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Abstract This paper proposes a power scaling (PS) technique aimed at mitigating the outage floor problem commonly encountered in multi-user full-duplex (FD) non-orthogonal multiple access (NOMA) systems. Two antenna modes denominated as adaptive antenna mode (AAM) and fixed antenna mode (FAM) are utilized in this work for L FD near users in close proximity. In addition, a combined method of selecting both antenna mode and near user integrated with PS is employed to improve the overall network performance. Moreover, a power allocation between the chosen near user and far user is considered. In the low-to-moderate power regions, by AAM and FAM, we achieve twice of full diversity gain and full diversity gain, respectively. The research presents mathematical expressions for deriving the average capacity and outage probability and supports the theoretical findings with simulation-based evidence. The results of this work show that PS method not only contributes to a greater spatial diversity but also leads to superior performance compared to traditional FD NOMA systems. Moreover, our proposed method overcomes the outage floor and capacity ceiling. Furthermore, the work can be developed to 6G massive MIMO technology in multi-user FD cooperative NOMA systems.

Abstract Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

Abstract Aiming at the problem that face images are easily interfered by occlusion factors in uncontrollable environments, and the complex structure of traditional convolutional neural networks leads to low expression recognition rates, slow network convergence speed, and long network training time, an improved lightweight convolutional neural network is proposed for facial expression recognition algorithm. First, the dilation convolution is introduced into the shortcut connection of the inverted residual structure in the MobileNetV3 network to expand the receptive field of the convolution kernel and reduce the loss of expression features. Then, the channel attention mechanism SENet in the network is replaced by the two-dimensional (channel and spatial) attention mechanism SimAM introduced without parameters to reduce the network parameters. Finally, in the normalization operation, the Batch Normalization of the backbone network is replaced with Group Normalization, which is stable at various batch sizes, to reduce errors caused by processing small batches of data. Experimental results on RaFD, FER2013, and FER2013Plus face expression data sets show that the network reduces the training times while maintaining network accuracy, improves network convergence speed, and has good convergence effects.

Abstract Flexible organic light-emitting diodes (OLEDs) are promising light sources for biomedical applications. However, the use of these flexible devices has been restricted by their short shelf lifetimes due to poor ambient stability. Here, the fabrication of a long-lived flexible OLED is reported by replacing air-sensitive metals such as aluminum, and alkali metals used as n dopants, with silver. In addition, to achieve stable and efficient flexible OLEDs we tuned the optical cavity length to the second-order interference maximum. The device design has simple encapsulation and leads to an improvement in the air stability of flexible OLEDs which show a shelf lifetime of greater than 130 days whereas the conventional structure exhibits degradation after only 12 days. The proposed design for making flexible OLEDs demonstrates a great potential for using the devices for wearable bioelectronic applications.

Abstract Fabricating high-conductive composites and constructing highly conductive networks are crucial for high-performance electrothermal film. In this study, an Ag nanowires/graphene (Ag/G) composite synthesized by liquid-phase exfoliation and in-situ photoreduction is mixed with carbon black (CB) to form a composite conductive ink, and a CB/Ag/G composite electrothermal film with a point-line-plane three-dimensional microstructure is obtained via blade coating process. Both the addition of Ag nanowires and a subsequent compression rolling treatment induce the establishment of the effective conductive network in the film, endowing it with an outstanding conductivity of 399.4 S cm−1. The film reaches a T s of 204 °C with an input voltage of 3.0 V, and is successfully applied in water heating and de-icing, demonstrating its extraordinary electrothermal performance and vast potential for practical applications. The film is also used as an electromagnetic shielding film and heat dissipation substrate, showing exceptional electromagnetic shielding (42.5 dB) and heat dissipation properties.

Abstract The unconstrained partitioned-block frequency-domain adaptive filter (PBFDAF) offers superior computational efficiency over its constrained counterpart. However, the correlation matrix governing the natural modes of the unconstrained PBFDAF is not full rank. Consequently, the mean coefficient behavior of the algorithm depends on the initialization of adaptive coefficients and the Wiener solution is non-unique. To address the above problems, a new theoretical model for the deficient-length unconstrained PBFDAF is proposed by constructing a modified filter weight vector within a system identification framework. Specifically, we analyze the transient and steady-state convergence behavior. Our analysis reveals that modified weight vector is independent of its initialization in the steady state. The deficient-length unconstrained PBFDAF converges to a unique Wiener solution, which does not match the true impulse response of the unknown plant. However, the unconstrained PBFDAF can recover more coefficients of the parameter vector of the unknown system than the constrained PBFDAF in certain cases. Also, the modified filter coefficient yields better mean square deviation (MSD) performance than previously assumed. The presented alternative performance analysis provides new insight into convergence properties of the deficient-length unconstrained PBFDAF. Simulations validate the analysis based on the proposed theoretical model.

Abstract Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption.

