Editor’s Choice Articles

The security of communication and computer systems is an increasingly important issue, nowadays pervading all areas of human activity (e.g., credit cards, website encryption, medical data, etc.). Furthermore, the development of high-speed and light-weight implementations of the encryption algorithms is fundamental to improve and widespread their application in low-cost, low-power and portable systems. In this scientific article, a high-speed implementation of the AES-128 algorithm is reported, developed for a short-range and high-frequency communication system, called Wireless Connector; a Xilinx ZCU102 Field Programmable Gate Array (FPGA) platform represents the core of this communication system since manages all the base-band operations, including the encryption/decryption of the data packets. Specifically, a pipelined implementation of the Advanced Encryption Standard (AES) algorithm has been developed, allowing simultaneous processing of distinct rounds on multiple successive plaintext packets for each clock period and thus obtaining higher data throughput. The proposed encryption system supports 220 MHz maximum operating frequency, ensuring encryption and decryption times both equal to only 10 clock periods. Thanks to the pipelined approach and optimized solutions for the Substitute Bytes operation, the proposed implementation can process and provide the encrypted packets each clock period, thus obtaining a maximum data throughput higher than 28 Gbit/s. Also, the simulation results demonstrate that the proposed architecture is very efficient in using hardware resources, requiring only 1631 Configurable Logic Blocks (CLBs) for the encryption block and 3464 CLBs for the decryption one. Full article

Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques. Full article

Recent advances in information and communication technology (ICT) have enabled interaction and cooperation between components of the transportation system, and cooperative eco-driving systems that apply ICT to eco-driving systems are receiving significant attention. A cooperative eco-driving system is a complex system that requires consideration of the electronic control unit (ECU) and vehicle-to-everything (V2X) communication. To evaluate these complex systems, it is needed to integrate simulators with expertise. Therefore, this study presents an integrated driving hardware-in-the-loop (IDHIL) simulator for the testing and evaluation of cooperative eco-driving systems. The IDHIL simulator is implemented by integrating the driving hardware-in-the-loop simulator and a vehicular ad hoc network simulator to develop and evaluate a hybrid control unit and cooperative eco-driving application for the connected hybrid electric vehicle (CHEV). A cooperative eco-driving speed guidance application is utilized to demonstrate the use of our simulator. The results of the evaluation show the improved fuel efficiency of the CHEV through a calculation of the optimal speed profile and the optimal distribution of power based on V2X communication. Finally, this paper concludes with a description of future directions for the testing and evaluation of cooperative eco-driving systems. Full article

The dynamic performance of the local control of single-phase voltage source inverters (VSIs) can be degraded when supplying to nonlinear loads (NLLs) in microgrids. When this control is based on the droop principles, a proper calculation of the active and reactive averaged powers (P–Q) is essential for a proficient dynamic response against abrupt NLL changes. In this work, a VSI supplying to an NLL was studied, focusing the attention on the P–Q calculation stage. This stage first generated the direct and in-quadrature signals from the measured load current through a second-order generalized integrator (SOGI). Then, the instantaneous power quantities were obtained by multiplying each filtered current by the output voltage, and filtered later by utilizing a SOGI to acquire the averaged P–Q parameters. The proposed algorithm was compared with previous proposals, while keeping the active power steady-state ripple constant, which resulted in a faster calculation of the averaged active power. In this case, the steady-state averaged reactive power presented less ripple than the best proposal to which it was compared. When reducing the velocity of the proposed algorithm for the active power, it also showed a reduction in its steady-state ripple. Simulations, hardware-in-the-loop, and experimental tests were carried out to verify the effectiveness of the proposal. Full article

Recently, the statistical analysis of muscle activation patterns highlighted that not only one, but several activation patterns can be identified in the gait of healthy adults, with different occurrence. Although its potential, the application of this approach in pathological populations is still limited and specific implementation issues need to be addressed. This study aims at applying a statistical approach to analyze muscle activation patterns of gait in Parkinson’s Disease, integrating gait symmetry and co-activation. Surface electromyographic signal of tibialis anterior and gastrocnemius medialis were recorded during a 6-min walking test in 20 patients. Symmetry between right and left stride time series was verified, different activation patterns identified, and their occurrence (number and timing) quantified, as well as the co-activation of antagonist muscles. Gastrocnemius medialis presented five activation patterns (mean occurrence ranging from 2% to 43%) showing, with respect to healthy adults, the presence of a first shorted and delayed activation (between flat foot contact and push off, and in the final swing) and highlighting a new second region of anticipated activation (during early/mid swing). Tibialis anterior presented five activation patterns (mean occurrence ranging from 3% to 40%) highlighting absent or delayed activity at the beginning of the gait cycle, and generally shorter and anticipated activations during the swing phase with respect to healthy adults. Three regions of co-contraction were identified: from heel strike to mid-stance, from the pre- to initial swing, and during late swing. This study provided a novel insight in the analysis of muscle activation patterns in Parkinson’s Disease patients with respect to the literature, where unique, at times conflicting, average patterns were reported. The proposed integrated methodology is meant to be generalized for the analysis of muscle activation patterns in pathologic subjects. Full article

