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Modern industrial technologies require raw materials of high purity from suppliers, especially due to the possible presence of inclusions of heterogeneous metals in them. The existence of metallic inclusions of heterogeneous metals in the raw materials leads to equipment failure, a decrease in the quality of output products, and, consequently, large financial losses. Since the main method of transporting raw materials in the industry is still a conveyor belt, this imposes additional conditions to control and remove metallic inclusions. For various reasons, current methods for removing metallic inclusions in the conveyor belt do not fully meet the needs of modern production. The main issue related to existing removal systems is the lack of intelligent interaction between these systems and the absence of information exchange between systems that detect and remove metallic inclusions. An alternative method for removing metallic inclusions of heterogeneous metals from loose medium has been proposed, which implies the tandem operation of the system that detects metallic inclusions and the system that removes them. The tandem operation of the two systems makes it possible to exchange information about a detected metallic inclusion and, as a result, to more flexibly use tools for the removal of metallic inclusion depending on the size and location of the metallic inclusion relative to the conveyor belt axis. At the same time, the control unit of the removal system makes it possible to control the conveyor belt itself, which allows the removal of complex metallic inclusions using the reverse of the electric drive of the belt, as well as enables a control check of the fact of removal. The developed algorithm of the removal system was implemented in the programming environment TIA-Portal. The introduction of this removal system could reduce the number of metallic inclusions in raw materials by 15‒20 %; moreover, its application is not limited to only one sector of the national economy

In this research, biometric authentication, which has been widely used for different purposes in the last quarter-century, is studied. Dorsal hand veins are used for biometric authentication. “Deep learning” (DL) and “generative adversarial networks” (GANs) are used together as keys in the study. A DL-GAN is obtained by combining deep learning and GAN. The developed DL-GAN method is tested on two separate databases. The adversarial network (DL-GAN) method is developed to increase the authentication process’s proportional value. For identity verification, dorsal hand veins with biometric physical properties are used. A multistep approach is used for selecting hand dorsal features, including preimage processing and effectively identifying individuals. The deep learning productive antinetwork method is used to effectively identify individuals based on the information obtained from the dorsal hand vein images. For the test in the study, two open access databases are used. These databases are the Jilin University - dorsal hand vein database and the 11K hands database. The results of the experiments performed on the dataset related to the dorsal hand vessels show that the DL-GAN method reaches an identity accuracy level of 98.36% and has an error rate of 2.47% and a standard accuracy of 0.19%. The accuracy of the experimental results in the second dataset is 96.43%, the equal error rate is 3.55% and the standard accuracy is 0.21%. The improved DL-GAN method obtains better results than physical biometric methods such as LBP, LPQ, GABOR, FGM, BGM and SIFT.

Visible light communications (VLC) technology is emerging as a candidate to meet the demand for interconnected devices’ communications. However, the costs of incorporating specific hardware into end-user devices slow down its market entry. Optical camera communication (OCC) technology paves the way by reusing cameras as receivers. These systems have generally been evaluated under static conditions, in which transmitting sources are recognized using computationally expensive discovery algorithms. In vehicle-to-vehicle networks and wearable devices, tracking algorithms, as proposed in this work, allow one to reduce the time required to locate a moving source and hence the latency of these systems, increasing the data rate by up to 2100%. The proposed receiver architecture combines discovery and tracking algorithms that analyze spatial features of a custom RGB LED transmitter matrix, highlighted in the scene by varying the cameras’ exposure time. By using an anchor LED and changing the intensity of the green LED, the receiver can track the light source with a slow temporal deterioration. Moreover, data bits sent over the red and blue channels do not significantly affect detection, hence transmission occurs uninterrupted. Finally, a novel experimental methodology to evaluate the evolution of the detection’s performance is proposed. With the analysis of the mean and standard deviation of novel K parameters, it is possible to evaluate the detected region-of-interest scale and centrality against the transmitter source’s ideal location.

The emergence of the Internet of Things (IOT) as a result of the development of the communications system has made the study of cyber security more important. Day after day, attacks evolve and new attacks are emerged. Hence, network anomaly-based intrusion detection system is become very important, which plays an important role in protecting the network through early detection of attacks. Because of the development in machine learning and the emergence of deep learning field, and its ability to extract high-level features with high accuracy, made these systems involved to be worked with real network traffic CSE-CIC-IDS2018 with a wide range of intrusions and normal behavior is an ideal way for testing and evaluation . In this paper , we test and evaluate our deep model (DNN) which achieved good detection accuracy about 90% .

