Editor’s Choice Articles

Rotor shaft position sensors are required to ensure the efficient and reliable control of Permanent Magnet Synchronous Machines (PMSM), which are often applied as traction motors in electrified automotive powertrains. In general, various sensor principles are available, e.g., resolvers and inductive- or magnetoresistive sensors. Each technology is characterized by strengths and weaknesses in terms of measurement accuracy, space demands, disturbing factors and costs, etc. Since the most frequently applied technology, the resolver, shows some weaknesses and is relatively costly, alternative technologies have been introduced during the past years. This paper investigates state-of-the-art position sensor technologies and compares their potentials for use in PMSM in automotive powertrain systems. The corresponding evaluation criteria are defined according to the typical requirements of automotive electric powertrains, and include the provided sensor accuracy under the influence of mechanical tolerances and deviations, integration size, and different electrical- and signal processing-related parameters. The study presents a mapping of the potentials of different rotor position sensor technologies with the target to support the selection of suitable sensor technologies for specified powertrain control applications, addressing both system design and components development. Full article

Embedded software has properties dependent on hardware (direct operation of address spaces, memory mapped I/O, interruption, etc.). Therefore, demands about the established method of formal verifications corresponding to those properties are increasing from the point of view of shorter development and high reliability. Our study aims at enabling a formal verification with Satisfiability Modulo Theories-Based Bounded Model Checking (SMT-Based BMC) of safety for embedded assembly codes. Our proposed method generates models of assembly codes in detail with the fixed-sized bit-vectors theory. The models generated by our method include interrupts, and the size of the models is reduced using Interrupt Handler Execution Reduction (IHER) technique. In this paper, we have developed the verification method of safety properties of embedded assembly program by combining SMT-Based Bounded Model Checking and Reduction of Interrupt Handler Executions. Moreover, we show the evaluation of our method by experiments using prototype model checker. Full article

The aim of design space exploration (DSE) is to identify implementations with optimal quality characteristics which simultaneously satisfy all imposed design constraints. Hence, besides searching for new solutions, a quality evaluation has to be performed for each design point. This process is typically very expensive and takes a majority of the exploration time. As nearly all the explored design points are sub-optimal, most of them get discarded after evaluation. However, evaluating a solution takes virtually the same amount of time for both good and bad ones. That way, a huge amount of computing power is literally wasted. In this paper, we propose a solution to the aforementioned problem by integrating efficient approximations in the background of a DSE engine in order to allow an initial evaluation of each solution. Only if the approximated quality indicates a promising candidate, the time-consuming exact evaluation is executed. The novelty of our approach is that (1) although the evaluation process is accelerated by using approximations, we do not forfeit the quality of the acquired solutions and (2) the integration in a background theory allows sophisticated reasoning techniques to prune the search space with the help of the approximation results. We have conducted an experimental evaluation of our approach by investigating the dependency of the accuracy of used approximations on the performance gain. Based on 120 electronic system level problem instances, we show that our approach is able to increase the overall exploration coverage by up to six times compared to a conservative DSE whenever accurate approximation functions are available. Full article

Capacitorless one-transistor dynamic random-access memory cells that use a polysilicon body (poly-Si 1T-DRAM) have been studied to overcome the scaling issues of conventional one-transistor one-capacitor dynamic random-access memory (1T-1C DRAM). Generally, when the gate length of a silicon-on-insulator (SOI) structure metal-oxide-silicon field-effect transistor (MOSFET) is reduced, its body thickness is reduced in order to suppress the short-channel effects (SCEs). TCAD device simulations were used to investigate the transient performance differences between thin and thick-body poly-Si DRAMs to determine whether reduced body thickness is also appropriate for those devices. Analysis of the simulation results revealed that operating bias conditions are as important as body thickness in 1T-DRAM operation. Since a thick-body device has more trapped hole charge in its grain boundary (GB) than a thin-body device in both the “0” and “1” states, the transient performance of a thick-body device is better than a thin-body device regardless of the Write”1” drain voltage. We also determined that the SCEs in the memory cells can be improved by lowering the Write”1” drain voltage. We conclude that an optimization method for the body thickness and voltage conditions that considers both the cell’s SCEs and its transient performance is necessary for its development and application. Full article

