Human localization based on spiking neural network in intelligent sensor networks
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This paper proposes a human localization method in sensor networks for monitoring elderly people. First, we explain the proposed intelligent sensor networks. Next, we apply a spiking neural network to extract feature points for human localization from a measurement data by sensor networks. Furthermore, we propose a learning method using spiking neural network based on the time series of measurement data. Finally, we discuss the effectiveness of proposed method through experimental results in a living room.Keywords:
Intelligent sensor
Feature (linguistics)
Activity Recognition
Intelligent Network
Developing with intelligent building application, the intelligent BAS had been grown up separating from traditional one. But, general speaking, the BAS still had been impressed that is a integration system by sequence control and short of intelligence comparing with industry complex large system. According to intelligent control theory, whatever objects are, the control core or head and controlled object must be consisted in system, and this control core must be intelligent. In this paper, depending on thinking of building control characteristics, we present the new concept that intelligent BAS is a complex large system, but it can be constructed a hierarchical large system, and the intelligent hierarchical control is its main approach. Basing on this point, we discuss the intelligent BAS network structure. On the other hand, for intelligent control requirement, the lower level sensor network and wireless sensor network in this BAS are described. Finally, some application suggestions of wireless sensor network in BAS are discussed in this paper.
Intelligent Control
Hierarchical control system
Intelligent sensor
Intelligent Network
Building Automation
Real-time Control System
Networked control system
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Intelligent fusion terminals are the core components of intelligent distribution stations in low-voltage distribution Internet of Things. To enhance the practical application of intelligent fusion terminals in distribution Internet of Things, a neural network-based intelligent fusion terminal testing method is proposed. The focus is on the neural network-based intelligent fusion terminal testing method and the construction of a neural network model based on modified incentive functions to support communication protocol recognition between intelligent fusion terminals and different devices, improving the detection efficiency of intelligent fusion terminals.
Intelligent Network
Intelligent sensor
Sensor Fusion
Intelligent Control
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Activity recognition is important to health care in smart homes. It provides information about the activities of the residents. Many health care services are based on it. To collect data about the activities, sensor networks consist of binary sensors are widely used. Activity recognition is performed based on their readings. Most of existing activity recognition methods are based on supervised classification algorithms. One drawback of these methods is that the classification model learned in one smart home environment usually cannot be used in another. For a new smart home environment, sufficient sensor readings have to be collected and labeled to learn the needed classification model. This process is time consuming and expensive. In this paper, we propose a method for smart home activity recognition with binary sensors. Our method utilizes the characteristics of binary sensors, the semantic information of the sensors and the activities, and the time information. The classification model learned with the data in one smart home environment can be used in the activity recognition in another, which has different sensor networks and label spaces. Experiments on real world datasets show the effectiveness of our method.
Activity Recognition
Home Automation
Smart environment
Binary classification
Intelligent sensor
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Intelligent services are essential for various services in a smart city. However, it is still difficult to find a perfect example of intelligent services in our real life. Recently, the term artificial intelligence service has been used more than the term intelligent service. However, it is still difficult to find examples of applying artificial intelligence services in the real world due to the minimal performance requirements to run the model smoothly. A technology for lightweighting intelligent models is being developed so that artificial intelligence models can operate even on low-performance devices. If a lightweight intelligent model can be freely used in low-performance devices, many intelligent services suitable for the IoT era will flourish. Therefore, we tried PoC service in a smart city using a lightweight intelligent model mounted on the Raspberry Pi, a low-performance device, and we confirmed the possibility.
Intelligent sensor
Intelligent Network
Ambient Intelligence
Smart City
Smart device
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This paper is concerned with constructing a prototype intelligent home environment for home service robot. In this environment, multi-pattern information can be represented by some intelligent artificial marks. Light-packs service robots can provide reliable and intelligent service by interacting with the environment through the wireless sensor networks. The intelligent space consists the following main components: smart devices with intelligent artificial mark; home server that connects the smart device and maintains the information through wireless sensor network; and the service robot that perform tasks in collaboration with the environment. In this paper, the multi-pattern information model is built, the construction of wireless sensor networks is presented, the smart and agilely home service is introduced. Fi- nally, the future direction of intelligent space system is discussed.
Service robot
Intelligent sensor
Intelligent Network
Home Automation
Intelligent environment
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Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.
Activity Recognition
Intelligent sensor
Smart environment
Home Automation
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Intelligent sensor
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Neural networks models and their training algorithms on a central computer with reference to a previously developed distributed sensor network are considered. The requirements for its intelligent node are formulated. Also the node's structure is offered which realises such intelligent functions, as sensor and other measuring channel components drift prediction using remote reprogramming.
Intelligent sensor
Intelligent Network
Sensor node
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In this paper, we briefly introduce the importance of intelligent surveillance sensor networks. Then we propose a very simple application framework for intelligent surveillance sensor networks.
Intelligent sensor
Intelligent Network
Sensor web
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Learning and recognizing human activities of daily living(ADL), is very useful and essential to build a pervasive home monitoring system. These monitoring technologies are indispensable for developing the next generation of smart houses. In this paper we describe a fuzzy logic system for recognizing activities in home environment using a set of sensors: physiological sensors (cardiac frequency, activity or agitation, posture and fall detection sensor), microphones, infrared sensors, debit sensors and state-change sensors. Motivated by the fact that fuzzy controllers have been successfully embedded within billions of dollars in commercial products, plus the characteristic of data providing from each sensor, the fusion of the different sensors has been performed by using fuzzy logic. This fuzzy logic approach allowed us to recognize several activities of daily living (ADLs) for ubiquitous healthcare.
Activity Recognition
Home Automation
Intelligent sensor
Sensor Fusion
Remote patient monitoring
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Citations (121)