In this work, the design of a novel Ka-band miniaturized bandpass filter with broad bandwidth is demonstrated by using inversely coupled U-shaped transmission lines. In the proposed filter, two transmission zeros can be generated within a cascaded U-shaped structure and it can also be proven that, by inversely coupling two stacked U-shaped transmission lines, the notch frequency at the upper stopband can be shifted to a lower frequency, which results in a smaller chip size. The key parameters affecting the performance of the proposed filter are investigated in detail with the effective lumped-element circuit illustrated. Fabricated in a 0.13-μm SiGe BiCMOS process, the proposed filter achieves an insertion loss of 3.6 dB at a frequency of 28.75 GHz and the measured bandwidth is from 20.75 GHz to 41 GHz. The return loss is better than −10 dB from 20.5 GHz to 39 GHz. The lower transmission zero is located at 11.75 GHz with a suppression of 54 dB while the upper transmission zero is around 67 GHz with an attenuation of 34.6 dB. The measurement agrees very well with the simulation results and the overall chip size of the proposed filter is 176 × 269 μm². Full article

The concept of Blockchain has penetrated a wide range of scientific areas, and its use is considered to rise exponentially in the near future. Executing short scripts of predefined code called smart contracts on Blockchain can eliminate the need of intermediaries and can also raise the multitude of execution of contracts. In this paper, we discuss the concept of Blockchain along with smart contracts and discuss their applicability in the Internet of Medical Things (IoMT) in the e-healthcare domain. The paper analyses the dimensions that decentralization and the use of smart contracts will take the IoMT in e-healthcare, proposes a novel architecture, and also outlines the advantages, challenges, and future trends related to the integration of all three. The proposed architecture shows its effectiveness with average packet delivery ratio, average latency, and average energy efficiency performance parameters when compared with traditional approaches. Full article

Convolutional neural networks (CNNs) have achieved state-of-the-art performance in numerous aspects of human life and the agricultural sector is no exception. One of the main objectives of deep learning for smart farming is to identify the precise location of weeds and crops on farmland. In this paper, we propose a semantic segmentation method based on a cascaded encoder-decoder network, namely CED-Net, to differentiate weeds from crops. The existing architectures for weeds and crops segmentation are quite deep, with millions of parameters that require longer training time. To overcome such limitations, we propose an idea of training small networks in cascade to obtain coarse-to-fine predictions, which are then combined to produce the final results. Evaluation of the proposed network and comparison with other state-of-the-art networks are conducted using four publicly available datasets: rice seeding and weed dataset, BoniRob dataset, carrot crop vs. weed dataset, and a paddy–millet dataset. The experimental results and their comparisons proclaim that the proposed network outperforms state-of-the-art architectures, such as U-Net, SegNet, FCN-8s, and DeepLabv3, over intersection over union (IoU), F1-score, sensitivity, true detection rate, and average precision comparison metrics by utilizing only (1/5.74 × U-Net), (1/5.77 × SegNet), (1/3.04 × FCN-8s), and (1/3.24 × DeepLabv3) fractions of total parameters. Full article

In this paper, a compact rectifier, capable of harvesting ambient radio frequency (RF) power is proposed. The total size of the rectifier is 45.4 mm × 7.8 mm × 1.6 mm, designed on FR-4 substrate using a single-stage voltage multiplier at 900 MHz. GSM/900 is among the favorable RF Energy Harvesting (RFEH) energy sources that span over a wide range with minimal path loss and high input power. The proposed RFEH rectifier achieves measured and simulated RF-to-dc (RF to direct current) power conversion efficiency (PCE) of 43.6% and 44.3% for 0 dBm input power, respectively. Additionally, the rectifier attained 3.1 V DC output voltage across 2 k$\Omega$ load terminal for 14 dBm and is capable of sensing low input power at −20 dBm. The work presents a compact rectifier to harvest RF energy at 900 MHz, making it a good candidate for low powered wireless communication systems as compares to the other state of the art rectifier. Full article

High-performance n-type organic semiconducting polymers are key components of next-generation organic electronics. Here, we designed and synthesized two electron deficient organic polymers composed of benzodifurandione-based oligo (p-phenylenevinylene) (BDOPV) and benzothiadiazole by Stille coupling polycondensation. BDOPV-benzothiadiazole-based copolymer (PBDOPV-BTT) possesses a D-A₁-D-A₂ type backbone with intramolecular charge–transfer interactions, while PBDOPV-BTTz is an all-acceptor polymer. The former has a higher molecular weight (M_n) of 109.7 kg∙mol⁻¹ than the latter (M_n = 20.2 kg∙mol⁻¹). The structural difference of these polymers was confirmed by the optical absorption spectra. PBDOPV-BTT showed a more bathochromically shifted absorption spectrum than PBDOPV-BTTz. The longer wavelength absorption of PBDOPV-BTT was due to the intramolecular charge transfer. Therefore, PBDOPV-BTT had a narrower band gap than PBDOPV-BTTz. However, this feature was not reflected by the lowest unoccupied molecular orbital (LUMO) levels. Both polymers displayed almost the same LUMO level of −3.8 eV. Accuracy of this observation was cross-verified by density functional theory (DFT) calculations. The electron-transporting properties were investigated by thin film transistors. PBDOPV-BTT showed an electron mobility (μ_e) of 1.02 × 10⁻² cm² V⁻¹ s⁻¹ under the optimized annealing conditions. PBDOPV-BTTz exhibited poorer transistor performances with the optimized μ_e of 9.54 × 10⁻⁶ cm² V⁻¹ s⁻¹. Finally, the grazing-incidence wide angle X-ray scattering (GIWAXS) measurements of both polymer films revealed the higher crystallinity of PBDOPV-BTT with the edge-on orientation. Full article

