scholarly journals New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study

2021 ◽  
Vol 32 (4) ◽  
pp. 913-928
Author(s):  
Trina Mouchahoir ◽  
John E. Schiel ◽  
Rich Rogers ◽  
Alan Heckert ◽  
Benjamin J. Place ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Peak Detection ◽  
Detection Performance ◽  
Interlaboratory Study

Criterion setting for objective, fourier analysis based pilot performance metrics

2004 ◽  
Author(s):  
Nicholas R. Johnson ◽  
Esa M. Rantanen ◽  
Donald A. Talleur
Keyword(s):  
Fourier Analysis ◽  
Performance Metrics ◽  
Pilot Performance ◽  
Criterion Setting

Understanding Target Detection Performance When Receiving Visual or Auditory Cues

2002 ◽  
Author(s):  
David C. Cibik ◽  
Erich W. Meyerhoff
Keyword(s):  
Target Detection ◽  
Detection Performance ◽  
Auditory Cues

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Education and the Relation to the Outside: A Little Real Reality

2015 ◽  
Vol 9 (3) ◽  
pp. 273-300 ◽  
Author(s):  
David Savat ◽  
Greg Thompson
Keyword(s):  
Performance Metrics ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Educational Institutions ◽  
Special Issue ◽  
Massive Open Online ◽  
Transformative Teaching ◽  
Control Society ◽  
Brave New World ◽  
Radical Transformation

One of the more dominant themes around the use of Deleuze and Guattari's work, including in this special issue, is a focus on the radical transformation that educational institutions are undergoing, and which applies to administrator, student and educator alike. This is a transformation that finds its expression through teaching analytics, transformative teaching, massive open online courses (MOOCs) and updateable performance metrics alike. These techniques and practices, as an expression of control society, constitute the new sorts of machines that frame and inhabit our educational institutions. As Deleuze and Guattari's work posits, on some level these are precisely the machines that many people in their day-to-day work as educators, students and administrators assemble and maintain, that is, desire. The meta-model of schizoanalysis is ideally placed to analyse this profound shift that is occurring in society, felt closely in the so-called knowledge sector where a brave new world of continuous education and motivation is instituting itself.

Tolerance for Clock Variance of LF-Band Wireless SS Communication Utilizing Peak Detection with Maximum Determination at the Center of Section Method

2009 ◽  
Vol 129 (12) ◽  
pp. 1195-1200
Author(s):  
Yukihiko Wada ◽  
Hajime Nakajima ◽  
Kazuhiko Sumi
Keyword(s):  
Peak Detection ◽  
Section Method

Interlaboratory Study of Standard Methods for Testing Multiwire Steel Prestressing Strand

PCI Journal ◽  
2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Rémy D. Lequesne ◽  
William N. Collins ◽  
Enrico Lucon ◽  
David Darwin ◽  
Ashwin Poudel
Keyword(s):  
Interlaboratory Study ◽  
Standard Methods ◽  
Prestressing Strand

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT

2017 ◽  
Vol 1 (3) ◽  
pp. 54
Author(s):  
BOUKELLOUZ Wafa ◽  
MOUSSAOUI Abdelouahab
Keyword(s):  
Performance Metrics ◽  
Radiation Treatment ◽  
Hybrid Methods ◽  
Dose Calculation ◽  
Absolute Error ◽  
Ct Images ◽  
General Technique ◽  
Therapy Field ◽  
The Brain

Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods. Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach. Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

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