• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3095-3111
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• 2018

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

• Journal of Navigation and Port Research
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• v.30 no.2
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• pp.167-172
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• 2006

In this paper, the issue of blind detection of Alamouti-type differential space-time (ST) ${\pi}/2$-shifted BPSK modulation in static Rayleigh fading channels is considered. We introduce a novel transformation to the received signal from each receiver antenna such that this binary ST modulation, which has a second-order transmit-diversity, is equivalent to QPSK modulation with second-order receive-diversity. The pre-detection combining of the result of transformation allows us to apply a low complexity detection technique specifically designed for receive-diversity, namely, scalar multiple-symbol differential detection (MSDD). With receiver complexity proportional to the observation window length, our receiver can achieve the performance 1.5dB better than that of conventional differential detection ST and 0.5dB worse than that qf a coherent maximum ratio combining receiver (with differential decoding) approximately.

• Journal of KIBIM
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• v.7 no.1
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• pp.18-27
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• 2017

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.83-86
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• 2007

In the blind Maximum-likelihood (ML) detection for Orthogonal Space-Time Block Codes (OSTBC), the problem of ambiguity in determining the symbols has been a great concern. A solution to this problem is to apply semi-blind ML detection, i.e., the blind ML decoding with pilot symbols or training sequence. In order to increase the performance, the number of pilot symbols or length of training sequence should be increased. Unfortunately, this leads to a significant decrease in system spectral efficiency. This work presents an approach to resolve the aforementioned issue by introducing a new method for constructing transmitted information symbol. Thus, by transmitting information symbols drawn from different modulation constellation, the ambiguity can be easily eliminated in blind detection. Also, computer simulations are implemented to verify the performance of the proposed approach.

• Journal of Communications and Networks
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• v.5 no.1
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• pp.33-42
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• 2003

In this paper a two-stage maximum-likelihood (ML) detection structure for group detection in DS/CDMA systems is presented. The first stage of the receiver is a linear filter, aimed at suppressing the effect of the unwanted (i.e., out-of-grout) users' signals, while the second stage is a non-linear block, implementing a ML detection rule on the set of desired users signals. As to the linear stage, we consider both the decorrelating and the minimum mean square error approaches. Interestingly, the proposed detection structure turns out to be a generalization of Varanasi's group detector, to which it reduces when the system is synchronous, the signatures are linerly independent and the first stage of the receiver is a decorrelator. The issue of blind adaptive receiver implementation is also considered, and implementations of the proposed receiver based on the LMS algorithm, the RLS algorithm and subspace-tracking algorithms are presented. These adaptive receivers do not rely on any knowledge on the out-of group users' signals, and are thus particularly suited for rejection of out-of-cell interference in the base station. Simulation results confirm that the proposed structure achieves very satisfactory performance in comparison with previously derived receivers, as well as that the proposed blind adaptive algorithms achieve satisfactory performance.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.139-145
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• 2019

Precautionary detection of chemical warfare agents (CWAs) has been an important global issue mainly owing to their toxicity. To achieve proper detection, many studies have been conducted to develop sensitive gas sensors for CWAs. In particular, metal-oxide semi-conductors (MOS) have been investigated as promising sensing materials owing to their abundance in nature and excellent sensitivity. In this review, we mainly focus on various MOS-based gas sensors that have been fabricated for the detection of two specific CWA simulants, 2-chloroethyl ethyl sulfide (2-CEES) and dimethyl methyl phosphonate (DMMP), which are simulants of sulfur mustard and sarin, respectively. In the case of 2-CEES, we mainly discuss $CdSnO_3-$ and ZnO-based sensors and their reaction mechanisms. In addition, a method to improve the selectivity of ZnO-based sensors is mentioned. Various sensors and their sensing mechanisms have been introduced for the detection of DMMP. As the reaction with DMMP may directly affect the sensing properties of MOS, this paper includes previous studies on its poisoning effect. Finally, promising sensing materials for both gases are proposed.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.203-208
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• 2023

Recently, with the growing interest in the field of autonomous driving, many researchers have been focusing on developing autonomous driving software platforms. In particular, we have concentrated on developing 3D object detection models that can improve real-time performance. In this paper, we introduce a self-constructed 3D LiDAR dataset specific to domestic environments and propose a VariFocal-based CenterPoint for the 3D object detection model, with improved performance over the previous models. Furthermore, we present experimental results comparing the performance of the 3D object detection modules using our self-built and public dataset. As the results show, our model, which was trained on a large amount of self-constructed dataset, successfully solves the issue of failing to detect large vehicles and small objects such as motorcycles and pedestrians, which the previous models had difficulty detecting. Consequently, the proposed model shows a performance improvement of about 1.0 mAP over the previous model.

