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This paper delivers an accurate approximation for adaptive threshold and optimal frame detection algorithms based on the robust multi-taper method aiming at an efficient spectrum sensing in cognitive radio systems. An appropriate adaptive thresholding allows for seamless vacation of unlicensed secondary users from certain bands upon primary users’ requests, while arbitrary optimal frame detection contributes to the computational and throughput demands. Simulation exercises corroborate the given analysis over Rayleigh channel and multiple-input multiple-output configuration and emphasize the critical role of adopting applicable adaptive threshold and optimal frame detection policies.

There are many application areas of sensor networks, such as surveillance, hospital monitoring, and home network. These are dependent on the secure operation of networks, and will have serious outcome if the networks is injured. An adversary can inject false data into the network through the compromising node. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false data during forwarding process. In this scheme, it is important that the choice of the threshold value since it trades off security and overhead. This paper presents an adaptive threshold value determining method in the SEF using fuzzy logic. The fuzzy logic determines a security distance value by considering the situation of the network. The Sensor network is divided into several areas by the security distance value, it can each area to uses the different threshold value. The fuzzy based threshold value can reduce the energy consumption in transmitting.

본 논문은 적외선 열영상에서 영상의 밝기와 대비 변화에 따라 적응적으로 화염 후보 영역을 검출하기 위한 적응 문턱치를 제안한다. 현장에 사용 되고 있는 화재 검출 시스템은 카메라의 설치 장소에 따라 얻어지는 영상의 밝기나 대비의 변화가 발생 하여고정된 문턱치를 적용하는 화재 검출 알고리즘의 성능이 변화하게 되므로 환경에 적응적인 문턱치가 필요하다. 제안하는 적응 문턱치를 이용한 화염 검출 알고리즘은 화염의 특성인 온도와 동적임 특성을 분석하여 화염을 검출 한다. 실험을 위해 고정 문턱치를이용한 화염 검출 알고리즘과 비교 하였으며 제안된 적응 문턱치를 이용한 화염 검출 알고리즘은 화염 검출률 91.42%이며 고정문턱치를 적용 하였을 때 보다 오검출률을 약 20%가 감소한다. 그리고 영상의 밝기와 대비 변화에 의한 검출 결과가 일정함을 보여준다.

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adaptsaccording to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed thresholdmethod since surveillance environment does not change, once the system installed. But it needs a adaptive threshold methodas requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behaviorof flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithmand conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detectionrate and false detection is reduced by 20% comparing to the conventional method.

In this paper, a new tap detection algorithm, which uses adaptive peak tracking, is suggested. A low pass filter is used to track the time varing peaks of tap signals. The obtained peak is used to determine the threshold value for tap detection. Through experimental data analysis, it is found that the threshold value should be around 20% of the peak acceleration. A three axis MEMS accelerometer and a microprocessor is used for the experiments to check the validity of the proposed algorithm. Through experiments, it is demonstrated that the proposed algorithm could detect time varying tap signals very effectively, regardless of the environments or the user variations.

본 논문에서는 기존 에너지 스펙트럼 센싱 할 경우 고정된 FA를 기반으로 정해진 임계값에 따라 센싱이 진행된다. 하지만 SNR 상태가 높다면 낮은 레벨의 SNR에서보 비해 오경보 확률이 일어날 확률이 상대적으로 적어진다. 따라서 90%로 이상의 검출확률을 얻는 구간에 대하여 FA를 제어하는 방법으로 오경보 확률을 높게 설정하지 않아도 검출확률이 유지 되는지를 확인한다. 따라서 CR사용자의 SNR 상태에 따라 FA값을 컨트롤하여 적응적 임계값을 얻는 시스템 모델을 제안하고 성능을 분석한다.

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

Spectrum sensing is a key functionalities of Cognitive radio networks (CRN). There are several methods to improve sensing techniques. In this paper, we present a robust detector using signal to noise ratio (SNR) with adaptive threshold (ADT) scheme in Cognitive Radio Networks (CRN). This technique has two detectors, energy detector (ED)&EDwithADT detector, out of these two detectors only one will perform sensing operation at a time. Selection of detector depends on condition between estimated SNR value (Se) and threshold (γ). Numerical results show that proposed ESNR_ADT scheme optimizes detection performance and outperforms the cyclostationary based sensing method and adaptive spectrum sensing (SS) by 30.5 % and 30 % at – 10 dB signal to noise ratio (SNR) respectively. It is also shown that the proposed scheme yields lesser sensing time than cyclostationary detection and adaptive SS scheme in the order of 5.2 ms and 1.0 ms at - 20 dB SNR respectively.

