Electronics

Distracted human driver detection is an important feature that should be included in most levels of autonomous cars, because most of these are still under development. Hereby, this paper proposes an architecture to perform this task in a fast and accurate way, with a full declaration of its details. The proposed architecture is mainly based on the MobileNet transfer learning model as a backbone feature extractor, then the extracted features are averaged by using a global average pooling layer, and then the outputs are fed into a combination of fully connected layers to identify the driver case. Also, the stochastic gradient descent (SGD) is selected as an optimizer, and the categorical cross-entropy is the loss function through the training process. This architecture is performed on the State-Farm dataset after performing data augmentation by using shifting, rotation, and zooming. The architecture can achieve a validation accuracy of 89.63%, a validation recall of 88.8%, a validation precision of 90.7%, a validation f1-score of 89.8%, a validation loss of 0.3652, and a prediction time of about 0.01 seconds per image. The conclusion demonstrates the efficiency of the proposed architecture with respect to most of the related work. Full article

In the domain of radiological diagnostics, accurately detecting and classifying brain tumors from magnetic resonance imaging (MRI) scans presents significant challenges, primarily due to the complex and diverse manifestations of tumors in these scans. In this paper, a convolutional-block-based architecture has been proposed for the detection of multiclass brain tumors using MRI scans. Leveraging the strengths of CNNs, our proposed framework demonstrates robustness and efficiency in distinguishing between different tumor types. Extensive evaluations on three diverse datasets underscore the model’s exceptional diagnostic accuracy, with an average accuracy rate of 97.52%, precision of 97.63%, recall of 97.18%, specificity of 98.32%, and F1-score of 97.36%. These results outperform contemporary methods, including state-of-the-art (SOTA) models such as VGG16, VGG19, MobileNet, EfficientNet, ResNet50, Xception, and DenseNet121. Furthermore, its adaptability across different MRI modalities underlines its potential for broad clinical application, offering a significant advancement in the field of radiological diagnostics and brain tumor detection. Full article

Gallium nitride high-electron-mobility transistor (GaN HEMT) power devices are favored in various scenarios due to their high-power density and efficiency. However, with the significant increase in the heat flux density, the junction temperature of GaN HEMT has become a crucial factor in device reliability. Since the junction temperature monitoring technology for GaN HEMT based on temperature-sensitive electrical parameters (TSEPs) is still in the exploratory stage, the TSEPs’ characteristics of GaN HEMT have not been definitively established. In this paper, for the common steady-state TSEPs of GaN HEMT, the variation rules of the saturation voltage with low current injection, threshold voltage, and body-like diode voltage drop with temperature are investigated. The influences on the three TSEPs’ characteristics are considered, and their stability is discussed. Through experimental comparison, it is found that the saturation voltage with low current injection retains favorable temperature-sensitive characteristics, which has potential application value in junction temperature measurement. However, the threshold voltage as a TSEP for certain GaN HEMT is not ideal in terms of linearity and stability. Full article

Musical large-scale form is investigated using an electronic dance music piece fed into a Finite-Difference Time-Domain physical model of the cochlea, which again is input into an Impulse Pattern Formulation (IPF) Brain model. In previous studies, experimental EEG data showed an enhanced correlation between brain synchronization and the musical piece’s amplitude and fractal correlation dimension, representing musical tension and expectancy time points within the large-scale form of musical pieces. This is also in good agreement with a FitzHugh–Nagumo oscillator model.However, this model cannot display temporal developments in large-scale forms. The IPF Brain model shows a high correlation between cochlea input and brain synchronization at the gamma band range around 50 Hz, and also a strong negative correlation with low frequencies, associated with musical rhythm, during time frames with low cochlea input amplitudes. Such a high synchronization corresponds to temporal lobe epilepsy, often associated with creativity or spirituality. Therefore, the IPF Brain model results suggest that these conscious states occur at times of low external input at low frequencies, where isochronous musical rhythms are present. Full article

Honeynet and honeypot originate as network security tools to collect attack information during the network being compromised. With the development of virtualization and software defined networks, honeynet has recently achieved many breakthroughs. However, existing honeynet architectures treat network attacks as interactions with a single honeypot which is supported by multiple honeypots to make this single one more realistic and efficient. The scale and depth of existing honeynets are limited, making it hard to capture complicated attack information. Existing honeynet frameworks also have low-level simulation of protected network and lacks test metrics. To address these issues, we design and implement a novel container-based comprehensive cyber deception honeynet architecture that consists of five modules, called HoneyFactory. Just like factory producing products according to customer preferences, HoneyFactory generates honeynet using containers based on business networks under protection. In HoneyFactory architecture, we propose a novel honeynet deception model based on hmm model to evaluate deception stage. We also design other modules to make this architecture comprehensive and efficient. Experiments show that HoneyFactory performs better than existing research in communication latency and connections per second. Experiments also show that HoneyFactory can effectively evaluate deception stage and perform deep cyber deception. Full article

Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization. Full article

The evolution of malware poses significant challenges to the security of cyberspace. Machine learning-based approaches have demonstrated significant potential in the field of malware detection. However, such methods are partially limited, such as having tremendous feature space, data inequality, and high cost of labeling. In response to these aforementioned bottlenecks, this paper presents an Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning (MalOSDF). Inspired by traditional code slicing technology, this paper proposes a feature engineering method based on opcode slice for malware detection to better capture malware characteristics. To address the challenges of high expert costs and unbalanced sample distribution, this paper proposes the SSEAL (Semi-supervised Ensemble Active Learning) algorithm. Specifically, the semi-supervised learning module reduces data labeling costs, the active learning module enables knowledge mining from informative samples, and the ensemble learning module ensures model reliability. Furthermore, five experiments are conducted using the Kaggle dataset and DataWhale to validate the proposed framework. The experimental results demonstrate that our method effectively represents malware features. Additionally, SSEAL achieves its intended goal by training the model with only 13.4% of available data. Full article

Multi-microgrids (MMGs) revolutionize integrating and managing diverse distributed energy resources (DERs), significantly enhancing the overall efficiency of energy systems. Unlike traditional power systems, MMGs comprise interconnected microgrids that operate independently or collaboratively. This innovative concept adeptly addresses challenges posed by pulsed load effects, capitalizing on the cooperative nature of interconnected microgrids. A coordinated MMG system effectively redistributes and shares the impact of pulsed loads, mitigating voltage fluctuations and ensuring sustained system stability. The proposed cooperative MMG scheme optimizes power distribution and load prioritization, facilitating the seamless allocation of surplus energy from neighboring microgrids to meet sudden surges in demand. This study focuses on DC standalone multi-microgrid systems, showcasing their inherent adaptability, resilience, and operational efficiency in managing pulse, variable, and unpredictable generation deficits. Several experiments on a laboratory-scale DC multi-microgrid validate the system’s robust performance. Notably, transient current fluctuations during pulse loads are promptly stabilized through the effective collaboration of microgrids. Variable load experiments reveal distinct behaviors, shedding light on the profound influence of control strategies. This research reveals the transformative potential of MMGs in addressing energy challenges, with a particular focus on DC standalone multi-microgrid systems. The findings underscore the adaptability and resilience of the proposed cooperative scheme, marking a significant stride in the evolution of modern power systems. Full article

A novel unidirectional hybrid PFC rectifier topology based on SEPIC and boost converters is proposed, which is applicable to various industrial applications such as electric vehicle charging stations, variable speed AC drives, and energy storage systems. Compared to other rectifiers, the proposed SEPIC-boost-based rectifier exhibits continuous current on the AC side, lower voltage stress on the active switches, a wider range of DC output voltage, no auxiliary DC-DC converters, and a high step-up static voltage gain operating with low input voltage and a low step-up static gain for the high-input-voltage operation. These traits allow the SEPIC-boost-based rectifier to utilize smaller input-side harmonic filtering inductors and adopt active switches with lower voltage ratings, resulting in reduced conduction losses. Additionally, the proposed rectifier features power factor correction and high boost/buck voltage-gain capabilities, simplifying control for electric vehicle charging and expanding its range of applications. In this paper, the operating principle of the novel topology is presented first, and then the mathematical model of the proposed rectifier is built. Based on this, the comparison between the proposed topology and conventional boost and SEPIC converters is given. Furthermore, the control strategy, including the high-power-factor control and the balancing control to the DC capacitor voltages, is discussed. Finally, to validate the accuracy of the proposed rectifier’s theoretical research, a 500-W SEPIC-boost rectifier system has been constructed in the laboratory, generating a 200/120 V_dc output voltage from a 155 V_pk/50 Hz power source. Full article

