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

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

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

Analyzing capacitor-based reconfigurable antennas using graph models

This paper discusses reconfigurable antennas using variable capacitors or varactors to achieve reconfiguration. We propose graph models as tools to understand reconfigurable antenna topologies and their configurations. Guidelines are set to model this type of reconfigurable antennas using graphs and examples are studied to investigate their optimal performance

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

Least squares support vector machines for fixed-step and fixed-set CDMA power control

This paper presents two machine learning based algorithms for CDMA power control. The least squares support vector machine (LS-SVM) algorithms classify eigenvalues estimates into sets of power control commands. A binary LS-SVM algorithm generates fixed step power control (FSPC) commands, while the one vs. one multiclass LS-SVM algorithm generates estimates for fixed set power control

Reducing Redundancies in Reconfigurable Antenna Structures Using Graph Models

We present an approach for reducing redundancies in the design of reconfigurable antenna structures using graph models. The basics of graph models, their rules, and how they can be applied in the design of switch-based reconfigurable antennas are introduced. Based on these rules, a methodology is developed and formulated to reduce the number of switches and parts in the antenna structure, without sacrificing the desired antenna functions. This approach not only optimizes the overall structure of the antenna but it also reduces cost and overall losses. Several examples are presented and discussed to demonstrate the validity of this new approach through simulations and measurements that present good agreement

Kernel antenna array processing

We introduce two support vector machine (SVM)-based approaches for solving antenna problems such as beamforming, sidelobe suppression, and maximization of the signal-to-noise ratio. A basic introduction to SVM optimization is provided and a complex nonlinear SVM formulation developed to handle antenna array processing in space and time. The new optimization formulation is compared with both the minimum mean square error and the minimum variance distortionless response methods. Several examples are included to show the performance of the new approachesPublicad