scholarly journals A Three-Feature Model to Predict Colour Change Blindness

Vision ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 61 ◽  
Author(s):  
Steven Le Moan ◽  
Marius Pedersen
Keyword(s):  
Linear Regression ◽  
Visual System ◽  
Change Blindness ◽  
Colour Change ◽  
Feature Model ◽  
Image Complexity ◽  
Proposed Model ◽  
Observer Experience ◽  
The Difference ◽  
Visual Changes

Change blindness is a striking shortcoming of our visual system which is exploited in the popular `Spot the difference’ game, as it makes us unable to notice large visual changes happening right before our eyes. Change blindness illustrates the fact that we see much less than we think we do. In this paper, we introduce a fully automated model to predict colour change blindness in cartoon images based on image complexity, change magnitude and observer experience. Using linear regression with only three parameters, the predictions of the proposed model correlate significantly with measured detection times. We also demonstrate the efficacy of the model to classify stimuli in terms of difficulty.

scholarly journals Feasibility study of assessing the Preclinical Alzheimer Cognitive Composite (PACC) score via videoconferencing

2021 ◽  
Author(s):  
Giulia Seghezzo ◽  
Yvonne Van Hoecke ◽  
Laura James ◽  
Donna Davoren ◽  
Elizabeth Williamson ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Clinical Practice ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Cognitive Tests ◽  
Strong Positive Correlation ◽  
Pearson Correlation Coefficient ◽  
Face To Face ◽  
The Difference

Abstract Background The Preclinical Alzheimer Cognitive Composite (PACC) is a composite score which can detect the first signs of cognitive impairment, which can be of importance for research and clinical practice. It is designed to be administered in person; however, in-person assessments are costly, and are difficult during the current COVID-19 pandemic. Objective To assess the feasibility of performing the PACC assessment with videoconferencing, and to compare the validity of this remote PACC with the in-person PACC obtained previously. Methods Participants from the HEalth and Ageing Data IN the Game of football (HEADING) Study who had already undergone an in-person assessment were re-contacted and re-assessed remotely. The correlation between the two PACC scores was estimated. The difference between the two PACC scores was calculated and used in multiple linear regression to assess which variables were associated with a difference in PACC scores. Findings Of the 43 participants who were invited to this external study, 28 were re-assessed. The median duration in days between the in-person and the remote assessments was 236.5 days (7.9 months) (IQR 62.5). There was a strong positive correlation between the two assessments for the PACC score, with a Pearson correlation coefficient of 0·82 (95% CI 0·66, 0·98). The multiple linear regression found that the only predictor of the PACC difference was the time between assessments. Interpretation This study provides evidence on the feasibility of performing cognitive tests online, with the PACC tests being successfully administered through videoconferencing. This is relevant, especially during times when face-to-face assessments cannot be performed.

Modeling and simulation of saturated double rotor induction machine

2018 ◽  
Vol 37 (3) ◽  
pp. 1139-1165
Author(s):  
Koosha Choobdari Omran ◽  
Ali Mosallanejad
Keyword(s):  
Temperature Effects ◽  
Power Efficiency ◽  
Reactive Power ◽  
Induction Machine ◽  
Transient Behavior ◽  
State Equations ◽  
Content Type ◽  
Proposed Model ◽  
The Difference ◽  
Park Transformation

Purpose Double rotor induction machine (DRIM) is a particular type of induction machine (IM) that has been introduced to improve the parameters of the conventional IM. The purpose of this study is to propose a dynamic model of the DRIM under saturated and unsaturated conditions by using the equations obtained in this paper. Also, skin and temperature effects are considered in this model. Design/methodology/approach First, the DRIM structure and its performance will be briefly reviewed. Then, to realize the DRIM model, the mathematical equations of the electrical and mechanical part of the DRIM will be presented by state equations in the q-d axis by using the Park transformation. In this paper, the magnetizing fluxes saturation is included in the DRIM model by considering the difference between the amplitudes of the unsaturated and saturated magnetizing fluxes. The skin and temperature effects are also considered in this model by correcting the rotor and stator resistances values during operation. Findings To evaluate the effects of the saturation and skin effects on DRIM performance and validate the model, the machine is simulated with/without consideration of saturation and skin effects by the proposed model. Then, the results, including torque, speed, stator and rotor currents, active and reactive power, efficiency, power factor and torque-speed characteristic, are compared. In addition, the performance of the DRIM has been investigated at different speed conditions and load variations. The proposed model is developed in Matlab/Simulink for the sake of validation. Originality/value This paper presents an understandable model of DRIM with and without saturation, which can be used to analyze the steady-state and transient behavior of the motor in different situations.

scholarly journals Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jun Li ◽  
Qiang Dong ◽  
Yan Fu
Keyword(s):  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Smart Devices ◽  
Data Sets ◽  
Rating Data ◽  
Temporal Effect ◽  
Proposed Model ◽  
Target User ◽  
The Difference

As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks

2022 ◽  
pp. 1-9
Author(s):  
P. Vijayalakshmi ◽  
K. Muthumanickam ◽  
G. Karthik ◽  
S. Sakthivel
Keyword(s):  
Clustering Algorithm ◽  
Water Drop ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Uterine Wall ◽  
Health Issues ◽  
Testing Dataset ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Difference

Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.

scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

ACCURATE ANALYSIS OF GLOBAL INTERCONNECTS IN NANO-FPGAs

NANO ◽  
2009 ◽  
Vol 04 (03) ◽  
pp. 171-176 ◽  
Author(s):  
DAVOOD FATHI ◽  
BEHJAT FOROUZANDEH
Keyword(s):  
Propagation Delay ◽  
Modeling Method ◽  
Accurate Analysis ◽  
Proposed Model ◽  
Simulation Results ◽  
The Difference ◽  
A New Technique ◽  
Global Interconnects ◽  
Technology Nodes ◽  
Model Technique

This paper introduces a new technique for analyzing the behavior of global interconnects in FPGAs, for nanoscale technologies. Using this new enhanced modeling method, new enhanced accurate expressions for calculating the propagation delay of global interconnects in nano-FPGAs have been derived. In order to verify the proposed model, we have performed the delay simulations in 45 nm, 65 nm, 90 nm, and 130 nm technology nodes, with our modeling method and the conventional Pi-model technique. Then, the results obtained from these two methods have been compared with HSPICE simulation results. The obtained results show a better match in the propagation delay computations for global interconnects between our proposed model and HSPICE simulations, with respect to the conventional techniques such as Pi-model. According to the obtained results, the difference between our model and HSPICE simulations in the mentioned technology nodes is (0.29–22.92)%, whereas this difference is (11.13–38.29)% for another model.

scholarly journals Light Leakage of Multidomain Vertical Alignment LCDs Using a Colorimetric Model in the Dark State

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Chuen-Lin Tien ◽  
Rong-Ji Lin ◽  
Shang-Min Yeh
Keyword(s):  
Liquid Crystal ◽  
Contrast Ratio ◽  
Liquid Crystal Displays ◽  
Vertical Alignment ◽  
Dark State ◽  
Proposed Model ◽  
Color Shift ◽  
Depolarization Of Light ◽  
The Difference ◽  
Color Filters

Light leakage from liquid crystal displays in the dark state is relatively larger and leads to a degraded contrast ratio and color shift. This work describes a novel colorimetric model based on the Muller matrix that includes depolarization of light propagating through liquid crystal molecules, polarizers, and color filters. In this proposed model, the chromaticity can be estimated in the bump and no-bump regions of an LCD. We indicate that the difference between simulation and measurement of chromaticity is about 0.01. Light leakage in the bump region is three times that in no-bump region in the dark state.

scholarly journals Distilling Deep Structural Facial Relationships for FAU Intensity Estimation

2021 ◽  
Author(s):  
Yingruo Fan ◽  
Jacqueline CK Lam ◽  
Victor On Kwok Li
Keyword(s):  
Structural Characteristics ◽  
Model Performance ◽  
Training Model ◽  
Model Parameters ◽  
Intensity Estimation ◽  
Structural Relationships ◽  
Proposed Model ◽  
Comparable Performance ◽  
Knowledge Distillation ◽  
The Difference

<div> <div> <div> <p>Facial emotions are expressed through a combination of facial muscle movements, namely, the Facial Action Units (FAUs). FAU intensity estimation aims to estimate the intensity of a set of structurally dependent FAUs. Contrary to the existing works that focus on improving FAU intensity estimation, this study investigates how knowledge distillation (KD) incorporated into a training model can improve FAU intensity estimation efficiency while achieving the same level of performance. Given the intrinsic structural characteristics of FAU, it is desirable to distill deep structural relationships, namely, DSR-FAU, using heatmap regression. Our methodology is as follows: First, a feature map-level distillation loss was applied to ensure that the student network and the teacher network share similar feature distributions. Second, the region-wise and channel-wise relationship distillation loss functions were introduced to penalize the difference in structural relationships. Specifically, the region-wise relationship can be represented by the structural correlations across the facial features, whereas the channel-wise relationship is represented by the implicit FAU co-occurrence dependencies. Third, we compared the model performance of DSR-FAU with the state-of-the-art models, based on two benchmarking datasets. Our proposed model achieves comparable performance with other baseline models, though requiring a lower number of model parameters and lower computation complexities. </p> </div> </div> </div>

scholarly journals Investigation of Predictability and Influence factors of the Achieved Lenticule Thickness in Small Incision Lenticule Extraction

2019 ◽  
Author(s):  
Fang Wu ◽  
Houfa Yin ◽  
Xinyi Chen ◽  
Yabo Yang
Keyword(s):  
Linear Regression ◽  
Linear Regression Analysis ◽  
Influence Factors ◽  
Small Incision Lenticule Extraction ◽  
Pupil Center ◽  
Small Incision ◽  
Significant Relationships ◽  
Ultrasound Pachymetry ◽  
Lenticule Extraction ◽  
The Difference

Abstract Background To evaluate the differences between the predicted and achieved lenticule thickness (ΔLT) after small incision lenticule extraction (SMILE) surgery and investigate relationships between ΔLT and refractive errors or lenticule depth in SMILE. Methods A total of 184 eyes from 184 consecutive patients who underwent SMILE were included in this prospective study. One eye for each patient was randomly selected and included for statistical analysis. An ultrasound pachymetry measurement and Scheimpflug camera corneal topography were obtained before and at 3 months after SMILE. The achieved lenticule thickness was calculated by comparing the preoperative examinations with postoperative examinations using ultrasound pachymetry and Pentacam software measurements. The pupil center and corneal vertex were selected as the 2 locations for measurement calculation on Pentacam. Analysis of variance (ANOVA) was performed to compare mean pachymetry values using different instruments. An independent t test was performed to evaluate the difference in ΔLT between different cap thicknesses. Linear regression analyses were performed between the VisuMax readout lenticule thicknesses and the measured maximum corneal change, the preoperative spherical equivalent (SE) and each ΔLT. Results On average, the achieved lenticule thickness measured with ultrasound pachymetry was 13.02 ± 8.87 μm thinner than the VisuMax readout lenticule thickness. Linear regression analysis showed significant relationships between the predicted and each achieved lenticule thickness. The preoperative SE was significantly related to each ΔLT (ultrasound: R2 =0.279; at corneal vertex: R2 =0.252; at pupil center R2 =0.246). The ΔLT measured by ultrasound pachymetry was significantly smaller in the thick cap group (cap thickness above 120 μm) than in the thin cap group (P < 0.01). Conclusions An overestimation of achieved lenticule thickness was found in this study. The ΔLT was related to the preoperative SE correction. Furthermore a lager ΔLT was found under a thin cap.

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