Electronics, Volume 7, Issue 12 (December 2018) – 113 articles

Technology improvements and cost reduction allow electrochemical energy storage systems based on Lithium-ion cells to massively be used in medium-power applications, where the low system cost is the major constraint. Battery pack maintenance services are expected to be required more often in the future. For this reason, a low-cost instrumentation able to characterize the cells of a battery pack is needed. Several works use low-cost programmable units as Li-ion cell tester, but they are generally based on proprietary-software running on a personal computer. This work introduces an open-source software architecture based on Python language to control common low-cost commercial laboratory instruments. The Python software application is executed on a Raspberry Pi board, which represents the control block of the hardware architecture, instead of a personal computer. The good results obtained during the validation process demonstrate that the proposed cell station tester features measurement accuracy and precision suitable for the characterization of Li-ion cells. Finally, as a simple example of application, the state of health of twenty 40 Ah LiFePO₄ cells belonging to a battery pack used in an E-scooter was successfully determined. Full article

The paper investigates $H_{\infty }$ consensus problem of heterogeneous multi-agent systems including agents with first- and second-order integrators in the presence of disturbance and communication time delays under Markov switching topologies. Based on current messages, outdated information stored in memory and communication time delay information from neighbors, a more general kind of distributed consensus algorithm is proposed, which is faster consensus convergence. By applying stochastic stability analysis, model transformation techniques and graph theory, sufficient conditions of mean square consensus and $H_{\infty }$ consensus are obtained, respectively. Finally, simulation examples are given to illustrate the effectiveness of obtained theoretical results. Full article

This paper considers the fuzzy control design of maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) for the interior permanent magnet synchronous motor (IPMSM) control system that is capable of reducing computation burden, improving torque output, and widening the speed range. In the entire motor speed range, three control methods, i.e., the MTPA, flux weakening, and MTPV methods may be applied depending on current and voltage statuses. The simulation using MATLAB/Simulink is first conducted and then in order to speed up the development, hardware-in-the-loop (HIL) is adopted to verify the effectiveness of the proposed fuzzy MTPA and MTPV control for the IPMSM system. Full article

This paper presents the design and analysis of a proportional resonant controller with a resonant harmonic compensator and switch-type fault current limiter, as a fault-ride through strategy for a three-phase, grid-connected photovoltaic (PV) system under normal conditions and asymmetrical faults. The switch-type fault limiter comprised of current-limiting inductors, a bridge rectifier, a snubber capacitor, linear transformers, and energy absorption bypass. Furthermore, a critical and analytical comparison of switch-type fault limiters is carried out, with the conventional crowbar as the fault-ride through strategy, in combination with a conventionally tuned proportional integrator controller. The designed fault-ride through strategies with proportional integrator and proportional resonant controllers with resonant harmonic compensators are tested at the point of common coupling of the photovoltaic system and at a distance of 19 km from the point of common coupling, in order to analyze the impacts of fault parameter with respect to location. A MATLAB/Simulink model of a 100 kW three-phase grid-connected photovoltaic system is used for analysis. The simulation results of the proposed switch-type fault limiter with proportional resonant controller effectively validate the stable, ripple-free, and robust response compared to all other configurations. In addition, it is also verified that the grid faults on the PV system have a significant impact on fault type, and less impact on fault location. Full article

The vulnerability of chaotic communication systems to noise in transmission channel is a serious obstacle for practical applications. Traditional signal processing techniques provide only limited possibilities for efficient filtering broadband chaotic signals. In this paper, we provide a comparative study of several denoising and filtering approaches: a recursive IIR filter, a median filter, a wavelet-based denoising method, a method based on empirical modes decomposition, and, finally, propose the new filtering algorithm based on the cascade of driven chaotic oscillators. Experimental results show that all the considered methods make it possible to increase the permissible signal-to-noise ratio to provide the possibility of message recognition, while the new proposed method showed the best performance and reliability. Full article

