• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.305-314
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• 2015

The work presented in this paper deals with a navigation problem for multiple mobile robot system in unknown indoor environments. The environment is completely unknown for all the robots and the surrounding information should be detected by the proximity sensors installed on the robots' bodies. In order to guide all the robots to move along collision-free paths and reach the goal positions, a navigation method based on the combination of a set of primary strategies has been developed. The indoor environments usually contain convex and concave obstacles. In this work, a danger judgment strategy in accordance with the sensors' data is used for avoiding small convex obstacles or moving objects which include both dynamic obstacles and other robots. For big convex obstacles or concave ones, a wall following strategy is designed for dealing with these special situations. In this paper, a state memorizing strategy is also proposed for the "infinite repetition" or "dead cycle" situations. Finally, when there is no collision risk, the robots will be guided towards the targets according to a target positioning strategy. Most of these strategies are achieved by the means of fuzzy logic controllers and uniformly applied for every robot. The simulation experiments verified that the proposed method has a positive effectiveness for the navigation problem.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.1-6
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• 2012

Using a current-mode multiple reset, a current-to-voltage(I-V) converter with a wide dynamic range was produced. The converter consists of a trans-impedance amplifier(TIA), an analog-to-digital converter(ADC), and an N-bit counter. The digital output of the I-V converter is composed of higher N bits and lower bits, obtained from the N-bit counter and the ADC, respectively. For an input current that has departed from the linear region of the TIA, the counter increases its digital output, this determines a reset current which is subtracted from the input current of the I-V converter. This current-mode reset is repeated until the input current of the TIA lies in the linear region. This I-V converter is realized using 0.35 ${\mu}m$ LSI technology. It is shown that the proposed I-V converter can increase the maximum input current by a factor of $2^N$ and widen the dynamic range by $6^N$. Additionally, the I-V converter is successfully applied to a photometric sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1060-1064
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• 2006

Data collected by sensors in field are transmitted to the base station gathering all of data. Because sensors have to gather data in surroundings and periodically transmit data to the base station, it makes energy consumed much. In this paper, we mose the scheme that is to avoid traffic congestion with achievement of energy efficiency, so collected data is transmitted efficiently. This is to adjust transmission rate differently in case of increasing or decreasing traffic and minimize the energy consumption with setting ideal options up basic CSMA(Carrier Sense Multiple Access) protocol in each sensor. Through the simulation, we find the ideal CSMA options and apply the proposed scheme of traffic control mechanism to them and analyze them, then show energy efficiency and effective traffic control mechanism.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.891-897
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• 2018

In this paper, we propose a blind estimation scheme for the antenna spacing of nonuniform linear array MIMO radar using distributed electronic sensors based on ESPRIT. We present a blind method to separate orthogonal waveforms of a MIMO radar based on the antenna spacing estimation. The estimated orthogonal waveforms of a MIMO radar can be used for disabling opponent MIMO radars.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.676-683
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• 2019

Wireless Sensor Networks (WSNs) consist of multiple tiny and power constrained sensors which use radio frequencies to carry out sensing in a designated sensor area. To effectively design and implement reliable WSN, it is critical to consider models, protocols, and algorithms that can optimize energy consumption of all the sensor nodes with optimal amount of packet delivery. It has been observed that deploying a single sink node comes with numerous challenges especially in a situation with high node density and congestion. Sensor nodes close to a single sink node receive more transmission traffic load compared to other sensors, thus causing quick depletion of energy which finally leads to an energy hole and sink hole problems. In this paper, we proposed the use of multiple energy efficient sink nodes with brute force technique under optimized parameters to improve on the number of packets delivered within a given time. Simulation results not only depict that, deploying N sink nodes in a sensor area has a maximum limit to offer a justifiable improvement in terms of packet delivery ratio but also offers a reduction in End to End delay and reliability in case of failure of a single sink node, and an improvement in the network lifetime rather than deploying a single static sink node.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.239-247
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• 2022

The existing studies on the localization in the GNSS (Global Navigation Satellite System) denied environment usually exploit low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit) sensors to replace the GNSS signals. However, the navigation system still requires GNSS signals for the normal environment. This paper presents an integrated GNSS/INS (Inertial Navigation System) navigation system which combines GNSS and multiple IMU sensors using extended Kalman filter in partially GNSS-denied environments. The position and velocity of the INS and GNSS are used as the inputs to the integrated navigation system. The Mahalanobis distance is used for novelty detection to detect the outlier of GNSS measurements. When the abnormality is detected in GNSS signals, GNSS data is excluded from the fusion process. The performance of the proposed method is evaluated using MATLAB/Simulink. The simulation results show that the proposed algorithm can achieve a higher degree of positioning accuracy in the partially GNSS-denied environment.

• Smart Structures and Systems
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• v.18 no.3
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• pp.405-423
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• 2016

In this study, the feasibility of using telecommunication single-mode optical fiber (SMF) as a distributed fiber optic strain and crack sensor was evaluated in concrete pavement monitoring. Tensile tests on various sensors indicated that the $SMF-28e^+$ fiber revealed linear elastic behavior to rupture at approximately 26 N load and 2.6% strain. Six full-scale concrete panels were prepared and tested under truck and three-point loads to quantify the performance of sensors with pulse pre-pump Brillouin optical time domain analysis (PPP-BOTDA). The sensors were protected by precast mortar from brutal action during concrete casting. Once air-cured for 2 hours after initial setting, half a mortar cylinder of 12 mm in diameter ensured that the protected sensors remained functional during and after concrete casting. The strains measured from PPP-BOTDA with a sensitivity coefficient of $5.43{\times}10^{-5}GHz/{\mu}{\varepsilon}$ were validated locally by commercial fiber Bragg grating (FBG) sensors. Unlike the point FBG sensors, the distributed PPP-BOTDA sensors can be utilized to effectively locate multiple cracks. Depending on their layout, the distributed sensors can provide one- or two-dimensional strain fields in pavement panels. The width of both micro and major cracks can be linearly related to the peak strain directly measured with the distributed fiber optic sensor.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.152-161
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• 2014

In this paper, a multi-node real-time diagnostic and management system based on zigbee sensor network is proposed, which is to monitor and diagnose multiple nodes as well as to control the data generated from the various multiple sensors collectively. The proposed system is designed to transmit the collected wireless and wired data to the server for monitoring and controling efficiently the condition for multi-nodes by taking the corresponding actions according to the analysis. The system is implemented to make it possible to manage the sensor data by classifying them, of which data are issued from the clustered sources with a number of the remote 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 sensors. The results shows that the proposed system has a good performance.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Journal of Sensor Science and Technology
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• v.4 no.4
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• pp.47-54
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• 1995

Sensory measurements are inevitably uncertain, and this may decrease the reliability of sensor feedback systems. In this paper we present a method to achieve reliable measurements using multiple sensors. Assuming sensors with unknown characteristics in dynamic surroundings, readings from the sensors are clustered and fused with the estimated degrees of credit. In the experiment performed with logical sensors the technique proposed is employed to adaptively estimate the measurand under the circumstances where an existing probabilistic technique is difficult to be applied. The technique proposed may be useful for automatic systems working in unstructured environments - for example, sensory robots employed for intelligent manipulation.