This PDF file contains the front matter associated with SPIE Proceedings Volume 12178, including the Title Page, Copyright information, Table of Contents, and Conference Committee listings.

Cold heading die is one of the key components of high speed screw cold heading machine.As the vulnerable parts, the die should be monitored and fault diagnosed. In this paper, the arithmetic average algorithm based on time domain is adopted to denoise the load signal of the mold, and then the load signal after denoising is processed by time-domain tolerance matching and frequance-domain principal value analysis, so as to identify faults. The simulation results show that the timedomain arithmetic average algorithm can effectively improve the signal-to-noise ratio (SNR), the obvious fault signals can be identified by time-domain tolerance matching, and the fault data close to the standard signal can be further judged by frequency domain principal value analysis. Tolerance matching in time domain and principal value analysis in frequency domain provide an effective means for diagnosing die faults of high speed screw cold heading machine.

When the adaptive antenna array encounters motion interference, the interference may overflow the null position which leads to a sharp decline in the output performance. At present, most of the solutions are to widen the null to improve the fault-tolerance of interference incidence angle. This will consume more degrees of freedom of the antenna array. For the adaptive antenna array with large number of elements, although it can provide enough degrees of freedom, the computational complexity is very large. To solve the above problems, a flexible adaptive null broadening beamforming based on symmetric non-overlapping subarray is proposed in this paper. The proposed method has three main contributions. The first is to widen the null to suppress the motion interference. Secondly, the proposed method flexibly controls the wide null according to the actual needs to reduce the waste of degrees of freedom. Finally, subarray level adaptive beamforming is used to replace element level adaptive beamforming, so as to reduce the computational complexity of the method. Simulation results verify the high performance and effectiveness of the proposed method.

In this paper, we derive closed expression of range-direction of arrival (R-DOA) information in phased-array radar. In our previous works, the theoretical expression of R-DOA information is proposed. Firstly, a uniform linear array system model is given, where R-DOA information is formulated as the mutual information between range, direction of arrival and the received signals with with complex additive white Gaussian noise. Then employing the a posteriori probability density function (PDF) of the range and direction of arrival to extract R-DOA information from echoes. Moreover, by dividing the integral region of the a posteriori PDF into the signal part and noise part, a closed approximate expression of R-DOA information is derived. Finally, numerical results are presented to verify the effectiveness of the proposed approximate closed expression.

Aiming at the performance analysis of portable planar microphone arrays under different attitude angles in practical applications, azimuth estimation error of sound source associated with array attitude angle is derived. The influence from pitch angle and roll angle is formulated as an impact factor and the corresponding investigation is provided, leading to an explicit relationship between azimuth estimation error bound and tolerance range of array attitude angle. Comparison analysis and numerical results demonstrate that this study provides a quantitative approach to evaluate the azimuth estimation performance for portable planar microphone arrays when attitude angle is considered.

Aiming at the problem of high false alarm rate due to the monitoring blind area of transmission line anti external damage early warning method, a real-time anti external damage early warning method of transmission line based on optical fiber sensing technology is proposed. The geometry of the target damaged by external force is a contour representation of the outer edge, and the edge points are used for target recognition. Optical fiber sensing technology is used to accurately locate and quantify the pressure change of the identified target, and the monitoring blind area is reduced according to the stress distribution. The anti external force damage algorithm is designed to extract and locate the moving area of the external force damage target and separate it from the background in the video. The target movement area is divided into three areas: line protection area, monitoring work area and danger warning area. According to the evaluation level, the real-time early warning of external damage prevention of transmission line is carried out. The simulation test results show that the realtime early warning method based on optical fiber sensing technology has lower false alarm rate than the anti external break early warning method based on CNN-RVM, ViBe motion detection algorithm and automatic clustering model. It can provide real-time and reliable early warning information.