A compact triple-band porous electromagnetic bandgap structure-loaded coplanar-waveguide-fed wearable antenna is introduced for applications of wireless body area networks. The porous structure is aimed to create a stopband or bandgap in the electromagnetic spectrum and increase breathability. The holes in the bottom electromagnetic bandgap surface increase the inductance, which in turn increases the bandwidth. The final design resonates at three bands with impedance bandwidths of 264 MHz, 100 MHz, and 153 MHz and maximum gains of 2.18 dBi, 6.75 dBi, and 9.50 dBi at 2.45 GHz, 3.5 GHz, and 5.5 GHz, respectively. In addition, measurements indicate that the proposed design can be deformed up to certain curvature and withstand human tissue loading. Moreover, the specific absorption rate remains within safe levels for humans. Therefore, the proposed antenna can suitably operate in the industrial, scientific, and medical, Bluetooth, Wi-Fi, and WiMAX bands for potential application to wireless body area networks.

In this study, a planar printed circuit board (PCB) coil with FR4 substrate was designed and simulated using the finite element method, and the results were analyzed in the frequency domain. This coil can be used in wireless power transfer (WPT) as a transmitter or receiver, eliminating wires. It can also be used as the receiver in radio frequency energy-harvesting (RF-EH) systems by optimizing the planar PCB coil to convert radio-wave energy into electricity, and it can be employed as an excitation (transmitter) or receiver coil in nuclear magnetic resonance (NMR) spectroscopy. This PCB coil can replace the conventional coil, yielding a reduced occupied volume, a fine-tuned design, reduced weight, and increased efficiency. Based on the calculated gain, power, and electromagnetic and electric field results, this planar PCB coil can be implemented in WPT, NMR spectroscopy, and RF-EH devices with minor changes. In applications such as NMR spectroscopy, it can be used as a transceiver planar PCB coil. In this design, at frequencies of 915 MHz and 40 MHz with 5 mm between coils, we received powers of 287.3 μW and 480 μW, respectively, which are suitable for an NMR coil or RF-EH system.

This paper presents methods for reducing the peak-to-average power ratio (PAPR) of the orthogonal time frequency space (OTFS) signal. These methods mainly consist of two operations: symbol interferometry (SI) and either μ-law or A-law companding. SI spreads the data of one OTFS symbol onto all symbols and is implemented using a simple inverse fast Fourier transform operation on each OTFS symbol. During the second operation, the PAPR of the OTFS signal is significantly reduced. For our performance analysis, the complementary cumulative distribution function, probability density function, and bit error rate are illustrated through simulations performed in MATLAB. The performance is also analyzed using a solid-state power amplifier at the transmitter and compared with OTFS, μ-law-based OTFS, and SI OTFS systems. The results indicate that the proposed OTFS system achieves a low PAPR.

In wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

Medical signal processing requires noise and interference-free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two-level transpose Vedic multiplier. The proposed architecture identifies the zero-noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero-noise segregation. This is performed independently for the zero- and nonzero-noise intervals. Finally, the finite impulse response is estimated as the sum of zero- and nonzero-noise inputs at any finite classification.

In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60,000 customer reviews with 14,806 neutral reviews, 19,567 negative reviews, and 25,627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency-inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

The performance of face recognition (FR) has reached a plateau for publicbenchmark datasets, such as labeled faces in the wild (LFW), celebrities in frontal-profile in the wild (CFP-FP), and the first manually collected, inthe-wild age database (AgeDB), owing to the rapid advances in convolutional neural networks (CNNs). However, the effects of faces under various finegrained conditions on FR models have not been investigated, owing to the absence of relevant datasets. This paper analyzes their effects under different conditions and loss functions using K-FACE, a recently introduced FR dataset with fine-grained conditions. We propose a novel loss function called MixFace, which combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness was experimentally demonstrated using various benchmark datasets.

Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short-term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1-score. The use of ResNet50V2 for feature extraction can improve model performance.

The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video-based point-cloud compression in the range of 300-700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real-time point-cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal-to-noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7-5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low-complexity requirements are required in lightweight device environments.

Music identification is widely regarded as a solved problem for music searching in quiet environments, but its performance tends to degrade in TV broadcast audio owing to the presence of dialogue or sound effects. In addition, constructing an accurate dataset for measuring the performance of background music monitoring in TV broadcast audio is challenging. We propose a framework for monitoring background music by automatic identification and introduce a background music cue sheet. The framework comprises three main components: music identification, music-speech separation, and music detection. In addition, we introduce the Cue-K-Drama dataset, which includes reference songs, audio tracks from 60 episodes of five Korean TV drama series, and corresponding cue sheets that provide the start and end timestamps of background music. Experimental results on the constructed and existing datasets demonstrate that the proposed framework, which incorporates music identification with music–speech separation and music detection, effectively enhances TV broadcast audio monitoring.

We propose a 10-GHz 2 2 phased-array radio frequency (RF) receiver with an 8-bit linear phase and 15-dB gain control range using 65-nm complementary metal-oxide-semiconductor technology. An 8 X 8 phased-array receiver module is implemented using 16 2 X 2 RF phased-array integrated circuits. The receiver chip has four single-to-differential low-noise amplifier and gaincontrolled phase-shifter (GCPS) channels, four channel combiners, and a 50-Ω driver. Using a novel complementary bias technique in a phase-shifting core circuit and an equivalent resistance-controlled resistor-inductor-capacitor load, the GCPS based on vector-sum structure increases the phase resolution with weighting-factor controllability, enabling the vector-sum phase-shifting circuit to require a low current and small area due to its small 1.2-V supply. The 2 X 2 phased-array RF receiver chip has a power gain of 21 dB per channel and a 5.7-dB maximum single-channel noise-figure gain. The chip shows 8-bit phase states with a 2.39 degree root mean-square (RMS) phase error and a 0.4-dB RMS gain error with a 15-dB gain control range for a 2.5 degree RMS phaseerror over the 10 to 10.5-GHz band.

