Traffic generator using Perlin Noise

Trabajo presentado a IEEE Global Communications Conference, Globecom 2012; Optical Networks and Systems (ONS) Symposium, 3-7 de diciembre de 2012. Anaheim (Estados Unidos)Study of high speed networks such as optical next generation burst or packet switched networks require large amounts of synthetic traffic to feed simulators. Methods to generate self-similar long range dependent traffic already exist but they usually work by generating large blocks of traffic of fixed time duration. This limits simulated time or require very high amount of data to be stored before simulation. On this work it is shown how self-similar traffic can be generated using Perlin Noise, an algorithm commonly used to generate 2D/3D noise for natural looking graphics. 1-dimension Perlin Noise can be interpreted as network traffic and used to generate long range dependent traffic for network simulation. The algorithm is compared to more classical approach Random Midpoint Displacement showing at traffic generated is similar but can be generated continuously with no fixed block size.This work was supported by the Spanish Ministry of Science and Innovation through the research project INSTINCT (TEC-2010-21178-C02-01). Also, the authors want to thank Spanish thematic network IPoTN (TEC2010-12250-E) and Public University of Navarre for funding through PIF grant

The Experience of Dynamic Lighting

The experience of the dynamic flux in daylighting is a complex relation between experiential and perceptual modalities, spatial presence of lighting qualities, and the architectural situation for the experience. In architectural practice, the understanding of daylight influx is key to the design of daylight openings and the experience of spatial form. However, current developments in light-emitting diodes (LED) light sources and adaptive software control systems allow for an enhanced correlation between daylight and artificial lighting, where the variations of the daylight are dynamically supplemented by variations in the artificial lighting. It is recommended that a particular type of Observational Instrument is developed, which situates detailed experiential investigations into the design potentials of integration of natural and artificial lighting and thereby enables differentiated dynamic lighting design in architecture

Automatic High-Fidelity 3D Road Network Modeling

Many computer applications such as racing games and driving simulations frequently make use of 3D high-fidelity road network models for a variety of purposes. However, there are very few existing methods for automatic generation of 3D realistic road networks, especially for those in the real world. On the other hand, vast road network GIS data have been collected in the past and used by a wide range of applications, such as navigation and evaluation. A method that can automatically produce 3D high-fidelity road network models from 2D real road GIS data will significantly reduce both the labor and time needed to generate these models, and greatly benefit numerous applications involving road networks. Based on a set of selected civil engineering rules for road design, this dissertation research addresses this problem with a novel approach which transforms existing road GIS data that contain only 2D road centerline information into 3D road network models. The proposed method consists of several components, mainly including road GIS data preprocessing, 3D centerline modeling and 3D geometry modeling. During road data preprocessing, topology of the road network is extracted from raw road data as a graph composed of road nodes and road links; road link information is simplified and classified. In the 3D centerline modeling part, the missing height information of the road centerline is inferred based on 2D road GIS data, intersections are extracted from road nodes and the whole road network is represented as road intersections and road segments in parametric forms. Finally, the 3D road centerline models are converted into various 3D road geometry models consisting of triangles and textures in the 3D geometry modeling phase. With this approach, basic road elements such as road segments, road intersections and traffic interchanges are generated automatically to compose sophisticated road networks. Results show that this approach provides a rapid and efficient 3D road modeling method for applications that have stringent requirements on high-fidelity road models

