The direct current micro-current standard source is widely used in fields such as calibration of electrical instruments,semiconduc-tor testing,and electrostatic detection,involving key technologies like current negative feedback regulation and equipotential shielding.There are mainly three basic implementation methods for the direct current micro-current standard source:resistive,capacitive,and ioniza-tion.Each technology has its own unique advantages and limitations.This paper reviews the research status of domestic and foreign direct current micro-current standard sources in recent years,investigates the current situation of domestic and foreign direct current micro-current standard sources,and points out that China started relatively late in this field and there is a gap compared with the international advanced level.
With the rapid development of the Internet and the widespread deployment of network infrastructure,the number of domain name servers(DNS)has continued to grow,accompanied by their expanding geographical scope.This expansion is characterized by imbalanced regional distribution,complex service hierarchies,and dynamic operational changes,which exacerbate security risks within the DNS ecosys-tem and create challenges for comprehensive oversight.To address these issues,this study establishes a large-scale DNS discovery system and conducts an in-depth analysis of the spatiotemporal evolutionary patterns and hierarchical dependencies among domain name servers.The findings reveal critical insights into the topological evolution of DNS infrastructure,offering actionable support for enhancing cyberse-curity governance and optimizing the strategic deployment of next-generation digital infrastructure.
This work proposes and simulates a high-linearity GaN HEMT device using Technology Computer Aided Design(TC AD).The device features a composite channel based on graded-composite AlGaN and InGaN layers.This composite channel forms a dual-channel dis-tribution consisting of both a three-dimensional electron gas(3DEG)and a two-dimensional electron gas(2DEG).The device achieves a large gate voltage swing(GVS)of 4.1 V,which is 2.2 V higher than that of a conventional abrupt heterojunction HEMT.The peak value of the second derivative of transconductance is 56％lower than that of the conventional device,demonstrating superior suppression of third-order intermodulation distortion(IMD3).Furthermore,the impact of self-heating effects on device linearity is investigated.The proposed de-vice structure holds promise for high-linearity applications.
Secondary radar systems primarily detect cooperative targets through an interrogation-response mechanism and are extensively applied in fields such as air traffic control.To address the issue of declining detection probability for dense tar-gets in traditional secondary radar systems,this pager analyzes the mechanism by which random response delay enhances the detection probability for dense targets,formulates the underlying mathematical problems,and validates the findings through theoretical derivations,calculations,and Monte Carlo simulations.The results demonstrate that the random response delay technique can effectively improve the detection performance of secondary radar systems for dense targets.Furthermore,un-der the premise of meeting detection probability requirements,the number of detectable dense targets exhibits a linear step-like relationship with the number of response positions.
Aiming at the key problems of high missed detection rate of initial fire,significant interference of complex background and diffi-cult balance between edge equipment deployment accuracy and real-time performance in UAV highway slope fire detection,this study pro-poses a lightweight visible light fire detection algorithm based on the improved YOLOv12 framework.The algorithm accurately extracts slope and road areas through instance segmentation technology to eliminate background interference.A small target enhancement feature pyramid(SOPAN)including high-resolution P2 layer and multi-path downsampling mechanism is designed to improve the detection ability of small targets,and a lightweight detection head(LSCD)based on shared convolution is constructed to achieve model complexity com-pression.The experimental results show that on the fusion UAV inspection data set,the proposed model achieves 92.5％mAP detection ac-curacy with only 1.8M parameters,which is 28.3％lower than the benchmark model YOLOv12n parameters.It provides an efficient solu-tion for real-time and high-precision fire warning at the UAV side.
This paper conducts a survey and analysis of Wi-Fi solutions that incorporate AI/ML technologies.Firstly,it outlines the chal-lenges associated with Wi-Fi technology characteristics and the evolution of the latest specifications.Secondly,it examines the feasibility of using AI/ML to improve Wi-Fi performance and user experience,along with the AI/ML use cases defined by the IEEE 802.11 TIG.After-wards,the paper proposes system architecture for integrating AI/ML into Wi-Fi networks and analyzes typical use cases for practical Wi-Fi deployments.Finally,it presents an overview of the current constraints in applying AI/ML to Wi-Fi systems and discusses potential direc-tions for future technological development.
