• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.1-8
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• 2016

Spectrum sensing in cognitive radio networks allows secondary users to sense the unused spectrum without causing interference to primary users. Cognitive radio requires more accurate sensing results from unused portions of the spectrum. Accurate spectrum sensing techniques can reduce the probability of false alarms and misdetection. In this paper, a two-stage spectrum sensing scheme is proposed for cooperative spectrum sensing in cognitive radio networks. In the first stage, spectrum sensing is executed for each secondary user using energy detection based on double adaptive thresholds to determine the spectrum condition. If the energy value lies between two thresholds, a fuzzy logic scheme is applied to determine the channel conditions more accurately. In the second stage, a fusion center combines the results of each secondary user and uses a fuzzy logic scheme for combining all decisions. The simulation results show that the proposed scheme provides increased sensing accuracy by about 20% in some cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.20-33
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• 2015

Spectrum sensing is a key component of cognitive radio. The prediction of the primary user status in a low signal-to-noise ratio is an important factor in spectrum sensing. However, because of noise uncertainty, secondary users have difficulty distinguishing between the primary signal and an unauthorized signal when an unauthorized user exists in a cognitive radio network. To resolve the sensitivity to the noise uncertainty problem, we propose an entropy-based spectrum sensing scheme to detect the primary signal accurately in the presence of an unauthorized signal. The proposed spectrum sensing uses the conditional entropy between the primary signal and the unauthorized signal. The ability to detect the primary signal is thus robust against noise uncertainty, which leads to superior sensing performance in a low signal-to-noise ratio. Simulation results show that the proposed spectrum sensing scheme outperforms the conventional entropy-based spectrum sensing schemes in terms of the primary user detection probability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.10
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• pp.838-844
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• 2013

In this paper, we propose a novel spectrum sensing scheme based on sensing time partitioning for cognitive radio systems in high traffic environments. Specifically, we partition a sensing time into K sub-sections, and then, propose a spectrum sensing scheme that determines if a primary user signal is present based on the sensing results on the partitioned sub-sections. From numerical results, it is confirmed that the proposed scheme outperforms the conventional schemes in high traffic environments.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.476-483
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• 2002

In the past 20 years or so, numerous methods to reduce speckle in SAR images have been proposed. The primary goal of these methods is to reduce speckle without destroying resolution and smearing edge information. But the experiments indicate that there is always a kind of tradeoff between smoothing out speckle and preserving edge information. In this paper, an optimal SAR speckle filter is developed. It can effectively smooth out speckle while preserve edge information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3302-3320
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• 2014

An allocation of sensing and reporting times is proposed to improve the sensing performance by scheduling them in an efficient way for cognitive radio networks with cluster-based cooperative spectrum sensing. In the conventional cooperative sensing scheme, all secondary users (SUs) detect the primary user (PU) signal to check the availability of the spectrum during a fixed sensing time slot. The sensing results from the SUs are reported to cluster heads (CHs) during the reporting time slots of the SUs and the CHs forward them to a fusion center (FC) during the reporting time slots of the CHs through the common control channels for the global decision, respectively. However, the delivery of the local decision from SUs and CHs to a CH and FC requires a time which does not contribute to the performance of spectrum sensing and system throughput. In this paper, a super-allocation technique, which merges reporting time slots of SUs and CHs to sensing time slots of SUs by re-scheduling the reporting time slots, has been proposed to sense the spectrum more accurately. In this regard, SUs in each cluster can obtain a longer sensing duration depending on their reporting order and their clusters except for the first SU belonged to the first cluster. The proposed scheme, therefore, can achieve better sensing performance under -28 dB to -10 dB environments and will thus reduce reporting overhead.

