Earticle

As the number of low Earth orbit satellites is increasing, new algorithms and high-performance computing resources are needed to handle the enormous real-time arrival data generated by satellites. Based on the requirements, this paper proposes a novel real-time adaptive speckle filtering selection algorithm for satellite synthetic aperture radar (SAR) images. The use of high-performance filters generates high-quality images whereas it introduces delays. Thus, a real-time adaptive algorithm which achieves time-average SAR image quality maximization subject to delays using Lyapunov optimization, where the delay is formulated with a queuing model. Evaluation results show that the proposed algorithm guarantees desired performance improvements.

본 연구에서는, 덴탈 파노라마 영상의 선예도 향상을 위해 새로운 유형의 적응형 파노라마 기술을 제안했다. 제안하는 기법은 동심원 운동에 대한 FBP 방법을 사용하여 재구성하는 디지털 토모신테시스를 기반으로 한다. 해당 지오메트리에 서는 불완전한 샘플링으로 인한 재구성 후 흐려짐 현상을 억제하기 위해 적합한 필터를 사용하여 필터링 프로세스를 구현했다. 제안한 방법의 타당성을 검증하기 위해 체계적인 시뮬레이션 및 재구성 영상의 특성을 조사하였다. 우리의 결과는 사전에 정의된 관심 평면에서 선명한 영상을 획득할 수 있음을 나타냈다. 결과적으로 제안하는 알고리즘은 영상 의 선예도가 명확한 결과를 가져오며 이는 덴탈 재구성 영상의 판독성을 증가시켜 진단에 도움이 될 것으로 예상한다.

In this study, we proposed a new type adaptive panoramic technique for improving the image sharpness. This imaging method is based upon the digital tomosynthesis using a filtered-backprojection (FBP) method for an iso-centric circular motion. We implemented a filtering process using the apodizing filter to suppress the blurring artifact due to the incomplete sampling in this geometry. To verify the feasibility of the suggested technique, we have performed systematic simulation works and investigated the image characteristics. Our results indicate that predefined planes-of-interest can be well focused with definite image sharpness and that the position of image layer center can be adjusted precisely with proper amounts of shift in the reconstruction.

본 논문에서는 과거벡터를 이용한 개선된 성능의 멀티밴드 구조의 부밴드 적응 필터 알고리즘을 제안한다. 기존 부 밴드 적응 필터 알고리즘은 적응 필터의 현재의 계수 벡터만을 가지고 다음 계수 벡터를 업데이트하기 때문에 주변 환경의 잡음이 높을 경우, 필터 계수의 심한 변동으로부터 취약하다는 단점이 있다. 이러한 문제를 해결하기 위해, 적응 필터의 과거계수 벡터와 평균을 취하는 방법을 활용하여 현재의 계수 벡터를 업데이트하는 방법을 제안한다. 제안하는 방법을 통해 처음으로 인접투사 방법을 활용한 부밴드 적응 필터링 알고리즘에 과거계수 벡터를 사용하였고, 부밴드 적응 필터의 계수벡터 개수를 적응적으로 설정하였다. 컴퓨터 시뮬레이션 결과, 제안된 알고리즘은 주변 환경의 잡음에 강인한 성능을 보인다. 또한, 출력신호의 오차를 통해 과거벡터 개수를 상황에 따라 적절히 조절함으로써 수렵 속도를 향상시켰다.

In this paper, an improved multiband-structured subband adaptive filter using past weight vectors is proposed. In case of high background noise, heavy fluctuation can occur in the estimated weight vector since the conventional subband adaptive filtering algorithm updates the next weight vector by using the present weight vector. To solve the problem, we propose a new method of updating the present weight vector in the multiband- structured subband adaptive filter by averaging present and past weight vectors. Through the proposed method, we use past weight vectors for subband adaptive filtering algorithm using an affine projection method at first, whereby the number of past weight vectors is adaptively determined. Computer simulation results demonstrate the robust performance of the proposed algorithm even in case of high background noise. Also, it is shown that the convergence speed can be improved by adaptively controlling the number of past weight vectors depending on the output error information.

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.

