Proceedings of the 3rd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies

This paper describes the design of a low cost miniature-cleaning robot CLEANtech equipped with navigation and obstacle detection sensor suite and powerful actuators to carry out dry and wet mopping in an enclosed environment. The present design is a modified wheeled mobile robot with added sensing and actuation blocks for carrying out the cleaning task within a specified work envelope. The advanced motion-planning algorithm enable this embedded robot to cover all possible paths to remove dirt and dust from the floor efficiently. Floor cleaning robots have found special attention during COVID-19 pandemic where healthcare providers have recommended special caretaking of body and environmental hygiene in order to successfully control the spread of the viral infection.

Taking the secondary field response of layered earth excited by magnetic dipole field source as the basic theory, an improved damping least squares method is adopted for frequency domain electromagnetic data inversion to solve the parameters such as thickness and conductivity of the media in each layer of underground space. Due to the significant difference in numerical values between conductivity parameters and thickness parameters, in the inversion calculation process, the order of magnitude of each element in the Jacobian matrix is adjusted to quickly converge the parameter vector to near the true value. This article first validates the proposed improved algorithm using an ideal three-layer geological model, and the results show that the traditional damping least squares method still cannot converge to the correct result after 100 iterations, while the improved algorithm can match the model parameters well after 20 iterations. Then a complex underground tunnel model was designed and the inversion results of model parameters were compared at signal-to-noise ratios of 15 and 20 dB. Research has found that when the signal-to-noise ratio is high, the inverted image is smoother. The inversion accuracy is high, with a maximum relative error of less than 6% in the inversion results of conductivity parameters, and a relative error of about 1.4% in the depth of the model body.

As peak shaving control were introduced to wind turbine control system, the turbine’s load descended and the distance between the tips and the tower become larger. But generator’s output become more unstable, periodical fluctuation of power carry out much more fatigue injury, which reduced the turbine’s life. Because this is a nonlinear system, the traditional linear system’s analysis method is hard to deal with this problem. This essay provides the mathematical model of the peak shaving control, then analysis the stability of this system with describing function. To enhance system stability, a predictive module based on a differential tracker is used to replace the existing power filter. Simulation experiments have proven the effectiveness and practicality of the stated strategy.

Synthetic Aperture Radar (SAR) image change detection has the capability to continuously acquire land surface information under all weather and time conditions, making it widely applicable across various fields. However, detecting small area changes using SAR images remains a challenging task, primarily due to the impact of speckle noise and the imbalance between change and no-change classes. To address these challenges, this paper proposes an improved change detection method based on coarse and fine classification. Firstly, a multi-scale superpixel reconstruction method (MSRDI) is employed to generate a Difference Image (DI). This method enhances image edges by utilizing local spatial information within superpixels across multiple scales. Secondly, a weighted two-stage center-constrained fuzzy C-means clustering algorithm (WTCCFCM) is introduced to prevent incorrect class migration. This algorithm uses parallel clustering to classify pixels into change, no-change, and intermediate classes. Finally, the principal component analysis network (PCANet) is used to train and finely classify the pseudo-label samples of the first two classes. Experimental results demonstrate the effectiveness of the proposed method and its ability to significantly suppress speckle noise.

The vibration parameters of the generator set are important indicators for monitoring its stability. In order to measure high, medium, and low frequency vibrations of medium-sized hydroelectric generating units, a low-frequency compensation circuit designed according to the frequency response compensation principle of the vibration sensor, extending the frequency characteristics to the region of low frequencies. The mathematical representation of the vibration sensor is magneto electric droplet instrument and designed a compensation circuit for it introduced in detail. Amplitude-frequency behavior and mathematical models pertaining to the sensor before and after compensation were compared and examined individually. The experimental setup results show that this design can expand the amplitude-frequency range into the low-frequency domain and has desirable sensitivity and linearity.

With the advancement of agricultural modernization, precision agriculture technology is increasingly valued. Weed identification and meristematic tissue localization are crucial for precise weed control, as they can effectively reduce the use of herbicides and improve weed control effectiveness. To meet the requirements for meristem center location in applications such as laser weed removal, a method for weed identification and meristem center location has been proposed. Firstly, weeds are identified using the designed convolutional neural network model. Based on weed identification, a series of matching templates are devised to locate skeleton intersections through matching techniques, ultimately pinpointing meristematic points. Experimental results indicate that the proposed method achieves an accuracy rate of 89.5% in weed identification. Following weed identification, the proposed method effectively determines the central point of the weed meristem, offering robust technical support for weed removal in precision agriculture.

In this paper, a test system for high-temperature reflectivity testing of absorbers in the frequency band range of 18–40 GHz is studied and designed, and the absorption characteristics of hollow corundum absorbers in the frequency band range of 18–40 GHz are successfully measured to verify whether they still have the absorption performance of specific frequencies in the high temperature environment. According to the test results, the feasibility of the reflectance test system is verified.