Online videos are the major source of internet traffic, and are about to become the largest majority. Increasing effort is aimed to developing more efficient video codecs. In order to compare existing and novel video codecs, this paper presents a simple but effective methodology to model their performance in terms of Rate Distortion (RD). A linear RD model in the dB variables, Peak Signal-to-Noise Ratio (PSNR) and Bitrate (BR), easily allows us to estimate the difference in PSNR or BR between two sets of encoding conditions. Six sequences from the MPEG test set with the same resolution, encoded at different BR and different Quantization Parameters, were used to create the data set to estimate each RD model. Three codecs (HEVC, EVC, and VVC) were compared with this methodology, after estimating their models. Fitting properties of each model and a performance comparison between the models are finally shown and discussed. Full article

It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology. Full article

Direct torque control (DTC) is considered one of the simplest and fastest control strategies used in motor drives. However, it produces large torque and flux ripples. Replacing the conventional two-level hysteresis torque controller (HTC) with a four-level HTC for a three-level neutral-point clamped (NPC) inverter can reduce the torque and flux ripples in interior permanent magnet synchronous motor (IPMSM) drives. However, the torque will not be controlled properly within the upper HTC bands when driving the IPMSM in the medium and high-speed regions. This problem causes the stator current to drop, resulting in poor torque control. To resolve this problem, a simple algorithm based on a torque error average calculation is proposed. Firstly, the proposed algorithm reads the information of the calculated torque and the corresponding torque reference to calculate the torque error. Secondly, the average value of torque error is calculated instantaneously as the reference torque changes. Finally, the average value of the torque error is used to indicate the operation of the proposed algorithm without the need for motor speed information. By using the proposed algorithm, the torque can be controlled well in all speed regions, and thus, a better stator current waveform can be obtained. Simulation and experimental results validate the effectiveness of the proposed method. Full article

In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32. Full article

To help address the global growing demand for food and fiber, selective breeding programs aim to cultivate crops with higher yields and more resistance to stress. Measuring phenotypic traits needed for breeding programs is usually done manually and is labor-intensive, subjective, and lacks adequate temporal resolution. This paper presents a Multipurpose Autonomous Robot of Intelligent Agriculture (MARIA), an open source differential drive robot that is able to navigate autonomously indoors and outdoors while conducting plant morphological trait phenotyping and soil sensing. For the design of the rover, a drive system was developed using the Robot Operating System (ROS), which allows for autonomous navigation using Global Navigation Satellite Systems (GNSS). For phenotyping, the robot was fitted with an actuated LiDAR unit and a depth camera that can estimate morphological traits of plants such as volume and height. A three degree-of-freedom manipulator mounted on the mobile platform was designed using Dynamixel servos that can perform soil sensing and sampling using off-the-shelf and 3D printed components. MARIA was able to navigate both indoors and outdoors with an RMSE of 0.0156 m and 0.2692 m, respectively. Additionally, the onboard actuated LiDAR sensor was able to estimate plant volume and height with an average error of 1.76% and 3.2%, respectively. The manipulator performance tests on soil sensing was also satisfactory. This paper presents a design for a differential drive mobile robot built from off-the-shelf components that makes it replicable and available for implementation by other researchers. The validation of this system suggests that it may be a valuable solution to address the phenotyping bottleneck by providing a system capable of navigating through crop rows or a greenhouse while conducting phenotyping and soil measurements. Full article

Currently, urban environments are the basic human habitat. Large and complex infrastructures coordinate urban life and make it possible in high-density scenarios. In this context, one basic infrastructure to be considered is communication networks. Nowadays, even emergency response teams (ERT) are dependent on those deployments. However, emergencies may cause important damage in those communication infrastructures and, then, a critical discoordination among ERT might appear, greatly affecting the ERT efficiency and response capacity. Different architectures to address this situation have been recently proposed, but none of them are able to dynamically adapt to highly variable situations such as emergency crisis. Therefore, in this paper, a new approach is proposed for ad hoc emergency wireless communications in urban environments based on 5G technologies and Cyber-Physical Systems (CPS). The proposed architecture consists of three layers, where different feedback control loops are defined and linked following the CPS paradigm. At physical level, the spectrum usage is monitored through CPS control loops, analyzing if power signals present a good quality. At network level, 5G virtualization technologies are employed to manage in a dynamic way the network configuration and user management. Finally, at service level, the global situation is analyzed to decide about what services from a catalogue, and according to their priority, can be deployed, including the amount of assigned resources. To evaluate the performance of the proposed solution, an experimental validation based on simulation techniques is also described. Full article

The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists’ expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 × 50 pixels with 125 bands in less than 1 s. Full article

