Editor’s Choice Articles

The world is developing an app that alerts my smartphone when a COVID-19 (COrona VIrus Disease 19) confirmed case comes near me. However, regardless of what will be put to practical use first, the COVID-19 tracking system should satisfy the issues of legalization of location tracking and scalability as a public platform used by the world. Additional problems need solutions related to real-time authentication for information gathering, blind naming and privacy of tracked persons, and quality of service on the Query/Reply procedure. This paper proposes the Software-Defined Networking Controller-centric global public platform to monitor and track information for the COVID-19 relevant people and provide real-time information disclosure services to world-wide Centers for Disease Control and Prevention (CDCs) and regular users. The CDC manages a list of people who needs to be monitored related to the COVID-19 and forcibly installs COVID-19 virtual Internet of Things (vIoT) nodes in the form of applications on their smartphones. In addition to these nodes, the vIoT support nodes also engage as information providers to improve the quality of information services. The design of our platform aims to ensure confidentiality and authentication services giving individually different secret keys. In addition, our platform meets system scalability and reduces Query/Reply latency, where the platform accommodates a large number of world-wide CDCs and persons in control per CDC. Full article

Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms. Full article

Carbon nanotubes field-effect transistors (CNTFETs) have been recently studied with great interest due to the intriguing properties of the material that, in turn, lead to remarkable properties of the charge transport of the device channel. Downstream of the full-wave simulations, the construction of equivalent device models becomes the basic step for the advanced design of high-performance CNTFET-based nanoelectronics circuits and systems. In this contribution, we introduce a strategy for deriving a compact model for a CNTFET that is based on the full-wave simulation of the 3D geometry by using the finite element method, followed by the derivation of a compact circuit model and extraction of equivalent parameters. We show examples of CNTFET simulations and extract from them the fitting parameters of the model. The aim is to achieve a fully functional description in Verilog-A language and create a model library for the SPICE-like simulator environment, in order to be used by IC designers. Full article

We examined the effects of aluminum (Al) capping layer thickness on the electrical performance and stability of high-mobility indium–gallium–tin oxide (IGTO) thin-film transistors (TFTs). The Al capping layers with thicknesses (t_Als) of 3, 5, and 8 nm were deposited, respectively, on top of the IGTO thin film by electron beam evaporation, and the IGTO TFTs without and with Al capping layers were subjected to thermal annealing at 200 °C for 1 h in ambient air. Among the IGTO TFTs without and with Al capping layers, the TFT with a 3 nm thick Al capping layer exhibited excellent electrical performance (field-effect mobility: 26.4 cm²/V s, subthreshold swing: 0.20 V/dec, and threshold voltage: −1.7 V) and higher electrical stability under positive and negative bias illumination stresses than other TFTs. To elucidate the physical mechanism responsible for the observed phenomenon, we compared the O1s spectra of the IGTO thin films without and with Al capping layers using X-ray photoelectron spectroscopy analyses. From the characterization results, it was observed that the weakly bonded oxygen-related components decreased from 25.0 to 10.0%, whereas the oxygen-deficient portion was maintained at 24.4% after the formation of the 3 nm thick Al capping layer. In contrast, a significant increase in the oxygen-deficient portion was observed after the formation of the Al capping layers having t_Al values greater than 3 nm. These results imply that the thicker Al capping layer has a stronger gathering power for the oxygen species, and that 3 nm is the optimum thickness of the Al capping layer, which can selectively remove the weakly bonded oxygen species acting as subgap tail states within the IGTO. The results of this study thus demonstrate that the formation of an Al capping layer with the optimal thickness is a practical and useful method to enhance the electrical performance and stability of high-mobility IGTO TFTs. Full article

The diagnostics of the condition of athletes has become a field of special scientific interest and activity. The aim of this study was to verify the effect of a long (100 km) run on a group of runners, as well as to assess the recovery time that is required for them to return to the pre-run state. The heart rate (HR) data presented were collected the day before the extreme physical effort, on the same day as, but after, the physical effort, as well as 24 and 48 h after. The Wavelet Transform (WT) and the Wavelet-based Fractal Analysis (WBFA) were implemented in the analysis. A tool was constructed that, based on quantitative data, enables one to confirm the completion of the recovery process that is related to the extreme physical effort. Indirectly, a tool was constructed that enables one to confirm the completion of the recovery process. The obtained information proves that the return to the resting state of the body after a significant physical effort can be observed after two days entirely through the analysis of the HR. Certain practical measures were used to differentiate between two substantially different states of the human body, i.e., pre- and post-effort states were constructed. The obtained results allow for us to state that WBFA appears to be a useful and robust tool in the determination of hidden features of stochastic signals, such as HR time signals. The proposed method allows one to differentiate between particular days of measurements with a mean probability of 92.2%. Full article

