Erratic Signals Research Articles

Ship detection and tracking from single ocean-bottom seismic and hydroacoustic stations.

We report in this study how ocean-bottom seismometers (OBS) can be used as passive sonars to automatically detect, localize, and track moving acoustic sources at the ocean surface. We developed single-station methods based on direction of arrival and on multi-path interference measurements capable of handling continuous erratic signals emitted by ships. Based on a Bayesian mathematical framework, we developed an azimuthal detector and a radial detector and combined them into a fully automatic tracker. We tested the developed algorithm on seismic and hydroacoustic data recorded in the Indian Ocean by an OBS deployed at 4300 m depth, 200 km west of La Réunion Island. We quantified the performances using archives of commercial-vessel trajectories in the area provided by the Automatic Identification System. Detectors demonstrate capabilities in the detection range up to 100 km from the OBS with azimuthal accuracies of a few degrees and with distance accuracies of a few hundred of meters. We expect the method to be easily transposed to any other kind of sources (such as marine mammals).

A Nested Attention Guided UNet++ Architecture for White Matter Hyperintensity Segmentation

White Matter Hyperintensity (WMH) is a common finding in Magnetic Resonance Imaging (MRI) of patients with cerebral infarction and is associated with poor prognosis. Accurate and rapid segmentation of WMH lesions is critical for clinicians to assess the risk of rebleeding and the long-term prognosis of thrombolytic patients. However, segmentation can be challenging due to the erratic signals of WMH in MRI, leading to imprecise results. Deep learning-based approaches have been proposed, but the dice similarity coefficient remains low. Atlas images are navigation maps that integrate various medical information expressions. In this study, we propose a nested attention-guided UNet++ framework that employs attention mechanisms to capture local and global features of WMH lesions using atlas images for segmentation. The framework consists of two modules, the atlas attention module, and the nested attention-guided nested U-Net module. The atlas attention module generates the atlas attention map, which is used as the input for the nested attention-guided nested U-Net module that generates the segmentation map of the FLAIR image. Experimental results demonstrate that the proposed NAUNet++ framework converges faster than conventional UNet and UNet++ approaches. Moreover, the nested architecture enhances recall and f1 scores of the segmentation results compared to the attention-guided approach.

An lp-space matching pursuit algorithm and its application to robust seismic data denoising via time-domain Radon transforms

Sparse solutions of linear systems of equations are essential in many applications of seismic data processing. These systems arise in many denoising algorithms, such as those that use Radon transforms. We have developed a robust matching pursuit (RMP) algorithm for the retrieval of sparse Radon domain coefficients. The algorithm is robust to outliers and, hence, is applicable for seismic data deblending. The classic matching pursuit (MP) algorithm is often adopted to approximate data by a small number of basis functions. It performs effectively for data contaminated with well-behaved, typically Gaussian, random noise. However, MP tends to identify the wrong basis functions when the data are contaminated by erratic noise such as source interference encountered in common-receiver and common-channel gathers of simultaneous source surveys. Incorporating an [Formula: see text] space inner product into the MP algorithm significantly increases its robustness to erratic signals. Deblending experiments with synthetic and field data examples indicate a significant signal-to-noise ratio improvement when one adopts a Radon denoiser computed via our RMP solver. We determine in detail the steps required to implement our [Formula: see text] space RMP algorithm when the basis functions are not given in an explicit form, as is the case with the time-domain Radon transform.

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Classification is presented as a pre-processing step in this study. The state of the subject is classified as the unmoving state with normal breathing (normal breathing class), unmoving state with no breathing (stop breathing class) or the state when the subject is moving (erratic signal class) before breathing estimation algorithms are applied. Estimation algorithms may be applied to obtain breathing rate if normal breathing class is detected or alarms may be generated if stop breathing is detected, and fine-grained classification of activities may be pursued if the erratic signal is detected. Experiments were performed using a single-channel pulse-modulated continuous wave radar with three subjects for a total of 135 min. In each experiment, the subject was continuously monitored for 15 min and the subject performed activities that resulted in a signal that belonged to one of the three classes. Besides extracting a feature that assessed the distribution of energy of the signal in the frequency domain, a novel nonlinear time series feature extraction method based on the higher-dimensional embedding technique was applied to ascertain periodicity of the reflected signal. Bayes classifier was used to classify each 5-s segment of radar returns. A 30-fold cross validation resulted in 97% of overall classification accuracy.