In this paper, a novel algorithm with high computational efficiency is proposed for the filter adaptation in a feedforward active noise control system. The proposed algorithm Zero Forcing Block Adaptive Filter (ZF-BAF) performs filter adaptation on a block-by-block basis in the frequency domain. Filtering is performed in the time domain on a sample-by-sample basis. Working in the frequency domain permits us to get sub-linear complexity, whereas filtering in the time domain minimizes the latency. Furthermore, computational burden is tunable to meet specific requirements about adaptation speed and processing load. No other parameter tuning according to the working condition is required. Computer simulations, performed in different realistic cases against other high-performing time and frequency-domain algorithms, show that achievable performances are comparable, or even better, with those of the algorithms perfectly tuned for each specific case. Robustness exhibited in the tests suggests that performances are expected to be even better in a wide range of real cases where it is impossible to know a priori how to tune the algorithms. Full article

This paper reports a thorough overview on the last developments concerning the vital sign detection and the human localization employing the multiple-input-multiple-output (MIMO) technology. The wireless motion and vital sign detection represents an outstanding research area aimed at monitoring the health conditions of human subjects and at detecting their presence in different environments with minimal concern. MIMO radars exhibit several interesting advantages over conventional single-input-single-output architectures mainly related to their angle detection capabilities and enhanced signal-to-noise ratio. This paper describes the main features and details the operating principles of MIMO technology. Thereafter, it summarizes the state-of-the-art of the available solutions with the purpose of fueling the research activities on this hot topic. Full article

Most electric vehicles use regenerative brakes, since this kind of braking system design recycles electromotive force to increase electric power endurance during braking. This research proposes a sensor-free, integrated driving and braking control system that uses a space-vector-pulse-width module to synthesize stator current by purpose. It calculates the rotor position angle of the motor by detecting variation in the stator current and completes a closed-loop control. When the motor receives a brake command, the controller changes the inverter-switching sequence to generate reverse torque and a magnetic field to complete the driving or braking function using field-oriented control (FOC). This provides a smoother and more accurate motor control than sinusoidal commands with Hall feedback. Compared to the regenerative brake and rheostatic brake, the proposed braking system has a powerful braking torque and shorter reaction time. Comparisons of reaction times for a modified four-wheel electric vehicle equipped with a permanent magnet synchronous motor under neutral-sliding-status, FOC based braking, and short-circuit braking were conducted. Full article

This paper presents a single-phase power filter with an energy storage bidirectional DC/DC converter, both of which are equipped with separate capacitor-based DC links that provides good transient response and reduce energy storage capacity. The device is dedicated to the compensation of active power surges generated by nonlinear loads characterized by intermittent operation, where the operating time of a device in relation to the idle time is relatively short. As a compensated and filtered object, the single-phase spot welding machine with a thyristor current controller was assumed. In the case of such devices, the feeder has to be dimensioned for the peak loads, which increase the cost of installation—due to the fact that the used components have to be oversized. The proposed solution can produce measurable economic benefits by reducing the rated power necessary to energize periodically operating loads and improve the indicators of electrical energy quality. An elaborated control algorithm based on a switchable control structure provides a fast and good transient response. The work contains a comprehensive analysis of storage sizing, confirmed by simulation results performed in the Matlab and Simulink environment. Based on the analyses carried out, the laboratory model of the device was implemented and experimental verification performed. Full article

This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning. Full article

The massive deployment of Small Cells (SCs) is increasingly being adopted by mobile operators to face the exponentially growing traffic demand. Using the millimeter-wave (mmWave) band in the access and backhaul networks will be key to provide the capacity that meets such demand. However, dimensioning and planning have become complex tasks, because the capacity requirements for mmWave links can significantly vary with the SC location. In this work, we address the problem of SC planning considering the backhaul constraints, assuming that a line-of-sight (LOS) between the nodes is required to reliably support the traffic demand. Such a LOS condition reduces the set of potential site locations. Simulation results show that, under certain conditions, the proposed algorithm is effective in finding solutions and strongly efficient in computational cost when compared to exhaustive search approaches. Full article

With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks. Full article

A gallium nitride (GaN)-based normally off metal–oxide–semiconductor field-effect transistor (MOSFET) using a dual-metal-gate (DMG) structure was proposed and fabricated to improve current drivability. Normally off operation with a high V_th of 2.3 V was obtained using a Cl₂/BCl₃-based recess etching process. The DMG structure was employed to improve current characteristics, which can be degraded by recess etching. The I_D and g_m of a DMG-based device with nickel (Ni)-aluminum (Al) were improved by 42.1% and 30.9%, respectively, in comparison to the performances of a single-metal-gate-based device with Ni because the DMG structure increased electron velocity in the channel region. This demonstrates that the DMG structure with a large work-function difference significantly improves the carrier transport efficiency. GaN-based recessed-gate MOSFETs based on the DMG structure hold promising potentials for high-efficiency power devices. Full article

In recent years, there has been a desire to improve electricity generation and consumption, to reach sustainability. Technological solutions today allow a rational use of electricity with good overall performance. Traditionally, from production to distribution, electrical energy is AC-supported for compatibility reasons and easy voltage level transformation. However, nowadays most electric loads need DC power to work properly. A single high-efficiency central AC-DC power converter may be advantageous in eliminating several less efficient AC-DC embedded converters, distributed all over a residential area. This paper presents a new single-phase AC-DC converter using one active bridge (most isolated topologies are based on the dual active bridge concept) and a high-frequency isolation transformer with low-value non-electrolytic capacitors, together with its control system design. The converter can be introduced into future low-voltage DC microgrids for residential buildings, as an alternative to several embedded AC-DC converters. Non-linear control techniques (sliding mode control and the Lyapunov direct method) are employed to guarantee stability in the output DC low voltage with near unity power factor compensation in the AC grid. The designed converter and controllers were simulated using Matlab/Simulink and tested in a lab experimental prototype using digital signal processing (DSP) to evaluate system performance. Full article

