Developing usability evaluation heuristics for augmented reality applications

There is no generic usability heuristics for Augmented Reality (AR) applications, thus, the aim of this thesis was to develop one. The development of the heuristics was carried out in phases. Based on a literature review, a preliminary version of the heuristics was developed, which was evaluated by four experts. As a result, six evaluation criteria were formed: 1) interaction methods and controls, 2) presentation of virtual objects, 3) relationship between virtual objects and real world, 4) information related to virtual objects, 5) suitability for the usage context and 6) physical comfort of use. The heuristics should be used with Nielsen's (1995) generic usability evaluation heuristics. The heuristics are not ready to be used as such, since it must still be tested in practice.

The essence of a tacit intersection : a heuristic inquiry into nature-based leisure and personal transformation

There are a considerable number of programs and agencies that count on the existence of a unique relationship between nature and human development. In addition, there are significant bodies of literature dedicated to understanding developmentally focused nature-based experiences. This research project was designed to flirther the understanding of this phenomenon. Consequently, the purpose of this research endeavour was to discover the essence ofthe intersection ofpersonal transformation and nature-based leisure, culminating in a rich and detailed account of this otherwise tacit phenomenon. As such, this research built on the assumption of this beneficial intersection of nature and personal transformation and contributes to the understanding ofhow this context is supporting or generating of selfactualization and positive development. Heuristic methods were employed because heuristics is concerned with the quality and essence of an experience, not causal relationships (Moustakas, 1990). Heuristic inquiry begins with the primary researcher and her personal experience and knowledge of the phenomenon. This study also involved four other coresearchers who had also experienced this phenomenon intensely. Co-researchers were found through purposeful and snowball sampling. Rich narrative descriptions of their experiences were gathered through in-depth, semi-structured interviews, and artifact elicitation was employed as a means to get at co-researchers' tacit knowledge. Each coresearcher was interviewed twice (the first interview focused on personal transformation, the second on nature) for approximately four and a half hours in total. Transcripts were read repeatedly to discern patterns that emerged from the study of the narratives and were coded accordingly. Individual narratives were consolidated to create a composite narrative of the experience. Finally, a creative synthesis was developed to represent the essence of this tacit experience. In conclusion the essence of the intersection of nature-based leisure and personal transformation was found to lie in the convergence of the lived experience of authenticity. The physical environment of nature was perceived and experienced to be a space and context of authenticity, leisure experiences were experienced as an engagement of authenticity, and individuals themselves encountered a true or authentic self that emanated from within. The implications of these findings are many, offering suggestions, considerations and implications from reconsidered approaches to environmental education to support for selfdirected human development.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Rich and famous: recognition-based judgment in the Sunday Times rich list

Recognition as a cue to judgment in a novel, multi-option domain (the Sunday Times Rich List) is explored. As in previous studies, participants were found to make use of name recognition as a cue to the presumed wealth of individuals. Names that were recognized were judged to be the richest name from amongst the set presented at above chance levels. This effect persisted across situations in which more than one name was recognized; recognition was used as an inclusion criterion for the sub-set of names to be considered the richest of the set presented. However, when the question was reversed, and a “poorest” judgment was required, use of recognition as an exclusion criterion was observed only when a single name was recognized. Reaction times when making these judgments also show a distinction between “richest” and “poorest” questions with recognition of none of the options taking the longest time to judge in the “richest” question condition and full recognition of all the names presented taking longest to judge in the “poorest” question condition. Implications for decision-making using simple heuristics are discussed.

Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes

The current state of the art in the planning and coordination of autonomous vehicles is based upon the presence of speed lanes. In a traffic scenario where there is a large diversity between vehicles the removal of speed lanes can generate a significantly higher traffic bandwidth. Vehicle navigation in such unorganized traffic is considered. An evolutionary based trajectory planning technique has the advantages of making driving efficient and safe, however it also has to surpass the hurdle of computational cost. In this paper, we propose a real time genetic algorithm with Bezier curves for trajectory planning. The main contribution is the integration of vehicle following and overtaking behaviour for general traffic as heuristics for the coordination between vehicles. The resultant coordination strategy is fast and near-optimal. As the vehicles move, uncertainties may arise which are constantly adapted to, and may even lead to either the cancellation of an overtaking procedure or the initiation of one. Higher level planning is performed by Dijkstra's algorithm which indicates the route to be followed by the vehicle in a road network. Re-planning is carried out when a road blockage or obstacle is detected. Experimental results confirm the success of the algorithm subject to optimal high and low-level planning, re-planning and overtaking.

Relax and fix heuristics to solve one-stage one-machine lot-scheduling models for small-scale soft drink plants

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Simple heuristics for the assembly line worker assignment and balancing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.

FPGA BASED DESIGN FOR ACCELERATED FAULT-TESTING OF INTEGRATED CIRCUITS

In the past few decades, integrated circuits have become a major part of everyday life. Every circuit that is created needs to be tested for faults so faulty circuits are not sent to end-users. The creation of these tests is time consuming, costly and difficult to perform on larger circuits. This research presents a novel method for fault detection and test pattern reduction in integrated circuitry under test. By leveraging the FPGA's reconfigurability and parallel processing capabilities, a speed up in fault detection can be achieved over previous computer simulation techniques. This work presents the following contributions to the field of Stuck-At-Fault detection: We present a new method for inserting faults into a circuit net list. Given any circuit netlist, our tool can insert multiplexers into a circuit at correct internal nodes to aid in fault emulation on reconfigurable hardware. We present a parallel method of fault emulation. The benefit of the FPGA is not only its ability to implement any circuit, but its ability to process data in parallel. This research utilizes this to create a more efficient emulation method that implements numerous copies of the same circuit in the FPGA. A new method to organize the most efficient faults. Most methods for determinin the minimum number of inputs to cover the most faults require sophisticated softwareprograms that use heuristics. By utilizing hardware, this research is able to process data faster and use a simpler method for an efficient way of minimizing inputs.