Electronics, Volume 11, Issue 4 (February-2 2022) – 167 articles

The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy. Full article

Optical coherence tomography (OCT) is an imaging technique based on interferometry of backscattered lights from materials and biological samples. For the quantitative evaluation of an OCT system, artificial optical samples or phantoms are commonly used. They mimic the structure of biological tissues and can provide a quality standard for comparison within and across devices. Phantoms contain medium matrix and scattering particles within the dimension range of target biological structures such as the retina. The aim was to determine if changes in speckle derived optical texture could be employed to classify the OCT phantoms based on their structural composition. Four groups of phantom types were prepared and imaged. These comprise different concentrations of a medium matrix (gelatin solution), different sized polystyrene beads (PBs), the volume of PBs and different refractive indices of scatterers (PBs and SiO₂). Texture analysis was applied to detect subtle optical differences in OCT image intensity, surface coarseness and brightness of regions of interest. A semi-automated classifier based on principal component analysis (PCA) and support vector machine (SVM) was applied to discriminate the various texture models. The classifier detected correctly different phantom textures from 82% to 100%, demonstrating that analysis of the texture of OCT images can be potentially used to discriminate biological structure based on subtle changes in light scattering. Full article

This article demonstrates the possibility of creating memory devices based on polycrystalline mayenite. In the course of the study, structural characterization (XRD, TEM) of ceramic samples of mayenite was carried out, as well as a study of the spectral (THz range) and electrophysical characteristics. Materials obtained by calcination at high (1360–1450 °C) temperatures in an inert argon atmosphere differ in the degree of substitution of oxygen anions O²⁻ for electrons, as indicated by the data on the unit cell parameters and dielectric constant coefficients in the range of 0.2–1.3 THz, as well as differences in the conducting properties of the samples under study by more than five orders of magnitude, from the state of the dielectric for C12A7:O²⁻ to the conducting (metal-like) material in the state of the C12A7:e⁻ electride. Measurements of the current–voltage characteristics of ceramic C12A7:e⁻ showed the presence of memristive states previously detected by other authors only in the case of single crystals. The study of the stability of switching between states in terms of resistance showed that the values of currents for states with high and low resistance remain constant up to 180 switching cycles, which is two times higher than the known literature data on the stability of similar prototypes of devices. It is shown that such samples can operate in a switch mode with nonlinear resistance in the range of applied voltages from –1.3 to +1.3 V. Full article

An intrusion detection system serves as the backbone for providing high-level network security. Different forms of network attacks have been discovered and they continue to become gradually more sophisticated and complicated. With the wide use of internet-based applications, cyber security has become an important research area. Despite the availability of many existing intrusion detection systems, intuitive cybersecurity systems are needed due to alarmingly increasing intrusion attacks. Furthermore, with new intrusion attacks, the efficacy of existing systems depletes unless they evolve. The lack of real datasets adds further difficulties to properly investigating this problem. This study proposes an intrusion detection approach for the modern network environment by considering the data from satellite and terrestrial networks. Incorporating machine learning models, the study proposes an ensemble model RFMLP that integrates random forest (RF) and multilayer perceptron (MLP) for increasing intrusion detection performance. For analyzing the efficiency of the proposed framework, three different datasets are used for experiments and validation, namely KDD-CUP 99, NSL-KDD, and STIN. In addition, performance comparison with state-of-the-art models is performed which suggests that the RFMLP can detect intrusion attacks with high accuracy than the existing approaches. Full article

Resilience plays an important role in improving robustness for robots in harsh environments such as planetary exploration and unstructured terrains. As a naturally compliant structure, tensegrity presents advantageous flexibility for enhancing resilience in robotic applications according to existing research. However, tensegrity robots to date are normally based on monolithic morphologies and are slow in locomotion. In this paper, we demonstrate how we adopt such flexibility to improve the robustness of wheeled robots by articulating modules with tensegrity mechanisms. The test results reveal the robot is resistant and resilient to external hazards in a fully passive manner owing to the continuous elasticity in the structure network. It possesses a good number of DoFs and can adapt to various terrains easily either with actuation or not. The robot is also capable of crawling locomotion aside from wheeled locomotion to traverse uneven surfaces and provide self-recovery from rollovers. It demonstrates good robustness and mobility at the same time compared with existing tensegrity robots and shows the competitiveness with conventional rigid robots in harsh scenarios. The proposed robot presents the capability of tensegrity robots with resilience, robustness, agility, and mobility without compromise. In a broader perspective, it widens the potential of tensegrity robots in practical applications. Full article

This paper proposes a modified Chua Corsage Memristor endowed with two symmetrical locally active domains. Under the DC bias voltage in the locally active domains, the memristor with an inductor can construct a second-order circuit to generate periodic oscillation. Based on the theories of the edge of chaos and local activity, the oscillation mechanism of the symmetrical periodic oscillations of the circuit is revealed. The third-order memristor circuit is constructed by adding a passive capacitor in parallel with the memristor in the second-order circuit, where symmetrical periodic oscillations and symmetrical chaos emerge either on or near the edge of chaos domains. The oscillation mechanisms of the memristor-based circuits are analyzed via Domains distribution maps, which include the division of locally passive domains, locally active domains, and the edge of chaos domains. Finally, the symmetrical dynamic characteristics are investigated via theory and simulations, including Lyapunov exponents, bifurcation diagrams, and dynamic maps. Full article

High current density and high brightness are critical factors for high-power and compact extended interaction oscillators (EIOs) which are operated in the terahertz (THz) waveband. The pseudospark-sourced (PS) sheet electron beam, which combines merits including high current density, a relatively big beam cross-section and no requirement for the external focusing magnetic field, is a good choice for application to high-frequency EIO. The pulse generated by the PS electron beam can last around tens of nanoseconds or even less, thus the EIO’s oscillation start-up time (OST) should be short enough. This paper researched how to reduce OST in an EIO driven by the PS sheet electron beam. The authors realized that the OST of EIO was very sensitive to the gap length under the equal period. The distribution of the electric field is optimized by adjusting the length of the gap. The strong electric field strength is conducive to the beam-wave interaction, and the OST is affected by the beam-wave interaction. When the gap length reaches a suitable value, the OST becomes the shortest. The simulation results showed the EIO’s shortest OST was 8 ns and the corresponding peak output power was 2 kW at 0.19 THz, while the current density was 500 A/cm². When current density reached 10,000 A/cm², the shortest OST could even be 1.9 ns. Full article

The Binarized Neural Network (BNN) is a Convolutional Neural Network (CNN) consisting of binary weights and activation rather than real-value weights. Smaller models are used, allowing for inference effectively on mobile or embedded devices with limited power and computing capabilities. Nevertheless, binarization results in lower-entropy feature maps and gradient vanishing, which leads to a loss in accuracy compared to real-value networks. Previous research has addressed these issues with various approaches. However, those approaches significantly increase the algorithm’s time and space complexity, which puts a heavy burden on those embedded devices. Therefore, a novel approach for BNN implementation on embedded systems with multi-scale BNN topology is proposed in this paper, from two optimization perspectives: hardware structure and BNN topology, that retains more low-level features throughout the feed-forward process with few operations. Experiments on the CIFAR-10 dataset indicate that the proposed method outperforms a number of current BNN designs in terms of efficiency and accuracy. Additionally, the proposed BNN was implemented on the All Programmable System on Chip (APSoC) with 4.4 W power consumption using the hardware accelerator. Full article