A dc-dc converter with asymmetric pulse-width modulation is presented for medium voltage applications, such as three-phase ac-dc converters, dc microgrid systems, or dc traction systems. To overcome high voltage stress on primary side and high current rating on secondary side, three dc-dc circuits with primary-series secondary-parallel structure are employed in the proposed converter. Current doubler rectifiers are used on the secondary side to achieve low ripple current on output side. Asymmetric pulse-width modulation is adopted to realize soft switching operation for power switches for wide load current operation and achieve high circuit efficiency. Current balancing cells with magnetic component are used on the primary side to achieve current balance in each circuit cell. The voltage balance capacitors are also adopted on primary side to realize voltage balance of input split capacitors. Finally, the circuit performance is confirmed and verified from the experiments with a 1.44 kW prototype. Full article

Whereas most of the work that analyses Synchronous Dataflow (SDF) stays in the dataflow framework, this work pushes its analysis into another framework level, thereby addressing issues that are not well addressed or are even unexplored in SDF. In this manner, the paper proposes a model-driven engineering (MDE) method, combining Synchronous Dataflow (SDF) and Petri nets, to highlight and reinforce their interoperability in digital signal processing applications, cyber-physical systems, or industrial applications. Improvements regarding the settlement and exploitation of the initial conditions associated with SDF are demonstrated; this issue is crucial for every cyber-physical system, since a system’s initial conditions are crucial to ensuring the system’s liveness. The improvements outlined in this work exploit an innovating mapping in the Place/Transition (P/T) Petri net domain that is intended to reduce and predict the total amount of initial data in SDF channels. The relevance of the firing semantics engaged with the equivalent Petri net model is discussed. This paper proposes a new approach to estimate whether an SDF has a static schedule by performing simulation and property verification of the equivalent-based P/T Petri net system achieved, framed by a Petri net invariant analysis and based on the stubborn set method of Petri nets. In this way, this new approach will allow mitigating the state explosion problem. Finally, a strategy is applied to two case studies to discover all the elementary circuits (static schedules) associated with the generated model’s state-space. Full article

This paper presents the design of a microcontroller controlled buck-boost DC-to-DC power converter system. The system contains two major subsystems, a Zeta type buck-boost power converter and a control unit and it contains two control loops. The inner-loop is a voltage regulator based on a Zeta type buck-boost converter. The outer-loop is for voltage and current regulation. The voltage/current regulation is achieved by controlling a light dependent resistor from the control unit. Computer simulations based on a MATLAB/SIMULINK model were successfully conducted to verify the design. In addition, a prototype system was built and successfully tested for a Li-ion battery charging application. Full article

Repetitive control is one of the most used control approaches to deal with periodic references/disturbances. It owes its properties to the inclusion of an internal model in the controller that corresponds to a periodic signal generator. However, there exist many different ways to include this internal model. This work presents a description of the different schemes by means of which repetitive control can be implemented. A complete analytic analysis and comparison is performed together with controller synthesis guidance. The voltage source inverter controller experimental results are included to illustrative conceptual developments. Full article

The memristive device is a fourth fundamental circuit element with inherent memory, nonlinearity, and passivity properties. Herein, we report on a cost-effective and rapidly produced ZnO thin film memristive device using the doctor blade method. The active layer of the developed device (ZnO) was composed of compact microrods. Furthermore, ZnO microrods were well spread horizontally and covered the entire surface of the fluorine-doped tin oxide substrate. X-ray diffraction (XRD) results confirmed that the synthesized ZnO was oriented along the c-axis and possessed a hexagonal crystal structure. The device showed bipolar resistive switching characteristics and required a very low resistive switching voltage (±0.8 V) for its operation. Two distinct and well-resolved resistance states with a remarkable 10³ memory window were achieved at 0.2-V read voltage. The developed device switched successfully in consecutive 10² switching cycles and was stable over 10² seconds without any observable degradation in the resistive switching states. In addition to this, the charge–magnetic flux curve was observed to be a single-valued function at a higher magnitude of the flux and became double valued at a lower magnitude of the flux. The conduction mechanism of the ZnO thin film memristive device followed the space charge limited current, and resistive switching was due to the filamentary resistive switching effect. Full article