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

Partial discharge (PD) usually reflects failures and potential hazards of equipment, so PD detection is important to protect the power system. The most reliable method now is the pulse current method (PCM), but the device of PCM is large and hard to carry. Ultraviolet (UV) pulse detection is another method to detect PD, which has a high precision, strong anti-interference ability, and a long effective distance. However, the existing detection system does not work well when the PD is weak and can hardly reflect the hidden trouble of equipment. This paper introduces an improved PD detection system, based on the UV Pulse Method, which is of high precision and can reflect early discharge. In this study, a corresponding detection device was also built. This device is handheld, non-contact, easy to use, and of high precision.

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine. Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211] The work of Sultan Zavrak was supported by the Scienti~c and Technological Research Council of Turkey (TUBITAK) through the 2211/C Ph.D. Scholarship Programme for Priority Areas. WOS:000544044400003 2-s2.0-85086985318

International audience; Today, industrial control systems (ICS) are connected to the Internet. The addition of computer capabilities to ICSs has improved performance, reduced costs, and many other benefits. However, ICSs are not designed with security in mind. These systems, therefore, present security risks related to their IT vulnerabilities. Applying IT's security solutions to the ICS are not enough due to the difference between these two systems represented by the interaction between the cyber and physical system in ICS. Hence, it seems relevant to rely on a physical model of the cyber-physical system to obtain an intrusion detection system (IDS) for those systems. Most of the IDSs are based on rules to define how the IDS would detect attacks on the system. These rules are generally used either to describe possible attacks scenarios on the systems or the normal system behavior. However, creating and maintaining handmade rules for a complex system could be very difficult. In this article, we propose a model-based approach that consists of generating IDS rules from a physical system model and the authorized behavior specification. Then we illustrate the proposed algorithm in a case study.

Activity of static loads and different activities of dynamic loads produced over the structure of a building have a negative effect causing deformations of the materials that form the structure. Reinforced concrete is the most used in building structural material, being deflection its most important deformation that affects bending elements fabricated with this material. This paper presents a new measurement system based on the response of a piezoelectric low cost sensor. Placing the device in the midspan of the beam where maximum deformation is produced, the sensor is able to collect deflection variations according to the loads the element is subjected to, showing produced variations in real time.

We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas.

In the 500 kV hybrid high-voltage direct current (HVDC) circuit breaker, there are five 110 kV fast mechanical switches (FMSs) in the nominal current path. When the detection system detects the failure of the hybrid circuit breaker, in order to protect the disconnectors or semiconductors, the FMSs are required to close quickly. The closing drive technology can be divided into two categories, based on the closing speed of the Thomson-coil actuator. One is based on the conventional closing drive, the open−close operating time of which is 20.0 ms. The other type is based on the fast closing drive, which is used under fault conditions and the total time is ∼7.5 ms. A 110 kV/3 kA prototype FMS employing an interrupter with a 30 mm maximum open gap and 4 kg total moving mass has been modelled based on the equivalent circuit method. Through the simulation, the effect of discharging time sequence on open−close operation has been studied, and the general timing control design principle has been provided.