Visible Light Communication (VLC) is a short-range optical wireless communication technology that has been gaining attention due to its potential to offload heavy data traffic from the congested radio wireless spectrum. At the same time, wireless communications are becoming crucial to smart manufacturing within the scope of Industry 4.0. Industry 4.0 is a developing trend of high-speed data exchange in automation for manufacturing technologies and is referred to as the fourth industrial revolution. This trend requires fast, reliable, low-latency, and cost-effective data transmissions with fast synchronizations to ensure smooth operations for various processes. VLC is capable of providing reliable, low-latency, and secure connections that do not penetrate walls and is immune to electromagnetic interference. As such, this paper aims to show the potential of VLC for industrial wireless applications by examining the latest research work in VLC systems. This work also highlights and classifies challenges that might arise with the applicability of VLC and visible light positioning (VLP) systems in these settings. Given the previous work performed in these areas, and the major ongoing experimental projects looking into the use of VLC systems for industrial applications, the use of VLC and VLP systems for industrial applications shows promising potential. Full article

This paper is focused on the design of a control approach, based on the detection of events and changing between two different conduction modes, to reach high efficiency over the entire power range, especially at medium and low power levels. Although the proposed control strategy can be generalized for different topologies and specifications, in this paper, the strategy is validated in a SiC-based synchronous boost DC/DC converter rated for 400 V to 800 V and 10 kW. Evaluation of the power losses and current waveforms of the converter for different conduction modes and loads predicts suitable performance of quasi-square wave mode with zero voltage switching (QSW-ZVS) conduction mode for low and medium power and of continuous conduction Mode with hard switching (CCM-HS) for high power. Consequently, this paper proposes a control strategy, taking advantage of digital control, that allows automatic adjustment of the conduction mode to optimize the performance for different power ranges. Full article

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study. Full article

A time-based matrix multiply-and-accumulate (MAC) operation for a neural computing system is described. A simple and compact time-based matrix MAC structure is proposed that can perform multiplication and accumulation simultaneously in a single multiplier structure, and the hardware complexity is not affected by the matrix input size. To enhance the linearity of the weight factor, an offset-free pulse-width modulator is introduced. The proposed MAC architecture operates at a low supply voltage of 0.5 V while it consumes MAC energy of 0.38 pJ with a 32 nm low-power (LP) predictive technology model (PTM) CMOS process. In addition, the near-subthreshold operation can remove the level shifter to interface between the MAC operator and digital circuits such as static random-access-memory (SRAM) because both can utilize the same level of the supply voltage. The proposed MAC is based on a digital intensive pulse-width modulation, and thus it can further improve its performance and area with more advanced technologies. Full article

There is a growing interest in chipless radio-frequency identification (RFID) technology for a number of Internet of things (IoT) applications. This is due to its advantages of being of low-cost, low-power, and fully printable. In addition, it enjoys ease of implementation. In this paper, we present a novel, compact, chipless radio-frequency identification (RFID) tag that can be read with either vertical or horizontal polarization within its frequency bandwidth. This increases the sturdiness and detection ability of the RFID system. In addition, the difference between the vertical and horizontal responses can be used for tag identification. The proposed tag uses strip length variations to double the coding capacity and thereby reduce the overall size by almost 50%. It has a coding capacity of 20 bits in the operating bandwidth 3 GHz–7.5 GHz, and its spatial density is approximately 11 bits/cm². The proposed tag has a 4.44 bits/GHz spectral capacity, 2.44 bits/cm²/GHz encoding capacity, a spatial density at the center frequency of 358.33 bits/λ², and an encoding capacity at the center frequency of 79.63 bits/λ²/GHz. A prototype is fabricated and experimentally tested at a distance of 10 cm from the RFID reader system. Then, we compare the measured results with the simulations. The simulated results are in reasonable agreement with the simulated ones. Full article

This paper presents an extensive comparison of two 28-nm CMOS technologies, i.e., standard and mm-wave-optimized (i.e., thick metals and intermetal oxides) back-end-of-line (BEOL). The proposed comparison is carried out at both component and circuit level by means of a quantitative analysis of the actual performance improvements due to the adoption of a mm-wave-optimized BEOL. To this end, stand-alone transformer performance is first evaluated and then a complete mm-wave macroblock is investigated. A 77-GHz down-converter for frequency modulated continuous wave (FMCW) long-range/medium range (LR/MR) radar applications is exploited as a testbench. For the first time, it is demonstrated that thicker metals and intermetal oxides do not guarantee significant improvements at mm-wave frequencies and a standard (low-cost) BEOL is competitive in comparison with more complex (expensive) ones. Full article

