• 대한의용생체공학회:의공학회지
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• 제43권4호
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.

• ETRI Journal
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• 제38권6호
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• pp.1153-1162
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• 2016

This paper addresses the resource allocation (RA) problem in multi-cell cognitive radio networks. Besides the interference power threshold to limit the interference on primary users PUs caused by cognitive users CUs, a proportional fairness constraint is used to guarantee fairness among multiple cognitive cells and the impact of imperfect spectrum sensing is taken into account. Additional constraints in typical real communication scenarios are also considered-such as a transmission power constraint of the cognitive base stations, unique subcarrier allocation to at most one CU, and others. The resulting RA problem belongs to the class of NP-hard problems. A computationally efficient optimal algorithm cannot therefore be found. Consequently, we propose a suboptimal RA algorithm composed of two modules: a subcarrier allocation module implemented by the immune algorithm, and a power control module using an improved sub-gradient method. To further enhance algorithm performance, these two modules are executed successively, and the sequence is repeated twice. We conduct extensive simulation experiments, which demonstrate that our proposed algorithm outperforms existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권6호
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• pp.1479-1495
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• 2012

Spectrum scarcity seems to be the most challenging issue to be solved in new wireless telecommunication services. It is shown that spectrum unavailability is mainly due to spectrum inefficient utilization and inappropriate physical layer execution rather than spectrum shortage. Daily increasing demand for new wireless services with higher data rate and QoS level makes the upgrade of the physical layer modulation techniques inevitable. Orthogonal Frequency Division Multiple Access (OFDMA) which utilizes multicarrier modulation to provide higher data rates with the capability of flexible resource allocation, although has widely been used in current wireless systems and standards, seems not to be the best candidate for cognitive radio systems. Filter Bank based Multi-Carrier (FBMC) is an evolutionary scheme with some advantages over the widely-used OFDM multicarrier technique. In this paper, we focus on the total throughput improvement of a cognitive radio network using FBMC modulation. Along with this modulation scheme, we propose a novel uplink radio resource allocation algorithm in which fairness issue is also considered. Moreover, the average throughput of the proposed FBMC based cognitive radio is compared to a conventional OFDM system in order to illustrate the efficiency of using FBMC in future cognitive radio systems. Simulation results show that in comparison with the state of the art two algorithms (namely, Shaat and Wang) our proposed algorithm achieves higher throughputs and a better fairness for cognitive radio applications.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2003년도 춘계학술대회 논문집
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• pp.101-106
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• 2003

This paper introduces the basic concept of computational modeling of perception processes for multimedia data. Such processes are modeled as hierarchical inter-and relationships amongst information in physical, physiological, psychological and cognitive layers in perception. Based on our framework, this paper gives the , algorithms for content-based retrieval for multimedia database systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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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.

• 인지과학
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• 제10권4호
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• pp.51-62
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• 1999

본 논문에서는 규칙기반 전문가시스템의 퍼지 생성규칙을 표현할 수 있는 퍼지페트리네트 표현을 제안한다. 퍼지페트리네트 표현을 기반으로, 전진추론 알고리즘과 후진추론 알고리즘으로 구성된 퍼지 추론 알고리즘을 제안한다. 본 논문이 제안한 알고리즘은 단순히 min과 max 계산만을 하는 기존의 알고리즘과는 달리 퍼지 생성규칙의 전제 부와 결론 부에 퍼지 개념의 유무에 따라 적절한 믿음 값 평가 함수을 사용하여 보다 더 인간적인 추론을 한다. 전진추론 알고리즘은 유한한 방향성 나무인 도달나무로 표현할 수 있다. 후진추론 알고리즘은 목표노드에서 시작노드까지의 후진추론 통로를 구한 후에 믿음 값 평가함수를 이용하여 목표노드의 믿음 값을 구한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3414-3434
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• 2016