• Proceedings of the KSRS Conference
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• v.1
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• pp.340-343
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• 2006

Remote sensing science is a rapidly growing field of Earth sciences. Since emergence and to present day, an extensive literature has evolved which traces the wide application of remote sensing in human activities. According to the ISI Web of Science in the 1975-2005 time span more then 20,000 papers were published on remote sensing. The number of papers grew exponentially with doubling period of about 6 years. Notwithstanding all specialized proceedings, there is a lot more remote sensing papers published in a vast list of source titles (up to 350 proceedings). Only 25% of retrieved papers are published in 10 proceedings which ISI assigns to subject category of remote sensing. In 2005 all these proceedings published 1291 articles and received cca 24,000 citations. Average impact factor of the proceedings is equal to 1.181 and average cited half-life is 7.1. It means that an average paper in remote sensing proceedings is cited more then once per year and half of citations the paper receive within the next 7 years after publication. The time line of remote sensing periodicals issued in 1927-1995 shows an exponential growth with doubling period about 15 years. After 1995 there is a prominent deviation from the exponential curve which shows the demand saturation for specialized proceedings. The features revealed are discussed in terms of dynamics and impact of remote sensing in current Earth sciences development.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.11a
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• pp.141-144
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• 2005

Sensing and interfacial evaluation of Ni nanowire strands/polymer composites were investigated using Electro-micromechanical technique. Electro-micromechanical techniques can be used as sensing method for micro damage, loading, temperature of interfacial properties. Using Ni nanowire strands/silicone composites with different content, load sensing response of electrical contact resistivity was investigated under tensile and compression condition. The mechanical properties of Ni nanowire strands with different type/epoxy composites were measured using uniformed cyclic loading and tensile test. Ni nanowire strands/epoxy composites showed humidity and temperature sensing within limited ranges, 20 vol% reinforcement. Some new information on temperature and humidity sensing plus loading sensing of Ni nanowire strands/polymer composites could be obtained from the electrical resistance measurement as a new concept of the nondestructive interfacial evaluation.

• Broadcasting and Media Magazine
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• v.11 no.1
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• pp.68-77
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• 2006

The Cognitive Radio (CR) technology is a promising solution for exploiting the limited spectrum resources and providing flexibility of spectrum usage. Future interactive broadcasting service can be realized by utilizing CR concept, since the up-link return channel can be found by the spectrum sensing method, which is core functional block of the CR system. In this paper, the spectrum sensing technologies of CR system is presented. First the system architecture of the CR with spectrum sensing block is presented. The suggested spectrum sensing technique consists of the coarse and the fine spectrum sensing. The coarse spectrum sensing technique adopted the wavelet transform to provide the multi-resolution sensing feature - Multi-Resolution Spectrum Sensing (MRSS). The fine spectrum sensing technique uses the beneficial properties of the autocorrelation function Analog Auto-Correlation (AAC). The simulation results for the proposed sensing technologies are presented for various incumbent signals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5249-5267
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• 2016

Cognitive radio is an efficient technique to improve spectrum efficiency and relieve the pressure of spectrum resources. In this paper, we investigate the spectrum sensing period in cooperative relay cognitive radio networks; analyze the relationship between the available capacity and the signal-to-noise ratio of the received signal of second users, the target probability of detection and the active probability of primary users. Finally, we derive the closed form expression of the optimal spectrum sensing period in terms of maximum throughput. We simulate the probability of false alarm and available capacity of cognitive radio networks and compare optimal spectrum sensing period scheme with fixed sensing period one in these performance. Simulation results show that the optimal sensing period makes the cognitive networks achieve the higher throughput and better spectrum sensing performance than the fixed sensing period does. Cooperative relay cognitive radio networks with optimal spectrum sensing period can achieve the high capacity and steady probability of false alarm in different target probability of detection. It provides a valuable reference for choosing the optimal spectrum sensing period in cooperative relay cognitive radio networks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.25-31
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• 2009

The recently developed sampling theory, "compressed sensing" is gathering huge interest in MR reconstruction area because of its feasibility of high spatio-temporal resolution of dynamic MRI which has been limited in conventional methods based on Nyquist sampling theory. Since dynamic MRI usually has high redundant information along temporal direction, this can be very sparsely represented in most of cases. Therefore, compressed sensing that exploits the sparsity of unknown images can be effectively applied in most of dynamic MRI. This review article briefly introduces currently proposed compressed sensing based dynamic MR imaging algorithms and other methods exploiting sparsity. By comparing them with conventional methods, you may have insight how the compressed sensing based methods can impact nearly every area of clinical dynamic MRI.