• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• The Journal of Information Technology
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• v.5 no.1
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• pp.79-98
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• 2002

This study examines the association between valuation and accrual information. According to accounting based valuation model, firm's value consists of net book value and abnormal earnings. Net book value and abnormal earnings are determined as the manager's accounting policy. Discretionary accruals may signal the manager's value expectation or be noisy factor of accounting variables. The results of this study are as follows; First discretionary accruals are associated to stock prices negatively but non-discretionary accruals are not to stock prices. This result suggests that discretionary accruals and non-discretionary accruals are the differential factors of the firm value. Second, the product term of discretionary accrual and net book value are associated to the stock price negatively but the product term of non-discretionary accrual and net book value are not associated to the stock price. the results indicate that discretionary accruals are noisy factors of net book value information. Third, the product term of discretionary accrual and net income are associated to the stock price negatively and the product tenn of non-discretionary accrual and net income are also associated to the stock price negatively, the results suggest that discretionary accruals are noisy factors of earnings.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.169-174
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• 2013

Speech recognition, the problem of performance degradation is the difference between the model training and recognition environments. Silence features normalized using the method as a way to reduce the inconsistency of such an environment. Silence features normalized way of existing in the low signal-to-noise ratio. Increase the energy level of the silence interval for voice and non-voice classification accuracy due to the falling. There is a problem in the recognition performance is degraded. This paper proposed a robust speech detection method in noisy environments using a silence feature normalization and voice energy maximize. In the high signal-to-noise ratio for the proposed method was used to maximize the characteristics receive less characterized the effects of noise by the voice energy. Cepstral feature distribution of voice / non-voice characteristics in the low signal-to-noise ratio and improves the recognition performance. Result of the recognition experiment, recognition performance improved compared to the conventional method.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1048-1060
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• 2012

In this paper, an adaptive temporal and spatial filter (ATSF) based on mean square error (MSE) estimation is proposed. ATSF is a block based de-noising algorithm. Each noisy block is selectively filtered by a temporal filter or a spatial filter. Multi-hypothesis motion compensated filter (MHMCF) and bilateral filter are chosen as the temporal filter and the spatial filter, respectively. Although there is no original video, we mathematically derivate a formular to estimate the real MSE between a block de-noised by MHMCF and its original block and a linear model is proposed to estimate the real MSE between a block de-noised by bilateral filter and its original block. Finally, each noisy block is processed by the filter with a smaller estimated MSE. Simulation results show that our proposed algorithm achieves substantial improvements in terms of both visual quality and PSNR as compared with the conventional de-noising algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.795-802
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from a noisy image. In order to estimate variance, information of neighboring region is used generally. The size of neighbor region is varied according to the regional characteristics of image. More accurate estimation of edge variance is due to smaller region of neighbor, on the other hands, larger region of neighbor is used to estimate the variance of flat region. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new method for determining the range of neighbors to estimate the variance in wavelet domain. Firstly, a significance map is constructed using the parent-child relationship of wavelet domain. Based on the number of the significant wavelet coefficients, the range of neighbors is determined and then the variance of the original signal is estimated using ML(maximum likelihood method. Experimental results show that the proposed method yields better results than conventional methods for image denoising.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.210-216
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• 2005

We Propose a new feature normalization scheme based on eigenspace for achieving robust speech recognition. In general, mean and variance normalization (MVN) is Performed in cepstral domain. However, another MVN approach using eigenspace was recently introduced. in that the eigenspace normalization Procedure Performs normalization in a single eigenspace. This Procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In this method. 39 dimensional feature distribution is represented using only a single eigenspace. However it is observed to be insufficient to represent all data distribution using only a sin91e eigenvector. For more specific representation. we apply unique na independent eigenspaces to cepstra, delta and delta-delta cepstra respectively in this Paper. We also normalize training data in eigenspace and get the model from the normalized training data. Finally. a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial recognition improvement over the basic eigenspace normalization.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.216-221
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• 1999

A mental task combined with noise background is an effective model of laboratory stress for study of psychophysiology of the autonomic nervous system (ANS). The intensity of the background noise significantly affects both a subjective evaluation of experienced stress level during test and the physiological responses associated with mental load in noisy environments. Providing tests of similar difficulties we manipulated the background noise intensity as a main factor influencing a psychophysiological outcome and the analyzed reactivity along withe the noise intensity dimension. The goal of this study was to identify the patterns of ANS responses and the relevant subjective stress scores during performance of word recognition tasks on the background of white noise (WN) of the different intensities (55, 70 and 85 dB). Subjects were 27 college students (19-24 years old). BIOPAC, Grass Neurodata System and AcqKnowlwdge 3.5 software were used to record ECG, PPG, SCL, skin temperature, and respiration. Experimental manipulations were effective in producing subjective and physiological responses usually associated with stress. The results suggested that the following potential autonomic mechanisms might be involved in the mediation of the observed physiological responses: A sympathetic activation with parasympathetic withdrawal during mild 55 and 70dB noise (featured by similar profiles) and simultaneous activation of sympathetic and parasympathetic systems during intense 85dB WN. The parasympathetic activation in this case might be a compensatory effect directed to prevent sympathetic domination and to maintain optimal arousal state for the successful performance on mental stress task. It should be mentioned that obtained results partially support Gellhorn's (1960; 1970) "tuning phenomenon" as a possible mechanism underlying stress response.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.198-203
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• 2003

We develop the method of estimating the endpoints of speech by jointly using the lip motion (visual speech) and speech being included in multimedia data and then propose a new speech recognition system (SRS) based on that method. The endpoints of noisy speech are estimated as follows : For each test word, two kinds of endpoints are detected from visual speech and clean speech, respectively Their difference is made and then added to the endpoints of visual speech to estimate those for noisy speech. This estimation method for endpoints (i.e. speech interval) is applied to form a new SRS. The SRS differs from the convention alone in that each word model in the recognizer is provided an interval of speech not Identical but estimated respectively for the corresponding word. Simulation results show that the proposed method enables the endpoints to be accurately estimated regardless of the amount of noise and consequently achieves 8 o/o improvement in recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2367-2374
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• 2014

This paper proposes a speech enhancement method which can be independently applied to different types of speech recognition systems. Feature compensation methods are well known to be effective as a front-end algorithm for robust speech recognition in noisy environments. The feature types and speech model employed by the feature compensation methods should be matched with ones of the speech recognition system for their effectiveness. However, they cannot be successfully employed by the speech recognition with "unknown" specification, such as a commercialized speech recognition engine. In this paper, a speech enhancement method is proposed, which is based on the PCGMM-based feature compensation method. The experimental results show that the proposed method significantly outperforms the conventional front-end algorithms for unknown speech recognition over various background noise conditions.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.103-114
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• 2019

Moving-target detection using frequency-modulated continuous-wave (FMCW) radar systems has recently attracted attention. Detection tasks are more challenging with noise resulting from signals reflected from strong static objects or small moving objects(clutter) within radar range. Robust Principal Component Analysis (RPCA) approach for FMCW radar to detect moving objects in noisy environments is employed in this paper. In detail, compensation and calibration are first applied to raw input signals. Then, RPCA via Gradient Descents (RPCA-GD) is adopted to model the low-rank noisy background. A novel update algorithm for RPCA is proposed to reduce the computation cost. Finally, moving-targets are localized using an Automatic Multiscale-based Peak Detection (AMPD) method. All processing steps are based on a sliding window approach. The proposed scheme shows impressive results in both processing time and accuracy in comparison to other RPCA-based approaches on various experimental scenarios.