Automatic identification of modal damping from Floquet analysis

A practical method is proposed to identify the mode associated with the frequency part of the eigenvalue of the Floquet transition matrix (FTM). From the FTM eigenvector, which contains the states and their derivatives, the ratio of the derivative and the state corresponding to the largest component is computed. The method exploits the fact that the imaginary part of this (complex) ratio closely approximates the frequency of the mode. It also lends itself well to automation and has been tested over a large number of FTMs of order as high as 250.

Automatic identification and enumeration of algae

A good understanding of the population dynamics of algal communities is vital in many ecological and pollution studies of freshwater and oceanic systems. Present methods require manual counting and identification of algae and can take up to 90 min to obtain a statistically reliable count on a complex population. Several alternative techniques to accelerate the process have been tried on marine samples but none have been completely successful because insufficient effort has been put into verifying the technique before field trials. The objective of the present study has been to assess the potential of in vivo fluorescence of algal pigments as a means of automatically identifying algae. For this work total fluorescence spectroscopy was chosen as the observation technique.

The automatic identification and prioritisation of criminal networks from police crime data

The identification of criminal networks is not a routine exploratory process within the current practice of the law enforcement authorities; rather it is triggered by specific evidence of criminal activity being investigated. A network is identified when a criminal comes to notice and any associates who could also be potentially implicated would need to be identified if only to be eliminated from the enquiries as suspects or witnesses as well as to prevent and/or detect crime. However, an identified network may not be the one causing most harm in a given area.. This paper identifies a methodology to identify all of the criminal networks that are present within a Law Enforcement Area, and, prioritises those that are causing most harm to the community. Each crime is allocated a score based on its crime type and how recently the crime was committed; the network score, which can be used as decision support to help prioritise it for law enforcement purposes, is the sum of the individual crime scores.

Automatic identification of terpenoid skeletons by feed-forward neural networks

Feed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the (13)C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an heuristic approach. The latter was derived from general considerations on terpenoid structures. (c) 2006 Elsevier B.V. All rights reserved.

Automatic identification by C-13 NMR of substituent groups bonded in natural product skeletons

The aim of this paper is to present a procedure that utilizes C-13 NMR for identification of substituent groups which are bonded to carbon skeletons of natural products. For so much was developed a new version of the program (MACRONO), that presents a database with 161 substituent types found in the most varied terpenoids. This new version was widely tested in the identification of the substituents of 60 compounds that, after removal of the signals that did not belong to the carbon skeleton, served to test the prediction of skeletons by using other programs of the expert system (SISTEMAT). (C) 2002 Elsevier B.V. Ltd. All rights reserved.

Optical Design for Automatic Identification and Portable Systems

This thesis proposes design methods and test tools, for optical systems, which may be used in an industrial environment, where not only precision and reliability but also ease of use is important. The approach to the problem has been conceived to be as general as possible, although in the present work, the design of a portable device for automatic identification applications has been studied, because this doctorate has been funded by Datalogic Scanning Group s.r.l., a world-class producer of barcode readers. The main functional components of the complete device are: electro-optical imaging, illumination and pattern generator systems. For what concerns the electro-optical imaging system, a characterization tool and an analysis one has been developed to check if the desired performance of the system has been achieved. Moreover, two design tools for optimizing the imaging system have been implemented. The first optimizes just the core of the system, the optical part, improving its performance ignoring all other contributions and generating a good starting point for the optimization of the whole complex system. The second tool optimizes the system taking into account its behavior with a model as near as possible to reality including optics, electronics and detection. For what concerns the illumination and the pattern generator systems, two tools have been implemented. The first allows the design of free-form lenses described by an arbitrary analytical function exited by an incoherent source and is able to provide custom illumination conditions for all kind of applications. The second tool consists of a new method to design Diffractive Optical Elements excited by a coherent source for large pattern angles using the Iterative Fourier Transform Algorithm. Validation of the design tools has been obtained, whenever possible, comparing the performance of the designed systems with those of fabricated prototypes. In other cases simulations have been used.

Automatic identification of discourse markers in dialogues: An in-depth study of like and well

The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, κ = 0.69), and 99% recall and 98% precision for well (97.5% accuracy, κ = 0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.

Towards Automatic Identification of Discourse Markers in Dialogs: the Case of 'Like'

This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.

Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.

Automatic Identification of False Friends in Parallel Corpora: Statistical and Semantic Approach

False friends are pairs of words in two languages that are perceived as similar but have different meanings. We present an improved algorithm for acquiring false friends from sentence-level aligned parallel corpus based on statistical observations of words occurrences and co-occurrences in the parallel sentences. The results are compared with an entirely semantic measure for cross-lingual similarity between words based on using the Web as a corpus through analyzing the words’ local contexts extracted from the text snippets returned by searching in Google. The statistical and semantic measures are further combined into an improved algorithm for identification of false friends that achieves almost twice better results than previously known algorithms. The evaluation is performed for identifying cognates between Bulgarian and Russian but the proposed methods could be adopted for other language pairs for which parallel corpora and bilingual glossaries are available.

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners—This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.