• Communications for Statistical Applications and Methods
• /
• v.19 no.4
• /
• pp.579-590
• /
• 2012

Local linear jump detection in a discontinuous regression function involves the choice of the bandwidth and the performance of a local linear jump detector depends heavily on the choice of the bandwidth. However, little attention has been paid to this important issue. In this paper we propose two fully data adaptive bandwidth selection methods for a local linear jump detector. The performance of the proposed methods are investigated through a simulation study.

• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.3
• /
• pp.76-79
• /
• 2014

Face recognition is very challenging because of the unexpected changes of pose, expression, and illumination. Facial detection in the mobile environments has additional difficulty since the computational resources are very limited. This paper discusses face detection based on frequency domain matched filtering in the mobile environments. Face detection is performed by a linear or phase-only matched filter and sequential verification stages. The candidate window regions are selected by a number of peaks of the matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering tests, which aim to remove false alarms among selected candidate windows. The algorithms are built with JAVA language on the mobile device operated by the Android platform. The simulation and experimental results show that real-time face detection can be performed successfully in the mobile environments.

• Biomedical Engineering Letters
• /
• v.8 no.4
• /
• pp.373-382
• /
• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1419-1422
• /
• 1997

This paper deals with the Appliation of robust fault detection problem in uncertain linear systems, having both model mismatch and noise. A robust fault detection method presented by Kwon et al.(1994) for SISO uncertain systems. Here we experimented this method to the diesel engine systems described by difference ARMA models. The model mismatch includes here linearization error as well as undermodeling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.710-713
• /
• 1996

This paper deals with the fault detection problem in uncertain linear multivariable systems having both model mismatch and noise. A robust detection presented by Kwon et al.(1994) for SISO systems has been here extended to the multivariable systems are derived. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• Journal of Digital Contents Society
• /
• v.19 no.9
• /
• pp.1787-1793
• /
• 2018

Shadow is a common phenomenon observed in natural scenes, but it has a negative influence on image analysis such as object recognition, feature detection and scene analysis. Therefore, the process of detecting and removing shadows included in digital images must be considered as a pre-processing process of image analysis. In this paper, the existing methods for acquiring 1D invariant images, one of the feature elements for detecting and removing shadows contained in a single natural image, are described, and a method for obtaining 1D invariant images based on linear regression has been proposed. The proposed method calculates the log of the band-ratio between each channel of the RGB color image, and obtains the grayscale image line by linear regression. The final 1D invariant images were obtained by projecting the log image of the band-ratio onto the estimated grayscale image line. Experimental results show that the proposed method has lower computational complexity than the existing projection method using entropy minimization, and shadow detection and removal based on 1D invariant images are performed effectively.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.3
• /
• pp.461-469
• /
• 2010

In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.4
• /
• pp.1504-1526
• /
• 2018

For uplink multi-user massive MIMO systems, conventional minimum mean square error (MMSE) linear detection method achieves near-optimal performance when the number of antennas at base station is much larger than that of the single-antenna users. However, MMSE detection involves complicated matrix inversion, thus making it cumbersome to be implemented cost-effectively and rapidly. In this paper, we first summarize in detail the state-of-the-art simplified MMSE detection algorithms that circumvent the complicated matrix inversion and hence reduce the computation complexity from ${\mathcal{O}}(K^3)$ to ${\mathcal{O}}(K^2)$ or ${\mathcal{O}}(NK)$ with some certain performance sacrifice. Meanwhile, we divide the simplified algorithms into two categories, namely the matrix inversion approximation and the classical iterative linear equation solving methods, and make comparisons between them in terms of detection performance and computation complexity. In order to further optimize the detection performance of the existing detection algorithms, we propose more proper solutions to set the initial values and relaxation parameters, and present a new way of reconstructing the exact effective noise variance to accelerate the convergence speed. Analysis and simulation results verify that with the help of proper initial values and parameters, the simplified matrix inversion based detection algorithms can achieve detection performance quite close to that of the ideal matrix inversion based MMSE algorithm with only a small number of series expansions or iterations.

• Journal of Communications and Networks
• /
• v.1 no.2
• /
• pp.118-133
• /
• 1999

In this paper we compare linear and non-linear inter-ference cancellation for systems employing code division multi-ple access (CDMA) techniques. Specifically, we examine linear and non-linear parallel interference cancellation(also called multi-stage cancellation) in relationship to other multiuser detection al-gorithms. We show the explicit relationship between parallel inter-ference cancellation and the decorrelator (or direct matrix inver-sion). This comparison gives insight into the performance of paral-lel interference cancellation (PIC) and leads to vetter approaches. We also show that non-linear PIC approaches with explicit chan-nel setimation can provide performance improvement over linear PIC, especially when using soft non-linear symbol estimates. The application of interference cancellation to non-linear modulation techniques is also presented along with a discussion on minimum mean-squared error(MMSE) symbol estimation techniques. These are shown to further improve the performance of parallel cancella-tion.

• Journal of Electrochemical Science and Technology
• /
• v.8 no.4
• /
• pp.274-281
• /
• 2017

This paper describes the simple fabrication of an electrode modified with electrochemically reduced graphene oxide (ERGO) for the simultaneous electrocatalytic detection of dopamine (DA), ascorbic acid (AA), and uric acid (UA). ERGO was formed on a glassy carbon (GC) electrode by the reduction of graphene oxide (GO) using linear sweep voltammetry. The ERGO/GC electrode was formed by subjecting a GO solution ($1mg\;mL^{-1}$ in 0.25 M NaCl) to a linear scan from 0 V to -1.4 V at a scan rate of $20mVs^{-1}$. The ERGO/GC electrode was characterized by Raman spectroscopy, Fourier transform infrared spectroscopy, contact angle measurements, electrochemical impedance spectroscopy, and cyclic voltammetry. The electrochemical performance of the ERGO/GC electrode with respect to the detection of DA, AA, and UA in 0.1 M PBS (pH 7.4) was investigated by differential pulse voltammetry (DPV) and amperometry. The ERGO/GC electrode exhibited three well-separated voltammetric peaks and increased oxidation currents during the DPV measurements, thus allowing for the simultaneous and individual detection of DA, AA, and UA. The detection limits for DA, AA, and UA were found to be 0.46, 77, and $0.31{\mu}M$ respectively, using the amperometric i-t curve technique, with the S/N ratio being 3.