Underwater positioning plays a crucial role in the operation of autonomous underwater vehicles (AUVs). When multiple AUVs collaborate for positioning, higher accuracy can typically be achieved. However, traditional cooperative positioning models often overlook the impact of hydroacoustic propagation delay on model mismatch, which can degrade positioning performance. To tackle this issue, this study proposes a master–slave AUV cooperative positioning framework that accounts for hydroacoustic propagation delay. Additionally, an adaptive unscented Kalman filter (AUKF) algorithm is introduced to address the problem. The effectiveness of the proposed method is evaluated through simulation experiments, where it is compared against the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results demonstrate that the new approach significantly enhances co-location accuracy compared to EKF and UKF.

The precision and real-time capability of monitoring the condition of electrical transformers are critical to ensuring the stable operation of power systems and enhancing the efficiency of power transmission. To improve both the accuracy and performance of electrical transformer condition monitoring, this paper proposes an enhanced DETR model, termed GLD-DETR, which incorporates the GOLD-YOLO method. The main contributions of our model are as follows: (1) Our model integrates the Re-param Block into an extended residual module within the backbone. This architecture enables the model to more accurately detect subtle changes in transformer characteristics across various power application scenarios, thereby enhancing the robustness of feature extraction. (2) We implement the Gather-and-Distribute (GD) mechanism, which allows the model to effectively utilize feature information derived from monitoring data. This mechanism facilitates the integration of state features from transformers at multiple scales, thereby preventing the loss of valuable information and ensuring comprehensive feature representation. (3) We introduce the novel application of the cascade attention mechanism, which diversifies attention by allocating different attention heads to various features. This enhancement improves the model’s ability to focus on the most critical state features, thereby enhancing the overall accuracy and reliability of transformer condition monitoring.

In this paper, a hybrid plasma waveguide with a V-shaped gain medium is proposed by combining a low refractive index material with a metallic material to achieve optical confinement at ultra-deep subwavelength scales using the tip of the gain medium. The results show that the optical confinement performance of the V-shaped gain medium can reach a better deep subwavelength level compared to the semicircular and rectangular gain mediums, while maintaining a large propagation length. At the optimal size of the gain medium width wrib = 200 nm, the quality factor is improved by 297 and 363% compared to the other two structures. In order to achieve a low-loss laser with high quality factor, considering the high loss of the traditional plasma material gold, this paper replaces the traditional plasma material gold with the material sodium, which has lower transmission loss and can reduce the gain threshold of the laser compared to the traditional plasma material gold. This waveguide can be used for communication in highly integrated systems.

In this paper, the coaxial cavity is used to study the interaction mechanism between the material and the strong microwave electric field, through the establishment of re-entrant coaxial resonant cavity model, analyze the principle of different coupling modes and comparison, the most used to feed the cavity using hole coupling. Subsequent experiments based on the re-entrant coaxial resonant cavity, the results show that the microwave action at the beginning of the non-thermal consequences, with the increase of the action time, the thermal consequences is gradually obvious, for the high-power microwave material properties characterization and testing provides a new method and insights.

Through-wall radar imaging (TWRI) is vital for detecting concealed objects in civil and military scenarios. Its performance, however, is often hindered by clutter signals, such as antenna coupling, wall reflections, and environmental noise, which degrade imaging quality. This paper proposes a novel clutter suppression framework utilizing multiple-input multiple-output (MIMO) radar. The approach employs subarray configurations to reduce clutter and enhance target features. Techniques such as singular value thresholding (SVT) and robust principal component analysis (RPCA) effectively mitigate reflective wall clutter in the early stages. To further refine image quality, coherence factor (CF) weighting intensifies target signals while suppressing ghost artifacts. Additionally, a discrete wavelet transform (DWT)-based fusion strategy is introduced to address residual clutter. Experimental results validate that the proposed method significantly improves target detection accuracy in TWRI scenarios.

Manufacturing optomechanical sensors on Silicon-On-Insulator (SOI) substrates often results in waveguide collapse due to the complete etching of the top silicon layer, which exposes the underlying silica to hydrofluoric acid. Alternatives involve using materials resistant to hydrofluoric acid to protect the silicon waveguide, complicating the process and hindering MEMS fabrication compatibility. This study introduces an innovative ridge waveguide design that maintains a 500 nm silicon layer with 400 nm etched on each side, effectively shielding the silica layer and preventing waveguide collapse. This approach simplifies the manufacturing process and ensures compatibility with the MEMS platform. Simulations, using a 2 um spot diameter tapered fiber lens coupled to a 150 um long ridge waveguide, demonstrate a transmission efficiency of 82–94% across the 1480–1630 nm wavelength range, peaking at 82.9% at the typical 1550 nm wavelength for optomechanical sensing, with an end-face coupling efficiency of 46.3%. Our method, requiring only electron beam lithography (EBL) for patterning followed by direct etching of the SOI substrate's sensor middle layer silica, simplifies the fabrication and enables the suspension of the sensor’s proof mass. This design, while making certain performance trade-offs, provides a robust and reliable solution for optomechanical sensor integration, simplifying the manufacturing process and ensuring compatibility with the MEMS platform.