Nanoscale Environment Sensing Scheme with Brownian Nanorod and Plasmon Resonator

Tracking a Brownian particle’s motion allows localized parameters at its immediate vicinity to be measured. In this, the introduction of a rod that is drawn by a tunable attractive force to a cylindrical pillar overcomes the problems of the particle drifting away from the venue of measurement as well as colliding with other particles. With nanoscale particles, fluorescence labeling suffers from photobleaching and erratic signal due to blinking, while monitoring the polarization of scattered light is limited by the accuracy of correlating the rotational state of the rod to intensity changes. Here, we advance having the cylindrical pillar operate as a surface plasmon-based optical resonator to sense the contacts of nanorods. Simulations with a one-dimensional summed difference expression developed to reduce the difficulty of analyzing a three-dimensional dataset comprising wavelength, rod orientation, and gap distance allowed us to confirm distinct changes in transmission at 600 nm across all orientation angles with contact to noncontact or vice versa. This allows application of a cutoff transmission threshold. The metric f, which defines the proportion of incidences when the nanorod moves freely under Brownian motion influence, showed reduction with normalized charge increase. Good linear sensitivity responses were found at specific ranges in the f versus normalized charge relationship, which when correlated with temperature T, showed df/dT to be maximal when the normalized charge product value was −200. From an uncertainty estimation conducted, a restriction to 1 standard deviation variation necessitated only O(10−2) seconds of sampling using standard photodetectors. This portends significant advantages when sensing environments that are changing temporally rapidly.

Security agent allocation to partially observable heterogeneous frontier segments

This article proposes a stochastic attention allocation and reactive scheduling model to prevent illegal border crossings. To intercept infiltrators, a limited pool of security agents is dynamically assigned to heterogeneous frontier segments that transmit erratic signals of crossing attempts by independent trespassers. The frontier segments may differ in terms of rates of crossing attempts, ease of crossing, and reliability of the detection systems. Due to the huge complexity of the agent scheduling decision, a relaxed Markovian model is proposed whose solution is a set of optimal steady-state allocation rates for sending security agents to any frontier segment where a crossing attempt is apparently taking place. This solution is used to derive a heuristic policy for dispatching security agents among the frontier segments based on the evolving signals. Simulation experiments demonstrate that the proposed heuristic outperforms other scheduling policies. Border crossing is just one example of a viable application for this attention allocation model, which can be extended and customized for a wide variety of other scenarios.

Generation of Irregular Disturbances and Solitarylike Waves in Transitional Boundary Layers

The interrelation between erratic signals and large-sized coherent structures excited in boundary layers transitioning to turbulence is investigated. To shed light on the issue of fundamental importance, a new dynamical system has been put forward recently on the basis of simple physical arguments assuming the Reynolds number to tend to infinity. Wave dispersion in the system is defined by the Laplacian of the self-induced pressure resulting from inviscid-inviscid interaction of the intermediate (adjustment) sublayer with the outer oncoming stream. This regime supersedes the triple-deck velocity pattern when the pulsation size attains sufficiently large magnitudes. In the present paper, asymptotic equations governing a three-dimensional disturbance field are expanded into a power series of a small parameter proportional to the magnitude of excess pressure. However, the final closed-form result does not depend on this parameter. The birth of erratic signals typical of transitional boundary-layer flow is demonstrated by computing the two-dimensional disturbance field downstream of a steady obstacle. In accordance with experimental findings, solitarylike coherent structures emerge amid the foam of erratic oscillations surviving the random buffeting by the surrounding fluid. The phase speed of solitarylike waves exceeds the speed of weaker pulsations; therefore, the large-sized waves leave behind the small-sized signals.

