Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

Systemic modulation of skeletal mineralization by magnesium implant promoting fracture healing: Radiological exploration enhanced with PCA-based machine learning in a rat femoral model

The clinical application of magnesium(Mg)and its alloys for bone fractures has been well supported by in vitro and in vivo trials.However,there were studies indicating negative effects of high dose Mg intake and sustained local release of Mg ions on bone metabolism or repair,which should not be ignored when developing Mg-based implants.Thus,it remains necessary to assess the biological effects of Mg implants in animal models relevant to clinical treatment modalities.The primary purpose of this study was to validate the beneficial effects of intramedullary Mg implants on the healing outcome of femoral fractures in a modified rat model.In addition,the mineralization parameters at multiple anatomical sites were evaluated,to investigate their association with healing outcome and potential clinical applications.Compared to the control group without Mg implantation,postoperative imaging at week 12 demonstrated better healing outcomes in the Mg group,with more stable unions in 3D analysis and high-mineralized bridging in 2D evaluation.The bone tissue mineral density(TMD)was higher in the Mg group at the non-operated femur and lumbar vertebra,while no differences between groups were identified regarding the bone tissue volume(TV),TMD and bone mineral content(BMC)in humerus.In the surgical femur,the Mg group presented higher TMD,but lower TV and BMC in the distal metaphyseal region,as well as reduced BMC at the osteotomy site.Principal component analysis(PCA)-based machine learning revealed that by selecting clinically relevant parameters,radiological markers could be constructed for differentiation of healing outcomes,with better performance than 2D scoring.The study provides insights and preclinical evidence for the rational investigation of bioactive materials,the identification of potential adverse effects,and the promotion of diagnostic capabilities for fracture healing.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Interference-induced suppression of particle emission from a Bose-Einstein condensate in lattice with time-periodic modulations

Emission of matter-wave jets from a parametrically driven condensate has attracted significant experimental and theoretical attention due to the appealing visual effects and potential metrological applications.In this work,we investigate the collective particle emission from a Bose-Einstein condensate confined in a one-dimensional lattice with periodically modulated interparticle interactions.We give the regimes for discrete modes,and find that the emission can be distinctly suppressed.The configuration induces a broad band,but few particles are ejected due to the interference of the matter waves.We further qualitatively model the emission process and demonstrate the short-time behaviors.This engineering provides a way to manipulate the propagation of particles and the corresponding dynamics of condensates in lattices,and may find application in the dynamical excitation control of other nonequilibrium problems with time-periodic driving.

Light-field modulation and optimization near metal nanostructures utilizing spatial light modulators

Plasmonic modes within metal nanostructures play a pivotal role in various nanophotonic applications.However,a significant challenge arises from the fixed shapes of nanostructures post-fabrication,resulting in limited modes under ordinary illumination.A promising solution lies in far-field control facilitated by spatial light modulators(SLMs),which enable on-site,real-time,and non-destructive manipulation of plasmon excitation.Through the robust modulation of the incident light using SLMs,this approach enables the generation,optimization,and dynamic control of surface plasmon polariton(SPP)and localized surface plasmon(LSP)modes.The versatility of this technique introduces a rich array of tunable degrees of freedom to plasmon-enhanced spectroscopy,offering novel approaches for signal optimization and functional expansion in this field.This paper provides a comprehensive review of the generation and modulation of SPP and LSP modes through far-field control with SLMs and highlights the diverse applications of this optical technology in plasmon-enhanced spectroscopy.

A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.

Improvement and security analysis of multi-ring discrete modulation continuous variable quantum secret sharing scheme

In order to avoid the complexity of Gaussian modulation and the problem that the traditional point-to-point communication DM-CVQKD protocol cannot meet the demand for multi-user key sharing at the same time, we propose a multi-ring discrete modulation continuous variable quantum key sharing scheme(MR-DM-CVQSS). In this paper, we primarily compare single-ring and multi-ring M-symbol amplitude and phase-shift keying modulations. We analyze their asymptotic key rates against collective attacks and consider the security key rates under finite-size effects. Leveraging the characteristics of discrete modulation, we improve the quantum secret sharing scheme. Non-dealer participants only require simple phase shifters to complete quantum secret sharing. We also provide the general design of the MR-DM-CVQSS protocol.We conduct a comprehensive analysis of the improved protocol's performance, confirming that the enhancement through multi-ring M-PSK allows for longer-distance quantum key distribution. Additionally, it reduces the deployment complexity of the system, thereby increasing the practical value.