The COVID-19 pandemic has limited people’s mobility to a certain extent, making it difficult to purchase goods and services offline, which has led the creation of a culture of increased dependence on online services. One of the crucial issues with using credit cards is fraud, which is a serious challenge in the realm of online transactions. Consequently, there is a huge need to develop the best approach possible to using machine learning in order to prevent almost all fraudulent credit card transactions. This paper studies a total of 66 machine learning models based on two stages of evaluation. A real-world credit card fraud detection dataset of European cardholders is used in each model along with stratified K-fold cross-validation. In the first stage, nine machine learning algorithms are tested to detect fraudulent transactions. The best three algorithms are nominated to be used again in the second stage, with 19 resampling techniques used with each one of the best three algorithms. Out of 330 evaluation metric values that took nearly one month to obtain, the All K-Nearest Neighbors (AllKNN) undersampling technique along with CatBoost (AllKNN-CatBoost) is considered to be the best proposed model. Accordingly, the AllKNN-CatBoost model is compared with related works. The results indicate that the proposed model outperforms previous models with an AUC value of 97.94%, a Recall value of 95.91%, and an F1-Score value of 87.40%. Full article

The Internet of things (IoT) enables mobile devices to connect and exchange information with others over the Internet with a lot of applications in consumer, commercial, and industrial products. With the rapid development of machine learning, IoT with image recognition capability is a new research area to assist mobile devices with processing image information. In this research, we propose the rotation-invariant multi-scale convolutional neural network (RIMS-CNN) to recognize rotated objects, which are commonly seen in real situations. Based on the dihedral group D4 transformations, the RIMS-CNN equips a CNN with multiple rotated tensors and its processing network. Furthermore, multi-scale features and shared weights are employed in the RIMS-CNN to increase performance. Compared with the data augmentation approach of using rotated images at random angles for training, our proposed method can learn inherent convolution kernels for rotational features. Experiments were conducted on the benchmark datasets: MNIST, FASHION-MNIST, CIFAR-10, and CIFAR-100. Significant improvements over the other models were achieved to show that rotational invariance could be learned. Full article

Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes. Full article

With the increased resolution and frame rates of video recordings, in combination with the current evolution towards video-on-demand streaming services and the user expecting ubiquitous wireless connectivity, it is necessary to design wireless communication systems that allow high-rate data transfer. The large bandwidths that are available in the mmWave frequency band allow such high data rates. In this paper, we provide an experimental and simulated indoor residential radio channel model at V-band frequencies and perform packet error rate and throughput measurements at 60 GHz using IEEE 802.11ad transceivers. We compare the path loss and throughput measurements to simulations using a network performance prediction tool. The path loss measurement results using an omnidirectional transmit antenna correspond well to generic indoor mmWave channel models. Double-directional path loss measurements show that generic models underestimate path loss of non-Line-of-Sight (NLOS) links. A ray-launching algorithm is designed and validated, and used for IEEE 802.11ad throughput estimation based on link budget calculations. The link budget underestimates the achieved throughput, when comparing to adaptive-rate MCS selection in a commercial transceiver, based on the measured signal-to-noise ratio. Packet error rate measurements confirm that, even for NLOS links, throughputs exceeding 1 Gbps are possible. Full article

By linking computational intelligence technology directly to urban transportation systems, a framework for scheduling traffic lights is proposed to enhance their flexibility in adaptation to traffic fluctuation. First, based on the flexible neural tree (FNT) theory, an algorithm for predicting the traffic flow is designed to obtain the variance tendency of traffic load. After that, a strategy for adjusting the duration of traffic signal cycle is designed to tackle the problem of overload or lightweight traffic flow in the next-time frame. While predetermining the duration of signal cycle in the next-time frame, from a utilization perspective, an elastic-adaption strategy for scheduling the separate phase’s green traffic lights is derived from the analytical solution, which is obtained from a designed trade-off scheduling optimization problem to increase the adaptability for the upcoming traffic flow. The experiment results show that the proposed framework can effectively reduce the delay and stopping rate of vehicles, and improves the adaptability for the upcoming traffic flow. Full article

