Multilevel Transmission Research Articles

SAR-NTV-YOLOv8: A Neural Network Aircraft Detection Method in SAR Images Based on Despeckling Preprocessing

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

Designing Anonymous Signature-Based Identity Authentication Scheme for Ocean Multilevel Transmission

Designing Anonymous Signature-Based Identity Authentication Scheme for Ocean Multilevel Transmission

A hierarchical LFFC control scheme for underactuated unmanned surface vessels based on unreliable communication interaction

This article investigates the leader-follower formation-containment (LFFC) control issue of underactuated unmanned surface vessels (USVs) system subject to unreliable communication interaction and the external disturbances. Firstly, an adaptive control scheme is established for the leaders to track the desired trajectory associated with preset formation configuration. Subsequently, by means of convex hull theory, a formation-containment control algorithm is developed to guide the followers into the specific area generated by the leaders. Nevertheless, the communication interaction among USVs is frequently unreliable, which is constrained by communication distance, equipment quality, and indeterminate cyber-attack. Worse still, this unreliability will magnify the unpredictable impact on system stability. Moreover, the signals generally undergo multilevel transmission from virtual leader to followers under LFFC structure, which will result in the amplification of unreliable ratio during the information transmissions. To address the aforementioned issues, an online-update auxiliary signal is constructed through the quasi-sliding mode manifold, which can suppress the impact of unreliable communication interaction. Theoretical analysis and numerical simulations are demonstrated to verify the feasibility and validity of the proposed control strategies.

Coupling Projection Synchronization of Three Chaotic Systems and Its Multilevel Secure Communication via DNA CRNs

The complete synchronization of two chaotic systems and its secure communication have been considered based on DNA chemical reaction networks (DNA CRNs). There are concerns about the security of the single-level chaotic secure communication system. Thereby a multi-level secure communication scheme is put forward via DNA CRNs in this work. Firstly, a three-variable chaotic system that consists of catalysis, double, fasciation, and annihilation modules is constructed using DNA CRNs. Secondly, according to the stability principle of nonlinear systems, DNA CRNs are adopted to realize the coupling projection synchronization of three chaotic systems, and the synchronization results are discussed. Finally, sine and cosine signals are designed using DNA CRNs. These signals are added into three systems to realize multi-level encryption and decryption. Furthermore, the interference terms are taken into consideration to test the robustness of the system. The results show that the proposed scheme has high security, and can efficiently encrypt and decrypt biological signals through multi-level transmission in the presence of interference.

Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks

According to the regulations of the hybrid access strategy in cognitive radio network, the secondary user (SU) needs to identify the primary user’s (PU) specific transmit power level to avoid unacceptable interference with the PU. However, the conventional transmit power recognition methods cannot accurately identify the transmit power in conditions with low signal-to-noise ratio, fading channels and the existence of noise uncertainty, since those methods are based on a fixed statistical theory to model the dynamic electromagnetic environment mathematically. To address these issues, a ResNet-based multi-level transmission power recognition (MTPR) architecture is presented in this paper. Furthermore, the proposed architecture is implemented in two cases with different observation data. In the first case, the received signal’s covariance matrix (CM) containing rich energy information is used as the observation data of CM-MTPR scheme. To further improve the identification accuracy, in the second case, the in-phase and quadrature-phase (IQ) data sampled from the received signal that preserves more original information is configured as the observation data of IQ-MTPR scheme. The IQ-MTPR scheme, however, consumes additional computing resources which forms a trade-off between identification performance and computational consumption with the CM-MTPR scheme. Simulation results demonstrate the identification performance of the proposed schemes.

Multilevel transmission contrast in optical structures with thin films of GeTe

Analysis of the optical properties: refractive index n(λ) and extinction coefficient k(λ), of thin films of phase-change materials obtained by photometric and ellipsometric methods allows to optimize optical characteristics reflection R, transmission T and absorption A. The interference of an electromagnetic wave in an absorbing thin film on a dielectric substrate significantly affects the values of the optical characteristics. Optimization of the optical characteristics is possible through the use of additional layers of dielectric materials, which are used to compensate for the difference in the refractive indices of the film with the substrate and air and minimize the reflection. This approach will increase the contrast of the transmission levels for the modulated optical signal in near infrared range. Calculations of the optical characteristics of thin films of germanium telluride in multi-layer structures are performed. Experimental samples of structures with antireflection layers of zinc sulfide have been prepared and their optical transmission characteristics have been investigated. The combination of multilayer structures for the implementation of contrast levels of transmission have been determined.