This paper proposes a novel high-gain antenna element that can be used in antenna arrays that only require a limited scan range. Each high-gain antenna element uses a linear sub-array of highly-coupled open-ended waveguides. The active central element of this sub-array is directly fed, while the remaining passive waveguides are reactively loaded. The loads are implemented by short-circuits positioned at various distances from the radiating aperture. The short-circuit positions control the radiation pattern properties and the scattering parameters of the array. The proposed sub-array antenna element is optimized in the presence of the adjacent elements and provides a high gain and a flat-top main lobe. The horizontal distance between the sub-array centers is large in terms of wavelengths, which leads to limited scanning capabilities in the E-plane. However, along the vertical axis, the element spacing is around 0.6 wavelength at the central frequency that is beneficial to achieve a wider scan range in the H-plane. We show that the sub-array radiation pattern sufficiently filters the grating lobes which appear in the array factor along the E-plane. To demonstrate the performance of the proposed array configuration, an array operating at 28.0 GHz is designed. The designed array supports scan angles up to ±7.5° along the E-plane and ±24.2° along the H-plane Full article

Vibrations of two typical bicycles are measured by means of road tests in bicycle lanes. The analysis of experimental results in terms of power spectral density (PSD) of the acceleration components shows that most of the energy associated to bicycle vibrations is concentrated in a low frequency band (<30 Hz). Since piezoelectric cantilever harvesters achieve the best performance in resonance and the resonant frequency is well above 30 Hz, specific tuning strategies are adopted. A novel mathematical model for simulating the electro-mechanical behaviour of a piezoelectric harvester equipped with an auxiliary oscillator is proposed. Calculated results show the potentialities of this tuning device in terms of generated voltage and stress inside the piezoelectric layer. Prototypes of harvesters equipped with auxiliary oscillators are built and tested in the laboratory obtaining the frequency response function (FRF) of generated voltage. Finally, the average electric power generated by these harvesters (which are assumed to be interfaced to an electronic load by a power management unit based on synchronous rectifying technique) is simulated by using the measured FRFs and PSDs of bicycle vibrations. Full article

In this paper, a multi-step packaging (MSP) concept for series-connected SiC-MOSFETs is analyzed. The parasitic capacitance generated by the dielectric isolation of each device in the stack has a significant impact on the dynamic behavior of SiC devices, which impacts the voltage-sharing performances. The study performed in this work reveals that the parasitic capacitance network introduced by the classical planar packaging unbalances the voltage across the series-connected SiC-MOSFETs. Therefore, a new drain-source parasitic capacitance network configuration provided by the MSP is proposed in order to improve the voltage balancing across the series-connected devices. The concept is introduced and analyzed thanks to equivalent models and time domain simulations. To verify the analysis, the voltage sharing between four series-connected 1.2 kV SiC MOSFETs is tested in a double pulse test setup. The experimental results confirm that the MSP has a better performance than the classical one in terms of voltage sharing. Furthermore, the proposed investigation shows that the MSP increases the middle point dv/dt of the switching cell. Sensitive analysis and thermal management considerations are also discussed in order to clarify the MSP limitations and indicate the ways to optimize the MSP from a thermal point of view. Full article

Two problems can cause control performance degradation on permanent magnet synchronous motor (PMSM) systems, namely, fluctuation of PMSM parameters and the time delay between current sampling and command value update. In order to reduce the influence of these problems, a new current-predictive control strategy is proposed in this article for medium- and high-speed PMSM. This strategy is based on the discrete mathematical model of PMSM. This new control strategy consists of two main steps: First, an integrator is applied to calculate current compensation value; second, the predictive current value is obtained through deadbeat-current predictive method. The stability of predictive control system is also proved in the article. With this deadbeat-current predictive control scheme, the real current can reach the desired value within one control-step. Based on this new current control method, Luenberger observer and phase-locked loop position tracker is applied in this article. Experimental results for 0.4 kW surface-mounted PMSM confirm the validity and excellent performance for parameters fluctuation of new current predictive control. Full article

Improving the energy efficiency of computer communication is becoming more and more important as the world is creating a massive amount of data, while the interface has been a bottleneck due to the finite bandwidth of electrical wires. Introducing supply voltage scalability is expected to significantly improve the energy efficiency of communication input/output (I/O) interfaces as well as make the I/Os efficiently adapt to actual utilization. However, there are many challenges to be addressed to facilitate the realization of a true sense of supply-scalable I/O. This paper reviews the motivations, background theories, design considerations, and challenges of scalable I/Os from the viewpoint of computer architecture down to the transistor level. Thereafter, a survey of the state-of-the-arts fabricated designs is discussed. Full article

