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In 1-out-of- q Oblivious Transfer (OT) protocols, a sender Alice is able to send one of q >= 2 messages to a receiver Bob, all while being oblivious to which message was transferred. Moreover, the receiver learns only one of these messages. Oblivious Transfer combiners take n instances of OT protocols as input, and produce an OT protocol that is secure if sufficiently many of the n original OT instances are secure. We present new 1-out-of- q OT combiners that are perfectly secure against active adversaries. Our combiners arise from secret sharing techniques. We show that given an Fq -linear secret sharing scheme on a set of n participants and adversary structure A , we can construct an n -server, 1-out-of- q OT combiner that is secure against an adversary corrupting either Alice and a set of servers in A , or Bob and a set of servers B with B is not an element of A . If the normalized total share size of the scheme is & ell; , then the resulting OT combiner requires & ell; calls to OT protocols, and the total amount of bits exchanged during the protocol is (q(2)+q+1)& ell; logq . We also present a construction based on 1-out-of-2 OT combiners that uses the protocol of Cr & eacute;peau, Brassard and Robert (FOCS 1986). This construction provides smaller communication costs for certain adversary structures, such as threshold ones: For any prime power q >= n , there are n -server, 1-out-of- q OT combiners that are perfectly secure against active adversaries corrupting either Alice or Bob, and a minority of the OT candidates, exchanging O(qnlogq) bits in total.

Conventional discontinuous pulse width modulation (DPWM) strategies for cascaded H-bridge static compensators suffer from poor inter-phase capacitor voltage balance control during unbalanced grid voltage conditions. Specifically, the logic-based algorithm that calculates the zero-sequence voltage (ZSV) for discontinuous operation interacts with the closed-loop capacitor voltage control. This paper proposes an optimal DPWM strategy based on a finite-set optimization that chooses the best ZSV candidate that minimizes the control interactions while clamping a converter voltage. Furthermore, the effect of the different ZSV candidates on the switching loss is also considered within the optimization. Extensive experimental study is provided to demonstrate the effectiveness of the proposed approach.

New digital technologies generate large amounts of information. This data is processed by Service Providers in order to improve and develop new services and products, but also to fund themselves. However, processing personal data may result in the extraction of sensitive information, which, in turn, may lead to jeopardizing the users' privacy. To mitigate this significant risk, the European Parliament and Council of the European Union elaborated the General Data Protection Regulation (GDPR). This regulation forces Service Providers to obtain Data Subjects' explicit consent prior to collecting and processing their personal data. Nevertheless, the GDPR's legislative text does not define how Service Providers must transparently demonstrate that they already have these consents. Moreover, most individuals do not know the rights they have over their personal data, neither does this regulation provide them with efficient methods to be aware of what third parties are doing with such data. In order to address this situation, we propose a lightweight blockchain-based GDPR-compliant personal data management platform. The new solution provides public access to immutable evidences that reflect the reached agreements between Data Subjects and Service Providers. In this way, Service Providers can effectively demonstrate that they are fulfilling the regulation, and Data Subjects are able to control and manage their personal data according to their legitimate rights. We have implemented the new system, and we have performed a detailed study which includes: GDPR-compliance, provided functionality, security and privacy issues, and the cost in terms of gas and US dollars of the different operations to be run on the blockchain.

The performance of diagnostic Computer-Aided Design (CAD) systems for retinal diseases depends on the quality of the retinal images being screened. Thus, many studies have been developed to evaluate and assess the quality of such retinal images. However, most of them did not investigate the relationship between the accuracy of the developed models and the quality of the visualization of interpretability methods for distinguishing between gradable and non-gradable retinal images. Consequently, this paper presents a novel framework called “FGR-Net” to automatically assess and interpret underlying fundus image quality by merging an autoencoder network with a classifier network. The FGR-Net model also provides an interpretable quality assessment through visualizations. In particular, FGR-Net uses a deep autoencoder to reconstruct the input image in order to extract the visual characteristics of the input fundus images based on self-supervised learning. The extracted features by the autoencoder are then fed into a deep classifier network to distinguish between gradable and ungradable fundus images. FGR-Net is evaluated with different interpretability methods, which indicates that the autoencoder is a key factor in forcing the classifier to focus on the relevant structures of the fundus images, such as the fovea, optic disk, and prominent blood vessels. Additionally, the interpretability methods can provide visual feedback for ophthalmologists to understand how our model evaluates the quality of fundus images. The experimental results showed the superiority of FGR-Net over the state-of-the-art quality assessment methods, with an accuracy of >89% and an F1-score of >87%. The code is publicly available at https://github.com/saifalkh/FGR-Net.

