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©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper a wearable system with a sensor embroidered on a textile substrate to detect urine leaks is presented. The system consists of a moisture textile capacitive sensor together with the signal conditioning and its wireless transmission to the cloud. The proposed system has been integrated on underwear and hospital sheet to detect the urine leakage on the diaper users and critical ill patient, respectively. The methodology used by the microcontroller to measure the sensor value is a charge/discharge method. The information is visualised through a computer or smartphone, where can be seen the current state of the sensor. The system has a warning set up to communicate any urine leakage. The experimental results show the functionality of the proposed system which could supply a new tool to hospitals, nursing homes or other institutions to detect when the patient diaper or sheet need to be removed. This tool can optimise the hospital protocol and improve the patient quality of life. This work was supported by Spanish Government-MINECO under Project TEC2016-79465-R and AGAUR-UPC(2020 FIB 00028).

In this paper, a method to develop embroidered textile strain resistive sensors is presented. The method is based on two overlapped zigzag conductive yarn patterns embroidered in an elastic textile. To demonstrate the functionality of the proposed configuration, a textile sensor embroidered with a conductor yarn composed of 99% pure silver-plated nylon yarn 140/17 dtex has been experimentally characterised for an elongation range from 0% to 65%. In order to show the sensor applicability, a second test with the sensor embroidered in a knee-pad has been done to evaluate the flexion knee angle from 180° to 300°. The experimental results show the usefulness of the proposed method to develop fabric strain sensors that can help to manufacture commercial applications on the healthcare sector. This work was supported by Spanish Government-MINECO under Project TEC2016-79465-R and AGAUR-UPC(2020 FI-B 00028).

This paper presents an offline deep learning approach focused to model and identify a 270 V-to-28 V DC-DC step-down converter used in on-board distribution systems of more electric aircrafts (MEA). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black-box, and trains a wavelet convolutional neural network (WCNN) that is able of accurately reproducing the behavior of the DC-DC converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with experimental data obtained from a setup that replicates the 28 V on-board distribution system of an aircraft. The results presented in this paper show a high correlation between measured and estimated data, robustness and low computational burden. This paper also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other DC-DC converters, depending on their requirements.

The need for improving the energy efficiency of existing buildings has driven to the implementation of building energy management systems (BEMS) that can help facilities manager to discover and identify problems that may cause energy wastage or affect to occupants’ comfort. Modern data-driven fault detection and diagnosis (FDD) make use of the data collected by the building BEMS to provide high accuracy in the revelation of heating, ventilation and air-conditioning (HVAC) system faults. However, these methods need a large amount of faulty data samples during the training, which is an uncommon situation in the real world. The main focus of this paper is to present a methodology to detect faults when the number of faulty samples is low. For this purpose, a regression-based methodology based on an adaptative neuro-fuzzy inference system (ANFIS) chiller model is developed using the data collected from a real use case. The model presents good results, that can be used for benchmarking the machine operation and detect the abnormal operation states.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The present paper provides a solution to operate multilevel dual active bridge (ML-DAB) converters built upon neutral point clamped switching legs in a full-bridge disposition and with any number of levels on either side of the converter. The main issue of such converters is addressing the inherent unbalance of the dc-link capacitor voltages, which hinders the proper operation and optimum utilization of the converter. The proposed solution comprises a generalized to N levels modulation and control scheme that allows operating these converters, while guaranteeing the proper balance of the dc-link capacitor voltages. The suitability of the proposed solution is verified through simulation and experimental tests performed in five different converter configurations, three of them with an asymmetric number of voltage levels, proving that capacitor voltage balance is achievable in a wide range of operation conditions. Moreover, the efficiency of the ML-DAB converters is demonstrated to be superior to the conventional two-level DAB case.

This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Anomaly detection in manufacturing processes is one of the main concerns in the new era of the Industry 4.0 framework. The detection of uncharacterized events represents a major challenge within the operation monitoring of electrical rotatory machinery. In this regard, although several machine learning techniques have been classically considered, the recent appearance of deep-learning approaches represents an opportunity in the field to increase the anomaly detection capabilities in front of complex electromechanical systems. However, each anomaly detection technique considers its own data interpretability and modelling strategy, which should be analyzed in front of the specificities of the data generated in an industrial environment and, specifically, by an electromechanical actuator. Thus, in this study, a comparison framework is considered including multiple fault scenarios in order to analyze the performance of four representative anomaly detection techniques, that is, one-class support vector machine, k-nearest neighbor, Gaussian mixture model and, finally, deep-autoencoder. The experimental results suggest that the use of the deep-autoencoder in the task of detecting anomalies of operation in electromechanical systems has a higher performance compared to the state of the art methods.

This paper is a postprint of a paper submitted to and accepted for publication in IET renewable power generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library. In the last few years, microgrids have become a potential solution to improve reliability and security in the supply of electricity to power systems. When a microgrid changes its operation mode from grid-connected to islanded, particular attention must be paid by the operation services. In the islanded mode, the microgrids’ frequency and voltage may reach undesirable values, harming the security and quality of the operation. This study aims to propose global and local strategies of load shedding to preserve the energy supply to high priority loads within quality standards. The focus of this study is to consider a microgrid operating only with primary control. In this sense, a proper microgrid time-continuous load flow is applied, considering the constant verification of the frequency and the voltage of all buses. Finally, a Monte Carlo simulation is used to validate the proposal presented and to give some indices that quantify the load shed in each period. The results show the superior performance of the proposed strategy compared to a state-of-the-art load shedding solution that does not consider the priority of loads.