With the expansion of the civil UAV (Unmanned Aerial Vehicle) market, UAVs are also increasingly being used in illegal activities such as espionage and snooping on privacy. Therefore, how to effectively control the activities of UAVs in cities has become an urgent problem to be solved. Considering the urban background and the radar performance of communication signals, a low-altitude target detection scheme based on 5G base stations is proposed in this paper. A 5G signal is used as the external radiation source, the method of transceiver separation is adopted, and the forward-scattered waves are used to complete the detection of UAV. This paper mainly analyzes the principle of forward scattering detection in an urban environment, where the forward-scattered wave of a target is stronger than the backward-reflected wave and contains both height difference and midline height information on the target. Based on the above theory, this paper proposes a forward-scattered wave recognition algorithm based on YOLOv3-FCWImageNet, which transforms the forward-scattered wave recognition problem into a target detection problem and accomplishes the recognition of forward-scattered waves by using the excellent performance of algorithms in the field of image recognition. Simulation results show that FCWImageNet can distinguish two different low-altitude targets effectively, and realize the monitoring and classification of UAVs. Full article

In this paper, a novel switching scheme using discontinuous pulse-width modulation (DPWM) for a zero-voltage switching (ZVS) grid-connected three-phase inverter is proposed. ZVS in the main and auxiliary switches was achieved. Moreover, the reverse recovery currents of the anti-parallel diodes in the main switches were suppressed. A circuit analysis was performed, and a simulation was carried out. Furthermore, a prototype of the ZVS grid-connected three-phase inverter was constructed to verify the effectiveness of the proposed PWM control scheme. Both the simulation and experimental results verified the validity of the proposed PWM control scheme. Full article

The Internet of Things (IoT) is a growing computational paradigm where all kinds of everyday objects are interconnected, forming a vast cyberphysical environment at the edge between the virtual and the real world. Since the emergence of the IoT, Multi-Agent Systems (MAS) technology has been successfully applied in this area, proving itself to be an appropriate paradigm for developing distributed, intelligent systems containing sets of IoT devices. However, this technology still lacks effective mechanisms to integrate the enormous diversity of existing IoT devices systematically. In this context, this paper introduces the concept of the IoT artifact as a new interface abstraction for the development of MAS based on IoT devices. The IoT artifact strictly conforms to the Agents and Artifacts (A&A) meta-model, and it also adopts the programming model of the SPADE multi-agent platform, providing both a consistent theoretical framework and a practical model for real-world applications. Full article

Switched-capacitor converters and their alternatives have been shown to provide high efficiency with high power densities on smaller volumes, and can thereby be a suitable choice for energy harvesting. This paper proposes a hybrid power architecture based on a switched-capacitor topology and a boost converter that can be used for such purposes. A switching capacitor circuit can achieve any voltage ratio, allowing a boost converter to increase the input voltage to higher voltage levels. The first stage is unregulated with high-efficiency voltage conversion. The boost stage provides a regulated voltage output on such a converter. Rather than cascading two converters, their operation is integrated for the output voltage regulation. One major problem of switched-capacitor converters is output voltage regulation, which is solved by the interconnection of the power stages. The simplicity and robustness of the solution provide the possibility to achieve higher voltage ratios than cascading boost converters and provide higher efficiency. The converter’s size and cost can be improved with the integration of switching capacitors in DC-DC converter structures. A converter prototype has been designed, modelled, and built for the input voltage level of 2 V and power level of 5 W. Full article