• Research in Community and Public Health Nursing
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• v.13 no.2
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• pp.363-375
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• 2002

Objectives: The aim of this study was to analyze the trend of research on cancer prevention and early detection in Korea, in order to suggest a future direction of research on cancer prevention and early detection for Koreans. Methods: A total of 97 studies published from 1980 to 2001 were analyzed according to the year of publication, research design, journal type, cancer type, major study concepts, and findings. Results: 1) The number of studies related to cancer prevention and early detection had increased rapidly since the year 1995. 2) The most frequently used research design in the studies was the descriptive study design (55.7%). 3) There were 10 master's theses on cancer prevention and early detection, and 10 studies published in the Korean Epidemiology Journal. 4) When classified by the published field, 47 studies (48.5%) were published in nursing journals, 46 studies (47.4%) were published in medical journals, and 4 studies (4.1%) were published in public health journals. 5) The major topics of the studies were cancer prevention (51.5%), early detection (44.4%), and cancer prevention and early detection (4.1%). 6) Breast cancer was the most largely addressed issue in the studies (N=25; 25.7%), followed by lung cancer (N=23; 23.7%), hepatoma (N=17; 17.5%), gastric cancer (N=16; 16.5%), other general type of cancer (N=6; 6.2%), colorectal cancer (N=5; 5.2%) and cervical cancer (N=5; 5.2%). Conclusion: It is suggested that there should be more studies on cancer prevention and early detection in the future, and, particularly, experimental studies to exam the effects of intervention on cancer prevention and early detection are considered necessary.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.10
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• pp.783-790
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• 2018

The frequency-hopping technique is one of the spread-spectrum techniques. Frequency hopping is a communication system in which the carrier frequency channel is hopped within the wideband. Therefore, a frequency-hopping system has such advantages as antijamming and low probability of intercept. This system is often used in military communications. Because frequency-hopping signal detection is difficult, it is an important research issue. A novel detection technique is proposed that can improve detection probability. When the received signal is transformed to a frequency domain sample by fast Fourier transform, spectral leakage lowers the detection probability. This problem can be solved by using the Hamming window, and the detection probability can be increased. However, in a frequency-hopping environment, the windowing technique lowers the detection probability. The proposed method solves this weakness. The simulation results show that the proposed detection technique improves the detection probability by as much as 13 %.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4661-4680
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• 2016

Cognitive radio (CR) technology is an effective solution to the spectrum scarcity issue. Collaborative spectrum sensing is known as a promising technique to improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, collaborative spectrum sensing is vulnerable to spectrum data falsification (SSDF) attack, where malicious users (MUs) may send false sensing data to mislead other secondary users (SUs) to make an incorrect decision about primary user (PUs) activity, which is one of the key adversaries to the performance of CRNs. In this paper, we propose a coalition based malicious users detection (CMD) algorithm to detect the malicious user in CRNs. The proposed CMD algorithm can efficiently detect MUs base on the Geary'C theory and be modeled as a coalition formation game. Specifically, SSDF attack is one of the key issues to affect the resource allocation process. Focusing on the security issues, in this paper, we analyze the power allocation problem with MUs, and propose MUs detection based power allocation (MPA) algorithm. The MPA algorithm is divided into two steps: the MUs detection step and the optimal power allocation step. Firstly, in the MUs detection step, by the CMD algorithm we can obtain the MUs detection probability and the energy consumption of MUs detection. Secondly, in the optimal power allocation step, we use the Lagrange dual decomposition method to obtain the optimal transmission power of each SU and achieve the maximum utility of the whole CRN. Numerical simulation results show that the proposed CMD and MPA scheme can achieve a considerable performance improvement in MUs detection and power allocation.