Graphene is a new kind of ultra-thin two-dimensional material,which has become a new hotspot in the field of electromagnetism because of its controllable electromagnetic properties.At the same time,frequency-selective surface(FSS)has always been a research hot-spot in artificial periodic materials.In this paper,a composite electromagnetic metamaterial periodic structure with a graphene-FSS is pro-posed.The transmission band of the structure is 34.45 GHz～36.98 GHz(absolute bandwidth is 2.53 GHz).The absorption bands are 31.18 GHz～32.95 GHz(the absolute bandwidth is 1.77 GHz)and 38.17 GHz～39.52 GHz(the absolute bandwidth is 1.35 GHz),respec-tively.Based on the study,a single-layer graphene periodic structure without FSS part is fabricated and tested.It can be concluded that the grapheme periodic structure has good absorption characteristic in the whole measured microwave and millimeter wave frequency band(8 GHz～40 GHz).The composite electromagnetic metamaterial structure made of graphene and FSS structures is a good new candidate to re-alize a new metamaterial stealth radome.
This article studies and proposes a data encryption method based on the UltraScale architecture for the protection of core func-tions and intellectual property rights of aviation communication airborne equipment,combined with the architectural characteristics of the UltraScale chip.This method jointly encrypts the DNA sequence numbers of UltraScale chips' PS and PL,as well as user-defined informa-tion,and designs a plaintext information verification format that uses offline encryption and online decryption to complete device encryption and decryption functions.The experimental results show that the design method proposed in this article is effective and feasible,the encryp-tion and decryption functions of the device are normal,the security of the device is improved,and the user's intellectual property is effec-tively protected.At present,this technology has been applied in various types of aviation communication equipment and has strong practical-ity.
This paper addresses the power-efficient mapping problem in multi-chiplet Networks-on-Chip(NoC)by proposing an improved Adaptive Genetic Algorithm(AGA).By introducing permutation encoding,a partial-mapping crossover operator,an adaptive swap mutation strategy,and a hybrid selection mechanism,the algorithm effectively resolves issues such as constraint conflicts,local optima,and solution space explosion that exist in traditional genetic algorithms for NoC mapping.Experiments were conducted on a 36-node 2D-Mesh topology with randomly generated communication task graphs,comparing the performance of AGA,Ant Colony Optimization(ACO),and Grey Wolf Optimizer(GWO).The results demonstrate that AGA significantly outperforms other algorithms in communication energy optimization,re-ducing total power consumption by 32.0％and 26.2％compared to GWO and ACO,respectively.Additionally,AGA exhibits superior global search capability and convergence stability.This research provides an efficient optimization method for power-efficient NoC design.
In order to meet the requirements of system lightweighting,miniaturization,and integration,a highly integrated L-band dual channel frequency conversion System-in-Package(SiP)module has been implemented based on 3D stacking technology.The SiP module adopts a multi-layer organic composite substrate stack,integrating two frequency conversion channels,a frequency source,and a microcon-troller.It is sealed with a ceramic tube shell and has a size of only 21 mm×16 mm×4.3 mm.The test results show that the module has a typi-cal frequency conversion gain of 43.5 dB,a gain flatness of less than 0.7 dB,out of band spurious suppression greater than 60 dBc,and chan-nel isolation greater than 50 dB,meeting the system's usage requirements.
As power network control systems become more and more intelligent,the same switching device must support the mixed trans-mission of multiple streams in order to ensure system security and flexibility,and the current mixed-traffic transmission mechanism is still insufficient in scheduling success rate and scheduling speed when facing a large number of mixed streams.To this end,this paper proposes a BTCO hybrid traffic co-optimization framework based on a meta-heuristic approach to jointly optimize the scheduling order and transmis-sion time to solve the hybrid traffic problem in TSNs.This research simulated and validated BTCO,and the results show that compared with existing constraint-based planning methods and heuristics,BTCO has a higher scheduling success rate,as well as faster scheduling speed,lower overall delay and jitter,and achieves efficient scheduling of hybrid flows.