Non-volatile memory cells are exposed to adversary attacks since any active countermeasure is useless when the device is powered off. In this context, this work proposes the association of two serial RRAM devices as a basic cell to store sensitive data, which could solve this bothersome problem. This cell has three states: ‘1’, ‘0’, and masked. When the system is powered off or the data is not used, the cell is set to the masked state, where the cell still stores a ‘1’ or a ‘0’ but a malicious adversary is not capable of extracting the stored value using reverse engineering techniques. Before reading, the cell needs to be unmasked and it is masked afterwards until the next reading request. The operation of the cell also provides robustness against side-channel attacks. The presented experimental results confirm the validity of the proposal. This research was supported in part by the Spanish Ministry of Science, Innovation and Universities under Grant PID2019-103869RB-C33/ AEI /10.13039/501100011033, and the FEDER program under Grant TEC2017-84321-C4-1-R.

Síndrome de fatiga crònica; Diferències de gènere; Variabilitat de la freqüència cardíaca Síndrome de fatiga crónica; Diferencias de género; Variabilidad del ritmo cardíaco Chronic fatigue syndrome; Gender differences; Heart rate variability In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS. This research was funded by “Ministerio de Ciencia e Innovación” of the Spanish Government, grant number PID2019-107473RB-C2.

With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km. This research was funded by Albora Technologies and Universitat Politècnica de Catalunya with industrial PhD grant number DI 082 from the Generalitat de Catalunya; This research was partially funded by the Spanish Ministry of Science and Innovation project RTI2018-094295-B-I00. P.C. has been partially supported by the NSF under Awards CNS-1815349 and ECCS-1845833

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A new embedded system is presented for real-time wave monitoring on coastal areas using SigFox communication. SigFox is a Low-Power Wide-Area Network technology that has been rarely used in coastal marine monitoring. The system is based on the low-power TD1205P module that includes a microcontroller, an accelerometer, a GNSS receiver and a SigFox transceiver. Each hour, the module estimates the wave’s maximum height (Hmax) and mean period (Tz), determines the GPS position, and wirelessly transmits the data through the SigFox network. The procedure for wave parameter estimation is based on the zero-upcrossing method using the vertical acceleration data. It was experimentally validated by attaching the embedded system to a moored buoy and comparing Hmax and Tz with that provided by a seafloor acoustic wave and current profiler, used as a reference. Results over a period of two months show a good match for Hmax but less for Tz, which cross-correlation values at zero lag of about 0.85 and 0.5, respectively. Power tests of the embedded system were also performed resulting in a lifetime estimation of 420 days with a battery pack of 3 Ah. This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under project TEC2016-76991-P and by the project MELOA from the European Commission’s Horizon 2020 research and Innovation program under Grant Agreement No. 776280. The first author has a grant from the Secretariat of Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia on the FI program (ref. BDNS 362582).

The proliferation of grid-connected converter interfaced energy sources in Smart Grids, enhance sustainability and efficiency as well as minimizing power losses and costs. However, concerns arise regarding the stability and reliability of future smart grids due to this wide integration of power electronic devices, which are recognized to affect the dynamic response of the system, especially during disturbances. For instance, apart from the lower damping of existing electromechanical modes, new low-frequency oscillations begin to appear. Yet, the ability of grid-connected converters to provide grid support functionalities can alleviate the aforementioned challenges. Relevant studies show that these functionalities can be enhanced even further, if information regarding the oscillation characteristics are available. Traditional methods for extracting modal information are very well suited for monitoring purposes, however, they pose certain limitations when considered for control applications. Therefore, this paper proposes a multi-band intelligent power oscillation damper (MiPOD) that exploits 1) the inherent characteristics of grid-connected converters to damp multiple power oscillations and 2) the modeling capabilities of Artificial Intelligence (AI) for predicting the frequency of electromechanical oscillations in the system, as operating conditions change. Essentially, the MiPOD integrates the AI model in the control loop of the converter to attenuate multiple modes of oscillation. The proposed controller is validated for different disturbances and randomly generated operating points in the two area system. Specifically, in this case the AI model is a Random Forest ensemble regressor that is developed for tracking two electromechanical modes. As it is shown, the MiPOD can improve the overall performance of the system under various contingency scenarios with only 6% of the corresponding total nominal capacity of synchronous generators. In addition, the monitoring and damping abilities of the MiPOD are demonstrated for a vast range of operating points just by tuning two parameters; the predicted oscillation frequencies of the local and inter-area mode. This work was supported by the European Commission under project INTERFACE - H2020-LC-SC3-2018-ES-SCC-824330 and by the Spanish Ministry of Science under project ENE2017-88889-C2-1-R.

This article presents a novel front-end circuit for remote grounded capacitive sensors. The circuit is insensitive to amplifier input capacitance and stray interconnecting-cable capacitances. It does not rely on neutralization or compensation techniques that require manual adjustments and high-accuracy reference components. The proposed solution reduces the circuit input capacitance below a few femptofarads using a simple but properly shielded custom transformer. In addition, the circuit keeps a nonlinearity error below 0.01% when measuring capacitances of units or tens of picofarad, even with up to 12-m interconnecting cables.

This paper will provide a classification of high efficiency switching power-gyrator structures and their use as cells for energy processing in photovoltaic solar facilities. Having into account the properties of these topologies presented in the article, their inclusion in solar facilities allows increasing the performance of the whole installation. Therefore, the design, simulation and implementation of a Gtype power gyrator are carried out throughout the text. In addition, in order to obtain the maximum power from the photovoltaic solar panel, a maximum power point tracking (MPPT) is mandatory in the energy processing path. Therefore, the practical implementation carried out includes a control loop of the power gyrator in order to track the aforementioned maximum power point of the photovoltaic solar panel.

This paper analyses a novel countermeasure to Leakage Power Analysis Attacks based on the application of a random Body Bias voltage level at the beginning of the encryption process. The countermeasure effectiveness is established through the development of a theoretical model of the Pearson correlation coefficient in the presence of a varying body bias under both noiseless assumptions and in the presence of algorithmic noise, and through simulations on a partial cryptosystem implemented in 28 nm FDSOI technology. A study of the effect of averaging power measurements is also developed and contrasted against Monte Carlo simulations of the countermeasure scheme, effectively providing a floor for the increase in required measurements to identify the secret key.

WOS:000539280100053; International audience; In this paper a distributed algorithm for ensuring Optimal Power Flows in hybrid AC-DC systems is proposed. While centralized OPF formulations require information of many variables of all the network nodes, the present approach relies on solving an OPF locally in each AC agent and AC-DC agent that only requires to exchange the voltages corresponding to adjacent nodes. Therefore, operation schemes based on this approach would be specially interesting when communications with the central unit fail. This algorithm is applied to a 3 DC - 5 AC bus system, showing that the obtained results coincide with those of the centralized formulation. Additionally, the dynamic behavior of this system operated under the proposed algorithm is analysed in a real-time platform in a realistic communication environment.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Connected and Autonomous Vehicles (CAVs) rely on Global Navigation Satellite Systems (GNSS), e.g., the Global Positioning System (GPS), for the provision of accurate location information for various functionalities including Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communication and self-navigation. However, GNSS- based location awareness is prone to spoofing attacks, where the attacker generates counterfeit satellite signals. This in turn poses a serious threat to the CAV, e.g., car, drone, etc., as well as the surrounding entities. Thus, this threat needs to be detected reliably and mitigated timely to prevent undesired consequences (e.g., damages, casualties, etc.). To this end, this work proposes a location verification solution that leverages in-vehicle sensor readings (e.g., accelerometer, etc.) and Signals of Opportunity (SoO), as an alternative source of location information. In particular, the multimodal sensor data with SoO location measurements are fused by means of a Kalman filter and the estimated fusion-based location is used to verify the location output of the GPS receiver. In case the GPS location deviates considerably from the fusion-based location, then a location spoofing attack is ascertained. Preliminary experimental results with real GPS and sensor data collected with a drone demonstrate the effectiveness of the proposed approach.

Alternate testing techniques have been progressively adopted as a promising solution due to their effectiveness against classical specification-based test methods. This work presents a built-in test system, which adaptively generates an indirect digital signature characterising the circuit under test, which is later used to diagnose the actual performances relying on a statistical dictionary-based diagnosis method. The system is composed of an integrated digital signature generator and a digital control and acquisition subsystem. The signature generator is based on a converter architecture, in which the analogue range can be adapted to the magnitude of the indirect measure using the well known information of the fault-free circuit. The digital subsystem controls the proposed architecture and stores the digital codes sent by the integrated digital signature generator. The digital signature generator has been designed and fabricated in an industrial 65 nm complementary metal–oxide–semiconductor technology from STMicroelectronics, whereas the digital control and acquisition subsystem have been prototyped in a field-programmable gate array. The fabricated system has been used to test a biquad filter affected by parametric variations. Successful experimental results are reported showing the capabilities of the proposed adaptive test system to diagnose circuit performances with discrepancies as low as 1% of the actual real value.

The increased dependency on electricity of modern society makes reliability of power transmission systems a key point. This goal can be achieved by continuously monitoring power grid parameters, so possible failure modes can be predicted beforehand. The deployment of Internet of Things (IoT) solutions and new low-cost sensors eases this task. This paper presents a self-powered IoT solution for real-time monitoring of different parameters of a high-voltage substation connector. This new family of power connectors is called SmartConnector, which incorporates a thermal energy harvesting system powering a microcontroller that controls a transmitter, and several electronic sensors to measure the temperature, current and voltage drop across the connector to estimate the electrical contact resistance (ECR) of the connector. These measurements are sent remotely via a Bluetooth 5 wireless communication module to a local gateway, which transfers the measured data to a database server for storage and further analysis and visualization. Different experiments are presented to validate the feasibility of the SmartConnector in terms of measurement accuracy in comparison with a wired system. The proposed thermal energy harvesting is tested to calculate the optimum data transfer rate needed for extending the lifetime of the SmartConnector. A shielding solution for the electronics operating under this high voltage environment is also proposed

Autonomous tracking of dynamic targets by the use of Unmanned Aerial Vehicles (UAVs) is a challenging problem that has practical applications in many scenarios. In this context, a fundamental aspect that must be addressed has to do with the position estimation of aerial robots and a target to control the flight formation. For non-cooperative targets, their position must be estimated using the on-board sensors. Moreover, for estimating the position of UAVs, global position information may not always be available (GPS-denied environments). This work presents a cooperative visual-based SLAM (Simultaneous Localization and Mapping) system that allows a team of aerial robots to autonomously follow a non-cooperative target moving freely in a GPS-denied environment. One of the contributions of this work is to propose and investigate the use of a target-centric SLAM configuration to solve the estimation problem that differs from the well-known World-centric and Robot-centric SLAM configurations. In this sense, the proposed approach is supported by theoretical results obtained from an extensive nonlinear observability analysis. Additionally, a control system is proposed for maintaining a stable UAV flight formation with respect to the target as well. In this case, the stability of control laws is proved using the Lyapunov theory. Employing an extensive set of computer simulations, the proposed system demonstrated potentially to outperform other related approaches

Acoustic emission (AE) analysis is a powerful potential characterisation method forfracture mechanism analysis during metallic specimen testing. Nevertheless, identifying and extracting each event when analysing the raw signal remains a major challenge. Typically, AEdetection is carried out using a thresholding approach. However, thoughextensively applied, this approach presents some critical limitationsdue to overlapping transients,differences in strength and low signal-to-noise ratio.To address these limitations, advancedmethodologies for detecting AE hits have been developedin the literature. The most prominently used are instantaneous amplitude, the short-termaverage to long-term average ratio,the Akaike information criterionandwaveletanalysis, each of which exhibits satisfactory performance and easeof implementationfordiverseapplications. However, their pronenessto errors in the presence of non-cyclostationary AEwavefrontsand the lack of thoroughcomparison for transient AE signalsare constraints to the wider application of these methodsin non-destructive testing procedures.In this studywith the aim of make aware about the drawbacks of the traditionalthreshold approach, a comprehensiveanalysis ofits limiting factorswhentaking in regard the AE waveformbehaviouris presented.Additionallyin a second section, a performance analysis of the main advanced representative-methods in the field is carried out throughacommon comparative framework, by analysing first, AE waves generated from a standardisedHsu-Nielsen testand second, adata frame of a highly active signal derivedfrom a tensile test.With the aim to quantify the performance with which theseAE detection methodologies work, for the first time in literature, time features as the endpoint and duration accuracies, as well as statistical metricsas accuracy, precisionand false detection rates, are studied.

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

IEEE In this manuscript, a novel robust torque control strategy for Permanent Magnet Assisted Synchronous Reluctance Machine drives applied to electric vehicles and hybrid electric vehicles is presented. Conventional control techniques can highly depend on machine electrical parameters, leading to poor regulation under electrical parameters deviations or, in more serious cases, instabilities. Additionally, machine control can be lost if field weakening is not properly controlled and, as a consequence, uncontrolled regeneration is produced. Thus, advanced control techniques are desirable to guarantee electric vehicle drive controllability in the whole speed/torque operation range and during the whole propulsion system lifetime. In order to achieve these goals, a combination of a robust second order current based Sliding Mode Control and a Look- up Table/Voltage Constraint Tracking based hybrid Field Weakening control is proposed, improving the overall control algorithm robustness under parameter deviations. The proposed strategy has been validated experimentally in a full scale automotive test bench (51 kW prototype) for being further implemented in real hybrid and electric vehicles.

Dynamic thermal line rating (DTLR) allows us to take advantage of the maximum transmission capacity of power lines, which is an imperious need for future smart grids. This paper proposes a real-time method to determine the DTLR rating of aluminum conductor steel-reinforced (ACSR) conductors. The proposed approach requires a thermal model of the line to determine the real-time values of the solar radiation and the ambient temperature, which can be obtained from weather stations placed near the analyzed conductors as well as the temperature and the current of the conductor, which can be measured directly with a Smartconductor and can be transmitted wirelessly to a nearby gateway. Real-time weather and overhead line data monitoring and the calculation of DTLR ratings based on models of the power line is a practical smart grid application. Since it is known that the wind speed exhibits important fluctuations, even in nearby areas, and since it plays a key role in determining the DTLR, it is essential to accurately estimate this parameter at the conductor’s location. This paper presents a method to estimate the wind speed and the DTLR rating of the analyzed conductor. Experimental tests have been conducted to validate the accuracy of the proposed approach using ACSR conductors.

The objective of this paper is to measure the zero-flow offset variation in ultrasonic Clamp-on flowmeters produced by using pipes made of materials with a nonhomogeneous and nonlinear behavior. The evaluation of this phenomenon has been tested by the experimental measurement, the experiments consist of measuring the variation in the flow measurement when in ``no-flow condition,'' the transducers are moved along the pipe. Experimental results reveal that the reciprocity criterion is not fulfilled when nonhomogeneous and nonlinear pipe materials are used. Consequently, in these types of pipes materials, it is necessary to calibrate the offset when transducers are installed, and probably will need recalibration if the transducers are displaced to another position on the pipe. However, in installations, where it is not possible to stop flow in order to calibrate, the flowmeter will have an error in the measured value and this error will depend on the acoustic characteristics of the pipe material.