Salmonella is a leading source of bacterial foodborne illness in humans, causing gastroenteritis outbreaks with bacteraemia occurrences that can lead to clinical complications and death. Eggs, poultry and pig products are considered as the main carriers of the pathogenic Salmonella for humans. To prevent this relevant zoonosis, key changes in food safety regulations were undertaken to improve controls in the food production chain. Despite these measures, large outbreaks of salmonellosis were reported worldwide in the last decade. Thus, new strategies for Salmonella detection are a priority for both, food safety and public health authorities. Such detection systems should provide significant reduction in diagnostic time (hours) compared to the currently available methods (days). Herein, we report on the discovery and characterization of nucleic acid probes for the sensitive and specific detection of live Salmonella within less than 8 h of incubation. We are the first to postulate the nuclease activity derived from Salmonella as biomarker of infection and its utility to develop innovative detection strategies. Our results have shown the screening and identification of two oligonucleotide sequences (substrates) as the most promising probes for detecting Salmonella – Sal-3 and Sal-5. The detection limits for both probes were determined with the reference Salmonella Typhimurium (STM 1) and Salmonella Enteritidis (SE 1) cultures. Sal-3 has reported LOD values around 105 CFU mL−1 for STM 1 and 104 CFU mL−1 for SE 1, while Sal-5 proves to be a slightly better probe, with LODs of 104 CFU mL−1 for STM 1 and 104 CFU mL−1 for SE 1. Both selected probes have shown the capability to recognize 49 out of 51 different Salmonella serotypes tested in vitro and the most frequent serotypes in porcine mesenteric lymph nodes as a standard sample used in fattening-pig salmonellosis baseline studies. Notably, our results showed 100% correlation between nuclease detection and the PCR-InvA or ISO-6579 standard method, underlining the great potential of this innovative nucleic acids technology to be implemented as a rapid method for food safety testing., This work was supported by MINNECO [Grant: PTQ-16-08414] (IM), The Centre for the Development of Industrial Technology [CDTI Grant: 20161256] (SOMAprobes), Departamento de Industria, Energia e Innovación of the Navarra government, Spain [Grant: 2016 PT071 and 2017 PT031], Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linköping University (Faculty Grant SFO-Mat-LiU No. 2009-00971). The Knut and Alice Wallenberg foundation is acknowledged for generous support (FJH).

The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy. The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.

This paper presents a UAV based remote sensing system for the detection of fawns in the meadows. There is a high demand because during pasture mowing many wild animals, especially roe deer fawns are killed by mowing machines. The system was tested in several real situations especially with differing weather and iluminating conditions. Its primary sensor is a lightweight thermal infrared camera. The images are captured onboard of the flight system and also transmitted as analog video stream to the ground station, where the user can follow the camera live stream on a monitor for manual animal detection. Beside a high detection rate a fast workflow is another very important objective for this application. Therefore a waypoint planning software was developed that accelerates the workflow. At adequate illuminating and weather conditions the presented UAV-based fawn detection via thermal imaging is a comfortable, fast and reliable method.

With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many fields, such as supply chain management, international payment, interbanking, and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this paper provides a review regarding the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction.

Introduction. The article deals with the optimization of a moving target signals detection systems against clutter on probabilistic criteria. The object of research is the detection system implementing coherent rejection of clutter followed by coherent or non-coherent accumulation of rejection residues. The purpose is to optimize rejector filter weight coefficients depending on the correlation properties of clutter on probabilistic criteria. Probabilistic characteristics of the detection systems. The expressions for the probability characteristics of the detection systems with coherent rejection clutter and subsequent coherent or non-coherent accumulation of rejection residues are obtained respectively. These expressions establish a functional relationship between the averaged on Doppler signal phase of the correct detection probability and correlation clutter parameters and detection system characteristics. The optimization procedure. The criteria to optimize the weight vector of the rejector filter is listed. These criteria make it possible to communicate the optimal weight vector with clutter parameters on the basis of non-linear programming methods. The quasi-Newton iterative procedure for finding the optimal vector is shown. The restrictions are introduced on samples rejector filter frequency response to achieve unimodal extremum. The numerical results of optimization. We consider the results of numerical optimization system of coherent rejection and subsequent non-coherent accumulation on probabilistic criteria. A comparison of their results with the same optimization of the rejector filter on energy criteria is conducted. Conclusion. The proposed optimization method of detection systems on probabilistic criteria allows to obtain significant gains in the efficiency of detection signals compared with the optimization on energy criteria and to implement the limit efficiency for this class of systems., В статье рассмотрена оптимизация систем обнаружения сигналов движущихся целей на фоне пассивных помех по вероятностному критерию. Получены выражения для вероятностных характеристик соответствующих систем обнаружения. Приведены критерии оптимизации весового вектора режекторного фильтра, позволяющие установить связь оптимального весового вектора с параметрами помехи на основе методов нелинейного программирования. Приведена квазиньютоновская итерационная процедура поиска оптимального вектора. Получены числовые результаты оптимизации системы обнаружения. Установлены существенные выигрыши в эффективности обнаружения сигналов по сравнению с оптимизацией систем обнаружения по энергетическому критерию., У статті розглянута оптимізація систем виявлення сигналів рухомих цілей на тлі пасивних перешкод за ймовірнісним критерієм. Отримані вирази ймовірнісних характеристик систем виявлення. Наведено критерії оптимізації вагового вектора режекторного фільтра, що дозволяють встановити зв'язок між оптимальним ваговим вектором та параметрами перешкоди на основі методів нелінійного програмування. Наведено приклад квазіньютонівської ітераційної процедури пошуку оптимального вектора. Встановлено істотні виграші в ефективності виявлення сигналів у порівнянні з оптимізацією систем виявлення за енергетичним критерієм.

