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We study how the so-called Rao-Blackwellization, which is a variance reduction technique via “conditioning” for Monte Carlo methods, can be judiciously applied for graph sampling through neighborhood exploration. Despite its popularity for Monte Carlo methods, it is little known for Markov chain Monte Carlo methods and has never been discussed for random walk-based graph sampling. We first propose...
Graph sampling techniques via random walk crawling have been popular for analyzing statistical characteristics of large online social networks due to simple implementation and provable guarantees on unbiased estimates. Despite the growing popularity, the ‘cost’ of sampling and its true impact on the accuracy of estimates still have not been carefully studied. In addition, the random walk-based methods...
Human can quickly and effortlessly focus on a few most interesting points in an image. Different observers tend to have the same fixations towards the same scene. In order to predict observer's fixations, eye gaze information can be used to reveal human attention and interest. This paper presents a real-time eye gaze tracking system. Haar cascade classifier is used to calculate the position of eye...
After a piece of information is released in Online Social Networks (OSNs), will it spread to the entire network or reach only a small population of users? In a time window of interest, how many users will forward or comment on this information? Limited effort has been made at this point to develop an effective model to address these issues, as the timesensitive nature of information spreading and...
In the big data era, massive news reports about the latest events are being published on the Web. To thoroughly understand an event, we have to read massive reports and keep clues in mind, which is very difficult and usually results in a one-sided interpretation. In this paper, we propose a multi-document summarization approach which summarizes reports of a particular social or political event automatically...
We are constructing and studying a unicycle robot which balances and steers itself by swinging its pendulum sidewards. Our unicycle robot, automatic lateral-pendulum unicycle (ALP Cycle), has three main parts: wheel, chassis and pendulum. It is an underactuated robot with only two control inputs and five degrees of freedom. Given the dynamic model of ALP Cycle which we derived previously, we have...
One of the challenging tasks in the domain of Tool Condition Monitoring (TCM) is feature selection. Feature selection is crucial as extracting all possible features and creating a model based on those features results in two major disadvantages, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. In this paper, four statistical feature selection methods...
In this paper we present a synthesized integral sliding mode controller (ISMC) that can stabilize an underactuated unicycle system. The unicycle consists of a wheel and a saddle that is modeled as an inverse pendulum. The only driving force is the torque applied to the shaft of the wheel, hence it is underactuated. Based on a linear approximation model, a linear controller using LQR method is designed...
In this work, a novel approach using probabilistic CMOS (PCOMS) technology is used to reduce the energy consumption of a fuzzy PID (proportional-integral-derivative) controller. Energy saving is achieved through designing a probabilistic circuit which deliberately reduces the supply voltage of some less significant bits. The fuzzy PID controller consists of 15 bits with floating point representation...
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