This work reduces the synchronization complexity by using a linear phase approximation to the phase of shaped-offset quadrature phase-shift keying, telemetry group version (SOQPSK-TG) signal. We also use the pulse truncation (PT) method to simplify the phase state. The modulator and coherent demodulator are implemented on the Field-Programmable Gate Array (FPGA). The bit error rate (BER) performance of the proposed method is obtained, which is close to the theoretical optimum performance.

Graph data structures are now widely used and detecting graph anomalies is a challenging task. Traditional anomaly detection methods can achieve good results in the case of low-latitude data, but in the face of today's unstructured graph structure data, they often seem to be powerless. Fortunately,GCN provides an effective method for processing graph data. Based on the idea of GCN network and traditional support vector data description, this paper proposes a graph anomaly detection method and makes corresponding experiments. Compared with traditional methods, better results have been achieved.

In this paper, a low-cost signal delay generator which can be used for lidar range gating is developed and implemented by MC100EP195 delay chip and STM32 microcomputer. LabVIEW is used to write the upper computer control software, and 4 delay chips cascade is adopted to realize 0-40ns delay range. By controlling the encoder and the upper computer, the delay accuracy of 10ps can be obtained. The inherent delay of the generator is 10ns, which can meet the requirements of lidar range gated detection and other high precision applications.

In radar signal processing, pulse compression by the matched filtering can maximize the output signal-to-noise ratio (SNR). However, range sidelobes are relatively high in the pulse compression output. In weather radar, since the reflectivity of spatially distributed weather target varies dramatically, the sidelobes of large scatterers may mask the adjacent small scatterers. Thus, ultra-low sidelobes are desired. An effective way to control the range sidelobes is the adaptive pulse compression (APC) based on the reiterative minimum mean square error (RMMSE). In this paper, we focus on the reduceddimension fast APC (FAPC) algorithm. The FAPC algorithm estimates the range profile by reiteration. It has much lower computational complexity than APC while maintaining the performance close to that of full-dimensional adaptive processing. Furthermore, the FAPC has good Doppler tolerance, which makes it a preferable choice for weather target detection.

Urine routine examination has moved towards automation in recent years. In order to improve the accuracy and recognition speed of the automatic urine analyzer, a urine sediment classification method based on the attention mechanism and reparameterization is proposed. Aiming at the problem of unclear images of some urine sediments and noise, image processing technology is used to pre-processed. Aiming at the problem of some classes are easy to be confused in classification, during network training, multiple branches are used for feature extraction, and the attention mechanism is used to focus on the key points in the image, which improves the accuracy of easily confused classes. Multi-branch can be equivalently replaced with a single branch during inference to improve the recognition speed. Experimental results show that the designed network can effectively improve the classification accuracy of urine sediment images.

The application of compressed sensing(GS) theory in ghost imaging reduces the sampling required for image reconstruction, thus improving the reconstruction efficiency. Due to its sparsity constraints on objects, the algorithm performs better on sparse and smooth images. Many studies have been carried out on sparse representation of objects and the solution of constraint equations. Different from the previous method, using GS method after orthogonalize the reference patterns as a pretreatment method to reconstruct image is proposed in this paper. We compare the simulation and experimental results of the original GS algorithm and the GS algorithm with using pretreated reference patterns. The results show that the pretreatment method improves the quality of reconstructed images in simulation and experimental. It is proved that the pretreatment method is a feasible method to improve the quality of reconstructed images.

Radar communication waveform design is crucial for integrated radar and communication equipment, and abstracts increasing attention. The integrated radar communication waveform is usually faced with a higher sidelobe issue, compared with the single radar waveform, which will reduce the detection performance of radar. In this paper, we propose a convolutional neural network (CNN) based sidelobe suppression method for the integrated radar communication waveform. Different from the conventional method, the proposed method transforms the sidelobe suppression into a signal recognition and classification problem. The simulation results show that when the signal-to-noise ratio is not less than 3dB, this method can make the peak sidelobe ratio of matched filtering reach below -50dB, which has a great improvement compared with the traditional sidelobe suppression method.

