A scalable distributed paradigm for multi-user interaction with tiled rear projection display walls

We present the first distributed paradigm for multiple users to interact simultaneously with large tiled rear projection display walls. Unlike earlier works, our paradigm allows easy scalability across different applications, interaction modalities, displays and users. The novelty of the design lies in its distributed nature allowing well-compartmented, application independent, and application specific modules. This enables adapting to different 2D applications and interaction modalities easily by changing a few application specific modules. We demonstrate four challenging 2D applications on a nine projector display to demonstrate the application scalability of our method: map visualization, virtual graffiti, virtual bulletin board and an emergency management system. We demonstrate the scalability of our method to multiple interaction modalities by showing both gesture-based and laser-based user interfaces. Finally, we improve earlier distributed methods to register multiple projectors. Previous works need multiple patterns to identify the neighbors, the configuration of the display and the registration across multiple projectors in logarithmic time with respect to the number of projectors in the display. We propose a new approach that achieves this using a single pattern based on specially augmented QR codes in constant time. Further, previous distributed registration algorithms are prone to large misregistrations. We propose a novel radially cascading geometric registration technique that yields significantly better accuracy. Thus, our improvements allow a significantly more efficient and accurate technique for distributed self-registration of multi-projector display walls.     

Scalable Multi‐view Registration for Multi‐Projector Displays on Vertically Extruded Surfaces

Recent work have shown that it is possible to register multiple projectors on non‐planar surfaces using a single uncalibrated camera instead of a calibrated stereo pair when dealing with a special class of non‐planar surfaces, vertically extruded surfaces. However, this requires the camera view to contain the entire display surface. This is often an impossible scenario for large displays, especially common in visualization, edutainment, training and simulation applications. In this paper we present a new method that can achieve an accurate geometric registration even when the field‐of‐view of the uncalibrated camera can cover only a part of the vertically extruded display at a time. We pan and tilt the camera from a single point and employ a multi‐view approach to register the projectors on the display. This allows the method to scale easily both in terms of camera resolution and display size. To the best of our knowledge, our method is the first to achieve a scalable multi‐view geometric registration of large vertically extruded displays with a single uncalibrated camera. This method can also handle a different situation of having multiple similarly oriented cameras in different locations, if the camera focal length is known.     

Adapting to Less Water: Household Willingness to Pay for Decentralised Water Systems in Urban Australia

In South East Queensland (SEQ), extended periods of drought and unprecedented population growth have resulted in a water strategy reliant on permanent water conservation measures. As a result, there has been increasing emphasis on the installation of decentralised water systems at the household level, in particular, rainwater tanks and greywater systems to ease the water shortage stress. Results from a survey of 590 households in SEQ reveal that willingness to pay (WTP) for rainwater tanks and greywater systems range from $800 to $7,400 and from $1,700 to $14,100, respectively. When compared to the actual market price, WTP is substantially lower and subsidies will be required to encourage adoption. Nonetheless, a subsidy of $500 can lead to 100 % uptake of greywater diversion devices. Hence, the policy implication is that not all devices are preferred and subsidising greywater diversion devices would lead to the highest level of uptake with the least amount of subsidy spending.     

Cooperative Target-capturing with Incomplete Target Information

This paper presents a distributed target-centric formation control strategy for multiple unmanned aerial vehicles (UAVs) in the presence of target motion uncertainty. The formation is maintained around a target using a combination of a consensus protocol and a sliding mode control law. Consensus helps in distributing the target information which is available only to a subset of vehicles. Sliding mode control compensates for the uncertainty in the target information. Hence, collectively the combined strategy enforces each of the vehicles to maintain its respective position in the formation. We show that if at least one vehicle in a group has target information with some uncertainty and the corresponding communication graph is connected, then a target-centric formation can be maintained. The performance of the proposed strategy is illustrated through simulations.     

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

Classifiability-based omnivariate decision trees

Top-down induction of decision trees is a simple and powerful method of pattern classification. In a decision tree, each node partitions the available patterns into two or more sets. New nodes are created to handle each of the resulting partitions and the process continues. A node is considered terminal if it satisfies some stopping criteria (for example, purity, i.e., all patterns at the node are from a single class). Decision trees may be univariate, linear multivariate, or nonlinear multivariate depending on whether a single attribute, a linear function of all the attributes, or a nonlinear function of all the attributes is used for the partitioning at each node of the decision tree. Though nonlinear multivariate decision trees are the most powerful, they are more susceptible to the risks of overfitting. In this paper, we propose to perform model selection at each decision node to build omnivariate decision trees. The model selection is done using a novel classifiability measure that captures the possible sources of misclassification with relative ease and is able to accurately reflect the complexity of the subproblem at each node. The proposed approach is fast and does not suffer from as high a computational burden as that incurred by typical model selection algorithms. Empirical results over 26 data sets indicate that our approach is faster and achieves better classification accuracy compared to statistical model select algorithms.     

