The assessment of leaf water content using leaf reflectance ratios in the visible,near‐, and short‐wave‐infrared

The common features of spectral reflectance from vegetation foliage upon leaf dehydration are decreasing water absorption troughs in the near‐infrared (NIR) and short‐wave‐infrared (SWIR). We studied which leaf water index in the NIR and SWIR is most suitable for the assessment of leaf water content and the detection of leaf dehydration from the laboratory standpoint. We also examined the influence of the thickness of leaves upon leaf water indices. All leaf water content indices examined exhibited basic correlations with the relative water content (RWC) of leaves, while the R ₁₃₀₀/R ₁₄₅₀ leaf water index also demonstrated a high signal strength and low variability (R ²>0.94). All examined leaf reflectance ratios could also be correlated with leaf thickness. The thickness of leaves, however, was not independent of leaf RWC but appeared to decrease substantially as a result of leaf dehydration.     

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.     

Estimating leaf carotenoid contents of shade-grown tea using hyperspectral indices and PROSPECT–D inversion

Quantifying carotenoid contents has many applications in agriculture, ecology, and health science. Hyperspectral reflectance has been one of the promising tools for this purpose. However, previous studies were based on measurements under relatively low light–stress conditions. Therefore, assessing its robustness by using measurements under various levels of stress is required. In this study, the measurements of reflectance and carotenoid contents were carried out with four shading treatments including open–0%, 35%, 75%, and 90% shading to generate various chlorophyll/carotenoid ratios. Then the performances of 15 published hyperspectral indices and PROSPECT–D inversion were evaluated based on our data set for estimating leaf carotenoid contents. According to the ratio of performance to deviation, R_NIR/R₅₁₀, R₇₂₀/R₅₂₁–1, and PROSPECT–D inversion were applicable for this purpose, although calibration of the absorption coefficients was required for PROSPECT–D. Using them, root mean square percentage errors of 4.53–5.46% were achieved. Given that total chlorophyll/carotenoid ratios could be a good indicator for evaluating environmental stress in plants, PROSPECT–D, which also estimates total chlorophyll and anthocyanin contents, could be a strong tool for controlling the qualities of shade-grown tea.     

Testing the capabilities of the new WorldView-3 space-borne sensor’s red-edge spectral band in discriminating and mapping complex grassland management treatments

The majority of grasslands are overused and poorly managed, globally. The overuse of these grasslands has resulted in the adoption of numerous management treatments as interventions for optimizing their productivity. However, there are limited comprehensive frameworks and objective precedents for monitoring these grasslands and rangelands. In that regard, understanding the effect of such rangeland management treatments on grassland productivity is, therefore, a critical step towards their effective conservation and sustainable management. This study sought to test the capabilities of the WorldView-3 (WV-3) satellite data derivatives in characterizing grasslands administered with different rangeland management treatments (i.e. mowing, grazing, burning, fertilizer application, and control: no-treatment), using discriminant analysis. We compared the accuracies obtained based on WV-3 standard visible and near-infrared bands and vegetation indices (VIs), excluding and including the red-edge. Results illustrate that incorporating the strategically positioned red-edge band improves the classification accuracy of the four different rangeland management treatments from 65% to 70%. Furthermore, the overall accuracy was 73% when standard VIs were used and it increased to 78% when the red-edge VIs were added to standard VIs. Other than the red-edge derivatives, the results of this study showed that the yellow, red, NIR-1, and NIR-2 bands were the most influential. The utility of fine spatial resolution sensors such as the newly launched WV-3, with strategically positioned bands (red-edge), could offer detailed information essential for the sustainable management of grasslands.     