A critical reduction in the total harmonic distortion in the output voltage of multilevel inverters can be obtained with an appropriate selection of the angles to switch the power semiconductors at low frequency. Among the methods used to obtain the required sequence of angles, it is possible to recognize both standard and optimized methods, like the Newton–Raphson method or the genetic algorithm, to mention a few. However, implementing the genetic algorithm implies using several configuration parameters, so it is difficult to know if the best option was chosen. This paper implements the Taguchi design of experiments, the Taguchi method, to obtain the appropriate switching angles for an multilevel inverter producing an output voltage of five and seven levels. Experimental results obtained from a laboratory prototype using the computed angles in the MATLAB GA solver demonstrate the superiority of the proposed method.

The electrochemical synthesis of polypyrrole (PPy) on laser-induced graphene (LIG) electrodes is investigated in this study, along with the gas sensing applications of these modified laser-tailored materials. A simple and scalable method for the low-cost, large-scale production of polypyrrole LIG nanocomposites (PPy@LIG NCs) is proposed. This nanocomposite is subsequently applied to design chemo-resistive flexible gas sensors to detect ammonia (NH3) levels at room temperature. Following a brief gas exposure, PPy@LIG NCs sensors demonstrate changes in resistance with a sensitivity 14 times higher than that of pure LIG. Moreover, excellent repeatability in results and a low detection limit of 1 ppm were achieved. The LIG formation and electrochemical synthesis of PPy were confirmed through Raman spectroscopy and FTIR spectroscopy. The analysis by FE-SEM and TEM verified the formation of PPy@LIG NCs. In conclusion, this work introduces the novel PPy@LIG nanocomposites and its application in gas sensing, showcasing their unique selectivity, enhanced sensitivity, and cost-effective production. These attributes mark a significant step forward, presenting new possibilities for the development of advanced gas-sensing systems aimed at improving air quality monitoring.

The discontinuous pulsewidth modulation (DPWM) in cascaded H-bridge (CHB) static compensators (StatComs) is achieved via adding a common mode voltage to the converter voltage references. Hence, in discontinuously operated CHB StatComs, the common mode voltage has two functions, namely, inter-phase cluster voltage control and clamping a converter voltage. These two functions are conventionally achieved by two independently designed control stages and interferences between them can cause the loss of cluster voltage control during unbalanced grid conditions, since the fundamental frequency common mode voltage for discontinuous operation is significant. To solve this control issue, this paper proposes a DPWM strategy with embedded inter-phase cluster voltage control using a finite control set model predictive control approach. The proposed predictive DPWM outperforms the conventional DPWM in switching loss reduction while preventing the cluster voltages from diverging during unbalanced grid conditions. The effectiveness of the proposed strategy is verified experimentally considering various grid conditions.

The cascaded H-bridge low-capacitance static compensator (LC-StatCom) deliberately reduces the capacitor size while allowing large oscillations in capacitor voltages. When a capacitor deteriorates, its capacitance decreases or/and its equivalent series resistance (ESR) increases, compromising StatCom performance and safety. For this reason, the condition monitoring of the capacitors is important to schedule maintenance before failure. This letter introduces a cost-effective and nonintrusive method to monitor the condition of capacitors online. The proposed method offers real-time monitoring without requiring additional hardware or signal injection. The method uses averaged low-frequency signals instead of instantaneous high-frequency signals that can be corrupted by undesired high-frequency noise. Specifically, the method takes advantage of the inherent twice-fundamental-frequency oscillations on the capacitor voltage and current to identify the ESR and capacitance values. The method takes into account the influence of antialiasing filters required for sampling process. The letter shows experimental results that validate the fast and accurate performance of the proposed capacitor condition monitoring.