Conventional IoT applications rely on seamless data collection from the distributed sensor nodes of Wireless Sensor Networks (WSNs). The energy supplied to the sensor node is limited and it depletes after each cycle of data collection. Therefore, data flow from the network to the base station may cease at any time due to the nodes with a dead battery. A replacement of the battery in WSNs is often challenging and requires additional efforts. To ensure the robust operation of WSNs, many fault recovery routing mechanisms have been proposed. Most of the previous fault recovery routing methods incur considerable delays in recovery and high overhead in either energy consumption or device cost. We propose an energy-efficient fail recovery routing method that is aimed to operate over a data aggregation network topology using a TDMA media access control (MAC). This paper introduces a novel fault recovery routing algorithm for TDMA-based WSNs. It finds an optimal neighbor backup parent (NBP) for each node in a way that reduces the energy consumption. The proposed method allows the NBPs to utilize the time slot of the faulty parent nodes, so it eliminates the overhead of TDMA rescheduling for NBPs. To evaluate the fault recovery performance and energy efficiency of the proposed method, we implemented it in C++ simulation program. Simulation experiments with an extensive set of network examples demonstrate that the proposed method can extend the network lifetime by 21% and reduce the energy consumption by 23% compared with the reference methods. Full article

Quantification of the actin cytoskeleton is of prime importance to unveil the cellular force sensing and transduction mechanism. Although fluorescence imaging provides a convenient tool for observing the morphology of the actin cytoskeleton, due to the lack of approaches to accurate actin cytoskeleton quantification, the dynamics of mechanotransduction is still poorly understood. Currently, the existing image-based actin cytoskeleton analysis tools are either incapable of quantifying both the orientation and the quantity of the actin cytoskeleton simultaneously or the quantified results are subject to analysis artifacts. In this study, we propose an image recognition-based actin cytoskeleton quantification (IRAQ) approach, which quantifies both the actin cytoskeleton orientation and quantity by using edge, line, and brightness detection algorithms. The actin cytoskeleton is quantified through three parameters: the partial actin-cytoskeletal deviation (PAD), the total actin-cytoskeletal deviation (TAD), and the average actin-cytoskeletal intensity (AAI). First, Canny and Sobel edge detectors are applied to skeletonize the actin cytoskeleton images, then PAD and TAD are quantified using the line directions detected by Hough transform, and AAI is calculated through the summational brightness over the detected cell area. To verify the quantification accuracy, the proposed IRAQ was applied to six artificially-generated actin cytoskeleton mesh work models. The average error for both the quantified PAD and TAD was less than 1.22 ${}^{\circ }$ . Then, IRAQ was implemented to quantify the actin cytoskeleton of NIH/3T3 cells treated with an F-actin inhibitor (latrunculin B). The quantification results suggest that the local and total actin-cytoskeletal organization became more disordered with the increase of latrunculin B dosage, and the quantity of the actin cytoskeleton showed a monotonically decreasing relation with latrunculin B dosage. Full article

Indoor emitter localization is a topic of continued interest for improving wireless security as wireless technologies continue to become more advanced. Conventional methods have focused on the localization of devices relative to multi-sensor systems owing to ease of implementation with pre-existing infrastructures. This work, however, focuses on enhancing wireless security via non-cooperative emitter localization in scenarios where only a single receiver can be employed. A vector sensor is simulated and experimentally developed that extracts three-dimensional signal characteristics for room-based emitter localization and is compared to conventional methodologies such as Received Signal Strength (RSS), Time of Arrival (ToA), and Direction of Arrival (DoA). The proposed method generates time-frequency fingerprints and extracts features through dimensionality reduction. A second stage extracts spatial parameters consisting of Channel State Information (CSI) and DoAs that are analyzed using a Gaussian Mixture Model (GMM) to segregate fine-grained regions of interest within each room where the non-cooperative emitter resides. Blind channel equalization cascaded with a least squares channel estimate is used for acquiring the CSI, whereas the DoAs are obtained by unique trigonometric properties of the vector sensing antenna. The results demonstrate that a vector sensor can improve non-cooperative emitter localization and enhance wireless security in indoor environments. Full article