Image indexing and content analysis in children’s picture books using a large-scale database

In this paper we introduce a visual database for children’s picture book and we also present an intelligent robot trained on this database. Firstly, a large-scale image dataset is built that contains image samples of book pages. It can be used to verify image indexing algorithms and content recognition algorithms. Secondly, we study the state-of-the-art algorithms in image matching and object recognition. Several approaches are presented and compared from the aspects of computational efficiency and recognition accuracy. In order to improve the speed we proposed a novel hierarchical algorithm for fast search. Finally, using this large-scale database we are able to build a robot that can read children’s picture books and initial experimental results are presented. We can see that both the training database and the algorithms are promising, yet there are still a few open challenges concerning the costs and robustness.

     

Modeling of a ship’s entry into water using the boundary element method

The mathematical model and algorithm of a numerical analysis of the dynamic entry of a body falling into incompressible fluid is considered. The boundary element method is used. The ways of reducing computational costs for the repeated solution of a boundary value problem for many instants on the basis of coordinate transformations are presented.     

Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

This paper analyzes the application of Moran’s index and Geary’s coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.

Impact of digital content on young children’s reading interest and concentration for books

This study investigates the influence of digital content on mobile devices including flash animations, app books, and augmented reality (AR) books on children's reading interest and reading concentration for books. The factors responsible for the aforementioned influence were also investigated. The results from this research show that flash animation can significantly increase the interest in reading and concentration. AR books and app books led children to become more interested in storybooks but significantly decreased their reading concentration. Following a comparison of the digital content, it was concluded that all the tested prototypes could positively increase the reading interest of children in the same-storyline books. However, prototypes with a high level of interactivity could have a negative influence on children’s reading concentration. The research findings are important in determining how to design digital content for children.     

Identification of material parameters for McGinty’s model using adaptive RBFs and optimization

In this paper, an optimization approach is adopted to obtain the 12 material parameters used in McGinty’s Model for AL6022 by minimizing the differences between simulation and experimental stress–strain curves. Since the differences between the two stress–strain curves are implicitly related to the change of material parameters, the metamodeling technique is utilized to create explicit, approximate functions of these relationships. Radial basis functions (RBFs), which are shown from previous studies to be effective for both low- and high-order nonlinear responses, are used for the metamodels that are adaptively updated in the optimization work. Two optimization formulation schemes are studied to address the issue of using inaccurate RBF models in optimization. The sampling, metamodeling, and optimization works are performed using the integrated optimization framework HiPPO.     

Optimization of extrusion blow molding processes using soft computing and Taguchi’s method

The objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural–Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchis experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.     

Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0-13.5 μm) imagery

High spatial and spectral resolution thermal infrared imagery (8.0-13.5 μm) from the SEBASS airborne sensor was used to analyze and map tree canopy spectral features at the State Arboretum of Virginia, near Boyce, Virginia. Fifty tree species were analyzed and about half were directly identified with varying degrees of success on the basis of spectral matched filtering that utilized laboratory-measured leaf spectra as the target signatures. Spectral averages of pixels extracted from SEBASS emissivity data compared favorably with laboratory spectra of leaves collected from individual tree species. Best results were obtained from species having relatively strong spectral contrast, wide and flat leaves, closed planophile canopies, and/or large canopy areas. Tree species having small leaves or unfavorable leaf orientations showed spectral attenuation likely resulting from cavity blackbody effects. Increased spatial resolution and better image calibration and atmospheric correction might lead to further improvements in thermal infrared plant species identification.     

Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network

Multimedia Tools and Applications - Alzheimer’s disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the...     

Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Since approximately 90% of the people with PD (Parkinson’s disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson’s disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson’s disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy.     

