Understanding Usability and User Experience of Web-Based 3D Graphics Technology

As Web-based interactive 3D graphics (Web 3D), popularly referred to as Virtual Reality, continue to become more affordable, research and development groups in various fields have been adopting Web 3D technology. In addition to simulation of 3D content, the ability to instantly display alternative looks has been recognized as an innovative way to improve communication in such fields as product design, architecture, and e-commerce. Despite substantial adoption of Web 3D, how and how much the technology benefits target users as well as the providers who choose to adapt the Web 3D technology are not well understood. Previous research has established that interactive 3D graphics provide users with unique human–computer interaction (HCI). However, little is known about how users experience the Web 3D graphics technology and how user–system interaction contributes to system usability. The purpose of this study is to build new knowledge of the user experience with interactive-3D graphics systems used for product demonstration. By testing the impact of the technology on the user–system interaction and usability and comparing this impact with that of conventional two-dimensional (2D) graphics, this study tries to better understand the Web 3D technology from an interdisciplinary view of technology acceptance, sense of presence, and HCI. The study investigated how system usability is affected by HCI in the context of a furniture-style preference survey. The results of the study display the clear advantage for Web 3D for usability and show that perceived usefulness and sense of presence both mediate the effect of the technology treatment on the usability outcomes. The contribution of this study is that it includes empirical data to show how Web 3D benefits users when adopted in the context of a product demonstration and how the advantage is obtained through the user's interaction with the Web 3D technology.     

EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling

Aligning shapes is essential in many computer vision problems and generalized Procrustes analysis (GPA) is one of the most popular algorithms to align shapes. However, if some of the shape data are missing, GPA cannot be applied. In this paper, we propose EM-GPA, which extends GPA to handle shapes with hidden (missing) variables by using the expectation-maximization (EM) algorithm. For example, 2D shapes can be considered as 3D shapes with missing depth information due to the projection of 3D shapes into the image plane. For a set of 2D shapes, EM-GPA finds scales, rotations and 3D shapes along with their mean and covariance matrix for 3D shape modeling. A distinctive characteristic of EM-GPA is that it does not enforce any rank constraint often appeared in other work and instead uses GPA constraints to resolve the ambiguity in finding scales, rotations, and 3D shapes. The experimental results show that EM-GPA can recover depth information accurately even when the noise level is high and there are a large number of missing variables. By using the images from the FRGC database, we show that EM-GPA can successfully align 2D shapes by taking the missing information into consideration. We also demonstrate that the 3D mean shape and its covariance matrix are accurately estimated. As an application of EM-GPA, we construct a 2D + 3D AAM (active appearance model) using the 3D shapes obtained by EM-GPA, and it gives a similar success rate in model fitting compared to the method using real 3D shapes. EM-GPA is not limited to the case of missing depth information, but it can be easily extended to more general cases.     

An object-based middleware supporting efficient interoperability on a smart home network

This paper proposes a novel object-based home network middleware for supporting the interoperability among home devices and smart grid devices. This middleware provides various types of abstract objects for flexible representation of heterogeneous home devices, which are classified based on their characteristics. It is also flexible enough to allow addition of new protocols and message conversion between different protocols through an abstraction layer, which are not supported by existing home network middlewares so that various protocols can be supported. As a result, it can be utilized to provide the interoperability among a variety of devices from sensors to typical home appliances as well as smart grid devices such as a home electric generator and a battery, which adopt different protocols. In order to demonstrate that home devices and smart grid devices are interoperable through the proposed middleware, we implement applications based on the middleware on a target platform consisting of embedded boards, sensors and laptops for emulation purposes of a home network. According to our emulation, this middleware can provide efficient interoperability among home devices and smart grid devices for future energy efficient home.     