In vehicular Ad Hoc Networks (VANETs), smart data dissemination is crucial for efficient exchange of traffic and road information. Given the dynamic nature of VANET, the challenge is to design an adaptive multi-hop broadcast scheme that achieves high reachability while efficiently utilizing the bandwidth by reducing the number of redundant transmissions. In this paper, we propose a novel intelligent fuzzy logic based density and distribution adaptive broadcast protocol for VANETs. The proposed protocol estimates the spatial distribution of vehicles in the network employing the Nearest Neighbor Distance method, and uses it to adapt the transmission range to enhance reachability. To reduce packet collisions, the protocol intelligently adapts the contention window size to the network density and spatial distribution. Bloom filter technique is used to reduce the overhead resulting from the inclusion of the neighbor IDs in the header of the broadcast message, which is needed in identifying the set of potential rebroadcasting vehicles. Our simulation results confirmed the effectiveness of the proposed scheme in enhancing reachability while efficiently utilizing bandwidth. Full article

Due to performance and energy requirements, FPGA-based accelerators have become a promising solution for high-performance computations. Meanwhile, with the help of high-level synthesis (HLS) compilers, FPGA can be programmed using common programming languages such as C, C++, or OpenCL, thereby improving design efficiency and portability. Stencil computations are significant kernels in various scientific applications. In this paper, we introduce an architecture design for implementing stencil kernels on state-of-the-art FPGA with high bandwidth memory (HBM). Traditional FPGAs are usually equipped with external memory, e.g., DDR3 or DDR4, which limits the design space exploration in the spatial domain of stencil kernels. Therefore, many previous studies mainly relied on exploiting parallelism in the temporal domain to eliminate the bandwidth limitations. In our approach, we scale-up the design performance by considering both the spatial and temporal parallelism of the stencil kernel equally. We also discuss the design portability among different HLS compilers. We use typical stencil kernels to evaluate our design on a Xilinx U280 FPGA board and compare the results with other existing studies. By adopting our method, developers can take broad parallelization strategies based on specific FPGA resources to improve performance. Full article

This paper presents the design and testing of a low-noise programmable voltage source. Such a piece of instrumentation is often required as part of the measurement setup needed to test electronic devices without introducing noise from the power supply (such as photodetectors, resistors or transistors). Although its construction is based on known configurations, here the discussion is focused on the characterization and the minimization of the output noise, especially at very low frequencies. The design relies on a digital-to-analog converter, proper lowpass filters, and a low-noise Junction Field-Effect Transistors (JFET) based voltage follower. Because of the very low level of output noise, in some cases we had to resort to cross-correlation in order to reduce the background noise of the amplifiers used for the characterization of the programmable source. Indeed, when two paralleled IF9030 JFETs are used in the voltage follower, the output noise can be as low as 3 nV/√Hz, 0.6 nV/√Hz and 0.4 nV/√Hz at 1 Hz, 10 Hz and 100 Hz, respectively. The output voltage drift was also characterized and a stability of ±25 µV over 3 h was obtained. In order to better appreciate the performance of the low-noise voltage source that we have designed, its noise performances were compared with those of a set-up based on one of the best low-noise solid-state voltage regulators available on the market. Actual measurements of the current noise in a type-II superlattice photodetector are reported in which the programmable source was used to provide the voltage bias to the device. Full article

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case. Full article

Multiple small rovers can repeatedly pick cotton as bolls begin to open until the end of the season. Several of these rovers can move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To develop such a multi-agent cotton-harvesting system, each cotton-harvesting rover would need to accomplish three motions: the rover must move forward/backward, turn left/right, and the robotic manipulator must move to harvest cotton bolls. Controlling these actions can involve several complex states and transitions. However, using the robot operating system (ROS)-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability for deploying multiple tasks to the rover and manipulator. In this study, a center-articulated hydrostatic cotton-harvesting rover, using a stereo camera to locate end-effector and pick cotton bolls, was developed. The robot harvested the bolls by using a 2D manipulator that moves linearly horizontally and vertically perpendicular to the direction of the rover’s movement. We demonstrate preliminary results in an environment simulating direct sunlight, as well as in an actual cotton field. This study contributes to cotton engineering by presenting a robotic system that operates in the real field. The designed robot demonstrates that it is possible to use a Cartesian manipulator for the robotic harvesting of cotton; however, to reach commercial viability, the speed of harvest and successful removal of bolls (Action Success Ratio (ASR)) must be improved. Full article

Despite the great success of Convolutional Neural Networks (CNNs) in various visual recognition tasks, the high computational and storage costs of such deep networks impede their deployments in real-time remote sensing tasks. To this end, considerable attention has been given to the filter pruning techniques, which enable slimming deep networks with acceptable performance drops and thus implementing them on the remote sensing devices. In this paper, we propose a new scheme, termed Pruning Filter with Attention Mechanism (PFAM), to compress and accelerate traditional CNNs. In particular, a novel correlation-based filter pruning criterion, which explores the long-range dependencies among filters via an attention module, is employed to select the to-be-pruned filters. Distinct from previous methods, the less correlated filters are first pruned after the pruning stage in the current training epoch, and they are reconstructed and updated during the next training epoch. Doing so allows manipulating input data with the maximum information preserved when executing the original training strategy such that the compressed network model can be obtained without the need for the pretrained model. The proposed method is evaluated on three public remote sensing image datasets, and the experimental results demonstrate its superiority, compared to state-of-the-art baselines. Specifically, PFAM achieves a 0.67% accuracy improvement with a 40% model-size reduction on the Aerial Image Dataset (AID) dataset, which is impressive. Full article