© 2017 IEEE. Personal use of this ma terial is permitted. Permission from IEEE must be obtained for al l other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, f or resale or redistribution to se rvers or lists, or reuse of any copyrighted compone nt of this work in other works This paper proposes a modulation strategy for multilevel multiphase diode-clamped dc–ac converters (also applicable to other functionally equivalent topologies) able to keep the dc-link capacitor voltages balanced under passive front ends, low frequency modulation indices (i.e., low number of switching transitions per fundamental cycle), any value of the amplitude modulation index, and any ac-side displacement power factor. A suitable phase voltage pattern with minimum number of switching transitions is presented for the n-level three-phase case. Subsequently, it is extended to higher number of switching transitions per fundamental cycle and to higher number of phases. Simulation results with three, four, and five levels; three and five phases; and several frequency-modulation-index values are presented to validate the proposed modulation strategy. Experimental results obtained with a four-level three-phase dc–ac converter prototype are also provided. The proposed modulation strategy enables the use of this type of converters in applications where the ac fundamental frequency may be close to the switching frequency, such as in high-power systems and variable-speed motor drives.

An important challenge of modern data centers is to reduce energy consumption, of which a substantial proportion is due to the network. Switches and NICs supporting the recent energy efficient Ethernet (EEE) standard are now available, but current practice is to disable EEE in production use, since its effect on real world application performance is poorly understood. This paper contributes to this discussion by analyzing the impact of EEE on MapReduce workloads, in terms of performance overheads and energy savings. MapReduce is the central programming model of Apache Hadoop, one of the most widely used application frameworks in modern data centers. We find that, while 1GbE links (edge links) achieve good energy savings using the standard EEE implementation, optimum energy savings in the 10 GbE links (aggregation and core links) are only possible, if these links employ packet coalescing. Packet coalescing must, however, be carefully configured in order to avoid excessive performance degradation. With our new analysis of how the static parameters of packet coalescing perform under different cluster loads, we were able to cover both idle and heavy load periods that can exist on this type of environment. Finally, we evaluate our recommendation for packet coalescing for 10 GbE links using the energy-delay metric. This paper is an extension of our previous work [1], which was published in the Proceedings of the 40th Annual IEEE Conference on Local Computer Networks (LCN 2015). This work was supported in part by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant 610456 (EUROSERVER), in part by the Spanish Government through the Severo Ochoa programme (SEV-2011-00067 and SEV-2015-0493), in part by the Spanish Ministry of Economy a nd Competitiveness under Contract TIN2012-34557 and Contract TIN2015-65316-P, and in part by the Generalitat de Catalunya under Contract 2014-SGR-1051 and Contract 2014-SGR-1272.

Although one of the key characteristics of High Performance Computing (HPC) infrastructures are their fast interconnecting networks, the increasingly large computational capacity of HPC nodes and the subsequent growth of data exchanges between them constitute a potential performance bottleneck. To achieve high performance in parallel executions despite network limitations, application developers require tools to measure their codes’ network utilization and to correlate the network’s communication capacity with the performance of their applications. This paper presents a new methodology to measure and understand network behavior. The approach is based in two different techniques that inject extra network communication. The first technique aims to measure the fraction of the network that is utilized by a software component (an application or an individual task) to determine the existence and severity of network contention. The second injects large amounts of network traffic to study how applications behave on less capable or fully utilized networks. The measurements obtained by these techniques are combined to predict the performance slowdown suffered by a particular software component when it shares the network with others. Predictions are obtained by considering several training sets that use raw data from the two measurement techniques. The sensitivity of the training set size is evaluated by considering 12 different scenarios. Our results find the optimum training set size to be around 200 training points. When optimal data sets are used, the proposed methodology provides predictions with an average error of 9.6% considering 36 scenarios. With the support of the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Expedient 2013BP_B00243). The research leading to these results has received funding from the European Research Council under the European Union’s 7th FP (FP/2007-2013) /ERC GA n. 321253. Work partially supported by the Spanish Ministry of Science and Innovation (TIN2012-34557)

In energy-harvesting applications, inductor-based switching dc/dc converters are usually employed to regulate the operating voltage of the energy transducer and to transfer the harvested energy to a storage unit. In such a context, this paper analyses the optimal inductor current of the converter that leads to maximum power efficiency. This is evaluated assuming a low-power photovoltaic (PV) module connected to a boost dc/dc converter operating in burst mode so as to reduce the switching losses. The theoretical analysis and the experimental results reported herein prove that this optimal inductor current does not depend on the power generated by the PV module provided that the control circuit is powered from the output, but it does on the output voltage level of the storage unit. Experimental tests with a commercial boost dc/dc converter show that the use of this optimal inductor current provides up to 10% increase in efficiency.

This study presents the analysis, design and implementation of a simple and cost-effective residential inductive contactless energy transfer system with multiple mobile clamps. The topology is based on the cascaded connection of a buck converter and a high-frequency resonant inverter loaded by several output passive rectifiers. The proposed system includes a sliding transformer to supply the mobile loads, leading to a safe and flexible location of loads. The theoretical analysis and design of the proposed system is based on a mathematical model derived using the first harmonic approximation. Selected experimental results are included to verify the system features. In comparison with conventional topology, the proposed system significantly improves efficiency, complexity and cost.

