Electronics

At present, a prevalent approach to speaker diarization is clustering based on speaker embeddings. However, this method encounters two primary issues. Firstly, it cannot directly minimize the diarization error during the training process; secondly, the majority of clustering-based methods struggle to handle speaker overlap in audio. A viable approach for addressing these issues involves adopting end-to-end speaker diarization (EEND). Nevertheless, training this EEND system generally requires lengthy audio inputs, which must be downsampled to allow efficient model processing. In this study, we develop a novel downsampling layer using blueprint separable convolution (BSConv) instead of depthwise separable convolution (DSC) as the foundational convolutional unit, which effectively preserves information from the original audio. Furthermore, we incorporate multi-scale feature aggregation (MFA) into the encoder structure to combine the features extracted by each conformer block to the output layer, consequently enhancing the expressiveness of the model’s feature extraction. Lastly, we employ the conformer as the backbone network to incorporate the proposed enhancements, resulting in an EEND system named BSAC-EEND. We assess our suggested methodology on both simulated and real datasets. The experiment indicates that our proposed EEND system reduces diarization error rate (DER) by an average of 17.3% for two-speaker datasets and 12.8% for three-speaker datasets compared to the baseline. Full article

The existing discrete cosine transform (DCT) differential quantization robust watermarking has poor robustness against JPEG compression, cropping, and combined attacks. To improve such issues, a pair of adjacent block coefficients are selected to reduce the offset and improve the robustness of the watermarking. Firstly, at adjacent positions of neighboring blocks, the differences of medium frequency coefficients are calculated, and then the differences are used to divide regions. Experimental results show that this method is more robust to various attacks than the existing DCT differential quantization robust watermarking. The accuracy of watermark extraction under a JPEG compression attack increased by 2%, while the error rates of watermark extraction under a cropping attack and a combination attack decreased by 4.4% and 9%. Full article

This article presents the results of the investigations concerning the influence of the printed circuit board (PCB) layout design on self and transfer transient thermal impedances characterizing thermal phenomena occurring in the network containing two power MOSFETs. The tested devices have the case D²PAK and are soldered to the PCB using the surface mount technology (SMT). The measurement method is described. The tested transistors are presented with the used PCBs on which they are mounted. The obtained measurement results of the mentioned thermal parameters of the tested transistors operating on all the tested PCBs are shown and discussed. The influence of a cooling area of the tested PCBs on the parameters describing self and transfer transient thermal impedances is analyzed. Full article

In order to further improve the control effect of proportion integral differential (PID) control and linear quadratic regulator (LQC) control, and improve vehicle ride comfort and enhance body stability, the 7 DOF semi-active suspension model was established, and the fractional order PI^λD^μ-LQR controller was designed by combining fractional order PI^λD^μ control theory and LQR control theory. The semi-active suspension model in this paper is more complex, and there are many parameters in the controller. The optimal weighting coefficient of 12 vehicle smoothness evaluation indicators and parameters K_p, K_i, K_d, λ and μ in the controller were founded by NSGA-II algorithm. After optimization, the optimized parameters were brought into the controller for random pavement simulation. Compared to the traditional passive suspension, fractional order PI^λD^μ individual control and LQR separate control, the simulation results show that the effect of fractional order PI^λD^μ-LQR control is very significant. The evaluation index of vehicle smoothness has been significantly improved, and the use of fractional order PI^λD^μ-LQR control has significantly improved the working performance of the suspension and improved the smoothness of the vehicle. At the same time, the adjusting force output of the actuator is very balanced, which inhibits the roll of the body and improves the anti-roll performance. After simulation, the excellent performance of the designed fractional PI^λD^μ-LQR controller was verified, and the introduced NSGA-II algorithm played an important role in the controller parameter tuning work, which shows that the fractional order PI^λD^μ-LQR controller and NSGA-II algorithm cooperate with each other to achieve good control effects. Full article