An ultrawide stopband lowpass filter is reported using three stepped impedance resonators with high selectivity. The filter extends the stopband frequency range and attenuation, and two quarter-wave open stubs and three circular ground slots are introduced. The lumped and distributed equivalent models are derived and analyzed. The corresponding results are validated experimentally in a fabricated prototype. The prototype lowpass filter has a 3 dB cutoff frequency (fc) of 2.9 GHz, and the stopband is extended up to 35 GHz (12.07fc), with an attenuation level better than 20 dB throughout. The passband-to-stopband transition (3 dB-20 dB) bandwidth is 0.18 GHz, and the roll-off factor is 135 dB/GHz at 30 dB. The insertion loss is 0.3 dB at 1.6 GHz. The normalized circuit size of the proposed filter with respect to the guided wavelength is 0.04.

Telecommunications through an electrically conductive medium require the use of carrier bands with very-low and ultralow frequencies to establish radiofrequency links in harsh environments. Recent advances in atomic magnetometers operating at very-low frequencies have facilitated the reception of digitally modulated signals. We demonstrate the transmission and reception of quadrature phase-shift keying (QPSK) signals using a multi-resonant loop antenna and atomic magnetometer, respectively. We report the measured error vector magnitude according to the symbol rate for QPSK modulation and analyze the bandwidth of a receiver based on the atomic magnetometer. The multi-resonant loop antenna noticeably enhances the bandwidth by over 70% compared with a single-loop antenna. QPSK modulation for a carrier frequency of 20 kHz and symbol rate of 150 symbols per second verifies the feasibility of demodulation, and the measured error vector magnitude and signal-to-noise ratio are 7.29% and 30.9 dB, respectively.

Abstract In this study, a novel nine‐level inverter using one voltage source, 10 unidirectional and one bidirectional power switches, and two capacitors has been proposed to utilize in ground power units (GPUs). Selective harmonic elimination method has been applied to reduce the output voltage's THD to less than 3% with 115/200 V and 400 Hz. The proposed converter utilized a lower number of devices to output a nine‐level staircase in comparison to existing converters. Also, the proposed converter uses inherent self‐voltage balancing for capacitors' voltage. So, the control algorithm gets simpler. In this study, the topology analysis, modulation algorithm, capacitor calculation, loss, efficiency, and performance analysis of the proposed topology have been presented. The proposed circuit has been compared to recently published papers in terms of efficiency, switch, capacitor, diode, and source numbers. The theoretical and experimental performance of the topology has been verified by simulation on PSCAD and PSIM software and 350 W experimental setup.

Abstract This paper proposes a predictive power control strategy for the three‐phase, six‐switch Vienna rectifier without grid voltage sensors to reduce the hardware cost and complexity of a high‐power PWM rectifier system. Firstly, an algorithm for calculating the AC‐side voltage in the αβ coordinate system is derived according to the operating principle of the Vienna rectifier, and a voltage observer is constructed by combining a second‐order low‐pass filter to estimate the grid voltage. Secondly, a soft start method is designed to solve the problem that the rectifier is prone to inrush current when it is started. Furthermore, the control method of grid voltage sensorless is combined with predictive power control with good dynamic characteristics and simple parameter settings to form the control strategy proposed in this paper. Finally, simulation analysis and experimental verification are carried out on the proposed control strategy. Simulation and experimental results show that the grid voltage estimation has high accuracy, a good surge current suppression effect, unit power factor operation, low input current harmonic content, and good dynamic and steady‐state performance. Therefore, the correctness and effectiveness of the strategy proposed in this paper are verified.

Abstract Power quality is a critical issue in power systems that can affect the performance and reliability of sensitive equipment. The dynamic voltage restorer (DVR) is a widely used technology for improving power quality, but it mainly focuses on mitigating voltage sags and often neglects the issue of harmonic distortion. This paper proposes a DVR voltage compensation strategy based on the discrete Fourier transform (DFT). This strategy utilizes the DFT to analyze the voltage waveform, separating the fundamental and desired harmonic components. With this strategy, the DVR can effectively compensate for voltage sag and achieve selective harmonic compensation, thereby improving the power quality of the power system. Additionally, a method utilizing hardware circuitry and an LED as a timestamp is proposed to dynamically estimate digital siginal processor (DSP) resource occupancy rate in real‐time, ensuring effective compensation while minimizing resource occupancy. A 3 kW DVR experimental prototype was constructed and tested to evaluate the practicality of the proposed strategy. The simulation and experimental results show that the method proposed in this paper successfully realizes the compensation of voltage sag and harmonics in the power system, and improves the stability and reliability of the power system.

Abstract The droop control strategy is popularly employed in DC microgrids. However, its virtual resistance will cause voltage deviation and reduce transient response. The DOB‐based method is proven to improve transient response in literature. However, it is analyzed in this study that this method will negatively influence the current sharing when employed in droop control. A weakened disturbance observation (WDOB) is proposed in this work to improve the drawbacks. To employ the proposed method, the equivalent models of the droop controller and the physical system are separately established, and several transformations are conducted. An auxiliary compensation is added and the current loop is considered as a whole to be transmitted into the control plant, making the traditional DOB method successfully adopted. It is obvious that the dynamic performance is improved, but it disabled virtual resistance in the steady‐state. And the current sharing cannot be achieved in a multi‐converter parallel system. The reason for this problem is analyzed from the control process and transfer function, and the WDOB solution is finally proposed. Through the proposed method, both aims of improving dynamic response and current sharing can be achieved, and the steady voltage deviation is much less than that of the traditional droop controller.

Abstract The study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

Abstract In order for full utilization of the switching devices and safe continued operation of the power converter at the same time, the junction temperature of the switching devices needs to be accurately monitored without shutting down the motor drive. This paper derives an analytical junction temperature calculation method by using Laplace transformation and thermal network theory. Based on an actual motor drive, a simulation model is established using platform for power electronic systems (PLECS). Through comparison, the derived method is proven to be able to calculate junction temperature accurately.

Abstract This paper proposes an extended DC‐DC converter with high voltage conversion ratio and soft‐switching ability. The proposed converter has active switched‐inductors, switched‐capacitors included in the conventional high gain converter and operates in continuous conduction mode (CCM). Simple auxiliary resonant elements are added on the primary leg of the converter to provide design freedom for soft‐switching operation. The significant merits of the proposed converter are lesser voltage and current stresses, high voltage gain with reduced component count and better efficiency. Additional feature of this converter is soft‐switching operation under different load conditions and duty ratios without considerably increasing stresses. The zero voltage switching (ZVS) turn‐on operation is obtained for all switching devices. Therefore, switching power losses are minimized greatly. This paper presents the description, principles of operation and steady state analysis in comparison with the existing high gain converters. The theoretical analysis is verified with a 350 W prototype operated at input is 20 V and output is 220 V. The overall efficiency achieved is 96.7%. The obtained results confirm ZVZCS operation at full load.

Abstract A distributed ledger is a shared and synchronized database across multiple designated nodes, often referred to as miners, validators, or peers. These nodes record, distribute, and access data to ensure security and transparency. However, these nodes can be compromised and manipulated by selectively choosing which user transactions to include, exclude, or reorder, thereby gaining an unfair advantage. This is known as a miner/maximal extractable value (MEV) attack. Existing solutions can be classified into various categories, such as MEV auction platforms and time-based ordering properties, which rely on private transaction Mempools. In this paper, we first identify some architectural weaknesses inherent in the latest proposals that divide the block creation and execution roles into separate functions: block builders and block executors. The existing schemes mainly suffer from the verifiability of the decryption process, where a corrupted builder or executor can simply deny the inclusion of specific targeted transactions by exploiting the fact that all transactions are in plain format. To address this, we propose an enhanced version that incorporates a verifiable decryption process. On a very high level, within our proposal, whenever an Executor or a Builder performs a decryption, the decrypted values must be broadcasted. This enables any entity in the network to publicly verify whether the decryption was executed correctly, thus preventing malicious behavior by either party from going undetected. We also define a new adversary model for MEV and conduct a comprehensive security analysis of our protocol against all kinds of potential adversaries related to MEV. Finally, we present the performance analysis of the proposed solution.

Abstract The realization and control of exchange bias (EB) are highly desirable for spintronic applications. All-oxide heterostructures comprised of ferromagnetic and antiferromagnetic/multiferroic oxides provide an ideal platform to enable the electric-field control of EB, promising for energy-efficient memory and logic devices. However, the low block temperature (TB) and small bias field (HEB) hinder further advances towards room-temperature applications. Here, we report an alternative approach to enhance the interface-induced EB by using ferrimagnetic double-perovskite with B-site cation ordering. In heterostructures comprised of double-perovskite Sr2FeReO6 (SFRO) and LaFeO3 (LFO), a high TB (about 250 K) and large HEB are observed, which is significantly larger than the counterparts with LFO and ferromagnetic oxides. Further analysis suggests that the cation-ordering and ferrimagnetic spin structure of the double-perovskite could contribute significantly to the enhanced exchanged bias when interfacing with G-type antiferromagnets. Our results open a new avenue for developing all-oxides heterostructures for future magnetic technologies.

Abstract Recently, MnTe was established as an altermagnetic material that hosts spin-polarized electronic bands as well as anomalous transport effects like the anomalous Hall effect. In addition to these effects arising from altermagnetism, MnTe also hosts other magnetoresistance effects. Here, we study the manipulation of the magnetic order by an applied magnetic field and its impact on the electrical resistivity. In particular, we establish which components of anisotropic magnetoresistance are present when the magnetic order is rotated within the hexagonal basal plane. Our experimental results, which are in agreement with our symmetry analysis of the magnetotransport components, showcase the existence of an anisotropic magnetoresistance linked to both the relative orientation of current and magnetic order, as well as crystal and magnetic order. Altermagnetism is manifested as a three-fold component in the transverse magnetoresistance which arises due to the anomalous Hall effect.