Multiple data analysis-based approaches have proven highly effective across diverse domains and transfer tasks in identifying bearing failures. However, current deep learning-centric feature extraction techniques often neglect the exploration and incorporation of prior knowledge. In light of this, this paper introduces a fault diagnosis method for bearings under variable operating conditions, leveraging deep learning and feature fusion. This method combines data-driven features with time–frequency domain features, seamlessly embedding them within the deep learning network framework to enhance the model's generalization capabilities. The extracted features are then fed into the joint distribution alignment mechanism, which simultaneously adjusts the edge distribution and conditional distribution in the source and target domains. In order to verify the effectiveness of the proposed method, we validate the fault data under different operating conditions in the Case Western Reserve University (CWRU) bearing fault dataset, and the experimental results show that the generalization and diagnostic performance of the proposed method in this paper have achieved better results.

This paper presents an insight into the key safety and reliability challenges of AI-based intelligent decision making in autonomous vehicles (AVs). The rapid advancement of Artificial Intelligence (AI) has brought unprecedented opportunities for innovation, transforming industries and enhancing human capabilities. However, the integration of AI into automotive has raised significant dependability and trust issues. This paper explores the safety aspects of AI, emphasizing the need for an explainable (XAI) framework that balances technological innovation with societal values. We examine key safety issues, proposed frameworks for responsible AI development, and the role of manufacturers, regulators and researchers in addressing these challenges.

Fixing time-domain electromagnetic detection equipment on a large load-bearing unmanned aerial vehicle can achieve rapid detection and structural inversion of underground space. This article constructs a two-dimensional underground tunnel model to simulate the real environment, and calculates the quadratic field response of the model under vertical magnetic dipole excitation in the air through the forward formula of time-domain electromagnetic method. Obtain the time-domain apparent conductivity map of the model through data processing of the quadratic field response. In order to make the inversion calculation easier to converge, the time-domain apparent conductivity was used as the initial value for inversion, and an optimization based inversion algorithm was adopted to invert the depth and conductivity of each layer of the medium in the model, and a structural inversion diagram of the underground space was drawn. The inversion map matches well with the constructed model.

In response to the problem of poor lubrication caused by the deterioration of lubricating oil in mechanical equipment, based on the monitoring of the electrochemical impedance characteristics of lubricating oil, a partial least squares regression analysis method is proposed to process the oil impedance monitoring data, and a grey equal dimensional filling model is used for deterioration prediction. Taking a certain type of natural gas compressor lubricating oil as the research object, a PLS model was constructed and data fitting analysis was carried out by initializing the electrochemical impedance data at multiple frequency points and extracting principal components. The results showed that the linear regression variance fitted by the partial least squares regression analysis method could accurately predict the deterioration state of the natural gas compressor lubricating oil. The grey equal dimensional filling model was used to predict the data, and combined with the PLS model for diagnostic analysis, the deterioration trend of the lubricating oil in the future period could be obtained, providing a basis for planning oil change cycles and formulating mechanical equipment maintenance strategies.

With the advent of the big data era, the application of high-dimensional data in various fields has become increasingly widespread. However, traditional statistical methods face many challenges when dealing with high-dimensional data, such as overfitting, multicollinearity and computational complexity. In this paper, we explore machine learning-based parameter estimation methods for high-dimensional statistical models, focusing on the application of supervised learning, unsupervised learning and deep learning techniques in high-dimensional data analysis. This research proposes an efficient parameter estimation framework by incorporating deep neural networks (DNNs), self-encoders, and regularization approaches. According to the experimental findings, the machine learning methodology outperforms conventional techniques in terms of resilience and accuracy of parameter estimation, particularly when handling complicated nonlinear connections and data noise. The limits of the current approaches are also examined in this work, along with suggestions for future research aimed at enhancing computing efficiency, enhancing model interpretability, and expanding the model's use with small sample sizes.

The welding deformation problem of pump truck arms has gradually attracted widespread attention. The current welding process lacks an effective prediction model, resulting in unstable welding quality and low production efficiency. This paper aims to establish a pump truck arm welding deformation prediction model combining CNN and LSTM, and collect multiple parameter data during the welding process, including welding temperature, welding speed, material thickness and stress distribution. Then, CNN and LSTM are combined to design and train the model, in which CNN is used to extract spatial features and LSTM is used to capture time series features. Finally, the model performance is optimized through cross-validation and hyperparameter tuning. The accuracy of the model in welding deformation prediction reaches 92.5%. The prediction model based on deep learning can effectively improve the deformation prediction accuracy of pump truck arm welding and provide strong support for the optimization of welding process.

In hybrid mode, a certain electric hybrid bus would produce abnormal squealing noise during the braking process. Combined with the subjective judgement of the noise source, the noise signal from the attachment location of transmission and the permanent magnet motor is collected and its time–frequency characteristics is analyzed based on the complex Morlet wavelet algorithm. In addition, the time–frequency domain information of abnormal noise is determined with the help of filtering and sound playback technology. It is found that abnormal noise is related to vehicle speed. The abnormal noise frequencies at various initial braking velocities were extracted in comparison to the biting sound frequencies of connected bevel gears between the gearbox and permanent magnet motor. It is found that the two frequencies are very close. Therefore, it is basically concluded that the abnormal noise of this bus is generated by the biting transmission of bevel gears. The abnormal noise was basically eliminated in the real vehicle test after the bevel gear pairs were carefully matched and modified before loading which further confirms the noise source. From above, it shows that the time–frequency analysis method based on complex Morlet wavelet provide an accurate method to identify the brake noise source.