Automatic video segmentation plays an important role in real-time MPEG-4 encoding systems. This paper presents a video segmentation algorithm for MPEG-4 camera system with change detection, background registration techniques and real time adaptive threshold techniques. This algorithm can give satisfying segmentation results with low computation load. Besides, it has shadow cancellation mode, which can deal with light changing effect and shadow effect. Furthermore, this algorithm also implemented real time adaptive threshold techniques by which the parameters can be decided automatically.

주행 중인 차량의 타이어와 노면 사이에서 발생하는 랜덤성 초음파신호의 주기성을 검출하여 타이어의 손상을 분별하는 방법에 대하여 연구하였다. 잡음으로부터 타이어손상에 의한 유효한 펄스를 검출하기 위해 적응 임계치를 설정하는 방법을 제안하였고, 이를 위한 전처리로서 신호의 랜덤성을 감소시키는 저역통과필터를 사용하였다. 자동차의 속도에 따라 검출된 펄스의 시간간격을 밀도함수화하고 피크점의 시간을 측정하여 타이어 손상물질을 검출하는 방법을 제안하였다. 손상물질이 한 개이고, 주행속도가 50km/h, 80km/h, 100km/h 일 때, 시간밀도함수의 제 1 피크시간 측정결과는 각각 169.8ms, 97.9ms, 81.8ms로 주행속도에 따라 계산한 타이어 회전주기의 오차범위내로 측정되었으며, 한 개 이상의 손상물질의 경우에는 각 피크시간의 합이 타이어 회전주기의 오차범위내로 측정되는 결과를 얻을 수 있었다.

The tire damage classification method is researched on the periodicity detection of ramdomness ultrasonic signals to occur at the driving vehicle tire. Setting method of adaptive threshold is proposed in order to valid pulse detection by tire damage in ultrasonic noise on the road and used low pass filter for decrease signal ramdomness as preprocessing. Time interval of detected pulse is setted the density function depend on the vehicle's speed and the method of tire damage detection is proposed that measuring the first peak's time of time density function.The result of time density function in case of one damage material, the first peak's time is measured within the error limit of tire's rotation period, 169.8ms and 97.9ms and 81.8ms , about the speed of 50km/h and 80km/h and 100km/h. In case of more than one damage material, the sum of each peak's time is measured within the error limit of tire's rotation period about the speed.

With increasing demand of new wireless applications and increasing number of wireless user’s problem of spectrum scarcity arises. Cognitive Radio technology supports dynamic spectrum access to address spectrum scarcity problem. Reliability of cognitive operation entirely depends upon how effectively task of spectrum sensing has been performed. Spectrum sensing is a process of discovering voids in spectrum which can be allocated to cognitive users opportunistically. There exists number of traditional spectrum sensing methods in literature for constant noise floor. Practically, noise spectrum density is uncertain under which performance of spectrum sensing scheme degrades. In this paper a new spectrum sensing adaptive algorithm considering noise uncertainty has been proposed. Simulation results of proposed scheme shows a constant detection probability has been achieved under noise uncertainty.

Energy detection performance in the signal to noise ratio (SNR) fluctuation needs to be improved. In order to obtain the highest probability of detection, a new cooperative spectrum sensing algorithm is proposed. Dynamic double energy thresholds and adaptive grid search are utilized to obtain the highest probability detection. Double thresholds are adaptive to noise fluctuation, and in order to obtain the best sensing performance, adaptive grid search is used firstly to find the optimal double thresholds. Simulation results show that the proposed algorithm has excellent robustness to noise fluctuation and good sensing performance even under the low signal to noise ratio.

There are still problems such as low detection accuracy and poor noise immunity in the application of the standard threshold difference algorithm in the signal detection of electrocardiosignal (ECG), in this paper, a medical monitoring model based on the adaptive threshold difference is proposed. First we use a nonlinear filter to filter the P wave and T wave which are low frequency in ECG signal. Then complex wave QRS will be tested. Then the algorithm will be more accuracy through the detection of the R-R interval length and the adjustment of threshold. Finally, the ECG signal will be test with quadratic spline wavelet twice, and the error judgment will be known through adaptive threshold difference. In the simulation experiments, after judging error by wavelet transformation and making the standard threshold difference algorithm optimize adaptively, algorithm showed excellent detection accuracy with and without noise.