In order to identify the OFDM radar signals of complex new systems,a united algorithm based on Fast Correlation-Based Filter Adaptive Boosting(FCBF-AdaBoost)and a radar signal identification method based on frequency domain analysis are constructed.After discretizing the frequency domain amplitude attribute set,the joint algorithm firstly filters and rejects the redundant and uncorrelated ampli-tude data of the input frequency domain data to form a frequency-domain subset after dimension reduction.Then,the data feature learning is carried out through the algorithm of weak classifier integration,and finally the model is trained through a large amount of radar data to real-ize the classification of the radar signals.The feasibility of the algorithm is verified by theoretical analysis.It is shown by simulation experi-ments at various SNR that the proposed algorithm can significantly improve the accuracy of OFDM radar signal recognition with the in-crease of SNR.The accuracy of recognition can reach more than 94％.
To meet the demands of emerging services such as global seamless coverage,emergency communications,IoT,and aviation and maritime applications,3GPP Release 19 has systematically enhanced NTN.Compared to the transparent forwarding-dominated architecture in Release 18,3GPP R19 NR NTN introduced a novel onboard regeneration processing architecture for the first time,enabling flexible de-ployment of RAN and core network functions on satellites.This significantly reduces end-to-end latency and enhances system capacity.The 3 GPP R19 NR NTN access network standard has been enhanced and expanded in multiple aspects compared to Release 18,making the 6G technical solution more comprehensive and further strengthening its capabilities,thereby providing a solid technical foundation for the com-mercialization of 5G NR NTN.This paper takes the challenges faced in the current development of 6G satellite Internet as the starting point and focuses on analyzing aspects such as the core architecture of R19 NR NTN,air interface and performance optimization,mobility and ser-vice continuity,terminal and scenario expansion,etc.Finally,the technological development of R20 was prospected.
As one of the key technologies in remote sensing-based 3D reconstruction,point cloud and image fusion faces challenges in tradi-tional approaches,such as high computational cost and difficulty in balancing efficiency and data precision.This paper proposes an efficient point cloud color fusion strategy that integrates image pyramids with bilinear interpolation,while introducing OpenMP for parallel optimiza-tion.The method leverages the pyramid structure to enable rapid multi-resolution localization and adaptive regional interpolation,effectively improving fusion efficiency while preserving fine details.Experiments conducted on two datasets demonstrate that the proposed approach maintains high fusion accuracy while achieving a 3.6～3.8 fold improvement in processing speed.The performance gain is especially notable for high-density point cloud data.This method not only provides high-precision data support for 3D modeling of power transmission corri-dors but also offers important technical support for automated inspection.
Under the background of the artificial intelligence era,regular expression matching technology plays a crucial role,particularly in data cleansing and data extraction domains,where it provides technical support for high-quality data processing required by large language model training.However,traditional regular expression matching algorithms suffer from performance bottlenecks that limit their application scope.To address this challenge,this paper proposes a data-driven high-performance regular expression matching algorithm,denoted as YJJFA algorithm.The algorithm reduces the number of input characters that need to be processed in the state transition table by partitioning it into optimal trusted regions and untrusted regions,while leveraging non-memory-access vector comparisons of the untrusted character set(UBCS)to achieve low-time-cost processing of trusted characters.Experimental results show that the YJJFA algorithm achieves a through-put rate of 17.88～53.81 Gb/s on L7filter rules,representing an order-of-magnitude performance improvement over conventional DFA imple-mentations.