In this paper the problem of sampling the field radiated by a planar source observed over a finite planar aperture located in the near-field is addressed. The problem is cast as the determination of the spatial measurement positions which allow us to discretize the radiation problem so that the singular values of the radiation operator are well-approximated. More in detail, thanks to a suitably warping transformation of the observation variables, the kernel function of the relevant operator is approximated by a band-limited function and hence the sampling theorem applied to achieved discretization. It results in the sampling points having to be non-linearity arranged across the measurement aperture and their number can be considerably lowered as compared to more standard sampling approach. It is shown that the proposed sampling scheme works well for measurement apertures that are not too large as compared to the source’s size. As a consequence, the method appears better suited for broad-side large antenna whose radiated field is mainly concentrated in front of the antenna. A numerical analysis is included to check the theoretical findings and to study the trade-off between the field accuracy representation (over the measurement aperture) and the truncation error in the estimated far-field radiation pattern. Full article

This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature. Full article

An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns. Full article

This paper presents a comprehensive development of a dynamic wireless power transfer (WPT) system for the charge of the battery onboard an electric vehicle (EV). The development starts from the assessment of the electrical specifications of the dynamic WPT system, goes through the design of its power stages, and concludes with its validation. In the design step, the structure of the coupling set, layout of the coils, configuration of the conversion stages and topology of the compensation networks are illustrated, as well as electrical sizing of the power stage components. In the validation step, the setup of a dynamic WPT system is described, and the results of experimental tests carried out with the pickup moving along the track are given. Full article

Malware is one of the most common security threats experienced by a user when browsing webpages. A good understanding of the features of webpages (e.g., internet protocol, port, URL, Google index, and page rank) is required to analyze and mitigate the behavior of malware in webpages. This main objective of this paper is to analyze the key features of webpages and to mitigate the behavior of malware in webpages. To this end, we conducted an empirical study to identify the features that are most vulnerable to malware attacks and its results are reported. To improve the feature selection accuracy, a machine learning technique called bagging is employed using the Weka program. To analyze these behaviors, phishing and botnet data were obtained from the University of California Irvine machine learning repository. We validate our research findings by applying honeypot infrastructure using the Modern Honeypot Network (MHN) setup in a Linode Server. As the data suffer from high variance in terms of the type of data in each row, bagging is chosen because it can classify binary classes, date classes, missing values, nominal classes, numeric classes, unary classes and empty classes. As a base classifier of bagging, random tree was applied because it can handle similar types of data such as bagging, but better than other classifiers because it is faster and more accurate. Random tree had 88.22% test accuracy with the lowest run time (0.2 sec) and a receiver operating characteristic curve of 0.946. Results show that all features in the botnet dataset are equally important to identify the malicious behavior, as all scored more than 97%, with the exception of TCP and UDP. The accuracy of phishing and botnet datasets is more than 89% on average in both cross validation and test analysis. Recommendations are made for the best practice that can assist in future malware identification. Full article

Cryogenic Electron Microscopy (Cryo-EM) has been established as one of the key players in Structural Biology. It can reconstruct a 3D model of the sample at the near-atomic resolution, which led to a Method of the year award by Nature, and the Nobel Prize in 2017. With the growing number of facilities, faster microscopes, and new imaging techniques, new algorithms are needed to process the so-called movies data produced by the microscopes in real-time, while preserving a high resolution and maximum of additional information. In this article, we present a new algorithm used for movie alignment, called FlexAlign. FlexAlign is able to correctly compensate for the shift produced during the movie acquisition on-the-fly, using the current generation of hardware. The algorithm performs a global and elastic local registration of the movie frames using Cross-Correlation and B-spline interpolation for high precision. We show that our execution time is compatible with real-time correction and that we preserve the high-resolution information up to high frequency. Full article

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach. Full article

Power electronics technology is widely used in several areas, such as in the railways, automotive, electric vehicles, and renewable energy sectors. Some of these applications are safety critical, e.g., in the automotive domain. The heat produced by power devices must be efficiently dissipated to allow them to work within their operational thermal limits. Moreover, numerous ageing effects are due to thermal stress, which causes mechanical issues. Therefore, the reliability of a circuit depends on its dissipation system, even if it consists of a simple passive heatsink mounted on the power device. During the Printed Circuit Board (PCB) production, an incorrect assembly of the heatsink can cause a worse heat dissipation with a significant increase of the junction temperatures (T_j). In this paper, three possible test strategies are compared for testing the correct assembling of heatsinks. The considered strategies are used at the PCB end-manufacturing. The effectiveness of the different test methods considered is assessed on a case study corresponding to a Power Supply Unit (PSU). Full article

