KEYWORDS: Algorithm development, Binary data, Data hiding, Digital watermarking, Detection and tracking algorithms, Optimal filtering, Data communications, Algorithms, Distortion, Detection theory
In the area of covert network communications, the focus has been on spread spectrum (SS) techniques using correlated
host data, applicable to many data hiding and covert communications applications. Our work relates to the Iterative
Generalized Least Squares (IGLS) blind signature recovery algorithm of Gkizeli et al. 1, and can be summarized as
follows: (1) We have performed extensive Monte Carlo simulations that characterize the convergence properties of the
algorithm as a function of signature length, host distortion, and number of hidden bits; (2) We have developed and
characterized the behavior of a fully blind extension of the IGLS algorithm, called the BC-IGLS (Blind IGLS using
Clustering); (3) We have developed and performed a characterization study of an extension to the IGLS algorithm, called
the MS-IGLS (Multi Signature IGLS), that performs blind extraction of multiple signatures in multi-user embedding
applications2.
We present a novel application of genetic algorithm (GA) to optimal feature set selection in supervised learning using support vector machine (SVM) for steganalysis. Steganalysis attempts to determine whether a cover object (in our case an image file) contains hidden information. This is a bivariate classification problem: the image either does or does not contain hidden data. Our SVM classifier uses a training set of images with known classification to "learn" how to classify images with unknown classification. The SVM uses a feature set, essentially a set of statistical quantities extracted from the image. The performance of the SVM classifier is heavily dependent on the feature set used. Too many features not only increase computation time but decrease performance, and too few features do not provide enough information for accurate classification. Our steganalysis technique uses entropic features that yield up to 240 features per image. The selection of an optimum feature set is a problem that lends itself well to genetic algorithm optimization. We describe this technique in detail and present a "GA optimized" feature set of 48 features that, for our application, optimizes the tradeoff between computation time and classification accuracy.
Many commercial steganographic programs use least significant bit (LSB) embedding techniques to hide data in 24-bit color images. We present the results from a new steganalysis algorithm that uses a variety of entropy and conditional entropy features of various image bitplanes to detect the presence of LSB hiding. Our technique uses a Support Vector Machine (SVM) for bivariate classification. We use the SVMLight implementation due to Joachims (available at http://svmlight.joachims.org/). A novel Genetic Algorithm (GA) approach was used to optimize the feature set used by the classifier. Results include correct identification rates as high as >98% and false positive rates as low as <2%. We have applied using the staganography programs stegHide and Hide4PGP. The hiding algorithms are capable of both sequential and distributed LSB embedding. The image library consisted of 40,000 digital images of varying size and content, which form a diverse test set. Training sets consisted of as many as 34,000 images, half "clean" and the other half a disjoint set containing embedded data. The hidden data consisted of files with various sizes and various information densities, ranging from very low average entropy (e.g., standard word processing or spreadsheet files) to very high entropy (compressed data). The testing phase used a similarly prepared set, disjoint from the training data. Our work includes comparisons with current state-of-the-art techniques, and a detailed study of how results depend on training set size and feature sets used.
This paper reports on a novel optical linearized directional coupler modulator in stoichiometric lithium niobate (SLN). The linearized design has important applications in analog and RF communications systems where fiber optic link performance depends critically on the spurious-free dynamic range of the modulator. Newly available SLN has several distinct advantages over the congruently grown crystals commonly used for high speed integrated optic devices, including higher electrooptic coefficient and better ferroelectric properties. The higher electrooptic coefficient yields lower drive voltage, while the enhanced ferroelectric properties enable better velocity-matched electrode structures using domain inverted waveguides. This paper addresses the operation of the linearized directional coupler design, and the critical advantages of the SLN substrate for implementing high-speed operation using velocity-matching.
SRICO has developed a revolutionary approach to physiological status monitoring using state-of-the-art optical chip technology. The company’s patent pending Photrode is a photonic electrode that uses unique optical voltage sensing technology to measure and monitor electrophysiological parameters. The optical-based monitoring system enables dry-contact measurements of EEG and ECG signals that require no surface preparation or conductive gel and non-contact measurements of ECG signals through the clothing. The Photrode applies high performance optical integrated circuit technology, that has been successfully implemented in military & commercial aerospace, missile, and communications applications for sensing and signal transmission. SRICO’s award winning Photrode represents a new paradigm for the measurement of biopotentials in a reliable, convenient, and non-intrusive manner. Photrode technology has significant applications on the battlefield for rapid triage to determine the brain dead from those with viable brain function. An ECG may be obtained over the clothing without any direct skin contact. Such applications would enable the combat medic to receive timely medical information and to make important decisions regarding identification, location, triage priority and treatment of casualties. Other applications for the Photrode include anesthesia awareness monitoring, sleep medicine, mobile medical monitoring for space flight, emergency patient care, functional magnetic resonance imaging, various biopotential signal acquisition (EMG, EOG), and routine neuro and cardio diagnostics.
KEYWORDS: Electrodes, Signal detection, Electrocardiography, Electroencephalography, Skin, Waveguides, Modulators, Interference (communication), Data acquisition, Signal processing
This paper describes a paradigm shift in the technology for sensing electro-physiological signals. In recent years, SRICO has been developing small lithium niobate photonic electrodes, otherwise called "Photrodes” for measuring EEG and ECG signals. These extrinsic fiber-optic sensing devices exploit the extremely high electrical input impedance of Mach-Zehnder Intensity (MZI) electro-optic modulators to detect microvolt and millivolt physiological signals. Voltage levels associated with electrocardiograms are typically on the order of several millivolts, and such signals can be detected by capacitive pickup through clothing, i.e., the Photrode may be used in a non-contact mode. Electroencephalogram signals, which typically have an amplitude of several microvolts, require direct contact with the skin. However, this contact may be dry, eliminating the need for conductive gels. The electrical bandwidth of this photonic electrode system stretches from below 0.1 Hz to many tens of kHz and is constrained mainly by the signal processing electronics, not by the Photrode itself. The paper will describe the design and performance of Photrode systems and the challenging aspects of this new technology.
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