Regular Languages of Thin Trees

An infinite tree is called thin if it contains only countably many infinite branches. Thin trees can be seen as intermediate structures between infinite words and infinite trees. In this work we investigate properties of regular languages of thin trees. Our main tool is an algebra suitable for thin trees. Using this framework we characterize various classes of regular languages: commutative, open in the standard topology, and definable in weak MSO logic among all trees. We also show that in various meanings thin trees are not as rich as all infinite trees. In particular we observe a collapse of the parity index to the level (1, 3) and a collapse of the topological complexity to co-analytic sets. Moreover, a gap property is shown: a regular language of thin trees is either weak MSO-definable among all trees or co-analytic-complete.     

Pseudo-inversion: closure properties and decidability

We consider a pseudo-inversion operation inspired by biological events, such as DNA sequence transformations, where only parts of a string are reversed. We define the pseudo-inversion of a string \(w = uxv\) to be the set of all strings \(v^Rxu^R\), where \(uv \ne \lambda \) and consider the operation from a formal language theoretic viewpoint. We show that regular languages are closed under the pseudo-inversion operation whereas context-free languages are not. Furthermore, we study the iterated pseudo-inversion operation and show that the iterated pseudo-inversion of a context-free language is recognized by a nondeterministic reversal-bounded multicounter machine. Finally, we introduce the notion of pseudo-inversion-freeness and examine closure properties and decidability problems for regular and context-free languages. We demonstrate that pseudo-inversion-freeness is decidable in polynomial time for regular languages and undecidable for context-free languages.     

Usability Principles for Augmented Reality Applications in a Smartphone Environment

Through the rapid spread of smartphones, users have access to many types of applications similar to those on desktop computer systems. Smartphone applications using augmented reality (AR) technology make use of users' location information. As AR applications will require new evaluation methods, improved usability and user convenience should be developed. The purpose of the current study is to develop usability principles for the development and evaluation of smartphone applications using AR technology. We develop usability principles for smartphone AR applications by analyzing existing research about heuristic evaluation methods, design principles for AR systems, guidelines for handheld mobile device interfaces, and usability principles for the tangible user interface. We conducted a heuristic evaluation for three popularly used smartphone AR applications to identify usability problems. We suggested new design guidelines to solve the identified problems. Then, we developed an improved AR application prototype of an Android-based smartphone, which later was conducted a usability testing to validate the effects of usability principles.     

The determinants of continuous use of social networking sites: An empirical study on Taiwanese journal-type bloggers’ continuous self-disclosure behavior

The popularity and growth of social networking sites (SNSs) have resulted in fierce competition among SNS providers. A critical concern of SNSs is to identify how to have users continuously use a specific SNS. This study examines the determinants of journal-type bloggers’ continuous self-disclosure. An analysis of 283 samples with continuous self-disclosure experiences in journal-type blogs shows that personal factors, specifically the habit of self-disclosure, are the major factors affecting bloggers’ continuous self-disclosure, followed by self-benefits. In contrast, social factors, such as positive audience feedback and social benefits, are not related to continuous self-disclosure. These results suggest that SNS providers must encourage users to develop the habit of using their web sites regularly. By providing differential benefits to users, SNS providers may attract users to continuously use their services.     

Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.     

A Y-channel design for improving zeta potential and surface conductivity measurements using the current monitoring method

This study presents a new Y-channel design for measuring the zeta potential and surface conductance of a solid-liquid pairing using the current monitoring technique. The new design improves the throughput and reliability of the testing apparatus since the displacement between two solutions can be repeated many times without interfering with the experiments. It also increases the accuracy of the measurement by producing sharper start and end transitions for the current–time plot of the solution displacement process. In this design, efforts have been made to minimize the effects of electrolysis, Joule heating and undesired pressure driven flow on the measurements. An improvement on the current–time slope analysis is also presented. The Y-channel design was validated by comparing zeta potential measurements to published results. The zeta potential of several biological buffers relevant to the microfluidic community in plasma treated PDMS/PDMS and PDMS/Glass microchannels are presented. Preliminary studies of surface conductivity measurements using the Y-channel design were also conducted and are briefly discussed.     

Study on the electrical conductivity and morphology of porous polypyrrole layers prepared electrochemically in the presence of pyridinium chlorochromate

The electrical conductivity and morphology of thick (up to 3 mm) porous polypyrrole (PPy) layers produced electrochemically from pyrrole in acetonitrile (ACN) solutions have been studied. The electrical conductivity of pressed porous layers ranges from 1 to 10 Scm^–1, which is about one order of magnitude less than that in films which were prepared under similar conditions but without PnClCr. Analysis of the temperature dependence of conductivity has confirmed the major role of hopping in relation to tunnelling in charge transport inside the PPy layers even at lower temperatures. Scanning electron microscopy (SEM) showed a globular structure, which is different from the usual cauliflower-like structure of PPy films prepared without any oxidizing agent. Globular particles of about 1–3 m diameter have been found under a thin smooth crust on the electrode side of the sample. Globular particles form linked chain-like or larger round formations poorly filling the space. Closely packed fibrils of about 20 nm diameter and over 100 nm in length were found inside the aggregates.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.