Betrachtung der effektiven k-Raum-Abdeckung von MR-Bildern: Phantom-Experimente mit Anwendung der Fast-Fourier-Transformation

2013 ◽  
Vol 185 (S 01) ◽  
Author(s):  
J Menke ◽  
G Helms ◽  
J Larsen
Keyword(s):  
Fourier Transformation ◽  
Fast Fourier Transformation

scholarly journals Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su
Keyword(s):  
Object Detection ◽  
Fourier Transformation ◽  
Active Contour ◽  
Active Contour Model ◽  
Salient Object Detection ◽  
Fast Fourier Transformation ◽  
Salient Object ◽  
Active Contour Models ◽  
Contour Model ◽  
Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

Generation of Wave-Field Time Histories by the Modulated Discrete Fourier Transformation

2003 ◽  
Author(s):  
Yousun Li
Keyword(s):  
Wave Field ◽  
Fourier Transformation ◽  
Time Instant ◽  
Discrete Fourier Transformation ◽  
Fast Fourier Transformation ◽  
Structural Members ◽  
Random Waves ◽  
Offshore Structure ◽  
Time Histories ◽  
The Time Domain

In the time domain simulation of the response of an offshore structure under random waves, the time histories of the wave field should be generated as the input to the dynamic equations. Herein the wave field is the wave surface elevation, the water particle velocities and accelerations at structural members. The generated time histories should be able to match the given wave-field spectral descriptions, to trace the structural member motions if it is a compliant offshore structure, and be numerically efficient. Most frequently used generation methods are the direct summation of a limited number of cosine functions, the Fast Fourier Transformation, and the digital filtering model. However, none of them can really satisfy all the above requirements. A novel technique, called the Modulated Discrete Fourier Transformation, has been developed. Under this method, the wave time histories at each time instant is a summation of a few time-varying complex functions. The simulated time histories have continuous spectral density functions, and the motions of the structural members are well included. This method seems to be superior to all the conventional methods in terms of the above mentioned three requirements.

Abstract 16924: Initial AMSA and Increased AMSA After CPR Predict Second Shock Success in Initially Shock-resistant VF During Out-of-hospital Cardiac Arrest

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Ulrich Herken ◽  
Weilun Quan
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Success Rate ◽  
Fourier Transformation ◽  
Amplitude Spectrum ◽  
Fast Fourier Transformation ◽  
Shock Delivery ◽  
Spectrum Area ◽  
Hospital Cardiac Arrest

Purpose: Amplitude spectrum area (AMSA), which is calculated from the ventricular fibrillation (VF) waveform using fast Fourier transformation, has been recognized as a predictor of successful defibrillation (DF) and as an index of myocardial perfusion and viability during resuscitation. In this study, we investigated whether a change in AMSA occurring during CPR would predict DF outcome for subsequent DF attempts after a failed DF. We hypothesized that a patient responding to CPR with an increase in AMSA would have an increased likelihood of DF success. Methods: This was a retrospective analysis of out-of-hospital cardiac arrest patients who received a second DF due to initially shock-resistant VF. A total of 193 patients with an unsuccessful first DF were identified in a manufacturer database of electrocardiographic defibrillator records. AMSA was calculated for the first DF (AMSA1) and the second DF (AMSA2) during a 2.1 sec window ending 0.5 sec prior to DF. A successful DF attempt was defined as the presence of an organized rhythm with a rate ≥ 40 / min starting within 60 sec from the DF and lasting for > 30 sec. After the failed first DF, all patients received CPR for 2 to 3 minutes before delivery of the second DF. Change in AMSA (dAMSA) was calculated as dAMSA = AMSA2 - AMSA1. Results: The overall second DF success rate was 14.5%. Multivariable logistic regression showed that both AMSA1 and dAMSA were independent predictors of second DF success with odds ratios of 1.24 (95% CI 1.12 - 1.38, p<0.001) and 1.27 (95% CI 1.16 - 1.41, p<0.001) for each mVHz change in AMSA or dAMSA, respectively. Conclusions: In initially DF-resistant VF, a high initial AMSA value predicted an increased likelihood of second shock success. An increase of AMSA in response to CPR also predicted a higher second shock success rate. Monitoring of AMSA during resuscitation therefore may be useful to guide CPR efforts, possibly including timing of second shock delivery. These findings also further support the value of AMSA as indicator of myocardial viability.

Using GPUs to compute fast Fourier transforms for crystal structure solution and refinement

2013 ◽  
Vol 46 (3) ◽  
pp. 594-600 ◽  
Author(s):  
ElSayed Mohamed Shalaby ◽  
Miguel Afonso Oliveira
Keyword(s):  
Crystal Structure ◽  
Fourier Transformation ◽  
Fourier Transforms ◽  
Fast Fourier Transformation ◽  
Parallel Calculations ◽  
The Past ◽  
Graphical Processing Units ◽  
Software Algorithms ◽  
Structure Solution ◽  
Graphical Processing

In the past few years, new hardware tools have become available for computing using the graphical processing units (GPUs) present in modern graphics cards. These GPUs allow efficient parallel calculations with a much higher throughput than microprocessors. In this work, fast Fourier transformation calculations used inSIR2011software algorithms have been carried out using the power of the GPU, and the speed of the calculations has been compared with that achieved using normal CPUs.

scholarly journals Normal and Abnormal Human Face Detection Based on DCT and FFT Techniques - A Proposed Method

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta ◽  
Vishal Goyal ◽  
Payal Bose
Keyword(s):  
Face Detection ◽  
Fourier Transformation ◽  
Input Image ◽  
The Other ◽  
Fast Fourier Transformation ◽  
Length Variation ◽  
Human Face ◽  
Discrete Cosine Transformation ◽  
Cosine Transformation ◽  
Human Face Detection

In today&rsquo;s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

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