Double Modulation Research Articles

Double modulation of the electric field in InGaAs/Si APD by groove rings for the achievement of THz gain-bandwidth product

Abstract Avalanche photodiode (APD) is commonly used as a receiver in optical communication and light detection and ranging (LIDAR), offering highly sensitive photodetection capabilities. A key strategy for improving the gain-bandwidth product (GBP) of the APD involves the optimization of the electric field distribution using the charge layer. However, traditional modulation methods to adjust the carrier transport and avalanche process using the charge layer often face challenges (inefficiency and non-uniformity). An InGaAs/Si APD based on the wafer bonding method with a GBP up to 1.03 terahertz (THz) is reported theoretically in this work. The charge layer and groove rings are inserted at the InGaAs/Si bonded interface to modulate the electric field in the APD effectively, demonstrating low dark current and reduced avalanche bias of the device. This approach induces a dramatic and rapid variation of the electric field at the interface while reducing the gradient of the electric field in the multiplication layer. Additionally, the indirect impact of the groove ring on mitigating the adverse effects of the lattice mismatch is pointed out, and the optimal doping concentration range of the charge layer is identified to enhance the modulation effect of the electric field for stronger impact ionization. These findings provide valuable insights for the next-generation InGaAs/Si APDs with high GBP for high-speed data transmission.

MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios

Bearing fault diagnosis is of great importance to ensure the safe and stable operation of mechanical equipment. The actual collected bearing fault signals are susceptible to strong noise interference and bearing samples for each fault state may be insufficient, which increases the difficulty of capturing effective features. Most of the existing diagnostic methods extract features from a single sensor signal for pattern recognition and fault diagnosis. The fault information provided by a single sensor is limited and incomplete, which is usually very difficult to meet the demand for accurate and reliable fault diagnosis in complex scenarios. To solve these problems, this paper proposes a multi-sensor residual convolutional fusion network (MRCFN) for intelligent fault diagnosis of bearings. Firstly, a convolutional pooling module (CPM) is coupled with the designed double ring residual module (DRRM) to rough feature extraction and deep feature mining, which not only captures the discriminative fault features from multi-sensor signal, but also avoids the performance degradation of network. Secondly, a spatial channel reconstruction module (SCRM) is further introduced to eliminate redundant information in the features and improve the network training efficiency. Finally, the presented global interactive perception fusion module (GIPFM) is connected with a classification block (CB) to globally fuse the features extracted from the acoustic and vibration signals and conduct automatic fault identification, which both can realize the complementarity and calibration of multi-sensor feature information and high precision diagnosis. The experiments and a series of comparisons are implemented on two datasets to efficaciously verify the superiority of the proposed method over five existing representative multi-sensor fusion diagnosis methods (i.e., FAC-CNN, MB-CNN, MsACNN, MRSDF, and IMSFDFL) under strong noise and small samples.

Land Cover Classification of Remote Sensing Imagery with Hybrid Two-Layer Attention Network Architecture

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects.

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.

Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting. This study aims to use metabolomics data to develop HerbMet, a high-performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus. We propose HerbMet, an AI-based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D-ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance. Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1-score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1-score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU. HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.

Ab initio calculations and crystal structure simulations for mixed layer compounds from the tetradymite series