Transient electromagnetic method is widely used in mineral resource exploration, hydrological environment exploration, archaeological excavation and other fields. However, it is difficult to find an absolutely ideal underground space in real life. Therefore, the factors affecting the intensity of the secondary field induced by transient electromagnetic method are studied. This article uses numerical simulation methods based on the forward calculation formula of transient electromagnetic method in layered geological models to study the influence of the transmission current waveform and the axial direction of the receiving antenna on the secondary field emphasis under two conditions: vertical magnetic dipole and horizontal magnetic dipole for the transmitting antenna. The results indicate that the secondary field response excited by a vertical magnetic dipole is more than twice that of a horizontal magnetic dipole. When using a vertical magnetic dipole as the emission method, the vertical component of the secondary field induced magnetic field is 1–2 orders of magnitude larger than the horizontal component. In all configuration modes, the secondary field induced magnetic field with a trapezoidal waveform is one order of magnitude larger than the half sine wave.

Typhoons can cause significant damage along their paths upon landfall, making accurate typhoon trajectory prediction crucial for disaster prevention. Traditional numerical forecasting methods, while comprehensive, require substantial computational resources and lack real-time capabilities. Existing learning-based methods primarily focus on extracting historical typhoon tracks and environmental information for short-term prediction, but their performance on long-term prediction remains suboptimal. To deal with this issue, we propose a physics- and data-driven hybrid approach for long-term typhoon trajectory prediction. Specifically, Weather Research and Forecasting (WRF) is used to obtain long-term typhoon path prediction features, which are combined with statistical features and spatial environmental features. The combined features are processed through a comprehensive Multi-ConvGRU model to extract spatio-temporal characteristics and predict typhoon trajectories. Experimental results demonstrate that the proposed method significantly improves long-term prediction accuracy by 62.96% compared to the original model.

The respirator is a critical auxiliary component in the operation of power transformers, and its abnormal condition may lead to reduced transformer performance or even failures. To achieve efficient and accurate detection of respirator anomalies, this paper proposes an end-to-end respirator anomaly detection method based on an improved RT-DETR model. The method first employs an enhanced Backbone for multi-scale feature extraction, introducing SE channel attention at the output of each scale to strengthen the features. Next, the method employs an efficient hybrid encoder to enable feature interaction within scales and fusion across different scales. Finally, the decoder refines the outputs iteratively to produce bounding boxes and their corresponding confidence values. Experiments on a self-built dataset reveal that this method delivers outstanding detection results, significantly boosting the overall detection accuracy while reducing the computational cost. Specifically, the proposed approach enhances the mAP@0.5 and mAP@0.5:0.95 metrics by 8.1% and 13.8%, respectively, compared to the baseline. Additionally, it reduces model parameters and computational load (FLOPs) by 17.5% and 14.5%, respectively. This provides an efficient and reliable solution for the intelligent monitoring of respirators.

A method of high-precision harnessing of the on-board clock has been studied, which mainly consists of determining the amount of clock frequency adjustment in the mode of ground-controlled calculation or on-board autonomous calculation, based on which the time–frequency signals output from the satellite can be regulated to synchronize with the ground standard time. Taking the analog high-precision clock maintained by the ground monitoring station as the reference signal, the time signals generated by the small rubidium clocks and high-stability crystals carried by the satellite are tested, and the feasibility of the proposed technical scheme is verified. The experimental results on the principle prototype of the designed time–frequency unit show that, after harnessing the satellite time–frequency reference source with high finesse, better accuracy and long-term stability can be obtained with minimal impact on the short-term stability of the time–frequency reference source itself. Among them, the maximum deviation between the time–frequency signal generated by the small rubidium clock and the reference signal of the ground monitoring station after harnessing is less than 0.25 ns; the peak phase error between the time–frequency signal generated by the high-stability crystal and the ground reference signal after harnessing is less than 0.8 ns.

The customized parts in the construction machinery scene have complex shapes and wide-size spans. There is still room for improvement in the current research’s adsorption accuracy and time cost. To address these issues, this paper proposed a three-stage adsorption pose search method based on DenseNet acceleration and the GWO (gray wolf optimization). First, the DenseNet-121 is chosen to classify the synthesized part image. Then, the GWO is used to search for the adsorption pose of the part classified as irregular type. Finally, we store the 1024 vector and the corresponding adsorption pose as key-value data in the database for new part image retrieval. The experiments show that the average classification accuracy is as high as 99.3%, while its time cost is 487 times lower than the center of gravity rotation search. Moreover, the proposed GWO algorithm has improved the adsorption score by 7.7% compared with the state of the art (SOTA), which fully demonstrates the speed and reliability of our method.