In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the combined use of Software-Defined Networking (SDN) and Network Virtualization (NV) is a promising solution, as such techniques allow to reduce the network management complexity. This work presents the PrioSDN Resource Manager (PrioSDN_RM), a resource management mechanism based on admission control for virtualized SDN-based networks. The proposed combination imposes bounds on the resource utilization for the virtual slices, which therefore share the network links, while maintaining isolation from each other. The presented approach exploits a priority-based runtime bandwidth distribution mechanism to dynamically react to load changes (e.g., due to alarms). The paper describes the design of the approach and provides experimental results obtained on a real testbed. Full article

As a typical artificial intelligence algorithm, the convolutional neural network (CNN) is widely used in the Internet of Things (IoT) system. In order to improve the computing ability of an IoT CPU, this paper designs a reconfigurable CNN-accelerated coprocessor based on the RISC-V instruction set. The interconnection structure of the acceleration chain designed by the predecessors is optimized, and the accelerator is connected to the RISC-V CPU core in the form of a coprocessor. The corresponding instruction of the coprocessor is designed and the instruction compiling environment is established. Through the inline assembly in the C language, the coprocessor instructions are called, coprocessor acceleration library functions are established, and common algorithms in the IoT system are implemented on the coprocessor. Finally, resource consumption evaluation and performance analysis of the coprocessor are completed on a Xilinx FPGA. The evaluation results show that the reconfigurable CNN-accelerated coprocessor only consumes 8534 LUTS, accounting for 47.6% of the total SoC system. The number of instruction cycles required to implement functions such as convolution and pooling based on the designed coprocessor instructions is better than using the standard instruction set, and the acceleration ratio of convolution is 6.27 times that of the standard instruction set. Full article

This paper proposes a comparative study of regulation schemes for charge-pump-based voltage generators using behavioral models in Verilog- Analog Mixed Signal (AMS) code. An accurate and simple model of the charge pump is first introduced. It allows reducing the simulation time of complex electronic systems made up by both analog and digital circuits while maintaining a good agreement with transistor-level simulations. Finally, a comprehensive comparative study of the different regulation schemes for charge pumps is reported which allows the designer to choose the most suitable topology for a given application and Charge Pump (CP) operative zone. Full article

Progress in light-emitting diodes (LEDs) based on ZnO/GaN heterojunctions has run into several obstacles during the last twenty years. While both the energy bandgap and lattice parameter of the two semiconductors are favorable to the development of such devices, other features related to the electrical and structural properties of the GaN layer prevent an efficient radiative recombination. This work illustrates some advances made on ZnO/GaN-based LEDs, by using high-thickness GaN layers for the p-region of the device and an ad hoc device topology. Heterojunction LEDs consist of a quasicoalesced non-intentionally doped ZnO nanorod layer deposited by chemical bath deposition onto a metal–organic vapor-phase epitaxy -grown epitaxial layer of p-doped GaN. Circular 200 μm-sized violet-emitting LEDs with a p-n contact distance as low as 3 μm exhibit a turn-on voltage of 3 V, and an emitting optical power at 395 nm of a few microwatts. Electroluminescence spectrum investigation shows that the emissive process can be ascribed to four different recombination transitions, dominated by the electron-hole recombinations on the ZnO side. Full article

Cytological study of the nasal mucosa (also known as rhino-cytology) represents an important diagnostic aid that allows highlighting of the presence of some types of rhinitis through the analysis of cellular features visible under a microscope. Nowadays, the automated detection and classification of cells benefit from the capacity of deep learning techniques in processing digital images of the cytological preparation. Even though the results of such automatic systems need to be validated by a specialized rhino-cytologist, this technology represents a valid support that aims at increasing the accuracy of the analysis while reducing the required time and effort. The quality of the rhino-cytological preparation, which is clearly important for the microscope observation phase, is also fundamental for the automatic classification process. In fact, the slide-preparing technique turns out to be a crucial factor among the multiple ones that may modify the morphological and chromatic characteristics of the cells. This paper aims to investigate the possible differences between direct smear (SM) and cytological centrifugation (CYT) slide-preparation techniques, in order to preserve image quality during the observation and cell classification phases in rhino-cytology. Firstly, a comparative study based on image analysis techniques has been put forward. The extraction of densitometric and morphometric features has made it possible to quantify and describe the spatial distribution of the cells in the field images observed under the microscope. Statistical analysis of the distribution of these features has been used to evaluate the degree of similarity between images acquired from SM and CYT slides. The results prove an important difference in the observation process of the cells prepared with the above-mentioned techniques, with reference to cell density and spatial distribution: the analysis of CYT slides has been more difficult than of the SM ones due to the spatial distribution of the cells, which results in a lower cell density than the SM slides. As a marginal part of this study, a performance assessment of the computer-aided diagnosis (CAD) system called Rhino-cyt has also been carried out on both groups of image slide types. Full article

Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases. Full article

Energy demand associated with the ever-increasing penetration of electric vehicles on worldwide roads is set to rise exponentially in the coming years. The fact that more and more vehicles will be connected to the electricity network will offer greater advantages to the network operators, as the presence of an on-board battery of discrete capacity will be able to support a whole series of ancillary services or smart energy management. To allow this, the vehicle must be equipped with a bidirectional full power charger, which will allow not only recharging but also the supply of energy to the network, playing an active role as a distributed energy resource. To manage recharge and vehicle-to-grid (V2G) operations, the charger has to be more complex and has to require a fast and effective control structure. In this work, we present a control strategy for an integrated on-board battery charger with a nine-phase electric machine. The control scheme integrates a fuzzy logic controller within a voltage-oriented control strategy. The control has been implemented and simulated in Simulink. The results show how the voltage on the DC-bus is controlled to the reference value by the fuzzy controller and how the CC/CV charging mode of the battery is possible, using different charging/discharging current levels. This allows both three-phase fast charge and V2G operations with fast control response time, without causing relevant distortion grid-side (Total Harmonic Distortion is maintained around 3%), even in the presence of imbalances of the machine, and with very low ripple stress on the battery current/voltage. Full article

This paper analyzes the economic feasibility of a business model for multi-Mobile Network Operators (MNOs) and Mobile Virtual Network Operators (MVNOs), which is an envisioned scenario in mobile telecommunications markets supported by 5G networks. A business model for the provision of service to end-users through an MVNO using the infrastructure support of two MNOs is proposed. We analyze the proposal though a model that captures both system and economic features. As regards the systems features, an MVNO provides service to final users using the infrastructure support of two MNOs. The agreement between MVNO and MNOs is such that the MVNO will split the network traffic between the two MNOs and will pay to each MNO for the traffic served through its infrastructure. As regards the economic features, the incentives are modelled through the user utilities and the operators’ profits; and game theory is used to model the strategic interaction between the users’ subscription decision and the MNO network capacities decision. We conclude that such a model is feasible from an economic point of view for all the actors. Full article

Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use of energy storage systems (ESSs) could be an effective solution to reduce the peak power request by CSs in PAs to the grid. Moreover, II-Life battery modules are a potential approach for cutting costs and implementing sustainable solutions. We propose a method to size ESSs coupled to CSs by using II-Life battery modules. Our methodology is based on the estimation of the residual cycles and the decrease in the supplied power due to the battery aging for defining the number of EV battery packs required for an ESS use case. Then, economic evaluations are presented to compare II-Life with the equivalent I-Life storage system. Full article

In the recent era, new information technologies have a significant impact on social networks. Initial integration of information and communication technologies (ICT) into city operations has promoted information city, ease of communication and principles of smart communities. Subsequently, the idea of the Internet of Things (IoT) with the specific focus of social IoT (SIoT) has contributed towards the smart cities (SC), which support the city operations with minimal human interaction. The user-generated data obtained by SIoT can be exploited to produce new useful information for creating citizen-centered smart services for SC. The aim of this research is twofold. Firstly, we used the concept of local and global trust to provide new services in SC based on popular online social networks (OSN) data used by the citizens. Secondly, the sustainability of the three different OSN is assessed. This paper investigates the social network domain with regard to the SC. Although in SC, OSN are increasing day by day, there is still an unresolved issue of trust among their users and also OSN are not much sustainable. In this research, we are analyzing the sustainability of different OSN for the SC. We employ datasets of three different social networks for our analyses. A local trust model is used to identify the central user within the local cluster while the global trust-based framework is used to identify the opinion leaders. Our analysis based on the datasets of Facebook, Twitter, and Slashdot unveil that filtration of these central-local users and opinion leaders result in the dispersion and significant reduction in a network. A novel model is being developed that outlines the relationship between local and global trust for the protection of OSN users in SC. Furthermore, the proposed mechanism uses the data posted by citizens on OSN to propose new services by mitigating the effect of untrusted users. Full article

