Structure overview of vegetation detection. A novel approach for efficient vegetation detection using an active lighting system

Fully autonomous navigation has been widely investigated for several decade of years; however, a safe and reliable navigation is still a daunting challenge in terrains containing vegetation. To improve the mobility capability of recent autonomous navigation systems, an additional vegetation detection function has been proposed. Since many proposals of generating vegetation classifier as well as suggestions of using different sensors to implement the function exist, a structured overview is required for vegetation detection in the context of outdoor navigation. Therefore, this paper studies and compares the accuracy and efficiency of existing vegetation detection approaches in a structured way. Furthermore, a new vision system set-up which combines CMOS sensor and Photo Mixer Device sensor with a near-infrared lighting system is also introduced to simultaneously provide depth, near-infrared and color images at high frame rate. Those near-infrared and color information are then used to compute vegetation index or train vegetation classifier to completely realize a real-time robust vegetation detection system. In this paper, a modification of the normalized difference vegetation index is devised, which is then defined as the new standard form of vegetation index for such vision system integrated with an additional lighting system. Finally, we will show the out-performance of the proposed approach in comparison with more conventional ones.     

Avoiding unnecessary information loss: correct and efficient model synchronization based on triple graph grammars

Model synchronization, i.e., the task of restoring consistency between two interrelated models after a model change, is a challenging task. Triple graph grammars (TGGs) specify model consistency by means of rules that describe how to create consistent pairs of models. These rules can be used to automatically derive further rules, which describe how to propagate changes from one model to the other or how to change one model in such a way that propagation is guaranteed to be possible. Restricting model synchronization to these derived rules, however, may lead to unnecessary deletion and recreation of model elements during change propagation. This is inefficient and may cause unnecessary information loss, i.e., when deleted elements contain information that is not represented in the second model, this information cannot be recovered easily. Short-cut rules have recently been developed to avoid unnecessary information loss by reusing existing model elements. In this paper, we show how to automatically derive (short-cut) repair rules from short-cut rules to propagate changes such that information loss is avoided and model synchronization is accelerated. The key ingredients of our rule-based model synchronization process are these repair rules and an incremental pattern matcher informing about suitable applications of them. We prove the termination and the correctness of this synchronization process and discuss its completeness. As a proof of concept, we have implemented this synchronization process in eMoflon, a state-of-the-art model transformation tool with inherent support of bidirectionality. Our evaluation shows that repair processes based on (short-cut) repair rules have considerably decreased information loss and improved performance compared to former model synchronization processes based on TGGs.

     

Enhanced Electrochemical CO2 Reduction of Cu@CuxO Nanoparticles Decorated on 3D Vertical Graphene with Intrinsic sp3‐type Defect

Defective 3D vertical graphene (VG) with a relatively large surface area, high defect density, and increased surface electrons is synthesized via a scalable plasma enhanced chemical vapor deposition method, together with a postsynthesis Ar‐plasma treatment (VG‐Ar). Subsequently, Cu@Cu_xO nanoparticles are deposited onto VG‐Ar (Cu/VG‐Ar) through a galvanostatic pulsed electrodeposition method. These Cu@Cu_xO nanocatalyst systems exhibit a superior electrochemical CO₂ reduction performance when compared to Cu‐based catalysts supported on commercial graphene paper or pristine VG without postsynthesis Ar‐plasma treatment. The Cu/VG‐Ar achieves the highest CO₂ reduction Faradaic efficiency of 60.6% (83.5% of which are attributed to liquid products, i.e., formate, ethanol, and n‐propanol) with a 5.6 mA cm^?2 partial current density at ?1.2 V versus reversible hydrogen electrode (RHE). The improved CO₂ reduction performance of Cu/VG‐Ar originates from the well‐dispersed Cu@Cu_xO nanoparticles deposited on the defective VG‐Ar. The intrinsic carbon defects on VG‐Ar can suppress the hydrogen evolution reaction as well as tune the interaction between VG and Cu@Cu_xO, thus impeding the excessive oxidation of Cu₂O species deposited on VG‐Ar. The defective VG‐Ar and stabilized Cu@Cu_xO enhances CO₂ adsorption and promotes electron transfer to the adsorbed CO₂ and intermediates on the catalyst surface, thus improving the overall CO₂ reduction performance.     

The category-theoretic solution of recursive metric-space equations

It is well known that one can use an adaptation of the inverse-limit construction to solve recursive equations in the category of complete ultrametric spaces. We show that this construction generalizes to a large class of categories with metric-space structure on each set of morphisms: the exact nature of the objects is less important. In particular, the construction immediately applies to categories where the objects are ultrametric spaces with ‘extra structure’, and where the morphisms preserve this extra structure. The generalization is inspired by classical domain-theoretic work by Smyth and Plotkin.For many of the categories we consider, there is a natural subcategory in which each set of morphisms is required to be a compact metric space. Our setting allows for a proof that such a subcategory always inherits solutions of recursive equations from the full category.As another application, we present a construction that relates solutions of generalized domain equations in the sense of Smyth and Plotkin to solutions of equations in our class of categories.Our primary motivation for solving generalized recursive metric-space equations comes from recent and ongoing work on Kripke-style models in which the sets of worlds must be recursively defined. We show a series of examples motivated by this line of work.     