Abstract Spin-orbit torque (SOT), arising from spin-orbit coupling-induced spin currents, provides efficient control of magnetization. SOT characterization involving harmonic Hall resistances is typically done in low-current regimes, distinct from high-current regimes, where SOT-induced magnetization switching occurs. In this study, we investigate azimuthal angle (ϕ)-dependent harmonic Hall resistances of a Pt/yttrium iron garnet (YIG) layer across a wide range of measurement currents. Under low-current conditions, conventional ϕ-dependent Hall resistances are observed; the first harmonic Hall resistance exhibits sin 2ϕ behavior and the second harmonic Hall resistance comprises cos ϕ and cos 3ϕ terms. Interestingly, with increasing current, higher-order angular-dependent terms become non-negligible, referring to the sin 4ϕ and sin 6ϕ terms for the first harmonic and the cos 5ϕ and cos 7ϕ terms for the second harmonic Hall resistances. We attribute this unconventional angular dependence to the nonlinear response of magnetization direction to SOT, emphasizing its relevance to understanding the magnetization dynamics during SOT-induced switching under large currents.

Abstract In the last few years, research on the detection of AI-generated videos has focused exclusively on detecting facial manipulations known as deepfakes. Much less attention has been paid to the detection of artificial non-facial fake videos. In this paper, we address a new forensic task, namely, the detection of fake videos of human body reenactment. To this purpose, we consider videos generated by the “Everybody Dance Now” framework. To accomplish our task, we have constructed and released a novel dataset of fake videos of this kind, referred to as FakeDance dataset. Additionally, we propose two forgery detectors to study the detectability of FakeDance kind of videos. The first one exploits spatial–temporal clues of a given video by means of hand-crafted descriptors, whereas the second detector is an end-to-end detector based on Convolutional Neural Networks (CNNs) trained on purpose. Both detectors have their peculiarities and strengths, working well in different operative scenarios. We believe that our proposed dataset together with the two detectors will contribute to the research on the detection of non-facial fake videos generated by means of AI.

Abstract Reflecting intelligent surface technology (RIS) is regarded as a key enabler of the sixth-generation (6G) communication system. It provides the ability to reshape radio channels through passively reflecting beams in a reconstructive manner. Furthermore, aerial RIS (ARIS) introduces more flexibility in providing line-of-sight (LOS) links. Unfortunately, most of the related research efforts supposed the system as a planar RIS mounted on a satellite, unmanned aerial vehicle (UAV), or balloon despite reported limitations of planar RISs. The essential problem in designing any planar RIS network resides in mutual orientation and alignment difficulty, especially under random fluctuation of position/orientation due to wind conditions or UAV wobbling in the hover state. So, this paper highlights spherical RIS (bubble) as the optimal choice for aerial beam routing where the orientation/rotation can be completely relaxed. It outperforms planar RIS in terms of RIS networking flexibility, dead zone relaxation, and coverage extension. Consequently, due to the added degrees of freedom, many new deployment scenarios/use cases are recommended such as introducing meta-bubbles as intermediate gateways between satellite and ground nodes and extending network infrastructure installation down to the client level to enhance its visibility and throughput. Simulations demonstrate the superiority of meta-bubbles in minimizing channel loss over successive multi-hop routing.

Abstract 3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and processing, especially in real-time systems. Instead of decoding the compressed data stream and subsequently conducting downstream tasks on the decompressed data, analyzing point clouds directly in their compressed domain has attracted great interest. In this paper, we dive into the realm of compressed point cloud classification (CPCC), aiming to achieve high point cloud classification accuracy in a bitrate-saving way by ensuring the bit stream contains a high degree of representative information of the point cloud. Edge information is one of the most important and representative attributes of the point cloud because it can display the outlines or main shapes. However, extracting edge points or information from point cloud models is challenging due to their irregularity and sparsity. To address this challenge, we adopt an advanced edge-sampling method that enhances existing state-of-the-art (SOTA) point cloud edge-sampling techniques based on attention mechanisms and consequently develop a novel CPCC method “CPCC-PES” that focuses on point cloud’s edge information. The result obtained on the benchmark ModelNet40 dataset shows that our model has superior rate-accuracy trade-off performance than SOTA works. Specifically, our method achieves over 90% Top-1 Accuracy with a mere 0.08 bits-per-point (bpp), marking a remarkable over 96% reduction in BD-bitrate compared with specialized codecs. This means that our method only consumes 20% of the bitrate of other SOTA works while maintaining comparable accuracy. Furthermore, we propose a new evaluation metric named BD-Top-1 Accuracy to evaluate the trade-off performance between bitrate and Top-1 Accuracy for future CPCC research.

