Two approaches for an adaptive multimedia transfer service for mobile environments

Mobile environments are characterized by varying locations and network access points of end users, by varying levels of network QoS and often also by limited connectivity and reliability. These aspects have to be considered specifically for distributed multimedia applications, typically involving large amounts of data and specific QoS requirements. This article presents concepts and experiences concerning adaptive services for asynchronous multimedia transmission in mobile environments. We describe two different approaches to solve the problems of adaptation to variable network QoS using generic mechanisms. The first approach takes advantage of a mobile queuing service and is based on our former work on a support platform for mobile computing (see Schill and Kümmel. Mobile Computing Special Issue of Distributed Systems Engineering Journal 1995; 128–41; Schill et al. Proceedings of IMC 96 Workshop on Information Visualization and Mobile Computing. Zentrum für graphische Datenverarbeitung, February 1996). The second approach uses the mobile agent paradigm in the context of an application partitioning model to gain flexibility and maintainability (see Schill et al. In: Papaioannou, Minar, editors, Proceedings. Mobile Agents in the Context of Competition and Cooperation (MAC3), a workshop at Autonomous Agents '99, 1999. p. 34–41.). Both approaches exploit software-based conversion to adapt multimedia data according to bandwidth availability. Decoupling of communication peers with automatic notifications about communication actions helps to address disconnection problems.     

Development of alternative reality environments for spacecraft habitat design evaluation

Virtual Reality - Alternative reality (XR) tools are becoming more commonplace in the realm of architecture, engineering, and construction (AEC); however, these digitally immersive technologies...     

Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches

Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns of local parameter estimates, multicollinearity between local coefficients, and their predictive accuracy, utilizing housing data for the metropolitan area of Vienna (Austria). The comparison covered the spatial expansion method (SEM), moving window regression (MWR), geographically weighted regression (GWR), and genetic algorithm-based eigenvector spatial filtering (ESF), an approach that had not previously been employed in real estate research. The results highlight the following strengths and limitations of each method: 1) In contrast to SEM, MWR, and GWR, ESF depicts more localized patterns of the parameter estimates and does not smooth local particularities. 2) ESF is less affected by multicollinearity between the local parameter estimates than MWR, GWR, and SEM. 3) Even though the in-sample explanatory power and prediction accuracy of ESF is superior compared to the competitors, repeated sampling indicates a limited out-of-sample fit and prediction accuracy, suggesting over-fitting tendencies. 4) The application of ESF is less intuitive than MWR and GWR, which are available off-the-shelf.     

An alternative scheme for perplexity estimation and its assessment for the evaluation of language models

Language models are usually evaluated on test texts using the perplexity derived from the model likelihood function computed on these texts (test set perplexity). In order to use this measure in the framework of a comparative evaluation campaign, we have developed an alternative scheme for estimating the test set perplexity. The method is derived from the Shannon game and based on a gambling approach on the next word to come in a truncated sentence. We also study the entropy bounds proposed by Shannon and based on the rank of the correct answer, in order to estimate a perplexity interval for non-probabilistic language models. The relevance of the approach is validated on an example. We then report the results of a preliminary comparative evaluation using the proposed scheme.     

Hypermedia environments and adaptive instruction

The relevant professional literature is reviewed and discussed regarding the following salient issues: (1) hypermedia environments enable learner control; (2) this is reflected in individuals' navigational paths through network-based subject matter; (3) these student tracks can be captured in log files; and (4) this information can be used by artificially intelligent tutors to implement adaptive instruction. Assuming (1) most schools, colleges, universities, and corporations will eventually offer distributed students network-based instruction for particular refresher preparation and certain core courses, and (2) adaptive intelligent tutors are crucial components of course management systems, recommendations for research, development, and evaluation, extracted from the discussion, are made to appropriate sponsors, academic administrators, faculty members, training managers, and instructional developers, interested in realizing on-line learning.     

An adaptive optimization technique for dynamic environments

The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments.     

Complementary menus: Combining adaptable and adaptive approaches for menu interface

This study proposes a method of coupling adaptable and adaptive approaches to the design of menus. The proposed complementary menu types incorporate both adaptability and adaptivity by dividing and allocating menu adaptation roles to the user and the system. Four different types of interface adaptation (i.e., adaptable with/without system support and adaptive with/without user control) were defined. They were implemented in a hypothetical prototype mobile phone via a hotlist (an additional collection of quickly accessible items). A controlled lab experiment was conducted to compare the menu types and investigate the effects of the system support in the adaptable menus and the user control in the adaptive menus. Twenty subjects participated in the experiment and performed menu selection tasks. Both performance and user satisfaction measures were collected. The results showed that adaptable and adaptive menus were superior to the traditional one in terms of both performance and user satisfaction. Providing system support to the adaptable menu not only increased the users’ perception of the efficiency of selection, but also reduced the menu adaptation time. Important implications for the design of menus are described and valuable insights into the menu interface adaptation were gained from the quantitative and qualitative analyses of the experimental results.