Linking contact tracing with genomic surveillance to deconvolute SARS-CoV-2 transmission on a university campus

Contact tracing and genomic data, approaches often used separately, have both been important tools in understanding the nature of SARS-CoV-2 transmission. Linked analysis of contact tracing and sequence relatedness of SARS-CoV-2 genomes from a regularly sampled university environment were used to build a multilevel transmission tracing and confirmation system to monitor and understand transmission on campus. Our investigation of an 18-person cluster stemming from an athletic team highlighted the importance of linking contact tracing and genomic analysis. Through these findings, it is suggestive that certain safety protocols in the athletic practice setting reduced transmission. The linking of traditional contact tracing with rapid-return genomic information is an effective approach for differentiating between multiple plausible transmission scenarios and informing subsequent public health protocols to limit disease spread in a university environment.

Performance Analysis of Dimming Methods in Visible Light Communication Systems

The Visible Light Communication (VLC) has been taken very attentions from many researchers due to its efficiency substructure. In specially, the VLC systems provide both lighting and data transmission at the same time. This paper has investigated the performance analyses of modulation schemes which support the brightness control for Visible Light Communication. In this sense, it has been focused on performance differences between M-ary VPPM (M-ary Variable Pulse Position Modulation) scheme and VPAPM (Variable Pulse Amplitude Position Modulation) which was proposed to ensure the multilevel transmission for VPPM scheme. In particular, a performance comparison has been given for both techniques with respect to Bit Error Rate by considering the same bit length consisted in a symbol. The investigated M-ary VPPM is modified by generating the signals of two power levels. Moreover, a VPAPM based-transmission model has been proposed to assure the accurate dimming target values under condition of long runs of same bits (1 s or 0 s) that encode the signal amplitude. However, the proposed system has lower data rate when compare to traditional VPAPM. Moreover, a receiver scheme has been suggested to decode received VPAPM signals. The performance of VPAPM demodulator architecture has been observed in terms of BER versus transmission distance between receiver and transmitter.

A 0.17 pJ/bit 28 Gb/s/pin Single-Ended PAM-4 Transmitter for On-Chip Short-Reach Unterminated Channels

This paper presents the design of a single-ended four-level pulse-amplitude modulation (PAM-4) transmitter for an on-chip short-reach unterminated channel. To achieve multi-output generation, a local voltage buffer consisting of a diode-connected device and a leaker transistor is introduced. By charge-sharing between a local reservoir capacitor and an unterminated channel, the proposed transmitter generates mid-level output voltages without using the DC current, thereby realizing multi-level signaling without significantly increasing the static current. A prototype chip was fabricated by 28 nm CMOS process, and the transmitter exhibits an energy efficiency of 0.17 pJ/bit at 28 Gb/s/pin, which is state-of-the-art energy efficiency as a multi-level transmitter having a data rate beyond 20 Gb/s.

Estimating transmission dynamics of SARS-CoV-2 at different intraspatial levels in an institutional outbreak

IntroductionLarge, localised outbreaks of COVID-19 have been repeatedly reported in high-density residential institutions. Understanding the transmission dynamics will inform outbreak response and the design of living environments that are more resilient to future outbreaks. MethodsWe developed an individual-based, multilevel transmission dynamics model using case, serology and symptom data from a 60-day cluster randomised trial of prophylaxes in a densely populated foreign worker dormitory in Singapore. Using Bayesian data augmentation, we estimated the basic reproduction number and the contribution that within-room, between-level and across-block transmission made to it, and the prevalence of infection over the study period across different spatial levels. We then simulated the impact of changing the building layouts in terms of floors and blocks on outbreak size. ResultsWe found that the basic reproduction number was 2.76 averaged over the different putative prophylaxes, with substantial contributions due to transmission beyond the residents’ rooms. By the end of ~60 days of follow up, prevalence was 64.4 % (95 % credible interval 64.2–64.6 %). Future outbreak sizes could feasibly be halved by reducing the density to include additional housing blocks, or taller buildings, while retaining the overall number of men in the complex. DiscussionThe methods discussed can potentially be utilised to estimate transmission dynamics at any high-density accommodation site with the availability of case and serology data. The restructuring of infrastructure to reduce the number of residents per room can dramatically slow down epidemics, and therefore should be considered by policymakers as a long-term intervention.

