Multi-model CFAR detection in FOliage PENetrating SAR images

A multi-model approach for Constant False Alarm Ratio (CFAR) detection of vehicles through foliage in FOliage PENetrating (FOPEN) SAR images is presented. Extreme value distributions and Location Scale properties are exploited to derive an adaptive CFAR approach that is able to cope with different forest densities. Performance analysis on real data is carried out to estimate the detection and false alarm probabilities in the presence of a ground truth

Computer Aided Detection of Microcalcifications Utilizing Texture Analysis

A comparative study of texture measures for the classification of breast tissue is presented. The texture features investigated include Angular Second Moments, Power Spectrum Analysis and a novel feature, Laws Energy Ratios. The texture study was accomplished as part of the development of a Model Based Vision (MBV) system for the automatic detection of microcalcifications. An overview of the Microcalcification Detection System is presented, which applies image differencing techniques, feature selection methods, and neural networks for locating microcalcification clusters in mammograms. The Power Spectrum Analysis feature set had the best overall performance with an 83% Probability of Detection and an average False ROl Rate of 2.17 ROIs per image over 53 mammograms. A combination of Laws Energy Ratio and Power Spectrum Analysis features selected using Ruck Saliency metrics achieved an increased Probability of Detection of 85% with an average 4 false ROIs per image

Spam Classification Using Machine Learning Techniques - Sinespam

Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam) messages, even as an expanding volume of such e-mail occupies server storage space and consumes network bandwidth. An ongoing challenge, therefore, rests within the development and refinement of automatic classifiers that can distinguish legitimate e-mail from spam. Some published studies have examined spam detectors using Naïve Bayesian approaches and large feature sets of binary attributes that determine the existence of common keywords in spam, and many commercial applications also use Naïve Bayesian techniques. Spammers recognize these attempts to prevent their messages and have developed tactics to circumvent these filters, but these evasive tactics are themselves patterns that human readers can often identify quickly. This work had the objectives of developing an alternative approach using a neural network (NN) classifier brained on a corpus of e-mail messages from several users. The features selection used in this work is one of the major improvements, because the feature set uses descriptive characteristics of words and messages similar to those that a human reader would use to identify spam, and the model to select the best feature set, was based on forward feature selection. Another objective in this work was to improve the spam detection near 95% of accuracy using Artificial Neural Networks; actually nobody has reached more than 89% of accuracy using ANN

Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities

PATTERN RECOGNITION INTEGRATED SENSING METHODOLOGIES (PRISMS) IN PHARMACEUTICAL PROCESS VALIDATION, REMOTE SENSING AND ASTROBIOLOGY

Modern analytical instrumentation is capable of creating enormous and complex volumes of data. Analysis of large data volumes are complicated by lengthy analysis time and high computational demand. Incorporating real-time analysis methods that are computationally efficient are desirable for modern analytical methods to be fully utilized. The use of modern instrumentation in on-line pharmaceutical process validation, remote sensing, and astrobiology applications requires real-time analysis methods that are computationally efficient. Integrated sensing and processing (ISP) is a method for minimizing the data burden and sensing time of a system. ISP is accomplished through implementation of chemometric calculations in the physics of the spectroscopic sensor itself. In ISP, the measurements collected at the detector are weighted to directly correlate to the sample properties of interest. This method is especially useful for large and complex data sets. In this research, ISP is applied to acoustic resonance spectroscopy, near-infrared hyperspectral imaging and a novel solid state spectral imager. In each application ISP produced a clear advantage over the traditional sensing method. The limitations of ISP must be addressed before it can become widely used. ISP is essentially a pattern recognition algorithm. Problems arise in pattern recognition when the pattern-recognition algorithm encounters a sample unlike any in the original calibration set. This is termed the false sample problem. To address the false sample problem the Bootstrap Error-Adjusted Single-Sample Technique (BEST, a nonparametric classification technique) was investigated. The BEST-ISP method utilizes a hashtable of normalized BEST points along an asymmetric probability density contour to estimate the BEST multidimensional standard deviation of a sample. The on-line application of the BEST method requires significantly less computation than the full algorithm allowing it to be utilized in real time as sample data is obtained. This research tests the hypothesis that a BEST-ISP metric can be used to detect false samples with sensitivity \u3e 90% and specificity \u3e 90% on categorical data

Effects of diabetes and aging on posture and acceleration thresholds during lateral translations

Research objectives. One source of falls in the elderly may be an inability to sufficiently adjust to transient postural perturbations or slips. Identifying useful predictors of fall potential, as well as factors that affect the ability of an individual to detect a movement of the standing support surface may provide insight into postural stability and methods to increase stability in elders. To do this, acceleration thresholds to short, precise, lateral platform translations and the resultant psychophysical responses of adults with early Type 2 diabetes to age-matched controls and young adults were measured. Methods. Using an innovative SLIP-FALLS platform, short (1, 2, 4, 8,and 16mm) lateral perturbations were presented to 21 individuals—9 young adults, 6 neurologically intact elder adults, and 6 elders with diabetes using a two-alternative forced choice (2AFC) protocol. All subjects underwent lower-limb nerve conduction velocity determination, air conduction velocity testing, Semmes-Weinstein monofilament thresholds, the Mini Mental Status Exam, and reaction time tests to touch, tone and high acceleration, 4mm super-threshold perturbations. Results. All three groups had significantly different thresholds at all small (\u3c4mm) movement lengths, with the diabetic neuropathy group having a markedly higher acceleration threshold (P \u3c 0.001); the healthy elderly, which, in turn, had markedly higher thresholds than young adults. Patients with neuropathy had significantly higher reaction times to platform movements and touches to the plantar sole, but not for auditory tones. Both elderly groups had a significantly higher reaction time to superthreshold platform movement than did young adults. Sensory tests revealed slower nerve conduction velocities, higher air conduction velocities, and lower cognitive ability in the diabetic group. Conclusions. A marked decrease in perception of very small moves due to aging and diabetic neuropathy could well have a detrimental effect on postural control mechanisms. The higher prevalence of falls in the elderly and elderly diabetics may be due to decreased perceptual ability, slower nerve conduction velocities, and slowing reaction times compounded by larger amounts of imparted energy needed for detection of a slipping event

Electric Powered Wheelchair Control with a Variable Compliance Joystick: Improving Control of Mobility Devices for Individuals with Multiple Sclerosis

While technological developments over the past several decades have greatly enhanced the lives of people with mobility impairments, between 10 and 40 percent of clients who desired powered mobility found it very difficult to operate electric powered wheelchairs (EPWs) safely because of sensory impairments, poor motor function, or cognitive deficits [1]. The aim of this research is to improve control of personalized mobility for those with multiple sclerosis (MS) by examining isometric and movement joystick interfaces with customizable algorithms. A variable compliance joystick (VCJ) with tuning software was designed and built to provide a single platform for isometric and movement, or compliant, interfaces with enhanced programming capabilities.The VCJ with three different algorithms (basic, personalized, personalized with fatigue adaptation) was evaluated with four subjects with MS (mean age 58.7±5.0 yrs; years since diagnosis 28.2±16.1 yrs) in a virtual environment. A randomized, two-group, repeated-measures experimental design was used, where two subjects used the VCJ in isometric mode and two in compliant mode.While still too early to draw conclusions about the performance of the joystick interfaces and algorithms, the VCJ was a functional platform for collecting information. Inspection of the data shows that the learning curve may be long for this system. Also, while subjects may have low trial times, low times could be related to more deviation from the target path