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Investigation of the process of the NO3–anion solvation is central to understanding the chemical and physical properties of its aqueous solutions. The importance of this topic can be seen in atmospheric chemistry, as well as in nuclear waste processing research. In this work, we used a particle swarm optimization technique driven by density functional theory to sample the potential energy surface of various microsolvated [NO3·(H2O)n]−(n= 1–12) clusters. We found that the charge transfer plays a crucial role in the stabilization of the investigated species. Moreover, by conducting ab initiomolecular dynamics simulations, we showed that at low concentrations (∼0.2 M) the NO3–species tend to be located on the surface of water solution. We also observed that the contact ion pair K+–NO3–undergoes a fast dissociation and each of the ions is solvated separately. As a result, from our calculations, we expect that at low concentration there could be oppositely signed concentration gradients for NO3–and K+ions in a thin water film.

We present a robot-assisted wayfinding system for the visually impaired in structured indoor environments. The system consists of a mobile robotic guide and small passive RFID sensors embedded in the environment. The system is intended for use in indoor environments, such as office buildings, supermarkets and airports. We describe how the system was deployed in two indoor environments and evaluated by visually impaired participants in a series of pilot experiments. We analyze the system’s successes and failures and outline our plans for future research and development.

We present an approach to human-robot interaction through gesture-free spoken dialogue. Our approach is based on passive knowledge rarefication through goal disambiguation, a technique that allows a human operator to collaborate with a mobile robot on various tasks through spoken dialogue without making bodily gestures. A key assumption underlying our approach is that the operator and the robot share a common set of goals. Another key idea is that language, vision, and action share common memory structures.We discuss how our approach achieves four types of human-robot interaction: command, goal disambiguation, introspection, and instruction-based learning. We describe the system we developed to implement our approach and present experimental results.

Many problems in information retrieval and related fields depend on a reliable measure of the distance or similarity between objects that, most frequently, are represented as vectors. This paper considers vectors of bits. Such data structures implement entities as diverse as bitmaps that indicate the occurrences of terms and bitstrings indicating the presence of edges in images. For such applications, a popular distance measure is the Hamming distance. The value of the Hamming distance for information retrieval applications is limited by the fact that it counts only exact matches, whereas in information retrieval, corresponding bits that are close by can still be considered to be almost identical. We define a “Generalized Hamming distance” that extends the Hamming concept to give partial credit for near misses, and suggest a dynamic programming algorithm that permits it to be computed efficiently. We envision many uses for such a measure. In this paper we define and prove some basic properties of the “Generalized Hamming distance”, and illustrate its use in the area of object recognition. We evaluate our implementation in a series of experiments, using autonomous robots to test the measure's effectiveness in relating similar bitstrings.

This article describes faqfinder, a natural language question‐answering system that uses files of frequently asked questions as its knowledge base. Unlike AI question‐answering systems that focus on the generation of new answers, faqfinderretrieves existing ones found in frequently asked question files. Unlike information‐retrieval approaches that rely on a purely lexical metric of similarity between query and document, faqfinderuses a semantic knowledge base (wordnet) to improve its ability to match question and answer. We include results from an evaluation of the system's performance and show that a combination of semantic and statistical techniques works better than any single approach.