Traditional methods, such as cell counting and dry cell weight measurement, are frequently employed for measuring microalgal density. However, these methods encounter challenges including low efficiency, dependence on the operator’s experience, and the potential for sample contamination. In this study, a luminance-tunable white-light panel LED luminarie is used to capture images of Nannochloropsis sp. and Chaetoceros sp. samples, effectively reducing interferences from the environmental background. Then, the average pixel color and the Euclidean distance from the standard white color of images are calculated to determine the color difference relative to the standard white. An arctan inverse tangent function based fitting model is developed based on the calculated color difference and microalgal density, which is subsequently used to estimate the unknown microalgal density. Compared to the traditional cell counting method, the proposed method captures images with greater uniformity and higher resistance to interference, achieving an average accuracy of over 91% in measuring microalgal density. In addition to its direct application in microalgal cultivation, the proposed method shows promise for application in water quality monitoring, early warning of algal blooms, and early detection of marine red tides.

The distribution transformer meter is an instrument used in distribution systems to measure various electrical parameters. Identifying its faults promptly and ensuring its proper functioning is critical for the daily management of the distribution network. To address the issue of fault identification in distribution transformer meters, this study applies a classification method based on Circular Dilated Convolutional Neural Network (CDIL-CNN) using 15-dimensional time-series measurement data from the meters. This paper analyzes the differences between CDIL-CNN and traditional convolutional neural networks (CNN) as well as temporal convolutional networks (TCN). Furthermore, the study compares the performance of CDIL-CNN with several existing models, highlighting its advantages in long-sequence classification tasks. Experimental results demonstrate that CDIL-CNN significantly outperforms other methods in classification accuracy, especially for long-sequence data.

The unmanned aerial vehicle electromagnetic detection system based on frequency domain electromagnetic method can play an important role in underground space exploration. In order to reduce the impact of the aircraft electronic system on the received signal, this paper designs an airborne extended measurement device, and based on engineering requirements, simplifies the structure and models the mechanics. On this basis, the strength and stiffness of the extended device are analyzed and verified, and the results indicate that the design in this article meets the requirements of safety and functionality in terms of mechanics.

In this paper, a silicon waveguide integrated accelerometer based on cavity optomechanical system is proposed. The accelerometer can be fabricated on SOI wafer with top silicon thickness of 250 nm by micro nano processing technology. The structure and working principle of the accelerometer are analyzed. The optical mode and transmission characteristics of the photonic crystal microcavity structure and silicon waveguide structure of the accelerometer are simulated. The edge coupling of silicon waveguide, the coupling between silicon waveguide and photonic crystal waveguide, and the coupling between photonic crystal waveguide and photonic crystal microcavity are simulated. According to the simulation results, the overall optical path loss is less than 18 dB. Due to the use of the simplest mode transformation structure, the processing technology of the integrated silicon waveguide cavity optomechanical accelerometer can be greatly simplified.

In dynamic mechanical measurements, due to the accuracy limitations of the sensor itself, environmental noise, and changes in measurement conditions, the measurement data of a single sensor often contains errors, which will affect the performance evaluation and fault diagnosis of the mechanical system. To address this problem, this paper proposes a dynamic error modeling and compensation method based on sensor fusion. This method uses the system identification method to dynamically model the sensor. On this basis, multi-sensor fusion technology is introduced to comprehensively process the data of different types of sensors. Error compensation technology is used to separate, correct and suppress errors in the fused data to further reduce measurement errors. The sensor fusion-based approach significantly reduces measurement errors in dynamic mechanical measurements. Specifically, the MPE value based on sensor fusion is between 0.05 mm and 0.24 mm, while the MPE value based on a single sensor is between 0.25 mm and 0.45 mm. Sensor fusion technology reduces the error introduced by a single sensor by integrating the data of multiple sensors. In addition, the measurement time based on sensor fusion is always lower than that of the single sensor method in different dynamic machines, showing that it also has advantages in measurement time. This paper not only improves the accuracy and efficiency of mechanical measurement, but also provides a means of error control for the fields of intelligent manufacturing and precision engineering, helping to promote the development of industrial automation technology.

Global Navigation Satellite Systems (GNSS) provides critical technical support for meteorological disaster warning and monitoring through real-time atmospheric data observation. Tropospheric delay, as one major errors source of GNSS, has been applied in meteorological disaster prediction. Understanding tropospheric delay variations is essential for improving GNSS-based disaster monitoring systems. Traditional prediction models rely heavily on empirical formulas, resulting in insufficient accuracy in their predictions, and some of them require measured meteorological data as input or large dataset for training. To deal with these issues, we propose a GNSS tropospheric delay prediction model based on Group Method of Data Handling (GMDH). By utilizing simple input parameters including latitude, longitude, altitude, and day of the year, zenith tropospheric delay (ZTD) is predicted with the GMDH model. Experimental results indicate that the proposed model outperforms Saastamoinen model, UNB3M model and LSTM. In Experiment 1, using the same 12 stations for both research and model training, the proposed model delivers ZTD prediction accuracy improvements of 47.53%, 56.98% and 37.58% compared to Saastamoinen model, UNB3M model and LSTM. In Experiment 2, using an additional 6 stations beyond those used for training, the improvements are 9.71%, 33.73% and 7.80%, respectively. Compared to LSTM, The running time is reduced by 5.00% and 4.14%, respectively.