Controlling Radio Frequency (RF) signals through switching technology is of interest to designers of modern wireless platforms such as Advanced Wireless services (AWS) from 2.18 GHz–2.2 GHz, mid-bands of sub-6 GHz 5G (2.5 GHz and 3.5 GHz), and 4G bands around 600 MHz/700 MHz, 1.7 GHz/2.1 GHz/2.3 GHz/2.5 GHz. This is because certain layout efficiencies can be achieved if suitable components are chosen to control these signals. The objective of this paper is to present a new model of an RF switch denoted as a Magnetostatic Responsive Structure (MRS) for achieving reconfigurable operation in 4G/5G antennas. In particular, the ABCD matrices of the MRS are derived from the S-parameter values and shown to be a good model from 100 kHz to 3.5 GHz. Furthermore, an overall agreement between simulations, analytical results, and circuit model values are shown. Full article

In this paper, the emergence of multistable behavior through the use of fractional-order-derivatives in a Piece-Wise Linear (PWL) multi-scroll generator is presented. Using the integration-order as a bifurcation parameter, the stability in the system is modified in such a form that produces a basin of attraction segmentation, creating many stable states as scrolls are generated in the integer-order system. The results here presented reproduce the same phenomenon reported in systems with integer-order derivatives, where the multistable regimen is obtained through a parameter variation. The multistable behavior reported is also validated through electronic simulation. The presented results are not only applicable in engineering fields, but they also enrich the analysis and the understanding of the implications of using fractional integration orders, boosting the development of further and better studies. Full article

Hybrid Electric Vehicles (HEVs) can be divided into three categories according to how the two propulsion systems (the thermal and the electric ones) supply the driving torque to the vehicle. When the torque is supplied only by an electric propulsion system, while the heat engine takes care of generating the electricity needed to operate the system, it is called a hybrid-series. Conversely, when both propulsion systems provide torque, the vehicle is identified with parallel hybrid wording. Among the parallel hybrids there is a particular configuration called Through-the-Road (TTR). In this configuration, the two propulsion systems are not mechanically connected to each other, but it is precisely the road that allows hybrid propulsion. This architecture, dating back to the early twentieth century, is still used by several manufacturers and carries with it peculiar configurations and control methods. It is also a configuration that fits well with the transformation of conventional vehicles into a hybrid. The paper presents a survey of the TTR HEV solution, evidencing applications, potentialities and limits. Full article

This paper presents an energy-efficient low power 10-b 8-MS/s asynchronous successive approximation register (SAR) analog-to-digital (ADC) converter. An inverted common-mode charge recovery technique is proposed to reduce the switching energy and to improve the linearity of the digital-to-analog converter (DAC). The proposed switching technique consumes only 149 CV_REF² switching energy for the 10-bit case. A rail-to-rail dynamic latch comparator is implemented with adaptive power control for better power efficiency. Additionally, to optimize the power consumption and performance of the logic part, a modified asynchronous type SAR control logic with digitally controllable delay cells is adopted. An on-chip reference voltage generator is also designed with an ADC core for practical use. The structure is realized using 55-nm complementary metal–oxide–semiconductor (CMOS) process technology. The proposed architecture achieves an effective number of bits (ENOB) of 9.56 bits and a signal-to-noise and distortion ratio (SNDR) level of 59.3 dB with a sampling rate of 8 MS/s at measurement level. The whole architecture consumes only 572 µW power when a power supply of 1 V is applied. Full article

The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10⁻⁵. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations. Full article

Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%. Full article

Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such as packet fragmentation, low node density, short contact duration, and network disruption. These features may result in the absence of a path between the source and destination nodes, which is one of the most challenging issues faced by this type of network. To overcome some of these problems, it is necessary to provide vehicular networks with sophisticated tools or methodologies to implement monitoring and management operations. However, designing efficient solutions for this type of network is not an easy task due to its particular characteristics. This paper elaborates on a comprehensive survey focusing on promising proposals to deal with monitoring and management functionalities in vehicular networks. This work aims not only to present the state of the art on monitoring and management solutions but also to analyze their benefits and drawbacks, identify open issues, and provide guidelines for further contributions. Full article