Abstract Accurate diagnosis of the stage of esophageal disorders is crucial in the treatment planning for patients with esophageal cancer and in improving the 5-year survival rate. The progression of esophageal cancer typically begins with precancerous esophageal disorders such as gastroesophageal reflux disease (GERD), esophagitis, and non-dysplasia Barrett’s esophagus, eventually advancing to low- and high-dysplasia Barrett’s esophagus and ultimately to esophageal adenocarcinoma (EAC). The majority of prior research efforts have primarily focused on the identification of general gastrointestinal (GI) tract diseases and the detection of esophageal cancer, with limited attention to the diverse spectrum of esophageal disorders. To address this gap, an innovative framework called Hybrid Model-Based Esophageal Disorder Diagnosis (HMEDD) is developed in this work. The primary goal of HMEDD is to enable early diagnosis of various esophageal disorders using gastroscopic images. HMEDD combines the feature extraction capabilities of an Esophageal Convolutional Neural Network (EsoNet) with the high classification accuracy of a Refined Logistic Regression (RLR) model. EsoNet comprises 14 weight layers and kernels $$\left(3\times 3\right)$$ 3 × 3 used for high-level deep feature learning. Esophageal disorders are classified using the RLR model, which is developed by fine-tuning hyperparameters in the traditional Logistic Regression (LR) model using Random Search Cross-Validation (RandomizedSearchCV). HMEDD is extensively validated using a data set containing numerous esophageal abnormalities captured through gastroscopic images. The results of this work demonstrate the effectiveness of HMEDD in accurately classifying different esophageal disorders, with an impressive accuracy of 92.15%. These findings will assist physicians in the accurate early diagnosis of esophageal disorders, ultimately preventing their progression to cancer.

Abstract Point clouds denote a prominent solution for the representation of 3D photo-realistic content in immersive applications. Similarly to other imaging modalities, quality predictions for point cloud contents are vital for a wide range of applications, enabling trade-off optimizations between data quality and data size in every processing step from acquisition to rendering. In this work, we focus on use cases that consider human end-users consuming point cloud contents and, hence, we concentrate on visual quality metrics. In particular, we propose a set of perceptually relevant descriptors based on principal component analysis (PCA) decomposition, which is applied to both geometry and texture data for full-reference point cloud quality assessment. Statistical features are derived from these descriptors to characterize local shape and appearance properties for both a reference and a distorted point cloud. The extracted statistical features are subsequently compared to provide corresponding predictions of visual quality for the distorted point cloud. As part of our method, a learning-based approach is proposed to fuse these individual predictors to a unified perceptual score. We validate the accuracy of the individual predictors, as well as the unified quality scores obtained after regression against subjectively annotated datasets, showing that our metric outperforms state-of-the-art solutions. Insights regarding design decisions are provided through exploratory studies, evaluating the performance of our metric under different parameter configurations, attribute domains, color spaces, and regression models. A software implementation of the proposed metric is made available at the following link: https://github.com/cwi-dis/pointpca .

Abstract This paper addresses the problem of 6D pose tracking of plane segments from point clouds acquired from a mobile camera. This is motivated by manual packing operations, where an opportunity exists to enhance performance, aiding operators with instructions based on augmented reality. The approach uses as input point clouds, by its advantages for extracting geometric information relevant to estimating the 6D pose of rigid objects. The proposed algorithm begins with a RANSAC fitting stage on the raw point cloud. It then implements strategies to compute the 2D size and 6D pose of plane segments from geometric analysis of the fitted point cloud. Redundant detections are combined using a new quality factor that predicts point cloud mapping density and allows the selection of the most accurate detection. The algorithm is designed for dynamic scenes, employing a novel particle concept in the point cloud space to track detections’ validity over time. A variant of the algorithm uses box size priors (available in most packing operations) to filter out irrelevant detections. The impact of this prior knowledge is evaluated through an experimental design that compares the performance of a plane segment tracking system, considering variations in the tracking algorithm and camera speed (onboard the packing operator). The tracking algorithm varies at two levels: algorithm ( $$A_{wpk}$$ A wpk ), which integrates prior knowledge of box sizes, and algorithm ( $$A_{woutpk}$$ A woutpk ), which assumes ignorance of box properties. Camera speed is evaluated at low and high speeds. Results indicate increments in the precision and F1-score associated with using the $$A_{wpk}$$ A wpk algorithm and consistent performance across both velocities. These results confirm the enhancement of the performance of a tracking system in a real-life and complex scenario by including previous knowledge of the elements in the scene. The proposed algorithm is limited to tracking plane segments of boxes fully supported on surfaces parallel to the ground plane and not stacked. Future works are proposed to include strategies to resolve this limitation.

Abstract Stretchable electronic circuits can seamlessly conform to irregular and dynamic surfaces with high integration. However, current stretchable configurations typically have limited stretchability due to the lack of robust connections between soft interconnects and rigid electronics. Here, we printed highly stretchable metal–polymer conductors on thermoplastic elastomers as interconnects. We developed electronic vests with porous surfaces for rigid electronics and introduced polyester hot-melt adhesives to strengthen connections between soft interconnects and rigid electronics. After thermal bonding, the adhesive penetrates the porous surface of electronic vests, creating a mechanical interlock and providing an adhesion force of 8.34 N/cm for the connection (3× higher than conductive adhesives). Thus, rigid electronics of different sizes and different pin counts can form strong connections to soft interconnects, achieving a maximum strain tolerance of ~700% (10× higher than conductive adhesives). We achieved highly integrated ultra-stretchable displays that can withstand stretching up to 220% without dead pixels.