Conventional sense amplifiers limit the performance of current RRAM computing-in-memory (CIM) macro circuits, resulting in high latency and energy consumption. This paper introduces a multi-bit quantization technology low-latency voltage sense amplifier (MQL-VSA). Firstly, the multi-bit quantization technology enhances circuit quantization efficiency, reducing the number of operational states in conventional VSA. Secondly, by simplifying the sequential logic circuits in conventional VSA, the complexity of sequential control signals is reduced, further diminishing readout latency. Experimental results demonstrate that the MQL-VSA achieves a 1.40-times decrease in readout latency and a 1.28-times reduction in power consumption compared to conventional VSA. Additionally, an 8-bit input, 8-bit weight, 14-bit output macro circuit utilizing MQL-VSA exhibited a 1.11times latency reduction and 1.04-times energy savings. Full article

In transportation electrification, power modules are considered the best choice for power switches to build a high-power inverter. Recently, several studies have presented prototypes that use parallel discrete MOSFETs and show similar overall output capabilities. This paper aims to compare the maximum output power and losses of inverters with different types (surface-mounted, through-hole-mounted and power modules) of commercially available switching devices, and, therefore, discuss the theoretical boundaries of each technology. The numerical analysis relies on detailed power loss and thermal models, with adjustments made for gate current and realistic parameters of the cooling system. The analysis includes two case studies with different targets, including minimum dimensional characteristics and maximum output power. The results demonstrate that discrete MOSFETs can provide improved capabilities in contrast to power modules under certain conditions. Full article

In the context of autonomous driving, sensing systems play a crucial role, and their accuracy and reliability can significantly impact the overall safety of autonomous vehicles. Despite this, fault diagnosis for sensing systems has not received widespread attention, and existing research has limitations. This paper focuses on the unique characteristics of autonomous driving sensing systems and proposes a fault diagnosis method that combines hardware redundancy and analytical redundancy. Firstly, to ensure the authenticity of the study, we define 12 common real-world faults and inject them into the nuScenes dataset, creating an extended dataset. Then, employing heterogeneous hardware redundancy, we fuse MMW radar, LiDAR, and camera data, projecting them into pixel space. We utilize the “ground truth” obtained from the MMW radar to detect faults on the LiDAR and camera data. Finally, we use multidimensional temporal entropy to assess the information complexity fluctuations of LiDAR and the camera during faults. Simultaneously, we construct a CNN-based time-series data multi-classification model to identify fault types. Through experiments, our proposed method achieves 95.33% accuracy in detecting faults and 82.89% accuracy in fault diagnosis on real vehicles. The average response times for fault detection and diagnosis are 0.87 s and 1.36 s, respectively. The results demonstrate that the proposed method can effectively detect and diagnose faults in sensing systems and respond rapidly, providing enhanced reliability for autonomous driving systems. Full article

Video anomaly detection is a critical component of intelligent video surveillance systems, extensively deployed and researched in industry and academia. However, existing methods have a strong generalization ability for predicting anomaly samples. They cannot utilize high-level semantic and temporal contextual information in videos, resulting in unstable prediction performance. To alleviate this issue, we propose an encoder–decoder model named SMAMS, based on spatiotemporal masked autoencoder and memory modules. First, we represent and mask some of the video events using spatiotemporal cubes. Then, the unmasked patches are inputted into the spatiotemporal masked autoencoder to extract high-level semantic and spatiotemporal features of the video events. Next, we add multiple memory modules to store unmasked video patches of different feature layers. Finally, skip connections are introduced to compensate for crucial information loss caused by the memory modules. Experimental results show that the proposed method outperforms state-of-the-art methods, achieving AUC scores of 99.9%, 94.8%, and 78.9% on the UCSD Ped2, CUHK Avenue, and Shanghai Tech datasets. Full article

This paper introduces an ultra-wideband (UWB) reflective metasurface that exhibits the characteristics of a linear to circular (LTC) polarization conversion. The LTC polarization conversion is an orthotropic pattern comprising two equal axes, v and u, which are mutually orthogonal. Additionally, it possesses a 45${}^{°}$ rotation with respect to the y-axis which extends vertically. The observed unit cell of the metasurface resembles a basic dipole shape. The converter has the capability to transform LP (linear polarized) waves into CP (circular polarized) waves within the frequency range 15.41–25.23 GHz. The band that contains its 3dB axial ratio lies within 15.41–25.23 GHz, which corresponds to an axial ratio (AR) bandwidth of 49.1%, and the resulting circular polarized wave is specifically a right-hand circular polarization (RHCP). Additionally, an LTC polarization conversion ratio (PCR) of over 98% is achieved within the frequency range between 15 and 24 GHz. A thorough theoretical investigation was performed to discover the underlying mechanism of the LTC polarization conversion. The phase difference $\Delta {\phi }_{\mu \nu }$ among the reflection coefficients of both the v- as well as the u-polarized incidences is approximately ±90${}^{°}$ that is accurately predictive of the AR of the reflected wave. This study highlights that the reflective metasurfaces can be used as an efficient LTC polarization conversion when the $\Delta {\phi }_{\mu \nu }$ approaches ±90${}^{°}$. The performance of the proposed metasurface enables versatile applications, especially in antenna design and polarization devices, through LTC polarization conversion. Full article