In this paper, a proportional-integral passivity-based controller (PI-PBC) is proposed to regulate the amplitude and frequency of the three-phase output voltage in a direct-current alternating-current (DC-AC) converter with an LC filter. This converter is used to supply energy to AC loads in hybrid renewable based systems. The proposed strategy uses the well-known proportional-integral (PI) actions and guarantees the stability of the system by means of the Lyapunov theory. The proposed controller continues to maintain the simplicity and robustness of the PI controls using the Hamiltonian representation of the system, thereby ensuring stability and producing improvements in the performance. The performance of the proposed controller was validated based on simulation and experimental results after considering parametric variations and comparing them with classical approaches. Full article

Heading the ball is an important skill in soccer. Head impacts are of concern because of the potential adverse health effects. Many elite players now wear GPS (that include inertial monitoring units) on the upper spine for location tracking and workload measurement. By measuring the maximum acceleration of the head and the upper spine, we calculated the acceleration ratio as an attenuation index for participants (n = 8) of different skill levels during a front heading activity. This would allow for in-field estimates of head impacts to be made and concussive events detected. For novice participants, the ratio was as high as 8.3 (mean value 5.0 ± 1.8), whereas, for experienced players, the mean ratio was 3.2 ± 1.5. Elite players stiffen the neck muscles to increase the ball velocity and so the torso acts as a support structure. Electromyography (EMG) signals that were recorded from the neck and shoulder before and after a training intervention showed a major increase in mean average muscle activity (146%, p = 3.39 × 10⁻⁶). This was accompanied by a major decrease in acceleration ratio (34.41%, p = 0.008). The average head-ball impact velocity was 1.95 ± 0.53 m/s determined while using optical motion capture. For this low velocity, the impact force was 102 ± 19 N, 13% of the published concussive force. The voluntary action of neck muscles decreases isolated head movements during heading. Coaches and trainers may use this evidence in their development of junior players. Full article

We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources. Full article

A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle. Full article

It is quite common for transceivers to operate with the RF receiver and transmitter working on different time slots. Typical applications are radars and transceivers in the field of communications. Generally, the receiver is turned off when the transmitter broadcasts and vice versa. This is done in order to prevent the transmitter from blinding the receiver or causing the RF low noise amplification (LNA) stage to saturate. When keeping a receiver active, some leakage of RF energy is inevitable, and therefore shielding is applied to mitigate spurious signals. However, there are many applications wherein the receiver cannot be turned off. To address these applications, we investigate the design and performance of a fully-analog self-jamming canceller able to operate in UHF (Ultra High Frequency) RFID devices. While the traditional cost to design and build this type of topology can be quite high, our proposal is based on a low-cost physical approach. In addition to using common SMT (Surface Mount Technology) devices, we leveraged a new piece of modular technology offered by X-Microwave which allows designers to easily produce RF solutions with a broad portfolio of modular system drop-in blocks. A prototype was realized and the measured results are in close agreement with theoretical simulations. Significant damping of the leaked signal in the receiving channel was realized. Full article

For performance-driven systems such as space-based applications, it is important to maximize the gain of radio-frequency amplifiers (RFAs) with a certain tolerance against radiation, temperature effects, and small form factor. In this work, we present a K-band, compact high-gain RFA using an f_T-doubler topology in a silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) technology platform. The through-silicon vias (TSVs), typically used for small-size chip packaging purposes, have been effectively utilized as an adjustable matching element for input impedance, reducing the overall area of the chip. The proposed RFA, fabricated in a modest 0.35 µm SiGe technology, achieves a gain of 14.1 dB at 20 GHz center frequency, and a noise figure (NF) of 11.2 dB at the same frequency, with a power consumption of 3.3 mW. The proposed design methodology can be used for achieving high gain, avoiding a complex multi-stage amplifier design approach. Full article

Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems. Full article

Fractional calculus in discrete-time systems is a recent research topic. The fractional maps introduced in the literature often display chaotic attractors belonging to the class of “self-excited attractors”. The field of fractional map with “hidden attractors” is completely unexplored. Based on these considerations, this paper presents the first example of fractional map without equilibria showing a number of hidden attractors for different values of the fractional order. The presence of the chaotic hidden attractors is validated via the computation of bifurcation diagrams, maximum Lyapunov exponent, 0–1 test, phase diagrams, complexity, and entropy. Finally, an active controller with the aim for stabilizing the proposed fractional map is successfully designed. Full article