In the operation of battery energy storage systems based on the cascaded H-bridge converter, it is beneficial to balance the state of charge of batteries in different submodules within the converter phase-arm. This is achieved by distributing the active power among the submodules. Although multiple methods have been proposed for this purpose, they face the challenge of rendering optimal active power distributions that maximize balancing speed while meeting power constraints in the battery energy storage system. To overcome this challenge, a model predictive control scheme is developed in this paper. The proposed method is remarkably robust against parametric uncertainties (battery voltage, capacity, etc.), as evidenced by its ability to tolerate a substantial 50% uncertainty in the parameters, resulting in a mere 0.05% steady-state error. Furthermore, because the predictive control can be executed at a low frequency, the computational burden is comparable to other existing methods.

This report presents the analysis, study, and simulation of an ultrafast charging station (UFCS) for electric vehicles (EVs) operating in steady state. The electrical architecture of the charging station uses an ac bus plus two dc buses and it is supported by a storage system based on batteries and super-capacitors. The power demand of the EVs is established taking into account the electric characteristics of their batteries and the availability of the station charging points. The analysis introduces a supervisory control based on a state machine description for different operating modes, which eventually facilitates fault detection in the electrical architecture. In addition, the study proposes different methods to handle the required energy for the charging demand and a procedure for the correct sizing of both the energy storage system and the input transformer. In laboratory experiments in a reduced-scale storage system, a SCADA supervision with CAN communication has proved successful in gathering data corresponding to modes of charge and discharge in batteries and super-capacitors, and subsequently displaying them on a computer screen.

Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state-of-the-art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi-scale-aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor-based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature-bas

To solve real-life problems for different smart city applications, using deep Neural Network, such as parking occupancy detection, requires fine-tuning of these networks. For large parking, it is desirable to use a cenital-plane camera located at a high distance that allows the monitoring of the entire parking space or a large parking area with only one camera. Today’s most popular object detection models, such as YOLO, achieve good precision scores at real-time speed. However, if we use our own data different from that of the general-purpose datasets, such as COCO and ImageNet, we have a large margin for improvisation. In this paper, we propose a modified, yet lightweight, deep object detection model based on the YOLO-v5 architecture. The proposed model can detect large, small, and tiny objects. Specifically, we propose the use of a multi-scale mechanism to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for detecting objects in a scene (i.e., in our case vehicles). The proposed multi-scale module reduces the number of trainable parameters compared to the original YOLO-v5 architecture. The experimental results also demonstrate that precision is improved by a large margin. In fact, as shown in the experiments, the results show a small reduction from 7.28 million parameters of the YOLO-v5-S profile to 7.26 million parameters in our model. In addition, we reduced the detection speed by inferring 30 fps compared to the YOLO-v5-L/X profiles. In addition, the tiny vehicle detection performance was significantly improved by 33% compared to the YOLO-v5-X profile.

We describe parallel and series resonant converters (PRC and SRC) via a unified set of input-dependent coordinates whose dynamics are intrinsically hybrid. We then propose hybrid feedback showing a self-oscillating behavior whose amplitude and frequency can be adjusted by a reference input ranging from zero to $\pi$ . For any reference value in that range, we give a Lyapunov function certifying the existence of a unique nontrivial hybrid limit cycle whose basin of attraction is global except for the origin. Our results are confirmed by experimental results on an SRC prototype.

