River basin restoration and management is crucial for assuring the continued delivery of ecosystem services and for limiting potential hazards. Human activity, whether directly or indirectly, can induce erosion processes and drastically change the landscape and alter vital ecological functions. Mapping erosion risk before future restoration-management projects will help to reveal the priority areas and develop a hierarchy ordered according to need. For this purpose, we used the Revised Universal Soil Loss Equation (RUSLE) erosion model. We also applied a novel technique called GPVI (Genetic Programming Vegetation Index) in the Martín River basin in NE Spain (2112 Km 2), which has a large coalfield located in the southern part of the basin. Approximately two-thirds (69%) of the area of the Martín basin presents low and medium soil loss rates, and one-third (31%) of the area presents high (18%), very high (10%) and irreversible (3%) erosion rates. The southern part of the basin is the most degraded and is strongly influenced by the topography. This work allows us to locate areas prone to erosional degradation processes to help create a buffer around the river and locate "spots" in need of restoration. We also checked the error estimation of the methodology because our soil maps do not include rock and bare rock areas. The usefulness of applying RUSLE for predicting degraded areas and the consequent directing of soil conservation-restoration actions at the basin scale is demonstrated. We highly recommend a field survey of the selected areas to prove the goodness of the model estimations.

This work describes a genetic programming (GP) approach that creates vegetation indices (VI's) to automatically detect the sum of healthy, dry, and dead vegetation. Nowadays, it is acknowledged that VI's are the most popular method for extracting vegetation information from satellite imagery. In particular, erosion models like the "Revised Universal Soil Loss Equation" (RUSLE) can use VI's as input to measure the effects of the RUSLE soil cover factor (C). However, the results are generally incomplete, because most indices recognize only healthy vegetation. The aim of this study is to devise a novel approach for designing new VI's that are bettercorrelated with C, using field and satellite information. Our approach consists on stating the problem in terms of optimization through GP learning, building novel indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of our approach in contrast with traditional indices like those of the NDVI and SAVI family. This study provides evidence that similar problems related to soil erosion assessment could be analyzed with our proposed methodology.

Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.

Abstract. This paper investigates the communication system of honeybees with the purpose of obtaining an intelligent approach for threedimensional reconstruction. A new framework is proposed in which the 3D points communicate between them to achieve an improved sparse reconstruction which could be used reliable in further visual computing tasks. The general ideas that explain the honeybee behavior are translated into a computational algorithm following the evolutionary computing paradigm. Experiments demonstrate the importance of the proposed communication system to reduce dramatically the number of outliers.

No abstract

This paper introduces a novel analogy with the way in which honeybee colonies operate in order to solve the problem of sparse and quasi dense reconstruction. To successfully solve increasingly complex problems, we must develop effective techniques for evolving cooperative solutions in the form of interacting coadapted subcomponents. A new adaptive behavior strategy is presented based on the "divide and conquer" approach used by the honeybee colony to solve search problems. The general ideas that explain the honeybee behavior are translated into a computational algorithm following the evolutionary computing paradigm. Experiments demonstrate the importance of the proposed communication system to reduce dramatically the number of outliers.

No abstract

Several studies have been conducted on alternatives to fossil fuels in internal combustion engines. In this work, we present an analysis of the optimization of a spark ignition engine (SIE), which operates with a mix of gasoline and hydrogen. Thus, this study tries to enrich the literature about the effect of using hydrogen in SIEs taking into account their performance and environmental issues. The experiments were conducted considering three independent variables: revolutions, the load produced by a dynamometer, and an electrolyte concentration (EC) to produce hydrogen. The effect of these factors was investigated on three response variables that are related to the performance of the engine: torque, hydrocarbon emissions (HC), and power. To achieve this optimization, we employed the design of experiments, the seemingly unrelated regression (SUR), and the desirability function. Once the models were fitted by SUR, the individual desirabilities were calculated and later aggregated into an overall desirability (D), which was optimized using the generalized reduced gradient (GRG) method. The results showed that, with a revolution of 2400 revolutions per minute (RPM), a load of 10 liters per minute (LPM), and an EC of 80 mL/gal, a reduction of approximately 51% of HC emissions was achieved, while the other two response variables demonstrated good performance.