Pictorial representations are widely used in human problem solving. For blind and visually impaired people, haptic interfaces can provide perceptual access to graphical representations. We propose line-based graphics as a type of graphics, which are suitable to be explored by blind and visually impaired people, and which can be successfully augmented with auditory assistance by speech or non-verbal sounds. The central prerequisite for realizing powerful assistive interaction is monitoring the users’ haptic exploration and in particular the recognition of exploratory events. The representational layers of line-based graphics as well as of exploration-event descriptions are specified by qualitative spatial propositions. Based on these representations, event recognition is performed by rule-based processes.
In the last decade, cooperative communication and network coding techniques gained rising attention. Based on these techniques, this paper introduces a novel protocol called cooperation and network coding based MAC (CNCMAC) for vehicular ad-hoc networks (VANETs). It employs cooperative automatic repeat request (ARQ) and network coding techniques to enhance throughput. The CNC-MAC protocol works in two phases. In the first phase - cooperation - data is sent to relay nodes. The second phase - network coding - minimizes received packets. The performance of the CNC-MAC protocol is compared with cooperative ARQ-MAC, another common protocol in this domain. The simulation results indicate that CNC-MAC outperforms ARQ-MAC.
Programmers working in a Unix-like environment can easily build custom tools by configuring and combining small filter programs in shell scripts. When leaving such a text-based world and entering one that is graphics-based, however, tool building is more difficult because graphical tools are typically not prepared to be easily re-programmed by their users. We propose a data-driven perspective on graphical tools that uses concise scripts as glue between data and views but also as means to express missing data transformations and view items. Given this, we built a framework in Squeak/Smalltalk that promotes low-effort tool construction; it works well for basic programming tools, such as code editors and debuggers, but also for other domains, such as developer chats and issue browsers. We think that this perspective on graphical tools can inspire the creation of new trade-offs in modularity for both data-providing projects and interactive views.
Vision is one of the most complex proficiencies we possess, but its underpinnings are still shrouded in mystery. Many great scientific minds have been engaged in the enterprise of modeling vision. This chapter takes a look at some of the history of this effort, stretching from the times of the ancient Greeks to recent developments in neural networks, and discusses how current techniques may play a role in furthering our understanding of vision.
Raman spectroscopy is used to identify unknown constituent minerals and their abundances since Raman spectra convey characteristic information about the sample's chemical structure. We present a novel method to identify constituting pure minerals in a mixture by comparing the measured Raman spectra with a reference database. Our method comprises of two major components: A novel scale-invariant spectral matching technique, that allows to compare measured spectra with the reference spectra from the database even when the band intensities are not directly comparable and an iterative unmixing scheme to decompose a measured spectrum into its constituent minerals and compute their abundances.
Robotic in-hand manipulation is very important for service robots as it is one of the key skills in housework. Although plenty of research has been carried out, it is still far way from real applications. One of the issues lies in uncertainty of interaction states. In this paper we research robot-object interactions with a concept called `haptic exploration'. With this concept, a robot hand tries to push an in-hand object slightly to explore the interaction state. In this process, both haptic and visual feedbacks are collected to estimate the interaction state. We firstly review spring based models proposed in our previous works to explain the mechanism behind the haptic exploration. In addition, in order to verify the feasibility of the proposed methods, two experiments are conducted. In the first experiment, the repeatability of push actions is verified. Furthermore, in the second experiment, both haptic and visual rewards are adopted to evaluate the pushes for a best in-hand manipulation action after the haptic exploration.
We present a monadic second-order logic which is extended by an expected value operator and show that this logic is expressively equivalent to probabilistic automata for both finite and infinite words. We give possible syntax extensions and an embedding of our probabilistic logic into weighted MSO logic. We further derive decidability results which are based on corresponding results for probabilistic automata.
In CT, the nonlinear attenuation characteristics of polychromatic X-rays cause beam hardening artifacts in the reconstructed images. Statistical algorithms can effectively correct beam hardening artifacts while providing the benefit of noise reduction. In practice, a big challenge for CT is the difficulty at acquiring accurate energy spectrum information, which hinders the efficiency of beam hardening correction approaches that require the spectrum as prior knowledge such as the statistical methods. In this paper, we used proposed energy spectrum binning approach for reducing prior knowledge from full spectrum to three energy bins to compare the results when applying parameters optimized for one spectrum to data measured using a different spectrum.
