Electronics

Our study is focused on the perceived professional efficacy of informatics in-service teachers with the experience of using micro-controller BBC micro:bit. In Slovakia, teaching using hardware is not typical. In addition, many teachers do not teach programming. BBC micro:bit is designed to be a tool for computer science (CS) teachers that should make a significant contribution to the innovation of CS teaching and enable CS teachers to implement CS lessons. The following research questions were asked. Q1: Is there a difference in the perceived efficacy to use teaching strategies based on experience with the micro:bit? Q2: Is there a relationship between the perceived efficacy of using teaching strategies and experience using the micro:bit? The research sample comprised N = 388 CS teachers employed in Slovak schools from the available selection. The research sample included CS teachers who participated in the project called ENTER. All participants have a grant, weekly online practices, supporting materials, and also consultant for implementation of a new teaching strategy. This study’s findings indicate that the use of a microcontroller such as the micro:bit has a positive impact on self-efficacy for instructional strategies, but not for classroom management. Full article

Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning. Full article

This paper presents the first design that combines pseudospark-sourced (PS) electron beams with a multiple-beam extended interaction oscillator (EIO). The PS electron beam is an excellent choice for driving EIOs because it has high current density and does not require a focusing magnetic field. The EIO with coaxial structure adopts the method of multiple electron beams, which plays a crucial role in improving the average output power. At the same frequency, the EIO operating in the high-order TM₃₁-3π mode has a larger cavity size than the EIO operating in the traditional TM₀₁-2π mode. The high-order TM₃₁-3π mode solves the problem of the EIO’s manufacture at high frequency. In order to verify the above points, a G-band PS multiple-beam EIO operating in TM₃₁-3π mode has been designed. The beam–wave interaction particle-in-cell simulation results show that the EIO’s peak output power is 39.2 kW at 217 GHz, and that its efficiency is around 6.1%. The EIO with six pencil beams operates at a voltage of 43 kV. The total current of the six electron beams is 15 A (equally distributed among the six beams), and the corresponding current density is about 5000 A/cm². Considering the ohmic loss and the effect of skin depth, the conductivity used in these simulations is 2 × 10⁷ S/m. The design is an excellent way to improve the output power of EIO operating at high frequency. Full article

With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet of Things to establish connections among the many components that make up the structure. As the notion of the Internet of Things (IoT) gains attraction, smart grids are being integrated into larger networks. The IoT is an integral part of smart grids since it enables beneficial services that improve the experience for everyone inside and individuals are protected because of tried-and-true life support systems. The reason for installing Internet of Things gadgets in smart structures is the primary focus of this investigation. In this context, the infrastructure behind IoT devices and their component units is of the highest concern. Full article

Information regarding newly added construction land can be extracted from high-resolution remote sensing images. The retrieval accuracy of land cover changes across the country has improved, and the illegal use of land is actively monitored. To address the imbalance between positive and negative training samples in extracting information regarding newly added construction land, a method for identifying newly added construction land by weakening the weight of negative samples was proposed. A focal loss function was used to weaken the negative samples’ weights and improve the overfitting U-net. Since the two parameters of the focal loss function are not independent of each other, they need to be selected at the same time. Therefore, this paper developed a formula for selecting the balance factor α based on a large number of experimental results. First, the GF-2 image was combined with the historical land change survey data and monitoring vector results to construct a dataset, and then the training dataset was input into a fully convolutional neural network (CNN) integrated with feature fusion and a focal loss function. Finally, the accuracy of the trained network model was verified. To demonstrate the applicability of the method of determining the parameters of the focal loss function, the validation set was divided into four subsets for accuracy verification. The experimental results showed that the F1-score of newly added construction land information extracted by this method reached 0.913, which is 0.078 and 0.033 higher than those of the U-net and the improved U-net. The parameters obtained by the method proposed in this study achieved the best results on the four verification sets, which shows that the method for extracting newly added construction land information and that for selecting parameters have strong applicability. Full article