This article describes the development of a radio frequency (RF) platform for electromagnetically modulated signals that makes use of a software-defined radio (SDR) to receive information from a novel magnetoelectric (ME) antenna capable of sensing low-frequency magnetic fields with ultra-low magnitudes. The platform is employed as part of research and development to utilize miniaturized ME antennas and integrated circuits for neural recording with wireless implantable devices. To prototype the reception of electromagnetically modulated signals from a sensor, a versatile Universal Software Radio Peripheral (USRP) and the GNU Radio toolkit are utilized to enable real-time signal processing under varying operating conditions. Furthermore, it is demonstrated how a radio frequency signal transmitted from the SDR can be captured by the ME antenna for wireless energy harvesting. Full article

A new cost-efficient, low-loss Ka-band diplexer designed in inverted microstrip gap waveguide technology is presented in this paper. Gap waveguide allows to propagate quasi-TEM modes in the air between two metal plates without the need for contact between them by using periodic metasurfaces. The diplexer is realized by using a bed of nails as AMC (Artificial Magnetic Conductor), first modeled with a PMC (Perfect Magnetic Conductor) surface for design simplification, and two fifth order end-coupled passband filters (BPFs) along with a power divider. The experimental verification confirms that the two channels centered at 24 GHz and 28 GHz with 1 GHz of bandwidth show measured insertion losses of 1.5 dB and 2 dB and 60 dB of isolation between them. A slight shift in frequency is observed in the measurements that can be easily explained by the variation in the permittivity of the substrate. Full article

In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver’s state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions. Full article

Switched-Mode Power Supplies (SMPSs) are a relevant source of conducted emissions, in particular in the frequency interval of supraharmonics, between 2 kHz and 150 kHz. When using sampled data for assessment of compliance, methods other than Fourier analysis should be considered for better frequency resolution, compact signal energy decomposition and a shorter time support. This work investigates the application of the Wavelet Packet Transform (WPT) and the Empirical Mode Decomposition (EMD) to measured recordings of SMPS conducted emissions, featuring steep impulses and damped oscillations. The comparison shows a general accuracy of the amplitude estimate within the variability of data themselves, with very good performance of WPT in tracking on stationary components in the low frequency range at some kHz. WPT performance however may vary appreciably depending on the selected mother wavelet for which some examples are given. EMD and its Ensemble EMD implementation show a fairly good accuracy in representing the original signal with a very limited number of base functions with the capability of exploiting a filtering effect on the low-frequency components of the signal. Full article

Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case applications. For example, sharing data between smart transport and smart industry can lead to other use-case applications such as intelligent logistics management and warehouse management. The benefits of such applications, however, can only be achieved if the shared data is of acceptable quality. There are three main practices in data quality (DQ) determination approaches that are restricting their effective use in data-shared platforms: (1) most DQ techniques validate test data against a known quantity considered to be a reference; a gold reference. (2) narrow sets of static metrics are used to describe the quality. Each consumer uses these metrics in similar ways. (3) data quality is evaluated in isolated stages throughout the processing pipeline. Data-shared IoT presents unique challenges; (1) each application and use-case in shared IoT has a unique description of data quality and requires a different set of metrics. This leads to an extensive list of DQ dimensions which are difficult to implement in real-world applications. (2) most data in IoT scenarios does not have a gold reference. (3) factors endangering DQ in shared IoT exist throughout the entire big-data model from data collection to data visualization, and data use. This paper aims to describe data-shared IoT and shared data pools while highlighting the importance of sharing quality data across various domains. The article examines how we can use trust as a measure of quality in data-shared IoT. We conclude that researchers can combine such trust-based techniques with blockchain for secure end-to-end data quality assessment. Full article

A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction. Full article

As one of the most vulnerable components to temperature and temperature cycling conditions in power electronics converter systems in these application fields as wind power, electric vehicles, drive system, etc., power semiconductor devices draw great concern in terms of reliability. Owing to the wide utilization of power semiconductor devices in various power applications, especially insulated gate bipolar transistors (IGBTs), power semiconductor devices have been studied extensively regarding increasing reliability methods. This study comparatively reviews recent advances in the area of reliability research for power semiconductor devices, including condition monitoring (CM), active thermal control (ATC), and remaining useful lifetime (RUL) estimation techniques. Different from previous review studies, this technical review is carried out with the aim of providing a comprehensive overview of the correlation between various enhancing reliability techniques and discussing the corresponding merits and demerits by using 144 related up-to-date papers. The structure and failure mechanism of power semiconductor devices are first investigated. Different failure indicators and recent associated CM techniques are then compared. The ATC approaches following the type of converter systems are further summarized. Furthermore, RUL estimation techniques are surveyed. This paper concludes with summarized challenges for future research opportunities regarding reliability improvement. Full article

