Tutoramento de plantas de ervilha visando à produção de grãos secos.

bitstream/CNPT-2010/40286/1/p-bp13.pd

Automated border control systems: biometric challenges and research trends

Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

New methodological approach to induce a differentiation phenotype in Caco-2 cells prior to post-confluence stage

BACKGROUND: Various differentiation-inducing agents or harvesting of spontaneously late post-confluence cultures have been used to differentiate the human colon carcinoma Caco-2 cell line. We report a new procedure to generate pre-confluent subcultures of Caco-2 population at various stages of differentiation without altering culture conditions. MATERIALS AND METHODS: Ultrastructural analysis, cell proliferation activity and biochemical markers of differentiation were evaluated at different passages. RESULTS: Subcultures of Caco-2 cells at pre-confluence, exhibiting progressive acquisition of a more benign differentiation phenotype, were generated. Early passages of Caco-2 cells showed a well-developed brush border and incomplete junctional apparatus; subsequent subcultures yielded cell populations with well-developed junctions similar to those of small intestinal cells. CONCLUSION: These culture conditions represent a new versatile model not only to progressively induce the differentiation program in Caco-2 cells at pre-confluence without changes of culture media, but also to explore mechanistic modes of drug transport and tumor development

A decision support system for wind power production

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach

Adaptive ECG biometric recognition : a study on re-enrollment methods for QRS signals

The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics

Automatic classification of acquisition problems affecting fingerprint images in automated border controls

Automated Border Control (ABC) systems are technologies designed to increase the speed and accuracy of the identity verifications performed at international borders. A great number of ABCs deployed in different countries use fingerprint recognition techniques because of their high accuracy and user acceptability. However, the accuracy of fingerprint recognition methods can drastically decrease in this application context due to user-sensor interaction factors. This paper presents two main contributions. The first of them consists in an experimental evaluation performed to search the main negative aspects that could affect the usability and accuracy in ABCs based on fingerprint biometrics. The mainly considered aspects consists in the presence of luggage and cleanness of the finger skin. The second contribution consists in a novel approach for automatically identifying the type of user-sensor interaction that caused quality degradations in fingerprint samples. This method uses a specific feature set and computational intelligence techniques to detect non-idealities in the acquisition process and to suggest corrective actions to travelers and border guards. To the best of our knowledge, this is the first method in the literature designed to detect problems in user-sensor interaction different from improper pressures on the acquisition surface. We validated the proposed approach using a dataset of 2880 images simulating different scenarios typical of ABCs. Results shown that the proposed approach is feasible and can obtain satisfactory performance, with a classification error of 0.098

Prognostic factors associated with survival and recurrence in resectable gastroesophageal cancer: retrospective analysis of 338 patients operated at the Hospital of Cremona in ten years' time

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Improving OSB wood panel production by vision-based systems for granulometric estimation

Oriented Strand Board (OSB) is a kind of engineered wood particle board widely adopted in manufacturing, construction and logistics. The production of OSB panels requires rectangular-shaped wood strands of specific size, arranged in layers to form the so-called \u201cmattress\u201d (mat) and bonded together with glue. The structural properties of the panel rely directly on the layer forming. In particular, the size distribution - namely granulometry - of the strands should fulfill standard measures to reach the mechanical properties expected from the panel. Offline granulometry of particle materials is the most commonly procedure used to evaluate the production process, but it is prone to several drawbacks owing to the manual intervention of human operators. Vision-based systems, instead, allow for performing granulometric analyses in an automatic and contactless manner. We propose a computer vision approach to estimate the granulometry of wood strands. The designed framework analyzes images of a mat of strands placed over a moving conveyor belt, and provides useful information and measurements to enhance the production of OSB panels. Because of the very large amount of wood strands on the mat, particle-overlapping is frequent and represents a main issue for measurement algorithms. In order to overcome this problem, our framework joins image processing and computational intelligence methods, such as edge detection and fuzzy color clustering. We tested the framework with real and synthetic images, useful to variate the conditions of the material's shape. The obtained results demonstrate the ability of our approach to evaluate the granulometry of the strands in real conditions, and robustness against the simulated variations of the production process

Emerging biometric technologies for Automated Border Control gates

Automated Border Control (ABC) gates, or shortly e-Gates, are systems able to verify automatically the identity of the travelers through the biometric traits, and to grant passage of the border. Biometric technologies make the clearance automation possible, with a positive impact on efficiency, effectiveness, security, and usability of the process. The e-Gate compares biometric data of the traveler from an electronic document against live acquisitions, using different biometric traits. The face emerged in this area as the primary trait used by the e-Gates, with fingerprint and iris more adopted in registered traveler programs. This paper analyzes the main biometric aspects relating to both the human-machine interaction and the technologies used for ABC, and presents the emerging solutions that can produce a performance enhancement

Biometric recognition in automated border control : a survey

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas