Mutual coupling between circular apertures on an infinite conducting ground plane and radiating into a finite width slab

The problem of electromagnetic coupling between two horns is of interest for the Microwave Reflectometer Ionization Sensor (MRIS) that will be used in the Aeroassist Flight Experiment (AFE). Laboratory measurements of mutual coupling between conical horns (using a flat metallic reflector to simulate a critically dense plasma outside) have shown a strong dependence on the finite dimensions of the shuttle tile over the apertures. Since both, the dielectric tile and the plasma outside the tile reflect microwaves, a study should be done to isolate the two mechanisms so that the MRIS reentry flight data can be interpreted correctly. Once the coupling due to the tile itself is determined then the location of the critial electron number density layers can be determined. As a first attempt to tackle this problem the Geometrical Theory of Diffraction was used to modify the existing solution to mutual coupling between apertures with infinite dielectric sheets. By using the equivalent current method, aperture theory to determine the radiated fields inside the dielectric tiles, and ray tracing the contributions to mutual coupling were determined. Results from two cases with different tile thicknesses have indicated that the main contribution to mutual coupling is due to diffraction from the bottom and top (back and front) wedges

Analysis of magnetic field levels at KSC

The scope of this work is to evaluate the magnetic field levels of distribution systems and other equipment at Kennedy Space Center (KSC). Magnetic fields levels in several operational areas and various facilities are investigated. Three dimensional mappings and contour are provided along with the measured data. Furthermore, the portion of magnetic fields generated by the 60 Hz fundamental frequency and the portion generated by harmonics are examined. Finally, possible mitigation techniques for attenuating fields from electric panels are discussed

Polarization-Tunable Antenna-Coupled Infrared Detector

An antenna-coupled infrared detector with polarization tuning over approximately 90 degrees by application of a bias voltage in the range of a few hundred millivolts. This mechanism of polarization tuning eliminates the need for bulk-optical polarization filters. When integrated into focal plane arrays, these detectors can be used in remote-sensing systems to facilitate enhanced image recognition, feature extraction and image-clutter removal. A preferred version of the antenna has longitudinal metal antenna arms extending outward from an infrared(IR) sensor in a spiral pattern, polarization tuning devices connected between the antenna arms, and a voltage for controlling the polarization tuning devices, wherein the polarization tuning devices enable real-time control of current distribution in the arms. The infrared(IR) sensors can be tunnel diodes, schottky diodes, photovoltaics, photoconductors, and pyroelectrics. Application areas can include earth-resource mapping, pollution monito

Beamforming Using Support Vector Machines

Support vector machines (SVMs) have improved generalization performance over other classical optimization techniques. Here, we introduce an SVM-based approach for linear array processing and beamforming. The development of a modified cost function is presented and it is shown how it can be applied to the problem of linear beamforming. Finally, comparison examples are included to show the validity of the new minimization approach.Publicad

Reducing complexity and improving the reliability of frequency reconfigurable antennas

In this paper the complexity and reliability of frequency reconfigurable antennas are presented. A new approach for decreasing the complexity of reconfigurable antennas while maintaining the reliability of such structures is discussed. An example is given to prove the validity of the proposed approaches

Least squares support vector machines for direction of arrival estimation

Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems. The paper presents a multiclass least squares SVM (LS-SVM) architecture for direction of arrival (DOA) estimation as applied to a CDMA cellular system. Simulation results show a high degree of accuracy, as related to the DOA classes, and prove that the LS-SVM DDAG (decision directed acyclic graph) system has a wide range of performance capabilities. The multilabel capability for multiple DOAs is discussed. Multilabel classification is possible with the LS-SVM DDAG algorithm presented

Least squares support vector machines for direction of arrival estimation with error control and validation

The paper presents a multiclass, multilabel implementation of least squares support vector machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system, the algorithm\u27s capabilities and performance must be evaluated. Specifically, for classification algorithms, a high confidence level must exist along with a technique to tag misclassifications automatically. The presented learning algorithm includes error control and validation steps for generating statistics on the multiclass evaluation path and the signal subspace dimension. The error statistics provide a confidence level for the classification accuracy

Διασυνοριακές Συγχωνεύσεις Εταιρειών στην Ευρωπαϊκή Ένωση

Το βασικό ζήτημα που εξετάζεται στην παρούσα εργασία είναι το επιτρεπτό των διασυνοριακών συγχωνεύσεων υπό το πρίσμα του Δικαίου της Ευρωπαϊκής Ένωσης, το ρυθμιστικό τους πλαίσιο σε ενωσιακό και εθνικό επίπεδο, καθώς και τα ζητήματα ιδιωτικού διεθνούς δικαίου που ανακύπτουν.The key issue addressed in this paper is the permissibility of cross-border mergers of companies in the light of European Union law, their legal framework at european and national level, and the issues of private international law that arise

Machine learning based CDMA power control

This paper presents binary and multiclass machine learning techniques for CDMA power control. The power control commands are based on estimates of the signal and noise subspace eigenvalues and the signal subspace dimension. Results of two different sets of machine learning algorithms are presented. Binary machine learning algorithms generate fixed-step power control (FSPC) commands based on estimated eigenvalues and SIRs. A fixed-set of power control commands are generated with multiclass machine learning algorithms. The results show the limitations of a fixed-set power control system, but also show that a fixed-set system achieves comparable performance to high complexity closed-loop power control systems