The density classification task is a prototypical consensus problem of distributed solution, usually addressed in the field of cellular automata. In short, this problem consists of finding the most frequent state in a...
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ISBN:
(纸本)9783030238872;9783030238865
The density classification task is a prototypical consensus problem of distributed solution, usually addressed in the field of cellular automata. In short, this problem consists of finding the most frequent state in a binary sequence, necessarily through a non-global process on which the automaton reaches uniform consensus about such state. In this regard, we formulate the task as an agent-based model, in which agents set up a connectivity pattern, here corresponding to a circulant graph, and update their internal states according to the majority rule. The performance of the model corresponds to the number of correctly classified densities, given a set of binary sequences. Therefore, our goal is to analyze the sensibility of the model's performance in terms of the connectivity pattern associated with it, configured as a circulant graph, under different orders, average degrees and connectivity arrangements.
We investigate the practical challenge of localized flood detection in real smart city environment using the fusion of physical sensor and social sensing models to depict a reliable and accurate flood monitoring and d...
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ISBN:
(纸本)9781728169972
We investigate the practical challenge of localized flood detection in real smart city environment using the fusion of physical sensor and social sensing models to depict a reliable and accurate flood monitoring and detection framework. Our proposed framework efficiently utilize the physical and social sensing models to provide the flood-related updates to the city officials. We deployed our flood monitoring system in Ellicott City, Maryland, USA and connect it to the social sensing module to perform the flood-related sensor and social data integration and analysis. Our ground-based sensor network model record and performs the predictive data analytic by forecasting the rise in water level (RMSE=0.2) that demonstrates the severity of upcoming flash floods whereas, our social sensing model helps collect and track the flood-related feeds from Twitter. We employ a pre-trained model and inductive transfer learning based approach to classify the flood-related tweets with 90% accuracy in the use of unseen target flood events. Finally our flood detection framework categorizes the flood relevant localized contextual details into more meaningful classes in order to help the emergency services and local authorities for effective decision making.
In many industrial sectors such as factory automation and process control sensor redundancy is required to ensure reliable and highly-available operation. Measured values from N-redundant sensors are typically subject...
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ISBN:
(纸本)9781728152974
In many industrial sectors such as factory automation and process control sensor redundancy is required to ensure reliable and highly-available operation. Measured values from N-redundant sensors are typically subjected to some voting scheme to determine a value which is used in further processing. In this paper we present a voting framework which allows the sensors and the voting scheme to be configured at system-configuration time. The voting scheme is designed as a Real Time Ethernet profile. We describe the structure of the voting system and the design and verification of the framework. We argue the applicability of this sub-system based on a successful prototype implementation.
Adaptive manufacturing systems consist of many autonomous agents working together in an ever-changing environment. Therefore, collectively deciding which agent performs what task is a key issue and widely studied. How...
Adaptive manufacturing systems consist of many autonomous agents working together in an ever-changing environment. Therefore, collectively deciding which agent performs what task is a key issue and widely studied. However, many approaches towards this issue assume (partially) centralized control, require implementing proprietary algorithms, or cannot provide any guarantees regarding their runtime or communication overhead. To address these problems, we investigate the use of distributed constraint optimization (DCOP) in this context: We present a DCOP model built on freely available algorithms to distribute the problem among the agents that cooperate to solve it. Furthermore, we compare this decentralized approach to a centralized one by measuring the runtime in a set of system configurations with an increasing number of agents. While the DCOP approach works well in small system configurations, our results indicate poor scalability compared to the central approach when increasing the number of agents. We conclude that, although the DCOP approach has desirable properties, it is unsuitable for larger practical applications with dozens or hundreds of agents.
Estimation of noise in signal dependant data can be done using estimators based on local statistics. In this research paper, a comparative analysis of estimators based on second order moment and local mean is done. An...
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ISBN:
(纸本)9781728148762
Estimation of noise in signal dependant data can be done using estimators based on local statistics. In this research paper, a comparative analysis of estimators based on second order moment and local mean is done. An empirical correction factor constant is introduced in the local mean estimator which makes the estimated standard deviation more converged to the input noise. The study was performed on T1 and T2 weighted synthetic MR Images from BrainWeb and comparative results were drawn. The algorithm was implemented using MATLAB 2015a.
With the rapid development of edge computing technology, the application of edge computing in smart grids has become more and more extensive. But edge computing has not yet been applied to the operation control of dis...
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With the rapid development of edge computing technology, the application of edge computing in smart grids has become more and more extensive. But edge computing has not yet been applied to the operation control of distributed power generation microgrid systems. This article proposes a microgrid-oriented edge computing architecture. First, we introduce the main functions of edge-cloud collaboration. Then we explain the construction plan of the architecture, including the realization of data processing, network communication and security mechanisms. Finally, we introduce the architecture application practice in a rural community in Central China.
Now a days distributedsystems are backbone of majority of the networked applications. It has multiple entities working collaboratively. This collaboration needs to be coordinated by an entity among the member entitie...
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Like rigid objects, also soft and elastic manufactured materials for industrial and biomedical applications are subjected to fatigue stress that might speed up the aging process and even cause premature failures. The ...
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ISBN:
(纸本)9781728152783
Like rigid objects, also soft and elastic manufactured materials for industrial and biomedical applications are subjected to fatigue stress that might speed up the aging process and even cause premature failures. The occurrence of early signs of damaging, like the arising of surface cracks, could avoid more severe critical events, especially when biomedical soft prosthesis are involved (such as artificial breast, stomach, bladder). A thin-film stretchable wireless sensor for surface monitoring is here proposed. The device is based on a densely distributed electrode exploiting, at the macro-scale, a Space-Filling Curve pattern, and a meandered profile in the micro-scale. Interconnection with a wrapped Radiofrequency Identification antenna permits to transmit the status of the electrode to remote, with no battery onboard. The device was manufactured by means of electron beam deposition over a thin elastomer. Surface defects of size larger than 0.9mm to 9mm can be detected with probability of 60% to 90%, respectively. Thanks to its double-scale meanderings, the sensor is highly tolerant to stretch keeping its shape nearly unchanged up to a 35% strain.
A standard aspect of the neurological examination involves the assessment of diadochokinesia. Diadochokinesia pertains the other swift alternation of an agonist and antagonist muscle pair, such as pronation and supina...
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ISBN:
(纸本)9781728188034
A standard aspect of the neurological examination involves the assessment of diadochokinesia. Diadochokinesia pertains the other swift alternation of an agonist and antagonist muscle pair, such as pronation and supination with respect to the forearm. For example, hemiparesis can manifest perceptible distinction for diadochokinesia with respect to the hemiplegic affected forearm and unaffected forearm. Standard techniques involve ordinal scales, which are inherently subjective, and the development of quantified methods has been recommended. The advent of conformal wearable and wireless inertial sensorsystems, such as the BioStamp nPoint with a lightweight and bandage-like profile, enables quantification of diadochokinesia with wireless access to a Cloud computing environment for post-processing. The gyroscope signal data quantifying diadochokinesia can be consolidated to a feature set for machine learning classification to differentiate between a hemiplegic affected forearm and unaffected forearm. Considerable machine learning classification accuracy is attained for distinguishing between a hemiplegic affected forearm and unaffected forearm based on the quantified gyroscope signal data acquired from a conformal wearable and wireless inertial sensor system.
Online data processing in sensor networks problem is of particular relevance in connection with the widespread use of such systems for restoring physical fields from local measurements, for example, in environmental m...
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