Abstract

We propose a signal detection mechanism for passive
SONAR. Since the detector must be agnostic to the transmitted signal, we design a detector based on the received signal energy.To account for the time-varying and unknown signal-to-noise ratio, clustering is employed to separate signal energy from noise
energy and the energy distribution parameters are estimated from the separated clusters of frame energies of the incoming signal. Our approach solves the composite hypothesis testing problem with parameters estimated from the data. The ability to adapt detector parameters from the signal results in a constant false alarm rate detector. The resulting Neyman-Pearson detector is analyzed using simulation experiments.

Abstract

The analysis of non-stationary signals in non-uniformly sampled data is a challenging task. Time-integrated methods, such as the generalised Lomb-Scargle (GLS) periodogram, provide a robust statistical assessment of persistent periodicities but are insensitive to transient events. Conversely, existing time-frequency methods often rely on fixed-duration windows or interpolation, which can be suboptimal for non-uniform data.
We introduce the non-uniform Stockwell-transform (NUST), a time-frequency framework that applies a localized density adaptive spectral analysis directly to non-uniformly sampled data.
NUST employs a doubly adaptive window that adjusts its width based on both frequency and local data density, providing detailed time-frequency information for both transient and persistent signals.
We validate the NUST on numerous non-uniformly sampled synthetic signals, demonstrating its superior time-localization performance compared to GLS.
Furthermore, we apply NUST to HARPS radial velocity data of the multi-planetary system HD 10180, successfully distinguishing coherent planetary signals from stellar activity.

Abstract

The completion of a Euclidean distance matrix (EDM) from sparse
and noisy observations is a fundamental challenge in signal pro-
cessing, with applications in sensor network localization, acoustic
room reconstruction, molecular conformation, and manifold learn-
ing. Traditional approaches, such as rank-constrained optimization
and semidefinite programming, enforce geometric constraints but
often struggle under sparse or noisy conditions. This paper intro-
duces a hierarchical Bayesian framework that places structured pri-
ors directly on the latent point set generating the EDM, naturally
embedding geometric constraints. By incorporating a hierarchical
prior on latent point set, the model enables automatic regularization
and robust noise handling. Posterior inference is performed using
a Metropolis-Hastings within Gibbs sampler to handle coupled la-
tent point posterior. Experiments on synthetic data demonstrate im-
proved reconstruction accuracy compared to deterministic baselines
in sparse regimes.

Abstract

Missing values in time-series is a frequently occurring problem in
multi-sensor data analysis. The current state-of-the-art imputation
networks don't generalize well across the benchmarks. Further,
some directly operate on space-time product graphs which may
minimize the information loss but are prohibitively expensive. We
propose Product Graph U-networks using Temporal pooling for Spa-
tiotemporal imputation (P-GUTS). It uses pooled representations
of the space-time sequence in the bottleneck part of the U-network
and is memory efficient. Since datasets can have differing levels of
redundancy, relying on a single view (pooled representation) of the
data can be limiting. Therefore, P-GUTS uses multiple U-Nets with
different pooling factors and aggregates the representations. We
demonstrate the effectiveness of P-GUTS on multiple real-world
imputation datasets and robustness across increasing missing rates.
P-GUTS also gives encouraging results in spatiotemporal forecast-
ing.

Abstract

Time-series data from sensor networks often has missing entries due to sensor failures,
poor communication, scheduled downtime, etc. Missing data imputation improves perfor-
mance of downstream tasks such as forecasting and anomaly detection. Therefore, highly
accurate imputation methods are desirable.
Multi-variate time series imputation methods using interpolation [1-3], or matrix factor-
ization [4, 5] have limited accuracy. Auto-regressive methods like ARIMA [6] and RNNs
like GRIN [7] are subject to error propagation and are slow. More recently, attention [8] has
been successfully used to design effective networks for various time series tasks - imputation
(SPIN, SPIN-H and Transformer) [9], forecasting [10, 11] and anomaly detection [12].
However, as evident from Figure 2, these state-of-the-art (SOTA) networks are effective
only on certain datasets. It motivates a more general meta-network which can effectively
aggregate these experts using a gating network. We provide the scope of improvement when
these frozen experts are selected optimally using an oracle. The possible reduction in error
can be as high as 28 − 40 percent by using existing imputation networks.