In this paper, a high silicon microring modulator (Si-MRM) based intensity modulation direct detection (IM/DD) systems that enabled by probabilistic shaping (PS) is demonstrated by simulation for the first time. The adopted probabilistic shaping method uses constant component distribution matching to transform uniformly distributed amplitude points into a unilateral Maxwell–Boltzmann distribution. The system is implemented by co-simulation between Matlab and VPI Transmission Maker, probabilistic shaping pulse amplitude modulation 4 (PS-PAM4) signal is used. The simulation results demonstrate that by adopting PS in the system, a 1.02 dB maximum receiver sensitivity improvement can be obtained at bit error rate (BER) of 3.8 × 10–3 compared to the case without employing PS. This study shows the performance improvement potential of PS technology in the scenario of Si-MRM based IM/DD systems.

Digital communication system has been widely used in military, broadcasting, mobile communication and other fields because of its strong anti-interference ability, high transmission quality and easy encryption (Jiang et al. in Bistatic SAR spatial-variant motion error compensation method via joint-refocusing of multi-subimages, pp. 190–195, May 12, 2023) [1]. This paper uses Simulink platform to simulate and analyze the digital communication system using BPSK modulation in AWGN and BSC channels, focusing on the performance of Hamming code, BCH code and convolutional code. In AWGN channel, by adjusting the signal-to-noise ratio (SNR), the bit error rate (BER) performance of different coding schemes is compared. The simulation results show that the convolutional code has the best performance under high SNR. In BSC channels, considering channel symmetry and error probability, Hamming codes and BCH codes perform well at low error probability, while convolutional codes maintain low BER even at high error probability.

Security threats to wireless networks are becoming increasingly prominent, and during data transmission, various network attacks and information leakage issues are becoming increasingly prominent. This paper, therefore, offers a wireless network security monitoring and communication transmission system developed through the use of Internet of Things technology to increase the security of wireless networks and improve data transmission reliability. First, a security monitoring platform is being set up, involving a real-time packet traffic analysis via DPI (Deep Packet Inspection) technology, where SVM (Support Vector Machines) will be used to identify abnormalities in behavior. The various security approaches are then incorporated into the system to ensure information dissemination’s security, including data encryption, identity authentication, and access control. Hence, experiments revealed that the system went below 5% levels of false alarms and decreased information transmission delay times to 55 ms after optimization. Thus, the system greatly improves the security monitoring capability of wireless networks and presents effective solutions to protect information security within the IoT environment.

In MIMO (Multiple-Input Multiple-Output) radar systems, optimizing phase-coded waveform sequences with low autocorrelation sidelobes and low inter-correlation peaks is crucial for improving the detection performance of the system. Traditional waveform design methods often fail to effectively capture the complex dependencies within the signal sequence, making it difficult to achieve globally optimal sidelobe suppression. To address this issue, this paper proposes an optimization method for orthogonal phase-coded signal sets based on the Long Short-Term Memory (LSTM) model. The method can model long-range dependencies in the waveform sequence and effectively capture the complex patterns in the phase-coded sequences. The model learns to minimize the loss function of autocorrelation and inter-correlation sidelobes, resulting in waveforms with good sidelobe suppression in the time domain. Compared with traditional optimization methods, the LSTM-based waveform design method can adaptively adjust the phase sequences, effectively reducing autocorrelation and inter-correlation sidelobes while maintaining good signal orthogonality. Experimental results verify that the generated waveforms significantly reduce sidelobe levels and enhance the system's signal processing performance, with broad application prospects.

Sensors are defined as devices to transform any natural energy into electric energy, such as movement, environment status, image pixels, traffic load of human body biotic signals, varied functions of sensors can build network with distributed and parallel structures. For sensor networks, routing algorithm can be introduced to describe the load balancing and properties of the distributed data collection system. We applied arbiter to adaptive load balancing by alternating each signal in a fair and evenly way to grant, Odd–even turn model is addressed to permit possible path while the fair arbiter select the path to achieve a balanced load distribution. Congestion information is also introduced to reach more balanced load, the congestion-aware routing applied fair arbiter can improve the network performance with under simulations. We also proposed several application fields for sensor network and its load balancing, covering inter-discipline research from human health, environmental protection, traffic regulation, imaging and farming.

Optical wireless communication (OWC) has emerged as a promising complementary technology for radio frequency (RF) communication due to its distinctive advantages, especially when integrated with the user-centric (UC) philosophy, resulting in significant enhancements in system performance. However, in scenarios that support user mobility, frequent handovers in UC-OWC networks can degrade the user equipment (UE) experience, thereby necessitating the design of a stable and efficient handover strategy. In this paper, we propose a Q-Learning based dual-end interactive handover strategy (QL-DIHO), which dynamically optimizes network association by alternately adjusting the AP and UE ends. To strike a balance between throughput performance and association continuity, we ingeniously incorporate an additional reward term related to historical association retention into the reward function. Our simulations demonstrate that, compared to the stateless handover strategy that ignores historical association, the proposed QL-DIHO strategy maintains superior association continuity, albeit with an acceptable reduction in throughput performance.

