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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.

Subsynchronous resonance (SSR) occurs mainly in series compensated transmission line connected to the wind power system. This phenomenon (SSR) can lead to electrical instability and potential turbine generator shaft failure. Thus, the protection system is an essential part to avoid the harm of SSR. In this paper, an improved fast detection method is proposed to detect the SSR phenomenon in a very short time. It consists of two stages; first, the positive or negative half-cycle time of each cycle of voltage signal is detected based on four steps, namely, signal conversion, comparison, integrator, and sample and hold; second, the obtained time is compared with the damping and trip threshold values, and the best action is determined (addressing or trip) based on the value of half-cycle time. The effectiveness and superiority of the improved fast SSR detection method are validated at different compensation levels and compared with the traditional method and subsynchronous oscillation relay (SSO). The simulation results prove that the improved method is more efficient in all types of SSR phenomenon; induction generator effect (IGE), control interactions (CI), and torsional interaction (TI) at different compensation levels. In addition, a comparative study among these three types of SSR with the proposed method is carried out, and the obtained results show that the CI is a suitable type with the proposed method.

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.

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.

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.

Spy app is a class of malware for mobile devices that allows an adversary to steal sensitive information. Detecting spy apps is challenging because they do not rely on classic malware techniques, for instance, they use standard services to store stolen data, and do not perform privileges escalation on the victim phone. Thus, their behavior is generally closer to the benign apps and poses new challenges for their detection. In this paper, we propose ASAINT: A Spy App Identification System based on Network Traffic. To the best of our knowledge, ASAINT is the first system capable of detecting spy apps in a network without any physical or software control of the victim mobile device. Core of our approach is a wide range of non-intrusive network detection methods designed by studying several popular spy apps. We test ASAINT on a self-collected dataset containing network traffic from both spy and benign applications, either on Android and iOS. Our result is an F1-score of 0.85 on average, that confirms the effectiveness of ASAINT. Moreover, our analysis provides a methodological classification of the exfiltration strategies used by spy apps in different operating systems. In sum, our work gives new and practical insights about the detection of modern spy apps, paving the way for future research in detecting this class of malware.

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

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.

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).

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.

Fuel cell technology is considered to be the 21st century of choice for clean and efficient power generation technology, due to a fuel cell itself is a very complex physical and chemical process, the input output are different kinds of physical quantities, so a practical fuel cell must have accurate monitoring and control the performance of these physical quantities. Are briefly introduced in this paper the characteristic and principle of the fuel cell, the function of fuel cell system and the fuel cell test system are analyzed, and made a specific fuel cell test system implementation scheme. The system Atmega32 is the core, can finish the collection work of various data quantity very well, theapplication fuel cell inspection meter can complete to the battery voltage and the current measurement. This system has great flexibility, except for the detection of PEMFC, it can also be adapted to different types of fuel cell detection.

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.

ISBN: 978-1-4503-4490-6 ; Conference Code:130944; International audience; Stance detection systems often integrate social clues in their algorithms. While the influence of social groups on stance is known, there is no evaluation of how well state-of-the-art community detection algorithms perform in terms of detecting like-minded communities, i.e. communities that share the same stance on a given subject. We used Twitter's social interactions to compare the results of community detection algorithms on datasets on the Scottish Independence Referendum and US Midterm Elections. Our results show that algorithms relying on information diffusion perform better for this task and confirm previous observations about retweets being better vectors of stance than mentions.

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.

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.

Automated detection of snow avalanches is an important tool for avalanche forecasting and for assessing the effectiveness of avalanche control measures at bad visibility. Avalanche detection systems are usually based on infrasound, seismic, or radar signals. Within this study, we compared three different types of avalanche detection systems: one avalanche radar, one infrasound array system consisting of four infrasound sensors, and a newly developed single sensor infrasound system. A special focus is given to the new single sensor system, which is a low cost, easy to install system, originally designed for the detection of debris flows and debris floods. Within this work, we analysed how this single sensor system could be adapted to detect also snow avalanches. All three systems were installed close to a road near Ischgl (Tyrol, Austria) at the avalanche-exposed Paznaun Valley. The valley is endangered by two avalanche paths which are controlled by several avalanche towers. The radar system detected avalanches accurately and reliably but was limited to the particular avalanche path towards which the radar beam was directed. The infrasound array could detect avalanches from all surrounding avalanche paths, however, with a higher effort for installation. The newly tested single infrasound sensor system was significantly cheaper and easier to install than the other two systems. It could also detect avalanches form all directions, although without information about the direction. In summary, each of the three different systems was able to successfully detect avalanches and had its particular strengths and weaknesses, which should be considered according to the specific requirements of a particular practical application.

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.