Abstract Density functional theory (DFT) is used to obtain structural information of seven members of the tetradymite homologous series: Bi2Te3 (tellurobismuthite), BiTe (tsumoite), Bi4Te3 (pilsenite), Bi5Te3, Bi2Te, Bi7Te3 (hedleyite), and Bi8Te3. We use the formula S(Bi2kTe3)·L[Bi2(k+1)Te3] as a working model (k = 1–4) where S and L are short and long modules in the structures. The relaxed structures show an increase in the a parameter and decrease in the interlayer distance (dsub) from Bi2Te3 (2.029 Å) to Bi8Te3 (1.975 Å). DFT-derived formation energy for each phase indicates that they are all thermodynamically stable. Scanning transmission electron microscopy (STEM) simulations for each of the relaxed structures show an excellent match with atom models. Simulated electron diffractions and reflection modulation along c* are concordant with published data, where they exist, and with the theory underpinning mixed-layer compounds. Two modulation vectors, q = γ·csub* (γ = 1.800–1.640) and qF = γF·dsub* (γF = 0.200–0.091), describe the distribution of reflections and their intensity variation along dsub* = 1/dsub. The γF parameter reinforces the concept of Bi2kTe3 and Bi2(k+1)Te3 blocks in the double module structures, and γ relates to dsub variation. Our model describing the relationship between γ and dsub allows prediction of dsub beyond the compositional range considered in this study, showing that phases with k >5 have values dsub within the analytical range of interlayer distance in bismuth. This, in turn, allows us to constrain the tetradymite homologous series between γ values of 1.800 (Bi2Te3) and 1.588 (Bi14Te3). Phase compositions with higher Bi/Te should be considered as disordered alloys of bismuth. These results have implications for mineral systematics and classification as they underpin predictive models for all intermediate structures in the group and can be equally applied to other mixed-layer series. Our structural models will also assist in understanding variation in the thermoelectric and topological insulating properties of new compounds in the broader tetradymite group and can support experimental work targeting a refined phase diagram for the system Bi-Te.

Methodology for substantiating the parameters of navigation signals with double meander modulation for local navigation systems based on land pseudo-satellites

Formulation of the problem. The development of local navigation systems based on land pseudo-satellites in order to solve the problem of insufficient noise immunity of global navigation satellite systems determines the relevance of the improvement of the theory of multicriteria synthesis of new types of navigation signals. Purpose. Ensuring high accuracy and continuity of navigation-time definitions with the level of out-of-band radiation reduced to compliance with electromagnetic compatibility requirements and an acceptable decrease in the energy efficiency of transmitters by developing a methodology for substantiating the parameters of navigation signals with double meander modulation for local navigation systems based on land pseudo-satellites. Results. The use of navigation signals with double meander modulation in local navigation systems based on land pseudo-satellites is proposed, and their advantages over currently used signals are shown. A methodology of multi-criteria substantiating of navigation signal parameters has been developed, taking into account spectral, correlation characteristics, as well as the effect of reducing the level of out-of-band radiation of land radio transmitting devices in order to ensure electromagnetic compatibility requirements on the time characteristics of the signal. Practical significance. The obtained results can be used by specialists in the field of local and global radio navigation systems when considering the introduction of new types of navigation signals that ensure high quality indicators of tracking their parameters, as well as the application of new and existing navigation signals in local navigation systems based on land pseudo-satellites.

MSFA-Net: A Multiscale Feature Aggregation Network for Semantic Segmentation of Historical Building Point Clouds

In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sparse and uneven nature of large-scale historical building point cloud scenes, most semantic segmentation methods opt to sample representative subsets of points, often leading to the loss of key features and insufficient segmentation accuracy of architectural components. Moreover, the geometric feature information at the junctions of components is cluttered and dense, resulting in poor edge segmentation. Based on this, this paper proposes a unique semantic segmentation network design called MSFA-Net. To obtain multiscale features and suppress irrelevant information, a double attention aggregation module is first introduced. Then, to enhance the model’s robustness and generalization capabilities, a contextual feature enhancement and edge interactive classifier module are proposed to train edge features and fuse the context data. Finally, to evaluate the performance of the proposed model, experiments were conducted on a self-curated ancient building dataset and the S3DIS dataset, achieving OA values of 95.2% and 88.7%, as well as mIoU values of 86.2% and 71.6%, respectively, further confirming the effectiveness and superiority of the proposed method.

Study on a rotational symmetry structure pulse forming network with low-impedance for compact pulse drivers.

A pulse forming network (PFN) is a significant component, contributing a lot to the overall dimension of pulse generators. In order to both reduce the size of PFN and improve the output waveform quality, this paper proposes a compact low-impedance PFN with a rotational symmetry structure. The PFN consists of four groups of Blumlein pulse forming units (PFUs) connected in parallel along the angular direction, and the spline curve structure is applied in each PFU, which achieves a higher space utilization rate. The theoretical maximum energy density of PFN is 6.6 J/L as the dimensions of PFN are φ500 × 138mm. Field-circuit co-simulation is carried out based on the spatial model of PFN and the double switch modulation circuit to analyze the effects of switch delay time (time between main switch and steep discharge switch), as well as the output port position affecting the output pulse waveform. The results show that the PFN is appropriate to achieve quasi-square wave pulse modulation as the switch delay time is 290ns with the output port positioned at the periphery. The verification experiments are also carried out. The results show that the PFN can generate a quasi-square wave pulse with an output voltage of 49.6kV, a pulse width of 83ns, and a peak power of 1 GW on a matched load. The output pulse exhibits a distinct flat top, with the fluctuation of the plateau being less than 3%.

