Electronics, Volume 9, Issue 12 (December 2020) – 225 articles

Smart grid technology enables active participation of the consumers to reschedule their energy consumption through demand response (DR). The price-based program in demand response indirectly induces consumers to dynamically vary their energy use patterns following different electricity prices. In this paper, a real-time price (RTP)-based demand response scheme is proposed for thermostatically controllable loads (TCLs) that contribute to a large portion of residential loads, such as air conditioners, refrigerators and heaters. Wind turbine generator (WTG) systems, solar thermal power systems (STPSs), diesel engine generators (DEGs), fuel cells (FCs) and aqua electrolyzers (AEs) are employed in a hybrid microgrid system to investigate the contribution of price-based demand response (PBDR) in frequency control. Simulation results show that the load frequency control scheme with dynamic PBDR improves the system’s stability and encourages economic operation of the system at both the consumer and generation level. Performance comparison of the genetic algorithm (GA) and salp swarm algorithm (SSA)-based controllers (proportional-integral (PI) or proportional integral derivative (PID)) is performed, and the hybrid energy system model with demand response shows the supremacy of SSA in terms of minimization of peak load and enhanced frequency stabilization of the system. Full article

Mobile devices and social media have been used to create empowering healthcare services. However, privacy and security concerns remain. Furthermore, the integration of interoperability biomedical standards is a strategic feature. Thus, the objective of this paper is to build enhanced healthcare services by merging all these components. Methodologically, the current mobile health telemonitoring architectures and their limitations are described, leading to the identification of new potentialities for a novel architecture. As a result, a standardized, secure/private, social-media-based mobile health architecture has been proposed and discussed. Additionally, a technical proof-of-concept (two Android applications) has been developed by selecting a social media (Twitter), a security envelope (open Pretty Good Privacy (openPGP)), a standard (Health Level 7 (HL7)) and an information-embedding algorithm (modifying the transparency channel, with two versions). The tests performed included a small-scale and a boundary scenario. For the former, two sizes of images were tested; for the latter, the two versions of the embedding algorithm were tested. The results show that the system is fast enough (less than 1 s) for most mHealth telemonitoring services. The architecture provides users with friendly (images shared via social media), straightforward (fast and inexpensive), secure/private and interoperable mHealth services. Full article

In order to detect the vehicle presence in parking slots, different approaches have been utilized, which range from image recognition to sensing via detection nodes. The last one is usually based on getting the presence data from one or more sensors (commonly magnetic or IR-based), controlled and processed by a micro-controller that sends the data through radio interface. Consequently, given nodes have multiple components, adequate software is required for its control and state-machine to communicate its status to the receiver. This paper presents an alternative, cost-effective beacon-based mechanism for sensing the vehicle presence. It is based on the well-known effect that, once the metallic obstacle (i.e., vehicle) is on top of the sensing node, the signal strength will be attenuated, while the same shall be recognized at the receiver side. Therefore, the signal strength change conveys the information regarding the presence. Algorithms processing signal strength change at the receiver side to estimate the presence are required due to the stochastic nature of signal strength parameters. In order to prove the concept, experimental setup based on LoRa-based parking sensors was used to gather occupancy/signal strength data. In order to extract the information of presence, the Hidden Markov Model (HMM) was employed with accuracy of up to 96%, while the Neural Network (NN) approach reaches an accuracy of up to 97%. The given approach reduces the costs of the sensor production by at least 50%. Full article

This paper presents an enhanced maximum power point tracking approach to extract power from photovoltaic panels. The proposed method uses an artificial neural network technique to improve the fractional open-circuit voltage method by learning the correlation between the open-circuit voltage, temperature, and irradiance. The proposed method considers temperature variation and can eliminate the steady-state oscillation that comes with conventional algorithms, which improves the overall efficiency of the photovoltaic system. A comparison with the traditional and most widely used algorithms is discussed and shows the difference in performance. The presented algorithm is implemented with a Ćuk converter and tested under various weather and irradiance conditions. The results validate the competitiveness of the algorithm against other algorithms. Full article

Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value. Full article

The industrial sector has a great opportunity to reduce its energy costs through distributed generation. In this sense, the potential of photovoltaic self-consumption systems in the industrial cooling and refrigeration sector is shown. Two industries with photovoltaic self-consumption installations are shown and the electricity consumption profile of this type of industry which has a remarkable basal electricity consumption during daytime is analyzed. The matching between consumption and photovoltaic generation profiles is provided through the self-consumption and self-sufficiency curves considering different reporting periods (monthly and annual). Moreover, a new index is presented: self-sufficiency index for sunshine hours, φ_SS,SH. This index evaluates the performance of the photovoltaic self-consumption system when facing the consumption only during sunshine hours. This index may complement the self-sufficiency index and may improve the analysis of this type of systems in the industrial sector. Self-consumption indices of 90% may be provided. Moreover, self-sufficiency indices for total (24 h) and for sunshine hours of 25% and 50%, respectively, for industry A, and 26% and 45% for industry B have been obtained. During daytime, half the load consumption in this type of industry may be covered by photovoltaics while achieving high levels of use of the photovoltaic energy generated. Full article

The semantic segmentation of a brain tumor is of paramount importance for its treatment and prevention. Recently, researches have proposed various neural network-based architectures to improve the performance of segmentation of brain tumor sub-regions. Brain tumor segmentation, being a challenging area of research, requires improvement in its performance. This paper proposes a 2D image segmentation method, BU-Net, to contribute to brain tumor segmentation research. Residual extended skip (RES) and wide context (WC) are used along with the customized loss function in the baseline U-Net architecture. The modifications contribute by finding more diverse features, by increasing the valid receptive field. The contextual information is extracted with the aggregating features to get better segmentation performance. The proposed BU-Net was evaluated on the high-grade glioma (HGG) datasets of the BraTS2017 Challenge—the test datasets of the BraTS 2017 and 2018 Challenge datasets. Three major labels to segmented were tumor core (TC), whole tumor (WT), and enhancing core (EC). To compare the performance quantitatively, the dice score was utilized. The proposed BU-Net outperformed the existing state-of-the-art techniques. The high performing BU-Net can have a great contribution to researchers from the field of bioinformatics and medicine. Full article

This paper proposes a small-footprint wake-up-word (WUW) recognition system for real noisy environments by employing the competing-words-based feature. Competing-words-based features are generated using a ResNet-based deep neural network with small parameters using the competing-words dataset. The competing-words dataset consists of the most acoustically similar and dissimilar words to the WUW used for our system. The obtained features are used as input to the classification network, which is developed using the convolutional neural network (CNN) model. To obtain sufficient data for training, data augmentation is performed by using a room impulse response filter and adding sound signals of various television shows as background noise, which simulates an actual living room environment. The experimental results demonstrate that the proposed WUW recognition system outperforms the baselines that employ CNN and ResNet models. The proposed system shows 1.31% in equal error rate and 1.40% false rejection rate at a 1.0% false alarm rate, which are 29.57% and 50.00% relative improvements compared to the ResNet system, respectively. The number of parameters used for the proposed system is reduced by 83.53% compared to the ResNet system. These results prove that the proposed system with the competing-words-based feature is highly effective at improving WUW recognition performance in noisy environments with a smaller footprint. Full article

One of the most recent speaker recognition methods that demonstrates outstanding performance in noisy environments involves extracting the speaker embedding using attention mechanism instead of average or statistics pooling. In the attention method, the speaker recognition performance is improved by employing multiple heads rather than a single head. In this paper, we propose advanced methods to extract a new embedding by compensating for the disadvantages of the single-head and multi-head attention methods. The combination method comprising single-head and split-based multi-head attentions shows a 5.39% Equal Error Rate (EER). When the single-head and projection-based multi-head attention methods are combined, the speaker recognition performance improves by 4.45%, which is the best performance in this work. Our experimental results demonstrate that the attention mechanism reflects the speaker’s properties more effectively than average or statistics pooling, and the speaker verification system could be further improved by employing combinations of different attention techniques. Full article