Energy harvesting has a vital role in building reliable Environmental Wireless Sensor Networks (EWSNs), without needing to replace a discharged battery. Solar energy is one of the main renewable energy sources that can be used to efficiently charge a battery. This paper introduces two solar energy harvesters and their power measurements at different light conditions in order to charge rechargeable AA batteries powering EWSN nodes. The first harvester is a primitive energy harvesting circuit that is built using elementary off-shelf components, while the second harvester is based on a commercial boost converter chip. To prove the effectiveness of harvesting solar energy, five EWSN nodes were distributed at a nature reserve (the Audubon Society of Western Pennsylvania, USA) and the sunlight at their locations was recorded for more than five months. For each recorded illumination, the corresponding harvested energy has been estimated and compared with the average energy consumption of the EWSN with the most power consumption. The results show that the daily harvested energy effectively compensates for the energy consumption of the EWSN nodes, and the battery charge capacity of 295 mAh can reliably support their daily dynamic energy consumption. Full article

Current control has, for decades, been one of the more challenging research fields in the development of power converters. Simple and robust nonlinear methods like hysteresis or sigma-delta controllers have been commonly used, while sophisticated linear controllers based on classical control theory have been developed for PWM-based converters. The one-cycle current control technique is a nonlinear technique based on cycle-by-cycle calculation of the ON time of the converter switches for the next switching period. This kind of controller requires accurate measurement of voltages and currents in order achieve a precise current tracking. These techniques have been frequently used in the control of power converters generating low-frequency currents, where the reference varies slowly compared with the switching frequency. Its application is not so common in active power filter current controllers due to the fast variation of the references that demands not only accurate measurements but also high-speed computing. This paper proposes a novel one-cycle digital current controller based on the minimization of the integral error of the current. Its application in a three-leg four-wire shunt active power filter is presented, including a stability analysis considering the switching pattern selection. Furthermore, simulated and experimental results are presented to validate the proposed controller. Full article

Fast steering mirrors (FSMs) are used to correct images observed by satellites. FSMs need to have large apertures and realize high precision and the positioning of the mirror in the tip-tilt and axial directions needs to be highly precise and highly responsive in order to capture large-scale, high-resolution images. An FSM with a large-diameter mirror supported by a compact magnetic suspension and driven by long-stroke voice coil motors (VCMs) is proposed in this paper. The magnetic suspension and VCM actuators enable the mirror to be highly responsive and to have long-range movement in the tip-tilt and axial directions without friction and wear. The magnetic suspension is a hybrid that has active control in the lateral directions and passive support in the tip-tilt and axial directions. An experimental FSM with an 80 mm diameter dummy mirror was fabricated and tested. The mirror’s driving ranges in the tip-tilt and axial directions were ±20 mrad and ±500 μm, respectively. Furthermore, the servo bandwidths in the tip-tilt and axial directions were more than 1 kHz and 200 Hz, respectively. Full article

Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety. Full article

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition. Full article

Long distances in the vicinities of railways are not exploited in terms of wind energy. This paper presents a scalable power electronics approach, aimed to harness the wind potential in a railway infrastructure. The key aspect of this proposal relies on both using the wind energy in the location, and the displaced air mass during the movement of a train along the railway, in order to produce electrical energy. Vertical Axis Wind Turbines (VAWT) are used in order to take advantage of the wind power, and widely used and well-known power converter techniques to accomplish the goal, showing MPPT techniques, parallelization of converters and power delivery with a Solid State Transformer (SST). Results are shown according simulations of the whole system, with and without train activity, resulting that 30.6 MWh of the energy could be generated without the train, and the energy generated with the assistance of the train could reach 32.3 MWh a year. Concluding that almost the $10\%$ of the energy could be provided by the assistance of the train. Full article

This paper studies an approach for detecting cyber attacks against networked cooperative systems (NCS) that are assumed to be working in a cyber-physical environment. NCS are prone to anomalies both due to cyber and physical attacks and faults. Cyber-attacks being more hazardous given the cooperative nature of the NCS may lead to disastrous consequences and thus need to be detected as soon as they occur by all systems in the network. Our approach deals with two types of malicious attacks aimed at compromising the stability of the NCS: intrusion attacks/local malfunctions on individual systems and deception/cyber-attacks on the communication between the systems. In order to detect and identify such attacks under switching communication topologies, this paper proposes a new distributed methodology that solves global state estimation of the NCS where the aim is identifying anomalies in the networked system using residuals generated by monitoring agents such that coverage of the entire network is assured. A cascade of predefined-time sliding mode switched observers is introduced for each agent to achieve a fast estimate of the global state whereby the settling time is an a priori defined parameter independently of the initial conditions. Then, using the conventional consensus algorithm, a set of residuals are generated by the agents that is capable of detecting and isolating local intrusion attacks and communication cyber-attacks in the network using only locally exchanged information. In order to prove the effectiveness of the proposed method, the framework is tested for a velocity synchronization seeking network of mobile robots. Full article

