scholarly journals Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

2022 ◽  
Vol 72 (1) ◽  
pp. 122-132
Author(s):  
Remadevi M. ◽  
N. Sureshkumar ◽  
R. Rajesh ◽  
T. Santhanakrishnan
Keyword(s):  
Long Range ◽  
Simulated Data ◽  
Interference Signal ◽  
Spectral Leakage ◽  
Low Snr ◽  
Eigenvector Analysis ◽  
Spatio Temporal ◽  
Ocean Environment ◽  
Space Time Adaptive Processing

Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets.

scholarly journals Supervised linear classification of Gaussian spatio-temporal data

2021 ◽  
Vol 62 ◽  
pp. 9-15
Author(s):  
Marta Karaliutė ◽  
Kęstutis Dučinskas
Keyword(s):  
Time Moment ◽  
Gaussian Random Field ◽  
Covariance Structure ◽  
Simulated Data ◽  
Spatial Location ◽  
Error Rates ◽  
Prior Probabilities ◽  
Structure Factors ◽  
Spatio Temporal

In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

Extracting Spatio-temporal Feature for Classification of Event-related Potentials*

2011 ◽  
Vol 38 (9) ◽  
pp. 866-871 ◽  
Author(s):  
Zhi-Hua HUANG ◽  
Ming-Hong LI ◽  
Yuan-Ye MA ◽  
Chang-Le ZHOU
Keyword(s):  
Event Related Potentials ◽  
Related Potentials ◽  
Spatio Temporal ◽  
Temporal Feature

scholarly journals Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani
Keyword(s):  
Conceptual Models ◽  
Future Generations ◽  
Temporal Data ◽  
Temporal Modeling ◽  
Temporal Models ◽  
Gis Data ◽  
Spatio Temporal ◽  
New Interpretation ◽  
Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

scholarly journals Detecting long-range interactions between migrating cells

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Metzner ◽  
F. Hörsch ◽  
C. Mark ◽  
T. Czerwinski ◽  
A. Winterl ◽  
...  
Keyword(s):  
Nk Cells ◽  
Tumor Cells ◽  
Long Range ◽  
Cooperative Behavior ◽  
Simulated Data ◽  
Movement Direction ◽  
Repulsive Interaction ◽  
Collagen Gels ◽  
Long Range Interactions ◽  
A Cell

AbstractChemotaxis enables cells to systematically approach distant targets that emit a diffusible guiding substance. However, the visual observation of an encounter between a cell and a target does not necessarily indicate the presence of a chemotactic approach mechanism, as even a blindly migrating cell can come across a target by chance. To distinguish between the chemotactic approach and blind migration, we present an objective method that is based on the analysis of time-lapse recorded cell migration trajectories: For each movement step of a cell relative to the position of a potential target, we compute a p value that quantifies the likelihood of the movement direction under the null-hypothesis of blind migration. The resulting distribution of p values, pooled over all recorded cell trajectories, is then compared to an ensemble of reference distributions in which the positions of targets are randomized. First, we validate our method with simulated data, demonstrating that it reliably detects the presence or absence of remote cell-cell interactions. In a second step, we apply the method to data from three-dimensional collagen gels, interspersed with highly migratory natural killer (NK) cells that were derived from two different human donors. We find for one of the donors an attractive interaction between the NK cells, pointing to a cooperative behavior of these immune cells. When adding nearly stationary K562 tumor cells to the system, we find a repulsive interaction between K562 and NK cells for one of the donors. By contrast, we find attractive interactions between NK cells and an IL-15-secreting variant of K562 tumor cells. We therefore speculate that NK cells find wild-type tumor cells only by chance, but are programmed to leave a target quickly after a close encounter. We provide a freely available Python implementation of our p value method that can serve as a general tool for detecting long-range interactions in collective systems of self-driven agents.

scholarly journals Relaxation of quasi-stationary states in long range interacting systems and a classification of the range of pair interactions

Open Physics ◽  
2012 ◽  
Vol 10 (3) ◽  
Author(s):  
Bruno Marcos ◽  
Andrea Gabrielli ◽  
Michael Joyce
Keyword(s):  
Long Range ◽  
Asymptotic Form ◽  
Pair Interaction ◽  
Collision Integral ◽  
Stationary States ◽  
Long Range Interactions ◽  
Interacting Systems ◽  
Dynamical Properties ◽  
Number Of Particles

AbstractSystems of particles interacting with long range interactions present generically ”quasi-stationary states” (QSS), which are approximately time-independent out of equilibrium states. In this proceedings, we explore the generalization of the formation of such QSS and their relaxation from the much studied case of gravity to a generic pair interaction with the asymptotic form of the potential v(r) ∼ 1/r γ with γ > 0 in d dimensions. We compute analytic estimations of the relaxation time calculating the rate of two body collisionality in a virialized system approximated as homogeneous. We show that for γ < (d − 1/2), the collision integral is dominated by the size of the system, while for γ > (d − 1/2), it is dominated by small impact parameters. In addition, the lifetime of QSS increases with the number of particles if γ < d − 1 (i.e. the force is not integrable) and decreases if γ > d − 1. Using numerical simulations we confirm our analytic results. A corollary of our work gives a ”dynamical” classification of interactions: the dynamical properties of the system depend on whether the pair force is integrable or not.

scholarly journals Machine Learning Based Track Classification and Estimation using Kalman Filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1700-1704
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Velocity Model ◽  
Supervised Machine Learning ◽  
Multiple Target ◽  
Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

scholarly journals Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

2019 ◽  
Vol 9 (12) ◽  
pp. 348 ◽  
Author(s):  
Ji-Hoon Jeong ◽  
Baek-Woon Yu ◽  
Dae-Hyeok Lee ◽  
Seong-Whan Lee
Keyword(s):  
Short Term Memory ◽  
Mental States ◽  
Critical Issue ◽  
High Accuracy ◽  
Classification Performance ◽  
Eeg Signals ◽  
Aviation Accidents ◽  
Spatio Temporal ◽  
Lstm Network

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

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