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.
KEYWORDS: Data modeling, Feature selection, Statistical analysis, Process control, Analog electronics, Image information entropy, Facility engineering, Statistical modeling, Algorithm development, Data storage
In evolutionary learning, the sine qua non is evolvability, which requires heritability of fitness and a balance between
exploitation and exploration. Unfortunately, commonly used fitness measures, such as root mean squared error (RMSE),
often fail to reward individuals whose presence in the population is needed to explain important data variance; and
indicators of diversity generally are not only incommensurate with those of fitness but also essentially arbitrary. Thus,
due to poor scaling, deception, etc., apparently relatively high fitness individuals in early generations may not contain
the building blocks needed to evolve optimal solutions in later generations. To reward individuals for their potential
incremental contributions to the solution of the overall problem, heritable information theoretic functionals are
developed that incorporate diversity considerations into fitness, explicitly identifying building blocks suitable for
recombination (e.g. for non-random mating). Algorithms for estimating these functionals from either discrete or
continuous data are illustrated by application to input selection in a high dimensional industrial process control data set.
Multiobjective information theoretic ensemble selection is shown to avoid some known feature selection pitfalls.
This paper surveys the various approaches used to apply evolutionary algorithms to develop artificial neural networks that solve pattern recognition, classification, and other tasks. These approaches are classified into four groups, each addressing one aspect of an artificial neural network: (a) evolving connection weights; (b) evolving neural architectures; (c) evolving an ensemble of networks; and (d) evolving node functions. Hybrid approaches are also discussed.
Several surveillance applications are characterized by the ability to gather information about the scene from more than one sensor modality, and heterogeneous sensor data must then be fused by the decision-maker. In this paper, we discuss the issues relevant to developing a model for fusion of information from audio and visual sensors, and present a framework to enhance decision-making capabilities. In particular, our methodology focuses on the issues of temporal reasoning, uncertainty representations, and coupling between features inferred from data streams coming from different sensors. We propose a conditional probability-based representation for uncertainty, along with fuzzy rules to assist decision-making, and a matrix representation of the coupling between sensor data streams. We also develop a fusion algorithm that utilizes these representations.
This paper presents neural network models for storing terminating and cyclic temporal sequences of patterns under synchronous, sequential and asynchronous dynamics. We use fully interconnected neural networks with asymmetric weight connections for synchronous and sequential dynamics and a layered neural network with feedback for asynchronous dynamics. The network were successfully implemented and the number of patterns that could be stored and recalled was approximately 12% of the size of the patterns in the network.