In this study, we examined the effects of the annealing atmosphere on the electrical performance and stability of high-mobility indium-gallium-tin oxide (IGTO) thin-film transistors (TFTs). The annealing process was performed at a temperature of 180 °C under N₂, O₂, or air atmosphere after the deposition of IGTO thin films by direct current magnetron sputtering. The field-effect mobility (μ_FE) of the N₂- and O₂-annealed IGTO TFTs was 26.6 cm²/V·s and 25.0 cm²/V·s, respectively; these values were higher than that of the air-annealed IGTO TFT (μ_FE = 23.5 cm²/V·s). Furthermore, the stability of the N₂- and O₂-annealed IGTO TFTs under the application of a positive bias stress (PBS) was greater than that of the air-annealed device. However, the N₂-annealed IGTO TFT exhibited a larger threshold voltage shift under negative bias illumination stress (NBIS) compared with the O₂- and air-annealed IGTO TFTs. The obtained results indicate that O₂ gas is the most suitable environment for the heat treatment of IGTO TFTs to maximize their electrical properties and stability. The low electrical stability of the air-annealed IGTO TFT under PBS and the N₂-annealed IGTO TFT under NBIS are primarily attributed to the high density of hydroxyl groups and oxygen vacancies in the channel layers, respectively. Full article

The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100 $\Omega$ to 5 G$\Omega$ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance. Full article

Lead halide perovskites possess outstanding optical characteristics that can be employed in the fabrication of phototransistors. However, due to low current modulation at room temperature, sensitivity to the ambient environment, lack of patterning techniques and low carrier mobility of polycrystalline form, investigation in perovskite phototransistors has been limited to rigid substrates such as silicon and glass to improve the film quality. Here, we report on room temperature current modulation in a methylammonium lead iodide perovskite (MAPbI₃) flexible transistor made by an extremely cheap and facile fabrication process. The proposed phototransistor has the top-gate configuration with a lateral drain–channel–source structure. The device performed in the linear and saturation regions both in the dark and under white light in different current ranges according to the illumination conditions. The transistor showed p-type transport characteristics and the field effect mobility of the device was calculated to be ~1.7 cm² V⁻¹ s⁻¹. This study is expected to contribute to the development of MAPbI₃ flexible phototransistors. Full article

This paper presents a methodology for the design of field-programmable gate array (FPGA)-based real-time simulators (RTSs) for power electronic circuits (PECs). The programmability of the simulator results from the use of an efficient and scalable overlay architecture (OA). The proposed OA relies on a latency-insensitive design (LID) paradigm. LID consists of connecting small processing units that automatically synchronize and exchange data when appropriate. The use of such data-driven architecture aims to ease the design process while achieving a higher computational efficiency. The benefits of the proposed approach is evaluated by assessing the performance of the proposed solver in the simulation of a two-stage AC–AC power converter. The minimum achievable time-step and FPGA resource consumption for a wide range of power converter sizes is also evaluated. The proposed overlays are parametrizable in size, they are cost-effective, they provide sub-microsecond time-steps, and they offer a high computational performance with a reported peak performance of 300 GFLOPS. Full article

Despite the convenience offered by e-commerce, online apparel shopping presents various product-related risks, as consumers can neither physically see nor try products on themselves. Augmented reality (AR) and virtual reality (VR) technologies have been used to improve the shopping online experience. Therefore, we propose an AR- and VR-based try-on system that provides users a novel shopping experience where they can view garments fitted onto their personalized virtual body. Recorded personalized motions are used to allow users to dynamically interact with their dressed virtual body in AR. We conducted two user studies to compare the different roles of VR- and AR-based try-ons and validate the impact of personalized motions on the virtual try-on experience. In the first user study, the mobile application with the AR- and VR-based try-on is compared to a traditional e-commerce interface. In the second user study, personalized avatars with pre-defined motion and personalized motion is compared to a personalized no-motion avatar with AR-based try-on. The result shows that AR- and VR-based try-ons can positively influence the shopping experience, compared with the traditional e-commerce interface. Overall, AR-based try-on provides a better and more realistic garment visualization than VR-based try-on. In addition, we found that personalized motions do not directly affect the user’s shopping experience. Full article

This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms in order to minimise power consumption. A reinforcement learning (RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with a Xilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms. Full article

Number comparison has long been recognized as one of the most fundamental non-modular arithmetic operations to be executed in a non-positional Residue Number System (RNS). In this paper, a new technique for designing comparators of RNS numbers represented in an arbitrary moduli set is presented. It is based on a newly introduced modified diagonal function, whose strictly monotonic properties make it possible to replace the cumbersome operations of finding the remainder of the division by a large and awkward number with significantly simpler computations involving only a power of 2 modulus. Comparators of numbers represented in sample RNSs composed of varying numbers of moduli and offering different dynamic ranges, designed using various methods, were synthesized for the 65 nm technology. The experimental results suggest that the new circuits enjoy a delay reduction ranging from over 11% to over 75% compared to the fastest circuits designed using existing methods. Moreover, it is achieved using less hardware, the reduction of which reaches over 41%, and is accompanied by significantly reduced power-consumption, which in several cases exceeds 100%. Therefore, it seems that the presented method leads to the design of the most efficient current hardware comparators of numbers represented using a general RNS moduli set. Full article

