An Adaptive Offset-Tracking Method Based on Deformation Gradients and Image Noises for Mining Deformation Monitoring

The offset-tracking method (OTM) utilizing SAR image intensity can detect large deformations, which makes up for the inability of interferometric synthetic aperture radar (InSAR) technology in large mining deformation monitoring, and has been widely used. Through lots of simulation experiments, it was found that the accuracy of OTM is associated with deformation gradients and image noises in the cross-correlation window (CCW), so CCW sizes should be selected reasonably according to deformation gradients and noise levels. Based on the above conclusions, this paper proposes an adaptive CCW selection method based on deformation gradients and image noises for mining deformation monitoring, and this method considers influences of deformation gradients and image noises on deformations to select adaptive CCWs. In consideration of noise influences on offset-tracking results, smaller CCWs are selected for large deformation gradient areas, and larger CCWs are selected for small deformation gradient areas. For some special areas, special CCWs are selected for offset-tracking. The proposed method is implemented to simulation and real experiments, and the experiment results demonstrate that the proposed method with high reliability and effectiveness can significantly improve the accuracy of OTM in mining deformation monitoring.

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible.

Intrinsic neural timescales related to sensory processing: Evidence from abnormal behavioural states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioural states. We address these questions by investigating the brain's intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anaesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity's intrinsic neural timescale is related to specifically sensory rather than motor information processing in the healthy brain.

Simplified Bounds on FHSs Set and Its Strictly Optimal Construction

Frequency-hopping sequences (FHSs) with favorite partial Hamming correlation properties are intensively needed in many synchronization and multiple-access systems. Strictly optimal FHS sets are a kind of FHS sets which has optimal Hamming correlation for any correlation window. In this paper, firstly we present simplified representations of the generalized Lempel–Greenberger bound on the partial Hamming autocorrelation of an FHS and the generalized Peng-Fan bound on the partial Hamming correlation of an FHS set, respectively. Secondly, we propose a direct construction of strictly optimal FHS sets, which interprets the previous construction proposed by Cai, Zhou, Yang and Tang. By choosing appropriate parameters and bijections, we present more flexible constructions of strictly optimal FHS sets.

MAPPING VELOCITY OF THE POTSDAM GLACIER, EAST ANTARCTICA USING LANDSAT-8 DATA

<p><strong>Abstract.</strong> Most of the glaciers have been retreating and thinning globally due to climate change. Glacier velocity is one such important parameter of glacier dynamics, which helps to understand the mass balance. The variations in velocity at different areas of the glacier can be used to identify the zones of ablation and accumulation. Zones of accumulation are identified as areas with higher velocity. This data is useful to incorporate in the glacier mass balance analysis. This study aims to derive the glacier velocity, using feature tracking technique for Potsdam glacier, east Antarctica. Feature tracking is an efficient way to derive glacier velocity, which is based on a cross-correlation algorithm that seeks offsets of the maximal correlation window on repeated satellite images. In this technique, two temporally different images are acquired for the same area and a distinct feature on both images is identified and the velocity is calculated with respect to the movement of that particular feature from one image to the other. Landsat-8 data for the year 2016 was used to derive velocity. Finer resolution promotes better feature tracking so the panchromatic band (band 8) of Landsat-8 OLI with a resolution of 15&amp;thinsp;m was utilized for deriving velocity. This technique was performed using COSI-Corr module in ENVI. This tool calculates displacement between the east-west and north-south directions, and the resultant velocity is calculated using the displacement in both directions and the temporal difference of two images. The velocity map generated at a resolution of 240&amp;thinsp;m showed that the resultant velocity ranged between 18.60 and 285.28&amp;thinsp;ma^&amp;minus;1. Bias and root mean square error (RMSE) have been calculated with respect to the point-by-point MEaSUREs data provided by National Snow and Ice Data Centre at 1000&amp;thinsp;m resolution. The RMSE was found to be 78.06&amp;thinsp;ma^&amp;minus;1 for 2016. The velocity for Potsdam glacier was also pictorially validated with the DGPS measurements from literature.</p>

How much averaging is necessary to cancel out cross-terms in noise correlation studies?

We present an analytical approach to jointly estimate the correlation window length and number of correlograms to stack in ambient noise correlation studies to statistically ensure that noise cross-terms cancel out to within a chosen threshold. These estimates provide the minimum amount of data necessary to extract coherent signals in ambient noise studies using noise sequences filtered in a given frequency bandwidth. The inputs for the estimation process are (1) the variance of the cross-correlation energy density calculated over an elementary time length equal to the largest period present in the filtered data and (2) the threshold below which the noise cross-terms will be in the final stacked correlograms. The presented theory explains how to adjust the required correlation window length and number of stacks when changing from one frequency bandwidth to another. In addition, this theory provides a simple way to monitor stationarity in the noise. The validity of the deduced expressions have been confirmed with numerical cross-correlation tests using both synthetic and field data.