Department Publications

We have developed a two-color, two-pathway coherent control technique to detect and measure weak optical transitions in atoms by coherently beating the transition amplitude for the weak transition with that of a much stronger transition. We demonstrate the technique in atomic cesium, exciting the transition via a strong two-photon transition and a weak controllable Stark-induced transition. We discuss the enhancement in the signal-to-noise ratio for this measurement technique over that of direct detection of the weak transition rate, and project future refinements that may further improve its sensitivity and application to the measurement of other weak atomic interactions.

This paper illustrates two techniques for improved estimation of the location of Mobile Stations (MS) in cellular Networks. The first approach is the statistical method in which signal properties are treated as random variables which are statistically dependent on the location of the transmitter and the receiver. Location estimation for a set of observed signal strengths at a specific location is done as an inference problem. In the second approach, Database Correlation, signal information seen by an MS is stored in the form of fingerprints. To estimate the location, difference between the measurement and the database fingerprints is calculated using a correlation algorithm. Database fingerprints with least difference are selected as the nearest fingerprints for the location to be estimated. The two techniques have been tested through extensive measurements in two environments, urban and sub urban, to verify their …

Map building and obstacle detection are basic tasks in mobile robot navigation. In this paper we present a probability based solution for map building which utilizes time of flight information from an ultrasonic sensor. In contrast to most of the available map building systems, this uses a single ultrasonic sensor, mounted on a rotating shaft. Discrete sonar observations taken at regular time intervals, by rotating the shaft in small step angles are incrementally merged into partial planes to produce a realistic representation of environment that is amenable to sonar localization. Readings of close proximity are averaged to suppress the transient errors and the results are further refined by using the sonar probability model to fill an occupancy grid and Bayesian combination for the purpose of data fusion. Experimental results obtained in a real indoor environment are presented to verify the validity of our algorithm.

We demonstrate a powerful technique for detecting weak atomic or molecular transitions that is based on homodyning two atomic transition amplitudes. The scheme is tested on a weak Stark-induced transition in cesium by exciting its ground 6s^ 2S 1/2 state to the 8s^ 2S 1/2 excited state by a 411 nm laser field. The strong local oscillator signal is a two-photon transition excited by an 822 nm laser field that connects the same ground and exited states. The Stark-induced transition can be regulated by controlling a DC field across a Cs cell. The simultaneous excitation of the two pathways results in a quantum interference that can be controlled by phase modulating either of the driving laser fields. The change in the relative phase between the laser fields results in a change in the relative phase between the transition amplitudes of the two paths. Thus, phase modulating one of the beams gives rise to an amplitude …

The simultaneous localization and map-building (SLAM) problem of robots with Extended Kalman Filter (EKF) framework were studied. Two types of sensing systems, ultrasound and computer vision, were mounted on the robot to perceive the environment so as to simultaneously localize the robot. The Kalman Filter (KF) is a linear, discrete-time, finite dimensional system with recursive structure that makes a digital computer well suited for its implementation. EKF provides a real-time solution to the navigation problem and to on-going estimation of the uncertainty acquired from vehicle motion and landmark observation.

Frequency Domain Adaptive Filters (FDAFs) are an important subject of current research in digital signal processing. One of the main reasons is that FDAFs have the ability to reduce complexity, especially when the filter length becomes very large. In Adaptive Filter (AF) configurations a convolution and a correlation have to be carried out. These operations can be done by using overlap methods in combination with Discrete Fourier Transforms (DFTs). Two well-known overlap methods are the so-called overlap-save and the overlap-add method. In literature 111 it is stated that for AF configurations the overlap-save method is to be preferred to the overlap-add method for complexity reasons. By introducing a new FDAF structure we will show that this statement is not correct and that an FDAF using the overlap-add method can be realized with the same number of DFTs as the FDAF using the overlap-save method

This paper presents the results of work carried out to apply and evaluate the use of network planning tool predictions to the Database Correlation Method (DCM), for location estimation in cellular environments. In Database Correlation Method (DCM), the location dependent signal information seen by a mobile station is stored in a database in a form of fingerprints. During the location estimation, the measured signal sample is compared with the fingerprints stored in the database to come up with an accurate estimation. Even though the method has proven superior in accuracy over other cellular positioning techniques, the higher demanding effort involved in database formation using field measurements has become a big challenge which prevents the technique being deployed in large dynamic networks. Formation of a database using the predictions from the network planning tools or theoretical propagation models would be a possible solution for the issue. Then, the motivation arises to evaluate the accuracy of the method using predicted fingerprints instead of measured ones.

Map building and obstacle detection are basic tasks in mobile robot navigation. In this paper we present a probability based solution for map building which utilizes time of flight information from an ultrasonic sensor. In contrast to most of the available map building systems, this uses a single ultrasonic sensor, mounted on a rotating shaft. Discrete sonar observations taken at regular time intervals, by rotating the shaft in small step angles are incrementally merged into partial planes to produce a realistic representation of environment that is amenable to sonar localization. Readings of close proximity are averaged to suppress the transient errors and the results are further refined by using the sonar probability model to fill an occupancy grid and Bayesian combination for the purpose of data fusion. Experimental results obtained in a real indoor environment are presented to verify the validity of our algorithm.