Presence, molecular characteristics and geosmin producing ability of actinomycetes isolated from South Korean terrestrial and aquatic environments

The unpleasant odor of drinking water is one of the major problems in many water utilities in the world. Actinomycetes have long been associated with odorous compounds. Considering the paucity of research on Actinomycetes producing odorous compounds in South Korea, presence of Actinomycetes, their molecular characteristics and ability to produce odorous compounds were investigated in this study. Findings confirmed the presence of Actinomycetes in surface soil, sediment, and water samples from four sites: two artificial lakes [Paldang and Cheongpyeong (CP)], and two streams [Gyeongan (GA) and Yangpyeong]. Surface soil and sediment from CP area had the greatest concentration of Actinomycetes (8.2 x 10(7) and 6.8 x 10(6) colony forming units (CFUs)/gram, dry weight, respectively). When water samples are considered, samples from GA had the highest concentration (1.9 x 10(2) CFU/mL). 16S rRNA sequencing and molecular phylogenetic analysis showed that Streptomyces was the dominant genus (64.1%). In addition, the isolated Actinomycetes synthesized 5.4 ng/L geosmin as demonstrated by thermal desorption unit-gas chromatograph/mass spectrometry analysis.     

PASU: A personal area situation understanding system using wireless camera sensor networks

In this paper, we present a personal area situation understanding (PASU) system, a novel application of a smart device using wireless camera sensor networks. The portability of a PASU system makes it an attractive solution for monitoring and understanding the current situation of the personal area around a user. The PASU system allows its user to construct a 3D scene of the environment and view the scene from various vantage points for better understanding of the environment. The paper describes the architecture and implementation of the PASU system addressing limitations of wireless camera sensor networks, such as low bandwidth and limited computational capabilities. The capabilities of PASU are validated with extensive experiments. The PASU system demonstrates the potential of a portable system combining a smart device and a wireless camera sensor network for personal area monitoring and situation understanding.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields

In this study, we present a new architecture of a granular neural network and provide a comprehensive design methodology as well as elaborate on an algorithmic setup supporting its development. The proposed neural network relates to a broad category of radial basis function neural networks (RBFNNs) in the sense that its topology involves a collection of receptive fields. In contrast to the standard architectures encountered in RBFNNs, here we form individual receptive fields in subspaces of the original input space rather than in the entire input space. These subspaces could be different for different receptive fields. The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-means clustering while the information granules in the multidimensional input space are formed by using the so-called context-based fuzzy C-means, which takes into account the structure being already formed in the output space. The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization [genetic algorithms (GAs), to be more specific] to determine the optimal input subspaces. A series of numeric studies exploiting synthetic data and data coming from the Machine Learning Repository, University of California at Irvine, provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.     

Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and information granulation

We introduce a new architecture of feed-forward neural networks called hybrid fuzzy set-based polynomial neural networks (HFSPNNs) that are composed of heterogeneous feed-forward neural networks such as polynomial neural networks (PNNs) and fuzzy set-based polynomial neural networks (FSPNNs). We develop their comprehensive design methodology by embracing mechanisms of genetic optimization and information granulation. The construction of information granulation-driven HFSPNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the resulting information granulation-driven genetically optimized HFSPNN results from a synergistic usage of the hybrid system generated by combining original fuzzy set-based polynomial neurons (FSPNs)-based FSPNN with polynomial neurons (PNs)-based PNN. The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling (GMDH) whose some essential parameters of the network being tuned with the use of genetic algorithms throughout the overall development process. Two general optimization mechanisms are explored. First, the structural optimization is realized via GAs while the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through extensive experimentation where we considered a number of modeling benchmarks (synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling).     

The fabrication of functional biosurface composed of iron storage protein, ferritin

A functional biosurface applicable to a biomemory device was fabricated using ferritin, which is one of the globular protein complexes consisting of 24 protein subunits, which can be classified as metalloproteins. For the fabrication of uniform ferritin layer, 11-MUA(11-mercaptoundecanoic acid) was used as a linker material. The formation of the ferritin layer was confirmed by surface plasmon resonance (SPR) spectroscopy, and the morphology of the immobilized ferritin was analyzed by scanning tunneling microscopy (STM). The electrochemical redox property investigation was accomplished by the cyclic voltammetry (CV) technique. These results of adsorbed ferritin on the modified electrode can be used for the fabrication of bioelectronics.     

Preparation and characterization of nanoporous polyimide films from triblock copolyimides

Nanoporous polyimide films were prepared from triblock copolyimides. A thermally labile polymer, poly(propylene glycol), was incorporated into polyimides based on 4,4-(hexafluoroisopropylidene)diphthalic anhydride to obtain triblock copolyimides. Nanofoams were formed by thermolysis of the labile block. The thermal properties of the copolymers were investigated by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The nanopores were characterized by scanning electron microscopy (SEM).