Modular multilevel converters (MMCs) represent the forefront of power converter technology, with wide-ranging applications across diverse industries. Over recent decades, substantial research and development efforts have been dedicated to enhancing MMCs’ performance. A significant challenge in power conversion processes is the presence of total harmonic distortion (THD) in output waveforms, which can have adverse effects on electrical equipment. In response, extensive studies have been conducted to address THD-related challenges by refining the control and operation of MMCs. This study investigates the effect of the amplitude modulation index (Ma) on the total harmonic distortion (THD) in nine-level MMC output waves. For this, a standard three-phase and nine-level MMC model was built and simulated in MATLAB/ Simulink environment, and the Ma value was shifted between 0.1 and 1.5. The output current and voltage waves were analyzed, and the optimal limits for the Ma values yielding the lowest THD values were determined. The simulation outcomes reveal a crucial Ma range between 0.6 and 1.2, where THD is significantly minimized. Ma values below 0.6 introduce significant harmonic distortion in the voltage waves, while values surpassing 1.2 lead to appreciable harmonic distortion in the current wave. This study contributes valuable insights for engineers and researchers and aids in the refinement of MMC control strategies and the mitigation of THD-related challenges in power systems. Full article

This paper focuses on the real-time implementation of an optimal high-performance control applied to an interleaved nonisolated DC/DC converter designed for fuel cell applications. Three-phase interleaved boost converters are utilized to minimize input current undulation, increase efficiency, and provide a high output voltage in order to ensure the performance of the FC stack. The proposed control strategy contains an outer loop that generates the reference current based on a two-degree-of-freedom PID controller. This controller provides a robust setpoint tracking and disturbance rejection, which improves the system’s response and efficiency. A fast inner regulation loop based on a super-twisting integral sliding mode (STISM) algorithm is developed to achieve a fixed converter output voltage, equitable phase current sharing, and fast regulation against load disturbances in failure operation. The STISM algorithm exhibits a rapid convergence property of the sliding mode and effectively avoids the chattering phenomena frequently observed in conventional sliding modes. The proposed controller’s gains are determined using the atom search optimization algorithm, which ensures exceptional reliability and a high degree of robustness and stability of the controllers under a variety of operational conditions. This method is inspired from the behavior of atoms and their electrons during the excitation process leading to a one-of-a-kind optimization technique which contributes to the controller’s reliability. Using Matlab-Simulink simulation tools, the efficacy and performance of the designed control have first been evaluated and assessed and compared with other optimization algorithms, and then with a dual loop based on a PID controller. Then, they have been verified by real-time hardware implementation on a 1.2 KW prototype FC converter driven by the dSPACE-1104 card under a variety of tests. The suggested approach offers impressive experimental results in dynamic and steady states. Full article

This publication is aimed at developing a concept of an innovative system for dimensioning and predicting changes in the coastal zone topography using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The 4DBatMap system will consist of four components: 1. Measurement data acquisition module. Bathymetric and photogrammetric measurements will be carried out with a specific frequency in the coastal zone using a UAV equipped with a Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and a photogrammetric camera, as well as a USV equipped with a GNSS Real Time Kinematic (RTK) receiver and a MultiBeam EchoSounder (MBES). 2. Multi-sensor geospatial data fusion module. Low-altitude aerial imagery, hydrographic and LiDAR data acquired using UAVs and USVs will be integrated into one. The result will be an accurate and fully covered with measurements terrain of the coastal zone. 3. Module for predicting changes in the coastal zone topography. As part of this module, a computer application will be created, which, based on the analysis of a time series, will determine the optimal method for describing the spatial and temporal variability (long-term trend and seasonal fluctuations) of the coastal zone terrain. 4. Module for imaging changes in the coastal zone topography. The final result of the 4DBatMap system will be a 4D bathymetric chart to illustrate how the coastal zone topography changes over time. Full article