A ferroelectric-gated fin-shaped field effect transistor (Fe-FinFET) is fabricated by connecting a Pb(Zr_0.2Ti_0.8)O₃-based ferroelectric capacitor into the gate electrode of FinFET. The ferroelectric capacitor shows coercive voltages of approximately −1.5 V and 2.25 V. The polarization-induced threshold voltage shift in the Fe-FinFET is investigated by regulating the gate voltage sweep range. When the maximum positive gate to source voltage is varied from 4 V to 2 V with a fixed starting negative gate to source voltage, the threshold voltage during the backward sweep is increased from approximately −0.60 V to 1.04 V. In the case of starting negative gate to source voltage variation from −4 V to −0.5 V with a fixed maximum positive gate to source voltage of 4 V, the threshold voltage during the forward sweep is decreased from 1.66 V to 0.87 V. Those results can be elucidated with polarization domain states. Lastly, it is observed that the threshold voltage is mostly increased/decreased when the positive/negative gate voltage sweep range is smaller/larger than the positive/negative coercive voltage, respectively. Full article

The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches. Full article

The growth of the world’s energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces an increasing need for management systems to supply such energy volume while guaranteeing, at the same time, the security and reliability of the microgrids. Locally distributed energy storage systems (ESS) may provide the capacity to temporarily decouple production and demand. In this sense, the most implemented ESS in local energy districts are small–medium-scale electrochemical batteries. However, hydrogen systems are viable for storing larger energy quantities thanks to its intrinsic high mass-energy density. To match generation, demand and storage, energy management systems (EMSs) become crucial. This paper compares two strategies for an energy management system based on hydrogen-priority vs. battery-priority for the operation of a hybrid renewable microgrid. The overall performance of the two mentioned strategies is compared in the long-term operation via a set of evaluation parameters defined by the unmet load, storage efficiency, operating hours and cumulative energy. The results show that the hydrogen-priority strategy allows the microgrid to be led towards island operation because it saves a higher amount of energy, while the battery-priority strategy reduces the energy efficiency in the storage round trip. The main contribution of this work lies in the demonstration that conventional EMS for microgrids’ operation based on battery-priority strategy should turn into hydrogen-priority to keep the reliability and independence of the microgrid in the long-term operation. Full article

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies. Full article

Optimization algorithms have been successfully applied to the automatic design of analog integrated circuits. However, many of the existing solutions rely on expensive circuit simulations or use fully customized surrogate models for each particular circuit and technology. Therefore, the development of an easily adaptable low-cost and efficient tool that guarantees resiliency to variations of the resulting design, remains an open research area. In this work, we propose a computationally low-cost surrogate model for multi-objective optimization-based automated analog integrated circuit (IC) design. The surrogate has three main components: a set of Gaussian process regression models of the technology’s parameters, a physics-based model of the MOSFET device, and a set of equations of the performance metrics of the circuit under design. The surrogate model is inserted into two different state-of-the-art optimization algorithms to prove its flexibility. The efficacy of our surrogate is demonstrated through simulation validation across process corners in three different CMOS technologies, using three representative circuit building-blocks that are commonly encountered in mainstream analog/RF ICs. The proposed surrogate is $69X$ to $470X$ faster at evaluation compared with circuit simulations. Full article

As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers. Full article

Industrial applications of terahertz (THz) technology are becoming more widespread. In particular, novel evaluation methods for essential rubber products are being developed. THz absorbance spectra of various rubber polymers and reagents enable visualization of filler dispersions and vulcanization reactions. Here, improved visualization of the vulcanization reaction in thick rubber samples is discussed. Silica macro-dispersion is also analyzed because it is a general filler in automobile tires and has been difficult to evaluate with conventional techniques. Full article

An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study. Full article

Physical layer encryption (PLE) is a new research trend for securing data in communication systems. However, conventional procedures in works on PLE are of high complexity and degrade the packet error rate (PER) performance of the system. They are therefore not yet suitable for IoT sensors’ transceiver, which has limited power and computational resource. In this paper, we propose a low complexity PLE method named as joint encryption-modulation (JEM) for small transceivers such as IoT sensors. In our JEM method, data is encrypted after modulation to preserve high security. Our JEM method does not make change the constellation of the modulation after encryption; therefore, the encryption does not degrade PER performance of the system as the conventional works do. Furthermore, the encryption is performed by XOR gates and multiplexers only. It is, therefore, low complexity. Our experiment results show that the JEM method improves about 3 dB of PER performance as compared with that of conventional works. JEM method can support multiple modulation types such as BPSK, QPSK, 16-256 QAM within a small hardware cost. Compared with conventional works, JEM’s hardware resource is reduced by 87.5% in terms of FPGA synthesis and 86.5% in terms of the ASIC circuit. ASIC static power consumption of JEM is reduced by 80.6%. Full article