Recognition and Analysis of Sports on Mental Health Based on Deep Learning.

This paper presents the purpose of sport recognition of mental health for users and analyzes and studies the recognition of mental health by sports based on deep learning. The recognition model of sport mental health state composed of data layer, logic layer and display layer is built. After fusing human health data with deep learning algorithm, the feature of human health mutual information is extracted, the feature into the recognition model of mental health state is inputted, and the recognition results of sport mental health mode after forward and reverse operation are outputted. The recognition data of sports on mental health status are obtained, which correspond to the link flowing through during multi-level transmission, calibrate the multi-level transmission point, and fuse and process the recognition information of sports on mental health status. The experimental results show that the loss value of the research method when analyzing the effect of sports on mental health enhancement is the smallest, the output result is reliable, can effectively improve the body mass index (BMI) of the human body, has the most controllable amount of data, and has good performance.

EVOLUTION AND INFLUENCING FACTORS OF THE GREEN DEVELOPMENT SPATIAL ASSOCIATION NETWORK IN THE GUANGDONG-HONG KONG-MACAO GREATER BAY AREA

Accurately grasping the structural characteristics and influencing factors of green development spatial association are significant for green coordinated development and ecological civilization construction in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This study evaluates the GBA’s green development performance from 2015 to 2019 based on duality theory, and uses social network analysis to explore the structural characteristics and evolution of the green development spatial association network, and then uses the exponential random graph model to reveal the influencing factors of network formation. we find that: (1) the GBA’s green development is steady. Its spatial association network became increasingly complex, and tends to be tight. (2) As important hubs, Guangzhou, Shenzhen, and Hong Kong have the dominant positions in the GBA’s green development spatial association network. Huizhou, Jiangmen, Zhaoqing, and Macao are at the edge of the network, and their interoperability with other cities is relatively weak. (3) Four subgroups exist in the GBA during different periods, with obvious gradient characteristics between them, and the multilevel transmission mechanism of the green development network gradually forms. (4) Economic development and urbanization level, ecological environment endowment, and geographical, institutional, and industrial proximity all have significant impacts on the formation of the GBA’s green development spatial association network.

Energy Attentive and Pre-fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Wireless sensor networks (WSNs) have been transforming over recent years with development in the design of smart real-time applications. However, it presents numerous challenges in terms of fault-tolerant communication, low latency, scalability, and transmission efficiency. It is extremely difficult for WSNs to detect runtime faults since they're unaware of the internal processes at work within the sensor node. As a result, valuable sensed information cannot reach its destination and performance starts degrading. Towards this objective, the proposed mechanism applies a novel pre-fault detection mechanism based on a fuzzy rule-based method for multilevel transmission in distributed sensor networks. The proposed mechanism uses a fuzzy rule set to make routing decisions. A fuzzy decision rule set is proposed to perform routing based on the fuzzy fault count status of a node. The proposed mechanism assists in identifying the fault in advance and determining the optimal routing path to save energy and improve network performance. In accordance with the node fault status, the data transmission rate is finalized to prevent further energy consumption. The results demonstrated that the proposed mechanism performed well on judgment evaluation metrics like the energy dissipation ratio, throughput, packet loss rate and communication delay.