Federated learning (FL) aims to address the challenges of data silos and privacy protection in artificial intelligence. Vertically federated learning (VFL) with independent feature spaces and overlapping ID spaces can capture more knowledge and facilitate model learning. However, VFL has both privacy and utility problems in framework construction. On the one hand, sharing gradients may cause privacy leakage. On the other hand, the increase in participants brings a surge in the feature dimension of the global model, which results in higher computation costs and lower model accuracy. To address these issues, we propose a vertically federated learning algorithm with correlated differential privacy (CRDP-FL) to meet FL systems’ privacy and utility requirements. A privacy-preserved VFL framework is designed based on differential privacy (DP) between organizations with many network edge devices. Meanwhile, feature selection is performed to improve the algorithm’s efficiency and model performance to solve the problem of dimensionality explosion. We also propose a quantitative correlation analysis technique for VFL to reduce the correlated sensitivity and noise injection, balancing the utility decline due to DP protection. We theoretically analyze the privacy level and utility of CRDP-FL. A real vertically federated learning scenario is simulated with personalized settings based on the ISOLET and Breast Cancer datasets to verify the method’s effectiveness in model accuracy, privacy budget, and data correlation. Full article

In this paper, a universal fractional order memelement (FOME) emulator is proposed based on the use of a voltage differentiating current conveyor (VDCC) as active block. The emulation circuit was implemented without an analog voltage multiplier and with only one type of grounded passive element—capacitors. Specially designed switching networks allow controlling the type of memelement and the emulator mode—floating or/and grounded, electronically controlled (by changing the bias voltage of the VDCC) FOMEs. The proposed emulator was theoretically analyzed, and the influence of possible non-idealities and parasitic effects was also been analyzed to reduce the undesirable effects by selecting the passive circuit elements. The proposed designs are very simple compared to most of the designs available in the literature and can operate in a wide frequency range (up to 50 MHz) and also satisfy the non-volatility test. All realized memelements can be used in incremental and decremental modes as well as in inverse configuration. The performance of the circuit was verified by HSPICE simulations using 0.18 μm TSMC process parameters and ±0.9 V power supply. The proposal is also supported by experimental results with off-the-shelf components (LM13700 and one AD844) in order to confirm the proposed solution’s workability. Full article

Wind and photovoltaic power generation connected to the independent power system can save fuel, reduce carbon emissions, and provide significant economic and environmental benefits. Influenced by the characteristics of light resources and wind resources, the wind and photovoltaic output active power is characterized by volatility and randomness, which affects the frequency stability of the independent power system. In order to evaluate the frequency stability, in this paper, the simulation model of an independent power system is established, and the simulation model of a diesel generator, wind and photovoltaic are connected. Through droop calculation and Simulink simulation, the frequency characteristics of an independent power system under different working conditions are analyzed, and the maximum absorption capacity of wind and photovoltaic is studied. In an independent power system, when the new energy output is 25% of the total output, all the new energy output is cut off, the frequency drops by 0.5 Hz, and the frequency fluctuation is within the specified range. Full article

In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%. Full article

Quantum cryptography protocols make it possible not only to ensure the protection of data transmitted in a communication channel from unauthorized access by intruders, but also to detect the existence of any attempted interception. This scientific direction is currently relevant, since it is related to the problem of security and data protection in current information and communication networks. The article is devoted to quantum cryptography; it describes the development of quantum protocols as quantum key distribution systems. Grounded on the laws of quantum mechanics, the elaboration of modifications of secure data transfer protocols is shown. The authors considered the best-known protocol to be BB84 of quantum key distribution; a more modern modification of this protocol is BB84 Info-Z. Comparative analysis of these has also been carried out. It has been established that the BB84-Info-Z quantum protocol works more efficiently than BB84 since its lower error threshold allows the interceptor to obtain much less information about the secret key. The authors put forward a new idea to improve the BB84 protocol (which has been quite outdated for almost 40 years), due to the increase in modern requirements for quantum cryptography protocols. The modification is called CSLOE-2022. It enables significant intensification of cryptographic strength and the entanglement degree of the interceptor (cryptanalyst), which greatly complicates the very possibility of intercepting information. The ultimate goal of the CSLOE-2022 modification is to complicate the eavesdropping process so much that it can be considered completely useless for an attacker in terms of wasting time and resources. The modification allows exceeding the known speed limit of key generation without repeaters since it uses two sources, the phases of which, in addition to the hundreds of kilometers of fiber between them, are very difficult to stabilize. Comparison of the protocols by working distance showed that for BB84, this distance does not exceed 70 km; for BB84-Info-Z it is similar, at no more than 70 km, and the modification of CSLOE-2022 proposed by the authors theoretically allows increasing the working distance of the quantum protocol to 511 km (7.3 times). Full article

