Towards reliable and scalable robot communication

The Robot Operating System (ROS) is the de facto standard platform for modern robots. However, communication between ROS nodes has scalability and reliability issues in practice. In this paper, we investigate whether Erlang’s lightweight concurrency and reliability mechanisms have the potential to address these issues. The basis of the investigation is a pair of simple but typical robotic control applications, namely two face-trackers: one using ROS publish/subscribe messaging, and the other a bespoke Erlang communication framework. We report experiments that compare five key aspects of the ROS and Erlang face trackers. We find that Erlang communication scales better, supporting at least 3.5 times more active processes (700 processes) than its ROS-based counterpart (200 nodes) while consuming half of the memory. However, while both face tracking prototypes exhibit similar detection accuracy and transmission latencies with 10 or fewer workers, Erlang exhibits a continuous increase in the total time taken to process a frame as more agents are added, and we identify the cause. A reliability study shows that while both ROS and Erlang restart failed computations, the Erlang processes restart 1000–1500 times faster than ROS nodes, reducing robot component downtime and mitigating the impact of the failures

Intelligent Joystick Sensing the User's Emotion and Providing Biofeedback

Development of an intelligent joystick is proposed which senses the user’s bio-signals and recognises the user’s emotion. It provides biofeedback to the user as well as the user’s emotional state information to the computer allowing human-computer interaction over sensitive environment. While the user is interacting with a computer via a joystick the bio-signals can be collected through the user’s fingers touching it. The collected bio-signals information is mapped on a two-dimensional space to find out the quality and intensity of emotion continuously and in a real-time manner. The intelligent joystick has application within several fields such as healthcare, sport and game industries. In such cases, the user can be influenced, or suffer from medical problems while under stress during interaction with the machines. The intelligent joystick will provide feedback to the user and alert alarm about unhealthy conditions through the embedded actuators and allow the machine to adapt with the users’ emotional state

Lecture on Stochastic Simulation Methods for Probabilistic Inference

Technique for approximate inference in Bayesian networks • Run repeated simulations of the world described by the network • Estimate the probabilities we are interested in by counting the frequencies with which relevant events occur Get probabilities from samples • If we could sample from a variable's posterior probability, we could estimate its posterior probability • Probabilities correspond to samples 2 Estimating Probabilities: An Example Suppose we wish to estimate the probability p that a certain drawing pin lands heads. We toss it 100 times and it comes up heads 35 times. What is our best guess for p? If we had tossed it once, and it had come up heads, what would be our guess for p then? 3 The Law of the Large Numbers Consider tossing a coin a large number of times, where the probability of heads on any toss is p. Let Sn be the number of heads that come after n tosses. Think of Sn as the number of successes The law of large numbers says that the probability that Sn n differs much from p becomes smaller and smaller as n gets bigger If ɛ> 0, then as n → ∞ it holds P ( | Sn n − p | < ɛ) →

On‐demand recent personal tweets summarization on mobile devices

Tweets summarization aims to find a group of representative tweets for a specific set of input tweets or a given topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (that is, consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this article we present a lightweight, personal, on-demand, topic modeling-based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM first preprocesses recent tweets in a user’s timeline and exploits Latent Dirichlet Allocation-based topic modeling to assign each preprocessed tweet to a topic. Then it generates a ranked list of relevant tweets, a topic label, and a topic summary for each of the topics. Our experimental study with real-world data sets demonstrates the superiority of TOTEM.Accepted versio

Back to the Future: Consistency-Based Trajectory Tracking

Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observable Markov decision processes with very large state spaces. The algorithm presented incrementally generates, rather than revises, an approximate belief state at any point by abstracting and summarizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial belief state when it remains consistent with observations and revisit past assumptions about the process' evolution when the belief state is ruled out. The system presented has been implemented and results on examples from the domain of spacecraft control are presented

Aspect-Oriented Programming is Quantification and Obliviousness

This paper proposes that the distinguishing characteristic of Aspect-Oriented Programming (AOP) systems is that they allow programming by making quantified programmatic assertions over programs written by programmers oblivious to such assertions. Thus, AOP systems can be analyzed with respect to three critical dimensions: the kinds of quantifications allowed, the nature of the actions that can be asserted, and the mechanism for combining base-level actions with asserted actions. Consequences of this perspective are the recognition that certain systems are not AOP and that some mechanisms are expressive enough to allow programming an AOP system within them. A corollary is that while AOP can be applied to Object-Oriented Programming, it is an independent concept applicable to other programming styles