Search

A possible source of natural reinforcement for bio-composites can be represented by lemongrass plant (Cymbopogon flexuosus), a clumped and perennial grass which belongs to the Poaceae family. This plant is extensively used for several applications such as pharmacology, food preservation and cosmetics but, to the best of our knowledge, few papers were published on its use as source for reinforcement of composites and no one article was focused on the comparison between lemongrass leaves and culms as potential source of natural reinforcement. To this aim, a preliminary investigation on leaf and culm fibers was carried out to compare their physical and chemical features as well as their tensile properties. Furthermore, bio-composites based on a biodegradable starch-derived matrix (MaterBi®) and lemongrass leaf and culm particles were manufactured via extrusion and compression molding. For both fillers, two compositions (i.e., 10% and 20 wt.%) were investigated in terms of morphological and mechanical properties.

In this paper we propose a new effective remote sensing tool combining hardware and software solutions as an extension of our previous work. In greater detail the tool consists of a low cost receiver subsystem for public weather satellites and a signal and image processing module for several tasks such as signal and image enhancement, image reconstruction and cloud detection. Our solution allows to manage data from satellites effectively with low cost components and portable software solutions. We aim at sampling and processing of the modulated signal entirely in software enabled by Software Defined Radios (SDR) and CPU computational speed overcoming hardware limitation such as high receiver noise and low ADC resolution. Since we want to extend our previous method to demodulate signals coming from various meteorological satellites, we propose a new high frequency receiving system designed to receive and demodulate signals transmitted at 1.7 GHz. The signals coming from satellites are demodulated, synchronized and enhanced by using low level image processing techniques, then cloud detection is performed by using the well known K-means clustering algorithm. The hardware and software architecture extensions make our solution able to receive and demodulate high frequency and bandwidth meteorological satellite signals, such as those transmitted by NOAA POES, NOAA GOES, EUMETSAT Metop, Meteor-M and FengYun.

Aim: PET imaging is increasingly utilized for radiation treatment planning. Nevertheless, accurate segmentation of PET images is a complex and unresolved problem. Aim of this work is the development of an automatic segmentation method of Biological Target Volume (BTV) in patients with cervical cancer. Materials and methods: Random walks (RW) is a graph-based method that represents a DICOM (Digital Imaging and COmmunications in Medicine) image as a graph. The voxels are its nodes and the edges are defined by a cost function which maps a change in image intensity to edge weights. Then, RW partitions the nodes into target and background subsets. To create an automatic method starting from previous work (A. Stefano, et al. A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study, in A. Petrosino, ed., Image Analysis and Processing - ICIAP 2013: LNCS, v. 8157, Springer Berlin Heidelberg, p. 711-720), we propose an automated RW seed localization approach. The algorithm identifies the PET slice with the highest SUVmax and a maximum of 10 target and 8 background seeds for each volume slice. The voxels with a SUV>95% of SUVmax are marked as target seeds. Then, the method explores the hottest voxel neighborhood through searching in 8 directions to identify the background voxels with a SUV

In this paper intertemporal decision making is analysed from a framework that defines the differences in value for decision options at present and future time points in terms of the extra amount of mental burden or work load that is expected to occur when a future option is chosen. It is shown how existing hyperbolic and exponential discounting models for intertemporal decision making both fit in this more general framework. Furthermore, a specific computational model for work load is adopted to derive a new discounting model. It is analysed how this intertemporal decision model relates to the existing hyperbolic and exponential intertemporal decision models. Finally, it is shown how this model relates to a transformation between subjective and objective time experience.

Social decision making under stressful circumstances may involve strong emotions and contagion from others, and requires adequate prediction and valuation capabilities. In this paper based on principles from Neuroscience an adaptive agent-based computational model is proposed to address these aspects in an integrative manner. Using this model adaptive decision making of an agent in an emergency evacuation scenario is explored. By simulation computational learning mechanisms are identified required for effective decision making of agents.

The paper focuses on the main advantages in the defnition and utilization of an open and modular e-learning software platform to support highly cognitive tasks performed by the main actors of the learning process. We present in detail the integration inside the platform of two intelligent agents devoted to talking with the student and to retrieving new information sources on the Web. The process is triggered as a reply to the system’s perception that the student feels discontented with the presented contents. The architecture is detailed, and some conclusions about the growth of the platform’s overall performance are expressed., Journal of e-Learning and Knowledge Society, Vol 6, No 2 (2010): Journal of e-Learning and Knowledge Society - Focus on: Artificial Intelligence and e-Learning

An algorithm is presented in this paper to calculate a semantic similarity measure inside an OWL ontology. The formulation is based on a combined measure taking into account the two most important aspects involved in the similarity computation. These are the structural properties of a concept, and the information content inside the ontology. We define a fuzzy system to blend these information sources with a training process over some ontologies. Finding a similarity measure between concepts of an ontology is a fundamental topic to accomplish information exchange on the Web. Through this measure it is possible to perform sophisticated queries over the web where the user is able to request concepts with a predefined similarity (or even dissimilarity) degree.

This paper describes the DynaLearn project, which seeks to address contemporary problems in science education by integrating well established, but currently independent technological developments, and utilize the added value that emerges. Specifically, diagrammatic representations are used for learners to articulate, analyse and communicate ideas, and thereby construct their conceptual knowledge. Ontology mapping is used to find and match co-learners working on similar ideas to provide individualised and mutually benefiting learning opportunities. Virtual characters are used to make the interaction engaging and motivating. The development of the workbench is tuned to fit key topics from environmental science curricula, and evaluated and further improved in the context of existing curricula using case studies. Through this approach, the DynaLearn project will deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge that fits the true nature of this expertise.

Facial animation denotes all those systems performing speech synchronization with an animated face model. These kinds of systems are named Talking Heads or Talking Faces. At the same time simple dialogue systems called chatbots have been developed. Chatbots are software agents able to interact with users through pattern-matching based rules. In this paper a Talking Head oriented to the creation of a Chatbot is presented. An answer is generated in form of text triggered by an input query. The answer is converted into a facial animation using a 3D face model whose lips movements are synchronized with the sound produced by a speech synthesis module. Our Talking Head exploits the naturalness of the facial animation and provides a real-time interactive interface to the user. Besides, it is specifically suited for being used on the web. This leads to a set of requirements to be satisfied, like: simple installation, visual quality, fast download, and interactivity in real time. The web infrastructure has been realized using the ClientServer model. The Chatbot, the Natural Language Processing and the Digital Signal Processing services are delegated to the server. The client is involved in animation and synchronization. This way, the server can handle multiple requests from clients. The conversation module has been implemented using the A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) technology. The output of the chatbot is given input to the Natural Language Processing (Comedia Speech), incorporating a text analyzer, a letter-to-sound module and a module for the generation of prosody. The client, through the synchronization module, computes the time of real duration of the animation and the duration of each phoneme and consequently of each viseme. The morphing module performs the animation of the facial model and the voice reproduction. As a result, the user will see the answer to question both in textual form and in the form of visual animation.