Abstract

We propose a data-driven method for direction-of-arrival (DOA) trajectory estimation. We use a deep complex architecture which leverages complex-valued representations to capture both magnitude and phase information in the received sensor array data. The network is designed to output the DOA trajectory parameters and amplitudes of the strongest source. Deviating from conventional methods, which attempt to estimate parameters for all sources simultaneously -- leading to assignment ambiguity and the problem of uncertain output dimensions, we adopt a sequential approach. The estimated source signal contribution is subtracted from the input to obtain a residual signal. This residual signal is then fed back into the network to identify the next strongest source and so on, making the proposed network reusable. We evaluate our network on simulated data of varying complexity. Results demonstrate the feasibility of such a reusable network and potential improvements can be explored in future.

Abstract

Time series data related to traffic and air quality are useful
indicators for urban planning but they frequently have missing values.
Imputation of multi-sensor time series data is thus a vital pre-processing
step for forecasting, anomaly detection and other downstream tasks. We
develop an efficient architecture - Sparse Attention-based Imputation
Network for Time series (SAINT) - which outperforms the state-of-
the-art imputation networks. Efficiency is achieved by separating the
computations on the space-time product graph sequentially into channel
independent temporal attention and sparse space-time transformer. This
channel independent network can reduce overfitting to effectively repre-
sent general temporal patterns. Sparse space-time transformer performs
message passing on the spatial graph conditioned on time. We consider
real-world datasets for evaluation - PEMS-BAY, METR-LA and AQI -
which are gathered from sensor networks in major cities. We demonstrate
the effectiveness and robustness of SAINT across complex missing data
scenarios. Additionally, SAINT generalizes well to short-term forecasting
and is practical for long-term forecasting with limited resources.

Abstract

We address the problem of network connectivity inference by observing interacting time-series signals at the nodes. The evolving nodal signals depend on the connectivity structure of the network thus allowing us to successfully invert for it. Our approach is demonstrated using the example of heat diffusion dynamics. To learn the graph, we combine our knowledge of the dynamical equation and the observed graph signals. Specifically, we perform approximate numerical discretization of the dynamical equation leading to a system of linear equations in the unknown graph topology. Simulations indicate feasibility of this method for accurate network inference.

Abstract

In a prior study, a novel deterministic compartmental model known as the SEIHRK model was introduced, shedding light on the pivotal role of test kits as an intervention strategy for mitigating epidemics. Particularly in heterogeneous networks, it was empirically demonstrated that strategically distributing a limited number of test kits among nodes with higher degrees substantially diminishes the outbreak size. The network's dynamics were explored under varying values of infection rate. In this research, we expand upon these findings to investigate the influence of migration on infection dynamics within distinct communities of the network. Notably, we observe that nodes equipped with test kits and those without tend to segregate into two separate clusters when coupling strength is low, but beyond a critical threshold coupling coefficient, they coalesce into a unified cluster. Building on this clustering phenomenon, we develop a reduced equation model and rigorously validate its accuracy through comprehensive simulations. We show that this property is observed in both complete and random graphs.

Abstract

Functional MRI research using naturalistic stimuli (like movies) can help examine brain networks supporting empathy. Applying graph learning methods to whole-brain time-series signals, we propose a novel fMRI signal processing pipeline that includes high-pass filtering, voxel-level clustering, and windowed graph learning with a sparsity-based approach. The fMRI dataset is from passive viewing of an 8-minute movie shown to 14 healthy volunteers. The emotion rating of the movies by age-matched 40 participants is the ground truth. We considered a total of 54 regions extracted from the AAL Atlas for the study. The sparsity-based graph learning method consistently outperforms in capturing variations in the emotion scale, achieving over 88% match of the graph cluster labels averaged across participants. Temporal analysis reveals a high fit to the emotion scale supported by the method's effectiveness in capturing dynamic connectomes through graph clustering, indicating the role of contextual inference in building empathy. While the edge-weight dynamics analysis underscores the superiority of sparsity-based learning, the connectome-network analysis highlights the crucial role of the Insula, Amygdala, and Thalamus in empathy, with lateral brain connections facilitating synchronized responses. Spectral filtering analysis by band-pass filter isolated the regions linked to emotional and empathy processing in scenes with high emotional context. We also observe strong similarities between the movies in graph cluster labels, connectome-network analysis, and spectral filtering-based analyses, revealing robust neural correlates of empathy. These findings contribute to a better understanding of the neural dynamics associated with empathy and identify regions specific to empathetic responses to naturalistic stimuli.