Security problems with network are significant, such as network failures and malicious attacks. Monitoring network traffic and detect anomalies of network traffic is one of the effective manner to ensure network security. In this paper, we propose a hybrid method for network traffic prediction and anomaly detection. Specifically, the original network traffic data is decomposed into high-frequency components and low-frequency components. Then, non-linear model Relevance Vector Machine (RVM) model and ARMA (Auto Regressive Moving Average) model are employed respectively for prediction. After combining the prediction, a self-adaptive threshold method based on Central Limit Theorem (LCT) is introduced for anomaly detection. Moreover, our extensive experiments evaluate the efficiency of proposed method.

Aiming at the problem of ground penetrating radar image denoising, a new adaptive image denoising algorithm based on nonsubsampled Contourlet transform is proposed. The algorithm firstly performs nonsubsampled Contourlet transform to the noise image, to obtain the coefficients of each directional sub band and each scale, then, according to the energy of the coefficient, the denoising threshold value is adjusted adaptively. Simulation results show that, compared with the wavelet threshold denoising algorithm, the proposed algorithm can effectively remove the Gauss white noise in the image, improve the peak signal to noise ratio (PNSR), while preserving the edge details of the image, it can improve the PSNR value and reduce the Gibbs phenomenon.

We propose a new lossless compression algorithm for hyper spectral images based on the third -order interband predictor and the backward pixel search scheme (IP3-BPS). Specifically, we propose an adaptive search threshold algorithm and a bi-directional pixel search scheme. The resulting algorithm takes the bi-directional pixel search and the backward pixel search with adaptive search threshold as the last two predictors; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 1997 images shows that it outperforms the original IP3-BPS algorithm, at lower computational complexity level.

Block-matching and 3D filtering (BM3D) denoising algorithm [1] proposed recently has a problem of computational burden especially for low noise level and a sharp performance drop for high noise level. To solve it, an improved version of BM3D is proposed. The solution combines the digital image characteristic with added noise pollution levels, and adaptively selects block-matching threshold in grouping stage. Experimental results demonstrate it outperforms not only in terms of objective criteria of PSNR and running time, but also in visual quality.

Typically, the previous load balancing methods for Flume which completely depends on the user-specified threshold does not adaptively deal with the performance change of the entire log processing system at runtime. Furthermore, their task-transferring algorithm aggravates the performance degradation of the overloaded node because the excessive data transfer to another node should be done on the overloaded node. In this paper, we propose a new load balancing method for Apache Flume by automatically and dynamically modifying threshold of node load status in accordance with the runtime performance of the system. This feature can be realized by monitoring both the increasing rate of incoming log information in the queue of each collector agent and its occupancy rate at the request of the overloaded or under-loaded collection node in a decentralized manner. The proposed method considerably alleviates the additional overhead incurred by the task migration and makes the load of the entire system as fair as possible by selecting the optimal task migration destination depending on the current load-state values of collector agents unlike the previous round-robin and random ones.

This study is devoted to overcome the underflow problem and poorly cost-effective limitation of model-set adaptive IMM algorithm. Cause of underflow problem in Novel-IMM is addressed firstly, based on which an underflow prevented selection probabilities (UPSP) algorithm is presented to solve this problem. This paper then presents a fast model-set adaptive (FAIMM) IMM algorithm based on steady state Kalman filters that decrease the computational burden greatly while keeping acceptable tracking accuracy. Finally, the threshold choosing strategy of UPSP algorithm is presented, which could make the FAIMM algorithm achieves ideal performance. Simulation results demonstrate that the FAIMM algorithm can be an effective estimator in real-time application.

본 논문에서는 웨이블릿 변환과 Haar 변환을 기반으로 적응적 문턱치와 가중치를 이용하여 의료영상의 화질 을 개선하는 알고리즘을 제안한다. 첫째, 화질이 저하된 의료영상에 대해 웨이블릿 변환을 수행하고 분해된 고주파 밴드에 대해 Haar 변환을 수행한다. 둘째, 고주파 각 밴드에 대해 적응적 문턱치를 이용하여 잡음을 제거한다. 셋째, 잡음이 제거된 고주파 밴드에 대해 적응적인 가중치를 이용하여 계수를 향상한 후, Haar 역변환 및 웨이블릿 역변환 을 수행하여 복원영상을 얻는다. 마지막 단계에서는 복원된 영상의 화소 값의 범위가 좁아졌으므로 비선형 히스토그 램 평활을 이용하여 화소 값의 범위를 조절하고 명암 대비가 좋은 향상된 영상을 얻는다.