Abstract In the inductive power transfer (IPT) system, using multiple coil branches instead of a whole large coil can effectively solve the thickness problem and save space. In that case, there are small parameter differences in mutual inductance and self‐inductance between the coil branches in parallel. These differences are amplified in high‐frequency IPT systems, resulting in different currents flowing through the different coil branches. The current unbalance will create many hazards. Therefore, this paper analyzes the key factors that cause current to unbalance and studies the equivalent circuit model of an inductive power transfer system with secondary multiple parallel branches. Moreover, this paper designs the compensation circuit and studies two kinds of current balance methods. Furthermore, the parameter design criteria are detailed to obtain a nearly ideal current balance effect. The characteristic analysis, parameter design, and verification are described in detail.

Abstract The presented work discusses issues related to the use of modern multiphase topologies of Dual Active Bridge (DAB)‐type converters. Converters of this type are widely used in most DC microgrid applications. The introduction emphasizes a comparative analysis between single‐phase and multi‐phase DAB topologies within high‐power DC microgrids, delving into their respective advantages, drawbacks, design procedures, and considerations based on the latest knowledge. The publication explores the comparison and selection of viable topologies for deployment in high‐power and high‐efficiency DC microgrids. The unified method of controlling 1‐phase and multi‐phase DAB converters was proposed in this design, simplifying the issues of DC microgrid control. All topologies were tested on the same controller concept. The study performs laboratory investigation of DAB 1‐phase and 3‐phase: Star–Star, and Star–Delta topologies. Attention was paid to maintaining uniform operating conditions of the system, contrary to studies known from the literature, all tests were carried out on the same laboratory stand and the same magnetic components in different configurations. Analytical and laboratory analyses of the Zero Voltage Switching (ZVS) region were performed, accounting for non‐linear phenomena. Based on these findings, an assessment of the system's performance in soft switching was carried out. The presented results were implemented in a simulation model and subsequently validated through tests on a constructed laboratory setup to ensure the proper operation of the system. This work meticulously presents and discusses variations in efficiency, dynamic response, phase current harmonic distribution, phase shift distribution, ZVS switching region, and more among the examined topologies. To ensure a fair comparison, the converter configuration for both simulation and laboratory models utilized identical components across all configurations.

Abstract In electric vehicles (EVs), the low voltage dc‐dc converter (LDC) with synchronous rectifier switches (SRs) can be used to reduce conduction loss caused by the high load current and improve the efficiency. Therefore, it is important to design a proper turn‐on time of SRs. As a popular driving strategy for the SRs, the drain–source voltage across the SRs is sensed to generate the gate driving signal. However, when LLC dc–dc converter operates at O mode, there will be a voltage ringing across the SRs. If the ringing voltage reaches to the threshold voltage −Vth,on, it would make SRs turn‐on early and results in abnormally working of the converter. The novelty and contribution of this paper lie in: (1) elaborating on the voltage ringing across the SRs, (2) the accurate ringing model is established during O mode, and (3) a simple, lossless, and low‐cost filter circuit is used to solve the negative effect of the ringing voltage for LLC converter at high load current. A 1.26 kW experimental prototype with ≈250–430 V wide input voltage and ≈9–16 V wide output voltage is implemented, 96.7% peak efficiency and 96% full load efficiency are achieved with a 3.12 kW/L power density. The theoretical analysis has been validated by the simulated and experimental results, and the SR LLC dc–dc converter with the SR works efficiently and reliably with the proposed solution at a high load current.

Abstract Due to galvanic isolation, bidirectional power transmission and extensive voltage adjustment, dual active bridge (DAB) exerts significant interface between renewable sources and electric vehicle (EV) storage port. However, for DAB operations, under light load or high voltage gain conditions, minimization of root‐mean‐square (RMS) current (MRMS) causes a restricted soft‐switching range. With variations of output power and operation modes, maintenance for all switches ZVS operation is difficult to maintain. Therefore, here, a segmented‐region optimum modulation (SROM) scheme based on extended‐phase‐shift (EPS) control is proposed to solve the aforementioned problems and solve complicated coupled relationship among different optimization methods. Firstly, based on voltage gain and transmission power, all EPS modes are divided clearly. Then, for switching loss reduction, fully‐power‐range ZVS (FZVS) is realized through transitions among EPS modes. Thirdly, either all semiconductors ZVS or not, most suitable optimization method is selected for different EPS regions by SROM. Further, to establish accurate boundaries among these optimization methods, relationship between inner phase‐shift ratio D1 and outer phase‐shift ratio D2 are quantified by unified parameter algorithm with unified parameter m. Therefore, for any EPS modes, efficiency is promoted comprehensively. A DAB‐based prototype is established and experimental results verify correctness and effectiveness of proposed method.

Abstract In this paper, a reduced‐switch‐count two‐phase interleaved LLC converter with precise current balancing is proposed by using switching‐control‐capacitor (SCC) technology. Compared with the conventional SCC‐LLC converter, the proposed two‐phase SCC‐LLC converter reduces the switch count with the SCC circuits, and achieves excellent current sharing under all the tolerance conditions between the two‐phase resonant elements. In the proposed converter, the SCC circuit is only used in the first phase. The design of the SCC circuit parameters is analyzed to reduce the voltage stress of the SCC circuit and to ensure that two‐phase currents are balancing with large tolerance of resonant elements. A 2.16‐kW GaN‐based two‐phase interleaved SCC‐LLC converter prototype is established for data center application, and the experimental results show that the 98.1% peak efficiency, the 97.6% full load efficiency, and higher than 96% efficiency at the wide range from 3‐A to 40‐A load current are achieved. The precise current sharing lower than 1% is achieved by using a cheap ceramic capacitor with high tolerance, which validates the performance of the converter.