Some algorithms running with compromised data select cache memory as a type of secure memory where data is confined and not transferred to main memory. However, cold-boot attacks that target cache memories exploit the data remanence. Thus, a sudden power shutdown may not delete data entirely, giving the opportunity to steal data. The biggest challenge for any technique aiming to secure the cache memory is performance penalty. Techniques based on data scrambling have demonstrated that security can be improved with a limited reduction in performance. However, they still cannot resist side-channel attacks like power or electromagnetic analysis. This paper presents a review of known attacks on memories and countermeasures proposed so far and an improved scrambling technique named random masking interleaved scrambling technique (RM-ISTe). This method is designed to protect the cache memory against cold-boot attacks, even if these are boosted by side-channel techniques like power or electromagnetic analysis.

This paper studies a multilevel multiphase dc-ac conversion system configured by a neutral-point-clamped converter fed by multiple battery packs connected in series. A virtual-vector modulation is selected and a state-of-charge (SoC) balancing control is designed to be able to employ the full battery bank capacity, even under different battery initial SoC values or different battery nominal capacities. The SoC balancing among battery packs is accomplished through the multilevel converter operation in a lossless manner, by simply distributing the dc-to-ac power flow among the batteries according to their SoC. A simple average system model is also presented, which allows performing very fast system simulations over long periods of time and serves as a convenient tool to tune the compensator parameters. The satisfactory performance of the proposed system configuration and control, which can be applied with any number of levels and phases, has been verified through simulations and experiments in a four-level three-phase dc-ac converter fed by three lithium-ion battery packs. The results prove the feasibility and advantages of the proposed system configuration, which can be used to implement conversion systems with different specifications combining several instances of a standard battery pack and a standard power semiconductor device

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The main goals of the CARAMEL project are to enhance the protection of modern vehicles against cybersecurity threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture for improving the security and privacy of communication for connected and au- tonomous driving. The proposed architecture includes: (i) multi- radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support; (ii) a MEC platform, where algorithms for detecting attacks are implemented; (iii) an intelligent On- Board Unit with anti-hacking features inside the vehicle; (iv) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application scenario, the interaction between the entities of the CARAMEL architecture is showcased in the case that the GPS locations used by vehicles are spoofed.

In this paper, we present a new method to mitigate the effect of the charge collected by trigate FinFET devices after an ionizing particle impact. The method is based on the creation of an internal structure that generates an electrical field that drives the charge generated by the ion track out of the sensitive device terminals. This electrical field is generated with the insertion of complementary doped regions near the active region of the device. We analyze the influence of the distance of those regions to the device, their depth into the substrate and their doping concentration to determine the optimal implementation which minimizes the collected charge. The impact on the device performance in terms of leakage current, threshold voltage, maximum transconductance and subthreshold voltage swing has also been investigated. Our results show that the added structures introduce negligible effects in performance degradation and total leakage current, at the cost of a small increase in area. The simulations performed with technology computer-aided design numerical (TCAD) tools in 22nm bulk FinFET technology show that the amount of charge collected by the device terminals can be reduced up to 50% for a linear energy transfer (LET) of 60 MeV-cm2/mg., Spanish Ministry of Economy (MINECO), European Union (EU) TEC2016-75151-C3-R

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works This article evaluates the benefits of having peripheral-triggered peripherals in a microcontroller unit (MCU) intended for low-power sensor applications. In such an architecture, the functionality is moved from the central processing unit (CPU) to the peripherals so that a peripheral is able to trigger another peripheral with non-CPU intervention. For the sensor data logging application under study, both energy consumption and measuring time are reduced by a factor of 2 with respect to the case of applying an interrupt-based approach that requires the CPU intervention.

The unwanted electro-forming process is unavoidable for the practical application of most resistive random access memory (ReRAM) devices, which is always being one of the obstacles for the massive commercialization of this novel electronic device. In this letter, a forming-free Pt/Ti/(TiO2-x )/Ta2O5/Pt based ReRAM device is demonstrated with an additional feature of the low operation voltage. The fitting result of the measured I-V curves reveals that resistive switching of the fabricated device is conducted by the electrons trapping/de-trapping process in deep-level electron traps of the TiO2-x layer, which is formed through the spontaneous “oxygen grabbing” reaction in the interface of Ti and Ta2O5 during the film deposition. The plentiful oxygen vacancy defects and the thin resistive switching zone (TiO2-x ) ensure the forming-free and low operating voltage characteristics. Using “oxygen grabbing” to pre-produce abundant electron trapping centers for the resistive switching provides a simple way for the fabrication of the forming-free ReRAM device with low operating voltage, aiming to the high-density and low-power memory applications.

A front-end for biopotential sensing in wearable medical devices has been recently proposed which is claimed to provide 100 GOinput impedance by manually matching two resistor pairs in a positive- and a negative-feedback loop around an operationalamplifier (op amp); the cost being that the equivalent input noise voltage doubles with respect to a simple non-inverting amplifier.The ECG acquired with capacitive (sic) electrodes through a cotton shirt is presented as a proof of the performance of theproposed circuit. It turns out, however, that the analysis ignores op amp’s input capacitance hence the effort to achieve a very highinput resistance seems futile. Further, cotton is highly hygroscopic hence not an appropriate dielectric, so that there is no proofthat the electrodes tested were actually capacitive. This comment addresses these two problems and some additional conceptualand methodological inaccuracies found in the paper.

