Signal Information Research Articles

Flexible Organic Chiral Crystals with Thermal and Excitation Modulation of the Emission for Information Transmission, Writing, and Storage.

Organic single crystals quickly emerge as dense yet light and nearly defect-free media for emissive elements. Integration of functionalities and control over the emissive properties is currently being explored for a wide range of these materials to benchmark their performance against organic emissive materials diluted in powders or films. Here, we report mechanically flexible emissive chiral organic crystals capable of an unprecedented combination of fast, reversible, and low-fatigue responses. UV-excited single crystals of both enantiomers of the material, 4-chloro-2-(((1-phenylidene)imino)methyl)phenol, exhibit a drastic yet reversible change in the emission color from green to orange-yellow within a second and can be cycled at least 2000 times. The photoresponse was found to depend strongly on the excitation intensity and temperature. Combining chirality, mechanical compliance, rapid emission switching, multiple responses, and writability by UV light, this material provides a unique and versatile platform for developing organic crystal-based materials for on-demand signal transfer, information storage, and cryptography.

Wavelength-tunable quasi-unidirectional spatiotemporal mode-locked solitons in an isolator-free Er-doped fiber laser

In this letter, we have demonstrated the realization of the wavelength-tunable spatiotemporal mode-locked (STML) operation in an Er-doped partial multimode fiber laser without the intracavity isolator. The laser adopts an all-fiber structure and the saturable absorption effect is attributed to nonlinear multimode interference (NL-MMI) from the single mode fiber-graded index multimode fiber-single mode fiber (SMF-GIMF-SMF) design. The STML laser can operate in the quasi-unidirectional regime with an emission strength difference of larger than 30 dB for the two counter-propagating lasers. And the switching between the dominating operating directions in the clockwise (CW) or counter-clockwise (CCW) directions can be easily achieved by adjusting the MM polarization controller (PC). Both STML conventional solitons (CSs) and the STML soliton molecules are obtained with a similar tunable wavelength from ∼1.56 μm to ∼1.58 μm for the opposite operating directions. Furthermore, the spatial dynamics of the STML soliton are also investigated in detail. The experimental results are of great help for us to explore nonlinear spatiotemporal dynamics and provide potential applications in fields of all-optical signal processing, fiber sensing and information coding.

Fully distributed adaptive asymptotical consensus tracking control of uncertain high‐order nonlinear multi‐agent systems with event‐triggered communication

AbstractIn this article, the distributed adaptive asymptotical consensus tracking control is considered for a class of high‐order nonlinear multi‐agent systems with matched unknown parameters under the event‐triggered communication mechanism. The controllers, adaptive laws and triggering conditions are presented under directed topology with a spanning tree, relieving communication burden among the connected agents since continuous monitoring of in‐neighbors is no longer needed. To handle the cross‐coupling term associated with asymmetric Laplacian matrix and unknown parameters of all agents when the candidate Lyapunov function is defined in terms of local consensus errors, adaptive immersion and invariance control technique is adopted. Adaptive estimators are also designed to estimate the constants related to global information, with which the proposed control scheme is fully distributed. It is shown that all closed‐loop system signals are globally uniformly bounded and all agents can track the reference signal asymptotically while ruling out Zeno behavior though a subset of the agents cannot have direct access to the reference signal information. A numerical example is illustrated to show the effectiveness of the proposed control protocol.

Perception of power quality disturbances using Fourier, Short-Time Fourier, continuous and discrete wavelet transforms

Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time–frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Cochlear Implant Users Experience the Sound-To-Music Effect

ABSTRACT The speech-to-song illusion is a robust effect where repeated speech induces the perception of singing; this effect has been extended to repeated excerpts of environmental sounds (sound-to-music effect). Here, we asked whether repetition could elicit musical percepts in cochlear implant (CI) users, who experience challenges with perceiving music due to both physiological and device limitations. Thirty adult CI users and thirty age-matched controls with normal hearing (NH) completed two repetition experiments for speech and nonspeech sounds (water droplets). We hypothesized that CI users would experience the sound-to-music effect from temporal/rhythmic cues alone, but to a lesser magnitude compared to NH controls, given the limited access to spectral information CI users receive from their implants. We found that CI users did experience the sound-to-music effect but to a lesser degree compared to NH participants. Musicality ratings were not associated with musical training or frequency resolution, and among CI users, clinical variables like duration of hearing loss also did not influence ratings. Cochlear implants provide a strong clinical model for disentangling the effects of spectral and temporal information in an acoustic signal; our results suggest that temporal cues are sufficient to perceive the sound-to-music effect when spectral resolution is limited. Additionally, incorporating short repetitions into music specially designed for CI users may provide a promising way for them to experience music.