Expressing inter-link constraints in OWL knowledge bases

Abstract: In this paper the Web Ontology Language (OWL) is examined to instantiate expert system knowledge bases intended for semantic Web applications. In particular, OWL is analyzed for expressing Unified Modeling Language (UML) representations that have been augmented with propositional logic asserted as inter‐link constraints. The motivation is ultimately to provide declarative propositional logic constraints that can be represented in UML and declaratively implemented using OWL and other constructs to realize semantic Web knowledge base repositories and databases to facilitate expert system applications. The results of this paper show that OWL is sufficient for capturing most inter‐link constraints asserted on generalization/specialization instances; however, OWL alone is inadequate for representing some inter‐link constraints asserted on associations. We propose enhancements to OWL via RDF extensions for the reification of associations into classes. These extensions mitigate all concerns that were identified in OWL as part of this study. The result is increased support of declarative constraint representations, which can be expressed in knowledge bases in the context of the semantic Web.     

Cleaning the spurious links in data

Data quality problems can arise from abbreviations, data entry mistakes, duplicate records, missing fields, and many other sources. These problems proliferate when you integrate multiple data sources in data warehousing, federated databases, and global information systems. A newly discovered class of erroneous data is spurious links, where a real-world entity has multiple links that might not be properly associated with it. The existence of such spurious links often leads to confusion and misrepresentation in the data records representing the entity. Although the data set is well known for its high-quality bibliographic information, collecting and maintaining the data from diverse sources requires enormous effort. Errors, including spurious links, are inevitable. To solve this problem, we use context information to identify spurious links. First, we identify data records that contain potential spurious links. We then determine the set of attributes that constitute each record's context. Experiments with three real-world databases have demonstrated that our approach can accurately identify spurious links. Comparing context information between data records can help solve the data quality problem of spurious links-that is, multiple links between data entries and real-world entities.     

Microporous Hydroxyapatite Ceramic Composites as Tissue Engineering Scaffolds: An Experimental and Computational Study

Bone‐tissue engineering mandates the development of multi‐functional bioactive porous hydroxyapatite (HAp) scaffolds. Herein, microwave sintered HAp/ZnO and HAp/Ag composite scaffolds with ≈5–19% porosity are developed using 0–30 vol% graphite as a porogen. The mechanical properties of the porous scaffold are analyzed in detail, revealing that even being more porous, the reinforcement of ZnO (9% porosity, hardness of 2.8 GPa, and toughness of 3.5 MPa.m^1/2) has shown to have better hardness and fracture toughness when compared to Ag (5% porosity, hardness of 1.6 GPa, and toughness of 2.6 MPa.m^1/2). The flexural strength obtained experimentally are complemented with a finite‐element technique that adopts microstructural features in visualizing the effect of porosity on stress distribution. The antibacterial efficacy and cytocompatibility of these composites are validated by increased metabolic activity and conspicuous cell‐matrix interactions. The anticipation of the results reveal that HAp/ZnO (9% porosity) and HAp/Ag (5% porosity) composites can be used as a potential multi‐functional bone implant scaffolds.

     

Effect of graphene concentration on performance of MEH:PPV/graphene nanocomposite based devices

Herein, we explore the effect of graphene concentration on performance of poly [2-methoxy-5-2-ethylhexoxy-p-phenylene vinylene] (MEH:PPV)/graphene nanocomposite based devices. The surface morphology of the nanocomposites analyzed through emission scanning electron microscopy suggest that increase in graphene concentration results in the formation of aggregation. Optical and structural properties of the nanocomposites examined through UV–Vis absorption and Raman spectra revealed that the addition of graphene has no affect on the conjugation length and structure of the MEH:PPV. The electrical characteristics of devices have been investigated by I–V measurement under dark and illumination at room temperature. The devices show increase in the current value and reduction on turn-on voltage with increase in the graphene concentration up to 5 wt%. However, increase in graphene concentration above 5 wt% leads to the performance degradation of the devices. Moreover, charge transport mechanisms of the devices have been explained through Ohmic behavior at lower voltages and trap charge limited conduction at higher voltages.