Over the past two years, there has been an increasing presence of integrated communication, navigation and remote sensing in technology planning and application design, with “Starlink” serving as a prominent example in the rapid advancement. As a result, the integrated communication, navigation and remote sensing technology is becoming an increasingly important research focus in the global development of mega-constellation of satellites. In this paper, we investigate the system, application and key technologies of integrated communication, navigation and remote sensing. After comprehensively analyzing the technical implementation at various levels and taking “Starlink” constellation as a typical case study, the development strategy and technical architecture of integrated communication, navigation and remote sensing was deeply discussed. On this basis, we proposed the flat self-organizing integration strategy and present a novel framework based on “task driven and empowerment on-demand” approach as a suggestion for future research.

This paper tackles the challenges of spectrum scarcity and network efficiency in the 5G IoV environment using dynamic spectrum sharing and network slicing, supported by deep learning. It aims to improve spectrum use and network resource allocation, addressing limited resources and uneven network load. The choice of 5G NR is due to its higher bandwidth and lower latency, crucial for IoV applications. A deep learning model monitors and predicts real-time spectrum usage, followed by a dynamic sharing mechanism adjusting allocation based on demand and network status. Network slicing then distributes traffic according to application requirements, ensuring QoS. Tested in a simulated environment, results show a 2% increase in spectrum utilization to 62% for 100 vehicles, latency reduced from 58 to 55 ms, and QoS satisfaction up from 78 to 80%. The proposed technology effectively resolves resource and service quality issues, providing a foundation for future intelligent IoV systems.

In this study, finite element modeling of cross-scale high-speed transmission links on composite ceramic substrates is conducted. The cross-scale transmission link is composed of micro-bumps connecting the chip, a redistribution layer (RDL) and through ceramic vias (TCVs) on low temperature co-fired ceramic (LTCC), an RDL and TCVs on high temperature co-fired ceramic (HTCC), and finally, a termination at the ball grid array (BGA). A parametric scan analysis is performed on critical parameters influencing transmission performance, including the thickness and spacing of the RDL. Optimization designs are applied accordingly. The findings indicate that optimizing the thickness of the RDL can significantly improve transmission performance and ensure signal stability. Adjusting the spacing between RDLs reduces electromagnetic interference and crosstalk, further enhancing signal integrity. The simulation results show that reduced return loss is achieved in the optimized transmission link, while low insertion loss is maintained over the 10 MHz to 30 GHz frequency range. Improved noise margins and signal stability are revealed by time-domain eye diagrams, confirming enhanced transmission quality. Multi-port signal path simulations verify that strong crosstalk resistance and efficient signal transmission are demonstrated by the transmission link in high-density, multi-channel systems.

Coplanar waveguide is a key structure in cross-scale transmission structure of System-in-Package (SiP). In this paper, an analytical model for the radiation field distribution of coplanar waveguide structure is established based on the analytical approximation method. In this model, the upper groung plane of the coplanar waveguide is treated as a binary slot antenna array. And, the Babinet’s principle and duality principle are used to make the binary slot array equivalent to the binary traveling-wave array. And, the mirror image principle is used to make the lower ground plane of the coplanar waveguide equivalent to the binary traveling-wave array, with same current amplitudes and opposite phases to the former. According to the analytical model proposed in this paper, the radiative directivity of the coplanar waveguide is analyzed and regulated, and parameter scanning analysis is performed on the length of the coplanar waveguide. Compared with the finite element model, the analytical model not only has less than 5 $$\%$$ % phase error in the main radiation direction and has lower time complexity , but also can be used to analyze and regulate the radiation characteristics of coplanar waveguides from theoretical level.

A simple single-layer dual-band dual-mode metasurface antenna for on/off-body wireless body area network (WBAN) communication is proposed. By properly adjusting the size and arrangement of the surrounding patches, the on-body omnidirectional mode is significantly enhanced. The antenna can excite off-body broadside pattern mode by introducing an arc-shaped slot at its center. The results show that the proposed antenna achieves a broadside radiation at lower frequency of 2.4 GHz band and an omnidirectional pattern at higher frequency of 5.8 GHz band.

This paper presents a breakthrough methodology for overcoming the intrinsic suppression limitations of mixers in low-order in-band spurious suppression within frequency conversion chains, including innovative design methodologies, circuit architectures, and comprehensive simulation and experimental validation results. Currently, in complex microwave frequency conversion systems operating under predetermined frequency planning schemes, the suppression of in-band low-order spurious components remains a persistent technical challenge that significantly impacts system performance and operational reliability. This study introduces an innovative approach where harmonic signals are extracted through LO signal path splitting. These LO-generated harmonics are then phase/amplitude-conditioned to produce cancellation signals, which undergo vector synthesis cancellation with intrinsic low-order in-band spurious components, thereby achieving a significant enhancement in spurious suppression capability beyond conventional mixer limitations. Currently, the spaceborne Ka/Ka-band frequency converters achieve an in-band spurious suppression capability of 32 dBc for the − RF + 6LO mixing component under room temperature conditions. Through the implementation of LO harmonic mixing to generate cancellation signals and their subsequent vector synthesis cancellation, the suppression capability has been enhanced to 70 dBc, achieving an improvement of approximately 40 dB compared to conventional methods. In contrast, the comparable spurious suppression capability of similar systems developed by Thales Alenia remains at approximately 20 dBc. Therefore, the low-order spurious suppression capability demonstrated in this study achieves internationally leading performance, surpassing existing state-of-the-art solutions by a significant margin.

