Pose estimation is one of the vital issues in mobile robot navigation. Odometry data can be fused with absolute position measurements to provide better and more reliable pose estimation. This paper deals with the determination of better relative localization of a two wheeled differential drive robot by means of odometry by considering the influence of parameters namely weight, velocity, wheel p...

This paper develops a new efficient estimator for the average treatment effect, if selection for treatment is on observables. The new estimator is linear in the first-stage nonparametric estimator. This simplifies the derivation of the means squared error (MSE) of the estimator as a function of the number of basis functions that is used in the first stage nonparametric regression. We propose an...

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as sum-rate. This work studies encoder cooperation in the setting of a discrete multiple access channel with two encoders and a single decoder. A node in the network that is connected to both encoders via rate-limited links, referred to as the cooper...

Failure to allow for autocorrelation of the disturbances in a regression model can lead to biased and inconsistent parameter estimates, particularly if the model is autoregressive. While consistent estimation methods are available which allow for autocorrelation, estimation is usually much easier when there is some assurance that autocorrelation is absent. In pursuit of such assurance the prese...

The measurement of the average received power is essential for power control and dynamic channel allocation in wireless communication systems. However, due to the effects of multipath fading and additive noise inherent to the wireless channel, there can be significant errors in such measurements. In this paper, the error statistics for average power measurements are considered; in particular, t...

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061...

The ability of a sensor node to determine its physical location within a network (Localization) is of fundamental importance in sensor networks. Interpretating data from sensors will not be possible unless the context of the data is known; this is most often accomplished by tracking its physical location. Existing research has focused on localization in static sensor networks where localization...

We have developed a new on-line error modeling and optimizationbased localization approach for sensor networks in the presence of distance measurement noise. The approach is solely based on the concept of consistency, and is developed specifically for the case of on-line localization, which refers to the situation when references are not available a priori. The localization problem is formulate...

Source localization from MEG data in real time requires algorithms which are robust, fully automatic, and very fast. We present two neural network systems which are able to localize a single dipole to reasonable accuracy within a fraction of a millisecond, even when the signals are contaminated by considerable noise. The first network is a multilayer perceptron (MLP) which takes the sensor meas...

This thesis introduces robust reconstruction and efficient localization methods for mobile Augmented Reality (AR). Robust Structure from Motion(SfM) methods are necessary to create sparse reconstructions of specific areas where localization and therefore AR should be possible. The resulting reconstructions serve as large, general tracking targets. Efficient localization methods are required to ...