A great variety of mechanisms have been proposed to protect structured databases with numerical and categorical attributes; however, little attention has been devoted to unstructured textual data. Textual data protection requires first detecting sensitive pieces of text and then masking those pieces via suppression or generalization. Current solutions rely on classifiers that can recognize a fixed set of (allegedly sensitive) named entities. Yet, such approaches fall short of providing adequate protection because in reality references to sensitive information are not limited to a predefined set of entity types, and not all the appearances of certain entity type result in disclosure. In this work we propose a more general and flexible based on the notion of word embedding. By means of word embeddings we build vectors that numerically capture the semantic relationships of the textual terms. Then we evaluate the disclosure caused by the terms on the entity to be protected according to the similarity between their vector representations. Our method also preserves the semantics (and, therefore, the utility) of the document by replacing risky terms with privacy-preserving generalizations. Empirical results show that our approach offers much more robust protection and greater utility preservation than methods based on named entity recognition. IEEE

The advanced modeling and estimation of overall system dynamics play a vital role in electric vehicles (EVs), as the selection of components in the powertrain and prediction of performance are the key market qualifiers. The state-space averaged model and small-signal transfer function model are useful for assessing long-term behavior in system-level analysis and for designing the controller parameters, respectively. Both models take less computation time but ignore the high-frequency switching dynamics. Therefore, these two models could be impractical for the development and testing of EV prototypes. On the other hand, the multi-domain model in available simulation tools gives in-depth information about the short-term behavior and loss analysis of power electronic devices in each subsystem, considering the switching dynamics in a long computation time. In this paper, a general mathematical framework for the dynamical analysis of complete EVs is presented using a unified, switched nonlinear model. This equation-based model runs faster than the available module-based simulation tools. Two other models, namely the time domain state-space averaged model and frequency domain small-signal transfer function model, are also developed from the switched nonlinear model for the analysis with less computation time. The design and performance of an EV with two different motors and its controllers are evaluated using the general mathematical framework.

Recently, there has been significant improvement in the power conversion efficiency of non-fullerene organic solar cells, exceeding 19%. In this article, we have fabricated conventional sandwich structure of ITO/PEDOT:PSS/PM6:Y7/PDINO/Ag reaching efficiency up to 17.3% along with high generated current density of 30.60 mA/cm2 for the optimized devices. The thickness of PM6:Y7 layer has been varied to investigate the impact on the overall device behavior. The obtained results showed that the PCE has been enhanced by 15% upon reducing its thickness from 150 nm to 100 nm. Accordingly, current-voltage measurements and impedance spectroscopy analysis through capacitance-voltage, Mott-Schottky analysis and capacitance frequency under different illumination intensities were conducted to evaluate the behaviour of the charge recombination due to thickness variation of the PM6:Y7 photoactive layer.

This paper presents a compact model for organic thin-film transistors fabricated in the staggered or coplanar device architecture which includes short-channel effects related to the potential barrier in the channel region, which manifest themselves as threshold voltage roll-off, drain-induced barrier lowering, and subthreshold swing degradation. Furthermore, the effect of non-linear injection due to a work-function mismatch between the source/drain contacts and the semiconductor is considered. The model includes a charge-based capacitance model considering overlap and fringing regions in short-channel multi-finger layout structures. Extensions include a model for drain-current variability, low-frequency noise and non-quasistatic effects. The introduction of physically meaningful fitting parameters provides a high degree of flexibility to the model. The equation package is verified using the results of measurements performed on transistors fabricated on flexible substrates and is available in Verilog-A for an efficient circuit simulation on different design platforms.

Distance-bounding protocols are security protocols with a time measurement phase used to detect relay attacks, whose security is typically measured against mafia-fraud and distance-fraud attacks. A prominent subclass of distance-bounding protocols, known as lookup-based protocols, use simple lookup operations to diminish the impact of the computation time in the distance calculation. Independent results have found theoretical lower bounds [Formula presented] and [Formula presented], where n is the number of time measurement rounds, on the security of lookup-based protocols against mafia and distance-fraud attacks, respectively. However, it is still an open question whether there exists a protocol achieving both security bounds. This article closes this question in two ways. First, we prove that the two lower bounds are mutually exclusive, meaning that there does not exist a lookup-based protocol that provides optimal protection against both types of attacks. Second, we provide a lookup-based protocol that approximates those bounds by a small constant factor. Our experiments show that, restricted to a memory size that linearly grows with n, our protocol offers strictly better security than previous lookup-based protocols against both types of fraud.