Self-assembly of spin-labeled antimicrobial peptides magainin 2 and PGLa in lipid bilayers

The cationic antimicrobial peptides PGLa and magainin 2 (Mag2) are known for their antimicrobial activity and synergistic enhancement in antimicrobial and membrane leakage assays. Further use of peptides in combinatory therapy requires knowledge of the mechanisms of action of both individual peptides and their mixtures. Here, electron paramagnetic resonance (EPR), double electron-electron resonance (DEER, also known as PELDOR) and electron spin echo envelope modulation (ESEEM) spectroscopies were applied to study self-assembly and localization of spin-labeled PGLa and Mag2 in POPE/POPG membranes with a wide range of peptide/lipid ratios (P/L) from ∼1/1500 to 1/50. EPR and DEER data showed that both peptides tend to organize in clusters, which occurs already at the lowest peptide/lipid molar ratio of 1/1500 (0.067 mol%). For individual peptides, these clusters are quite dense with intermolecular distances of the order of ∼2 nm. In the presence of a synergistic peptide partner, these homo-clusters are transformed into lipid-diluted hetero-clusters. These clusters are characterized by a local surface density that is several times higher than expected from a random distribution. ESEEM data indicate a slightly different insertion depth of peptides in hetero-clusters when compared to homo-clusters.

Braille glove design toward interdisciplinary approach for visually impaired people: developing independent sensing design with MXene and embroidery

This study developed a wearable device designed to aid visually impaired people (VIP) who are unfamiliar with Braille and/or struggle to learn and use Braille employing the design thinking process. VIP often face challenges in learning and using Braille, and even with existing equipment, including smart technologies, they frequently experience aesthetic and emotional discomfort beyond usability and accessibility. The interdisciplinary research team conducted mixed-method research following the double diamond module of the design thinking process. Initially, qualitative interviews to analyze VIP’s Braille utilization were conducted to derive design factors. Subsequently, identified design factors were applied to conduct experiments with appropriate embroidery methods and connection structures using MXene-coated yarn. The strategies effectively enhanced pressure sensing performance by two times and realized highly localized and independent sensing to enable Braille recognition. Finally, a specific design for the wearable Braille recognition glove was proposed that incorporated these findings. The proposed wearable glove, equipped with a high-performance Braille recognition sensor, is designed to enhance the psychological and aesthetic satisfaction of VIP. The glove’s universal design and accompanying technical support allow VIP to maintain their existing Braille reading methods while enjoying enhanced performance and avoiding social stigmas.

Spin–orbit coupling effects on localization and correlated tunneling for two interacting bosons in a double-well potential

We theoretically study the tunneling dynamics of two interacting spin–orbit-coupled (SOC) atoms trapped in a periodically perturbed double-well potential. We find that the phenomenon of coherent destruction of tunneling (CDT) can exist only for certain values of SOC, and two different mechanisms for the appearance of CDT are identified in this system. One is the conventional CDT resulting from quasi-energy degeneracy, while the other CDT originates from the dark Floquet state with zero quasi-energy for all values of the driving parameters. We discover that under double modulation combining the double-well potential shaking and a time-periodic Zeeman field, it is possible to realize spin-flipping single-atom Rabi tunneling and the CDT induced by the dark Floquet state at any value of SOC strength, which is not accessible with a single-drive field. Furthermore, we show that the detuning of Zeeman field with respect to the multiphoton energy is particularly significant in the case of the correlated two-particle tunneling mediated by SOC. We expect that these results will stimulate further exploration of the many-body dynamics in the interacting systems and expand the possibilities for manipulating the spin dynamics.