Modulation of Electronic States in Bimetallic-doped Nitrogen-Carbon Based Nanoparticles for Enhanced Oxygen Reduction Kinetics

Controlling the local electronic structure of active ingredients to improve the adsorption desorption characteristics of oxygen-containing intermediates over the electrochemical liquid-solid interfaces is a critical challenge in the field of oxygen reduction reaction(ORR)catalysis.Here,we offer a simple approach for modulating the electronic states of metal nanocrystals by bimetal co-doping into carbon-nitrogen substrate,allowing us to modulate the electronic structure of catalytic active centers.To test our strategy,we designed a typical bimetallic nanoparticle catalyst(Fe-Co NP/NC)to flexibly alter the reaction kinetics of ORR.Our results from synchrotron Xray absorption spectroscopy and X-ray photoelectron spectroscopy showed that the co-doping of iron and cobalt could optimize the intrinsic charge distribution of Fe-Co NP/NC catalyst,promoting the oxygen reduction kinetics and ultimately achieving remarkable ORR activity.Consequently,the carefully designed Fe-Co NP/NC exhibits an ultra-high kinetic current density at the operating voltage(71.94 mA/cm^(2)at 0.80 V),and the half-wave potential achieves 0.915 V,which is obviously better than that of the corresponding controls including Fe NP/NC,Co NP/NC.Our findings provide a unique perspective for optimizing the electronic structure of active centers to achieve higher ORR catalytic activity and faster kinetics.

Radar Signal Intra-Pulse Modulation Recognition Based on Deep Residual Network

In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).

Interfacial Electronic Modulation of Dual-Monodispersed Pt–Ni_(3)S_(2) as Efficacious Bi-Functional Electrocatalysts for Concurrent H_(2) Evolution and Methanol Selective Oxidation

Constructing the efficacious and applicable bifunctional electrocatalysts and establishing out the mechanisms of organic electro-oxidation by replacing anodic oxygen evolution reaction(OER) are critical to the development of electrochemicallydriven technologies for efficient hydrogen production and avoid CO_(2) emission. Herein, the hetero-nanocrystals between monodispersed Pt(~ 2 nm) and Ni_(3)S_(2)(~ 9.6 nm) are constructed as active electrocatalysts through interfacial electronic modulation, which exhibit superior bi-functional activities for methanol selective oxidation and H_(2) generation. The experimental and theoretical studies reveal that the asymmetrical charge distribution at Pt–Ni_(3)S_(2) could be modulated by the electronic interaction at the interface of dual-monodispersed heterojunctions, which thus promote the adsorption/desorption of the chemical intermediates at the interface. As a result, the selective conversion from CH_(3)OH to formate is accomplished at very low potentials(1.45 V) to attain 100 m A cm^(-2) with high electronic utilization rate(~ 98%) and without CO_(2) emission. Meanwhile, the Pt–Ni_(3)S_(2) can simultaneously exhibit a broad potential window with outstanding stability and large current densities for hydrogen evolution reaction(HER) at the cathode. Further, the excellent bi-functional performance is also indicated in the coupled methanol oxidation reaction(MOR)//HER reactor by only requiring a cell voltage of 1.60 V to achieve a current density of 50 m A cm^(-2) with good reusability.

Nano-Au-decorated hierarchical porous cobalt sulfide derived from ZIF-67 toward optimized oxygen evolution catalysis:Important roles of microstructures and electronic modulation

Enhancing both the number of active sites available and the intrinsic activity of Co-based electrocatalysts simultaneously is a desirable goal.Herein,a ZIF-67-derived hierarchical porous cobalt sulfide decorated by Au nanoparticles(NPs)(denoted as HP-Au@CoxSy@ZIF-67)hybrid is synthesized by low-temperature sulfuration treatment.The well-defined macroporous-mesoporous-microporous structure is obtained based on the combination of polystyrene spheres,as-formed CoxSy nanosheets,and ZIF-67 frameworks.This novel three-dimensional hierarchical structure significantly enlarges the three-phase interfaces,accelerating the mass transfer and exposing the active centers for oxygen evolution reaction.The electronic structure of Co is modulated by Au through charge transfer,and a series of experiments,together with theoretical analysis,is performed to ascertain the electronic modulation of Co by Au.Meanwhile,HP-Au@CoxSy@ZIF-67 catalysts with different amounts of Au were synthesized,wherein Au and NaBH4 reductant result in an interesting“competition effect”to regulate the relative ratio of Co^(2+)/Co^(3+),and moderate Au assists the electrochemical performance to reach the highest value.Consequently,the optimized HP-Au@CoxSy@ZIF-67 exhibits a low overpotential of 340 mV at 10 mA cm^(-2)and a Tafel slope of 42 mV dec-1 for OER in 0.1 M aqueous KOH,enabling efficient water splitting and Zn-air battery performance.The work here highlights the pivotal roles of both microstructural and electronic modulation in enhancing electrocatalytic activity and presents a feasible strategy for designing and optimizing advanced electrocatalysts.

