A COMPARISON OF HAZE REMOVAL ALGORITHMS AND THEIR IMPACTS ON CLASSIFICATION ACCURACY FOR LANDSAT IMAGERY

The quality of Landsat images in humid areas is considerably degraded by haze in terms of their spectral response pattern, which limits the possibility of their application in using visible and near-infrared bands. A variety of haze removal algorithms have been proposed to correct these unsatisfactory illumination effects caused by the haze contamination. The purpose of this study was to illustrate the difference of two major algorithms (the improved homomorphic filtering (HF) and the virtual cloud point (VCP)) for their effectiveness in solving spatially varying haze contamination, and to evaluate the impacts of haze removal on land cover classification. A case study with exploiting large quantities of Landsat TM images and climates (clear and haze) in the most humid areas in China proved that these haze removal algorithms both perform well in processing Landsat images contaminated by haze. The outcome of the application of VCP appears to be more similar to the reference images compared to HF. Moreover, the Landsat image with VCP haze removal can improve the classification accuracy effectively in comparison to that without haze removal, especially in the cloudy contaminated area

Global stabilization for triangular formations under mixed distance and bearing constraints

This paper addresses the triangular formation control problem for a system of three agents under mixed distance and bearing constraints. The main challenge is to find a fully distributed control law for each agent to guarantee the global convergence towards a desired triangular formation. To solve this problem, we invoke the property that a triangle can be uniquely determined by the lengths of its two sides together with the magnitude of the corresponding included angle. Based on this feature, we design a class of control strategies, under which each agent is only responsible for a single control variable, i.e., a distance or an angle, such that the control laws can be implemented in local coordinate frames. The global convergence is shown by analyzing the dynamics of the closed-loop system in its cascade form. Then we discuss some extensions on more general formation shapes and give the quadrilateral formation as an example. Simulation results are provided to validate the effectiveness of the proposed control strategies

Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning (DL) models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different for pediatrics across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to analyze the generalizability of deep adult lung segmentation models to the pediatric population and improve performance through a systematic combinatorial approach consisting of CXR modality-specific weight initializations, stacked generalization, and an ensemble of the stacked generalization models. Novel evaluation metrics consisting of Mean Lung Contour Distance and Average Hash Score are proposed in addition to the Multi-scale Structural Similarity Index Measure, Intersection of Union, and Dice metrics to evaluate segmentation performance. We observed a significant improvement (p < 0.05) in cross-domain generalization through our combinatorial approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.Comment: 11 pages, 7 figures, and 8 table

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pre-trained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.Comment: 40 pages, 8 tables, 7 figures, 3 supplementary figures and 4 supplementary table

Pontine infarction with pure motor hemiparesis or hemiplegia: A prospective study

BACKGROUND: The study aimed to prospectively observe the clinical and neuroimaging features of pontine infarction with pure motor hemiparesis (PMH) or hemiplegia at early stage. METHODS: In 118 consecutive selected patients with the first-ever ischemic stroke within 6 hours after onset, fifty of them presented with PMH or hemiplegia and had negative acute computed tomography (CT) scans, then magnetic resonance imaging (MRI) confirmed the corresponding infarcts in pons or cerebrum. The clinical and neuroimaging features of the pontine infarctions were compared with those of cerebral infarctions. RESULTS: The pontine infarction with PMH or hemiplegia accounted for 10.2% (12/118) of all first-ever ischemic stroke patients and 24% (12/50) of the patients with both PMH or hemiplegia and acute negative CT scans. Compared to the patients with cerebral infarction, the patients with pontine infarction had more frequency of diabetes mellitus (50.0% vs 5.3%, P = 0.001), nonvertiginous dizziness at onset (58.3% vs 21.1%, P = 0.036) and a progressive course (33.3% vs 2.6%, P = 0.011). CONCLUSION: The pontine infarction may present as PMH or hemiplegia with more frequency of nonvertiginous dizziness, a progressive course and diabetes mellitus. MRI can confirm the infarct location in the basal pons at early stage after stroke onset

A New Heat Transfer Correlation for Turbulent Flow of Air With Variable Properties in Noncircular Ducts

Turbulent flow and heat transfer of air with variable properties in a set of regular polygonal ducts and circular tube have been numerically simulated. All the ducts have the same hydraulic diameter as their characteristic lengths in the Reynolds number. The flow is modeled as three-dimensional (3D) and fully elliptic by using the finite volume method and the standard k-e turbulence model. The results showed that the relatively strong secondary flow could be observed with variable properties fluid. For the regular polygonal ducts, the local heat transfer coefficient along circumferential direction is not uniform; there is an appreciable reduction in the corner region and the smaller the angle of the corner region, the more appreciable deterioration the corner region causes. The use of hydraulic diameter for regular polygonal ducts leads to unacceptably large errors in turbulent heat transfer determined from the circular tube correlations. Based on the simulation results, a correction factor is proposed to predict turbulent heat transfer in regular polygonal ducts

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation, where radar echo maps were used to predict their consequent moment, so as to recognize potential severe convective weather events. However, these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation, due to the size limitation of convolution filter, lack of global feature, and less attention to features from previous states. To address the problems, this paper proposes a CEMA-LSTM recurrent unit, which is embedded with a Contextual Feature Correlation Enhancement Block (CEB) and a Multi-Attention Mechanism Block (MAB). The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction; the MAB uses a position and channel attention mechanism to capture global features of radar echoes. Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets. Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTM over recent models, e.g., PhyDNet, MIM and PredRNN++, etc. In particular, compared with the second-ranked model, its average POD, FAR and CSI have been improved by 3.87%, 1.65% and 1.79%, respectively on the FREM, and by 1.42%, 5.60% and 3.16%, respectively on the CIKM 2017