A novel closed-loop capacitance-to-pulse width converter (CPC) suitable for single element capacitive sensors that use sinusoidal excitation is presented in this paper. Its operation is realized using a new configuration based on a simple, yet effective, auto-balancing scheme. The hardware prototype of the proposed CPC is relatively less complex to implement than those presented so far in the literature. It provides a quasi-digital output at a high update rate. Additionally, the output is insensitive to parasitic capacitances of the sensor. The output possesses high linearity, with respect to change in the sensor capacitance, ranging +/-5 pF, with a nominal capacitance as high as 200 pF. It exhibits a maximum non-linearity error of 0.061%FS. The output of the prototype has a resolution of 13.31 bits. Also, its response time for a step-change in the sensor capacitance is about 13 ms. This sophisticated and inexpensive closed-loop CPC is a perfect fit as an interfacing circuit for single element capacitive sensors.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Thispaperpresents the design and structure of aversatileworkbench simulator forevaluating the performance of a five-phase inverter andPermanent Magnet assisted Synchronous Reluctance Motor(PMa-SynRM). The simulatorallows for adding variations tothe modulationtechniques, changingthe inverter structure’s semiconductordevice, and calculatingtheinverter’spower losses. Itcanalso facilitate observingthe current, voltage,andthe jointtemperature ofthe semiconductors devices. Furthermore,wecanobtain a perform that is close to anactualPMa-SynRM, dependingon the desired conditionsof speed and torque. The workbench simulator wasdevelopedby combining three software: Matlab/Simulink, PLECSand Altair Flux.

Purpose: the aim of this paper is to minimize the risks of new product development and shorten time-to-market, particularly for high-tech enterprise where the complexity of the product generates vast amount of failure mode. Design/methodology/approach: first, the concept of Critical Consideration Factor (CCF) is introduced based on product-specific technical characteristics, expected lifetime, and yield requirement to identify and prioritize the critical failure mode in the subsequent Failure Mode and Effect Analysis (FMEA), followed by process characterization on the high-risk failure mode and Critical Parameter Management (CPM) practice to realize a robust mass production system of the developed technology. The application on the development of advanced flexible substrate and surface finishes fabrication technique is presented. Findings: through the proposed methodology, the risk level of each potential failure mode can be accurately quantified to identify the critical variables. With process characterization, reliability of the product is ensured. Consequently, significant reduction in development resources and time-to-market can be achieved. Practical implications: the development strategy allows high tech enterprises to achieve a balanced ecosystem in which value created through adaption of new technology/product can be thoroughly captured through commercialization in a timely manner with no field failure. Originality/value: the proposed development strategy utilizes a unique approach with thorough considerations that enables high tech enterprise to deliver new product with rapid time-to-market without sacrificing product lifetime reliability, which is key to achieve competitive advantage in the highly dynamic market.

© 2016 Elsevier Ltd. A novel nonlinear image encryption scheme based on a fully phase nonzero-order joint transform correlator architecture (JTC) in the Gyrator domain (GD) is proposed. In this encryption scheme, the two non-overlapping data distributions of the input plane of the JTC are fully encoded in phase and this input plane is transformed using the Gyrator transform (GT); the intensity distribution captured in the GD represents a new definition of the joint Gyrator power distribution (JGPD). The JGPD is modified by two nonlinear operations with the purpose of retrieving the encrypted image, with enhancement of the decrypted signal quality and improvement of the overall security. There are three keys used in the encryption scheme, two random phase masks and the rotation angle of the GT, which are all necessary for a proper decryption. Decryption is highly sensitivity to changes of the rotation angle of the GT as well as to little changes in other parameters or keys. The proposed encryption scheme in the GD still preserves the shift-invariance properties originated in the JTC-based encryption in the Fourier domain. The proposed encryption scheme is more resistant to brute force attacks, chosen-plaintext attacks, known-plaintext attacks, and ciphertext-only attacks, as they have been introduced in the cryptanalysis of the JTC-based encryption system. Numerical results are presented and discussed in order to verify and analyze the feasibility and validity of the novel encryption-decryption scheme.

Temperature rise simulations are one of the key steps in the design of high-voltage substation connectors. These simulations help minimizing the number of experimental tests, which are power consuming and expensive. The conventional approach to perform these simulations relies on finite element method (FEM). It is highly desirable to reduce the number of required FEM simulations since they are time-consuming. To this end, this paper presents a data-driven modeling approach to drastically shorten the required simulation time. The data-driven approach estimates the thermal response of substation connectors from the data provided by a reduced number of FEM simulations of different operating conditions, thus allowing extrapolating the thermal response to other operating conditions. In the study, a partitioning method is also applied to enhance the performance of the learning stage of a set of data-driven methods, which are then compared and evaluated in terms of simulation time and accuracy to select the optimal configuration of the data-driven model. Finally, the complete methodology is validated against simulation tests.

