Electronics — Open Access Journal

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

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about $31.64\%$ by reducing the MC effect by about 4 dB. Full article

A highly linear fully self-biased class AB current buffer designed in a standard 0.18 $\mathsf{\mu }$m CMOS process with 1.8 V power supply is presented in this paper. It is a simple structure that, with a static power consumption of 48 $\mathsf{\mu }$W, features an input resistance as low as $89\mathsf{\Omega }$, high accuracy in the input–output current ratio and total harmonic distortion (THD) figures lower than −60 dB at 30 $\mathsf{\mu }$A amplitude signal and 1 kHz frequency. Robustness was proved through Monte Carlo and corner simulations, and finally validated through experimental measurements, showing that the proposed configuration is a suitable choice for high performance low voltage low power applications. Full article

Different industrial and medical situations require the non-invasive extraction of information from the inside of bodies. This is usually done through tomographic methods that generate images based on internal body properties. However, the image reconstruction involves a mathematical inverse problem, for which accurate resolution demands large computation time and capacity. In this paper we explore the use of Machine Learning to develop an accurate solver for reconstructing Electrical Impedance Tomography images in real-time. We compare the results with the Iterative Gauss-Newton and the Primal Dual Interior Point Method, which are both largely used and well-validated solvers. The approaches were compared from the qualitative as well as the quantitative viewpoints. The former was focused on correctly detecting the internal body features. The latter was based on accurately predicting internal property distributions. Experiments revealed that our approach achieved better accuracy and Cohen’s kappa coefficient (97.57% and 94.60% respectively) from the qualitative viewpoint. Moreover, it also obtained better quantitative metrics with a Mean Absolute Percentage Error of 18.28%. Experiments confirmed that Neural Networks algorithms can reconstruct internal body properties with high accuracy, so they would be able to replace more complex and slower alternatives. Full article

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement. Full article

This paper deals with the definition and analysis of a complete electronic system for the detection and monitoring of stability characteristics in complex scenarios such as structural elements or environmental events. For instance, it may be successfully adopted to detect rockfall events on protection barriers, as well as to monitor landslides or the integrity of structures like bridges and buildings. The system is completely autonomous thanks to the implementation of an energy harvesting architecture and realizes a wireless sensor network whose nodes are auto-configurable, making it possible to freely arrange them in situ. The continuously collected data are relative to acceleration, inclination, position, and temperature of each node. These data are transmitted and stored on a remote web server devoted to the automatic management of alarms and accessible for data consulting. The proposed system is currently operating in different experimental fields in Italy. Full article

The last decade has witnessed a significant reduction in prices and an increased performance of electronic components, coupled with the influence of the shift towards the generation of open resources, both in terms of knowledge (open access), programs (open-source software), and components (open hardware). This situation has produced different effects in today’s society, among which is the empowerment of citizens, called makers, who are themselves able to generate citizen science or build assembly developments. Situated in the context described above, the current study follows a Do-It-Yourself (DIY) approach. In this way, it attempts to define a conceptual design of an Internet of Things (IoT) node, which is reproducible at both physical and behavioral levels, to build IoT nodes which can cover any scenario. To test this conceptual design, this study proposes a sensorization node to monitor meteorological phenomena. The node is called SEnviro (node) and features different improvements such as: the possibility of remote updates using Over-the-Air (OTA) updates; autonomy, using 3G connectivity, a solar panel, and applied energy strategies to prolong its life; and replicability, because it is made up of open hardware and other elements such as 3D-printed pieces. The node is validated in the field of smart agriculture, with the aim of monitoring different meteorological phenomena, which will be used as input to disease detection models to detect possible diseases within vineyards. Full article

The monitoring and control of renewable energy sources (RESs) based on DC (Direct Current) microgrids (DC MG) are gaining much consideration at this time. In comparison with the isolated individual control of converters in a microgrid, DC microgrids provide better voltage regulation and harmonized energy generation/consumption. To address the inherent vulnerability of communication links, robust methods have been proposed that improve the resilience of communication-based control. However, the failure of the communication links in microgrid control layers remains a considerable issue that may lead to one or more nodes being disconnected and operating as a communication island. Such types of communication islanding may cause the unpredictable behavior of the system and further destabilization may lead to a cascaded failure. This paper proposes a fast algorithm to detect and evaluate network connectivity based on the information stored at every node in the form of a look-up table. The control structure has been modified under communication islanding, and a communication connectivity observer is used at every node to detect and address power flow issues under communication islanding. The proposed method has been verified through mathematical analysis, simulation, and experimental results. Full article

