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Object Recognition: Distributed Architecture Based on Heterogeneous Devices to Integrate Sensor Information
Abstract
Object recognition is a necessary task for many areas of technology, such as robot navigation or the intelligent reconstruction of environments in order a robot can interact with these objects. This article presents an architecture that integrates distributed heterogeneous information to recognise objects. The architecture uses devices that can process sensory data locally to send information to other devices. In order to perform data processing actions, the devices must have a layer of intelligent connectivity. These devices are called Smart Resources that offer to the distributed nodes the processed sensor data by means of information services. To validate the architecture, a system with two smart resources, equipped with different sensors, has been implemented. Experiments carried out show that it is better to select objects as soon as possible to improve the object recognition rate. Consequently, in the distributed system, devices should, when possible, deliver the process in advance.
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The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot’s belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.
This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.
The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client's requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client's requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.
In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.
A significant obstacle for service robots is the execution of complex tasks in real environments. For example, it is not easy for service robots to find objects that are partially observable and are located at a place which is not identical but near the place where the robots saw them previously. To overcome the challenge effectively, robot knowledge represented as a semantic network can be extremely useful. This paper presents an ontology-based unified robot knowledge framework that integrates low-level data with high-level knowledge for robot intelligence. This framework consists of two sections: knowledge description and knowledge association. Knowledge description includes comprehensively integrated robot knowledge derived from low-level knowledge regarding perceptual features, part objects, metric maps, and primitive behaviors, as well as high-level knowledge about perceptual concepts, objects, semantic maps, tasks, and contexts. Knowledge association uses logical inference with both unidirectional and bidirectional rules. This characteristic enables reasoning to be performed even when only a partial information is available. The experimental results that demonstrate the advantages of using the proposed knowledge framework are also presented.
Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.
The primary goal of pattern recognition is supervised or
unsupervised classification. Among the various frameworks in which
pattern recognition has been traditionally formulated, the statistical
approach has been most intensively studied and used in practice. More
recently, neural network techniques and methods imported from
statistical learning theory have been receiving increasing attention.
The design of a recognition system requires careful attention to the
following issues: definition of pattern classes, sensing environment,
pattern representation, feature extraction and selection, cluster
analysis, classifier design and learning, selection of training and test
samples, and performance evaluation. In spite of almost 50 years of
research and development in this field, the general problem of
recognizing complex patterns with arbitrary orientation, location, and
scale remains unsolved. New and emerging applications, such as data
mining, web searching, retrieval of multimedia data, face recognition,
and cursive handwriting recognition, require robust and efficient
pattern recognition techniques. The objective of this review paper is to
summarize and compare some of the well-known methods used in various
stages of a pattern recognition system and identify research topics and
applications which are at the forefront of this exciting and challenging
field
Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning.
In the current context of distributed systems, the communications have moved from a model based on connected nodes, to a model that must connect processes, or including connect data. There are many paradigms, mechanisms and architectures to give support to these distributed systems. Most of these mechanisms are extensions or evolutions of distributed node based architecture where the knowledge about the physical node is necessary. To isolate communications from the physical aspects of distributed systems a middleware, centered on the data, and oriented to connect processes has been developed. The middleware, called “CKMultipeer”, allows processes to communicate data between them. Communication is integrated without worrying about the network configuration and management. This article describes the implementation of the middleware, as well as the tests developed. Through the design of a graphical interface, the communications performance is analyzed.
Smart devices are becoming increasingly commercially available. However, uptake of these devices has been slow and abandonment swift, which indicates that smart devices may not currently meet the needs of users. To advance an understanding of the ways users benefit from, are challenged by, and abandon smart devices, we asked a group of users to purchase smart sensing devices to advance themselves towards a personal, self-defined goal. We found that participants abandoned devices because they did not fit with the their conceptions of themselves, the data collected by devices were perceived to not be useful, and device maintenance became unmanageable. Participants used devices because they had developed routines and because devices were useful, satisfied curiosity, and held hope for potential benefit to them. We propose ways to reduce barriers, motivate use, and argue for envisioning an additional function of these devices for short-term interventions, in addition to standard long-term use.
In the last few years, Industrial Information Integration Engineering (IIIE) has attracted much attention by the information and communications technology (ICT) community. However, despite of the dynamic nature of this research area, a systematic and extensive review of recent research on IIIE is unavailable. Accordingly, this study conducts an intensive literature review on IIIE and presents an overview of IIIE's content, scope and findings, and potential research opportunities by examining existing literatures from 2006 to 2015 in all databases within Web of Science. Altogether, 497 papers related to IIIE are grouped into 37 research categories and reviewed. The results add knowledge to the existing ones by answering what the current level of development on IIIE is and what the potential future research directions of IIIE are.
In this paper, we present a framework based on 3D range data to solve the problem of simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic environments. The basic idea is to use an octree based Occupancy Grid representation to model dynamic environment surrounding the vehicle and to detect moving objects based on inconsistencies between scans. The proposed method for the discrimination between moving and stationary objects without a priori knowledge of the targets is the main contribution of this paper. Moreover, the detected moving objects are classified and tracked using Global Nearest Neighbor (GNN) technique. The proposed method can be used in conjunction with any type of range sensors however we have demonstrated it using the data acquired from a Velodyne HDL-64E LIDAR sensor. The merit of our approach is that it allows for an efficient three dimensional representation of a dynamic environment, keeping in view the enormous amount of information provided by 3D range sensors.
With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion.
IntroductionModelling Context-Aware SystemsMobility AwarenessSpatial AwarenessTemporal Awareness: Coordinating and SchedulingICT System AwarenessExercisesReferences
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