Range and range rate system

A video controlled solid state range finding system which requires no radar, high power laser, or sophisticated laser target is disclosed. The effective range of the system is from 1 to about 200 ft. The system includes an opto-electric camera such as a lens CCD array device. A helium neon laser produces a source beam of coherent light which is applied to a beam splitter. The beam splitter applies a reference beam to the camera and produces an outgoing beam applied to a first angularly variable reflector which directs the outgoing beam to the distant object. An incoming beam is reflected from the object to a second angularly variable reflector which reflects the incoming beam to the opto-electric camera via the beam splitter. The first reflector and the second reflector are configured so that the distance travelled by the outgoing beam from the beam splitter and the first reflector is the same as the distance travelled by the incoming beam from the second reflector to the beam splitter. The reference beam produces a reference signal in the geometric center of the camera. The incoming beam produces an object signal at the camera

40 Years of Church Growth: A View from the Theological Tower

I present these evaluative thoughts about the first forty years of the Church Growth Movement, and especially the first twenty-five years of the movement in North America (1970- 1995), with the humility appropriate to one person’s perspective on so vast and diverse a phenomenon as that of the Church Growth Movement

Music arranged for elementary school orchestra

Thesis (M.M.E.)--Boston Universit

Methods to Address Extreme Class Imbalance in Machine Learning Based Network Intrusion Detection Systems

Despite the considerable academic interest in using machine learning methods to detect cyber attacks and malicious network traffic, there is little evidence that modern organizations employ such systems. Due to the targeted nature of attacks and cybercriminals’ constantly changing behavior, valid observations of attack traffic suitable for training a classifier are extremely rare. Rare positive cases combined with the fact that the overwhelming majority of network traffic is benign create an extreme class imbalance problem. Using publically available datasets, this research examines the class imbalance problem by using small samples of the attack observations to create multiple training sets that reflect a realistic class imbalance. A variety of techniques to alleviate the imbalance are examined including under sampling the majority class and three techniques to over sample the minority attack observations by creating new synthetic observations. We test these methods on four of the most popular machine learning classifiers. We examine two single model classifiers, artificial neural networks and support vector machines, and two ensemble methods, gradient boosting and random forests. We find that under sampling generally outperforms oversampling techniques and that the ensemble methods both outperform single models. We show that the apparent superiority of the ensemble methods may be illusory due to the “laboratory conditions” of using well-crafted public datasets. By introducing an element of noise into the training data, we show that neural networks’ robustness to noise make it the preferred approach in real world settings where the more sophisticated ensemble methods fail. We also present a technique where neural networks are used to select features from the noisy dataset that improve the performance of random forests and gradient boosting allowing for the creation of an improved ensemble classifier