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

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

For higher integration density, X-band power amplifiers (PAs) with CMOS technology have been widely discussed in recent publications. However, with reduced power supply voltage and device size, it is a great challenge to design a compact PA with high output power and power-added efficiency (PAE). In the proposed design, a 40-nm standard CMOS process is used for higher integration with other RF building blocks, compared with other CMOS PA designs with larger process node. Transistor cells are designed with neutralization capacitors to increase stability and gain performance of the PA. As a trade-off among gain, output power, and PAE, the transistor cells in driving stage and power stage are biased for class A and class AB operation, respectively. Both transistor cells consist of two transistors working in differential mode. Furthermore, transformer-based matching networks (TMNs) are used to realize a two-stage X-band CMOS PA with compact size. The PA achieves an effective conductivity (EC) of 117.5, which is among the highest in recently reported X-band PAs in CMOS technology. The PA also attains a saturated output power ($P_{sat}$) of 20.7 dBm, a peak PAE of $22.4\%$, and a gain of 25.6 dB at the center frequency of 10 GHz under a 1 V supply in 40-nm CMOS. Full article

This paper presents a switched capacitor technique for bus-pumping compensation in a half-bridge class-D amplifier. The proposed approach, in addition to the almost complete reduction of the bus-pumping effect, allows the half-bridge class-D amplifier to preserve maximum energy efficiency. The studied hardware implementation of the proposed technique demonstrates its advantages of high efficiency, simple circuit, and low cost. The principal design and operating principles are analyzed and described. The experimental static characteristics and time-domain waveforms for the proposed technique are shown to verify its feasibility. Full article

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

This paper proposes a novel pulse width modulation (PWM) for a three-level neutral point clamped (NPC) voltage source inverter (VSI). When the conventional PWM method is used in three-level NPC VSI, dead time is required to prevent a short circuit caused by the operation of complementary devices on the upper and lower arms. However, current distortion is increased because of the dead time and it can also cause a voltage unbalance in the dc-link. To solve this problem, we propose a zero dead-time width modulation (ZDPWM) which does not require dead time used in complementary operation. The proposed technique applies the offset voltage to the space vector pulse width modulation (SVPWM) reference voltage for the same modulation index (MI) as the conventional SVPWM, but any complementary switching operation needs dead time. In addition, the proposed method is divided into four operation sections using the reference voltage and phase current to operate switching devices which flow the current depending on the section. This ZDPWM method is simply implemented by carrier and reference voltage that reduce the current distortion, because complementary operation that needs dead time is not implemented. However, the operation section is delayed due to the sampling delay that occurs during the experiment. Therefore, in this paper, we conduct a modeling of sampling delay to improve the delay of operation section. To verify the principle and feasibility of the proposed ZDPWM method, a simulation and experiment are implemented. Full article

This paper describes a clocked AC-DC charge pump to enable full integration of power converters into a sensor or radio frequency (RF) chip even with low open circuit voltage magnetostrictive vibration energy transducer operating at a low resonant frequency of 10 Hz to 1 kHz. The frequency of the clock to drive an AC-DC charge pump was up-converted with an on-chip oscillator to increase output power of the charge pump without significantly increasing the circuit area. A model of the system including the charge pump and vibration energy transducer is shown. It was validated by HSPICE simulation and measured, resulting in a prototype chip with an area of 0.11 mm² fabricated in a 65 nm 1 V CMOS process. The fabricated charge pump was also measured together with a magnetostrictive transducer. The charge pump converted the power from the transducer to an output power of 4.2 μW at an output voltage of 2.0 V. The output power varied below 3% over a wide input frequency of 10 Hz to 100 kHz, which suggests that universal design of the clocked AC-DC charge pump can be used for transducers with different resonant frequencies. In a low-input voltage region below 0.8 V, the proposed circuit has higher output power compared with the conventional circuits. Full article

Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis (HLS) tools. Various topologies are implemented and benchmarked, and a comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor. Full article

This paper presents the bandwidth improvement for dual-band power divider using complex isolation network while maintaining physical port separation. The conventional port-extended power dividers suffered from narrow system bandwidth. A rigorous analysis revealed that such problem was mainly due to the limited impedance bandwidth caused by the odd-mode bisected network. Moreover, the isolation bandwidth provided by the parallel L-C topology in the conventional approach was also limited. To overcome such technical issues, a serial L-C topology was proposed. Derivations of the impedance bandwidth through even- and odd-mode network analysis have been performed and optimal system bandwidth could be achieved when the reflection coefficients of the corresponding bisected networks exhibited minimum frequency dependence. Based on the theoretical analysis, simultaneous achievement of bandwidth broadening, size compactness, and physical port extension at both frequencies is possible with optimum combinations of the design parameters. The experimental results evidenced that other than the improvement in system bandwidth, the fabricated prototype featured low extra insertion loss, good isolation across the bands, and compactness in size while maintaining physical separation between the split ports compared with previously published works. Full article

This paper focuses on proposing a new access barring scheme for internet of things (IoT) devices in long term evolution advanced (LTE/LTE-A) and 5G networks. Massive number of IoT devices communicating simultaneously is one of the hallmarks of the future communication networks such as 5G and beyond. The problem of congestion also comes with this massive communication for which access barring is one of the solutions. So, it is required that sophisticated access barring techniques are designed such that the congestion is avoided and these devices get served in less time. Legacy access barring schemes like access class barring (ACB) and extended access barring (EAB) suffer from high energy consumption and high access delay respectively. However, our proposed scheme provides less energy consumption than ACB while giving less access delay than EAB. The proposed scheme maximizes the success probability while reducing the number of collisions at the same time. The scheme is based on an approximation of the number of IoT devices based on details available to the eNodeB of the number of idle, successful and collided preambles. Extensive Matlab simulations are performed to validate our claims and analysis. Full article

Online communication platforms face security and privacy challenges, especially in broad ecosystems, such as online social networks, where users are unfamiliar with each other. Consequently, employing trust management systems is crucial to ensuring the trustworthiness of participants, and thus, the content they share in the network. WhatsApp is one of the most popular message-based online social networks with over one billion users worldwide. Therefore, it is considered an attractive platform for cybercriminals who spread malware to gain unauthorized access to users’ accounts to steal their data or corrupt the system. None of the few trust management systems proposed in the online social network literature have considered WhatsApp as a use case. To this end, this paper introduces WhatsTrust, a trust management system for WhatsApp that evaluates the trustworthiness of users. A trust value accompanies each message to help the receiver make an informed decision regarding how to deal with the message. WhatsTrust is extensively evaluated through a strictly controlled empirical evaluation framework with two well-established trust management systems, namely EigenTrust and trust network analysis with subjective logic (TNA-SL) algorithms, as benchmarks. The experimental results demonstrate WhatsTrust’s dominance with respect to the success rate and execution time. Full article

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

This paper proposes a new hybrid Ćuk-type converter employing two inductors built on the same core which can be successfully used in applications requiring an output voltage considering higher than the input one. With few components added, in the proposed converter the static conversion ratio can be easily extended becoming wider compared to the classical Ćuk topology. At the same duty cycle range the output voltage is higher than in the classical Ćuk converter. The output voltage remains with negative polarity and with a reduced ripple. An advantage of the new converter is given by its two degrees of freedom. A DC and AC analysis is carried out, device stresses are evaluated and a comparative analysis of the proposed hybrid Ćuk topology to other indirect converters has also been performed. All the equations necessary for designing the converter are provided. The simulations performed together with the practical experiments carried out, all results confirm that the theoretical considerations are correct and validate the features that the proposed converter can provide a higher static conversion ratio without operating at high duty cycles. Full article