A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriage-ways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems.

This paper presents the results of a safety impact assessment, providing quantitative estimates of the safety impacts of ten intelligent transport systems (ITS) which were designed to improve safety, mobility and comfort of vulnerable road users (VRUs). The evaluation method originally developed to assess safety impacts of ITS for cars was now adapted for assessing safety impacts of ITS for VRUs. The main results of the assessment showed that nine ITS included in the quantitative safety impact assessment affected traffic safety in a positive way by preventing fatalities and injuries. At full penetration the highest effects were obtained for Pedestrian and Cyclists Detection System + Emergency Braking (PCDS+EBR), VRU Beacon System (VBS) and Intersection Safety (INS). The estimates for PCDS+EBR showed the maximum reduction of 7.5% on all road fatalities at full penetration, which comes down to an medium estimate of around 1,900 fatalities saved per year in the EU-28 when applying the 2012 accident data and 100% penetration rate. The results regarding future scenarios showed the highest effects in number of reduced fatalities per system in the European Union (EU)-28 in 2030 for PCDS+EBR (-200 fatalities), Blind Spot Detection (BSD) (-22 fatalities), INS (-20 fatalities) and VBS (-11 fatalities). © 2017 The Institution of Engineering and Technology.

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms.In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task.The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors. HighlightsWe propose a new extensible and adaptable cloud-based fall detection framework.An ontology-based approach is used to model the situation of patient and caregivers.The captured information from the sensors is combined with contextual information.Existing fall detection systems can easily be integrated to improve accuracy.Different combinations of fall detection systems and sensors can be used.

The Italian National Research Council is a public company with multiple locations and many decentralized human resources departments. With decentralized human resources management, each location controls its own individual personnel issues such as attendances and leaves. One of the goals of decentralized human resources departments is to give autonomy to different locations, allowing them to adapt to the specific scientific climate of the individual site. However, the actual result of this form of human resources management is often disorganization, failure to follow unified standards or an ineffective application of those standards. This article describes a distributed and federated approach to Human Resource Management that is being adopted by the Italian National Research Council (CNR). In particular, this article describes the time and attendance software implemented by the Institute of Informatics and Telematics (IIT). The aim of the article is to describe the architecture of the system, the decision-making and choices regarding implementation adopted as part of the project, the main software components and their ways of interaction and communication.

Yuri Kwon,1 Sang Hoon Park,1 Ji-Won Kim,1 Yeji Ho,1 Hyeong-Min Jeon,1 Min-Jung Bang,1 Gu-In Jung,1 Seon-Min Lee,3 Gwang-Moon Eom,1,2 Seong-Beom Koh,3 Jeong-Whan Lee,1,2 Heung Seok Jeon41Biomedical Engineering, 2Research Institute of Biomedical Engineering, Konkuk University, Chungju, Korea; 3Department of Neurology, Korea University College of Medicine, Seoul, Korea; 4Department of Computer Engineering, Konkuk University, Chungju, KoreaPurpose: Freezing of gait (FOG), increasing the fall risk and limiting the quality of life, is common at the advanced stage of Parkinson’s disease, typically in old ages. A simple and unobtrusive FOG detection system with a small calculation load would make a fast presentation of on-demand cueing possible. The purpose of this study was to find a practical FOG detection system.Patients and methods: A sole-mounted sensor system was developed for an unobtrusive measurement of acceleration during gait. Twenty patients with Parkinson’s disease participated in this study. A simple and fast time-domain method for the FOG detection was suggested and compared with the conventional frequency-domain method. The parameters used in the FOG detection were optimized for each patient.Results: The calculation load was 1,154 times less in the time-domain method than the conventional method, and the FOG detection performance was comparable between the two domains (P=0.79) and depended on the window length (P

International audience; In this study we propose an approach for increasing mobility scooter safety by detecting and avoiding obstacles. The detection system is based on obstacle detection and implemented using infrared and ultrasonic devices. The system is fully embedded and is composed of six infrared and three ultrasonic devices which allow: far detection for crossing objects or normal speed obstacle detection; close detection for low speed obstacles; side obstacle detection when turning. Three experiments have been carried out using our system in an indoor environment using different object shapes and sizes, crossing distances and turning angles. The system was able to detect all obstacles in the mobility scooters path with the exception of transparent flat surfaces. Obstacles passing in front were also successfully detected and when turning into walls the system correctly detected them.