Abstract Due to high computation complexity, traditional design methods for LLC converter usually consider limited kinds of performance, and the process for searching the optimal parameter scheme is discrete, which might cause the missing of real optimal solution. In order to solve these problems, a multi‐objectives optimization design method for the LLC converter is proposed in this paper. According to the numerical calculation method, a closed parameter space for resonant parameters is established under the constraints of operation mode, switching frequency, zero voltage switching (ZVS), and voltage stress of resonant capacitor. Besides, the root‐mean‐square (RMS) value of resonant current and secondary current, as well as the core loss of the transformer, are selected as the optimization objectives. Non‐dominated sorting genetic algorithm (NSGA‐II) is utilized to deal with these multi‐objective formulations. In order to reduce the computation complexity, a data‐driven method, called adaptive polynomial approximation (APA), is selected to obtain the explicit expressions of the parameter space and the optimization objectives. Then, by substituting this simplified surrogate model into the NSGA‐II algorithm, the optimal parameter scheme is obtained. The comprehensive comparison analysis is performed, and a 400 W/48 V experimental prototype was built to verify the theoretical analysis, which shows that the proposed method features the highest efficiency of 95%.

Abstract This paper proposes a new DC–DC bipolar resonance converter that combines a dual‐active‐bridge and a multi‐port resonance Buck‐Boost converter. This structure uses a resonance network between the input and output ports, which has the following advantages: it creates a constant current supply in the open‐loop mode; it keeps the current phase of the output ports constant under different loads; and it allows reversing the power from any output port by adjusting the phase shift of its switches, if the converter is connected to a DC microgrid. In this way, power can be exchanged among each of the output ports and input ports. The advantages of this converter are bidirectional power exchange, power exchange between output ports, soft switching, high efficiency, integration of transformer parasitic elements, and the modular capability to connect multiple resources at the input. Furthermore, the proposed structure of the bipolar converter has been implemented for verification purposes, and its results are presented in three modes: open loop, voltage control, and current control. Additionally, the results of different modes of power sharing and their bidirectionally are shown.

Abstract Three‐coil inductive power transfer (IPT) charging system can not only achieve inherent constant current (CC) to constant voltage (CV) transition but also tolerate load open‐circuit. However, due to the cross‐coupling between the auxiliary coil and the receiver coil, the output voltage in CV mode may deviate from the CV voltage significantly, which makes the design freedom of the three‐coil coupler low. By replacing the auxiliary coil with a transformer, an improved auxiliary circuit is proposed in this paper to address the cross‐coupling issue and to increase the design freedom of the loosely coupled transformer (LCT). The improved auxiliary circuit can achieve load short‐circuit and open‐circuit protection. Besides, the optimization design of LCT is easy to achieve by load impedance matching. The operating principle and parameters design of the improved auxiliary circuit are discussed. A 1 kW experimental prototype is built to verify the feasibility of the proposed method.

Abstract Environmental issues and the global need to increase economically sustainable access to electricity addressed and boosted scientific research and use of Distributed Energy Resources, such as solar Photovoltaic (PV). Multi‐port power converters can be of great interest for a compact and efficient interface between PV, storage units, and DC loads. The Triple Active Bridge (TAB) shows interesting advantages in terms of isolation and Zero Voltage Switching capabilities over wide load and input voltage ranges. This work aims to develop a TAB prototype for a NanoGrid (NG) application, analyzing the possibility of a direct interface of PV modules, storage units, and DC loads, without the use of intermediate conversion stages. The TAB prototype uses Gallium Nitride devices, and an evaluation of the overall efficiency is provided, useful to compare with other isolated three‐port converters. Through an analytical approach, a TAB has been sized and optimized to meet specific application requirements. MATLAB/Simulink simulations have been done for the sizing validation and the control strategy design. A prototype has been developed and experimental measurements have been compared with simulation results. The TAB is proposed as a key element of DC NGs, but applications of interest are also automotive, More Electric Aircraft, and naval applications.

Abstract LLC series resonant converter (LLC‐SRC) has attracted much attention due to its simple circuit structure and easy realization of soft‐switching. To get a better efficiency, rectifier stage can be realized by the active switches instead of the diodes. Meanwhile, more control variables can be selected besides the switching frequency, such as single phase shifted (SPS) angle, dual phase shifted (DPS) angle, and extended phase shifted (EPS) angle. Considering the simplicity of the implementation of the control strategy, this paper focused on the converter model with frequency and SPS control. Different from the common modelling method with the Fundamental Harmonic Approximation (FHA), the precise time‐domain mathematic model is built. By a comparison between the two modelling results, it can be proved the FHA model may be failure in some operating condition while the proposed time‐domain model can still show a good accuracy. According to the proposed model, two simple fixed‐frequency SPS control strategies are proposed. The first one can realize infinite soft‐switching margin with no restrictions on the transformer magnetizing inductance. The second one can greatly improve the efficiency of the converter. All the theoretical results are verified by a 1‐kW experiment prototype.