In recent decades, power consumption has become an essential factor in attracting the attention of integrated circuit (IC) designers. Multiple-valued logic (MVL) and approximate computing are some techniques that could be applied to integrated circuits to make power-efficient systems. By utilizing MVL-based circuits instead of binary logic, the information conveyed by digital signals increases, and this reduces the required interconnections and power consumption. On the other hand, approximate computing is a class of arithmetic computing used in systems where the accuracy of the computation can be traded-off for lower energy consumption. In this paper, we propose novel designs for exact and inexact ternary multipliers based on carbon-nanotube field-effect transistors (CNFETs). The unique characteristics of CNFETs make them a desirable alternative to MOSFETs. The simulations are conducted using Synopsys HSPICE. The proposed design is compared against existing ternary multipliers. The results show that the proposed exact multiplier reduces the energy consumption by up to 6 times, while the best inexact design improves energy efficiency by up to 35 time compared to the latest state-of-the-art methods. Using the imprecise multipliers for image processing provides evidence that these proposed designs are a low-power system with an acceptable error. Full article

We present a new data hiding method based on Adaptive BTC Edge Quantization (ABTC-EQ) using an optimal pixel adjustment process (OPAP) to optimize two quantization levels. The reason we choose ABTC-EQ as a cover media is that it is superior to AMBTC in maintaining a high-quality image after encoding is executed. ABTC-EQ is represented by a form of $trio(Q_1,Q_2,\left[Q_3\right]$ , BM) where Q is quantization levels $(Q_1\le Q_2\le Q_3)$ , and BM is a bitmap). The number of quantization levels are two or three, depending on whether the cover image has an edge or not. Before embedding secret bits in every block, we categorize every block into smooth block or complex block by a threshold. In case a block size is 4x4, the sixteen secret bits are replaced by a bitmap of the smooth block for embedding a message directly. On the other hand, OPAP method conceals 1 bit into LSB and 2LSB respectively, and maintains the quality of an image as a way of minimizing the errors which occur in the embedding procedure. The sufficient experimental results demonsrate that the performance of our proposed scheme is satisfactory in terms of the embedding capacity and quality of an image. Full article

This paper presents the influence of different pulse width modulation (PWM) methods on losses and thermal stresses in SiC power modules used in a three-phase inverter. The variation of PWM methods directly impacts instantaneous losses on these semiconductors, consequently resulting in junction temperature swing at the fundamental frequency of the converter’s output current. This thermal cycling can significantly reduce the lifetime of these components. In order to determine semiconductor losses, one needs to characterize SiC devices to calculate the instantaneous power. The characterization methodology of the devices, the calculation of instantaneous power and temperature of SiC dies, and the influence of the different PWM methods are presented. A 15-kVA inverter is built in order to obtain experimental results to confirm the characterization and loss calculation, and we show the best PWM methods to increase efficiency and reliability of the three-phase inverter for specific aircraft applications. Full article

This paper presents a novel hierarchical Internet of Things (IoT)-based scheme for Microgrid-Enabled Intelligent Buildings to achieve energy digitalization and automation with a renewable energy self-consumption strategy. Firstly, a hierarchical structure of Microgrid-Enabled Intelligent Buildings is designed to establish a two-dimensional fusion layered architecture for the microgrid to interact with the composite loads of buildings. The building blocks and functions of each layer are defined specifically. Secondly, to achieve transparent information fusion and interactive cooperation between the supply-side and demand-side, a state transition mechanism driven by a combination of time and events is proposed to activate the real-time and mutual response of generation and loads dynamically. Thirdly, based on the above hierarchical fusion structure and data-driven state transition mechanism, a power balance control algorithm driven by a self-consumption strategy is further proposed to achieve the autonomous balance of supply and demand. Finally, the IoT Microgrid Laboratory at Aalborg University is introduced to show how to implement this novel hierarchical IoT-based scheme in a Microgrid-Enabled Intelligent Building, and the power consensus control method based on the state transition mechanism is verified to achieve a renewable energy self-consumption strategy. Full article