Physically Unclonable Functions (PUFs) have become ubiquitous as part of the emerging cryptographic algorithms. Strong PUFs are also predominantly addressed as the suitable variant for lightweight device authentication and strong single-use key generation protocols. This variant of PUF can produce a very large number of device-specific unique identifiers (CRPs). Consequently, it is infeasible to store the entire CRP space of a strong PUF into a database. However, it is potential to use Machine Learning to provide an estimated model of strong PUF for enrollment. An estimated model of PUF is a compact solution for the designer’s community, which can provide access to the full CRP space of the PUF with some probability of erroneous behavior. To use this solution for enrollment, it is crucial on one hand to ensure that PUF is safe against a model-building attack. On the other hand, it is important to ensure that the ML-based enrollment will be performed efficiently. In this work, we discuss these factors, and we present a formalized procedure of ML-based modeling of PUF for enrollment. We first define a secure sketch which allows modelability of PUF only for a trusted party. We then highlight important parameters which constitute the cost of enrollment. We show how an ML-based enrollment procedure should use these parameters to evaluate the enrollment cost prior to enrolling a large group of PUF-enabled devices. We introduce several parameters as well to control ML-based modeling in favor of PUF enrollment with minimum cost. Our proposed ML-based enrollment procedure can be considered a starting point to develop enrollment solutions for protocols which use an estimated model of PUF instead of a CRP database. In the end, we present a use-case of our ML-based enrollment method to enroll 100 instances of 2-XOR Arbiter PUFs and discuss the evaluative outcomes. Full article

Persistent Organic Pollutants (POPs) are toxic and difficult to degrade, which will cause huge damages to human life and the ecological environment. Therefore, based on historical measurements, it is important to use intelligent methods and data analysis technologies to build an intelligent prediction system to accurately predict the future POPs concentrations in advance. This work has extremely important significance for policy formulation, human health, environmental protection and the sustainable development of society. Since the POPs concentrations sequence contains both linear and nonlinear components, this paper proposes an intelligent data analysis system combining autoregressive integrated moving average (ARIMA) and long short-term memory network (LSTM) to analyze and predict the POPs concentrations in the Great Lakes region. ARIMA is used to capture linear components while LSTM is used to process nonlinear components, which overcomes the deficiency of single models. Moreover, a one-class SVM algorithm is used to detect outliers during data preprocessing. Bayesian information criterion and grid search methods are also used to obtain the optimal parameter combinations of ARIMA and LSTM, respectively. This paper compares our intelligent data analysis system with other single baseline models by using multiple evaluation indicators and finds that our system has the smallest MAE, RMSE and SMAPE values on all datasets. Meanwhile, our system can predict the trends of concentration changes well and the predicted values are closer to true values, which prove that it can effectively improve the precision of prediction. Finally, our system is used to predict concentration values of sites in the Great Lakes region in the next 5 years. The predicted concentrations present a large fluctuation trend in each year, but the overall trend is downward. Full article

EEG-based emotion recognition can help achieve more natural human-computer interaction, but the temporal non-stationarity of EEG signals affects the robustness of EEG-based emotion recognition models. Most existing studies use the emotional EEG data collected in the same trial to train and test models, once this kind of model is applied to the data collected at different times of the same subject, its recognition accuracy will decrease significantly. To address the problem of EEG-based cross-day emotion recognition, this paper has constructed a database of emotional EEG signals collected over six days for each subject using the Chinese Affective Video System and self-built video library stimuli materials, and the database is the largest number of days collected for a single subject so far. To study the neural patterns of emotions based on EEG signals cross-day, the brain topography has been analyzed in this paper, which show there is a stable neural pattern of emotions cross-day. Then, Transfer Component Analysis (TCA) algorithm is used to adaptively determine the optimal dimensionality of the TCA transformation and match domains of the best correlated motion features in multiple time domains by using EEG signals from different time (days). The experimental results show that the TCA-based domain adaptation strategy can effectively improve the accuracy of cross-day emotion recognition by 3.55% and 2.34%, respectively, in the classification of joy-sadness and joy-anger emotions. The emotion recognition model and brain topography in this paper, verify that the database can provide a reliable data basis for emotion recognition across different time domains. This EEG database will be open to more researchers to promote the practical application of emotion recognition. Full article

