Evaluating the Road Damage of Flexible Pavement Using Digital Image

Project funding becomes the main problem in the evaluation of pavement conditions using mechanical tools. It is due to the price of these tools is quite expensive, added to that one type of tool only measures one particular condition. visual inspection method is a good solution to sort out the problems because it is quite practical, simple and efficient. However, there is a weakness in evaluating the damage of roads visually. The visual assessment method is highly subjective, depending on the assessor. Considering the weakness of the road damage assessment method visually, it is necessary to create an algorithm or a method in detecting and calculating the amount of the road damages quickly and precisely. This research offers the use of digital camera to detect the damages of the roads. The first step of the algorithm process is done by taking pictures using digital camera, so that it is resulting digital images to be processed. The result will give information about the types of road damages and the damage value of the road as well as monitoring the structural strength of the road material (core drill test) and the influence of traffic load on the damage roa

DESIGN OF NOVEL ELECTRONIC JACQUARD WITH MASTER-SLAVE ARCHITECTURE & DESIGN PARTITIONING

In India, very rarely one can find Computer controlled Power looms and Handlooms, because of cost involved in the technology, dearth of indigenous manufacturers of electronic jacquards suited for Indian looms because of the complexity of the technologies involved - mechanical, electrical, electronics & computers. This paper proposes Hardware & Software design and Implementation details of Electronic Jacquard Control System based on Master-Slave Architecture and Design Partitioning, which provides some specific advantages over the existing EJCS systems, for Indian Handlooms & Power looms

An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for Wireless Sensor Networks

Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values

Detecção e Rastreamento de Veículos em Movimento para Automóveis Robóticos Autônomos

Neste trabalho, foi investigado o problema de detecção e rastreamento de objetos em movimento (detection and tracking of moving objects - DATMO) para automóveis robóticos autônomos. DATMO envolve a detecção de objetos em movimento no ambiente ao redor do robô e a estimativa do estado (e.g., posição, orientação e velocidade) dos objetos ao longo do tempo. O robô precisa estimar o estado de cada objeto ao longo do tempo, de forma que possa predizer o estado destes objetos alguns segundos mais tarde para fins de mapeamento, localização e navegação. Foram estudadas várias abordagens para a solução deste problema e foi proposto um sistema de detecção e rastreamento de múltiplos veículos em movimento no ambiente ao redor do automóvel robótico autônomo usando um sensor Light Detection and Ranging (LIDAR) 3D. O sistema proposto opera em três etapas: segmentação, associação e rastreamento. A cada varredura do sensor, após a conversão dos dados do sensor em uma nuvem de pontos 3D, na etapa de segmentação os pontos associados ao plano do solo são removidos; a nuvem de pontos é segmentada em agrupamentos de pontos 3D usando a distância Euclidiana, sendo que cada agrupamento representa um objeto no ambiente ao redor do automóvel robótico; nesta etapa os agrupamentos relacionados a meio-fios são também removidos. Na etapa de associação, os objetos observados na varredura atual do sensor são associados aos mesmos objetos observados na varredura anterior usando o algoritmo do vizinho mais próximo (nearest neighbor). Finalmente, na etapa de rastreamento, o estado (posição, orientação e velocidade) dos objetos é estimado usando um filtro de partículas. Os objetos com velocidade acima de um determinado limiar são considerados veículos em movimento. O desempenho do sistema de DATMO proposto foi avaliado usando dados de um sensor LIDAR 3D, além de dados de outros sensores, coletados ao longo de uma volta pelo anel viário do campus da Universidade Federal do Espírito Santo (UFES). Os resultados experimentais mostraram que o sistema de DATMO proposto foi capaz de detectar e rastrear com bom desempenho múltiplos veículos em movimento

Sea-Ice Detection from RADARSAT Images by Gamma-based Bilateral Filtering

Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%