(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets. Full article

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration. Full article

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks. Full article

Bipolar DC grids have become an adequate solution for high-power microgrids. This is mainly due to the fact that this configuration has a greater power transmission capacity. In bipolar DC grids, any distributed generation system can be connected through DC-DC converters, which must have a monopolar input and a bipolar output. In this paper, a DC-DC converter based on the combination of single-ended primary-inductor converter (SEPIC) and Ćuk converters is proposed, to connect a photovoltaic (PV) system to a bipolar DC grid. This topology has, as main advantages, a reduced number of components and a high efficiency. Furthermore, it can contribute to regulate/balance voltage in bipolar DC grids. To control the proposed converter, any of the techniques described in the literature and applied to converters of a single input and single output can be used. An experimental prototype of a DC-DC converter with bipolar output based on the combination of SEPIC and Ćuk converters was developed. On the other hand, a perturb and observe method (P and O) has been applied to control the converter and has allowed maximum power point tracking (MPPT). The combined converter was connected in island mode and in parallel with a bipolar DC microgrid. The obtained results have allowed to verify the behavior of the combined converter with the applied strategy. Full article

We present biphasic stimulator electronics developed for a wearable functional electrical stimulation system. The reported stimulator electronics consist of a twenty four channel biphasic stimulator. The stimulator circuitry is physically smaller per channel and offers a greater degree of control over stimulation parameters than existing functional electrical stimulator systems. The design achieves this by using, off the shelf multichannel high voltage switch integrated circuits combined with discrete current limiting and dc blocking circuitry for the frontend, and field programmable gate array based logic to manage pulse timing. The system has been tested on both healthy adults and those with reduced upper limb function following a stroke. Initial testing on healthy users has shown the stimulator can reliably generate specific target gestures such as palm opening or pointing with an average accuracy of better than 4 degrees across all gestures. Tests on stroke survivors produced some movement but this was limited by the mechanical movement available in those users’ hands. Full article

The efficient multi-modal fusion of data streams from different sensors is a crucial ability that a robotic perception system should exhibit to ensure robustness against disturbances. However, as the volume and dimensionality of sensory-feedback increase it might be difficult to manually design a multimodal-data fusion system that can handle heterogeneous data. Nowadays, multi-modal machine learning is an emerging field with research focused mainly on analyzing vision and audio information. Although, from the robotics perspective, haptic sensations experienced from interaction with an environment are essential to successfully execute useful tasks. In our work, we compared four learning-based multi-modal fusion methods on three publicly available datasets containing haptic signals, images, and robots’ poses. During tests, we considered three tasks involving such data, namely grasp outcome classification, texture recognition, and—most challenging—multi-label classification of haptic adjectives based on haptic and visual data. Conducted experiments were focused not only on the verification of the performance of each method but mainly on their robustness against data degradation. We focused on this aspect of multi-modal fusion, as it was rarely considered in the research papers, and such degradation of sensory feedback might occur during robot interaction with its environment. Additionally, we verified the usefulness of data augmentation to increase the robustness of the aforementioned data fusion methods. Full article

The Internet-of-Things (IoT) has gained significant importance in all aspects of daily life, and there are many areas of application for it. Despite the rate of expansion and the development of infrastructure, such systems also bring new concerns and challenges. Security and privacy are at the top of the list and must be carefully considered by designers and manufacturers. Not only do the devices need to be protected against software and network-based attacks, but proper attention must also be paid to recently emerging hardware-based attacks. However, low-cost unit software developers are not always sufficiently aware of existing vulnerabilities due to these kinds of attacks. To tackle the issue, various platforms are proposed to enable rapid and easy evaluation against physical attacks. Fault attacks are the noticeable type of physical attacks, in which the normal and secure behavior of the targeted devices is liable to be jeopardized. Indeed, such attacks can cause serious malfunctions in the underlying applications. Various studies have been conducted in other research works related to the different aspects of fault injection. Two of the primary means of fault attacks are clock and voltage fault injection. These attacks can be performed with a moderate level of knowledge, utilizing low-cost facilities to target IoT systems. In this paper, we explore the main parameters of the clock and voltage fault generators. This can help hardware security specialists to develop an open-source platform and to evaluate their design against such attacks. The principal concepts of both methods are studied for this purpose. Thereafter, we conclude our paper with the need for such an evaluation platform in the design and production cycle of embedded systems and IoT devices. Full article

We investigate, by means of numerical simulations, the lowest magnetic field level that can be detected with a given relative accuracy with a sensor based on a double-dot device fabricated in a high-mobility two-dimensional electron gas. The double dot consists of a cavity delimited by an input and an output constriction, with a potential barrier exactly in the middle. In conditions of perfect symmetry, a strong conductance enhancement effect appears as a consequence of the constructive interference between symmetric trajectories. When the symmetry is broken, for example by the presence of an applied magnetic field, this enhancement effect is suppressed. We explore the design parameter space and assess the minimum magnetic field value that can be measured with a given accuracy in the presence of flicker noise. Full article