Pulsed Modulation in Electro-Hyperthermia

Modulated electro-hyperthermia (mEHT) is one of the novel oncological treatments with many preclinical and clinical results showing its advantages. The basis of the method is the synergy of thermal and nonthermal effects, similar to the thermal action of conventional hyperthermia combined with ionizing radiation (radiotherapy). The electric field and the radiofrequency current produced both the thermal and nonthermal processes. The thermal effects produce the elevated temperature as a thermal background to optimize the nonthermal impacts. The low frequency amplitude modulation ensures accurate targeting and promotes immunogenic cell death to develop the tumor specific memory T cells disrupting the malignant cells by immune surveillance. This process (abscopal effect) works like a vaccination. The low frequency amplitude modulation is combined in the new method with the high power pulses for short time, increasing the tumor distortion ability of the electric field. The new modulation combination has much deeper penetration triplicating the active thickness of the effective treatment. The short pulse absorption increases the safety and decreases the thermal toxicity of the treatment, making the treatment safer. The increased power allows for reduced treatment time with the prescribed dose.

Security-Enhanced Directional Modulation Based on Two-Dimensional M-WFRFT

Directional modulation(DM)is one of the most promising secure communication techniques.However,when the eavesdropper is co-located with the legitimate receiver,the conventional DM has the disadvantages of weak anti-scanning capability,anti-deciphering capability,and low secrecy rate.In response to these problems,we propose a twodimensional multi-term weighted fractional Fourier transform aided DM scheme,in which the legitimate receiver and the transmitter use different transform terms and transform orders to encrypt and decrypt the confidential information.In order to further lower the probability of being deciphered by an eavesdropper,we use the subblock partition method to convert the one-dimensional modulated signal vector into a twodimensional signal matrix,increasing the confusion of the useful information.Numerical results demonstrate that the proposed DM scheme not only provides stronger anti-deciphering and anti-scanning capabilities but also improves the secrecy rate performance of the system.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

A Few-Shot Learning-Based Automatic Modulation Classification Method for Internet of Things

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

Exercise-induced modulation of miR-149-5p and MMP9 in LPS-triggered diabetic myoblast ER stress: licorice glycoside E as a potential therapeutic target

Background:This study explores the relationship between endoplasmic reticulum(ER)stress and diabetes,particularly focusing on the impact of physical exercise on ER stress mechanisms and identifying potential therapeutic drugs and targets for diabetes-related sepsis.The research also incorporates traditional physical therapy perspectives,emphasizing the genomic insights gained from exercise therapy in disease management and prevention.Methods:Gene analysis was conducted on the GSE168796 and GSE94717 datasets to identify ER stress-related genes.Gene interactions and immune cell correlations were mapped using GeneCard and STRING databases.A screening of 2,456 compounds from the TCMSP database was performed to identify potential therapeutic agents,with a focus on their docking potential.Techniques such as luciferase reporter gene assay and RNA interference were used to examine the interactions between microRNA-149-5p and MMP9.Results:The study identified 2,006 differentially expressed genes and 616 miRNAs.Key genes like MMP9,TNF-α,and IL1B were linked to an immunosuppressive state.Licorice glycoside E demonstrated high affinity for MMP9,suggesting its potential effectiveness in treating diabetes.The constructed miRNA network highlighted the regulatory roles of MMP9,IL1B,IFNG,and TNF-α.Experimental evidence confirmed the binding of microRNA-149-5p to MMP9,impacting apoptosis in diabetic cells.Conclusion:The findings highlight the regulatory role of microRNA-149-5p in managing MMP9,a crucial gene in diabetes pathophysiology.Licorice glycoside E emerges as a promising treatment option for diabetes,especially targeting MMP9 affected by ER stress.The study also underscores the significance of physical exercise in modulating ER stress pathways in diabetes management,bridging traditional physical therapy and modern scientific understanding.Our study has limitations.It focuses on the microRNA-149-5p-MMP9 network in sepsis,using cell-based methods without animal or clinical trials.Despite strong in vitro findings,in vivo studies are needed to confirm licorice glycoside E’s therapeutic potential and understand the microRNA-149-5p-MMP9 dynamics in real conditions.