Geographically varying relationships between population flows from Wuhan and COVID-19 cases in Chinese cities

ABSTRACT The COVID-19 epidemic widely spread across China from Wuhan, Hubei Province, because of huge migration before 2020 Chinese New Year. Previous studies demonstrated that population outflows from Wuhan determined COVID-19 cases in other cities but neglected spatial heterogeneities of their relationships. Here, we use Geographically Weighted Regression (GWR) model to investigate the spatially varying influences of outflows from Wuhan. Overall, the GWR model increases explanatory ability of outflows from Wuhan by 20%, with the adjusted R 2 increasing from ~0.6 of Ordinary Least Squares (OLS) models to ~0.8 of GWR models. The coefficient between logarithmic of outflows from Wuhan and COVID-19 cases in other cities is generally less than 1. The sub-linear scaling relationship indicates the increasing returns of outflows was restrained, proving the epidemic was efficiently controlled outside Hubei at the beginning without obvious local transmissions. Coefficients in GWR models vary in cities. Not only cities around Wuhan but also cities having close connections with Wuhan experienced higher coefficients, showing a higher vulnerability of these cities. The secondary or multi-level transmission networks deserve to be further explored to fully uncover influences of migrations on the COVID-19 pandemic.

Analysis and optimization of a natural gas multi-stage expansion plant integrated with a gas engine-driven heat pump

Excessive preheating energy consumption is a persistent issue in natural gas expansion power plants. In this paper, a novel system is proposed to solve this problem by using multi-stage expansion and gas engine-driven heat pumps (GHP). A dynamic model based on phase equilibrium theory is established to predict hydrate formation. A control strategy with continuously variable transmission (CVT) for optimizing engine efficiency is investigated through simulation and experiment. Finally, the efficiency and economic performance are compared among GHP, electric heat pump (EHP), and cogeneration (CHP) in a case study. The results show that the engine efficiency of CVT is 4.89% higher than that for multi-level transmission. Compared to single-stage expansion, multi-stage design can reduce preheating temperature by 46%, preheating load by 4.41%, and improve the performance coefficient (COP) of GHP 1.4-fold. The COP of GHP is 33.38% higher than that of EHP, and the primary energy ratio (PER) is 1.81 times that of CHP accordingly. The annual average profit margin for GHP is 1.89 times that of CHP and 7.15 times that of EHP. Thus, the proposed system has significant energy savings and economic advantages.

Miniaturization of an Offshore Platform with Medium-Frequency Offshore Wind Farm and MMC-HVDC Technology

Offshore wind power has great development potential, for which the key factors are reliable and economical wind farms and integration systems. This paper proposes a medium-frequency wind farm and MMC-HVDC integration system. In the proposed scheme, the operating frequency of the offshore wind farm and its power collection system is increased from the conventional 50/60 Hz rate to the medium-frequency range, i.e., 100–400 Hz; the offshore wind power is transmitted to the onshore grid via the modular multilevel converter-based high-voltage direct current transmission (MMC-HVDC). First, this paper explains the principles of the proposed scheme in terms of the system topology and control strategy aspects. Then, the impacts of increasing the offshore system operating frequency on the main parameters of the offshore station are discussed. As the frequency increases, it is shown that the actual value of the electrical equipment, such as the transformers, the arm inductors, and the SM capacitors of the rectifier MMC, can be reduced, which means smaller platforms are required for the step-up transformer station and the converter station. Then, the system operation characteristics are analyzed, with the results showing that the power losses in the system increase slightly with the increase of the offshore AC system frequency. Based on time domain simulation results from power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC), it is noted that the dynamic behavior of the system is not significantly affected with the increase of the offshore AC system frequency in most scenarios. In this way, the technical feasibility of the proposed offshore platform miniaturization technology is proven.

High-Power Gallium Nitride HIFU Transmitter With Integrated Real-Time Current and Voltage Measurement.

