Arun Subramanian

A copula-based semi-parametric approach for footstep detection using seismic sensor networks

In this paper, we consider the problem of detecting the presence of footsteps using signal measurements from a network of seismic sensors. Since the sensors are closely spaced, they result in correlated measurements. A novel method for detection that exploits the spatial dependence of sensor measurements using copula functions is proposed. An approach for selecting the copula function that is most suited for modeling the spatial dependence of sensor observations is also provided. The performance of the proposed approach is illustrated using real footstep signals collected using an experimental test-bed consisting of seismic sensors.

A coalitional game for distributed estimation in wireless sensor networks

We consider a collaborative estimation problem using dependent observations in a wireless sensor network, where each sensor aims to maximize its estimation performance in terms of Fisher information (FI) by forming coalitions with other sensors and collaborating within a coalition. The energy consumed by the sensors increases with the size of the coalition and hence we prove that grand coalition will not form. We investigate the formation of non-overlapping coalitions such that each sensors performance is maximized under a specific energy constraint. We decouple marginal and dependent components of FI obtained from the joint distribution by using copula theory. We introduce the concept of diversity gain and redundancy loss and demonstrate how a copula based formulation allows us to characterize these concepts. Distributed estimation problem is formulated as a coalitional game. A merge-and-split algorithm is used for finding an optimal partition. Stability of the proposed algorithm for this game is discussed. Finally, numerical results are discussed.

Detection of Dependent Heavy-Tailed Signals

This paper examines the problem of detection of dependent α-stable signals. Measurements of several phenomena exhibit non-Gaussian, heavy-tailed behavior in their probability density functions (p.d.f.); we use the class of α-stable distributions to characterize these signals. When two sensors make simultaneous measurements of such phenomena, these heavy-tailed realizations are dependent across sensors. The intersensor dependence is modeled using copulas. We consider a two-sided test in the Neyman-Pearson framework and present an asymptotic analysis of the generalized likelihood test (GLRT). Both, nested and non-nested models are considered in the analysis. The performance of the proposed scheme is evaluated numerically on simulated data, as well as indoor seismic data. With appropriately selected models, our results demonstrate that a high probability of detection can be achieved for false alarm probabilities of the order of 10-4.

Ultrahigh resolution 3D model of murine heart from micro-CT and serial confocal laser scanning microscopy images

This study involves the reconstruction of a distortion-free ultrahigh-resolution 3D model of a whole murine heart. This is achieved by multimodal registration of serial images generated by confocal laser scanning microscopy (CLSM) with the aid of a micro-CT 3D image as a template. High-resolution information from CLSM is utilized for study of fine soft tissue structures in 3D, including fiber orientation and gap junctions. CLSM requires physical sectioning of the sample resulting in missing tissue and in various degrees of tissue distortion depending on thickness. The micro-CT data are distortion free and provide complete information on whole-object interfaces both external and internal. However, they do not provide information on the soft-tissue fine structure. In this project, we used a micro-CT image as template to spatially co-register all the individual CLSM images and to correct the resulting volume for distortion

Feature selection and occupancy classification using seismic sensors

In this paper, we consider the problem of indoor surveillance and propose a feature selection scheme for occupancy classification in an indoor environment. The classifier aims to determine whether there is exactly one occupant or more than one occupant. Data are obtained from six seismic sensors (geophones) that are deployed in a typical building hallway. Four proposed features exploit amplitude and temporal characteristics of the seismic time series. A neural network classifier achieves performance ranging between 77% to 95% on the test data, depending on the type of construction of the location in the building being monitored.

A data-driven personnel detection scheme for indoor surveillance using seismic sensors

This paper describes experiments and analysis of seismic signals in addressing the problem of personnel detection for indoor surveillance. Data was collected using geophones to detect footsteps from walking and running in indoor environments such as hallways. Our analysis of the data shows the significant presence of nonlinearity, when tested using the surrogate data method. This necessitates the need for novel detector designs that are not based on linearity assumptions. We present one such method based on empirical mode decomposition (EMD) and functional data analysis (FDA) and evaluate its applicability on our collected dataset.

Neuronal nuclei localization in 3D using level set and watershed segmentation from laser scanning microscopy images

Abnormalities of the number and location of cells are hallmarks of both developmental and degenerative neurological diseases. However, standard stereological methods are impractical for assigning each cells nucleus position within a large volume of brain tissue. We propose an automated approach for segmentation and localization of the brain cell nuclei in laser scanning microscopy (LSM) embryonic mouse brain images. The nuclei in these images are first segmented by using the level set (LS) and watershed methods in each optical plane. The segmentation results are further refined by application of information from adjacent optical planes and prior knowledge of nuclear shape. Segmentation is then followed with an algorithm for 3D localization of the centroid of nucleus (CN). Each volume of tissue is thus represented by a collection of centroids leading to an approximate 10,000-fold reduction in the data set size, as compared to the original image series. Our method has been tested on LSM images obtained from an embryonic mouse brain, and compared to the segmentation and CN localization performed by an expert. The average Euclidian distance between locations of CNs obtained using our method and those obtained by an expert is 1.58±1.24 µm, a value well within the ~5 µm average radius of each nucleus. We conclude that our approach accurately segments and localizes CNs within cell dense embryonic tissue.

Large volume reconstruction from laser scanning microscopy using micro-CT as a template for deformation compensation

In biomedical research, there is an increased need for reconstruction of large soft tissue volumes (e.g. whole organs) at the microscopic scale from images obtained using laser scanning microscopy (LSM) with fluorescent dyes targeting selected cellular features. However, LSM allows reconstruction of volumes not exceeding a few hundred ìm in size and most LSM procedures require physical sectioning of soft tissue resulting in tissue deformation. Micro-CT (&mgr;CT) can provide deformation free tomographic image of the whole tissue volume before sectioning. Even though, the spatial resolution of &mgr;CT is around 5 &mgr;m and its contrast resolution is poor, it could provide information on external and internal interfaces of the investigated volume and therefore could be used as a template in the volume reconstruction from a very large number of LSM images. Here we present a method for accurate 3D reconstruction of the murine heart from large number of images obtained using confocal LSM. The volume is reconstructed in the following steps: (i) Montage synthesis of individual LSM images to form a set of aligned optical planes within given physical section; (ii) Image enhancement and segmentation to correct for non-uniform illumination and noise; (iii) Volume matching of a synthesized physical section to a corresponding sub-volume of &mgr;CT; (iv) Affine registration of the physical section to the selected &mgr;CT sub-volume. We observe correct gross alignment of the physical sections. However, many sections still exhibit local misalignment that could be only corrected via local nonrigid registration to &mgr;CT template and we plan to do it in the future.

Social media data assisted inference with application to stock prediction

The access to the massive amount of social media data provides a unique opportunity to the signal processing community for extracting information that can be used to infer about unfolding events. It is desirable to investigate the convergence of sensor networks and social media in facilitating the data-to- decision making process and study how the two systems can complement each other for enhanced situational awareness. In this paper, we propose a copula-based joint characterization of multiple dependent time series from sensors and social media. As a proof-of-concept, this model is applied to the fusion of Google Trends (GT) data and stock price data of Apple Inc. for prediction, where the stock data serves as a surrogate for sensor data. Superior prediction performance is demonstrated, by taking the non-linear dependence among social media data and sensor data into consideration.