This work is concerned with the mechanical design and the description of the different components of a new mobile base for a lightweight mobile manipulator. These kinds of mobile manipulators are normally composed of multiple lightweight links mounted on a mobile platform. This work is focused on the description of the mobile platform, the development of a new kinematic model and the design of a control strategy for the system. The proposed kinematic model and control strategy are validated by means of experimentation using the real prototype. The workspace of the system is also defined. Full article

The sum throughput of a cellular network can be improved when nearby devices employ direct communications using a resource sharing technique. Multicast device-to-device (M-D2D) communication is a promising solution to accommodate higher transmission rates. In an M-D2D communication, a multicast group is formed by considering a transmitter that can transmit the same information to multiple receivers by considering the transmission link conditions. In this paper, we focus on the uplink interference generated due to the non-orthogonal sharing of resources between the cellular users and M-D2D groups. To mitigate the interference, we propose a spectrum reuse-based resource allocation and power control scheme for M-D2D communication underlaying an uplink cellular network. We formulate the throughput optimization problem by considering the fractional frequency reuse (FFR) method within a multicell cellular network. In addition, a metaheuristic-tabu search algorithm is developed that maximizes the probability of finding optimal solutions by minimizing uplink interference. To analyze fairness resource distribution among users, we finally consider Jain’s fairness index. Simulation results show that the proposed scheme can improve the coverage probability, success rate, spectral efficiency, and sum throughput of the network, compared with a random resource allocation scheme without a metaheuristic-tabu search algorithm. Full article

A compact antenna with a 6 mm ground clearance for 4G and 5G metal frame mobile phones is proposed in this paper. The proposed antenna consists of a coupled line, a ground branch, a monopole branch, and a tuning line. The ground branch and the coupled line are used to obtain the lower band (698–960 MHz), the monopole branch is used to improve the match at the lower band and obtain the higher band (1710–2690, 3400–3800 MHz), and the tuning line is used to improve the match at the higher band. The novelty of the proposed antenna is that more modes are excited and work together to obtain multiple working bands, by using the coupled line and the folded branches with the help of the tuning line, and then nine bands are obtained under the conditions of a 6 mm, only, ground clearance and a metal frame environment. A prototype has been fabricated and measured. The measured −6 dB impedance bandwidths are 345 MHz (0.685–1.03 GHz) and 2.16 GHz (1.67–3.83 GHz) at the lower and higher bands, respectively. The LTE700, GSM850, GSM900, DCS, PCS, UMTS, LTE2300, and LTE2500 bands for 2G, 3G, 4G, and the 3.5 GHz band that is possible for 5G are covered. The measured efficiencies and patterns are also presented. Full article

In this study, a novel wetness and moisture concentration analysis approach is presented. A finite element method is utilized for the solution technique mainly using thermal and surface effect elements. Numerical results obtained from the current approach are compared against other existing finite element-based solutions and the newly introduced peridynamics theory. For numerical analysis, a reflow soldering stage is simulated for a multi-material system with time-dependent saturated moisture concentrations. Different solubility activation energies and temperature conditions are considered. Numerical results demonstrate that the developed methodology can make accurate predictions under different conditions and it is more general than some other existing models which are limited to certain conditions. Full article

This note presents an optimal design method to enhance image quality in optical image stabilization (OIS) systems. First of all, performance limitations of conventional methods are shown and secondly, a new design framework based on convex optimization is proposed. The resulting controller essentially stabilizes the closed loop systems because the proposed method is derived from Lyapunov stability. From the test results, it is confirmed that this method reduces the effect of hand vibrations and makes images sharp. Additionally, it is shown that the proposed method is also effective in robot vision and recognition rate of deep neural network (DNN) based traffic signs and pedestrians detection in automotive applications. This note has three main contributions. First, performance limitations of the conventional method are shown. Second, from the relation between sensitivity and complementary sensitivity functions, an indirect design method for performance improvement is proposed, and finally, stability guaranteed optimal design is proposed. Unlike conventional methods, the proposed method does not require addition filters to suppress resonances of the plant and this note highlights phases of the closed loop systems on removing external vibrations. Full article

A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g., ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties. Full article