The paper studies the estimation of the angular difference between adjacent sources in a uniform linear array. When there is additive Gaussian noise, in order to obtain the eigenvalue expression related to the signal, we can apply singular value decomposition, getting the eigenvalue of the spatial covariance matrix. One of the feature values is related to the degree of difference between the two sources, and the expression of the angular difference estimation is derived using this difference. The larger the observed samples, the more precise the estimation. The experimental results prove our theoretical analysis. Meanwhile, the feasibility of the proposed estimation method has been proved. Importantly, the results of this paper have pragmatic guiding meaning for array signal processing.

Aiming at the problem of insufficient analysis of the influence of self-defense jamming on the radar measurement to determine the target position, the mean and standard deviation of the conversion error under different jamming are derived. The angle and distance measurement errors of the radar to the target under different jamming are analyzed, and the coordinate conversion equation is used to derive the mean and standard deviation of the conversion error under different jamming. The simulation results show that the self-defense noise jamming makes the conversion error more random and the error standard deviation increases; the self-defense deception jamming makes the coordinate conversion produce a fixed error. It shows that under the influence of different self-defense jamming, the positioning of the target by the radar produces different fixed error and random error, the performance of the radar is greatly affected. The target penetration success probability is guaranteed.

To realize the long-period and automated data collection of lunar radiation and eliminate the geometric errors of the lunar hyperspectral image during the observation process, this paper proposes a slit-type spectrometer observation method based on the rotating table of the equatorial mount. This method uses a wide field finder-scope to realize automatic moon tracking and positioning; at the same time, it corrects the drift angle and oversampling of the collected raw data. The results show that the full moon situation is the same as the non-full moon situation. This method can effectively find and track the position of the moon's sphere center, and it also corrects the drift and over-sampling of the moon image, realizes accurate and automated long-term observation of the moon, reduces observation errors, and saves observation costs.

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple cameras. With the advancement of deep network and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. In this paper, we propose a simple yet effective Multi-Scale Horizontal (MSH) model for person Re-ID task. Firstly, the model consists of a novel Multi-branch network which adopted residual network ResNet50. There are two branches in our network: global branch and local branch. In local branch, the model slice a person into different parts in multi-scales. Secondly, we present a mix pooling method which considering both average and maximum pooling method. Finally, we employ triple loss and softmax loss as the loss function of the network. Experiments on two datasets (Market1501 and DukeMTMC-reID) demonstrate the advantage of the proposed model.

Microwave staring correlated imaging (MSCI) is an imaging method that forms a temporal-spatial stochastic radiation field (TSSRF) and correlates the echo with the known stochastic radiation field to obtain target scattering information. The randomness of TSSRF is the necessary condition for MSCI, and analyzing the spatio-temporal randomness of correlated imaging is the basis for optimizing radar parameters. This paper first introduces the multiple-input multiple-output (MIMO) system into radar correlation imaging for increasing radar detection channels. Then we construct the spatial ambiguity function of MIMO to analyze the randomness of the radiation field in MIMO MSCI. Finally, we analyze the influence of the receiving array geometry and different compounding methods of the multi-channel signal on the randomness of the radiation field and the imaging quality. Simulation experiments verify the relationship between the space-time randomness in MIMO MSCI and transceiver aperture and the compound of the received signals.

Passive electronic reconnaissance guiding radar active detection is the main method for the integrated radar ECM system to give full play to the integrated advantages. In order to optimize the overall detection efficiency of shipborne integrated radar ECM system under guidance, this paper analyzes the target detection time optimization and airspace optimization, and gives some suggestions on guiding detection from the perspective of technical support and tactical use through simulation calculation. The simulation data and conclusions can be used as the basis for the operation of shipborne integrated radar ECM system, and have guiding significance for the commander’s operational decision-making.

