Each channel samples below the Nyquist rate such that the overall sampling rate is preserved. However, this scheme suffers from two practical limitations that make its implementation difficult. First, the inherent anti-aliasing filter of the samplers distorts the wideband signal. Second, it requires accurate time shifts on the order of the signal's Nyquist rate, which are challenging to maintain. In this work, we propose an alternative multi-channel sampling scheme, the wideband demodulator for optical waveforms (WINDOW), based on analog RF demodulation, where each channel aliases the spectrum using a periodic mixing function before integration and sampling. We show that intentionally using the inherent ADC filter to perform integration increases the signal to noise ratio (SNR). We demonstrate both theoretically and through numerical experiments that our system outperforms interleaved sampling in terms of signal recovery and symbol estimation in the presence of both thermal and quantization noise but is slightly less robust to timing jitter. The main contribution of this work is the application of RF demodulation concepts proposed in the context of sub-Nyquist sampling, e.g. random demodulator and modulated wideband converter, to optical communication signals in the Nyquist regime. We develop a sampling scheme that presents an alternative for optical links where thermal noise in the receiver is the bottleneck.

Fully automatic gain control beamformer for smart antenna array system

Purpose The aim of this investigation is to make the Bessel beamformer fully automatic when it is combined with Root MUSIC (MUltiple SIgnal Classification) and is based on the array processing in the application of beamforming and Direction of Arrival (DOA) algorithm for source position. The concept is analyzed for modified Bessel beamformer with Root MUSIC to form a non-blind array processing technique which is then used to focus a beam towards a desired user and place nulls towards interferers for capacity improvement. Design/methodology/approach The idea is manipulated for modified Bessel beamformer with Root MUSIC relies on MATLAB software with the goal to obtain high efficiency in terms of signal recovery, directive gain by minimizing MSE and place nulls towards interferers. Findings The finding of the analysis is presented in adaptive environment and provided in the tables. The paper shows that the presented method leads to superior results and can be used as a better alternative in many practical applications to avoid operator involvement. Research limitations/implications The adaptive processes described in the paper are still limited to simulation due to non availability of real system for testing. Practical implications The paper can be extended to a next level by implementing the proposed algorithms in the real time environment using FPGA technology. Originality/value This is our original work that can offer scientific contribution to signal processing community and may provide better results as an automatic beamformer.

Design and performance analysis of live model of Bessel beamformer for adaptive array system

Purpose – The purpose of this paper is to design and analyze the performance of live model of Bessel beamformer for thorough comprehension of beamforming in adaptive environment and compared with live model of least mean square (LMS) in terms of gain and mean square error (MSE). It presents the principal elements of communication system. The performance of designed live model is tested for its efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format into the MATLAB workspace. These adaptive techniques are illustrated by appropriate examples. Design/methodology/approach – The proposed algorithm framework relies on MATLAB software with the goal to obtain high efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format. It is assumed that this audio signal is only the message or the baseband signal received by the computer. Here the authors consider computer (laptop) as a base station containing adaptive signal processing algorithm and source (mobile phone) as a desired user, so the experiment setup is designed for uplink application (user to base station) to differentiate between desired signal, multipath and interfering signals as well as to calculate their directions of arrival. Findings – The presented adaptive live model is reliable, robust and lead to a substantial reduction of MSE, signal recovery in comparison with the LMS technique. The paper contains experimental data. Obtained results are presented clearly and the conclusion comes directly from the presented experimental data. The paper shows that the presented method leads to superior results in comparison with the popular LMS method and can be used as a better alternative in many practical applications. Research limitations/implications – The adaptive processes described in the paper are still limited to simulation. It is because of the non-availability of real system for testing, therefore chosen research approach that is platform of MATLAB is opted for simulation. Therefore, researchers are encouraged to test the proposed algorithms on real system if possible. Practical implications – The paper contains experimental data. The paper's impact on the society is acceptable. These implications are consistent with the findings and the conclusions of the paper. However, there is a need to extend this paper to a next level by implementing the proposed algorithms in the real time environment using FPGA technology. Social implications – This research will improve the signal quality of wireless cellular system by increasing capacity and will reduce the total cost of the system so that cost toward subscribers be decreased. Originality/value – The live model presented in this paper is shown to provide better results. It is the original work and can provide scientific contribution to signal processing community.

Statistical estimation for hyper shrinkage

A new shrinkage technique in wavelet domain called hyper shrinkage that uses hyperbolic function for improved denoising is explained. The methodology is statistically significant in terms of signal recovery and improving signal-to-noise ratio over both hard and soft shrinkage. A mathematical treatment of proposed shrinkage function shows an improvement over the existing shrinkage function in terms of variance and bias. A simulation of mathematical model shows effectiveness of the proposed shrinkage function.