• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.97-103
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• 2014

In spectrum sensing, there is a tradeoff between the probability of missed detection and the probability of a false alarm according to the value of the sensing threshold. Therefore, it is important to determine the sensing threshold suitable to the environment of cognitive radio networks. In this study, we consider a cognitive radio-based ad hoc network where secondary users directly communicate by using the same frequency band as the primary system and control their transmit power on the basis of the distance between them. First, we investigate a condition in which the primary and the secondary users can share the same frequency band without harmful interference from each other, and then, propose an algorithm that controls the sensing threshold dynamically on the basis of the transmit power of the secondary user. The analysis and simulation results show that the proposed sensing threshold control algorithm has low probabilities of both missed detection and a false alarm and thus, enables optimized spectrum sharing between the primary and the secondary systems.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.391-395
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• 2011

Cognitive radio (CR) technique is a useful tool for improving spectrum utilization by detecting and using the vacant frequency bands while avoiding interference to the primary user. The sensing performance in a CR network can be improved by allowing some CR users to perform cooperative spectrum sensing. In this paper, we propose a new sensing algorithm that utilizes an adaptive energy threshold for cooperative spectrum sensing in which a changeable energy threshold is adopted by the CR users for improving local sensing performance. Through the proposed scheme, the reliability of global decision can be enhanced mainly due to the improvement in local sensing performance.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2368-2372
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• 2005

We proposed a novel method for object recognition using the Smart tag system in the previous research. We identified the object easily, but could not assure the object pose, because the threshold problem was not solved. So we propose a new method to solve this threshold problem. This method uses a smart tag to decide the threshold by recording color information of the image when the object feature is extracted. This method records the original of the object color information at the smart tag first. And then it records the object image information, the circumstance image information and the sensors information continuously when the object feature is extracted through the experiments. Finally, it estimates the current threshold by recorded information. This method can be applied the threshold to each objects. And it can solve the difficult threshold decision problem easily. To approve the possibility of our method, we implemented our approach by using easy and simple techniques as possible.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.61-65
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• 2013

In this paper, we propose system model to solve conventional threshold problem of using fixed false alarm for energy spectrum sensing. Spectrum sensing reliability is ensured when Secondary user have high SNR. Thus, it is not reasonable using fixed optional false alarm without considering CR user's SNR. So, we propose adaptive threshold method. adaptive threshold is decided by controling FA according to CR user's SNR.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.66-70
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• 2013

In this paper, Threshold setting method is proposed to improve detection probability for cooperative sensing. Even if cooperative users have all same false alarm rate, each user has different threshold due to pass ad-hoc channel. threshold level is related to detection probability. So, we select the highest threshold among cooperative users and then share threshold information for getting the high detection probability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.253-263
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• 2015

Lately, spectrum sensing performance has been improved by using cooperate spectrum sensing which each results of sensing of several secondary users are reported to the fusion center. Using Cognitive Radio, secondary user is able to share a bandwidth allocated to primary user. In this paper, we propose a new decentralized cooperative spectrum sensing scheme which compensates the performance degradation of existing decentralized cooperative spectrum sensing considering the error probability of the channel which sensed result of the secondary user is delivered to the fusion center in decentralized cooperative spectrum sensing. In addition, a sensing threshold optimization of minimizing the error probability of decentralized cooperative spectrum sensing is introduced by deriving the equation and the optimal sensing threshold has been confirmed to maximize the decentralized cooperative spectrum sensing performance.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.251-255
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• 2011

In this paper, we investigate an adaptive cognitive radio (CR) scheme where a sensing duration and a detection threshold for spectrum sensing are adaptively determined according to the channel condition in a fading channel. We optimize the sensing duration and detection threshold of a secondary user to maximize the performance of the secondary user guaranteeing a primary user's secure communication. In addition, we analyze the effect of channel fading on the optimization of the sensing duration and detection threshold. Our numerical results show that the performance of the adaptive CR scheme can be drastically improved if a secondary user can take the advantage of channel information between primary and secondary users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2755-2773
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• 2015

Cooperative spectrum sensing increases the detection performance in a cognitive radio network, based on the number of sensing nodes. However, as the number of sensing nodes increases, the reporting overhead linearly increases. This paper proposes two kinds of cooperative spectrum sensing with limited reporting in a centralized cognitive radio network, a soft combination with threshold-based reporting (SC-TR) and a soft combination with contention-based reporting (SC-CR). In the proposed SC-TR scheme, each sensing node reports its sensing result to the fusion center through its own reporting channel only if the observed energy value is higher than a decision threshold. In the proposed SC-CR scheme, sensing nodes compete to report their sensing results via shared reporting channels. The simulation results show that the proposed schemes significantly reduce the reporting overhead without sacrificing the detection performance too much.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.