• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.629-644
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• 2010

A real-time image georeferencing system requires all inputs to be determined in real-time. The intrinsic camera parameters can be identified in advance from a camera calibration process while other control information can be derived instantaneously from real-time GPS/INS data. The bottleneck process is tie point acquisition since manual operations will be definitely obstacles for real-time system while the existing extraction methods are not fast enough. In this paper, we present a fast-and-automated image matching technique based on the KLT tracker to obtain a set of tie-points in real-time. The proposed work accelerates the KLT tracker by supplying the initial guessed tie-points computed using the GPS/INS data. Originally, the KLT only works effectively when the displacement between tie-points is small. To drive an automated solution, this paper suggests an appropriate number of depth levels for multi-resolution tracking under large displacement using the knowledge of uncertainties the GPS/INS data measurements. The experimental results show that our suggested depth levels is promising and the proposed work can obtain tie-points faster than the ordinary KLT by 13% with no less accuracy. This promising result suggests that our proposed algorithm can be effectively integrated into the real-time image georeferencing for further developing a real-time surveillance application.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.291-296
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• 2008

Wireless network technologies are becoming more widely used in industrial environment. The industrial communication system requires a real-time delivery of messages. The sensors periodically senses the physical environment and wants to deliver the data in real-time to the sink. This paper proposes a real-time protocol for periodic messages on wireless sensor and actor networks to be used in industrial communications. The proposed protocol delivers the data message using the shortest path from the source and the nearest actor. The protocol considers the energy consumption by reducing the number of broadcast messages during flooding. We have evaluated the performance of the proposed protocol using QualNet simulator. The simulation results show that the data messages have been delivered in real-time and the number of broadcast messages is reduced from 90% to 35% compared the existing protocols.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8B
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• pp.1003-1011
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• 2011

In this paper, a real-time multi-source sensor data management system based on wireless sensor network is proposed and implemented. The proposed management system is designed to transmit the wireless data to the server in order to monitor and control the multi-source target's status efficiently by analyzing them. The proposed system is implemented to make it possible to control and transmit the wireless sensor data by classifying them, of which data are issued from the clustered sources composed of a number of the remote multiple sensors. In order to evaluate the performance of the proposed system, we measure and analyze both the transmission delay time according to the distance and the data loss rate issued from multiple data sources. From these results, it is verified that the proposed system has a good performance.

• Journal of Sensor Science and Technology
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• v.16 no.5
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• pp.361-368
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• 2007

Small size real-time ECG signal analysis function by QRS-complex detection was put into sensor nodes. Wireless sensor nodes attached on the patient’s body transmit ECG data continuously in normal u-healthcare system. So there are heavy communication traffics between sensor nodes and gateways. New developed platform for real-time analysis of ECG signals on sensor node can be used as an advanced diagnosis and alarming system for healthcare. Sensor node does not need to transmit ECG data all the time in wireless sensor network and to server PC via gateway. When sensor node detects suspicion or abnormality in ECG, then the ECG data in the network was transmitted to the server PC for further powerful analysis. This system can reduce data packet overload and save some power in wireless sensor network. It can also increase the server performance.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.18-27
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• 2013

Real-time monitoring is one of the prime necessities in a weapon flight test that is required for the efficient and timely collection of large amounts of high-rate sampled data acquired by an event-trigger. The wireless sensor network is a good candidate to resolve this requirement, especially considering the inhospitable environment of a weapon flight test. In this paper, we propose a priority based multi-channel MAC protocol with CSMA/CA over a single radio for a real-time monitoring of a weapon flight test. Multi-channel transmissions of nodes can improve the network performance in wireless sensor networks. Our proposed MAC protocol has two operation modes: Normal mode and Priority Mode. In the normal mode, the node exploits the normal CSMA/CA mechanism. In the priority mode, the node has one of three grades - Class A, B, and C. The node uses a different CSMA/CA mechanism according to its grade that is determined by a signal level. High grade nodes can exploit more channels and lower backoff exponents than low ones, which allow high grade nodes to obtain more transmission opportunities. In addition, it can guarantee successful transmission of important data generated by high grade nodes. Simulation results show that the proposed MAC exhibits excellent performance in an event-triggered real-time application.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1003-1006
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• 2004

In this paper have developed a system for monitoring and processing the real time sensor data in remote site through network. For realizing this system, measurement equipment and protocol are used to transmit the measurement data to remote server and to process measurement data. In server part, the received data from remote site sensor is converted to text or graphic charts for user. The measurement device in sensor part receives the sensor data form sensor and store the received data to its internal memory for transmitting data to server part through Internet. Also the measurement device can receive data form server. The temperature sensor is connected to the measurement device located in laboratory and the measurement device measures temperature of laboratory which can be confirmed by user through Internet. We have developed a server programworking on the Linux to store measurement data from measurement device to server memory. The program is use for SNMP(Simple Network Management Protocol) to exchange data with measurement device. Also the program changes the measurement data into text and graphic charts for user display. The program is use apache PHP program for user display and inquiry. The real time temperature measurement system can be apply for many parts of industry and living.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.92-96
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• 2011

Nowadays, the researches study high rate and real-time query applications seem to be real-time query scheduling protocols and energy aware real time query protocols. Also the WSNs should provide the quality of data in real time query applications that is more and more popular for wireless sensor networks (WSNs). Thus we propose the quality of data function to merge into energy efficiency called energy join quality aware realtime query scheduling (EJQRTQ). Our work calculate the energy ratio that considers interference of queries, and then compute the expected quality of query and allocate slots to real-time preemptive query scheduler.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.1-19
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• 2010

Recently, it is necessary to process real time sensor data, which is generated from ubiquitous environments. Data, which are written by XML, are small, but, large volumes of data. Therefore, weneed to use an efficient method for processing a large amount of it. An XQuery has two types for sensor data: one is to get sensor identification and value from sensor data; the other is restructuring for user's convenience. Existing XQuery engines don't have efficient method for batch processing of sensor data. This paper proposed the twig query processing over reverse path summary, and we developed and applied restructuring batch processing method for real time processing of a large amount of sensor data. Finally, we do performance evaluation using XMark and RFID EPC data, and comparison analysis with MonetDB/XQuery and Berkeley DB XML.