A compact single-photon counting module that can accurately control the bias voltage and hold-off time is developed in this work. The module is a microcontroller-based system which mainly consists of a microcontroller, a programmable negative voltage generator, a silicon-based single-photon avalanche diode, and an integrated active quench and reset circuit. The module is 3.8 cm × 3.6 cm × 2 cm in size and can communicate with the end user and be powered through a USB cable (5 V). In this module, the bias voltage of the single-photon avalanche diode (SPAD) is precisely controllable from −14 V ~ −38 V and the hold-off time (consequently the dead time) of the SPAD can be adjusted from a few nanoseconds to around 1.6 μs with a setting resolution of ∼6.5 ns. Experimental results show that the module achieves a minimum dead time of around 28.5 ns, giving a saturation counting rate of around 35 Mcounts/s. Results also show that at a controlled reverse bias voltage of 26.8 V, the dark count rate measured is about 300 counts/s and the timing jitter measured is about 158 ps. Photodetection probability measurements show that the module is suited for detection of visible light from 450 nm to 800 nm with a 40% peak photon detection efficiency achieved at around 600 nm. Full article

A 100 Gb/s quad-lane SerDes receiver with a phase-interpolator (PI)-based quarter-rate all-digital clock and data recovery (CDR) is presented. The proposed CDR utilizes a multi-phase multiplying delay-locked loop (MDLL) to generate the eight-phase reference clocks, which achieves multi-phase frequency multiplication with a small area and less power consumption. The shared MDLL generates and distributes eight-phase clocks to each CDR. The proposed CDR uses a new initial phase tracker that uses a preamble to achieve a fast lock time of about 12 ns and to provide a constant output data sequence. The CDR utilizes quarter-rate 2x-oversampling architecture, and the PI controller is designed full custom to minimize the loop latency. To improve the dithering jitter performance of the recovered clock, the decimation factor of the CDR can be adjustable. Also, a new continuous-time linear equalizer (CTLE) receiver was adopted to reduce power consumption and achieved a data rate of 25 Gb/s/lane. The proposed SerDes receiver with a digital CDR is implemented in 40 nm CMOS technology. The 100 Gb/s four-channel SerDes receiver (4 CTLEs + 4 CDRs + MDLL) occupies an active area of only 0.351 mm² and consumes 241.8 mW, which achieves a high energy efficiency of 2.418 pJ/bit. Full article

Diode-like threshold switching and high on/off ratio characteristics by using an Al/Ag/Al₂O₃/TiN conductive bridge resistive random access memories (CBRAM) have been obtained. The 5 nm-thick Al₂O₃ device shows superior memory parameters such as low forming voltage and higher switching uniformity as compared to the 20 nm-thick switching layer, owing to higher electric field across the material. Capacitance-voltage (CV) characteristics are observed for the Ag/Al₂O₃/TiN devices, suggesting the unipolar/bipolar resistive switching phenomena. Negative capacitance (NC) at low frequency proves inductive behavior of the CBRAM devices due to Ag ion migration into the Al₂O₃ oxide-electrolyte. Thicker Al₂O₃ film shows diode-like threshold switching behavior with long consecutive 10,000 cycles. It has been found that a thinner Al₂O₃ device has a larger on/off ratio of >10⁸ as compared to a thicker one. Program/erase (P/E) cycles, read endurance, and data retention of the thinner Al₂O₃ oxide-electrolyte shows superior phenomena than the thicker electrolyte. The switching mechanism is also explored. Full article

A new and compact four-pole wide-band planar filter-antenna design is proposed in this article. The effect of the dielectric material type on the characteristics of the design is also investigated and presented. The filter-antenna structure is formed by a fourth-order planar band-pass filter (BPF) cascaded with a monopole microstrip antenna. The designed filter-antenna operates at a centre frequency of 2.4 GHz and has a relatively wide-band impedance bandwidth of about 1.22 GHz and a fractional bandwidth (FBW) of about 50%. The effects of three different types of substrate material, which are Rogers RT5880, Rogers RO3003, and FR-4, are investigated and presented using the same configuration. The filter-antenna design is simulated and optimised using computer simulation technology (CST) software and is fabricated and measured using a Rogers RT5880 substrate with a height (h) of 0.81 mm, a dielectric constant of 2.2, and a loss tangent of 0.0009. The structure is printed on a compact size of 0.32 λ₀ × 0.30 λ₀, where λ₀ is the free-space wavelength at the centre frequency. A good agreement is obtained between the simulation and measurement performance. The designed filter-antenna with the achieved performance can find different applications for 2.4 GHz ISM band and 4G wireless communications. Full article

The effects of post-deposition annealing (PDA) on the formation of polarization-charge inversion at ultrathin Al₂O₃/Ga-polar GaN interfaces are assessed by the analysis of energy band bending and measurement of electrical conduction. The PDA-induced positive interface charges form downward energy band bending at the Al₂O₃/GaN interfaces with polarization-charge inversion, which is analyzed using X-ray photoelectron spectroscopy. Net charge and interface charge densities at the Al₂O₃/GaN interfaces are estimated after PDA at 500 °C, 700 °C, and 900 °C. The PDA temperatures affect the formation of charge densities. That is, the charge density increases up to 700 °C and then decreases at 900 °C. Electrical characteristics of GaN Schottky diodes with ultrathin Al₂O₃ layers exhibit the passivation ability of the Al₂O₃ surface layer and the effects of polarization-charge inversion through PDA. This result can be applied to improvement in GaN-based electronic devices where surface states and process temperature work important role in device performance. Full article