Relevance to industry

The evaluation experiment conducted in this study may provide valuable information to designers of adaptive or adaptable menus. Adding system support to adaptable menu would be an attractive option to consider. Also, the results of a user survey provide useful information to the practitioners in mobile phone industry on the features users accessed most frequently.     

Nonadaptive, semi-adaptive and adaptive approaches to signal processing problems in nondestructive evaluation

In signal processing problems associated with estimation or pattern recognition in nondestructive evaluation (NDE), the degree of adaption can vary between wide limits, and is related to the availability or usability of a priori information. We will discuss several examples of NDE signal processing problems involving varying degrees of adaption, namely: (1) a completely nonadaptive approach to the extraction of low frequency properties of scatterers, (2) a semi-adaptive approach to the same problem, and (3) a completely adaptive approach to the classification of acoustical emission signals.     

Learning support environments: Rationale and evaluation

In this paper we argue that existing understanding of human cognition has much to offer the design of instructional systems and materials, and that new technologies, such as hypertext, in harness with traditional techniques, provide opportunities for extending the mapping of cognitive principles to instructional design. Following a review of types of knowledge, cognitive styles and strategies within a CAL framework, the advantages of learning support environments over other CAL approaches are discussed. Such environments are presented as extensions to hypertext which incorporate various generic features for learning applications. The need for evaluation of learning which is dynamic and internal to the learning tasks is stressed.     

New identification approaches for disturbed models

Recently, much research has been conducted in the field of identification of the linear models. In general, these methods use a time-domain estimate or a frequency-domain estimate. In this paper, the time-domain estimate and the frequency-domain estimate were combined to identify the autoregressive exogenous noise (ARX) interference model. The concept of a general prediction error criterion is introduced for the time-domain estimate. An optimal frequency estimation is introduced for the frequency-domain estimate. A new identification method, called the empirical frequency-domain optimal parameter estimate, is proposed for disturbed systems. It is fully applied and developed for the output error model and a specific case or the ARX model. The algorithm theoretically provides the globally optimum frequency-domain estimate of the model. Some simulations are included to illustrate the new identification method.     

X-Board: an egocentric adaptive AR assistant for perception in indoor environments

Virtual Reality - Augmented reality (AR) has the potential to become an effective assistive technology for emergencies in the future. However, raw AR content can confuse users’ visual...     

Evolution of adaptive synapses: robots with fast adaptive behavior in new environments.

This paper is concerned with adaptation capabilities of evolved neural controllers. We propose to evolve mechanisms for parameter self-organization instead of evolving the parameters themselves. The method consists of encoding a set of local adaptation rules that synapses follow while the robot freely moves in the environment. In the experiments presented here, the performance of the robot is measured in environments that are different in significant ways from those used during evolution. The results show that evolutionary adaptive controllers solve the task much faster and better than evolutionary standard fixed-weight controllers, that the method scales up well to large architectures, and that evolutionary adaptive controllers can adapt to environmental changes that involve new sensory characteristics (including transfer from simulation to reality and across different robotic platforms) and new spatial relationships.     

Approximate models for adaptive feedback linearization

A result obtained by Sastry and Isidori in 1989 on the adaptive stabilization and tracking of affine systems with a well defined global relative degree and linear parametric uncertainty is reviewed, and a generalization is developed to include bounded output disturbances. From this, we develop neurocontrol results, for linear weight approximants with compactly supported basis functions, where the disturbance term is used to handle the approximation error. In contrast with many results in the neurocontrol literature the control is a state feedback law, taking measurements from the original coordinates and not from the normal form. A localweights result is given, and under some nonlinearity constraints from Sastry and Isidori a global result is proved, where the dimension of the controller varies according to the size of the initial conditions and tracking signal.     

