Redistribution Index – Detection of an Outdated Prior Information in the Discrete Cosine Transformation-based EIT Algorithm

2021 ◽  
Author(s):  
Rongqing Chen ◽  
Knut Moeller
Keyword(s):  
Prior Information ◽  
Discrete Cosine Transformation ◽  
Cosine Transformation

scholarly journals Detection of Outdated Structural Priors in the Discrete Cosine Transformation-based Electrical Impedance Tomography Algorithm

2021 ◽  
Vol 7 (2) ◽  
pp. 676-679
Author(s):  
Rongqing Chen ◽  
Knut Moeller
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Prior Information ◽  
Simulated Data ◽  
Treatment Decision ◽  
Simulation Models ◽  
Discrete Cosine Transformation ◽  
Impedance Tomography ◽  
Cosine Transformation ◽  
Structural Prior

Abstract Morphological prior information incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm can improve the interpretability of EIT reconstructions in clinical applications. However, an outdated structural prior can yield a misleading reconstruction compromising the accuracy of the clinical diagnosis and the appropriate treatment decision. In this contribution, we propose a redistribution index scaled between 0 and 1 to quantify the possible error in a DCT-based EIT reconstruction influenced by structural prior information. Two simulation models of different tissue atelectasis and collapsed ratios were investigated. Outdated and updated structural prior information were applied to obtain different EIT reconstructions using this simulated data, with which the redistribution index was calculated and compared. When the difference between prior and reality (the redistribution index) became larger and exceeded a threshold, this was considered as an indicator of an outdated prior information. The evaluation result shows the potential of the redistribution index to detect outdated prior information in a DCT-based EIT algorithm.

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’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.

scholarly journals STUDI DAN IMPLEMENTASI STEGANOGRAFI METODE ALGORITMA DAN TRANSFORMASI PADA CITRA JPEG

Teknika ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 46-58
Author(s):  
Timothy John Pattiasina
Keyword(s):  
Frequency Domain ◽  
Wavelet Transformation ◽  
Spatial Domain ◽  
Discrete Cosine Transformation ◽  
Cosine Transformation

Steganografi adalah seni dan ilmu menulis atau menyemhunyikan pesan tersembunyi dengan suatu cara sehingga selain si pengirim dan si penerima, tidak ada seorangpun yang mengetahui atau menyadari bahwa ada suatu pesan rahasia. lstilah steganografi termasuk penyemhunyian data digital dalam komputer Ada beberapa metode steganografi, salah satunya adalah metode Algorithms and Transformation. Metode menyembunyikan data dalam fungsi matematika yang disebut algoritma compression. Dua fungsi tersebut adalah Discrete Cosine Transformation (DCT) dan Wavelet Transformation. Fungsi DCT dan Wavelet yaitu untuk mentransformasikan data dari satu tempat (domain) ke tempat (domain) yang lain. Fungsi DCT yaitu mentransformasi data dari tempat spatial (spatial domain) ke tempat fiekuensi (frequency domain).

scholarly journals Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yu Pang ◽  
Limin Jia ◽  
Zhan Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Efficiency ◽  
Remaining Useful Life ◽  
Discrete Cosine Transformation ◽  
Time Frequency ◽  
Cosine Transformation ◽  
Useful Life ◽  
Spectrum Coding ◽  
Low Dimensional

In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.

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