Slope detection criterion robust to sparse 2D data

2021 ◽  
Author(s):  
Dmitry Bocharov ◽  
Alexey Kroshnin ◽  
Dmitry Nikolaev
Keyword(s):  
Detection Criterion ◽  
2D Data

Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain

2003 ◽  
Vol 42 (05) ◽  
pp. 215-219
Author(s):  
G. Platsch ◽  
A. Schwarz ◽  
K. Schmiedehausen ◽  
B. Tomandl ◽  
W. Huk ◽  
...  
Keyword(s):  
Data Sets ◽  
3D Mri ◽  
Clinical Routine ◽  
Fusion Procedure ◽  
3D Data ◽  
Mri Scans ◽  
2D Data ◽  
Time Required ◽  
The Brain ◽  
Manual Correction

Summary: Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and Method: In 32 patients regional cerebral blood flow was measured using 99mTc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

Enhanced Information Recovery in 2D On-and Off-Resonance Nutation NQR using the Maximum Entropy Method

1996 ◽  
Vol 51 (5-6) ◽  
pp. 337-347 ◽  
Author(s):  
Mariusz Maćkowiak ◽  
Piotr Kątowski
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Theoretical Description ◽  
Entropy Method ◽  
Zero Field ◽  
Resonance Effects ◽  
Information Recovery ◽  
Truncation Errors ◽  
Enhanced Resolution ◽  
2D Data

Abstract Two-dimensional zero-field nutation NQR spectroscopy has been used to determine the full quadrupolar tensor of spin - 3/2 nuclei in serveral molecular crystals containing the 3 5 Cl and 7 5 As nuclei. The problems of reconstructing 2D-nutation NQR spectra using conventional methods and the advantages of using implementation of the maximum entropy method (MEM) are analyzed. It is shown that the replacement of conventional Fourier transform by an alternative data processing by MEM in 2D NQR spectroscopy leads to sensitivity improvement, reduction of instrumental artefacts and truncation errors, shortened data acquisition times and suppression of noise, while at the same time increasing the resolution. The effects of off-resonance irradiation in nutation experiments are demonstrated both experimentally and theoretically. It is shown that off-resonance nutation spectroscopy is a useful extension of the conventional on-resonance experiments, thus facilitating the determination of asymmetry parameters in multiple spectrum. The theoretical description of the off-resonance effects in 2D nutation NQR spectroscopy is given, and general exact formulas for the asymmetry parameter are obtained. In off-resonance conditions, the resolution of the nutation NQR spectrum decreases with the spectrometer offset. However, an enhanced resolution can be achieved by using the maximum entropy method in 2D-data reconstruction.

scholarly journals Local Convexity-PreservingC2Rational Cubic Spline for Convex Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Abbas ◽  
Ahmad Abd Majid ◽  
Jamaludin Md. Ali
Keyword(s):  
Shape Parameter ◽  
Cubic Spline ◽  
Convex Curve ◽  
Shape Feature ◽  
Local Convexity ◽  
Shape Parameters ◽  
Numerical Examples ◽  
2D Data ◽  
Industrial Demand ◽  
Made In

We present the smooth and visually pleasant display of 2D data when it is convex, which is contribution towards the improvements over existing methods. This improvement can be used to get the more accurate results. An attempt has been made in order to develop the local convexity-preserving interpolant for convex data usingC2rational cubic spline. It involves three families of shape parameters in its representation. Data dependent sufficient constraints are imposed on single shape parameter to conserve the inherited shape feature of data. Remaining two of these shape parameters are used for the modification of convex curve to get a visually pleasing curve according to industrial demand. The scheme is tested through several numerical examples, showing that the scheme is local, computationally economical, and visually pleasing.

scholarly journals COMPARATIVE MEASUREMENTS OF HEAD ANGULAR MOVEMENTS USING A CAMERA SYSTEM AND A GYROSCOPE SYSTEM

2014 ◽  
Vol 54 (4) ◽  
pp. 295-300 ◽  
Author(s):  
Vladimir Socha ◽  
Patrik Kutilek ◽  
Ondrej Cakrt ◽  
Rudolf Cerny
Keyword(s):  
Head Movements ◽  
Head Orientation ◽  
Quiet Stance ◽  
Body Segment ◽  
Mean Velocity ◽  
Camera System ◽  
Rehabilitation Process ◽  
Gyroscope System ◽  
The Mean ◽  
2D Data

Assessments of body-segment angular movements are very important in the rehabilitation process. Head angular movements are measured and analyzed for use in studies of stability and posture. However, there is no methodology for assessing angular movements of the head, and it has not been verified whether data measured by fundamentally different MoCap systems will lead to the same results. In this study, we used a camera system and a 3DOF orientation tracker placed on the subject’s head, and measured inclination (roll) and flexion (pitch) during quiet stance. The total length and the mean velocity of the traces of the pitch versus roll plots were used to measure and analyze head orientation. Using these methods, we are able to model the distribution of the measured 2D data, and to evaluate stability and posture. The results show that the total lengths and the mean velocities related to the 3DOF orientation tracker do not differ significantly from the total lengths and the mean velocities of traces related to the IR medical camera. We also found that the systems are not interchangeable, and that the same type of system must be used each time. The designed methods can be used for studies not only of head movements but also of movements of other segments of the human body, and can be used to compare other types of MoCap systems, depending on the requirements for a specific rehabilitation examination.

A Novel Detection Criterion for Weak<tex>$m$</tex>-Ary Signals and Its Application to UltraWideband Multiple Access Systems

2005 ◽  
Vol 54 (6) ◽  
pp. 2024-2036 ◽  
Author(s):  
I. Song ◽  
J. Koo ◽  
H. Kwon ◽  
S.R. Park ◽  
S.R. Lee ◽  
...  
Keyword(s):  
Multiple Access ◽  
Detection Criterion

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

scholarly journals VISUALIZATION OF E-COMMERCE DATA USING AUGMENTED REALITY

2020 ◽  
Vol 5 (19) ◽  
pp. 104-122
Author(s):  
Azzan Amin ◽  
Haslina Arshad ◽  
Ummul Hanan Mohamad
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Data Visualization ◽  
Data Science ◽  
Moving Average ◽  
Open Data ◽  
Visual Presentation ◽  
Depth Dimension ◽  
2D Data ◽  
Visualization Application

Data visualization is viewed as a significant element in data analysis and communication. As the data engagement becomes more and more complex, visual presentation of data does help users understand the data. So far, two-dimensional (2D) data visuals are often used for the data visualization process, but the lack of depth dimension leads to inefficient and limited understanding of the data. Therefore, the effectiveness of augmented reality (AR) in data visualization was studied through the development of an AR Data Visualization application using E-commerce data. Machine learning models are also involved in the development of this AR application for the provision of data using predictive analysis functions. To provide quality E-commerce data and an optimal machine learning model, the data science process is carried out using the python programming language. The E-commerce data selected for this study is open data taken through the Kaggle Website. This database has 9994 data numbers and 21 attributes. This AR data visualization application will make it easier for users to understand the E-commerce data in-depth through the use of AR technology and be able to visualize the forecasts for sales profit based on the algorithm model "Auto-Regressive Integrated Moving Average" (ARIMA).

scholarly journals 2D Information Space Based Action Recognition

2019 ◽  
Vol 9 (1S) ◽  
pp. 295-298
Keyword(s):  
Convex Hull ◽  
Action Recognition ◽  
Video Sequence ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
New Approach ◽  
Human Object ◽  
Background Data ◽  
2D Data

Video based human action recognition has attained more attraction from the researchers and it predominates in the field of computer vision and pattern recognition. In this paper we deliver a new approach to suppress the background data and to extract 2D data of foreground human object of the video sequence. A combination of convex hull area, convex hull perimeter, solidity and eccentricity is used to represent the feature vector. Experiments are conducted on Weizmann video dataset to assess how the system is doing. The discriminative nature of the feature vectors assures accuracy in action recognition.

Investigation of the Advantages and Limitations of Forward Linear Prediction for Processing 2D Data Sets

1997 ◽  
Vol 35 (8) ◽  
pp. 505-519 ◽  
Author(s):  
William F. Reynolds ◽  
Margaret Yu ◽  
Raul G. Enriquez ◽  
Ismael Leon
Keyword(s):  
Linear Prediction ◽  
Data Sets ◽  
2D Data
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