Self-organization of complex adaptive systems as a society of rational agents

In this paper, we aim at providing a general theoretical framework for designing complex adaptive systems as a society of rational agents. We term entities with their own interest agents. They are also rational in the sense that they only do what they want to do and what they think is in their own best interest. We formulate the dynamic interaction among those rational agents as competitive and cooperative problems. We obtain the equilibrium behavior in the long-run, and characterize the collective behavior of these rational agents. We show how complex collective behavior can emerge from the locally optimal behavior of each agent. We also describe why and how they organize themselves into a multilevel hierarchical organization with nesting structures. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

An architecture for adaptive fuzzy control in industrial environments

The paper presents an architecture for adaptive fuzzy control of industrial systems. Both conventional and adaptive fuzzy control can be designed. The control methodology can integrate a priori knowledge about the control and/or about the plant, with on-line control adaptation mechanisms to cope with time-varying and/or uncertain plant parameters. The paper presents the fuzzy control software architecture that can be integrated in industrial processing and communication structures. It includes four distinct modules: a mathematical fuzzy library, a graphical user interface (GUI), fuzzy controller, and industrial communication. Three types of adaptive fuzzy control methods have been studied, and compared: (1) direct adaptive, (2) indirect adaptive, and (3) combined direct/indirect adaptive. An experimental benchmark composed of two mechanically coupled electrical DC motors has been employed to study the performance of the presented control architectures. The first motor acts as an actuator, while the second motor is used to generate nonlinearities and/or time-varying load. Results indicate that all tested controllers have good performance in overcoming changes of DC motor load.     

Roundtable: Envisioning the Future for Multiprocessor SoC

Multiprocessor SoCs are no longer an advanced research topic for academia. Ninety percent of SoCs designed in 130 nm include at least one CPU. Most popular multimedia platforms are already multiprocessor SoCs. This roundtable brings together key players from the semiconductor industry and from academia to discuss the challenges and opportunities brought by this new technology.     

Collaborative virtual learning environments: design and evaluation

E-learning systems have gone through a radical change from the initial text-based environments to more stimulating multimedia systems. Such systems are Collaborative Virtual Environments, which could be used in order to support collaborative e-learning scenarios. The main aim of this paper is to aid educational designers in selecting, designing and evaluating three dimensional collaborative virtual environments in order to gain the pedagogical benefits of Computer Supported Collaborative Learning. Therefore, this paper initially discusses the potential of three dimensional networked virtual environments for supporting collaborative learning. Furthermore, based on a two-step platform selection process this paper (a) presents and compares three dimensional multi-user virtual environments for supporting collaborative learning and (b) validates the most promising solution against a set of design principles for educational virtual environments. According to these principles, an educational environment has been implemented on top of the selected platform in order to support collaborative e-learning scenarios. The design of this environment is also presented. In addition, this paper presents the results of three small scale studies carried out in a tertiary education department, to assess the educational environment. This environment has been evaluated based on a hybrid evaluation methodology for uncovering usability problems, collecting further requirements for additional functionality to support collaborative virtual learning environments, and determining the appropriateness of different kinds of learning scenarios.

Exploiting focal points among alternative solutions: Two approaches

Focal points refer to prominent solutions of an interaction, solutions to which agents are drawn. This paper considers how automated agents could use focal points for coordination in communication‐impoverished situations. Coordination is a central theme of Distributed Artificial Intelligence. Much work in this field can be seen as a search for mechanisms that allow agents with differing knowledge and goals to coordinate their actions for mutual benefit. Additionally, one of the main assumptions of the field is that communication is expensive relative to computation. Thus, coordination techniques that minimize communication are of particular importance. Our purpose in this paper is to consider how to model the process of finding focal points from domain‐independent criteria, under the assumption that agents cannot communicate with one another. We consider two alternative approaches for finding focal points, one based on decision theory, the second on step‐logic. The first provides for a more natural integration of agent utilities, while the second more successfully models the difficulty of finding solutions. For both cases, we present simulations over randomly generated domains that suggest that focal points can act as an effective heuristic for coordination. This revised version was published online in June 2006 with corrections to the Cover Date.     

Deriving software architectural models from requirements models for adaptive systems: the STREAM-A approach

Some quality attributes are known to have an impact on the overall architecture of a system, so that they are required to be properly handled from the early beginning of the software development. For example, adaptability is a key concern for autonomic and adaptive systems, which brings to them the capability to alter their behavior in response to changes on their surrounding environments. In this paper, we propose a Strategy for Transition between Requirements and Architectural Models for Adaptive systems (STREAM-A). In particular, we use goal models based on the i* (i-Star) framework to support the design and evolution of systems that require adaptability. To obtain software architectures for such systems, the STREAM-A approach uses model transformations from i* models to architectural models expressed in Acme. Both the requirements and the architectural model are refined to accomplish the adaptability requirement.