The Ministry of National Defense of South Korea is currently acquiring various surveillance and reconnaissance assets to improve its independent surveillance and reconnaissance capabilities. With the deployment of new strategic and tactical surveillance and reconnaissance assets, the amount of information collected will increase significantly, and military intelligence capable of handling greater complexity will be needed to process such information. As a consequence, it will no longer be possible to handle the increased workload through a manual analysis conducted by intelligence analysists. Further, the number of intelligence analysists is expected to decrease in the near future owing to a reduction in the total number of troops, thereby exacerbating the need to apply artificial intelligence technology to process military intelligence tasks more quickly and accurately. In this study, a method is introduced for determining the ways to prioritize the AI technology domains applied to military intelligence. Consequently, among the five stages used, the processing stage has the highest priority. The application of AI technology to all the stages of information circulation may be ideal. Nevertheless, among various military intelligence domains, the one that affords the highest effectiveness of such an application should be prioritized. This is owing to resource and defense budget limitations. Full article

At present, the key to underwater sensor network (UWSN) research is to provide personalized network support for many underwater applications. In order to achieve this goal, people need a general UWSN. Most of the current UWSN architecture is based on the traditional network, which are limited to a single hardware platform and software platform. Facing the current numerous underwater applications and heterogeneous networks, the UWSN is unable to provide personalized network services according to different application requirements. In this paper, we propose a heterogeneous network framework called MMNET (multimodal network) based on the idea of multimodality, aiming to achieve the compatibility of heterogeneous networks and the scalability of the new architecture. In addition, in the face of the complexity of heterogeneous networks and the personalized needs of network applications, the resource allocation is expressed as a personalized recommendation problem. The distributed personalized recommendation algorithm is used to configure personalized network resources for applications. Each node only needs to solve its own problems, instead of exchanging channel state information by using a distributed algorithm, so the computational complexity can be greatly reduced and signaling is overhead. Finally, we give a special example to prove that our network framework provides a good application. Full article

This meta-analysis aims to assess the effectiveness of virtual reality cognitive training (VRCT) and conventional computer-based cognitive training (CBCT) in five specific cognitive domains (i.e., global cognitive function (GCF), memory (Mem), executive function (EF), language (Lang) and visuospatial skills (VS)) of individuals with mild cognitive impairment. A total of 320 studies were yielded from five electronic databases. Eighteen randomized controlled trials met the PRISMA criteria, with 10 related to VRCT and 8 related to CBCT. A random-effect model was used in determining the main effect of cognitive training in five specific cognitive domains. VRCT provided the largest effect size on VS and Lang while the smallest on EF. CBCT provided the largest effect size on Mem and Lang while the smallest on EF. VRCT and CBCT generate an opposite effect on VS. VRCT outweighs CBCT in treatment effectiveness of GCF, EF, Lang and VS. More immersive and interactive experiences in VRCT may help individuals with MCI better engage in real-life experiences, which supports skill generalization and reduces external distractions. CBCT tends to improve Mem but no definite conclusions can be made. Further investigation with more stringent research design and specific protocol are required to reach consensus about the optimum intervention regime. Full article

This article presents an ablative method of cutting masks from ultra-thin metal foils using nanosecond laser pulses. As a source of laser radiation, a pulsed fiber laser with a wavelength of 1062 nm with the duration of pulses from 15 to 220 nanoseconds (ns), was used in the research. The masks were made of stainless-steel foil with thicknesses of 30 µm, 35 µm, and 120 µm. Channels of different lengths from 50 to 300 µm were tested. The possibilities and limitations of the presented method are described. The optimization of the cutting process parameters was performed using the experiment planning techniques. A static, determined complete two-level plan (SP/DC 2⁴) was used. On the basis of the analysis of the test structures, we designed and produced precise shading masks used in the process of organic field effect transistor (OFET) electrode evaporation. The ablative method proved suitable to produce masks with canals of minimum lengths of 70 µm. It offers facile, fast, and economically viable shadow mask fabrication for organic electronics applications, which moreover might enable fast prototyping and circuit design. Full article

