Coherence dynamics of two-mode condensates in asymmetric potentials

Detection of weak forces with an accuracy beyond the standard quantum limit holds promise both for fundamental research and for technological applications. Schemes involving ultracold atoms for such measurements are now considered to be prime candidates for increased sensitivity. In this paper we use a combination of analytical and numerical techniques to investigate the possible subshot-noise estimation of applied force fields through detection of coherence dynamics of Bose-condensed atoms in asymmetric double-well traps. Following a semiclassical description of the system dynamics and fringe visibility, we present numerical simulations of the full quantum dynamics that demonstrate the dynamical production of phase squeezing beyond the standard quantum limit. Nonlinear interactions are found to limit the achievable amount to a finite value determined by the external weak force.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.