The complex industrial environment of the 21st century is equipped with the Internet of Things platform, with the objective of real-time operational visibility, improved device management and predictive maintenance. To unleash the focused importance of its policy, a secure connectivity must be realized through a range of existing and dissimilar devices and data sources. During the conceptualization phase, the authors aimed to compel the following: (a) that restriction of access should be based on the presence of unexpected device actions that may point to a security breach, and (b) ensure the safety of the system by constant tracking of connected devices and data. In this paper, a policy-driven, zero-trust defense model is proposed to address numerous vulnerable entry points, validate device access to legitimate enterprise functions, quarantine unsecure devices, and trigger automated warnings and policy validation for hardware, software, network connectivity and data management. To handle active scanning, bots, passive auditing, outbound threat management, and device interconnections, an experimental environment was put up. This environment provides holistic visibility and a persistent view of all resources, including those that were previously unknown. A steady stream of reliable and authenticated data has helped to develop and adjust a scalable implementation strategy by avoiding recognized anomalous traps. Actual data was aggregated and analyzed to assess the proposed methodology. Comparative analysis of ‘device exposure view, attack path analysis, controlled view of devices, comprehensive vulnerability evaluation, and effective communication of cyber risk’ has proved the effectiveness of the proposed methodology. Full article

This paper reports on a CMOS fully integrated optoelectronic sensing system composed of a Si photodiode and a transimpedance amplifier acting as the electronic analog front-end for the conditioning of the photocurrent generated by the photodiode. The proposed device has been specifically designed and fabricated for wearable/portable/implantable biomedical applications. The massive employment of sensor systems in different industrial and medical fields requires the development of small sensing devices that, together with suitable electronic analog front ends, must be designed to be integrated into proper standard CMOS technologies. Concerning biomedical applications, these devices must be as small as possible, making them non-invasive, comfortable tools for patients and operating with a reduced supply voltage and power consumption. In this sense, optoelectronic solutions composed of a semiconductor light source and a photodiode fulfill these requirements while also ensuring high compatibility with biological tissues. The reported optoelectronic sensing system is implemented and fabricated in TSMC 180 nm integrated CMOS technology and combines a Si photodiode based on a PNP junction with a Si area of 0.01 mm² and a transimpedance amplifier designed at a transistor level requiring a Si area of 0.002 mm² capable to manage up to nanoampere input currents generated by the photodiode. The transimpedance amplifier is powered at a 1.8 V single supply showing a maximum power consumption of about 54 μW, providing a high transimpedance gain that is tunable up to 123 dBΩ with an associated bandwidth of about 500 kHz. The paper reports on both the working principle of the developed ASIC and the experimental measurements for its full electrical and optoelectronic characterizations. Moreover, as case-examples of biomedical applications, the proposed integrated sensing system has also been validated through the optical detection of emulated standard electrocardiography and photoplethysmography signal patterns. Full article

Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy optimization research, a deep learning framework is selected according to the research problems, and a DRL stereoscopic project policy model based on the asynchronous advantage actor–critic (A3C) algorithm, which uses two groups of neural networks, is constructed. The optimized stereoscopic projection policy is obtained by the interactive learning between the DRL model and the simulation. The effectiveness of the cooperative optimization policy between the DRL and the simulation experiment is verified. Full article

The Internet of Things is a system of interconnected smart devices that can communicate with each other and with other devices over the Internet, with or without human-to-human or human-to-computer interaction. Although IoT devices, which have IPs, make living easier, they are also a threat to the security and privacy of people. This research work presents a solution to the problem of security in the network of IoT, based on the idea of implementing the blockchain in IoT. Blockchain is a decentralized technology that adds blocks at the end of the chain. It saves the hash value for every block, and corresponds to the previous block. The decentralized behavior of blockchain is best for IoT as an extensive network, because IoT must not have a single point of failure, and one entity must not decide what to do. All the capable storage devices will save the same data entered from any device, removing the risk of receiving altered data. Full article

In this paper, a novel ultra-wideband (UWB) printed antenna with quadruple band-notched characteristics is proposed and investigated. The quadruple band rejections are achieved by etching two interdigital capacitance slots on each side of the ground plane, embedding a curled C-shaped slot on the circle patch, and adding a curled split-ring resonator on the backside of the antenna. Interdigital inductance slots can obtain a narrower notched band than general structures due to their high inductance, thereby preserving some valuable frequencies. Adjusting the tail branch’s length and distance of the curled C-shaped slot and the curled split-ring resonator can control the notch frequency and width. Finally, the proposed antenna operates from 2.9–11 GHz (VSWR < 2) with four band stops (VSWR > 2) for rejecting WiMAX, WLAN, and downlink of X-band satellite communication. Furthermore, the difference between the experimental results and the expected value is less than 3%. The proposed antenna can accurately filter out narrow-band signals. Full article