Electric vehicle batteries have the problem of low output voltage, so the application of a high-gain converter is a research hotspot. The symmetrical dual-switch high gain converter has the merits of simple structure, low voltage and current stress, and low EMI. Due to the deterioration of circuit performance caused by circuit parasitic parameters under high frequency operating conditions, the former analysis under low frequency condition cannot satisfy the requirements for performance evaluation. To clarify whether the symmetrical dual-switch high-gain converter can maintain its operating characteristics under high-frequency operating conditions, this paper establishes the converter model considering parasitic parameters, and deduces the sneak circuit modes at high frequency. The effects of parasitic parameters at high frequency on voltage gain, switch stress, and symmetrical operating are analyzed, which is beneficial for the selection and optimization of power devices. This paper believes that considering parasitic parameters may reduce the output gain of the symmetrical double-switch high-gain converter considering parasitic parameters under high frequency conditions, increase the switching stress, and affect the symmetry of the circuit operation when the parasitic parameter values are different. Finally, an experimental platform rated on 200 W with 200 kHz switching frequency is established, and experimental verification is given to verify the analysis. Full article

The skyrocketing growth in the number of Internet of Things (IoT) devices has posed a huge traffic demand for fifth-generation (5G) wireless networks and beyond. In-band full-duplex (IBFD), which is theoretically expected to double the spectral efficiency of a half-duplex wireless channel and connect more devices, has been considered as a promising technology in order to accelerate the development of IoT. In order to exploit the full potential of IBFD, the key challenge is how to handle network interference (including self-interference, co-channel interference, and multiuser interference) more effectively. In this paper, we propose a simple yet efficient user grouping method, where a base station (BS) serves strong downlink users and weak uplink users and vice versa in different frequency bands, mitigating severe network interference. First, we aim to maximize a minimum rate among all of the users subject to bandwidth and power constraints, which is formulated as a nonconvex optimization problem. By leveraging the inner approximation framework, we develop a very efficient iterative algorithm for solving this problem, which guarantees at least a local optimal solution. The proposed iterative algorithm solves a simple convex program at each iteration, which can be further cast to a conic quadratic program. We then formulate the optimization problem of sum throughput maximization, which can be solved by the proposed algorithm after some slight modifications. Extensive numerical results are provided to show not only the benefit of using full-duplex radio at BS, but also the advantage of the proposed user grouping method. Full article

A reconfigurable electromagnetic surface has been studied to realize the adjustable orbital angular momentum (OAM) beams for real-time wireless communication and dynamic target detection in the future. OAM mode switching realized by many previous designs suffers from low gains without OAM beam scanning. In this article, a 1-bit reconfigurable reflectarray antenna is designed, fabricated, and tested for the real-time control of OAM mode switching and large-angle vortex beam scanning in three-dimensional space. The proposed reflectarray surface is composed of 1-bit electronically reconfigurable cells, and the size is 24 λ × 24 λ with 2304 units. The reconfigurable element is designed by using a radiation patch loading a PIN diode with effective control of two states, “ON” and “OFF”, for the demand of 180° phase difference. The reflectarray surface can be assigned to a code sequence of 0 or 1 by the Field-Programmable Gate Array (FPGA) in real time. Henceforth, the coding surface can dynamically control the generation of high-gain OAM beams, where only the optimized phase distributions on the surface need to be changed according to demand. To verify the concept, a large-scale reflectarray surface is fabricated and measured with an oblique feed at 15°. Different OAM-carrying phase distributions for different OAM beam states are calculated and tested. The test results show that the OAM mode switching between l = 1 and l = 2 is realized, and other variable modes such as l = 3 or l = 5 can also be achieved by modifying the phase encoding sequence. Furthermore, the direction of the vortex beams can be accurately controlled with gains over 20 dBi, and the large-angle vortex beam scanning is verified. Therefore, all results demonstrate that the proposed 1-bit reconfigurable reflectarray is efficient for the regulation and control of OAM-carrying beams for the demand of real-time dynamic wireless communications in the future. Full article

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca²⁺), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca²⁺(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients’ urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes. Full article