Wireless broadband access networks have been positioning themselves as a good solution for manufacturers and users of IoT (internet of things) devices, due mainly to the high data transfer rate required over terminal devices without restriction of information format. In this work, a review of two Radio over Fiber strategies is presented. Both have excellent performance and even offer the possibility to extend wireless area coverage where mobile networks do not reach or the 802.11 network presents issues. Radio Frequency over Fiber (RFoF) and intermediate Frequency over Fiber (IFoF) are two transmission strategies compatible with the required new broadband services and both play a key role in the design of the next generation integrated optical–wireless networks, such as 5G and Satcom networks, including on RAU (Remote Antenna Unit) new functionalities to improve their physical dimensions, employing a microelectronic layout over nanometric technologies. Full article

This paper proposes a control method for a digital signal processor based series-series (SS) compensated wireless power transfer system. In the control method, load resistance and mutual inductance are identified simultaneously, and output voltage can be estimated by using only the primary side voltage and current without direct feedback from the secondary side circuit. Since this estimation method requires a complex mathematical calculation procedure, a digital signal processor is used in this system. One of the major disadvantages of using a digital controller in this system is a limitation of sampling rates. Therefore, in this paper, several current reconstruction methods with limited sampling rates are investigated and applied. As a result, this controller not only reduces the cost of the system but also shows good estimation performance within the limited digital controller unit resource. The proposed control concept is verified by experimental results with a 48W laboratory prototype. Full article

A dual-band bandpass filter (BPF) composed of a coupling structure and a bent T-shaped resonator loaded by small L-shaped stubs is presented in this paper. The first band of the proposed BPF covers 4.6 to 10.6 GHz, showing 78.9% fractional bandwidth (FBW) at 7.6 GHz, and the second passband is cantered at 11.5 GHz with a FBW of 2.34%. The bent T-shaped resonator generates two transmission zeros (TZs) near the wide passband edges, which are used to tune the bandwidth of the first band, and the L-shaped stubs are used to create and control the narrow passband. The selectivity performance of the BPF is analyzed using the transfer function extracted from the lumped circuit model verified by a detailed even/odd mode analysis. The BPF presents a flat group delay (GD) of 0.45 ns and an insertion loss (IL) less than 0.6 dB in the wide passband and a 0.92 IL in the narrow passband. A prototype of the proposed BPF is fabricated and tested, showing very good agreement between the numerically predicted and measured results. Full article

The histological assessment of glomeruli is fundamental for determining if a kidney is suitable for transplantation. The Karpinski score is essential to evaluate the need for a single or dual kidney transplant and includes the ratio between the number of sclerotic glomeruli and the overall number of glomeruli in a kidney section. The manual evaluation of kidney biopsies performed by pathologists is time-consuming and error-prone, so an automatic framework to delineate all the glomeruli present in a kidney section can be very useful. Our experiments have been conducted on a dataset provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital. This dataset is composed of 26 kidney biopsies coming from 19 donors. The rise of Convolutional Neural Networks (CNNs) has led to a realm of methods which are widely applied in Medical Imaging. Deep learning techniques are also very promising for the segmentation of glomeruli, with a variety of existing approaches. Many methods only focus on semantic segmentation—which consists in segmentation of individual pixels—or ignore the problem of discriminating between non-sclerotic and sclerotic glomeruli, so these approaches are not optimal or inadequate for transplantation assessment. In this work, we employed an end-to-end fully automatic approach based on Mask R-CNN for instance segmentation and classification of glomeruli. We also compared the results obtained with a baseline based on Faster R-CNN, which only allows detection at bounding boxes level. With respect to the existing literature, we improved the Mask R-CNN approach in sliding window contexts, by employing a variant of the Non-Maximum Suppression (NMS) algorithm, which we called Non-Maximum-Area Suppression (NMAS). The obtained results are very promising, leading to improvements over existing literature. The baseline Faster R-CNN-based approach obtained an F-Measure of 0.904 and 0.667 for non-sclerotic and sclerotic glomeruli, respectively. The Mask R-CNN approach has a significant improvement over the baseline, obtaining an F-Measure of 0.925 and 0.777 for non-sclerotic and sclerotic glomeruli, respectively. The proposed method is very promising for the instance segmentation and classification of glomeruli, and allows to make a robust evaluation of global glomerulosclerosis. We also compared Karpinski score obtained with our algorithm to that obtained with pathologists’ annotations to show the soundness of the proposed workflow from a clinical point of view. Full article

Designing an optimal iterative learning control is a huge challenge for linear and nonlinear dynamic systems. For such complex systems, standard Norm optimal iterative learning control (NOILC) is an important consideration. This paper presents a novel NOILC error convergence technique for a discrete-time method. The primary effort of the controller is to converge the error efficiently and quickly in an optimally successful way. A new iterative learning algorithm based on feedback based on reliability against input disruption was proposed in this paper. The illustration of the simulations authenticates the process suggested. The numerical example simulated on MATLAB@2019 and the mollified results affirm the validation of the designed algorithm. Full article