Facial attribute editing refers to the task of modifying facial images by altering specific target facial attributes. Existing approaches typically rely on the combination of generative adversarial networks and encoder–decoder architectures to tackle this problem. However, current methods may exhibit limited accuracy when dealing with certain attributes. The primary objective of this research is to enhance facial image modification based on user-specified target facial attributes, such as hair color, beard removal, or gender transformation. During the editing process, it is crucial to selectively modify only the regions relevant to the target attributes while preserving the details of other unrelated facial attributes. This ensures that the editing results appear more natural and realistic. This study introduces a novel approach called MAGAN (Combining GRU Structure and Additive Attention with AGU—Adaptive Gated Units). Moreover, a discriminative attention mechanism is introduced to automatically identify key regions in the input images that are relevant to facial attributes. This mechanism concentrates attention on these regions, enhancing the model’s ability to accurately capture and analyze subtle facial attribute features. The method incorporates external attention within the convolutional layers of the encoder–decoder architecture, facilitating the modeling of linear complexity across image regions and implicitly considering correlations among all data samples. By employing discriminative attention in the discriminator, the model achieves more precise attribute editing. To evaluate the effectiveness of MAGAN, experiments were conducted on the CelebA dataset. The average precision of facial attribute generation in images edited by our model stands at 91.83%. PSNR and SSIM for reconstructed images are 32.52 and 0.957, respectively. In comparison with existing methodologies (AttGAN, STGAN, MUGAN), noteworthy enhancements have been achieved in the domain of facial attribute manipulation. Full article

The use of resource-constrained devices is rising nowadays, and these devices mostly operate with sensitive data. Consequently, security is a key issue for these devices. In this paper, we propose a compact ECC (elliptic curve cryptography) architecture for resource-constrained devices based on López–Dahab (LD) projective point arithmetic operations on GF(2^m). To achieve an efficient area-power hardware ECC implementation, an efficient digit-serial multiplier is developed. The proposed multiplier is built on a Bivariate Polynomial Basis representation and a modified Radix-n Interleaved Multiplication (mRnIM) method (for area and power complexities reduction). Furthermore, the LD-Montgomery point multiplication algorithm is adjusted for accurate scheduling in the compact ECC architecture to eliminate data reliance and improve signal management. Meanwhile, the area complexity is reduced by reuse of resources, and clock gating and asynchronous counter are exploited to reduce the power consumption. Finally, the proposed compact ECC architecture is implemented over GF(2^m) (m = 163, 233, 283, 409, and 571) on Xilinx FPGAs’ (Field-Programmable Gate Array) Virtex 5, Virtex 6, and Virtex 7, showing that the efficiency of this design outperforms to date when compared to reported works individually. It utilizes less area and consumes low power. The FPGA results clearly demonstrate that the proposed ECC architecture is appropriate for constraint-resources devices. Full article

The low-frequency narrowband components (known as lines) in the radiated noise of underwater acoustic targets are an important feature of passive sonar detection. Conventional adaptive line enhancer (ALE) based on the least mean square algorithm has limited performance under colored background noise and low signal-to-noise ratio (SNR). In this paper, by combining the frequency domain sparse model of lines and maximum correntropy criterion (MCC), a β-adaptive l₀-MCC-ALE is proposed to solve the above-mentioned problem. The proposed ALE uses a sparse-driven MCC algorithm to update the weight vector in the frequency domain to further suppress the colored background noise. For the problem that the value of parameter β is sensitive to the performance, β is updated adaptively according to the frequency response of ALE in each iteration. Simulation and real data processing results show that the proposed ALE is insensitive to the given parameter β and has excellent performance for line enhancement. Compared with conventional ALE, the SNR of lines can be improved by 7~8 dB. Full article

Radio frequency (RF) fingerprints have been an emerging research topic for the last decade. Numerous algorithms for recognition have been proposed. However, very few algorithms for the accurate extraction of IQI and PA nonlinearity are available, especially when multiple paths are considered. In this study, we present a scheme that uses the transmitter in-phase/quadrature-phase imbalance (IQI) and the power amplifier (PA) nonlinearity as RF fingerprint features in time-division duplexing (TDD) OFDM systems, which are always considered to be harmful to data transmission. The scheme consists of two round trips with four steps for two cases: in the first, the IQI and PA nonlinearity are unknown at the terminal; in the second, they are known at the terminal. A channel state information (CSI)-tracking algorithm based on the sliding-window least squares method is first adopted at the terminal. In case A, the obtained CSI is sent to the base station (BS) to remove its impact there; in case B, this removal is conducted directly by using pre-equalization at the terminal. Then, by following a sequential iterative approach, the IQI and nonlinearity are individually calculated. Theoretical analyses reveal how CSI estimation errors influence subsequent estimates at the BS in these two cases. Furthermore, the approximate unbiasedness is verified. The theoretical variance and Cramer–Rao lower bound (CRLB) are also given. It is indicated that the theoretical minimum variance in case B is lower than that in case A from the perspective of the CRLB. The numerical results demonstrate the efficiency of the scheme in comparison with existing techniques in the literature. Full article