High photoelectric conversion efficiency and fast relaxation time of FA_(0.4)MA_(0.6)PbI_(3) applied in ultrafast modulation of terahertz waves

Active control of terahertz(THz)waves is attracting tremendous attentions in terahertz communications and active photonic devices.Perovskite,due to its excellent photoelectric conversion performance and simple manufacturing process,has emerged as a promising candidate for optoelectronic applications.However,the exploration of perovskites in optically controlled THz modulators is still limited.In this work,the photoelectric properties and carrier dynamics of FA_(0.4)MA_(0.6)PbI_(3)perovskite films were investigated by optical pumped terahertz probe(OPTP)system.The ultrafast carrier dynamics reveal that FA_(0.4)MA_(0.6)PbI_(3)thin film exhibits rapid switching and relaxation time within picosecond level,suggesting that FA_(0.4)MA_(0.6)PbI_(3)is an ideal candidate for active THz devices with ultrafast response.Furthermore,as a proof of concept,a FA_(0.4)MA_(0.6)PbI_(3)-based metadevice with integrating plasma-induced transparency(PIT)effect was fabricated to achieve ultrafast modulation of THz wave.The experimental results demonstrated that the switching time of FA_(0.4)MA_(0.6)PbI_(3)-based THz modulator is near to 3.5 ps,and the threshold of optical pump is as low as 12.7μJ cm^(-2).The simulation results attribute the mechanism of ultrafast THz modulation to photo-induced free carriers in the FA_(0.4)MA_(0.6)PbI_(3)layer,which progressively shorten the capacitive gap of PIT resonator.This study not only illuminates the potential of FA_(0.4)MA_(0.6)PbI_(3)in THz modulation,but also contributes to the field of ultrafast photonic devices.

A Novel Multiple DBC-staked units Package to Parallel More Chips for SiC Power Module

Silicon carbide(SiC) power modules play an essential role in the electric vehicle drive system. To improve their performance, reduce their size, and increase production efficiency, this paper proposes a multiple staked direct bonded copper(DBC) unit based power module packaging method to parallel more chips. This method utilizes mutual inductance cancellation effect to reduce parasitic inductance. Because the conduction area in the new package is doubled, the overall area of power module can be reduced. Entire power module is divided into smaller units to enhance manufacture yield, and improve design freedom. This paper provides a detailed design, analysis and fabrication procedure for the proposed package structure. Additionally, this paper offers several feasible solutions for the connection between power terminals and DBC untis. With the structure, 18dies were paralleled for each phase-leg in a econodual size power module. Both simulation and double pulse test results demonstrate that, compared to conventional layouts, the proposed package method has 74.8% smaller parasitic inductance and 34.9% lower footprint.

From VIB‑to VB‑Group Transition Metal Disulfides:Structure Engineering Modulation for Superior Electromagnetic Wave Absorption

The laminated transition metal disulfides(TMDs),which are well known as typical two-dimensional(2D)semiconductive materials,possess a unique layered structure,leading to their wide-spread applications in various fields,such as catalysis,energy storage,sensing,etc.In recent years,a lot of research work on TMDs based functional materials in the fields of electromagnetic wave absorption(EMA)has been carried out.Therefore,it is of great significance to elaborate the influence of TMDs on EMA in time to speed up the application.In this review,recent advances in the development of electromagnetic wave(EMW)absorbers based on TMDs,ranging from the VIB group to the VB group are summarized.Their compositions,microstructures,electronic properties,and synthesis methods are presented in detail.Particularly,the modulation of structure engineering from the aspects of heterostructures,defects,morphologies and phases are systematically summarized,focusing on optimizing impedance matching and increasing dielectric and magnetic losses in the EMA materials with tunable EMW absorption performance.Milestones as well as the challenges are also identified to guide the design of new TMDs based dielectric EMA materials with high performance.