Contains fulltext : 136826.pdf (Publisher’s version ) (Open Access) Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a much higher malignancy rate. Therefore, accurate detection of this type of pulmonary nodules is crucial. In this work, a computer-aided detection (CAD) system for subsolid nodules in computed tomography images is presented and evaluated on a large data set from a multi-center lung cancer screening trial. The paper describes the different components of the CAD system and presents experiments to optimize the performance of the proposed CAD system. A rich set of 128 features is defined for subsolid nodule candidates. In addition to previously used intensity, shape and texture features, a novel set of context features is introduced. Experiments show that these features significantly improve the classification performance. Optimization and training of the CAD system is performed on a large training set from one site of a lung cancer screening trial. Performance analysis on an independent test from another site of the trial shows that the proposed system reaches a sensitivity of 80% at an average of only 1.0 false positive detections per scan. A retrospective analysis of the output of the CAD system by an experienced thoracic radiologist shows that the CAD system is able to find subsolid nodules which were not contained in the screening database.

Conference of 2013 3rd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and Their Applications, ANIMMA 2013 ; Conference Code:102802; International audience; In the safety analysis for the core of the 4th Generation Reactors, the Total Instantaneous Blockage (TIB) is a hypothetic accident scenario involving the melting of the blocked subassembly with a risk of propagation to the neighbouring subassemblies. To avoid this latter consequence a detection system has to scram the reactor. For Superphénix or EFR project a Delayed Neutron Detection Integrated (DND I) was considered as efficient to limit the melting to the first neighbouring subassemblies. Nonetheless for the CFV core the objective of improving the safety leads to limit the melting to the blocked subassembly. For this purpose, the CEA has launched a program development to find a new detection method. This paper provides a brief review of the feedback of R&D, progress and program on the various early non-nuclear detection methods investigated by the CEA: • Temperature measurement at the subassemblies outlet by thermocouples. The advantage of this method is that it will require no additional instrumentation to that already present for continuous monitoring. • Temperature measurement at the subassemblies outlet by Optical Fibers Bragg Grating (OFBG). This technology has the electromagnetic immunity, compactness and short response time. • Temperature measurement at the subassemblies outlet by ultrasound. The measuring point is located closer to the head subassembly and the response time could be shorter. • Acoustic detection of sodium boiling. Boiling occurs early in the accident progress and the area to be monitored may be covered by few sensors. • Subassemblies loss of flow detection by eddy-current flowmeters. This method seems logically the easiest and the most immediate method to detect a blockage. To date, none of these methods has been fully demonstrated to be feasible. It should be noted that temperature measurement methods will probably consist of the detection of a low increase rate using specific signal processing. These methods have been compared according to their performance that may be expected and their maturity level. The shorter time to qualifying is for eddy-current flowmeters and thermocouples.

With increasing demand for solar energy, the need for reliable inspection systems has increased tremendously. In this paper, a combination of air coupled ultrasonics and Lamb waves have been utilized to frame a crack detection system. Monocrystalline and polycrystalline silicon wafers of 200 ?m thickness were used for experimentation and theoretical Lamb mode dispersion curves were plotted for Lamb wave generation in these wafers. A set of defect-free and cracked samples were scanned and the results are presented in this paper. A complete laboratory based cracked detection system was developed and the demonstrated to detect cracks in silicon wafers. � Springer Science+Business Media, LLC 2011.