Adaptive filters are commonly used to remove disturbances corrupting a signal, particularly when a reference signal correlated to the disturbance is available. Accompanying random noise, sinusoidal disturbance or other undesired signals can be removed from the desired signal. When applied to images, the term adaptive filter commonly refers to a filter which changes its features based on the local statistical characteristics of the image and noise (within each image segment). It has also been proposed previously to use the LMS adaptive filter for 2-D image filtering, mostly for random noise removal. In this paper, it is offered to extend the use of the 2-D LMS adaptive filter to other image disturbances, such as a varying frequency sinusoidal disturbance, and the removal of an undesired added image from the desired one. Moreover, it is proposed here that in some cases it may be useful to convert the two-dimensional image to a one-dimensional signal (by chaining its row or columns), and apply a 1-D LMS adaptive filter. The 2-D LMS adaptive filter is demonstrated to work well in restoring an image corrupted by a random noise, a sinusoidal disturbance or a disturbance of an undesired image added to the desired one. In some particular conditions an adaptive filter with no reference signal may also be used to remove the undesired disturbance from the image.

In this paper, we propose an adaptive filtering scheme of connection requests for the defense of malicious energy consumption attacks against wireless computing devices with limited energy budget. The energy consumption attack tries to consume the battery energy of a wireless device with repeated connection requests and shut down the wireless device by exhausting its energy budget. The proposed scheme blocks a connection request of the energy consumption attack in the middle, if the same connection request is repeated and its request result is failed continuously. In order to avoid the blocking of innocuous mistakes of normal users, the scheme gives another chance to allow connection request after a fixed blocking time. The scheme changes the blocking time adaptively by comparing the message arriving ate during non-blocking period and that during blocking period. Evaluation shows that the proposed defense scheme saves up to 94% energy consumption compared to the non-defense case.

The ECG (electrocardiogram) is a system that provides patient’s critical heart activity. The adaptive electrocardiogram filter will be designed to reduce noise caused by external systems & body artifacts. This paper aims to help to reduce the noise interference in the ECG signals and better diagnose results. Some of the most common examples of noise that the ECG filter would need to remove in order to give useful results includes power line interference, motion artifacts, muscle contraction, electrode contact noise and interference caused due other electronic equipment. ECG signals are weak and easily susceptible to noise and interference. In this paper I have presented an implementation of Least Mean Squares (LMS).

This paper presents the enhancement of speech signals in a noisy environment based on Wavelet Based Adaptive filtering (WAF). In this technique, the speech signals contaminated with noise are processed through the WAF. The adaptive algorithm based on least squares, updates the weighting factors of the filter to keep the difference between input and desired signal at a minimum level to eliminate the unwanted noise and interferences. The performance of the proposed method is evaluated by computing the Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Root-Mean-Square-Error (RMSE), Percentage Root Mean Square Difference (PRD) after denoising. The investigation on speech signals contaminated by noise has demonstrated that the performance of the proposed method is stable and reliable in the noisy environment.

This paper focuses ocular artifacts separation of EEG signals using Noise Assisted Bi-variate adaptive based filtering. In order to facilitate clinical diagnosis and/or implement so-called brain computer interface (BCI), detecting the rhythmic activity from EEG data recorded in a noisy environment is crucial. The pre-processing of EEG signal is mandatory due to highly interference with the EEG signal. Electro-oculogram (EOG) is the most important interference that misinterpret significantly of the EEG signal for brain activity measurements. To suppress EOG data, we have used a newly developed model with empirical mode decomposition (EMD) named as noise assisted EMD (NEMD). Because the complex signals have a mutual dependence between the real and imaginary parts, so it is possible to analyses both parts simultaneously using NEMD. Here, the EEG signal and white Gaussian noise (reference signal) are combined to produce complex signal which is decomposed using NEMD to extract complex intrinsic mode functions (IMFs). Then the low frequency trend (EOG) and high frequency components (purified EEG) of recorded EEG signals are obtained partial reconstruction on the basis of the energy distribution of their intrinsic mode functions. The experimental results show that the NEMD based data adaptive filtering technique performs better.

In this paper, we propose an adaptive frequency domain channel estimation method based on a modified Kalman filtering, which requires low pilot overhead, for estimating and tracking time-varying channel in orthogonal frequency division multiplexing (OFDM) systems. The proposed frequency domain Kalman filtering channel estimation achieves the minimum pilot overhead by exploiting periodically inserted pilot symbols and decision directed symbols between them. The pilot overhead efficiency and tracking performance of the proposed method is studied through simulation. Simulation results show that the proposed method has adequate tracking performance with a pilot overhead of below 5%.

Removal of noises from real-time speech signal is a typical problem. The signal interference initiated by background noise is a major problem in voice communication systems. Adaptive Filtering methods have emerged as an important technology for communication systems. This technique has been employed to improve the quality of the speech signal by cancelling the undesirable phenomenon such as acoustic noise. In this paper, for the removal of additional noise from speech signal an adaptive filter has been designed using LMS, NLMS, SLMS and VSS-LMS algorithms. This paper presents the instigation of Least Mean Square algorithm (LMS), Normalized Least Mean Square algorithm (NLMS), Sign Least mean square algorithm (SLMS) and Variable step size (VSS) algorithm on an infinite impulse response (IIR) filter using adaptive filter toolkit of LabVIEW software. User interface is designed using LabVIEW to obtain the learning curves for these adaptive algorithms. The final results show the comparison of the performance of the entire proposed algorithms with each other. The complete performance of the designed system in terms of stability and convergence rate has been observed.

The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) can produce accurate results if four or more GPS satellites are tracked. However, in GPS attenuated signal environment the errors of a low accuracy MEMS/GPS system rapidly grow to the unacceptable level. A tightly coupled integration scheme is utilized to improve the performance and reliability of the low accuracy integrated system in the areas such as tunnels, tall buildings, urban canyon, and forest canopy. This model is capable to detect the GPS fault and to track the errors of the integrated system even when less than four satellites are being tracked. Practically in INS/GPS integration, the system noises are not known correctly. Therefore, an Adaptive Kalman filter is proposed to merge the data of the two systems accurately. The algorithms are tested using the real data of MEMS-IMU (STIM300) and a single frequency NovAtel GPS receiver for land navigations. The integration results indicate a significant improvement in the accuracy of attitude, velocity and position parameters. Moreover, gyro drift which is the main source of errors in INS parameters is significantly reduced.

Aiming at the problems that the luminance information is not enough and the edge information can not be preserved easily in the process of the image fusion, an effective image fusion method based on nonsubsampled contourlet transform (NSCT) and adaptive guided image filtering is proposed in this paper. Firstly the source images are decomposed by NSCT with multi-scale, multi-direction and shift-invariant properties. The fusion rule of the low-frequency subband coefficients employs the local correlation energy to improve the energy and information of the subband coefficients. To acquire good effects with edge-preserving and noise reduction, the adaptive guided image filtering is introduced to the high-frequency subband coefficients as the fusion rule for the first time, and it can make use of halo-free edge slope enhancement in the fusion process. Finally, the fused image is reconstructed by the inverse NSCT. Experimental results demonstrate that the comprehensive performances of the proposed method are improved in the fusion information, edge and luminance.

In this paper, we employ the adaptive comb filtering for effective noise reduction in mobile communication environment. Adaptive comb filtering is a well- known method for noise reduction, but requires the correct pitch period and must be applied just in voiced speech frames. To satisfy these requirements we use two kinds of information extracted from speech packets, one of which is the pitch period information measured precisely by a speech coder and the other is the frame rate information related to a decision on speech or silence frame. Experiments on speech recognition system confirm the efficiency of this method. Feature parameters employing this method give superior performance in noise environment to those extracted directly from output speech.

In B-scan data measured by a pulse-type monostatic borehole radar, target signatures are seriously obscured by two clutters that differ in orientation and intensity. The primary clutter appears as a nearly constant time delay, which is caused by internal ringing between antenna and transceiver in the radar system. The secondary clutter occurs as an oblique time delay due to the guided borehole wave along the logging cable of the radar antenna. This issue led us to perform adaptive filtering processing for orientation-based clutter removal. This letter describes adaptive filtering processing consisting of a combination of edge detection, data rotation, and eigenimage filtering. We show that the hyperbolic signatures of a dormant air-filled tunnel target can be more distinctly enhanced by applying the proposed approach to the B-scan data, which are measured in a well-suited test site for underground tunnel detection.

Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so on. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training.

We had proposed fast thermal stress estimation methodology for the components on system board when the system is stationary within specific ambient air temperature. Now, we will propose one efficient thermal stress estimation methodology, block adaptive filtering methodology, for the FPD electronic system board which is enclosed by mechanical cover.