Through-silicon vias (TSVs) are critical elements in 3-D integrated circuits susceptible to defects during fabrication and lifetime. It is desirable to detect defective TSVs in the early steps of the fabrication process to prevent stacking yield loss. Thus, the development of effective prebond testing techniques becomes of great importance. In this direction, recent research effort has been devoted to the development of two main prebond techniques: 1) prebond probing and 2) built-in self-test (BIST) techniques. The prebond probing poses economic and technological challenges, whereas current BIST proposals have disadvantages for certain solutions. Hence, there is still a need for an effective methodology in terms of fault coverage, area overhead, and test time. This paper proposes a BIST technique based on a simple unbalanced circuit comparing the behavior of two TSVs. Electrical simulation results show the viability of the proposal to detect weak defects, i.e., resistive opens and resistive bridges, adding reasonable area overhead in a short-test application time. Furthermore, an experimental design is built on a 65-nm technology, where resistive open defects are intentionally injected. Automated test equipment measurements confirm the simulation results.

This paper proposes a microcontroller-based measurement system to detect and confirm the presence of a subject in a chair. The system relies on a single Force Sensing Resistor (FSR), which is arranged in the seat of the chair, that undergoes a sudden resistance change when a subject/object is seated/placed over the chair. In order to distinguish between a subject and an inanimate object, the system also monitors small-signal variations of the FSR resistance caused by respiration. These resistance variations are then directly measured by a low-cost general-purpose microcontroller unit (MCU) without using either an analogue processing stage or an analogue-to-digital converter. Two versions of such a MCU-based circuit are presented: one to prove the concept of the measurement, and another with a smart wake-up (generated by the sudden resistance change) intended to reduce the energy consumption. The feasibility of the proposed measurement system is experimentally demonstrated with subjects of different weight sitting at different postures, and also with objects of different weight. The MCU-based circuit with a smart wake-up shows a standby current consumption of 800 nA, and requires an energy of 125 &micro, J to carry out the measurement after the wake-up.

A crucial aspect for selecting optimal areas for commercial advertising is the probability with which that publicity will be seen. This paper presents a method based on top-view camera measurement, where the probability of viewing is estimated based on the trajectories and movements of the heads of the passerby individuals in the area of interest. Using a camera with a depth sensor, the heads of the people in the range of view can be detected and modeled. That allows to determine the orientation of the head which is used to estimate the direction of vision. A tracking by detection algorithm allows to compute the trajectory of each user. The attention given at each advertising spot is estimated based on the trajectories and head orientations of the individuals in the area of interest.

The surface resistance of state-of-the-art REBa2Cu3O7−x coated conductors has been measured at 8 GHz versus temperature and magnetic field. We show that the surface resistance of REBa2Cu3O7−x strongly depends on the microstructure of the material. We have compared our results to those determined by the rigid fluxon model. The model gives a very good qualitative description of our data, opening the door to unravel the effect of material microstructure and vortex interactions on the surface resistance of high temperature superconductors. Moreover, it provides a powerful tool to design the best coated conductor architecture that minimizes the infield surface resistance. We have found that the surface resistance of REBa2Cu3O7−x at 50 K and up to 9 T is lower than that of copper. This fact poses coated conductors as strong candidate to substitute copper as a beamscreen coating in CERN’s future circular collider. To this end we have also analyzed the secondary electron yield (SEY) of REBa2Cu3O7−x and found a compatible coating made of sputtered Ti and amorphous carbon that decreases the SEY close to unity, a mandatory requirement for the beamscreen chamber of a circular collider in order to prevent the electron-cloud phenomenon.

Microprobing is commonly used by adversaries to extract firmware or cryptographic keys from microcontrollers. We introduce the calibratable lightweight invasive attack detector (CaLIAD) to detect microprobing attacks. The CaLIAD measures timing imbalances between lines that are caused by the capacitive load of a probe. Compared to protection mechanisms from industry, it does not require an additional protection layer such as meshes do; in contrast to bus encryption, it does not introduce delay cycles. Compared to state-of-the-art low area probing detectors, it can be calibrated and, thus, allows compensating manufacturing variations as well as small layout imbalances. This capability allows us to significantly reduce the detection margin compared to the prior art while maintaining the low rate of false positives. We can finally show that capacitive loads of 23 fF or less can be detected, depending on how the CaLIAD is used. This includes all state-of-the-art commercial microprobes we are aware of.

This work aims to experimentally characterize the energy consumption of analog-to-digital converters (ADC) embedded into microcontroller units (MCU) operating in low power for autonomous sensor applications. The study is carried out at low and high operating frequencies, and for different numbers of conversion bits. In addition, this work not only takes into account the energy to do the conversion, but also the wake-up energy, which is implicit to any conversion performed by an embedded ADC. The contribution of this wake-up energy can be quite significant, especially when the conversion is performed at high frequency. Experimental tests have been done using a commercial low-power MCU (MSP430FR5969 from Texas Instruments) that includes a 12- bit ADC.

Memristor devices are two-terminal nanoscale circuit elements that exhibit nonvolatile information storing and can be manufactured in ultra-dense arrays with low-power operation. Although, theoretically, memristors are strong candidates for novel memory and computing applications, the fabricated devices show high variability, both device-to-device and cycle-to-cycle, such as varying switching behaviour and maximum (R MAX ) and minimum (R MIN ) resistance values. Those limitations in the device’s R MAX /R MIN ratio suppress the wide use of memristors in memory or logic applications, thus, this work presents the enhancement of this ratio on actual memristor devices, namely Knowm memristors, due to the introduction of external noise as a beneficial disturbance, following the nonlinear system phenomenon known as Stochastic Resonance.