Enhancing the efficiency and cost-effectiveness of magnetocardiography by optimal channel selection for cardiac diagnosis

Background. Magnetocardiography (MCG) is a non-invasive and non-contact technique that measures weak magnetic fields generated by the heart. It is highly effective in the diagnosis of heart abnormalities. Multichannel MCG provides detailed spatio-temporal information of the measured magnetic fields. While multichannel MCG systems are costly, usage of the optimal number of measurement channels to characterize cardiac magnetic fields without any appreciable loss of signal information would be economically beneficial and promote the widespread use of MCG technology. Methods. An optimization method based on the sequential selection approach is used to choose channels containing the maximum signal information while avoiding redundancy. The study comprised 40 healthy individuals, along with two subjects having ischemic heart disease and one subject with premature ventricular contraction. MCG measured using a 37 channel MCG system. After revisiting the existing methods of optimization, the mean error and correlation of the optimal set of measurement channels with those of all 37 channels are evaluated for different sets, and it has been found that 18 channels are adequate. Results. The chosen 18 optimal channels exhibited a strong correlation (0.99 ± 0.006) between the original and reconstructed magnetic field maps for a cardiac cycle in healthy subjects. The root mean square error is 0.295 pT, indicating minimal deviation. Conclusion. This selection method provides an efficient approach for choosing MCG, which could be used for minimizing the number of channels as well as in practical unforeseen measurement conditions where few channels are noisy during the measurement.

A Dual Fusion Pipeline to Discover Tactical Knowledge Guided by Implicit Graph Representation Learning

Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of combat intentions. In this work, we fill the above challenge by proposing a dual fusion pipeline introducing graph representation learning into sequence learning to construct tactical behavior sequence graphs expressing implicit martial knowledge, named TBGCN. Specifically, the TBGCN utilizes graph representation learning to represent prior knowledge by building a graph to induce deep learning paradigms, and sequence learning finds the hidden representation from the target’s serialized data. Then, we employ a fusion module to merge two such representations. The significance of integrating graphs with deep learning lies in using the artificial experience of implicit graph structure guiding adaptive learning, which can improve representation ability and model generalization. Extensive experimental results demonstrate that the proposed TBGCN can effectively discover tactical knowledge and significantly outperform the traditional and deep learning methods.

Evolving Dual-Threshold Bienenstock-Cooper-Munro Learning Rules in Echo State Networks.

The strengthening and the weakening of synaptic strength in existing Bienenstock-Cooper-Munro (BCM) learning rule are determined by a long-term potentiation (LTP) sliding modification threshold and the afferent synaptic activities. However, synaptic long-term depression (LTD) even affects low-active synapses during the induction of synaptic plasticity, which may lead to information loss. Biological experiments have found another LTD threshold that can induce either potentiation or depression or no change, even at the activated synapses. In addition, existing BCM learning rules can only select a set of fixed rule parameters, which is biologically implausible and practically inflexible to learn the structural information of input signals. In this article, an evolved dual-threshold BCM learning rule is proposed to regulate the reservoir internal connection weights of the echo-state-network (ESN), which can contribute to alleviating information loss and enhancing learning performance by introducing different optimal LTD thresholds for different postsynaptic neurons. Our experimental results show that the evolved dual-threshold BCM learning rule can result in the synergistic learning of different plasticity rules, effectively improving the learning performance of an ESN in comparison with existing neural plasticity learning rules and some state-of-the-art ESN variants on three widely used benchmark tasks and the prediction of an esterification process.

A Variable Carrier Generation for Heterodyne LDV with an Optical Phase-locked Loop

A heterodyne laser Doppler vibrometer (LDV) requires a frequency shifter to generate a carrier for the vibration information in the detector signal. The carrier frequency should be carefully selected in order to obtain the intended measurement range with a demodulation bandwidth that avoids existing noise sources. The traditional frequency shift in heterodyne LDV is realized with an acoustooptical modulator, which can only generate a fixed carrier frequency. In this paper, a variable carrier generation method based on an optical phase locked loop (OPLL) is demonstrated. Our setup implements a feedback loop to control the phase of the second laser to synchronize a reference laser. In lock-in status the carrier for the vibration information associated with a local oscillator signal. In this paper, we obtain the laser diode parameters relevant to performance and on the design of the photodetector and loop filter. Finally, the performance of the lock-in OPLL, variable carrier as well as velocity measurement is reported and the reliability of the proposed method is evaluated.

Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.

As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities. This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution. Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively. In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study.

Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG. Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia. Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention. The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

Residual Shrinkage ViT with Discriminative Rebalancing Strategy for Small and Imbalanced Fault Diagnosis

In response to the challenge of small and imbalanced Datasets, where the total Sample size is limited and healthy Samples significantly outweigh faulty ones, we propose a diagnostic framework designed to tackle Class imbalance, denoted as the Dual-Stream Adaptive Deep Residual Shrinkage Vision Transformer with Interclass–Intraclass Rebalancing Loss (DSADRSViT-IIRL). Firstly, to address the issue of limited Sample quantity, we incorporated the Dual-Stream Adaptive Deep Residual Shrinkage Block (DSA-DRSB) into the Vision Transformer (ViT) architecture, creating a DSA-DRSB that adaptively removes redundant signal information based on the input data characteristics. This enhancement enables the model to focus on the Global receptive field while capturing crucial local fault discrimination features from the extremely limited Samples. Furthermore, to tackle the problem of a significant Class imbalance in long-tailed Datasets, we designed an Interclass–Intraclass Rebalancing Loss (IIRL), which decouples the contributions of the Intraclass and Interclass Samples during training, thus promoting the stable convergence of the model. Finally, we conducted experiments on the Laboratory and CWRU bearing Datasets, validating the superiority of the DSADRSViT-IIRL algorithm in handling Class imbalance within mixed-load Datasets.

Positional multi-length and mutual-attention network for epileptic seizure classification

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.

Polyolefin ductile-brittle transition temperature predictions by machine learning

Polymers show a transition from ductile-to brittle fracture behavior at decreasing temperatures. Consequently, the material toughness has to be determined across wide temperature ranges in order to determine the Ductile-Brittle Transition Temperature This usually necessitates multiple impact experiments. We present a machine-learning methodology for the prediction of DBTTs from single Instrumented Puncture Tests Our dataset consists of 7,587 IPTs that comprise 181 Polyethylene and Polypropylene compounds. Based on a combination of feature engineering and Principal Component Analysis, relevant information of instrumentation signals is extracted. The transformed data is explored by unsupervised machine learning algorithms and is used as input for Random Forest Regressors to predict DBTTs. The proposed methodology allows for fast screening of new materials. Additionally, it offers estimations of DBTTs without thermal specimen conditioning. Considering only IPTs tested at room temperature, predictions on the test set hold an average error of 5.3°C when compared to the experimentally determined DBTTs.

Speech Enhancement Using Joint DNN-NMF Model Learned with Multi-Objective Frequency Differential Spectrum Loss Function

We propose a multi-objective joint model of non-negative matrix factorization (NMF) and deep neural network (DNN) with a new loss function for speech enhancement. The proposed loss function (LMOFD) is a weighted combination of a frequency differential spectrum mean squared error (MSE)-based loss function (LFD) and a multi-objective MSE loss function LMO. The conventional MSE loss function computes the discrepancy between the estimated speech and clean speech across all frequencies, disregarding the process of changing amplitude in the frequency domain which contains valuable information. The differential spectrum representation retains spectral peaks that carry important information. Using this representation helps to ensure that this information in the speech signal is reserved. Also, on the other hand, noise spectra typically have a flat shape and as the differential operation makes the flat spectral partly close to zero, the differential spectrum is resistant to noises with smooth structures. Thus, we propose using a frequency-differentiated loss function that considers the magnitude spectrum differentiations between the neighboring frequency bins in each time frame. This approach maintains the spectrum variations of the objective signal in the frequency domain, which can effectively reduce the noise deterioration effects. The multi-objective MSE term LMO is a combined two-loss function related to the NMF coefficients which are the intermediate output targets, and the original spectral signals as the actual output targets. The use of encoded NMF coefficients as low-dimensional structural features for DNN serves as prior knowledge and helps the learning process. LMO is used beside LFD to take advantage of both the properties of the original and the differential spectrum in the training loss function. Moreover, a DNN-based noise classification and fusion strategy (NCF) is proposed to exploit a discriminative model for noise reduction. The experiments reveal the improvements of the proposed approach compared to the previous methods.

