Integration of task level planning and diagnosis for an intelligent robot

A satellite floating space is diagnosed with a telerobot attached performing maintenance or replacement tasks. This research included three objectives. The first objective was to generate intelligent path planning for a robot to move around a satellite. The second objective was to diagnose possible faulty scenarios in the satellite. The third objective included two tasks. The first task was to combine intelligent path planning with diagnosis. The second task was to build an interface between the combined intelligent system with Robosim. The ability of a robot to deal with unexpected scenarios is particularly important in space since the situation could be different from time to time so that the telerobot must be capable of detecting that the situation has changed and the necessity may exist to alter its behavior based on the new situation. The feature of allowing human-in-the-loop is also very important in space. In some extreme cases, the situation is beyond the capability of a robot so our research project allows the human to override the decision of a robot

Scaling laws for molecular communication

In this paper, we investigate information-theoretic scaling laws, independent from communication strategies, for point-to-point molecular communication, where it sends/receives information-encoded molecules between nanomachines. Since the Shannon capacity for this is still an open problem, we first derive an asymptotic order in a single coordinate, i.e., i) scaling time with constant number of molecules $m$ and ii) scaling molecules with constant time $t$. For a single coordinate case, we show that the asymptotic scaling is logarithmic in either coordinate, i.e., $\Theta(\log t)$ and $\Theta(\log m)$, respectively. We also study asymptotic behavior of scaling in both time and molecules and show that, if molecules and time are proportional to each other, then the asymptotic scaling is linear, i.e., $\Theta(t)=\Theta(m)$.Comment: Accepted for publication in the 2014 IEEE International Symposium on Information Theor

Fairness in overloaded parallel queues

Maximizing throughput for heterogeneous parallel server queues has received quite a bit of attention from the research community and the stability region for such systems is well understood. However, many real-world systems have periods where they are temporarily overloaded. Under such scenarios, the unstable queues often starve limited resources. This work examines what happens during periods of temporary overload. Specifically, we look at how to fairly distribute stress. We explore the dynamics of the queue workloads under the MaxWeight scheduling policy during long periods of stress and discuss how to tune this policy in order to achieve a target fairness ratio across these workloads

Analysis of recent type Ia supernova data based on evolving dark energy models

We study characters of recent type Ia supernova (SNIa) data using evolving dark energy models with changing equation of state parameter w. We consider sudden-jump approximation of w for some chosen redshift spans with double transitions, and constrain these models based on Markov Chain Monte Carlo (MCMC) method using the SNIa data (Constitution, Union, Union2) together with baryon acoustic oscillation A parameter and cosmic microwave background shift parameter in a flat background. In the double-transition model the Constitution data shows deviation outside 1 sigma from LCDM model at low (z < 0.2) and middle (0.2 < z < 0.4) redshift bins whereas no such deviations are noticeable in the Union and Union2 data. By analyzing the Union members in the Constitution set, however, we show that the same difference is actually due to different calibration of the same Union sample in the Constitution set, and is not due to new data added in the Constitution set. All detected deviations are within 2 sigma from the LCDM world model. From the LCDM mock data analysis, we quantify biases in the dark energy equation of state parameters induced by insufficient data with inhomogeneous distribution of data points in the redshift space and distance modulus errors. We demonstrate that location of peak in the distribution of arithmetic means (computed from the MCMC chain for each mock data) behaves as an unbiased estimator for the average bias, which is valid even for non-symmetric likelihood distributions.Comment: 12 pages, 6 figures, published in the Phys. Rev.

The International Volatility of Growth

Growth in the world economy is not shared equally among all countries, with some growing faster, some slower and some not at all. The cross-country distribution of growth is a useful tool for analysing the inequality of growth. The appropriately-weighted first moment of this distribution is world growth, while the second measures cross-country volatility. This paper introduces a methodology to examine the cross-country distribution of growth, and the components of its volatility. Using data from the Penn World Table, we find countries within geographic regions are seeing a harmonisation of growth, but between regions there is increasing dispersion.Growth, Cross-Country Distribution, Volatility