This paper presents a piecewise linear-elliptic (PLE) droop control scheme to improve the dynamic behavior of islanded microgrids. Islanded microgrids are typically vulnerable to voltage and frequency fluctuations, particularly if a combination of high- and low-inertia power generation units are used in a microgrid. The intermittent nature of renewable energy sources can cause sudden power mismatches, and thus, voltage and frequency fluctuations. The proposed PLE droop control scheme can be employed in a battery energy storage system (BESS) to effectively mitigate voltage and frequency fluctuations in an islanded microgrid. Though the PLE shape can be implemented for any droop control scheme, it has been applied for active power-frequency (P-f) and reactive power-voltage (Q-v) droops in this paper. In addition, the dynamic response of a battery-fed smart inverter equipped with the proposed PLE droops has been compared with the results obtained from a linear droop control scheme in an islanded microgrid containing high- and low-inertia power-generation units. In this paper, the results of several case studies are presented to confirm the capability of the PLE droop control in mitigating voltage and frequency fluctuations in islanded microgrids. Full article

End-effector-based robots are widely adopted by physiotherapists and caregivers as support in the delivery of the rehabilitation training to the patient. The validation of these devices presents critical aspects, since the system performance must be assessed analyzing the movement performed by the subject limb, i.e., elements outside the device. This paper presents a multi-sensor approach for the validation of an innovative end-effector-based device, comparing different measurement strategies for evaluating the system effectiveness in imposing an expected training. The study was performed monitoring the movement induced by the device on the upper limb of a young male healthy subject during a set of fictitious rehabilitation sessions. The kinematic structure of the device is characterized by a compact differential mechanism with two degrees of freedom. A sequence of repetitions of a planar reaching pattern was analyzed as illustrative training task. A kinematic model of subject and system was developed, and the kinematics of a set of specific landmark points on the subject limb was evaluated. Data obtained from two measurement systems were compared: (1) an optoelectronic system with two cameras and eight skin passive markers, and (2) two triaxial accelerometers. Results were analyzed in MATLAB and R environment, revealing a high repeatability of the limb movement. Although both the measurement systems allow evaluating the acceleration of subject’s arm and forearm, accelerometers should be preferred for punctual analysis, like components optimizations, whereas optical markers provide a general overview of the system, particularly suitable for the functional design process. Full article

The outbreak of the respiratory disease caused by the new coronavirus (COVID-19) has caused the world to face an existential health crisis. To contain the infectious disease, many countries have quarantined their citizens for several weeks to months and even suspended most economic activities. To track the movements of residents, the governments of many states have adopted various novel technologies. Connecting billions of sensors and devices over the Internet, the so-called Internet of Things (IoT), has been used for outbreak control. However, these technologies also pose serious privacy risks and security concerns with regards to data transmission and storage. In this paper, we propose a blockchain-based system to provide the secure management of home quarantine. The privacy and security attributes for various events are based on advanced cryptographic primitives. To demonstrate the application of the system, we provide a case study in an IoT system with a desktop computer, laptop, Raspberry Pi single-board computer, and the Ethereum smart contract platform. The obtained results prove its ability to satisfy security, efficiency, and low-cost requirements. Full article

Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems. Full article

The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency. Full article

Wind power is positioned as one of the fastest-growing energy sources today, while also being a mature technology with a strong capacity for creating employment and guaranteeing environmental sustainability. However, the stochastic nature of wind may affect the integration of power plants into power systems and the availability of generation capacity. In this sense, as in the case of conventional power plants, wind power installations should be able to help maintain power system stability and reliability. To help achieve this objective, a significant number of countries have developed so-called grid interconnection agreements. These are designed to define the technical and behavioral requirements that wind power installations, as well as other power plants, must comply with when seeking connection to the national network. These documents also detail the tasks that should be conducted to certify such installations, so these can be commercially exploited. These certification processes allow countries to assess wind turbine and wind power plant simulation models. These models can then be used to estimate and simulate wind power performance under a variety of scenarios. Within this framework, and with a particular focus on the new Spanish grid code, the present paper addresses the validation process of dynamic wind turbine models followed in three countries—Spain, Germany and South Africa. In these three countries, and as a novel option, it has been proposed that these models form part of the commissioning and certification processes of wind power plants. Full article