Physical Unclonable Functions are security primitives that exploit the variation in integrated circuits’ manufacturing process, and, as a result, each instance processes applied stimuli differently. This feature can be used to provide a unique fingerprint of the electronic device, or as an interesting alternative to classic key storage methods. Due to their nature, they are often considered an element of the Internet of Things nodes. However, their application heavily depends on resource consumption. Lightweight architectures are proposed in the literature but are technology-dependent or still introduce significant hardware overhead. This paper presents a lightweight, Strong PUF based on ring oscillator architecture, which offers small hardware overhead and sufficient security levels for resource-constrained Internet of Things devices. The PUF design utilizes a Linear Feedback Shift Register-based scramble module to generate many challenge–response pairs from a small number of ring oscillators and a control module to manage the response generation process. The proposed PUF can be used as a Weak PUF for key generation or a Strong PUF for device authentication. Full article

Encapsulation is a critical factor in object-oriented programming languages and design patterns. Nevertheless, programs written in languages like Java may encounter broken encapsulation due to the lack of sufficient supply for ownership and immutability. As a result, this paper introduces SlimeJava, an ownership system extension based on abstract syntax trees and annotation utilization of Java that aims to help programmers prevent representation exposure. We show the features of the proposal with a motivating example using the Memento pattern. We then discuss how the utilization of annotations realizes ownership and why it is effective in avoiding representation exposure issues by comparing it with existing approaches. In the end, a quantitative performance evaluation was conducted to prove that SlimeJava does not cause a substantial overhead in execution time compared to native Java. Full article

This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author’s device, created by the “Łukasiewicz” Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device. Full article

Artificial intelligence (AI) is one of the most promising technologies based on machine learning algorithms. In this paper, we propose a workflow for the implementation of deep neural networks. This workflow attempts to combine the flexibility of high-level compilers (HLS)-based networks with the architectural control features of hardware description languages (HDL)-based flows. The architecture consists of a convolutional neural network, SqueezeNet v1.1, and a hard processor system (HPS) that coexists with acceleration hardware to be designed. This methodology allows us to compare solutions based solely on software (PyTorch 1.13.1) and propose heterogeneous inference solutions, taking advantage of the best options within the software and hardware flow. The proposed workflow is implemented on a low-cost field programmable gate array system-on-chip (FPGA SOC) platform, specifically the DE10-Nano development board. We have provided systolic architectural solutions written in OpenCL that are highly flexible and easily tunable to take full advantage of the resources of programmable devices and achieve superior energy efficiencies working with a 32-bit floating point. From a verification point of view, the proposed method is effective, since the reference models in all tests, both for the individual layers and the complete network, have been readily available using packages well known in the development, training, and inference of deep networks. Full article

This article investigates an adaptive tracking control problem for a six degrees of freedom (6-DOF) nonlinear quadrotor unmanned aerial vehicle (UAV) with a variable payload mass. The changing payload introduces time-varying parametric uncertainties into the dynamical model, rendering a static control strategy no longer effective. To handle this issue, two adaptive schemes are developed to maintain the uncertainties in the translational and rotational dynamics. Initially, a virtual proportional derivative (PD) is designed to stabilize the horizontal position; however, due to an unknown and time-varying mass, an adaptive controller is proposed to generate the total thrust of the UAV. Furthermore, an adaptive controller is designed for the rotational dynamics, to handle parametric uncertainties, such as inertia and external disturbance parameters. In both schemes, a standard adaptive scheme using the certainty equivalence principle is extended and designed. A stability analysis was conducted with rigorous analytical proofs to show the performance of our proposed controllers, and simulations were implemented to assess the performance against other existing methods. Tracking fitness and total control efforts were calculated and compared with closed-loop adaptive tracking control (CLATC) and adaptive sliding mode control (ASMC). The results indicated that the proposed design better maintained UAV stability. Full article