This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and $C{O}_2$ emissions due to the grid’s connection. Thus, we present a multi-objective two-stage optimization to reduce the impact of the charging station on the environment, as well as the costs. The first stage of the optimization provides an energy schedule, taking into account the PV forecast, the hourly grid’s $C{O}_2$ emissions factor, the electricity price, and the initial state of charge of the BESS. The output from this first stage corresponds to the maximum power permitted to be delivered to the EV by the grid. Then, the second stage of the optimization is based on model predictive control that looks to manage the energy flow from the grid, the PV, and the BESS. The proposed EMS is validated using an actual PV/BESS charging station located at the University of Trieste, Italy. Then, this paper presents an analysis of the performance of the charging station under the new EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees a reduction of 32% in emissions and 29% in energy costs. Full article

Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their training time is very long. Aiming at the low segmentation accuracy of various diseased leaf images caused by different sizes, colors, shapes, blurred speckle edges and complex backgrounds of traditional U-Net, a lightweight multi-scale extended U-Net (LWMSDU-Net) is constructed for CDLIS. It is composed of encoding and decoding sub-networks. Encoding the sub-network adopts multi-scale extended convolution, the decoding sub-network adopts a deconvolution model, and the residual connection between the encoding module and the corresponding decoding module is employed to fuse the shallow features and deep features of the input image. Compared with the classical U-Net and multi-scale U-Net, the number of layers of LWMSDU-Net is decreased by 1 with a small number of the trainable parameters and less computational complexity, and the skip connection of U-Net is replaced by the residual path (Respath) to connect the encoder and decoder before concatenating. Experimental results on a crop disease leaf image dataset demonstrate that the proposed method can effectively segment crop disease leaf images with an accuracy of 92.17%. Full article

In this paper, we propose a mechanism of orientation detection system based on edge-orientation selective neurons. We assume that there are neurons in the V1 that can generate response to object’s edge, and each neuron has the optimal response to specific orientation in a local receptive field. The global orientation is inferred from the aggregation of local orientation information. An orientation detection system is further developed based on the proposed mechanism. We design four types of neurons for four local orientations and used these neurons to extract local orientation information. The global orientation is obtained according to the neuron with the most activation. The performance of this orientation detection system is evaluated on orientation detection tasks. From the experiment results, we can conclude that our proposed global orientation mechanism is feasible and explainable. The mechanism-based orientation detection system shows better recognition accuracy and noise immunity than the traditional convolution neural network-based orientation detection systems and EfficientNet-based orientation detection system, which have the most accuracy for now. In addition, our edge-orientation selective cell based artificial visual system can greatly save time and learning cost compared to the traditional convolution neural network and EfficientNet. Full article

The energy transition towards renewable energies is crucial for the sustainable development of a society based on hydrocarbons. The current level of penetration and growth of wind energy in electric power systems is evident and many researchers have presented new methods for simulating and representing the electrical and mechanical characteristics of variable-speed wind turbines. However, complete mathematical models developed and implemented, for example, in MATLAB/Simulink^® software, require significant computational efforts that could make grid studies impractical when its scale tends to increase. To contribute to facing this issue, this paper proposes an extended simplified model for a variable-speed wind turbine that considers the dynamic behavior of its mechanical system and includes an approximate representation of the power electronic converter. This approach broadens the scope of studies related to grid frequency control and power quality (fast-frequency response, primary frequency control, and voltage control, among others), considerably reducing the computational burden. Several validations of the proposed simplified model are presented, including comparisons with a doubly fed induction generator-based wind turbine model (phasor type) from the MATLAB/Simulink^® library, and laboratory experiments under controlled conditions. The results show a good fit of the proposed simplified model to the MATLAB/Simulink^® model, with minimal delays about 3% of the wind turbine inertia constant. Moreover, with the proposal, the computational time is reduced by up to 80% compared to a detailed model. This time reduction is achieved without penalizing the numerical accuracy and the estimation quality of the real behavior of the variable-speed wind turbine. Full article