This paper investigates the effect of decorating graphene with zinc oxide (ZnO) nanoparticles (NPs) for the detection of NO2. In this regard, two graphene sensors with different ZnO loadings of 5 wt.% and 20 wt.% were prepared, and their responses towards NO2 at room temperature and different conditions were compared. The experimental results demonstrate that the graphene loaded with 5 wt.% ZnO NPs (G95/5) shows better performance at detecting low concentrations of the target gas than the one loaded with 20 wt.% ZnO NPs (G80/20). Moreover, measurements under dry and humid conditions of the G95/5 sensor revealed that the material is very sensitive to ambient moisture, showing an almost eight-fold increase in NO2 sensitivity when the background changes from dry to 70% relative humidity. Regarding sensor selectivity, it presents a significant selectivity towards NO2 compared to other gas compounds.

This paper proposes a discontinuous modulation (DM) scheme for cascaded H-bridge (CHB) static compensators (StatComs) with star configuration. The proposed DM scheme guarantees zero steady-state active power in each converter phase-arm, even in the presence of severe grid imbalance conditions, thus remaining decoupled from the inter-phase capacitor voltage balancing controller. Moreover, the proposed DM scheme exploits the possibility of clamping to the zero-voltage level, thus providing a more flexible discontinuous operation than conventional DM methods. This involves lower zero-sequence-voltage injection requirements and lower switching losses. The proposed modulation scheme is experimentally corroborated and its performance under different grid voltage conditions is compared with traditional modulation schemes.

In this work, we investigate organic electrochemical transistors (OECTs) as a novel artificial electronic device for the realization of synaptic behavior, bioelectronics, and a variety of applications. A numerical method considering the Poisson-Boltzmann statistics is introduced to reproduce associated charge densities, electrostatics and switching properties of OECTs. We shed light on the working principle of OECTs by taking into account the ionic charge distribution in the electrolyte and incomplete ionization of the organic semiconductor describing the underlying electrochemical redox reaction. This enables analyzing the OECTs electrical performance as well as a simplified chemical properties via an electrical double layer, doping and de-doping of the OMIEC layer. We have fabricated, characterized, simulated and analyzed OECTs based on PEDOT:PSS, and we show that the proposed model reveals important properties of the device’s working mechanism. The model shows a good agreement with the experimental data of the fabricated devices.

In recent years, there has been a significant increase in the number of collisions between vehicles and vulnerable road users such as pedestrians, cyclists, road workers and more recently scooter riders, especially in urban streets. This work studies the feasibility of enhancing the detection of these users by means of CW radars because they have a low radar cross section. Since the speed of these users is usually low, they can be confused with clutter due to the presence of large objects. To this end, this paper proposes, for the first time, a method based on a spread spectrum radio communication between vulnerable road users and the automotive radar consisting of modulating a backscatter tag, placed on the user. In addition, it is compatible with low-cost radars that use different waveforms such as CW, FSK or FMCW, and hardware modifications are not required. The prototype that has been developed is based on a commercial monolithic microwave integrated circuit (MMIC) amplifier connected between two antennas, which is modulated by switching its bias. Experimental results with a scooter, under static and moving conditions, using a low-power Doppler radar at a 24 GHz band compatible with blind spot radars, are provided.

In this paper, multiple reconfigurable intelligent surface (RIS) boards are deployed to enhance millimeter wave (mmWave) communication in a harsh blockage environment, where mmWave line-of-sight (LoS) link is completely blocked. Herein, RIS-user association should be considered to maximize the users' achievable data rate while assuring load balance among the installed RIS panels. However, maximum received power (MRP) based RIS-user association will overload some of the RIS boards while keeping others unloaded, which causes RIS load to unbalance and decreases the users' achievable data rate. Instead, in this paper, an online learning methodology using centralized multi-player multi-armed bandit (MP-MAB) with arms' load balancing is proposed. In this formulation, mmWave users, RIS boards, and achievable users' rates act as the bandit game players, arms, and rewards. During the MAB game, the users learn how to avoid associating with the heavily loaded RIS boards, maximizing their achievable data rates, and balancing the RIS loads. Three centralized MP-MAB algorithms with arms' load balancing are proposed from the family of upper confidence bound (UCB) MAB algorithms. These algorithms are UCB1, Kullback-Leibler divergence UCB (KLUCB), and Minimax optimal stochastic strategy (MOSS) with arms' load balancing, i.e., UCB1-LB, KLUCB-LB, and MOSS-LB. Numerical analysis ensures the superior performance of the proposed algorithms over MRP-based RIS-user association and other benchmarks.

