A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Simplified Successive Intervals Scaling

A simplified version of successive intervals scaling is described. Scale values for various datasets obtained with simplified successive intervals scaling are approximately linearly related to those obtained with traditional successive intervals scaling and with the method of pair comparisons. Simplified successive intervals scaling can be used with any number of stimuli and is as easy to apply...

Tracking Pedestrians across Multiple Microcells Based on Successive Bayesian Estimations

We propose a method for tracking multiple pedestrians using a binary sensor network. In our proposed method, sensor nodes are composed of pairs of binary sensors and placed at specific points, referred to as gates, where pedestrians temporarily change their movement characteristics, such as doors, stairs, and elevators, to detect pedestrian arrival and departure events. Tracking pedestrians in ...

Bayesian Group Tracking Method for Space Debris

With the intense increase in space debris, it is necessary to efficiently track and catalog the extensive dense clusters of space debris. As the main instrument for LEO space surveillance, a ground-based radar system is usually limited by its resolution while tracking small space debris with high density. Thus, the obtained measurement information could have been seriously missed, which makes t...

Anaxiomatization of Thurstone's Successive Intervals

Bayesian Modelling of Harmonic Signals for Polyphonic Music Tracking

Musical signals can be well represented as a sum of harmonically related sinusoids motivated by consideration of the sound generation mechanisms of many musical instruments. We consider the problem of estimating the harmonic model parameters in a Bayesian framework which has the potential to incorporate a priori knowledge about the structure of the data and of its parameters. Constraints can be...