Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

Surrogate-based optimization of a periodic rescheduling algorithm

Periodic rescheduling is an iterative method for real-time decision-making on industrial process operations. The design of such methods involves high-level when-to-schedule and how-to-schedule decisions, the optimal choices of which depend on the operating environment. The evaluation of the choices typically requires computationally costly simulation of the process, which—if not sufficiently efficient—may result in a failure to deploy the system in practice. We propose the continuous control parameter choices, such as the re-optimization frequency and horizon length, to be determined using surrogate-based optimization. We demonstrate the method on real-time rebalancing of a bike sharing system. Our results on three test cases indicate that the method is useful in reducing the computational cost of optimizing an online algorithm in comparison to the full factorial sampling.     

Toward Xeno-Free Differentiation of Human Induced Pluripotent Stem Cell-Derived Small Intestinal Epithelial Cells

The small intestinal epithelium has an important role in nutrition, but also in drug absorption and metabolism. There are a few two-dimensional (2D) patient-derived induced pluripotent stem cell (iPSC)-based intestinal models enabling easy evaluation of transcellular transport. It is known that animal-derived components induce variation in the experimental outcomes. Therefore, we aimed to refine the differentiation protocol by using animal-free components. More specifically, we compared maturation of 2D-cultured iPCSs toward small intestinal epithelial cells when cultured either with or without serum, and either on Geltrex or on animal-free, recombinant laminin-based substrata. Differentiation status was characterized by qPCR, immunofluorescence imaging, and functionality assays. Our data suggest that differentiation toward definitive endoderm is more efficient without serum. Both collagen- and recombinant laminin-based coating supported differentiation of definitive endoderm, posterior definitive endoderm, and small intestinal epithelial cells from iPS-cells equally well. Small intestinal epithelial cells differentiated on recombinant laminin exhibited slightly more enterocyte specific cellular functionality than cells differentiated on Geltrex. Our data suggest that functional small intestinal epithelial cells can be generated from iPSCs in serum-free method on xeno-free substrata. This method is easily converted to an entirely xeno-free method.     

Self-assembled polymeric solid films with temperature-induced large and reversible photonic-bandgap switching

In aqueous solutions the response of polymers and biological matter to external conditions, such as temperature and pH, is typically based on the hydrophobic/hydrophilic balance and its effects on the polymer conformation. In the solid state, related concepts using competing interactions could allow novel functions. In this work we demonstrate that polymeric self-assembly, reversibility of hydrogen bonding, and polymer-additive phase behaviour allow temperature response in the solid state with large and reversible switching of an optical bandgap. A complex of polystyrene-block-poly(4-vinylpyridinium methanesulphonate) and 3-n-pentadecylphenol leads to the supramolecular comb-shaped architecture with a particularly long lamellar period. The sample is green at room temperature, as an incomplete photonic bandgap due to a dielectric reflector is formed. On heating, hydrogen bonds are broken and 3-n-pentadecylphenol additionally becomes soluble in polystyrene, leading to a sharp and reversible transition at approximately 125 degrees C to uncoloured material due to collapse of the long period. This encourages further developments, for example, for functional coatings or sensors in the solid state.     

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.     

Personalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis

In this paper we present results of unsupervised cross-lingual speaker adaptation applied to text-to-speech synthesis. The application of our research is the personalisation of speech-to-speech translation in which we employ a HMM statistical framework for both speech recognition and synthesis. This framework provides a logical mechanism to adapt synthesised speech output to the voice of the user by way of speech recognition. In this work we present results of several different unsupervised and cross-lingual adaptation approaches as well as an end-to-end speaker adaptive speech-to-speech translation system. Our experiments show that we can successfully apply speaker adaptation in both unsupervised and cross-lingual scenarios and our proposed algorithms seem to generalise well for several language pairs. We also discuss important future directions including the need for better evaluation metrics.