High-Intensity Focused Ultrasound (HIFU) therapy provides a non-invasive technique with which to destroy cancerous tissue without using ionizing radiation. To drive large single-element High-Intensity Focused Ultrasound (HIFU) transducers, ultrasound transmitters capable of delivering high powers at relevant frequencies are required. The acoustic power delivered to a transducers focal region will determine the treated area, and due to safety concerns and intervening layers of attenuation, control of this output power is critical. A typical setup involves large inefficient linear power amplifiers to drive the transducer. Switched mode transmitters allow for a more compact drive system with higher efficiencies, with multi-level transmitters allowing control over the output power. Real-time monitoring of power delivered can avoid damage to the transducer and injury to patients due to over treatment, and allow for precise control over the output power. This study demonstrates a transformer-less, high power, switched mode transmit transmitter based on Gallium-Nitride (GaN) transistors that is capable of delivering peak powers up to 1.8kW at up to 600 Vpp, while operating at frequencies from DC to 5MHz. The design includes a 12 b 16MHz floating Current/Voltage (IV) measurement circuit to allow real-time high-side monitoring of the power delivered to the transducer allowing use with multi-element transducers.

Design of intelligent integrated monitoring system under multistation fusion platform

With the continuous advancement of the national energy strategy of China, constructing multistation fusion platform (MSFP) of substations, energy storage stations, and data center stations by taking advantage of the remaining resources of substations is a development trend. The monitoring of equipment operation and environmental parameters is a powerful guarantee for the stable and reliable operation of MSFP. Thus, this study developed an intelligent integrated monitoring system construction method that consists of state perception, information fusion, and decision and control layers. First, the state of equipment and environmental parameters were monitored efficiently through built-in status signals and multisensing techniques. Then, the multilevel transmission network, composite data structure, and data interface are integrated to achieve the collection of multisite information. Moreover, the device object model and station-level digital model were designed. Finally, the abnormal fault diagnosis of equipment, data fusion regulation, and coordinated optimization control were completed by using knowledge graph, data optimization and neural network modeling, and hierarchical analysis of multiple application scenarios in MSFP. The intelligent monitoring system can monitor and control the equipment, operation, and environment in the MSFP efficiently, thereby providing technical support for the new development of China’s electric power industry.

Slit Diffraction Spectrum Manipulation in Non-Paraxial Regions via the Spatial-Spectral Correspondence Relationship

In the past, a two-dimensional aperture diffraction of light in the non-paraxial region could only be studied using the Huygens integral without functional forms. This work presents a special case—a one dimension slit where the functional form can be obtained. The monochromatic light intensity distributions are investigated in detail. Using the correspondence relationship, the diffracted spectra of polychromatic light in that region can be readily found. Three interesting spectral effects are described: spectral switches, multi-level data transmission, and optical wavelength ruler. Since the functional form is derived without approximation, it is applicable to a region very near to the slit, including the wavelength region or even sub-wavelength scale. Thus, for light with micron-order wavelength (visible to near infrared (NIR) band), these results are valuable to micro- or nano-optics, especially for studies of the spatial intensities or spectral characteristics in the non-paraxial region.

High-Speed Optical Digital-to-Analog Converter Operation of Compact Two-Segment All-Silicon Mach–Zehnder Modulator

An optical digital-to-analog converter (DAC) is a promising solution for the efficient generation of multilevel modulation signals without a high-speed electrical DAC and a large swing linear driver. We report the design and characterization of an all-Si segmented Mach–Zehnder modulator for an optical DAC transmitter. The modulator comprises a forward-biased positive intrinsic negative (PIN) phase shifter integrated with a passive resistance and capacitance (RC) equalizer (PIN-RC) to optimize a tradeoff between modulation efficiency and analog bandwidth. We successfully confirmed an eye diagram of 90-Gbaud NRZ signal owing to a wide 3-dB bandwidth of 43.9 GHz. Additionally, a 70-Gbaud four-level pulse amplitude modulation (PAM4) waveform can be obtained via the optical DAC operation using a lumped segmented modulator because of its broad electro–optic bandwidth and uniform characteristics. A two-segmented modulator has a compact footprint of 300 μm × 600 μm. Error-free performances based on the patterns of the PRBS11 of NRZ signals were obtained at 50–70 Gbaud. The bit-error-rates of 60-Gbaud PAM4 and 70-Gbaud PAM4 were less than the 7% hard and 25.5% soft decision forward error correction limits, respectively. The optical DAC transmitter using an all-Si segmented PIN-RC modulator is suitable for next-generation high-speed multilevel transmitters.