Aiming at the relatively difficult problems of speech signal enhancement and de-noising under low signal-to-noise ratio and strong background noise, this paper adopts three variable step size LMS adaptive filtering algorithms: variable step size LMS algorithm based on sigmoid function product, variable step size LMS algorithm based on adjusting the optimal exponential factor of sigmoid function and variable step size LMS algorithm based on sigmoid function feedback de-noise The speech signals in different noise environments such as factory noise and ocean noise are de-noised. After the pure speech signal is superimposed with - 10dB, - 5dB, 0dB and 5dB Gaussian white noise, pink noise, factory noise and marine noise respectively, the variable step size LMS adaptive noise cancellation processing is carried out. SNR, PESQ and other objective evaluation algorithms are used to evaluate the sound quality of the enhanced speech signal. The simulation and calculation results show that the three variable step size LMS algorithms used in this paper have good de-noising effect on speech signals with strong background noises of low signal-to-noise ratio, and the variable step size LMS algorithm based on sigmoid function feedback control has better de-noising effect than the other two algorithms under this condition.

A gridless direction of arrival (DOA) estimation method for the two-dimensional (2-D) minimum redundant matrix (MRA) in nonuniform noise is proposed. First, a positive semi-definite programming model for restoring covariance matrix in 2- D MRA is established, and the denoising problem is transformed into a noise-free covariance matrix recovery problem. Second, the 2-D angle estimation is obtained by the unitary esprit method. This method can suppress the influence of nonuniform noise and has high estimation accuracy. Besides, it can also realize the automatic matching of azimuth and elevation angles. Simulations and experiments comparison confirm the performance of the proposed method.

The traditional synthetic aperture radar (SAR) image can only detect a piece of target area, but can not accurately locate a single vehicle target in the target group. In order to improve the accuracy and efficiency of dense vehicle detection in SAR images, a method of segmentation detection for dense vehicle targets was proposed to make full use of the advantages of CSAR (Circular SAR) in acquiring target scattering information from all directions. The method separates the entangled adjacent targets through image segmentation, and then uses the clustering method based on azimuth angle to locate the target precisely. Experimental results show that the detection probability of the proposed method is 93.18% and 91.84% respectively for high-frequency and low-frequency CSAR image dense vehicle targets, which can locate the densely packed vehicle targets in the scene more accurately, reduce the false alarm rate, and realize the detection of dense vehicle targets. Compared with other dense vehicle target detection methods, the detection performance of this method is better.

Traditional ghost imaging usually suffers from two problems. First, it usually offers poor images even if the measurement numbers are much larger than the pixels of images to be imaged. Second, the number of samples of ghost imaging should be lower. In this paper, a Group-based sparse representation ghost imaging (GSRGI) reconstruction scheme is proposed. The proposed GSRGI uses the structure group as the basic unit of sparse representation, which is composed of patches with similar structures, fully reflects the local sparsity and non-local self-similarity of natural images. To make GSRGI computational complexity lower, we combine the method of image blocking with computational ghost imaging. Through numerical simulation and experiment, GSRGI can obtain high image quality under a low sampling rate, it is better than other schemes in both quantitative analysis and visual perception.

In the field of directional sound source transmission, an important device for electro-acoustic energy conversion is the ultrasonic transducer. To achieve sound source clustering, it is necessary to make the wavelength of the sound wave much smaller than the diameter of the loudspeaker, in the range of ordinary audible sound frequencies, theoretically requires a traditional loudspeaker with a diameter of up to thousands of meters, which is unrealistic and cannot make the sound source directional. Using the characteristics of high frequency and short wavelength of ultrasound to modulate low frequency acoustic signals to high frequency ultrasound, the modulated signal is emitted by an emitting transducer, which is demodulated by the non-linear characteristics of air during propagation to produce low frequency audible sound, so that the directional transmission of sound waves is obtained. In this chapter, the basic working principle of the parametric emission array is studied, the factors affecting the directivity of the transducer array are analysed, the expression for the directivity of the uniform linear transducer array is derived, and the Matlab simulation test program is written according to the formula, and the three factors affecting the directivity of the acoustic beam, such as array element spacing, array size and emission frequency, are simulated and analysed one by one and conclusions are drawn.