Among non-destructive inspection (NDI) techniques, General Visual Inspection (GVI), global or zonal, is the most widely used, being quick and relatively less expensive. In the aeronautic industry, GVI is a basic procedure for monitoring aircraft performance and ensuring safety and serviceability, and over 80% of the inspections on large transport category aircrafts are based on visual testing, both directly and remotely, either unaided or aided via mirrors, lenses, endoscopes or optic fiber devices coupled to cameras. This paper develops the idea of a global and/or zonal GVI procedure implemented by means of an autonomous unmanned aircraft system (UAS), equipped with a low-cost, high-definition (HD) camera for carrying out damage detection of panels, and a series of distance and trajectory sensors for obstacle avoidance and inspection path planning. An ultrasonic distance keeper system (UDKS), useful to guarantee a fixed distance between the UAS and the aircraft, was developed, and several ultrasonic sensors (HC-SR-04) together with an HD camera and a microcontroller were installed on the selected platform, a small commercial quad-rotor (micro-UAV). The overall system concept design and some laboratory experimental tests are presented to show the effectiveness of entrusting aircraft inspection procedures to a small UAS and a PC-based ground station for data collection and processing. Full article

Pulse-Wave Doppler (PWD) ultrasound has been applied to the detection of blood flow for a long time; recently the same method was also proven effective in the monitoring of industrial fluids and suspensions flowing in pipes. In a PWD investigation, bursts of ultrasounds at 0.5–10 MHz are periodically transmitted in the medium under test. The received signal is amplified, sampled at tens of MHz, and digitally processed in a Field Programmable Gate Array (FPGA). First processing step is a coherent demodulation. Unfortunately, the weak echoes reflected from the fluid particles are received together with the echoes from the high-reflective pipe walls, whose amplitude can be 30–40 dB higher. This represents a challenge for the input dynamics of the system and the demodulator, which should clearly detect the weak fluid signal while not saturating at the pipe wall components. In this paper, a numerical demodulator architecture is presented capable of auto-tuning its internal dynamics to adapt to the feature of the actual input signal. The proposed demodulator is integrated into a system for the detection of the velocity profile of fluids flowing in pipes. Simulations and experiments with the system connected to a flow-rig show that the data-adaptive demodulator produces a noise reduction of at least of 20 dB with respect to different approaches, and recovers a correct velocity profile even when the input data are sampled at 8 bits only instead of the typical 12–16 bits. Full article

Over the past few years, power quality (PQ) monitoring has become of paramount importance for utilities and users since poor PQ generates negative consequences. In monitoring, fast detection and accurate classification of PQ disturbances (PQDs) are desirable features. In this work, a new method to detect and classify PQDs is proposed. The proposal takes advantage of the low computational resources of both a phasor measurement unit (PMU)-based signal processing scheme and the homogeneity approach. To classify the PQDs, if–then–else rules are used. To validate and test the proposal, synthetic and real signals of sags, swells, interruptions, notching, spikes, harmonics, and oscillatory transients are considered. For the generation of real signals, a PQD generator based on a power inverter is used. In the proposed method, the PMU information is directly used to classify sags, swells, and interruptions, whereas the homogeneity index is used to distinguish among the remaining PQDs. Results show that the proposal is an effective and suitable tool for PQ monitoring. Full article

The advancement in electrical load forecasting techniques with new algorithms offers reliable solutions to operators for operational cost reduction, optimum use of available resources, effective power management, and a reliable planning process. The focus is to develop a comprehensive understanding regarding the forecast accuracy generated by employing a state of the art optimal autoregressive neural network (NARX) for multiple, nonlinear, dynamic, and exogenous time varying input vectors. Other classical computational methods such as a bagged regression tree (BRT), an autoregressive and moving average with external inputs (ARMAX), and a conventional feedforward artificial neural network are implemented for comparative error assessment. The training of the applied method is realized in a closed loop by feeding back the predicted results obtained from the open loop model, which made the implemented model more robust when compared with conventional forecasting approaches. The recurrent nature of the applied model reduces its dependency on the external data and a produced mean absolute percentage error (MAPE) below 1%. Subsequently, more precision in handling daily grid operations with an average improvement of 16%–20% in comparison with existing computational techniques is achieved. The network is further improved by proposing a lightning search algorithm (LSA) for optimized NARX network parameters and an exponential weight decay (EWD) technique to control the input error weights. Full article

Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting. Full article

This paper presents a robotic platform, PiBot, which was developed to improve the teaching of robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera as the main sensor of the robotic platform. Furthermore, higher-level commands were provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and schools without the physical robot or that cannot afford to obtain one, can nevertheless practice, learn and teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot. Full article

A nuclear fusion reactor requires a radiation-hardened sensor readout integrated circuit (IC), whose operation should be tolerant against harsh radiation effects up to MGy or higher. This paper proposes radiation-hardening circuit design techniques for an instrumentation amplifier (IA), which is one of the most sensitive circuits in the sensor readout IC. The paper studied design considerations for choosing the IA topology for radiation environments and proposes a radiation-hardened IA structure with total-ionizing-dose (TID) effect monitoring and adaptive reference control functions. The radiation-hardened performance of the proposed IA was verified through model-based circuit simulations by using compact transistor models that reflected the TID effects into complementary metal–oxide–semiconductor (CMOS) parameters. The proposed IA was designed with the 65 nm standard CMOS process and provides adjustable voltage gain between 3 and 15, bandwidth up to 400 kHz, and power consumption of 34.6 μW, while maintaining a stable performance over TID effects up to 1 MGy. Full article

Digital speckle correlation method is widely used in the areas of three-dimensional deformation and morphology measurement. It has the advantages of non-contact, high precision, and strong stability. However, it is very complex to be carried out with low speed software implementation. Here, an improved full pixel search algorithm based on the normalized cross correlation (NCC) method considering hardware implementation is proposed. According to the field programmable gate array (FPGA) simulation results, the speed of hardware design proposed in this paper is 2000 faster than that of software in single point matching, and 600 times faster than software in multi-point matching. The speed of the presented algorithm shows an increasing trend with the increase of the template size when performing multipoint matching. Full article

Currently, researchers face new challenges in order to compensate or even reduce the noxious phenomenon known as bias-temperature instability (BTI) that is present in modern metal-oxide-semiconductor (MOS) technologies, which negatively impacts the performance of semiconductor devices. BTI remains a mystery in the way that it evolves in time, as well as the responsible mechanisms for its appearance and the further degradation it produces on MOS devices. The BTI phenomenon is usually associated with an increase of MOS transistor’s threshold voltage; however, this work also addresses BTI as a change in MOSFET’s drain current, transconductance, and the channel’s resistivity. In this way, we detail a physics-based model to get a better insight into the prediction of threshold voltage degradation for aging ranges going from days to years, in 180-nm MOS technology. We highlight that a physics-based BTI model improves accuracy in comparison to lookup table models. Finally, simulation results for the inclusion of such a physics-based BTI model into BSIM3v3 are shown in order to get a better understanding of how BTI impacts the performance of MOS devices. Full article

The imperfect array degrades the direction finding performance. In this paper, we investigate the direction finding problem in uniform linear array (ULA) system with unknown mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, the sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem into a sparse reconstruction problem. In the sparse-based model, the off-grid errors are introduced by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution assumptions of unknown parameters including the noise variance, the off-grid vector, the received signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction finding methods significantly in the array system with unknown mutual coupling effect. Full article

With the constant increase of energy consumption in the world, the efficiency of systems and equipment is becoming more important. Uninterruptible Power Supply (UPS) is an equipment that provides safe and reliable supply for critical load systems, that is, systems where a supply interruption can lead to economical or even human losses. The Double Conversion UPS is the most complete UPS class in terms of load protection, regulation, performance, and reliability, however, it has lower efficiency and higher cost because of its high number of power converters. Silicon Carbide devices are emerging as an opportunity to construct power converters with higher efficiency and higher power density. The main purpose of this work is to design a three-phase AC-DC-AC converter using Silicon Carbide for Double Conversion UPS applications. The aim is to maximize efficiency and minimize volume and mass. The methodologies to size and choose the main hardware components are described in detail. Experimental results obtained with the prototype prove the high efficiency and high power density achievable with Silicon Carbide Metal Oxide Semiconductor Field Effect Transistor (MOSFETs). Full article