In electromagnetic compatibility (EMC) testing, accurately extracting the radiation emission characteristics from the Equipment Under Test (EUT) in complex electromagnetic environments remains a daunting task. This paper presents a solution by introducing of a frequency-domain electromagnetic interference cancellation method based on the Adaptive Singular Envelope Iterative Variational Mode Decomposition (ASEI-VMD). The process begins with the application of the Adjacent Singular Envelope Entropy Ratio (ASEER) as an evaluation metric to adaptively discern the number of decomposition layers and the penalty factor, allowing the effective decomposition of signals from various channels into modal signals sharing the same center frequency and bandwidth. The singular envelope entropy of each VMD mode is computed as the basis for assessing the efficacy of components in the VMD. This step distinguishes the signals encapsulating the EUT frequency information, which are then isolated for cancellation to retrieve the actual EUT-radiated emission signal. Simulation and experimental data validate the efficacy of this approach. Uniquely, it neither places demands on the testing site nor requires prior conditions for the EUT. The retrieved signal exhibits a correlation of over 96% with the source signal, maintaining a signal-power error below 3 dB. It is suitable for conducting electromagnetic radiation emission testing in complex environments. Full article

Image composition involves the placement of foreground objects at an appropriate scale within a background image to create a visually realistic composite image. However, manual operations for this task are time-consuming and labor-intensive. In this study, we propose an efficient method for foreground object placement, comprising a background feature extraction module (BFEM) designed for background images and a foreground–background cross-attention feature fusion module (FBCAFFM). The BFEM is capable of extracting precise and comprehensive information from the background image. The fused features enable the network to learn additional information related to foreground–background matching, aiding in the prediction of foreground object placement and size. Our experiments are conducted using the publicly available object placement assessment (OPA) dataset. Both quantitative and visual results demonstrate that FTOPNet effectively performs the foreground object placement task and offers a practical solution for image composition tasks. Full article

Electric vehicles (EVs) have had a meteoric rise in acceptance in recent decades due to mounting worries about greenhouse gas emissions, global warming, and the depletion of fossil resource supplies because of their superior efficiency and performance. EVs have now gained widespread acceptance in the automobile industry as the most viable alternative for decreasing CO₂ production. The battery is an integral ingredient of electric vehicles, and the battery management system (BMS) acts as a bridge between them. The goal of this work is to give a brief review of certain key BMS technologies, including state estimation, aging characterization methodologies, and the aging process. The consequences of battery aging limit its capacity and arise whether the battery is used or not, which is a significant downside in real-world operation. That is why this paper presents a wide range of recent research on Li-ion battery aging processes, including estimations from multiple areas. Afterward, various battery state indicators are thoroughly explained. This work will assist in defining new relevant domains and constructing commercial models and play a critical role in future research in this expanding area by providing a clear picture of the present status of estimating techniques of the major state indicators of Li-ion batteries. Full article

Modeling the interactions among individuals in a group is essential for group activity recognition (GAR). Various graph neural networks (GNNs) are regarded as popular modeling methods for GAR, as they can characterize the interaction among individuals at a low computational cost. The performance of the current GNN-based modeling methods is affected by two factors. Firstly, their local receptive field in the mapping layer limits their ability to characterize the global interactions among individuals in spatial–temporal dimensions. Secondly, GNN-based GAR methods do not have an efficient mechanism to use global activity consistency and individual action consistency. In this paper, we argue that the global interactions among individuals, as well as the constraints of global activity and individual action consistencies, are critical to group activity recognition. We propose new convolutional operations to capture the interactions among individuals from a global perspective. We use contrastive learning to maximize the global activity consistency and individual action consistency for more efficient recognition. Comprehensive experiments show that our method achieved better GAR performance than the state-of-the-art methods on two popular GAR benchmark datasets. Full article