Research on speed predictive control system of intelligent connected vehicle based on V2X

To enhance the efficiency of urban road traffic and reduce waiting time at traffic lights, this paper proposes a V2X based speed prediction control method for intelligent connected vehicles. Firstly, this paper uses PreScan to build an intelligent network environment model based on V2X communication technology. Then, the traffic signal and target vehicle information transmitted by V2X are analyzed and processed, leading to the design of an intelligent connected vehicle speed prediction control system. Finally, comparative experiments were conducted using HIL between the conventional vehicle speed decision control system and the V2X-based intelligent connected vehicle speed prediction control system under various operating conditions. The experimental results demonstrate that the proposed speed prediction control system can reduce waiting time for traffic lights and enhance road traffic efficiency. Therefore, this paper’s designed speed prediction control system is both effective and feasible, providing a reference for future research on intelligent connected vehicle speed decision control.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

An efficient and optimized cooperative indoor localization for B5G communication

SummaryUltra‐accurate indoor localization/positioning technology is rapidly gaining interest in 5G and beyond (B5G) communications. Achieving 0.1 m level of precise indoor localization is a significant challenge that can be resolved using channel state information (CSI) of wireless signals from nearby access points. The complex indoor propagation issues like shadowing, blockage effects, and multi‐path fading diminish CSI, affecting precise position/location accuracy. Thus, to achieve ultra‐accurate localization, a novel MOIL framework is proposed using a deep learning BiLSTM model and CSI features in an efficient and optimized manner. The proposed MOIL integrates two functional blocks, SecLOP and CordNet. SecLOP conceals multi‐path effects and noise to optimize the input information using sequential responses of signals. CordNet minimizes precise position/location errors by taking all combinations of optimized signal features from the SecLOP block. Extensive simulation experiments were conducted to validate MOIL in different indoor scenarios, such as line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS). Moreover, through a substantial ablation study, the efficiency of the proposed framework is validated/verified. The results outperformed existing works by providing cm‐level accuracy of 1 cm in LOS and 1.5 cm in NLOS scenarios. The proposed work outperformed the literature by 31.3% in LOS and 25.30% in NLOS scenarios.

The digital economy and markup: Based on herd behavior and peer effect

The relationship between digital economy and signal information on firms‘ markup has been overlooked and not well explained, especially in the emerging economies. When there is information asymmetry or too much information, the export decisions of firms are not completed independently, but the result of the comprehensive trade-off of the export signals issued by the 'neighbors'. This paper discusses the impact of the development of digital economy on the markup of export firms by distinguishing two perspectives of export signal, namely herd behavior and peer effect. We find that the digital economy has a positive impact on firm markup. There is substitution effect in digital economy and export signals, and digital economy acts price bonus by influencing export signals. The growing popularity of market signals could offset some of the advantages of the digital economy on export markup for companies. In addition, our paper finds that foreign-owned firms, large-scale firms, highly competitive industries and non-digital economy industries adjust their markups more significantly when they were affected by export signals.

Evaluation of C/N0 raw observation positioning by smartphone based on indoor asynchronous pseudolite

With the rapid development of the economy and information services, there is an increasing demand for indoor positioning. However, Global Navigation Satellite System (GNSS) signals face difficulties in penetrating buildings, making indoor positioning challenging. As a complementary solution to outdoor GNSS signals, pseudolites have shown promising results in various scenarios, and have become one of the research hotspots in the field of indoor positioning. Previous studies on indoor positioning with pseudolites have focused on issues such as initial position determination and carrier phase ambiguity resolution. However, most of these studies have remained in the research stage due to data post-processing and complex calculations. To overcome the limitations associated with the aforementioned factors, this paper proposes a real-time indoor positioning method that utilizes only a smartphone equipped with a built-in GNSS chip. By effectively obtaining pseudolite signal information, the smartphone establishes a simple model based on the distance and received power between the pseudolite and the smartphone. Kalman filtering is employed to mitigate the effects of multipath and occlusion. This simple and low-complexity model enables fast estimation of the user's distance from the pseudolite, thereby facilitating indoor positioning services. In this paper, the effectiveness and low complexity of the method are verified through data collection and experiments in the actual scene of the underground garage. The valuation results show that the root mean square error (RMSE) of its navigation and positioning is better than 3 m, which can meet the needs of daily life applications.