Particle positioning techniques for dusty plasma experiments

Video microscopy is a widely applied diagnostic to investigate the structure and the dynamics of particles in dusty plasmas. Reliable algorithms are required to accurately recover particle positions from the camera images. Here, four different particle positioning techniques have been tested on artificial and experimental data of dusty plasma situations. Two methods that rely on pixel-intensity thresholds were found to be strongly affected by pixel-locking errors and by noise. Two other methods-one applying spatial bandpass filters and the other fitting polynomials to the intensity pattern-yield subpixel resolution under various conditions. These two methods have been shown to be ideally suited to recover particle positions even from small-scale fluctuations that are used to derive the normal mode spectra of finite dust clusters.     

Audio indexing: primary components retrieval

This work addresses the soundtrack indexing of multimedia documents. Our purpose is to detect and locate sound unity to structure the audio dataflow in program broadcasts (reports). We present two audio classification tools that we have developed. The first one, a speech music classification tool, is based on three original features: entropy modulation, stationary segment duration (with a Forward–Backward Divergence algorithm) and number of segments. They are merged with the classical 4 Hz modulation energy. It is divided into two classifications (speech/non-speech and music/non-music) and provides more than 90% of accuracy for speech detection and 89% for music detection. The other system, a jingle identification tool, uses an Euclidean distance in the spectral domain to index the audio data flow. Results show that is efficient: among 132 jingles to recognize, we have detected 130. Systems are tested on TV and radio corpora (more than 10 h). They are simple, robust and can be improved on every corpus without training or adaptation.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.     

Dense versus Sparse Approaches for Estimating the Fundamental Matrix

There are two main strategies for solving correspondence problems in computer vision: sparse local feature based approaches and dense global energy based methods. While sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimised interest points, dense energy based methods mark the state of the art in optical flow computation. The goal of our paper is to show that this separation into different application domains is unnecessary and can be bridged in a natural way. As a first contribution we present a new application of dense optical flow for estimating the fundamental matrix. Comparing our results with those obtained by feature based techniques we identify cases in which dense methods have advantages over sparse approaches. Motivated by these promising results we propose, as a second contribution, a new variational model that recovers the fundamental matrix and the optical flow simultaneously as the minimisers of a single energy functional. In experiments we show that our coupled approach is able to further improve the estimates of both the fundamental matrix and the optical flow. Our results prove that dense variational methods can be a serious alternative even in classical application domains of sparse feature based approaches.     

Generation of an adaptive simulation driven by product trajectories

The tracking of products trajectories involves major challenges in simulation generation and adaptation. Positioning techniques and technologies have become available and affordable to incorporate more deeply into workshop operations. We present our 2-year effort into developing a general framework in location and manufacturing applications. We demonstrate the features of the proposed applications using a case study, a synthetic flexible manufacturing environment, with product-driven policy, which enables the generation of a location data stream of product trajectories over the whole plant. These location data are mined and processed to reproduce the manufacturing system dynamics in an adaptive simulation scheme. This article proposes an original method for the generation of simulation models in discrete event systems. This method uses the product location data in the running system. The data stream of points (product ID, location, and time) is the starting point for the algorithm to generate a queuing network simulation model.     

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.     

A model for teaching psychotherapy theory through an integrative structure.

This article discusses a model for teaching psychotherapy theory through an integrative structure from the start of graduate students' training. This model articulates an ordering structure for the reputed 400+ so-called "theories" of psychotherapy. The rationale for such a structure highlights one dimension among several--that is, the recognition that a vast majority of mental health practitioners describe their orientation as eclectic or integrative. Professionals in training are encouraged to use this structure as an organizing principle to create the underpinnings for future professional development. The structure informs all aspects of a graduate-level course, including its syllabus, the textbooks selected, the reader, learning objectives, and tools for learning outcome assessment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Microstereophotolithography using a liquid crystal display as dynamic mask-generator

 A new laser microstereophotolithography process has been developed in our laboratory to manufacture three-dimensional parts with a high accuracy. As usual in laser stereophotolithography or laser microstereophotolithography, the part is manufactured layer by layer by a light-induced space-resolved polymerization. Until now, in all the already existing microstereophotolithography devices a layer is manufactured vector by vector, by moving the part beneath the initiating light source which remains motionless. We developed a simpler and easier process, in which we can manufacture an entire layer by irradiating its whole surface only once: we used a liquid crystal display as a dynamic generator of masks. In the device we set up, we need only one mobile element, the z translator, all the others are fixed. We manufactured several different 3D microparts, in particular a piece of bevel microgearing with helicoidal cogs, the volume of which is less than half a cubic millimetre. Received: 14 December 1995 / Accepted: 16 September 1996     

Energy Partitions and Image Segmentation

We address the issue of low-level segmentation for real-valued images. The proposed approach relies on the formulation of the problem in terms of an energy partition of the image domain. In this framework, an energy is defined by measuring a pseudo-metric distance to a source point. Thus, the choice of an energy and a set of sources determines a tessellation of the domain. Each energy acts on the image at a different level of analysis; through the study of two types of energies, two stages of the segmentation process are addressed. The first energy considered, the path variation, belongs to the class of energies determined by minimal paths. Its application as a pre-segmentation method is proposed. In the second part, where the energy is induced by a ultrametric, the construction of hierarchical representations of the image is discussed.     

Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification

This paper examines the applicability of some learning techniques to the classification of phonemes. The methods tested were artificial neural nets (ANN), support vector machines (SVM) and Gaussian mixture modeling (GMM). We compare these methods with a traditional hidden Markov phoneme model (HMM), working with the linear prediction-based cepstral coefficient features (LPCC). We also tried to combine the learners with linear/nonlinear and unsupervised/supervised feature space transformation methods such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), springy discriminant analysis (SDA) and their nonlinear kernel-based counterparts. We found that the discriminative learners can attain the efficiency of HMM, and that after the transformations they can retain the same performance in spite of the severe dimension reduction. The kernel-based transformations brought only marginal improvements compared to their linear counterparts.