We make an attempt to develop a novel approach to describing the initial stage of the active mode-locking in semiconductor laser structures based on analyzing the properties of dispersion relations in terms of stability for small initial perturbations. Nonlinear process of shaping optical pulses is interpreted as manifesting instability of diffusion type. For the purposes of experimental investigations, the auto-manual opto-electronic measuring system detecting average time parameters inherent in ultra-short optical pulse trains has been designed. This system is able to register auto-correlation functions of the second order exploiting the interferometric technique as well as to identify a pulsed character of the incoming light radiation. Experimental confirmations of appearing the diffusive instability within the active mode-locking process in semiconductor laser structures operating in the near infrared range are presented., CONACyT, Mexico: project # 61237-F

Objective To evaluate the performance of the recently introduced method based on detection of human cytomegalovirus (HCMV) pp67 mRNA in blood by the nucleic acid sequence-based amplification (NucliSens), in comparison to semiquantitative detection of pp65 HCMV antigen in white blood cells, in relation to development of clinical HCMV disease. Methods Thirty patients, recipients of renal transplants, were monitored prospectively for the presence of pp67 mRNA, the presence and level of pp65 antigenemia, IgG and IgM antibodies, and the development of clinical HCMV disease. A total of 148 samples were examined during the observation period. Results Twenty-five samples were positive for pp67-mRNA and 45 samples contained at least one pp65 positive cell, with 68% agreement between the two assays. Both assays predicted correctly the development of clinical disease in five patients, giving a sensitivity of 100%. However, the specificity of the pp67-mRNA test was 72%, and of the pp65 antigenemia test from 20 to 64%, depending on the level of antigenemia chosen for cut-off. pp67-RNA appeared somewhat earlier than pp65 antigenemia, and responded earlier to treatment. Sero-conversion and appearance of IgM antibodies were of very little clinical value. Conclusion Both the pp67-mRNA and the pp65 antigenemia assay predicted correctly the development of clinical HCMV disease in renal transplant recipients. However, the specificity of both tests with respect to development of HCMV disease, especially the pp65 antigen test was moderate. Significantly positive tests not necessarily prove the development of clinical disease. Testing for pp67-mRNA may improve the diagnosis and management of HCMV disease in renal transplant patients.

Requirements on fire and explosion detection systems continue to rise, especially in safety-sensitive areas. This paper presents a test bench for explosion detection systems. Theoretical computations of the flame temperature as a function of fuel/air ratio and maximum occurring combustion pressure specify the boundary conditions. Additionally, a series of experiments is performed to validate the theoretical results and to characterize the generated deflagrations. Due to its modular construction the test bench is able to create hydrocarbon-based deflagrations of adjustable scale. A high safety standard is guaranteed by filling the fuel and enabling the ignition device in separated steps

Computer-aided diagnosis (CAD) has great potential to improve radiologists' diagnostic performance. However, the reported performance of the radiologist-CAD team is lower than what might be expected based on the performance of the radiologist and the CAD system in isolation. This indicates that the interaction between radiologists and the CAD system is not optimal. An important factor in the interaction between humans and automated aids (such as CAD) is trust. Suboptimal performance of the human automation team is often caused by an inappropriate level of trust in the automation. In this review, we examine the role of trust in the radiologist-CAD interaction and suggest ways to improve the output of the CAD system so that it allows radiologists to calibrate their trust in the CAD system more effectively. Observer studies of the CAD systems show that radiologists often have an inappropriate level of trust in the CAD system. They sometimes under-trust CAD, thereby reducing its potential benefits, and sometimes over-trust it, leading to diagnostic errors they would not have made without CAD. Based on the literature on trust in human automation interaction and the results of CAD observer studies, we have identified four ways to improve the output of CAD so that it allows radiologists to form a more appropriate level of trust in CAD. Designing CAD systems for appropriate trust is important and can improve the performance of the radiologist-CAD team. Future CAD research and development should acknowledge the importance of the radiologist-CAD interaction, and specifically the role of trust therein, in order to create the perfect artificial partner for the radiologist. This review focuses on the role of trust in the radiologist-CAD interaction. The aim of the review is to encourage CAD developers to design for appropriate trust and thereby improve the performance of the radiologist-CAD team. (C) 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

A new multichannel Raman spectrometer is described. Each component of the optical part (sample compartment, sample adjustment provisions, filters, fore-monochromator, spectrograph) have been carefully designed to build a high quality, high reliability and easy to use instrument. The detection system uses a self-scanned photodiode array and the read-out electronics and data acquisition system are based on electronic circuits and logics specially developed to give a high dynamic range with low noise. Tests show that the Raman spectra recorded with this new instrument are of quality comparable to that obtained from a conventional single channel Raman spectrometer while the time required to acquire a spectrum is greatly reduced.