This paper proposes a single-stage AC-DC rectifier with power factor correction (PFC), high-frequency isolation and bidirectional power conversion capability for on-board battery charger (OBC) applications. The proposed converter is based on the interleaving technique and the Dual Active Bridge (DAB) operation, applying the phase-shift control to regulate the power flow. In addition to topology, this article presents a control strategy for reducing low-frequency power ripples transferred to the secondary side without any additional component and hence maintaining overall size and cost. The single-phase OBC can interchange active power with the grid to charge batteries while performing grid-to-vehicle (G2V) functionality or transferring energy back to the grid via vehicle-to-grid (V2G) mode. The theoretical analysis of the converter including modulation strategy and feedback control scheme are presented. The proposed topology and control strategy have been verified by experimental results of a 650 W SiC-based prototype. Full article

A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems. Full article

Conjugated polymers have emerged as promising materials for next-generation electronics. However, in spite of having several advantages, such as a low cost, large area processability and flexibility, polymer-based electronics have their own limitations concerning low electrical performance. To achieve high-performance polymer electronic devices, various strategies have been suggested, including aligning polymer backbones in the desired orientation. In the present paper, we report a simple patterning technique using a polydimethylsiloxane (PDMS) mold that can fabricate highly aligned nanowires of a diketopyrrolopyrrole (DPP)-based donor–acceptor-type copolymer (poly (diketopyrrolopyrrole-alt-thieno [3,2-b] thiophene), DPP-DTT) for high-performance field effect transistors. The morphology of the patterns was controlled by changing the concentration of the DPP-based copolymer solution (1, 3, 5 mg mL⁻¹). The molecular alignment properties of three different patterns were observed with a polarized optical microscope, polarized UV-vis spectroscopy and an X-ray diffractometer. DPP-DTT nanowires made with 1 mg mL⁻¹ solution are highly aligned and the polymer field-effect transistors based on nanowires exhibit more than a five times higher charge carrier mobility as compared to spin-coated film-based devices. Full article

Precise localization plays a crucial role in autonomous driving applications. As Global Position System (GPS) signals are often susceptible to interference or even not fully available, odometry sensors can precisely calculate positions in urban environments. However, the cumulative error is thus originated with time increasing. This paper proposes an effective empirical formula to model such unbounded cumulative errors from noisy relative measurements. Furthermore, a recursive cumulative error expression has been established by calculating the first and second moments of the Ackermann model. Finally, based on the developed formula, numerical experiments have also been conducted to verify the validity of the proposed model. Full article

In this paper, a new realization of electronically tunable voltage output second-order low-pass (LP) and band-pass (BP) filter is presented. The circuit has a multiple-input single-output structure, and LP and BP outputs are provided using the same structure. One electronically variable second-generation voltage conveyor (VCII), whose impedance at the Y port can be electronically varied using a control current (I_con), two capacitors, and one resistor are used. By changing the value of I_con, the impedance value at the Y port can be electronically varied; therefore, the value of ω₀ can be tuned. This feature helps to reduce the number of passive components used. Interestingly, the LP and BP outputs are provided at the low-impedance Z port of the VCII, and there is no need for an extra voltage buffer for practical use. The circuit enjoys a simple realization consisting of only 24 MOS transistors. Simulation results using PSpice and 0.18 μm CMOS parameters are provided. The value of ω₀ can be varied from 1.2 MHz to 1.7 MHz, while I_con varies from 0 to 50 µA, with a power consumption variation from 244 µW to 515 µW. Full article

Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis. Full article

This paper presents a high-voltage power source to produce glow-discharge plasma in the frame of a specific application. The load has two well-differentiated types of behavior. To start the system, it is necessary to apply a high voltage, up to 15 kV, to produce air-dielectric breakdown. Before that, the output current is zero. Contrarily, under steady state, the output voltage is smaller (a few hundred volts) while the load requires current-source behavior to maintain a constant glow in the plasma. The amount of current must be selectable by the operator in the range 50–180 mA. Therefore, very different voltage gains are required, and they cannot be easily attained by a single power stage. This work describes why the LC-parallel resonant topology is a good single stage alternative to solve the problem, and shows how to make the design. The step-up transformer is the key component of the converter. It provides galvanic isolation and adapts the voltage gain to the most favorable region of the LC topology, but it also introduces non-avoidable reactive components for the resonant net, determining their shape and, to some extent, their magnitude. In the paper, the transformer’s constructive details receive special attention, with discussion of its model. The experimental dynamic tests, carried out to design the control, show load behavior that resembles negative resistance. This fact makes any control loop prone to instability. To compensate this effect, a resistive ballast is proposed, eliminating its impact on efficiency with a novel filter design, based on an inductor, connected in series with the load beyond the voltage-clamping capacitor. The analysis includes a mathematical model of the filtering capacitor discharge through the inductor during the breakdown transient. The model provides insight into the dimensions of the inductor, to limit the discharge current peak and to analyze the overall performance on steady state. Another detail addressed is the balance among total weight, efficiency and autonomy, which appears if the filter inductor is substituted for a larger battery in autonomous operation. Finally, a comprehensive set of experimental results on the real load illustrate the performance of the power source, showing waveforms at breakdown and at steady state (for different output currents). Additionally, the detector’s constructive principles are described and its experimental performance is explored, showing results with two different types of metallic pollutants in water. Full article

The correctness of autonomous driving software is of utmost importance, as incorrect behavior may have catastrophic consequences. Formal model-based engineering techniques can help guarantee correctness and thereby allow the safe deployment of autonomous vehicles. However, challenges exist for widespread industrial adoption of formal methods. One of these challenges is the model construction problem. Manual construction of formal models is time-consuming, error-prone, and intractable for large systems. Automating model construction would be a big step towards widespread industrial adoption of formal methods for system development, re-engineering, and reverse engineering. This article applies active learning techniques to obtain formal models of an existing (under development) autonomous driving software module implemented in MATLAB. This demonstrates the feasibility of automated learning for automotive industrial use. Additionally, practical challenges in applying automata learning, and possible directions for integrating automata learning into the automotive software development workflow, are discussed. Full article

Wi-Fi-based human activity recognition is emerging as a crucial supporting technology for various applications. Although great success has been achieved for location-dependent recognition tasks, it depends on adequate data collection, which is particularly laborious and time-consuming, being impractical for actual application scenarios. Therefore, mitigating the adverse impact on performance due to location variations with the restricted data samples is still a challenging issue. In this paper, we provide a location-independent human activity recognition approach. Specifically, aiming to adapt the model well across locations with quite limited samples, we propose a Channel–Time–Subcarrier Attention Mechanism (CTS-AM) enhanced few-shot learning method that fulfills the feature representation and recognition tasks. Consequently, the generalization capability of the model is significantly improved. Extensive experiments show that more than 90% average accuracy for location-independent human activity recognition can be achieved when very few samples are available. Full article

Neutral-point-clamped (NPC) power conversion topologies are among the most popular multilevel topologies in current industrial products and in industrial and academic research. The proper operation of multilevel three-phase NPC DC–AC converters requires the use of specific pulse-width modulation (PWM) strategies that maintain the DC-link capacitor voltage balance and concurrently optimize various performance factors such as efficiency and harmonic distortion. Although several such PWM strategies have been proposed in the literature, their formulation is often complex and/or covers only particular cases and operating conditions. This manuscript presents a simple formulation of the original virtual-vector-based PWM, which enables capacitor voltage balance in every switching cycle. The formulation is presented, for the general case, in terms of basic phase voltage modulating signals, with no reference to space vectors, involving any number of levels and for any operating conditions, including the overmodulation region. The equivalence of the presented formulation to the original PWM strategy is demonstrated through simulation under different scenarios and operating conditions. Thus, this manuscript offers in a one-stop source a simple, effective, and comprehensive PWM formulation to operate multilevel three-phase NPC DC–AC converters with any number of levels in any operating condition. Full article