Strategic Assessment of Lisbon’s Accessibility and Mobility Problems from an Equity Perspective

Accessibility planning with reference to sustainability and equity principles has been advocated as the best approach to deal with the urban mobility complexity. It has enabled the development of more sustainable and fair policies in relation to access provision. However, despite this paradigm shift, many planning initiatives in practice are still focused on assessing alternatives and proposing solutions, instead of centering on the understanding and assessment of problems as the primary activity of planning. Therefore, in order to contribute for the problem-oriented paradigm in the accessibility planning, this work proposes a strategic assessment methodology for unequal and inequitable distribution problems of accessibility and mobility. This methodology relies on spatial analysis techniques and allows the characterization of accessibility and mobility conditions, as well as the diagnosis of accessibility and mobility problems and their causal relationships. It was applied for the case of Lisbon and the results of the performed assessment allowed an intelligent reading of the problems considered. Specifically, it was found that Lisbon presents an unequal and inequitable distribution of job accessibility and mobility by private car and public transport, and also that job accessibility, along with other transportation, land-use and socioeconomic variables, impact the mobility levels of its citizens.     

Investigation of the relationship between Ironworker’s gait stability and different types of load carrying using wearable sensors

Load carrying can cause changes in gait stability. Maintaining gait stability (body balance) on a steel frame is an important factor in the prevention of accidents. However, the effects of load carrying on a restricted surface (width), where ironworkers usually perform their jobs, have rarely been investigated. In this context, this study investigates the effect of different types of load-carrying scenarios on the gait stability of ironworkers. Accordingly, to identify the relationships between the weight of load, type of load-carrying method, type of load-carrying tools, and gait stability, represented as the dynamic time warping (DTW) values, an experiment was conducted under 15 different load-carrying conditions. The change in DTW according to the different load conditions was as follows: For one-handed load-carrying using a toolbox, the average DTW values were 23.31 at a weight of 3 kg, 23.80 at 6 kg, 33.20 at 10 kg, 65.84 at 16 kg, and 103.50 at 20 kg. Moreover, for one-handed load-carrying using a bucket, the average DTW values were 23.07 at 3 kg, 24.76 at 6 kg, 34.35 at 10 kg, 77.52 at 16 kg, and 108.28 at 20 kg. Finally, in the case of both-handed load carrying, the average DTW values were 24.31 at 6 kg, 28.11 at 10 kg, 27.23 at 16 kg, 37.90 at 20 kg, and 40.96 at 32 kg. As the weight of the load increased, the DTW values in almost all the experimental cases also increased. Furthermore, the DTW values for bucket carrying and tool box carrying showed clear differences above a certain weight (16 kg). Thus, the results of the experiments indicate that different types of load-carrying conditions significantly affect the ironworkers’ gait stability as they walk on a steel frame. Finally, the calculated DTW values were compared with the subjective perceived ratings to validate the experimental results. The DTW value was highly correlated with the subjective perceived rating scores (Pearson coefficient: 0.929). The results of this study will facilitate the use of gait stability measurements to enhance the construction safety of ironworkers.     

Laminate optimization of blended composite structures using a modified Shepard’s method and stacking sequence tables

This article presents an optimization tool for the stacking sequence design of blended composite structures. Enforcing blending ensures the manufacturability of the optimized laminate. A novel optimization strategy is proposed combining a genetic algorithm (GA) for stacking sequence tables with a multi-point structural approximation using a modified Shepard’s interpolation in stiffness-space. A successive approximation approach is used where the set of design points used to create the structural approximations is successively enriched using the elite of the previous step. Additional improvement in the generality and efficiency of the algorithm is obtained by using load approximations thus enabling the implementation of a wide range of stress-based design criteria. A multi-panel, blended composite problem is used as an application to demonstrate the performance of the developed tool. The optimization is performed with mass as the objective to be minimized, subjected to strength and buckling constraints. The results presented show that completely blended and feasible stacking sequence designs can be obtained, having their structural performance close to the theoretical continuous optimum itself. Additionally, the multi-point Shepard’s approximation shows a considerable saving in computational costs, while the limitations of inexpensive stiffness-matching optimizations are observed.     

Fast computation of 3D Meixner’s invariant moments using 3D image cuboid representation for 3D image classification

Multimedia Tools and Applications - In this paper, we propose a new fast computation method of 3D discrete orthogonal invariant moments of Meixner. This proposed method is based on two fundamental...