The cascaded H-bridge (CHB) low-capacitance static compensator (LC-StatCom) has a limited negative-sequence current injection capability compared to a conventional CHB StatCom, due to the comparatively larger oscillations on the capacitor voltages. Placing limits on the ability to provide negative-sequence current to the grid for a given capacitor size is required to prevent overmodulation. This paper shows that these limits can be maximised by injecting an optimal third-harmonic circulating current. The effects of injecting the third-harmonic current on the capacitor voltages, both in balanced and unbalanced grid voltage conditions, are analyzed. Specifically, the procedure allows the knowledge of the potential capability of the LC-StatCom when injecting negative-sequence currents. The procedure is based on a linear programming approach. Although the procedure is applicable to any CHB StatCom, regardless of the submodule capacitor size, it is particularly important in LC-StatComs. To corroborate the proposed analysis, simulation and experimental results under balanced and unbalanced grid voltage conditions are presented.

The unstable on and off state trajectories of a boost converter supplying a constant power load (CPL) are appropriately combined using sliding-mode control (SMC) with a suitable surface to yield a stable trajectory that reaches a specified equilibrium point. Selecting the optimum surface involves a comparative analysis among potential candidates in terms of stability, disturbance rejection, conduction losses and inrush current. The performance as switching function of polynomials of degree zero (non-zero constant), degree one (affine function) and degree 2 (quadratic function) are analyzed in depth to conclude that the affine function leads to the best static and dynamic results. The theoretical predictions are verified by means of simulations and measurements in a prototype.

Low Emission Zones (LEZ)s are areas where access restrictions to polluting vehicles are enforced. These infrastructures have become a main mechanism in large cities to deal with urban traffic and environmental pollution. A main problem of practical LEZs is that they generally depend on a camera network that identifies users and jeopardizes their privacy. In the literature, there are some privacy-preserving works that rely on camera-free approaches; however, they still suffer from a major issue: they depend on centralized entities to manage the vehicles’ accesses/departures and their corresponding fee payment. Those centralized entities represent a critical single point of failure in the system, endangering its security and availability. In order to address this situation, this paper proposes a new scheme that decentralizes the LEZ management, dealing with vehicle accesses as blockchain transactions, and pricing and charging them using smart contracts. In order to validate the deployability of the new scheme in real scenarios, it has been implemented and tested in both a controlled environment and a low-traffic street. The evaluation of the smart contracts’ costs in terms of gas has been included in the performed tests. The results obtained are satisfactory and show the feasibility of the new proposal.

The advent of decentralised systems and the continuous collection of personal data managed by public and private entities require the application of measures to guarantee the privacy of individuals. Due to the necessity to preserve both the privacy and the utility of such data, different techniques have been proposed in the literature. Microaggregation, a family of data perturbation methods, relies on the principle of k-anonymity to aggregate personal data records. While several microaggregation heuristics exist, those based on the Travelling Salesman Problem (TSP) have been shown to outperform the state of the art when considering the trade-off between privacy protection and data utility. However, TSP-based heuristics suffer from scalability issues. Intuitively, methods that may reduce the computational time of TSP-based heuristics may incur a higher information loss. Nevertheless, in this article, we propose a method that improves the performance of TSP-based heuristics and can be used in both small and large datasets effectively. Moreover, instead of focusing only on the computational time perspective, our method can preserve and sometimes reduce the information loss resulting from the microaggregation. Extensive experiments with different benchmarks show how our method is able to outperform the current state of the art, considering the trade-off between information loss and computational time.