Wireless Sensor Networks (WSNs) is a group of spatially dispersed autonomous sensor devices, named motes. These motes have a microcontroller, sensors, are powered by AA or AAA batteries, and mainly have the ability to communicate using the IEEE 802.15.4 standard. The motes communicate between them inside the WSN exchanging packets using a multi-hop routing. They use a very low amount of power (below 100 mW). This limits the maximum communication distance between motes within the WSN. Usually, one mote acts as a gateway to other networks and this mote is also called sink or simply Base Station (BS), and the data collected by the sensors of each mote are sent to this mote. The maximum distance between the BS and the nearest mote is below 100 m because of the power limitations of the motes. If the WSN-BS distance is above this boundary, the communication will surely fail. We propose a new technique in order to achieve a long range communication from the WSN, for instance to communicate to a Low Earth Orbit (LEO) satellite. Many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications are found, however the synchronization of clocks is an almost impossible task given the simplicity and cheapness of the architecture of the motes. To overcome this problem, we propose a new technique, named Stochastic Collaborative Beamforming (SCB), in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. This gain is obtained from interfering independent signals coming from each mote of the WSN, using a repetition scheme. Although it does not get all the nominal gain that could be obtained in case of a perfect synchronization, it does get a sufficient gain to reach the BS with limited power consumption. Full article

Three different insulator layers SiN_x, SiON, and SiO₂ were used as a gate dielectric and passivation layer in AlGaN/GaN metal–insulator–semiconductor high-electron-mobility transistors (MIS-HEMT). The SiN_x, SiON, and SiO₂ were deposited by a plasma-enhanced chemical vapor deposition (PECVD) system. Great differences in the gate leakage current, breakdown voltage, interface traps, and current collapse were observed. The SiON MIS-HEMT exhibited the highest breakdown voltage and I_on/I_off ratio. The SiN_x MIS-HEMT performed well in current collapse but exhibited the highest gate leakage current density. The SiO₂ MIS-HEMT possessed the lowest gate leakage current density but suffered from the early breakdown of the metal–insulator–semiconductor (MIS) diode. As for interface traps, the SiN_x MIS-HEMT has the largest shallow trap density and the lowest deep trap density. The SiO₂ MIS-HEMT has the largest deep trap density. The factors causing current collapse were confirmed by Photoluminescence (PL) spectra. Based on the direct current (DC) characteristics, SiN_x and SiON both have advantages and disadvantages. Full article

Currently, the technology of microelectromechanical systems is widely used in the development of high-frequency and ultrahigh-frequency devices. The most important requirements for modern and advanced devices of the ultra-high-frequency range are the reduction of weight and size characteristics, power consumption with an increase in their functionality, operating frequency and level of integration. Radio frequency microelectromechanical switches are developed using the technology of the manufacture of CMOS-integrated circuits. Integrated radio frequency control circuits require low control voltages, the high ratio of losses to the isolation in the open and closed condition, high performance and reliability. This review is devoted to the analytical approach based on the knowledge of materials, basic performance indices and mechanisms of failure, which can be used in the development of radio-frequency microelectromechanical switches. Full article

A new embedded chaotic cryptosystem is introduced herein with the aim to encrypt digital images and performing speech recognition as an external access key. The proposed cryptosystem consists of three technologies: (i) a Spartan 3E-1600 FPGA from Xilinx; (ii) a 64-bit Raspberry Pi 3 single board computer; and (iii) a voice recognition chip manufactured by Sunplus. The cryptosystem operates with four embedded algorithms: (1) a graphical user interface developed in Python language for the Raspberry Pi platform, which allows friendly management of the system; (2) an internal control entity that entails the start-up of the embedded system based on the identification of the key access, the pixels-entry of the image to the FPGA to be encrypted or unraveled from the Raspberry Pi, and the self-execution of the encryption/decryption of the information; (3) a chaotic pseudo-random binary generator whose decimal numerical values are converted to an 8-bit binary scale under the VHDL description of $mod\left(255\right)$; and (4) two UART communication algorithms by using the RS-232 protocol, all of them described in VHDL for the FPGA implementation. We provide a security analysis to demonstrate that the proposed cryptosystem is highly secure and robust against known attacks. Full article