Classical key generation is complicated to update and key distribution generally requires fixed infrastructures. In order to eliminate these restrictions researchers have focused much attention on physical-layer (PHY-layer) based key generation methods. In this paper, we present a PHY-layer fingerprints based fuzzy key generation scheme, which works to prevent primary user emulation (PUE) attacks and spectrum sensing data falsification (SSDF) attacks, with multi-node collaborative defense strategies. We also propose two algorithms, the EA algorithm and the TA algorithm, to defend against eavesdropping attacks and tampering attacks in mobile cognitive radio networks (CRNs). We give security analyses of these algorithms in both the spatial and temporal domains, and prove the upper bound of the entropy loss in theory. We present a simulation result based on a MIMO-OFDM communication system which shows that the channel response characteristics received by legitimates tend to be consistent and phase characteristics are much more robust for key generation in mobile CRNs. In addition, NIST statistical tests show that the generated key in our proposed approach is secure and reliable.

• 한국인터넷방송통신학회논문지
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• 제15권2호
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• pp.45-49
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• 2015

본 논문에서는 인지 통신의 스펙트럼 감지 기법에 에너지 하베스팅 기법을 적용하여 2차 송신단의 전력 소모 없이 스펙트럼을 감지할 수 있을 뿐만 아니라 전력을 저장할 수 있는 방안을 제안하였다. 감지 및 수집 알고리즘은 에너지 하베스팅 기법으로 수집되는 전력량을 임계값과 비교하여 1차 네트워크의 스펙트럼 사용 유무를 판단하며 2차 송신단이 메시지를 전송하려는 경우, 1차 네트워크가 사용 중이라면 주파수를 변경하여 스펙트럼의 사용 유무를 판단하게 된다. 또한 전송하려는 메시지를 가지지 않는 경우, 지속적으로 전력을 수집하게 된다. 따라서 에너지 하베스팅 기법을 스펙트럼 감지 기법에 적용할 경우, 2차 네트워크의 전력이 낭비되는 문제점을 제거하고 전력을 충전하게 되므로 인지 네트워크의 활용도 및 효율성을 증가시킬 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2101-2119
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• 2013

The spectrum allocation is an attractive issue for mobile cognitive radio (CR) network. However, the time-varying characteristic of the spectrum allocation is not fully investigated. Thus, this paper originally deduces the probabilities of spectrum availability and interference constrain in theory under the mobile environment. Then, we propose a prediction mechanism of the time-varying available spectrum lists and the dynamic interference topologies. By considering the node mobility and primary users' (PUs') activity, the mechanism is capable of overcoming the static shortcomings of traditional model. Based on the mechanism, two prediction-based spectrum allocation algorithms, prediction greedy algorithm (PGA) and prediction fairness algorithm (PFA), are presented to enhance the spectrum utilization and improve the fairness. Moreover, new utility functions are redefined to measure the effectiveness of different schemes in the mobile CR network. Simulation results show that PGA gets more average effective spectrums than the traditional schemes, when the mean idle time of PUs is high. And PFA could achieve good system fairness performance, especially when the speeds of cognitive nodes are high.

• 한국인터넷방송통신학회논문지
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• 제11권4호
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• pp.195-201
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• 2011

주파수 자원을 효율적으로 이용하기 위한 기술인 인지무선통신(CR : Cognitive radio) 기술은 주파수 자원난을 해결하기 위하여 연구되고 있다. CR은 1차 사용자에 할당된 주파수를 2차 사용자가 이용할 수 있도록 한다. 그러나 무선채널에서는 페이딩과 음영지역의 영향으로 스펙트럼 센싱에 있어서 절충안이 필요해진다. 또한, 다양한 CR 시스템을 검출하고 구분하기 위해서 새로운 센싱 알고리즘이 필요하다. 그러므로 본 논문에서는 워터마킹 기술을 이용한 스펙트럼 센싱 알고리즘을 제안하여, Kasami-sequence와 M-sequence를 워터마킹 시퀀스로 이용했을 경우의 검출 성능을 비교한다.