Satish G. Iyengar

A Parametric Copula-Based Framework for Hypothesis Testing Using Heterogeneous Data

We present a parametric framework for the joint processing of heterogeneous data, specifically for a binary classification problem. Processing such a data set is not straightforward as heterogeneous data may not be commensurate. In addition, the signals may also exhibit statistical dependence due to overlapping fields of view. We propose a copula-based solution to incorporate statistical dependence between disparate sources of information. The important problem of identifying the best copula for binary classification problems is also addressed. Computer simulation results are presented to demonstrate the feasibility of our approach. The method is also tested on real-data provided by the National Institute of Standards and Technology (NIST) for a multibiometric face recognition application. Finally, performance limits are derived to study the influence of statistical dependence on classification performance.

On the Detection of Footsteps Based on Acoustic and Seismic Sensing

In this work, we present a copula-based framework for integrating signals of different but statistically correlated modalities for binary hypothesis testing problems. Specifically, we consider the problem of detecting the presence of a human using footstep signals from seismic and acoustic sensors. An approach based on canonical correlation analysis and copula theory is employed to establish a likelihood ratio test. Experimental results based on real data are presented.

Fusing Dependent Decisions for Hypothesis Testing With Heterogeneous Sensors

In this paper, we consider a binary decentralized detection problem where the local sensor observations are quantized before their transmission to the fusion center. Sensor observations, and hence their quantized versions, may be heterogeneous as well as statistically dependent. A composite binary hypothesis testing problem is formulated, and a copula-based generalized likelihood ratio test (GLRT) based fusion rule is derived given that the local sensors are uniform multilevel quantizers. An alternative computationally efficient fusion rule is also designed which involves injecting a deliberate random disturbance to the local sensor decisions before fusion. Although the introduction of external noise causes a reduction in the received signal-to-noise ratio (SNR), it is shown that the proposed approach can result in a detection performance comparable to the GLRT detector without external noise, especially when the number of quantization levels is large.

On noise-enhanced distributed inference in the presence of Byzantines

The problem of Byzantine (malicious sensors) threats in a distributed detection framework for inference networks is addressed. Impact of Byzantines is mitigated by suitably adding Stochastic Resonance (SR) noise. Previously, Independent Malicious Byzantine Attack (IMBA), where each Byzantine decides to attack the network independently relying on its own observation was considered. In this paper, we present further results for Cooperative Malicious Byzantine Attack (CMBA), where Byzantines collaborate to make the decision and use this information for the attack. In order to analyze the network performance, we consider KL-Divergence (KLD) to quantify detection performance and minimum fraction of Byzantines needed to blind the network (αblind) as a security metric. We show that both KLD and αblind increase when SR noise is added at the honest sensors. When SR noise is added to the fusion center, we analytically show that there is no gain in terms of αblind or the network-wide performance measured in terms of the deflection coefficient. We also model a game between the network and the Byzantines and present a necessary condition for a strategy (SR noise) to be a saddle-point equilibrium.

A parametric copula based framework for multimodal signal processing

We present a framework for the joint processing of multimodal data such as audio-video data streams. We first consider the problem of estimating the joint distribution of statistically dependent multimodal random variables. We discuss the issues involved and provide a copula based solution. Application of this approach to solve a multisensor fusion problem for the detection of a random event is also discussed.

Quantifying EEG synchrony using copulas

In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimers disease. Results on real data are presented.

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.

An adaptive Kalman filter technique for context-aware heart rate monitoring

Traditional physiological monitoring systems convert a persons vital sign waveforms, such as heart rate, respiration rate and blood pressure, into meaningful information by comparing the instant reading with a preset threshold or a baseline without considering the contextual information of the person. It would be beneficial to incorporate the contextual data such as activity status of the person to the physiological data in order to obtain a more accurate representation of a persons physiological status. In this paper, we proposed an algorithm based on adaptive Kalman filter that describes the heart rate response with respect to different activity levels. It is towards our final goal of intelligent detection of any abnormality in the persons vital signs. Experimental results are provided to demonstrate the feasibility of the algorithm.

A semi-supervised Hidden Markov model-based activity monitoring system

Most existing human activity classification systems require a large training dataset to construct statistical models for each activity of interest. This may be impractical in many cases. In this paper, we proposed a semi-supervised HMM based activity monitoring system, that adapts the HMM for a specific subject from a general model in order to alleviate the requirement of a large training data set. In addition, using two triaxial accelerometers, our system not only identifies simple events such as sitting, standing and walking, but also recognizes the behavior or a more complex activity by temporally linking the events together. Experimental results demonstrate the feasibility of our proposed system.

Multibiometrics for Human Identification: Biometric Authentication: A Copula-Based Approach

Biometrics involves the design of automatic human recognition systems that use physical features such as face, fingerprints, iris or behavioral traits such as gait or rate of keystrokes, etc. as passwords. For example, in building access control applications, a person’s face may be matched to templates stored in a database consisting of all enrolled users. Decision to allow or deny entry is then taken based on the similarity score generated by the face matching algorithm. Such security systems that rely on biometrics have several advantages over the conventional ones where alphanumeric personal identification numbers (PINs) are provided to the users. For example, a PIN, if leaked, may be used by an unauthorized person causing serious security concerns. However, a person’s physical signature belongs only to that individual and it is very difficult if not impossible to emulate it. Further, biometric systems may be more convenient and user-friendly as there is no code to remember or any token to carry. However, there exist several limitations. Biometric traits such as face and voice change with age. One may be required to update the systems’ database to counter this time variabity. Environmental noise and noise in the acquisition system further affect the accuracy and reliability of the system. Overlap between physical features or inter-class similarity (e.g., twins with identical facial features) limits the system’s ability to distinguish between the classes. There also exist intra-class variations due to differences between the acquired biometric signature of an individual requesting the access and his/her template registered in the database. Apart from noise sources stated above, these differences may also stem from the psychological and behavioral variations of an individual at different instances of time. One method to overcome these limitations is to consider combining multiple sources of information. It