Synthetic data approach for traffic sign recognition

Dissertação de mestrado em Computer ScienceCurrently, Advanced Driver Assistance Systems (ADAS) have been gradually increasing their presence in everyday life, thanks in part to its ability to recognize several distinct types of objects in the road, namely, traffic signs. These systems employ Convolutional Neural Networks (CNNs), a type of classification algorithms that relies on an enormous amount of data in order to be effective. Current traffic sign datasets suffer from a scarcity of samples due to the necessity of compiling and labeling them manually. Such task is highly resource and time consuming. Thus, researches resort to other mechanisms to deal with this problem, such as increasing the architectural complexity of the neural networks or performing data augmentation. This work addresses the data shortage issue by exploring the feasibility of developing a synthetic dataset. Such set would not require gathering and labelling manually thousands of real word traffic sign images, requiring only easily collectable information and no human intervention. The only data required is a set of templates for each sign given that a particular sign may have more than one template. This is required to cope with outdated pictograms that are still present in streets and roads. We apply several colour and geometric processing methods to the templates aiming to achieve a look similar to real signs, from the CNN point of view. One of such methods is the usage of Perlin noise to both simulate shadows and avoid the clean and homogeneous look that templates have. Two use cases for synthetic data usage are presented: considering the synthetic dataset as a standalone training set, and merging synthetic data with real samples when real data is available. The first option provided results that not only clearly surpass any previous attempt on using synthetic data for traffic sign recognition, but are also encouragingly placing the accuracies obtained close to state-of-the-art results, with much simpler networks. The second approach provided results on three distinct test datasets that consistently beat state-of-the-art results, either in accuracy or in simplicity of the network.Atualmente, Sistemas Avançados de Assistência ao Condutor têm vindo a aumentar gradualmente a sua presença no quotidiano graças, em parte, à sua capacidade de reconhecer vários objetos distintos na estrada, nomeadamente, sinais de trânsito. Estes sistemas empregam Redes Neuronais Convolucionais (CNNs), um tipo de algoritmos de classificação que dependem de unia enorme quantidade de dados de forma a serem eficientes. Os conjuntos de dados de sinais de trânsito atuais sofrem de escassez de amostras devido à necessidade de as compilar e rotular manualmente. Tal tarefa consome imenso tempo e recursos. Por conseguinte, investigadores recorrem a outros mecanismos para serem capazes de lidar com esse problema, tais como, aumentar a complexidade arquitetural das redes neuronais ou efetuar data augmentation. Desta forma, este trabalho aborda a questão da escassez de dados, explorando a viabilidade do desenvolvimento de um conjunto de dados sintéticos. Tal conjunto não exigiria recolher e rotular manualmente milhares de imagens de sinais de trânsito, necessitando apenas de informação facilmente recolhida sem intervenção humana. Os únicos dados necessários são um conjunto de modelos para cada sinal uma vez que um sinal particular pode apresentar mais que um modelo. Tal é necessário para lidar com pictogramas desatualizados que ainda se encontram nas ruas e estradas. Aplicamos vários métodos de processamento de cor e geometria aos templates visando obter uma aparência semelhante a sinais reais, do ponto de vista da CNN. Um desses métodos é a utilização do ruído de Perlin para simular sombras e evitar a aparência limpa e homogênea que os modelos apresentam. Dois casos de uso com dados sintéticos são apresentados: considerar o conjunto de dados sintético como um conjunto de treino independente, e unir dados sintéticos com amostras reais sempre que estas estiverem disponíveis. A primeira opção forneceu resultados que, não apenas superam claramente qualquer tentativa anterior de usar dados sintéticos para reconhecimento de sinais de trânsito, como também colocam as precisões obtidas próximas dos resultados do estado da arte, com redes muito mais simples. A segunda abordagem forneceu resultados em três conjuntos de dados de teste distintos que superam consistentemente os resultados do estado da arte, tanto na precisão quanto na simplicidade da rede

Testing Autonomous Robot Control Software Using Procedural Content Generation

We present a novel approach for reducing manual effort when testing autonomous robot control algorithms. We use procedural content generation, as developed for the film and video game industries, to create a diverse range of test situations. We execute these in the Player/Stage robot simulator and automatically rate them for their safety significance using an event-based scoring system. Situations exhibiting dangerous behaviour will score highly, and are thus flagged for the attention of a safety engineer. This process removes the time-consuming tasks of hand-crafting and monitoring situations while testing an autonomous robot control algorithm. We present a case study of the proposed approach – we generated 500 randomised situations, and our prototype tool simulated and rated them. We have analysed the three highest rated situations in depth, and this analysis revealed weaknesses in the smoothed nearness-diagram control algorithm

Interactive Video Game Content Authoring using Procedural Methods

This thesis explores avenues for improving the quality and detail of game graphics, in the context of constraints that are common to most game development studios. The research begins by identifying two dominant constraints; limitations in the capacity of target gaming hardware/platforms, and processes that hinder the productivity of game art/content creation. From these constraints, themes were derived which directed the research‟s focus. These include the use of algorithmic or „procedural‟ methods in the creation of graphics content for games, and the use of an „interactive‟ content creation strategy, to better facilitate artist production workflow. Interactive workflow represents an emerging paradigm shift in content creation processes used by the industry, which directly integrates game rendering technology into the content authoring process. The primary motivation for this is to provide „high frequency‟ visual feedback that enables artists to see games content in context, during the authoring process. By merging these themes, this research develops a production strategy that takes advantage of „high frequency feedback‟ in an interactive workflow, to directly expose procedural methods to artists‟, for use in the content creation process. Procedural methods have a characteristically small „memory footprint‟ and are capable of generating massive volumes of data. Their small „size to data volume‟ ratio makes them particularly well suited for use in game rendering situations, where capacity constraints are an issue. In addition, an interactive authoring environment is well suited to the task of setting parameters for procedural methods, reducing a major barrier to their acceptance by artists. An interactive content authoring environment was developed during this research. Two algorithms were designed and implemented. These algorithms provide artists‟ with abstract mechanisms which accelerate common game content development processes; namely object placement in game environments, and the delivery of variation between similar game objects. In keeping with the theme of this research, the core functionality of these algorithms is delivered via procedural methods. Through this, production overhead that is associated with these content development processes is essentially offloaded from artists onto the processing capability of modern gaming hardware. This research shows how procedurally based content authoring algorithms not only harmonize with the issues of hardware capacity constraints, but also make the authoring of larger and more detailed volumes of games content more feasible in the game production process. Algorithms and ideas developed during this research demonstrate the use of procedurally based, interactive content creation, towards improving detail and complexity in the graphics of games

Evolving Efficient Floor Plans For Hospital Emergency Rooms

Genetic Algorithms find wide use in optimization problems across many fields of research, including crowd simulation. This paper proposes that genetic algorithms could be used to create better floor plans for hospital emergency rooms, potentially saving critical time in high risk situations. The genetic algorithm implemented makes use of a hospital-specific crowd simulation to accurately evaluate the effectiveness of produced layouts. The results of combining genetic algorithms with a crowd simulation are promising. Future work may improve upon these results to produce better, more optimal hospital floor plans

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio