scholarly journals Data-Driven Redundant Transform Based on Parseval Frames

2020 ◽  
Vol 10 (8) ◽  
pp. 2891 ◽  
Author(s):  
Min Zhang ◽  
Yunhui Shi ◽  
Na Qi ◽  
Baocai Yin
Keyword(s):  
Singular Values ◽  
Data Driven ◽  
Dual Frame ◽  
Two Phase ◽  
Sparse Models ◽  
Sparse System ◽  
Parseval Frames ◽  
Proposed Model ◽  
Objective Quality

The sparsity of images in a certain transform domain or dictionary has been exploited in many image processing applications. Both classic transforms and sparsifying transforms reconstruct images by a linear combination of a small basis of the transform. Both kinds of transform are non-redundant. However, natural images admit complicated textures and structures, which can hardly be sparsely represented by square transforms. To solve this issue, we propose a data-driven redundant transform based on Parseval frames (DRTPF) by applying the frame and its dual frame as the backward and forward transform operators, respectively. Benefitting from this pairwise use of frames, the proposed model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame pair to be Parseval frames, the singular values and condition number of the learnt redundant frames, which are efficient values for measuring the quality of the learnt sparsifying transforms, are forced to achieve an optimal state. We formulate a transform pair (i.e., frame pair) learning model and a two-phase iterative algorithm, analyze the robustness of the proposed DRTPF and the convergence of the corresponding algorithm, and demonstrate the effectiveness of our proposed DRTPF by analyzing its robustness against noise and sparsification errors. Extensive experimental results on image denoising show that our proposed model achieves superior denoising performance, in terms of subjective and objective quality, compared to traditional sparse models.

scholarly journals Stable Sparse Model with Non-Tight Frame

2020 ◽  
Vol 10 (5) ◽  
pp. 1771 ◽  
Author(s):  
Min Zhang ◽  
Yunhui Shi ◽  
Na Qi ◽  
Baocai Yin
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Tight Frame ◽  
Closed Form Expression ◽  
Dual Frame ◽  
Two Phase ◽  
Sparse Models ◽  
Frame Operator ◽  
Sparse Model ◽  
Sparse System

Overcomplete representation is attracting interest in image restoration due to its potential to generate sparse representations of signals. However, the problem of seeking sparse representation must be unstable in the presence of noise. Restricted Isometry Property (RIP), playing a crucial role in providing stable sparse representation, has been ignored in the existing sparse models as it is hard to integrate into the conventional sparse models as a regularizer. In this paper, we propose a stable sparse model with non-tight frame (SSM-NTF) via applying the corresponding frame condition to approximate RIP. Our SSM-NTF model takes into account the advantage of the traditional sparse model, and meanwhile contains RIP and closed-form expression of sparse coefficients which ensure stable recovery. Moreover, benefitting from the pair-wise of the non-tight frame (the original frame and its dual frame), our SSM-NTF model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame bounds and applying a second-order truncated series to approximate the inverse frame operator, we formulate a dictionary pair (frame pair) learning model along with a two-phase iterative algorithm. Extensive experimental results on image restoration tasks such as denoising, super resolution and inpainting show that our proposed SSM-NTF achieves superior recovery performance in terms of both subjective and objective quality.

scholarly journals A Real Data-Driven Analytical Model to Predict Happiness

2021 ◽  
Author(s):  
Aditya Chakraborty ◽  
Chris P. Tsokos
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Analytical Model ◽  
Well Being ◽  
Developed Countries ◽  
Data Driven ◽  
Individual Risk ◽  
Proposed Model ◽  
High Degree

Abstract Purpose: Philosophers and many modern-day researchers are convinced by the fact that the pursuit of happiness is the ultimate goal for humankind. Aristotle believed that the utmost goal of human life was eudaimonia (interpreted as “happiness,” “human flourishing,” or “a good life.”). Recently, many economists and physiologists have been doing applied research in the areas of subjective well-being (SWB) or happiness and trying to understand how it improves the quality of life of individual beings. Thus, searching for a data-driven analytical model is crucial to predict SWB and enhance the quality of lifeMethods: Our present study utilizes the world happiness database obtained from the Gallup World Poll on the happiness of 156 countries. However, our study focuses on using only the data of fifty-four developed countries, based on the human development index (HDI). We have developed a non-linear analytical model that predicts the average happiness score based on eleven risk factors with a high degree of accuracy. We also compared our analytical model with three other statistical models, and our model outperformed the rest of the three in terms of RMSE and MAE. Results: Our analytical model includes five important findings. The response of the proposed model is the average score of happiness of individuals in developed countries. In addition to predicting the happiness score, our model identifies the individual risk factors and their corre-sponding interactions that significantly contribute to happiness. We rank these risk factors by their percentage of contributions to the happiness score. We also proceed to rank the developed countries with respect to their predicted happiness score from our developed model. From our study, we found Finland being number one, followed by Denmark. The U.S is fifth and Romania being 54th.Conclusion: The proposed model offers other useful information on the subject area. Our ana-lytical model has been validated and tested to be of high quality, and our prediction of happiness is with a high degree of accuracy. We created a survey questionnaire (appendix 1) based on the data that can be used along with our model by any company for the strategic planning or decision making.

