Automatic system for thermal damage detection in manufacturing process with Internet monitoring

This work involved the development of a smart system dedicated to surface burning detection in the grinding process through constant monitoring of the process by acoustic emission and electrical power signals. A program in Visual Basic® for Windows® was developed, which collects the signals through an analog-digital converter and further processes them using burning detection algorithms already known. Three other parameters are proposed here and a comparative study carried out. When burning occurs, the newly developed software program sends a control signal warning the operator or interrupting the process, and delivers process information via the Internet. Parallel to this, the user can also interfere in the process via Internet, changing parameters and/or monitoring the grinding process. The findings of a comparative study of the various parameters are also discussed here. Copyright © 2006 by ABCM.

Wavelets and decision trees for target detection over sea surface using cosmo-skymed SAR data

We are investigating the combination of wavelets and decision trees to detect ships and other maritime surveillance targets from medium resolution SAR images. Wavelets have inherent advantages to extract image descriptors while decision trees are able to handle different data sources. In addition, our work aims to consider oceanic features such as ship wakes and ocean spills. In this incipient work, Haar and Cohen-Daubechies-Feauveau 9/7 wavelets obtain detailed descriptors from targets and ocean features and are inserted with other statistical parameters and wavelets into an oblique decision tree. © 2011 Springer-Verlag.

A study on automatic methods based on mathematical morphology for Martian dust devil tracks detection

This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor. © 2011 Springer-Verlag.

On data-centric misbehavior detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is a very important problem with wide range of implications, including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. It is therefore more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alerts with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. After misbehavior is detected, we do not revoke all the secret credentials of misbehaving nodes, as done in most schemes. Instead, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes. © 2011 IEEE.

Delay time detection and accommodation for a networked DC motor control

This paper presents a control method that is effective to reduce the degenerative effects of delay time caused by a treacherous network. In present application a controlled DC motor is part of an inverted pendulum and provides the equilibrium of this system. The control of DC motor is accomplished at the distance through a treacherous network, which causes delay time in the control signal. A predictive technique is used so that it turns the system free of delay. A robust digital sliding mode controller is proposed to control the free-delay system. Due to the random conditions of the network operation, a delay time detection and accommodation strategy is also proposed. A computer simulation is shown to illustrate the design procedures and the effectiveness of the proposed method. © 2011 IEEE.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Validation of a reference control for an SYBR-Green fluorescence assay-based real-time PCR for detection of bovine herpesvirus 5 in experimentally exposed bovine embryos

The objective of this study was to optimize an internal control to improve SYBR-Green-based qPCR to amplify/detect the BoHV-5 US9 gene in bovine embryos produced invitro and experimentally exposed to the virus. We designed an SYBR-Green-based binding assay that is quick to perform, reliable, easily optimized and compares well with the published assay. Herein we demonstrated its general applicability to detect BoHV-5 US9 gene in bovine embryos produced invitro experimentally exposed to BoHV-5. In order to validate the assay, three different reference genes were tested; and the histone 2a gene was shown to be the most adequate for normalizing the qPCR reaction, by considering melting and standard curves ( p<0.05). On the other hand, no differences were found in the development of bovine embryos invitro whether they were exposed to BoHV-5 reference and field strains comparing to unexposed embryos. The developed qPCR assay may have important field applications as it provides an accurate BoHV-5 US9 gene detection using a proven reference gene and is considerably less expensive than the TaqMan qPCR currently employed in sanitary programs. © 2013 Elsevier Ltd.

Análise de riscos: um modelo de assinatura de risco voltada para segurança da informação

Pós-graduação em Ciência da Computação - IBILCE

Proposta de uma ferramenta de visualização e realidade virtual para o monitoramento de tráfego de redes de computadores

Pós-graduação em Ciência da Computação - IBILCE

Electrochemical detection method

Electrochemical detection method and related aspects Herein is disclosed an electrochemical test method comprising (i) comparing how a plurality of immittance functions and/or components thereof vary with a change in a parameter of interest for a first system, and then selecting an immittance function or component thereof for use in an electrochemical test; (ii) carryingout an electrochemical test step for a second system to determine at least one value for the immittance function or component thereof selected in step (i), and then, by using a quantitative relationship between the selected immittance function and the parameter of interest, determining a value in the parameter of interest. A computer program and apparatus are also disclosed herein.

Role of the clinical pharmacist in detection of drug therapy problemas in critically impatients: experience report

This is an experience report on clinical pharmacy in New York, United States of America, in a teaching hospital, describing the results of drug therapy monitoring in critically ill patients, as well as interventions to solve or prevent identified drug therapy problems. The cross-sectional study was conducted by the clinical staff at the Surgical Intensive Care Unit during August 20th to 24th, 2012. Blood counts, serum levels of certain antibiotics, microbiological cultures and their antibiotic susceptibility, possible drug interactions, dosage of each drug prescribed and the compatibility between the route of administration and pharmaceutical form were assessed daily through review of electronic medical records. Twenty seven patients were followed up and 16 drug therapy problems were identified: Unnecessary drug therapy (seven), adverse drug reaction (four), needs additional drug therapy (two), noncompliance (two) and dosage too low (one). After evaluation, the drug therapy problems and their pharmaceutical interventions were reported to clinical pharmaceutical responsible for the Surgical ICU, as well as the multidisciplinary team. Further, the clinical outcomes were monitored and interventions were classified as to its acceptance. Data demonstrate that clinical pharmacists can contribute to the security and proper use of medications, as the trigger tools for intensive monitoring helps in early detection of drug therapy problems and patient safety.

A Security Framework for Wireless Sensor Networks Utilizing a Unique Session Key

Key management is a core mechanism to ensure the security of applications and network services in wireless sensor networks. It includes two aspects: key distribution and key revocation. Many key management protocols have been specifically designed for wireless sensor networks. However, most of the key management protocols focus on the establishment of the required keys or the removal of the compromised keys. The design of these key management protocols does not consider the support of higher level security applications. When the applications are integrated later in sensor networks, new mechanisms must be designed. In this paper, we propose a security framework, uKeying, for wireless sensor networks. This framework can be easily extended to support many security applications. It includes three components: a security mechanism to provide secrecy for communications in sensor networks, an efficient session key distribution scheme, and a centralized key revocation scheme. The proposed framework does not depend on a specific key distribution scheme and can be used to support many security applications, such as secure group communications. Our analysis shows that the framework is secure, efficient, and extensible. The simulation and results also reveal for the first time that a centralized key revocation scheme can also attain a high efficiency.

Revisiting the Security of the ALRED Design and Two of Its Variants: Marvin and LetterSoup

The ALRED construction is a lightweight strategy for constructing message authentication algorithms from an underlying iterated block cipher. Even though this construction's original analyses show that it is secure against some attacks, the absence of formal security proofs in a strong security model still brings uncertainty on its robustness. In this paper, aiming to give a better understanding of the security level provided by different authentication algorithms based on this design strategy, we formally analyze two ALRED variants-the MARVIN message authentication code and the LETTERSOUP authenticated-encryption scheme,-bounding their security as a function of the attacker's resources and of the underlying cipher's characteristics.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.