The images reconstructed from single image domains suffer from structural and non-local aliasing artefacts,and the multimodal super-resolution reconstruction network ignores irrational feature fusion due to modality-specific differences.To address this problem,a mul-timodal magnetic resonance image super-resolution reconstruction algorithm based on dual-domain synergy is proposed in this paper.By means of dual-domain synergy,local features in the image domain containing structural information and global features in the frequency do-main containing frequency distributions are combined to eliminate the effects of misalignment relationships between different modalities.The deformable cross-modal attention module is designed to adaptively migrate the high-frequency complementary information in T1 modal-ity to T2 modality;the implicit attention mechanism achieves the super-resolution reconstruction at arbitrary scales.The experimental results show that DDCMC has better visual quality than other single-domain reconstructed images.
In order to solve the technical problem of poor antenna isolation between adjacent frequency bands in BeiDou third-generation satellite navigation scene,this paper proposes a dual-band dual-polarized BeiDou transceiver antenna with high isolation,which can effec-tively solve the problem that BDS B1 band is susceptible to interference when BDS L band and BDS B1 band work at the same time.By in-troducing a broadband high isolation coupler,the high isolation of the antenna radiation patch feed port is realized.Secondly,the compact coupling feeding method is used to realize the miniaturization design of the antenna.Finally,the dual-band dual-polarized BeiDou antenna is verified.The measured isolation between the antennas is 37.8 dB.The test results are in good agreement with the design results,which veri-fies the effectiveness of the design and can meet the multi-frequency application requirements of the BeiDou third-generation satellite navi-gation antenna.
The image quality of the Scanning Electron Microscope(SEM)is highly dependent on the accuracy and stability of the output signals from its scanning circuitry.However,the impact of scanning signals on image quality remains insufficiently explored in existing re-search.To address this,a transmission mechanism from scanning signals to image quality was established,and the influence of scanning sig-nal performance metrics on the image was analyzed.Using high-precision digital multimeters and oscilloscopes,actual scanning signals were accurately measured.Data processed via the endpoint fitting method enabled quantification of error levels in both field and line scan-ning signals under a 512×512 pixel mode.Furthermore,MATLAB was employed to simulate the impact of various errors on a standard grid image,and measured signal data were imported to evaluate their effect in practical imaging.The results indicate that the scanning signals ex-hibit good overall linear performance,with an average error of 1.884 pixels in the central region,superior to the 2.749 pixels observed in the peripheral region.
For multi-chiplet heterogeneous complex structures,traditional functional testing methods are unable to accurately locate internal interconnection faults.A boundary-scan-based interconnect testing method for multi-chiplet is proposed,which enables the detection of inter-connect faults between chiplets.Based on the IEEE 1149.1 boundary-scan protocol and an optimized interconnect testing algorithm,this method uses an Automatic Test Equipment(ATE)testing system to identify faulty internal interconnection lines,thereby accurately detecting chip faults and defects.Compared with traditional testing methods,the boundary-scan-based interconnect testing method for multi-chiplet is stable and reliable.It can accurately pinpoint internal interconnection faults within the chip,significantly improves testing efficiency,and en-sures the reliable application of chiplet systems.
Utilizing unmanned aerial vehicles(UAVs)to charge energy-constrained sensor nodes can effectively mitigate the problem of ser-vice interruption caused by node energy depletion.However,due to the limited energy capacity of UAVs,minimizing the mission time while ensuring complete charging of sensor nodes is crucial for reducing UAV energy consumption.To address this challenge,this paper formu-lates an optimization problem aimed at minimizing UAV mission time,based on the UAV energy consumption model,the sensor node en-ergy harvesting model,and the remaining energy levels of the nodes.Furthermore,a UAV scheduling algorithm is proposed,which trans-forms the optimization problem into a classic Traveling Salesman Problem(TSP)through node clustering and anchor point selection meth-ods,and employs a genetic algorithm for path planning.Simulation experiments using synthetic data are conducted to evaluate the proposed algorithm.The results demonstrate that the proposed UAV flight strategy effectively reduces mission time under the same energy consump-tion conditions.
