• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권4호
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.

• 한국멀티미디어학회논문지
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• 제19권5호
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• pp.910-917
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• 2016

Sensors have limited resources in sensor networks, so efficient use of energy is important. Representative clustering methods, LEACH, LEACHC, TEEN generally use direct transmission methods from cluster headers to the sink node to pass collected data. However, the communication distance of the sensor nodes at low cost and at low power is not long, it requires a data transfer through the multi-hop to transmit data to the sink node. In the existing cluster-based sensor network studies, cluster process and route selection process are performed separately in order to configure the routing path to the sink node. In this paper, in order to use the energy of the sensor nodes that have limited resources efficiently, a cluster-based multi-hop routing protocol which merges the clustering process and routing process is proposed. And the proposed method complements the problem of uneven cluster creation that may occur in probabilistic cluster methods and increases the energy efficiency of whole sensor nodes.

• Journal of information and communication convergence engineering
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• 제5권4호
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• pp.300-304
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• 2007

Wireless sensor networks represent a new and exciting communication paradigm which could have multiple applications in future wireless communication. Therefore, performance analysis of such a wireless sensor network paradigm is needed in complex wireless channel. Wireless networks could be an important means of providing ubiquitous communication in the future. In this paper, the BER performance of uniform distributed wireless sensor networks is evaluated in non-Gaussian noise channel. Using an analytical approach, the impact of Av. BER performance relating the coherent BPSK system at the end of a multi-hop route versus the spatial density of sensor nodes and impulsive noise parameters A and $\Gamma$ is evaluated.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2002년도 춘계학술발표대회 개요집
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• pp.49-52
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• 2002

A vision sensor measure range data using laser light source, This sensor generally use patterned laser which shaped single line. But this vision sensor cannot satisfy new trend which needs faster and more precise processing. The sensor's sampling rate increases as reduced image processing time. However, the sampling rate can not over 30fps, because a camera has mechanical sampling limit. If we use multi line laser pattern, we will measure multi range data in one image. In the case of using same sampling rate camera, number of 2D range data profile in one second is directly proportional to laser line's number. For example, the vision sensor using 5 laser lines can sample 150 profiles per second in best condition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.699-701
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• 1995

An input estimation technique is derived in multi-sensor environment. The proposed approach distribute the computational burden of input estimation to each local sensor and fusion center without loss of its optimality. The performances of proposed method in 2-sensor system are compared with those in single sensor system. Simulation results show that a reliable maneuvering target tracking system can be constructed in multi-sensor environment via proposed approach.

• 한국생산제조학회지
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• 제24권1호
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• pp.103-109
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• 2015

This paper introduces a multi-purpose smart fusion sensor. Normally, this type of sensor can contribute to energy savings specifically related to lighting and heating/air conditioning systems by detecting individuals in an office building. If a fire occurs, the sensor can provide information regarding the presence and location of residents in the building to a management center. The system consists of four sensors: a thermopile sensor for detecting heat energy, an ultrasonic sensor for measuring the distance of objects from the sensor, a fire detection sensor, and a passive infrared sensor for detecting temperature change. The system has a wireless communication module to provide the management center with control information for lighting and heating/air conditioning systems. We have also demonstrated the usefulness of the proposed system by applying it to a real environment.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.582-587
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• 2004

Ultrasonic sensors are widely used in mobile robot applications to recognize external environments, because they are cheap, easy to use, and robust under varying lighting conditions. However, the recognition of objects using a ultrasonic sensor is not so easy due to its characteristics such as narrow beam width and no reflected signal from a inclined object. As one of the alternatives to resolve these problems, use of multiple sensors has been studied. A sequential driving system needs a long measurement time and does not take advantage of multiple sensors. Simultaneous and pulse coding driving system of ultrasonic sensor array cannot measure short distance as the length of the code becomes long. This problem can be resolved by multi-frequency driving of ultrasonic sensors, which allows multi-sensors to be fired simultaneously and adjacent objects to be distinguished. Accordingly, this paper presents a simultaneous and multi-frequency driving system for an ultrasonic sensor array for object recognition. The proposed system is designed and implemented using a DSP and FPGA. A micro-controller board is made using a DSP, Polaroid 6500 ranging modules are modified for firing the multi-frequency signals, and a 5-channel frequency modulated signal generating board is made using a FPGA. To verify the proposed method, experiments were conducted in an environment with overlapping signals, and the flight distances for each sensor were obtained from filtering of the received overlapping signals and calculation of the time-of-flights.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.647-648
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• 2012

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.649-650
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• 2012

• 센서학회지
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• 제15권1호
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• pp.7-12
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• 2006

The multi-functional one-chip sensor has been fabricated to reduce output variation under various water environment. There were a temperature sensor, a piezoresistive type pressure sensor, and a electrode type conductivity sensor in the fabricated one-chip sensor. This sensor was measured water depth in the range of $0{\sim}180cm$, temperature in the range of $0{\sim}50^{\circ}C$, and salinity in the range of 0 $0wt%{\sim}5wt%$, respectively. Since the change of water depth in solution environment depends on various factors such as salinity, latitude, temperature, and atmospheric pressure, the water depth sensor is needed to be compensated. We tried to compensate the salinity and temperature dependence for the pressure in water by using lookup-table method.