By using an adaptive threshold and weight based on the wavelet transform and Haar transform, a novel image enhancement algorithm is proposed. First, a medical image was decomposed with wavelet transform and all high-frequency sub-images were decomposed with Haar transform. Secondly, noise in the frequency domain was reduced by the proposed soft-threshold method. Thirdly, high-frequency coefficients were enhanced by the proposed weight values in different sub-images. Then, the enhanced image was obtained through the inverse Haar transform and wavelet transform. But the pixel range of the enhanced image is narrower than a normal image. Lastly, the image’s histogram was stretched by nonlinear histogram equalization. Experiments showed that the proposed method can be not only enhance an image’s details but can also preserve its edge features effectively.

독립 협동형 감시시스템인 이차감시레이더(SSR)의 위치 정보의 해상도를 높이기 위해 다변측정감시시스템(MLAT)이 항공 감시시스템에 적용되고 있다. 다변측정감시시스템은 쌍곡선(hyperbola) 또는 쌍곡면(hyperboloid) 위치 측정법을 이용하는 시스템으로 4개 이상의 지상 수신기에서 TOA (time of arrival)값을 측정하고 이를 중앙처리장치에서 각 수신기 상호간의 수신시각차 (TDOA; time difference of arrival)를 이용하여 항공기의 위치를 계산한다. TDOA (time difference of arrival) 알고리즘을 이용하는 다변측정감시시스템의 위치 측정 해상도를 높이기 위해서는 먼저 항공기에 탑재된 트랜스폰더에서 송출된 신호가 수신기에 수신되는 시간인 TOA(time of arrival)를 정밀하게 측정하여야 한다. 본 논문에서는 다변측정감시시스템의 구성에 대해 간략히 설명하였으며 TOA를 측정하기 위한 방법 중 하나인 ATD (adaptive threshold detector)에 대하여 간략히 설명하였다. ATD는 동일 신호에 대한 수신기간 수신한 신호의 진폭 차에 의해 발생하는 펄스 위치 편차 문제를 해결하는 방식이다. 본 논문에서는 ATD의 종류 중 LAS (level adjuster System)과 CDS (constant-fraction discriminator system)에 대한 시뮬레이션 결과와 기본적인 임계값을 적용한 펄스 위치 계산 시뮬레이션 결과를 비교하여 ATD의 성능을 분석하였다.

MLAT which is an independent cooperative surveillance system is applied to increase the positon resoultin of secondary survelliance radar. MLAT uses the hyperboic or hyperboloid position mesurement algorithm. Central processing unit of MLAT calculates target position using time difference of arrival (TDOA) which can be solved from time of arrival (TOA) information of each receivers (at least 4 receivers). To increase position resolution of MLAT which use TDOA, TOA which is transfer time from tranmitter to receiver shold be calculated with precision time resolution in receiver. This paper explained the MLAT system briefly and explained ATD which is one of means of calcuating pulse position. ATD is applied to solve the deviation of pulse position due to different amplitude of signals in mulitiple receivers. In this paper, to analysis the performance of ATD, the simulation result of LAS and CDS was compared with the simulation result of basic threshold method.

카메라 영상에 의한 자동차 번호판 인식시스템은 영상 획득, 번호판 추출, 전처리, 문자 분리, 문자 인식 등 크게 5자기의 핵심 부분으로 구성된다. 따라서 자동차 번호판 인식시스템의 성능을 향상시키기 위해서는 이들 부분들 각각의 성능의 최적화가 필요하다. 본 연구는 자동차 번호판 인식시스템의 여러 단계 중 전처리에 해당하는 번호판 영역의 이진화에 관한 연구로서, 기존의 단일 임계치 방법과 다중 임계치 방법이 해결하지 못했던 부분을 보완하는 새로운 다중 임계치 방법을 제안한다. 본 논문에서 제안하는 다중 임계치 알고리즘(Adaptive Multi-threshold Algorithm)을 사용함으로써 gray-level 번호판 영상에 대해서 보다 깨끗한 이진 영상을 얻을 수 있었으며, 또한 이 알고리즘은 번호판 영역의 밝기값이 고르지 않은 영상에 대해서도 효율적인 알고리즘 임을 알 수 있었다.