Owning to the application of artificial intelligence and big data analysis in industry, automobiles, aerospace, and other fields, the high-bandwidth candidate, time-sensitive networking (TSN), is introduced into the data communication network. Apart from keeping the safety-critical and real-time requirements, it faces challenges to satisfy large traffic transmission, such as sampled video for computer vision. In this paper, we consider task scheduling and time-sensitive network together and formalize them into a first-order-constraints satisfy module theory (SMT) problem. Based on the result of the solver, we build flow-level scheduling based on IEEE 802.1 Qbv. By splitting the flow properly, it can reduce the constraint inequality as the traffic grows more than the traditional frame-based programming model and achieve near 100% utilization. It can be a general model for the deterministic task and network scheduling design. Full article

This paper proposes a concept of battery-assisted battery charger with maximum power point tracking for DC energy transducer such as thermoelectric generator and photo voltaic generator, and shows experimental results to prove the concept. The DC energy transducer is connected in series with a battery to increase the voltage. The plus terminal for the DC energy transducer is connected with the input terminal of a DC-DC buck converter, whereas the battery is connected with the output terminal of the converter. Thus, the current is boosted from the input to the output. When the net current to the battery is positive, the system works as a battery charger. To extract the as much power from the DC energy transducer as possible for high charging efficiency, maximum power point tracking is introduced. The converter was designed in 180 nm 3V CMOS with a silicon area of 1.05 mm². The concept was experimentally proven by varying the reference voltages to control the input voltage. An all-solid-state battery was charged up from 2.2 V to 2.3 V in two hours by the converter with a flexible thermoelectric generator which had an open-circuit voltage of 0.6 V. Full article

Time-sensitive networks enable the high-quality mixed transmission of various types of business flows. However, the Time-Aware Scheduler mechanism fails to address the issue of interference in data flows with the same priority. This paper conducts an in-depth analysis of the store-and-forward mechanism in switches, combining it with the characteristics of critical GOOSE and SV-type flows in substations. By introducing methods such as setting offsets and allocating redundant time slots to the data flow of the transmitter in the TAS scheduling mechanism, all factors that cause conflicting interference to the data flow transmission in the TSN network are solved, and the uncertain queuing delay is eliminated. The proposed scheduling algorithm, compared to the TAS scheduling algorithm of the FIFO rule, achieves a maximum reduction of 34.35% in the transmission delay of critical business flows, while the jitter is controlled below 10 μs. Compared to the strict priority algorithm, it reduces the transmission delay by 40.26% while maintaining the standard deviation of delay within 1.59%. The maximum transmission delay and the minimum transmission delay of the data flow scheduled in this paper are between the theoretical boundary values without queuing delay, which satisfies the deterministic transmission of critical business flows under high load conditions, and provides support for future substation integrated networking and high load applications. Full article

A double-edge-triggered digital low dropout regulator (DLDO) is proposed with a built-in adaptive voltage-controlled oscillator (VCO) clock (AVC) for a system-on-chip (SoC) application. To achieve a fast transient response, the main comparator generates the comparison result at the rising edge of the AVC, and this result is sampled by the coarse or fine bidirectional shifter register at the falling edge of the AVC. Furthermore, the clock frequency can be boosted from 8 MHz at the steady state to 50 MHz by the AVC when the output current suffers from a sudden change, and it can also be adjusted in real-time according to the output voltage, which avoids the oscillation phenomenon and decreases the power consumption during the recovery process. To further lower the power consumption, the self-clock comparator replaces the conventional static comparator in the transient detector. The post-simulation results show that the proposed DLDO consumes a quiescent current of 95.13 μA in the steady state, and drives a maximum load current of 25 mA at the supply power of 0.6 V with an active area of 0.053-mm² in a 180 nm CMOS process. When the load current jumps from 0.5 mA to 25 mA at the edge of 100 ps, the undershoot voltage and overshoot voltage are only 335 mV with the recovery time of 2.7 μs and 47.6 mV with the recovery time of 2.1 μs at the total on-chip capacitor of 50 pF, respectively, resulting in two competitive figures of merits (FoMs) than the previous works. Full article