Double pulsed current adopted in local dry underwater WAAM to simultaneously enhanced strength and ductility of 308 L multi-layer component

The double pulsed current waveform modulation was applied to address the severe challenges of greatly declined strength and ductility caused by continuous heat accumulation and water cooling in local dry underwater wire arc additive manufacturing (LDU-WAAM) process. The microstructures and mechanical performance of 308 L stainless steel multi-layer components prepared by LDU-WAAM using single pulsed waveform (SPW) and double pulsed waveform (DPW) were comprehensively investigated in this work. The results demonstrate that the microstructures of LDU-WAAM parts were both composed of abundant columnar crystals and few equiaxed crystals. Extremely directional columnar crystals dominated in each deposited layer centre prepared by SPW, while the weakened growth tendency of columnar crystal and uniform microstructure were achieved by DPW. DPW exhibited outstanding stirring action in LDU-WAAM process, which was conducive to the refined grain, improved liquidity of deposited metal, increased ferrite content, dislocation density, and austenite deformed twinning content compared with the SPW multi-layer component. Hence, the average microhardness, ultimate tensile strength, and elongation of DPW deposited layers were improved to 204.7 HV, 659.2 MPa, and 24.9 %, respectively, which enhanced by 10.1 %, 12.5 %, and 37.5 % than that of SPW sample. The research results provide a new technique optimization method to simultaneous enhance the strength and ductility of LDU-WAAM process and promote the landing application of LDU-WAAM in underwater on-site repair.

A deep neural network approach with attention mechanism to improve the quality of target observation for UAVs

Owing to the limitations of onboard equipment, the direct acquisition of high-resolution images in unmanned aerial vehicle (UAV) target observation tasks is a challenge. However, with the development of deep learning, super-resolution reconstruction has become a popular technique for enhancing image resolution. Though, current super-resolution reconstruction methods can enhance UAV target observation images to some extent, most of them are unable to strike a balance between the number of parameters, computational cost, and reconstruction effect. Furthermore, limited research has been conducted for overcoming the cascade coupling problem in UAV target observation images. To solve these problems and provide a lightweight, fast, and high-quality super-resolution method for UAV target observation images, an enhanced attention dense cross network (EADCN) is proposed. This method includes excellent attention mechanisms, such as the double large kernel attention module (D-LKA) and multi-aware channel attention distillation block (MAADB). In addition to benchmark datasets, a UAV target observation dataset (UAV-TOD) is created to evaluate the performance of EADCN. The experimental results demonstrate that the EADCN has fewer parameters, low computational complexity, high accuracy, and fast processing speed, making it suitable for deployment in UAVs.

Bidirectional Power Control Strategy for On-Board Charger Based on Single-Stage Three-Phase Converter

To solve the problems of large switching losses and the need for large-capacity electrolytic capacitances in three-phase DC/AC on-board chargers for vehicle-to-grid (V2G) applications, this paper proposes a single-stage bidirectional high-frequency isolated converter that eliminates the need for large-capacity capacitances. Combined with the proposed modulation scheme, it can theoretically reduce the switching loss by about two-thirds with the three-phase converter compared with the conventional modulation scheme, improving the converter’s operating efficiency and power density. Firstly, based on the characteristics of the proposed topology, a hybrid modulation scheme is proposed, which combines a phase-shift modulation scheme based on double modulation waves and a sawtooth carrier with a 1/3 modulation scheme, and the theoretical feasibility of the hybrid modulation scheme is verified using a mathematical modeling equation. Secondly, this paper provides a detailed analysis of the four operating modes of the two full-bridge circuits and the commutation process of the three-phase converter within 1/6 of the fundamental frequency cycle (P1 modulation interval). Then, the control strategy is given for the constant-current and constant-voltage charging and constant-current discharging for electric vehicle batteries. Finally, simulation results verify the correctness of the proposed topology and modulation scheme in vehicle–grid interaction.

A fusion gas load prediction model with three-way residual error amendment

Accurately predicting gas load is crucial for optimal planning and scheduling of natural gas production. Existing machine learning or deep learning-based prediction methods primarily aim to improve accuracy. However, these methods face challenges such as overlapped features, significant background noise, and residual errors. In response to these challenges, we proposed a fusion prediction model, known as the Double Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) with Three-way Residual Error Amendment (DT-LGBM-3WREA). To address the overlapped feature issue in time series data, we utilize the STL module for data decomposition. To handle background noise, the network incorporates a double TCN module specifically designed for trend and seasonal components. Thus, it can better avoid the transmission of noise between modules. To address residual error, we introduce three-way decision methodology and develop the 3WREA module, which aims to avoid local optima and random factors from perturbing the prediction accuracy. This approach mitigates the influence of remainder item on overall prediction performance. Experimental results demonstrate that the DT-LGBM-3WREA model excels in gas load prediction scenarios, surpassing state-of-the-art methods.

A Single Sampling Double Update Modulation Method to Enhance Low-Carrier Ratio Operation

A Single Sampling Double Update Modulation Method to Enhance Low-Carrier Ratio Operation

Single Atom Ru Doped Ni2P/Fe3P Heterostructure for Boosting Hydrogen Evolution for Water Splitting.

Modulating the chemical composition and structure has been considered as one of the most promising strategies for developing high-efficient water splitting catalysts. Here, a single-atom Ru doped Ni2P/Fe3P catalyst is synthesized by introducing the dispersed Ru atoms to adjust Ni2P/Fe3P heterostructure. Single atom Ru provides effective hydrogen evolution reaction (HER) active sites for boosting catalytic activities. The catalyst with only 0.2wt.% content of Ru exhibits an overpotential of 19.3mV at 10mA cm-2, which is obviously lower than 146.1mV of Ni2P/Fe3P. Notably, an alkaline overall water electrolyzer based on Ru-Ni2P/Fe3P catalysts achieves a cell voltage of 1.47V and operates over 600h at 10mA cm-2, which is superior to that of benchmark RuO2//Pt/C (1.61V). The theoretical calculations further confirm that Ru single atom doping can effectively optimize the hydrogen/water adsorption free energy of the active site and therefore improve the HER activity of heterostructure. This work provides a valuable reference to design high-activity and durability catalyst for water splitting through the double modulation of interface-effect and atomic doping.

Double self-reinforced coordination modulation constructing stable Ni4+ for water oxidation

Nitrate ligands and Co atoms not only promote the formation of Ni4+ by accelerating deprotonation and attracting electrons, but also adsorb the electron-withdrawing bridge hydroxyl to stabilize Ni4+.

Sensorless Speed Control of PMSM in Low-Speed by High Frequency Pulsating Injection with Double Modulation Error Compensation

A novel improvement scheme for the High Frequency Pulsating Injection-Sinusoidal (HFPI-S) method that considers the error function is proposed and implemented for PMSM sensorless control. The limitations in extracting the error function and dealing with signal noise at low-speed could appear the estimation errors in rotor position within the HFPI-S system. In this study, a new model for the proposed method, including the addition of dual modulation as error compensation in the process of extracting rotor position and enhancing the observer for conventional rotor position signals was focused. The results of this study showed that the extraction of the error function that is expanded could obtain more accurate results. Therefore, the addition of dual modulation and the observer enhancement could be effectively applied to maintain the accuracy of rotor position estimation even under low-speed and loaded conditions.

Analysis of water and refrigerant-based PV/T systems with double glass PV modules: An experimental and computational approach

Water and refrigerant-based photovoltaic/thermal (PV/T) systems have substantial promise as sustainable energy solutions for commercial and residential buildings. Integrated photovoltaic and solar thermal systems outperform discrete systems in conversion efficiency and space utilization. There is a dearth of quantitative comparative studies on these systems despite their functional similarities. This study offers a comparative analysis of PV/T systems employing water and refrigerant as heat transfer fluids. Both systems are characterized by identical size and collector design. Prototypedevelopment, experimental research, and parametric assessments using computer models are used to attainthis target. Furthermore, the design of the PV/T collector in this research study involved the utilization of a double glass PV module instead of a tedlar back sheet PV module. Based on ambient conditions, refrigerant-based PV/T systems can reach a final water temperature of 7 °C to 14.9 °C higher than water-based systems. In low irradiance conditions, the refrigerant-based system generates hot water more reliably than the water-based system.