Scale-dependence of phylogenetic signal in ecological traits of ectoparasites

The non-independence of traits among closely related species is a well-documented phenomenon underpinning modern methods for comparative analyses or prediction of trait values in new species. Surprisingly such studies have mainly focused on life-history or morphological traits of free-living organisms, ignoring ecological attributes of parasite species in spite of the fact that they are critical for conservation and human health. We tested for a phylogenetic signal acting on two ecological traits, abundance and host specificity, using data for 218 flea species parasitic on small mammals in 19 regions of the Palaearctic and Nearctic, and a phylogenetic tree for these species. We tested for the presence of a phylogenetic signal at both regional and continental scales using three measures (Abouheif/Moran's I, Pagel's λ, and Blomberg et al.'s K). Our results show 1) a consistent positive phylogenetic signal for flea abundance, but only a weaker and erratic signal for host specificity, and 2) a clear dependence on scale, with the signals being stronger at the continental scale and relatively weaker or inconsistent at the regional scale. Whenever values of Blomberg et al.'s K were found significant, they were < 1 suggesting that the effects of phylogeny on the evolution of abundance and host specificity in fleas are weaker than expected from a Brownian motion model. The most striking finding is that, within a continental fauna, closely-related flea species are characterized by similar levels of abundance, though this pattern is weaker within local assemblages, possibly eroded by local biotic or abiotic conditions. We discuss the link between history (represented by phylogeny) and pattern of variation among species in morphological and ecological traits, and use comparisons between the Palaearctic and Nearctic to infer a role of historical events in the probability of detecting phylogenetic signals.

Neural network model for global alignment incorporating wafer and stage distortion

One of the most crucial emerging challenges in lithography is achieving rapid and accurate alignment under a wide variety of conditions brought about by the different overlying films occluding the marks. The problem is exacerbated by planarizing processes such as chemical mechanical polishing that reduce the topographical contrast used to view the marks and by distortion of the wafer and of the stage. Thus, an effective learning process is needed to rapidly acquire the best possible positional information from an array of the marks across the wafer. In this article, a neural network model for global alignment is described. The wafer and stage distortions can be incorporated into the model. The algorithm finds the best fit for the wafer distortion and at the same time compensates for both stage and wafer distortion. The algorithm can also learn to identify and ignore any alignment marks that yield significantly erratic signals. A few common distortion functions will also be used to test the model. Preliminary simulation results show alignment errors &amp;lt;5 nm in the presence of Gaussian noise with σ=30 and 500 nm sinusoidal stage distortion. Also the simulations show that increasing the magnitude of stage distortion has no impact on the results. It will be shown that considering wafer distortion can reduce the alignment error more than 10 times in the case of bowed and distorted wafers.

Electromyography of the opercularis muscle of Rana catesbeiana: an amphibian tonic muscle.

The opercularis muscle of Rana catesbeiana originates on the suprascapular cartilage of the shoulder girdle and inserts on the otic opercular element. It is part of the levator scapulae musculature and lies dorsomedial to the levator scapulae superior and inferior muscles. Bipolar electrode recordings from all three muscles show electrical activity linked to cyclical firing of the posterior intermandibularis muscle, an important ventilatory muscle. The opercularis muscle shows low amplitude, erratic signals when animals are submerged. Upon emergence of the snout region, the opercularis muscle shows rhythmic low amplitude activity at twice the rate of buccal pumping. Lung ventilation is synchronized with this rhythm and at ventilation the opercularis muscle shows higher amplitude activity. Upon submergence, opercularis activity again shows low level activity with no rhythmic pattern. Opercularis muscle activity has a major low frequency component (about 30 Hz) that probably corresponds to activity of tonic muscle fibers. Higher frequency signals (about 200-250 Hz) comparable to those of the levator scapulae muscles are also present and probably represent activity of phasic muscle fibers. Activity of the opercularis muscle is correlated with conditions in which aerial respiration is possible, and this pattern of activity supports an opercularis role in aerial hearing and/or detection of substrate vibrations. As far as we know, this is the first report of electromyographic analysis of a vertebrate tonic muscle.