Wireless communication technology drives the cyber-physical integration of digital twins in smart manufacturing. Orthogonal frequency-division multiple access (OFDMA), a promising multi-user transmission mode, can efficiently support the transmission of small frames to a group of sensors simultaneously. However, in time-sensitive scenarios, ensuring real-time data connectivity and monitoring remains a key challenge for OFDMA applications in smart factories utilizing digital twins. In this paper, we propose an OFDMA-based time-triggered scheduling strategy for low-latency communication in wireless sensor networks deployed in digital twin-enabled smart factories. Conflict-free scheduling constraint models based on integer programming (IP) are established, including latency constraints, RU continuity constraints, and TTI-RU constraints, for the joint scheduling of transmission time intervals and resource units. We further abstract the two-dimensional scheduling problem into a one-dimensional scheduling problem of time-frequency resource blocks (RBs), which decreases the number of constraints and expands the scheduling scale within limited time. We apply IP to design a scheduling method with minimal total end-to-end latency as the optimization goal. The experimental results demonstrate the advantages of RB-based scheduling in terms of latency and scheduling scale.

In addressing the challenges of privacy protection and the lack of personalization in data sharing within the Power Internet of Things (PIoT), this paper proposes a lightweight retrieval method based on attribute differentiation. We design a personalized retrieval model that incorporates attribute distinction and introduce an edge-cloud collaborative lightweight search method along with a hybrid lightweight search framework. The proposed personalized retrieval approach generates data replicas tailored to the attributes of electricity users, thereby reducing the number of replicas and alleviating computational burdens. Simulation results demonstrate that this solution achieves lower computational overhead and higher search efficiency.

This paper proposes a novel approach to mitigate propagation losses in terahertz (THz) communication, a key enabler for future 6G networks, by integrating reconfigurable intelligent surfaces (RIS) with a proximal gradient method (PGM) optimization framework. Operating in the 0.1–10 THz range, THz communication offers ultrahigh data rates and low latency but faces challenges from severe propagation losses caused by atmospheric absorption and scattering. The proposed RIS, comprising passive reflective elements, dynamically adjusts the phase shifts to enhance signal strength and coverage. A PGM-based algorithm optimizes these shifts and effectively mitigates the losses under varying conditions. Simulations demonstrated that the PGM-optimized RIS significantly reduced path loss, lowered outage probability, and enhanced ergodic capacity, particularly over long distances. These results highlight the potential of RIS-PGM integration to address THz propagation challenges and advance high-speed low-latency communications for future 6G networks.

Global Navigation Satellite System (GNSS) is indispensable in disaster monitoring, especially during extreme weather events such as extreme precipitation and typhoons. However, extreme precipitation can cause short-term anomalies in the Total Electron Content (TEC), leading to ionospheric errors that compromise GNSS accuracy and hinder effective disaster monitoring. Consequently, accurate determination of TEC is thus crucial for mitigating these errors and enhancing GNSS performance under such conditions. This paper focuses on the impact of extreme precipitation on TEC prediction accuracy and innovatively introduces specific humidity as an additional feature. This leads to the development of a Feature-Extended Long Short-Term Memory (LSTM) Model, designed to improve short-term ionospheric TEC prediction under challenging weather conditions. The prediction accuracy of the proposed model was evaluated using ionospheric TEC data, along with corresponding specific humidity, global geomagnetic index (Kp), and equatorial geomagnetic index (Dst) data. Experimental results demonstrate that the proposed Feature-Extended LSTM Model significantly improves prediction accuracy for TEC over 3–48-h forecast periods compared to both the Autoregressive Integrated Moving Average Model (ARIMA) model and the Non-Feature-Extended LSTM Model. Specifically, for a 48-h forecast period, the RMSE and MAE of the proposed model are improved by 42.88% and 48.29%, respectively, compared to the ARIMA model, and by 17.42% and 15.82%, respectively, compared to the Non-Feature-Extended LSTM Model.

The cable is a key component of large-span cable-stayed bridges to ensure structural safety. Real-time monitoring and intelligent assessment of the cable condition is crucial for the safe operation of bridges. Latest technologies such as the Internet of Things (IoT) provide more convenient means and tools in this field. This paper analyses the hierarchical structure and key technologies of the Internet of Things, studies the sensors for cable force collection, and proposes the scheme and engineering key points of the monitoring system based on IoT technology. The test results show that the system can effectively and accurately monitor bridge cable forces and has good application value for improving bridge safety and reducing construction costs.