scholarly journals Model and Data-Driven System Portfolio Selection Based on Value and Risk

2019 ◽  
Vol 9 (8) ◽  
pp. 1657
Author(s):  
Hechuan Wei ◽  
Boyuan Xia ◽  
Zhiwei Yang ◽  
Zhexuan Zhou
Keyword(s):  
Portfolio Selection ◽  
Remaining Useful Life ◽  
Pareto Set ◽  
Data Driven ◽  
Multiple Systems ◽  
Proposed Model ◽  
Driven System ◽  
Data Driven Approach ◽  
Overall Performance

System portfolio selection is a kind of tradeoff analysis and decision-making on multiple systems as a whole to fulfill the overall performance on the perspective of System of Systems (SoS). To avoid the subjectivity of traditional expert experience-dependent models, a model and data-driven approach is proposed to make an advance on the system portfolio selection. Two criteria of value and risk are used to indicate the quality of system portfolios. A capability gap model is employed to determine the value of system portfolios, with the weight information determined by correlation analysis. Then, the risk is represented by the remaining useful life (RUL), which is predicted by analyzing time series of system operational data. Next, based on the value and risk, an optimization model is proposed. Finally, a case with 100 candidate systems is studied under the scenario of anti-missile. By utilizing the Non-dominated Sorting Differential Evolution (NSDE) algorithm, a Pareto set with 200 individuals is obtained. Some characters of the Pareto set are analyzed by discussing the frequency of being selected and the association rules. Through the conclusion of the whole procedures, it can be proved that the proposed model and data-driven approach is feasible and effective for system portfolio selection.

Feasibility and Clinical Implications of Remotely Guided Ultrasound Examinations

2020 ◽  
Vol 91 (7) ◽  
pp. 592-596
Author(s):  
Quinn Dufurrena ◽  
Kazi Imran Ullah ◽  
Erin Taub ◽  
Connor Leszczuk ◽  
Sahar Ahmad
Keyword(s):  
Clinical Utility ◽  
Regional Wall Motion ◽  
Subjective Assessment ◽  
Clinical Implications ◽  
Pilot Studies ◽  
Objective Quality ◽  
The Us ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

BACKGROUND: Remotely guided ultrasound (US) examinations carried out by nonmedical personnel (novices) have been shown to produce clinically useful examinations, at least in small pilot studies. Comparison of the quality of such exams to those carried out by trained medical professionals is lacking in the literature. This study compared the objective quality and clinical utility of cardiac and pulmonary US examinations carried out by novices and trained physicians.METHODS: Cardiac and pulmonary US examinations were carried out by novices under remote guidance by an US expert and independently by US trained physicians. Exams were blindly evaluated by US experts for both a task-based objective score as well as a subjective assessment of clinical utility.RESULTS: Participating in the study were 16 novices and 9 physicians. Novices took longer to complete the US exams (median 641.5 s vs. 256 s). For the objective component, novices scored higher in exams evaluating for pneumothorax (100% vs. 87.5%). For the subjective component, novices more often obtained clinically useful exams in the assessment of cardiac regional wall motion abnormalities (56.3% vs. 11.1%). No other comparisons yielded statistically significant differences between the two groups. Both groups had generally higher scores for pulmonary examinations compared to cardiac. There was variability in the quality of exams carried out by novices depending on their expert guide.CONCLUSION: Remotely guided novices are able to carry out cardiac and pulmonary US examinations with similar, if not better, technical proficiency and clinical utility as US trained physicians, though they take longer to do so.Dufurrena Q, Ullah KI, Taub E, Leszczuk C, Ahmad S. Feasibility and clinical implications of remotely guided ultrasound examinations. Aerosp Med Hum Perform. 2020; 91(7):592–596.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

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