Search results for "Sensor fusion"
showing 10 items of 64 documents
Sensor-Assisted Machining
2008
This chapter provides comprehensive knowledge regarding the application of various sensors and monitoring techniques to machining processes, cutting tools and machine tools. A number of monitoring methods used in manufacturing and relevant sensors are characterized. In particular, three most frequently used sensors, i.e., force, acceleration and acoustic emission, are presented and their characteristics and advantages are highlighted. The architectures of multiple-sensor tool-monitoring systems for detection of tool wear and breakage and collision for machine tools, including details of sensor fusion, signal processing and decision-making, are presented. Moreover, the concepts of sensor-bas…
Stability and Noises Evaluation of Fuzzy Kalman UAV Navigation System.
2009
In this paper a new Fuzzy/Kalman navigation system for Unmanned Aerial Vehicles (UAV) is presented. A closed loop velocity Fuzzy navigation system is proposed for stabilizing the UAV in a reference trajectory generated dynamically and for obtaining a forward velocity command. The Kalman's filter (KF) is included in the feedback line of the fuzzy control system to filter the internal noise of the sensors and to evaluate the external noise due to possible perturbations of the nominal motion. The efficiency of the navigation system has been shown through experimental tests in a Matlab environment.
Fuzzy Data Fusion for Real-World Mapping Using 360° Rotating Ultrasonic Sensor
1997
Abstract Mobile robot perception of the external environment is limited by the features of the used sensor. An useful technique used to improve robot perception is data fusion. This paper presents an approach to build a map of an unknown environment applying fuzzy data fusion methods to data acquired through an ultrasonic sensor. Conditioning of these data and motion control of the mobil robot by fuzzy data fusion are also described. The resulting two dimensional map is used for path planning and navigation. The proposed approach is exrperimentally tested using real distance measures acquired by a 360° rotating sensor.
Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
2021
A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contamin…
Distributed and proximity-constrained C-means for discrete coverage control
2018
In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks. The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations. To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs. The proposed co…
Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources
2020
This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observations, from a plethora of different sensors, measuring states, fluxes, processes and variables, at unprecedented spatial and temporal resolutions. Earth observation is well equipped with remote sensing systems, mounted on satellites and airborne platforms, but it also involves in-situ observations, numerical models and social media data streams, among other data sources. Data-driven approaches, and ML techniques in particular, are the natural choice to extract significant i…
Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments
2017
This paper deals with the extremely fascinating area of “fusing” the outputs of sensors without any knowledge of the ground truth. In an earlier paper, the present authors had recently pioneered a solution, by mapping it onto the fascinating paradox of trying to identify stochastic liars without any additional information about the truth. Even though that work was significant, it was constrained by the model in which we are living in a world where “the truth prevails over lying”. Couched in the terminology of Learning Automata (LA), this corresponds to the Environment (Since the Environment is treated as an entity in its own right, we choose to capitalize it, rather than refer to it as an “…
Depth Enhancement by Fusion for Passive and Active Sensing
2012
This paper presents a general refinement procedure that enhances any given depth map obtained by passive or active sensing. Given a depth map, either estimated by triangulation methods or directly provided by the sensing system, and its corresponding 2-D image, we correct the depth values by separately treating regions with undesired effects such as empty holes, texture copying or edge blurring due to homogeneous regions, occlusions, and shadowing. In this work, we use recent depth enhancement filters intended for Time-of-Flight cameras, and adapt them to alternative depth sensing modalities, both active using an RGB-D camera and passive using a dense stereo camera. To that end, we propose …
Multitemporal fusion of Landsat and MERIS images
2011
Monitoring Earth dynamics from current and future observation satellites is one of the most important objectives for the remote sensing community. In this regard, the exploitation of image time series from sensors with different characteristics provides an opportunity to increase the knowledge about environmental changes, which are needed in many operational applications, such as monitoring vegetation dynamics and land cover/use changes. Many studies in the literature have proven that high spatial resolution sensors like Landsat are very useful for monitoring land cover changes. However, the cloud cover probability of many areas and the 15-days temporal resolution restrict its use to monito…
Recent advances in remote sensing image processing
2009
Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation…