scholarly journals Experimental Analysis of Performance Comparison on Both Linear Filter and Bidirectional Condential Technique for Spatial Domain Optical Flow Algorithm

1970 ◽  
Vol 7 (2) ◽  
pp. 156-167
Author(s):  
Darun Kesrarat ◽  
Vorapoj Patanavijit
Keyword(s):  
Optical Flow ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Motion Vector ◽  
Research Work ◽  
Spatial Domain ◽  
Performance Comparison ◽  
Video Sequences ◽  
Linear Filter ◽  
Analysis Of Performance

Optical flow is a method for classifying the density velocity or motion vector (MV) in a degree of pixel basis for motion classification of image in video sequences. In actual situation, many unpleasant situations usually generate noises over the video sequences. These unpleasant situations corrupt the performance in efficiency of optical flow. In turn to increase the efficiency of the MV, this research work proposes the performance comparison on linear filter and bidirectional confidential technique for spatial domain optical flow algorithms. Our experiment concentrates on the 3 classical spatial based optical flow algorithms (such as spatial correlation-based optical flow (SCOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)). Different standard video sequences such as AKIYO, CONTAINER, COASTGUARD, and FOREMAN are comprehensively evaluated to demonstrate the effectiveness results. These video sequences have differences in aspect of action and speed in foreground and background. These video sequences are also debased by the Additive White Gaussian Noise (AWGN) at different noise degree (such as AWGN at 25 dB, 20 dB, and 15 dB consequently). Peak Signal to Noise Ratio (PSNR) is utilized as the performance index in our observation.

scholarly journals Tracking of Blood Movement in the Heart using Elongated Horizontal Large Diamond Search Pattern and Optical Flow Technique for Enabling the Detection of Atrial Septal Defect

2019 ◽  
Vol 8 (6) ◽  
pp. 1862-1870
Keyword(s):  
Atrial Septal Defect ◽  
Optical Flow ◽  
Septal Defect ◽  
Motion Vector ◽  
Research Work ◽  
Ground Truth ◽  
Block Matching ◽  
Search Pattern ◽  
Diamond Search ◽  
Large Diamond

In major congenital heart ailments comes Atrial Septal Defect (ASD). Many techniques viz. ECG, MRI, Ultrasound etc., are in use to detect ASD. Performance of heart is analyzed by the Doctors through test results or observations of heart Ultrasound. In this research work it is proved that the disorder ASD is detected using the technique of block matching and optical flow. These algorithms enables the doctors or experts to detect ASD automatically and hence reduces the dependency on humans. This paper proposes a new Elongated Horizontal Large Diamond Search Pattern (EHLDSP) and Optical Flow algorithm for detection of ASD based on MV. Experimentation was carried out on A4C view of 2D Echo Cardiogram, collected form the hospitals as well as from open available database. Ground truth image is used by the cardiologist to compare the results. In this paper, performance of the proposed Elongated Horizontal Large Diamond Search Pattern (EHLDSP) method is compared with other techniques on the basis of cost functions like PSNR and computation complexity. EHLDSP algorithm is used to check the pixel movements and to calculate Motion Vector (MV), followed by the estimation of the displacement of blood cells from either right to left or vice versa. This will help in the reduction of dependency on specialized doctors or human factor can be reduced. For automatic detection of abnormality, research is going on in this area to make it fast and accurate. Proposed algorithm reduces the average computation by providing speed improvements of about 91% over FS/ES and about 6% over existing DS. Also, EHLDSP has shown improvements in the PSNR value in comparison with other methods.

scholarly journals Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

2019 ◽  
Vol 8 (12) ◽  
pp. 4257-4261
Keyword(s):  
Cognitive Radio ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Research Work ◽  
Radio Networks ◽  
Decision Threshold ◽  
Operating Characteristics ◽  
Low Snr ◽  
Unique System ◽  
Proposed Model

This paper represents the unique system model for cognitive radio based on the energy spotting method to enhance the performance of the accuracy by managing the queue regarding energy-samples and also estimating their average in order to characterize the decision-threshold. Consequently, these typical values summed and estimated over the sum of the samples are repeatedly correlated and analyzed with the recent energy values to determine whether the frequency band is vacant or occupied most accurately. The energy spotting technique’s performance is analyzed and estimated analytically for distinct decision-thresholds. Conventionally Such evaluations interprets that; the advances made to energy spotting algorithm which have enhanced the sensing accuracy in spectrum under the differing signal-to-noise ratio values. Consequently, we shown the utilities and advantages of proposed model that increases the cognitive radio ability. The performance has measured by utilizing the AWGN (Additive White Gaussian Noise) channel and receiver operating-characteristics curves varying under various SNR values alike as: -20 dB, -15 dB, -5 dB, 0 dB, 5db and 10db. With small-tradeoffs among the detection and false-alarm probabilities, the model increases and enhances the ability of spectrum sensing mechanism greatly in the lower SNR situations while tested with number of samples. By that, improving the conventional performance by increasing the sensing accuracy of cognitive radio networks under the low SNR have been the promising achievement of this research work.

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

Performance Comparison between DPSK and OQPSK modulation approaches in multi environments channels with Matlab Simulink models

2019 ◽  
Vol 7 (1) ◽  
pp. 30-39
Author(s):  
Fatima faydhe Al- Azzawi ◽  
Faeza Abas Abid ◽  
Zainab faydhe Al-Azzawi
Keyword(s):  
Phase Shift ◽  
Fading Channels ◽  
Additive White Gaussian Noise ◽  
Block Codes ◽  
Performance Comparison ◽  
Phase Shift Keying ◽  
Differential Phase Shift ◽  
Matlab Simulink ◽  
Communication Industry ◽  
Shift Keying

Phase shift keying modulation approaches are widely used in the communication industry. Differential phase shift keying (DPSK) and Offset Quadrature phase shift keying (OQPSK) schemes are chosen to be investigated is multi environment channels, where both systems are designed using MATLAB Simulink and tested. Cross talk and unity of signals generated from DPSK and OQPSK are examined using Cross-correlation and auto-correlation, respectively. In this research a proposed system included improvement in bit error rate (BER) of both systems in  the additive white Gaussian Noise (AWGN) channel, by using the convolutional and block codes, by increasing the ratio of energy in the specular component to the energy in the diffuse component (k) and  the diversity order BER in the fading channels will be improved in both systems.    

scholarly journals Identification and authentication for wireless transmission security based on RF-DNA fingerprint

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xueli Wang ◽  
Yufeng Zhang ◽  
Hongxin Zhang ◽  
Xiaofeng Wei ◽  
Guangyuan Wang
Keyword(s):  
Radio Frequency ◽  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Wireless Transmission ◽  
Dna Fingerprint ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Security Issue ◽  
Rf Devices

Abstract For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean (μ) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when SNR≥5 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.

scholarly journals Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

2021 ◽  
pp. 1-15
Author(s):  
Poovarasan Selvaraj ◽  
E. Chandra
Keyword(s):  
Real Time ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Time Delay Estimation ◽  
Variational Model ◽  
Speech Signals ◽  
Frequency Noise ◽  
Intrinsic Mode Functions ◽  
Real Time Applications

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

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