Estimating northern peatland CO2 exchange from MODIS time series data

Studies using satellite sensor-derived data as input to models for CO₂ exchange show promising results for closed forest stands. There is a need for extending this approach to other land cover types, in order to carry out large-scale monitoring of CO₂ exchange. In this study, three years of eddy covariance data from two peatlands in Sweden were averaged for 16-day composite periods and related to data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled photosynthetic photon flux density (PPFD). Noise in the time series of MODIS 250 m vegetation indices was reduced by using double logistic curve fits. Smoothed normalized difference vegetation index (NDVI) showed saturation during summertime, and the enhanced vegetation index (EVI) generally gave better results in explaining gross primary productivity (GPP). The strong linear relationships found between GPP and the product of EVI and modeled PPFD (R² = 0.85 and 0.76) were only slightly stronger than for the product of EVI and MODIS daytime 1 km land surface temperature (LST) (R² = 0.84 and 0.71). One probable reason for these results is that several controls on GPP were related to both modeled PPFD and daytime LST. Since ecosystem respiration (ER) was largely explained by diurnal LST in exponential relationships (R² = 0.89 and 0.83), net ecosystem exchange (NEE) was directly related to diurnal LST in combination with the product of EVI and modeled PPFD in multiple exponential regressions (R² = 0.81 and 0.73). Even though the R² values were somewhat weaker for NEE, compared to GPP and ER, the RMSE values were much lower than if NEE would have been estimated as the sum of GPP and ER. The overall conclusion of this study is that regression models driven by satellite sensor-derived data and modeled PPFD can be used to estimate CO₂ fluxes in peatlands.     

Heuristics for dynamic and stochastic routing in industrial shipping

Maritime transportation plays a central role in international trade, being responsible for the majority of long-distance shipments in terms of volume. One of the key aspects in the planning of maritime transportation systems is the routing of ships. While static and deterministic vehicle routing problems have been extensively studied in the last decades and can now be solved effectively with metaheuristics, many industrial applications are both dynamic and stochastic. In this spirit, this paper addresses a dynamic and stochastic maritime transportation problem arising in industrial shipping. Three heuristics adapted to this problem are considered and their performance in minimizing transportation costs is assessed. Extensive computational experiments show that the use of stochastic information within the proposed solution methods yields average cost savings of 2.5% on a set of realistic test instances.     

Influence of solar zenith angles on observed trends in the NOAA/NASA 8‐km Pathfinder normalized difference vegetation index over the African Sahel

The strong systematic change in solar zenith angles (SZA) due to annual orbital drift of the NOAA satellites has raised the suspicion of the influence of residual illumination on the calibrated normalized difference vegetation index (NDVI) derived from the Pathfinder AVHRR Land (PAL) database. The aim of this work is to analyse if trends in AVHRR NDVI from 1982 to 2000 over the Sahel region in Africa depend on variations in SZA.

The analysis uses both ordinary least squares regression and cointegration to analyse possible linear dependencies between NDVI and SZA on a per satellite basis. Tests for integration and cointegration fail to find any significant evidence for either. This, together with the ability of simple deterministic models to explain primarily SZA constitutes evidence against integration and cointegration, indicating that linear relationships can be examined using ordinary linear regression. Regression gives no consistent relationship between NDVI and SZA and the explanatory power (R ²) of the regression is low (on average 0.08).

However there is some evidence for downward bias in NDVI due to nonlinear interactions between NDVI and SZA when SZA is large (80°) leading to the conclusion that PAL data from the year 2000 should not be used for analyses in these environments.     

Applicability of leaf area index products for boreal regions of Sweden

Leaf area index (LAI) of boreal ecosystems was estimated with optical instruments at the Laxemar and the Forsmark investigation areas in Sweden. The aim was to study relationships between LAI and normalized difference vegetation index (NDVI), and to evaluate the applicability of the moderate resolution imaging spectroradiometer (MODIS) LAI product for small boreal regions. Relationships between optically-estimated LAI and NDVI were significant for different forest types in Laxemar and for Forsmark, effective LAI was correlated to the NDVI for all sites. NDVI-estimated LAI was used for evaluating accuracy of the MODIS LAI product and the comparison showed no correlation in Forsmark, whereas they were correlated in Laxemar. MODIS LAI was, on average, 2.28 higher than NDVI-based LAI, and it showed larger scatter. Scale issues were the main explanation for the high MODIS LAI, since heterogeneous landscapes with open areas were seen as forest in the large pixels of the MODIS LAI product.     

Variance inflation in high dimensional Support Vector Machines

Many important machine learning models, supervised and unsupervised, are based on simple Euclidean distance or orthogonal projection in a high dimensional feature space. When estimating such models from small training sets we face the problem that the span of the training data set input vectors is not the full input space. Hence, when applying the model to future data the model is effectively blind to the missed orthogonal subspace. This can lead to an inflated variance of hidden variables estimated in the training set and when the model is applied to test data we may find that the hidden variables follow a different probability law with less variance. While the problem and basic means to reconstruct and deflate are well understood in unsupervised learning, the case of supervised learning is less well understood. We here investigate the effect of variance inflation in supervised learning including the case of Support Vector Machines (SVMS) and we propose a non-parametric scheme to restore proper generalizability. We illustrate the algorithm and its ability to restore performance on a wide range of benchmark data sets.     

Monitoring of Probabilistic Timed Property Sequence Charts

Run‐time monitoring is an important technique to detect erroneous run‐time behaviors. Several techniques have been proposed to automatically generate monitors from specification languages to check temporal and real‐time properties. However, monitoring of probabilistic properties still requires manual generation. To overcome this problem, we define a formal property specification language called Probabilistic Timed Property Sequence Chart (PTPSC). PTPSC is a probabilistic and timed extension of the existing scenario‐based specification formalism Property Sequence Chart (PSC). We have defined a formal grammar‐based syntax and have implemented a syntax‐directed translator that can automatically generate a probabilistic monitor which combines timed B”uchi automata and a sequential statistical hypothesis test process. We validate the generated monitors with a set of experiments performed with our tool WS‐PSC Monitor. Copyright © 2011 John Wiley & Sons, Ltd.