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time. Full article

A novel technique for estimating the asymptotic stability region of nonlinear autonomous polynomial systems is established. The key idea consists of examining the optimal Lyapunov function (LF) level set that is fully included in a region satisfying the negative definiteness of its time derivative. The minor bound of the biggest achievable region, denoted as Largest Estimation Domain of Attraction (LEDA), can be calculated through a Generalised Eigenvalue Problem (GEVP) as a quasi-convex Linear Inequality Matrix (LMI) optimising approach. An iterative procedure is developed to attain the optimal volume or attraction region. Furthermore, a Chaotic Particular Swarm Optimisation (CPSO) efficient technique is suggested to compute the LF coefficients. The implementation of the established scheme was performed using the Matlab software environment. The synthesised methodology is evaluated throughout several benchmark examples and assessed with other results of peer technique in the literature. Full article

This study explores an info-structural model of cognition for non-interacting agents affected by human sensation, perception, emotion, and affection. We do not analyze the neuroscientific or psychological debate concerning the human mind working, but we underline the importance of modeling the above cognitive levels when designing artificial intelligence agents. Our aim was to start a reflection on the computational reproduction of intelligence, providing a methodological approach through which the aforementioned human factors in autonomous systems are enhanced. The presented model must be intended as part of a larger one, which also includes concepts of attention, awareness, and consciousness. Experiments have been performed by providing visual stimuli to the proposed model, coupling the emotion cognitive level with a supervised learner to produce artificial emotional activity. For this purpose, performances with Random Forest and XGBoost have been compared and, with the latter algorithm, 85% accuracy and 92% coherency over predefined emotional episodes have been achieved. The model has also been tested on emotional episodes that are different from those related to the training phase, and a decrease in accuracy and coherency has been observed. Furthermore, by decreasing the weight related to the emotion cognitive instances, the model reaches the same performances recorded during the evaluation phase. In general, the framework achieves a first emotional generalization responsiveness of 94% and presents an approximately constant relative frequency related to the agent’s displayed emotions. Full article

Among movement disorders, essential tremor is by far the most common, as much as eight times more prevalent than Parkinson’s disease. Although these two conditions differ in their presentation and course, clinicians do not always recognize them, leading to common misdiagnoses. Proper and early diagnosis is important for receiving the right treatment and support. In this paper, the development of a portable and reliable tremor classification system based on a wearable device, enabling clinicians to differentiate between essential tremor and Parkinson’s disease-associated one, is reported. Inertial data were collected from subjects with a well-established diagnosis of tremor, and analyzed to extract different sets of relevant spectral features. Supervised learning methods were then applied to build several classification models, among which the best ones achieved an average accuracy above 90%. Results encourage the use of wearable technology as effective and affordable tools to support clinicians. Full article

Blockchain is an emerging technology that has shaken the financial sector, and which is already perceived as having an impact. A blockchain is a network of many interconnected nodes, both trustworthy and malicious, which can reach a consensus and generate valid data. The resulting information is packed into a block and permanently saved on the network in a tamper-proof way. In this paper, we propose an adaptation of blockchain for securely storing data in a vehicular-based network. Our approach can work for storing data such as traffic events and user reputation. The proposed solution has two interconnected components: the Intelligent Transportation System (ITS) blockchain and the reputation system. The paper presents synthetic tests which validate the use cases of the solution: users reporting speeds and alerts behind which we see a fair reputation system penalising the (wrong/false) users. Full article

Nowadays, lower-limb prostheses are reaching real-world usability especially on ground-level walking. However, some key tasks such as stair walking are still quite demanding. Providing haptic feedback about the foot placement on the steps might reduce the cognitive load of the task, compensating for increased dependency on vision and lessen the risk of falling. Experiments on intact subjects can be useful to define the feedback strategies prior to clinical trials, but effective methods to assess the efficacy of the strategies are few and usually rely on the emulation of the disability condition. The present study reports on the design and testing of a wearable haptic feedback system in a protocol involving intact subjects to assess candidate strategies to be adopted in clinical trials. The system integrated a sensorized insole wirelessly connected to a textile waist belt equipped with three vibrating motors. Three stimulation strategies for mapping the insole pressure data to vibrotactile feedback were implemented and compared in terms of intuitiveness and comfort perceived during level and stair walking. The strategies were ranked using a relative rating approach, which highlighted the differences between them and suggested guidelines for their improvement. The feedback evaluation procedure proposed could facilitate the selection and improvement of haptic feedback strategies prior to clinical testing. Full article

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-day attacks. The aim is to build an IDS model with high recall while keeping the miss rate (false-negatives) to an acceptable minimum. Two well-known IDS datasets are used for evaluation—CICIDS2017 and NSL-KDD. In order to demonstrate the efficacy of our model, we compare its results against a One-Class Support Vector Machine (SVM). The manuscript highlights the performance of a One-Class SVM when zero-day attacks are distinctive from normal behaviour. The proposed model benefits greatly from autoencoders encoding-decoding capabilities. The results show that autoencoders are well-suited at detecting complex zero-day attacks. The results demonstrate a zero-day detection accuracy of 89–99% for the NSL-KDD dataset and 75–98% for the CICIDS2017 dataset. Finally, the paper outlines the observed trade-off between recall and fallout. Full article