Virtual Rendering based Second Life Mobile Application to Control Ambient Media Services

In this paper we propose the development details of a mobile client that allows virtual 3D avatar interaction and virtual 3D annotation control in Second Life. We established adaptation based virtual rendering of the Second Life client and encoded the real-time frames into video stream, which is suitable for mobile client rendering. Additionally, we re-mapped the touch-based interaction of the user and feed that to the Second Life client in a form of keyboard and mouse interactions. As a proof of concept, we annotated a virtual environment object in Second Life and linked that with a media service by UPnP [5]. Further, we captured the mobile interaction of the user and provided controller interface to change states of the media object through the virtual object interaction. We argue that by using the mobile Second Life virtual interface the user has a better look to monitor and control the home appliances. We present illustration of the prototype system and show its application in a smart environment setup

Open data-set of seven Canadian Cities

Open data has attracted huge attention for the construction of smart city in terms of delivering useful city information to citizens and interacting with citizens from the city council perspective. In this paper, we present an overview of the current status and issues of open data opened by different seven Canadian cities. We start by presenting the characters of open data, followed by data format conclusion and detailed dataset explaination for each Canadian city (e.g., Calgary, Halifax, Surrey, Waterloo, Ottawa, Vancouver, and Toronto), including the different data catalogues and their detailed characteristics. Next, we discuss the state-of-the-art of the tools and applications developed over each city's open data. Here, we not only illustrate the most successful examples, but particularly consider the potential issues due to the characters of the city datasets. This paper is not only beneficial for a government, which can compare its open data status with that of the Canadian cities but also quite useful for users or companies interested in tool development over open city data

Haptic handwritten signatures: the effect of deconcentrated dissimilarities on manifold extraction

The use of a haptic-based handwritten signatures has an intrinsic biometric nature and an important potential in user identification/authentication because it incorporates tactile information. However, in order to exploit this potential for constructing decision systems, it is necessary to gain an appropriate understanding of the internal structure of the data, which in relational representations tend to be very highly dimensional. Most machine learning techniques i) are affected by the curse of dimensionality, ii) use algorithms involving distances (usually Euclidean), but in high dimensional spaces they suffer from the concentration phenomenon. This paper explores the behavior of different strategies for distance deconcentration of haptic data when used for nonlinear unsupervised mappings into low dimensional spaces. An aposteriori use of class information shows that deconcentration transformations improve class cohesion and separation, which can improve the performance of machine learning algorithms.Peer reviewed: YesNRC publication: Ye

Visualization of handwritten signatures based on haptic information

The problem of user authentication is a crucial component of many solutions related to defense and security. The identification and verification of users allows the implementation of technologies and services oriented to the intended user and to prevent misuse by illegitimate users. It has become an essential part of many systems and it is used in several applications, particularly in the military. The handwritten signature is an element intrinsically endowed with specificity related to an individual and it has been used extensively as a key element in identification/authentication. Haptic technologies allow the use of additional information like kinesthetic and tactile feedback from the user, thus providing new sources of biometric information that can be incorporated within the process in addition to the traditional image-based sources. While work had been done on using haptic information for the analysis of handwritten signatures, most efforts have been oriented to the direct use ofmachine learning techniques for identification/verification. Comparatively fewer targeted information visualization and understanding the internal structure of the data. Here a variety of techniques are used for obtaining representations of the data in low dimensional spaces amenable to visual inspection (two and three dimensions). The approach is unsupervised, although for illustration and comparison purposes, class information is used as qualitative reference. Estimations of the intrinsic dimension for the haptic data are obtained which shows that low dimensional subspaces contains most of the data structure. Implicit and explicit mappings techniques transforming the original high dimensional data to lowdimensional spaces are considered. They include linear and nonlinear, classical and computational intelligence based methods: Principal Components, Sammon mapping, Isomap, Locally Linear Embedding, Spectral Embedding, t-Distributed StochasticNeighbour Embedding, Generative Topographic Mapping, Neuroscale and Genetic Programming. They provided insight about common and specific characteristics found in haptic signatures, their within/among subjects variability and the important role of certain types of haptic variables. The results obtained suggest ways how to design new representations for identification and verification procedures using tactile devices.Peer reviewed: YesNRC publication: Ye

Identity verification based on handwritten signatures with haptic information using genetic programming

In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naive Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).Peer reviewed: YesNRC publication: Ye

Nonlinear robust adaptive sliding mode control design for miniature unmanned multirotor aerial vehicle

This paper addresses the stability and tracking control problem of miniature unmanned multirotor aerial vehicle (MUMAV) in the presence of bounded uncertainty. The uncertainty may appear from unmodeled dynamics, underactuated property, input disturbance and flying environment. Nonlinear robust adaptive sliding mode control algorithm is designed by using Lyapunov function. Robust adaptation laws are designed to learn and compensate the bounded parametric uncertainty. Lyapunov analysis shows that the proposed algorithms can guarantee asymptotic stability and tracking control property of the linear and angular dynamics of MUMAV system. Compared with other existing control methods, the proposed design is very simple and easy to implement as it does not require multiple design steps, augmented auxiliary signals and exact bound of the uncertainty. Experimental results on a miniature unmanned quadrotor aerial vehicle are presented to illustrate of effectiveness of the proposed design for real-time applications

Distributed robust adaptive finite-time voltage control for microgrids with uncertainty

Consensus based distributed robust adaptive finite-time secondary voltage control is designed for inverter-based islanded AC microgrids. The design combines decentralized local states information with the states of the neighboring distributed generators with directed communication topology. Robust control algorithms are used locally for each distributed generator to deal with uncertainty. Lyapunov and terminal sliding mode theory uses to guarantee that the proposed distributed control design can restore voltage to the reference value in finite-time. Analysis shows that the finite-time robust consensus can force the voltage of the distributed generators to reach the designed terminal sliding surface in finite-time and remain there. The proposed distributed secondary controller does not require a priori knowledge of the nonlinear dynamical model and uncertainty associated with microgrids

Observer-based force reflecting robust coordination control for networked bilateral shared telerobotic system

This paper proposes observer-based output feedback force reflecting robust coordination control for networked bilateral shared telerobotic system with asymmetrical delay and uncertainty. We design state feedback-based force reflecting control algorithm provided that all the states are available for feedback. We then replace the unknown velocity states by model-free observer to develop output feedback-based force reflecting robust coordination control algorithm for bilateral shared autonomous system. The coordination control algorithm is designed by combining delayed position and velocity states with the reflected interaction forces from human and environment. Robust adaptive control theory is employed to deal with uncertainty. The stability analysis is shown by using Lyapunov method. The method does not require linear matrix inequality and uncertainty. Compared with other force reflection methods, the design uses reflected forces from both interaction between master and human and between slave and environment. Experimental results are presented to demonstrate the validi