Thunderstorm poses a serious threat to aviation safety, therefore thunderstorm identification is very important for airborne weather data. In this paper, we present a thunderstorm identification method, which combines SCI algorithm with TITAN algorithm to identify not only the thunderstorm cells in the thunderstorm cluster, but also the thunderstorm profile. The simulated airborne scanning data is constructed using ground-based weather radar reflectivity data. The simulation results show that this method can effectively identify thunderstorm cells, including their centroids and profiles.

The existing monitoring technologies of obstructive sleep apnea/hypopnea syndrome (OSAHS) face many problems, such as invasion discomfort and privacy security. Aiming at these problems, non-contact microphone array technology is utilized in this work. Firstly, two microphones on both sides of the bedhead are used to synchronously acquire the snoring signals of the patient all night. Secondly, the high-band energy ratio (HER) and spatial cross-correlation deviation (SCCD) between the two array element channels are proposed as the spatial propagation features of snoring. Thirdly, the supine and lateral positions are indirectly recognized by the head orientation classification through unsupervised clustering, which provides a new feasible idea for the detection of sleep body positions.

Crowd counting algorithm began to develop towards the combination of counting and localization. However, the existing methods pay little attention to the localization task, so we propose a crowd counting and localization network based on codec structure, named CALNet. At the end of encoding, the network efficiently fuses different scale information through Adaptive Feature Fusion (AFF) module, to weaken the influence of change of head scale and camera perspective. In the decoding part, we designed the Attention Enhancement Upsampling (AEU) module to obtain high-quality regression map, which uses high-level semantics to guide the model to fuse shallow features. In addition, due to the problem that the existing label maps can not give good consideration to the counting and localization tasks, we proposed a new label map generation method. The new label map not only ensures the simple counting mode, but also has excellent localization performance, and it can be easily migrated to the existing counting methods. The experimental results show that our approach can achieve good results in counting accuracy and localization accuracy.

In order to address the detection and segmentation of partial blur for natural images,a no-reference and training-free algorithm was proposed.Firstly,the test image was re-blurred by a Gaussian low-pass filter.Secondly,the pixel-wise singular value decomposition within N×N neighborhoods for both the test image and the re-blurred image were conducted to obtain the ratio of the end singular value to all singular value.According to the ratio,the blurriness distribution function was structured and the blurriness distribution map was drawed.Finally,combined with the K-means clustering algorithm and the erosion and dilation operations, the test image could be segmented into blur region and clear region.Experiment results show that the proposed approach can effectively detect and segment the partial blur image and behave well at precision, recall and F-score.

The contamination caused by noise to the image leads to the image missing key information or reflect the role of the real situation. Wavelet denoising based on the spatial domain has a certain denoising effect on the noise. When doing wavelet decomposition on the image, because the wavelet function has the feature of flexibility in base selection, different choices of wavelet basis functions have different denoising effects on the noisy image. Simulation experiments are conducted by Python for several commonly used wavelet basis functions using the control variables method. The final experimental results are evaluated by PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity).

The microwave landing radar provides the GNC (guidance, navigation and control) sub-system with the range and velocity relative to the Martian surface during the EDL (entry, descent, landing) phase, to ensure the safety for mars landing rover. Microwave landing radar adopts a linear frequency modulation continuous wave(LFCW) system and is equipped with four radar beams, each of which can measure range and velocity at the same time. Continuous wave radar has the characteristics of no blind range and high measurement accuracy. However, due to the leakage of transmission and receiving, the range will be limited, and the bottom of landing rover can cause multi-path problems and special sand and dust effect ,to the microwave landing radar system design brings many problems. In order to meet the requirements of far-range and highaccuracy, and be responsible for the range and velocity measurement in the mars environment, the microwave landing radar adopted high isolation system design and multi-path suppression algorithm, and carried out radar performance verification under various environments such as sand and dust, which effectively solved the problem of continuous wave radar and ensured the complete success of China's first mars exploration landing on the surface of mars.

Owing to the complexity of sound propagation and the limitations of available samples, traditional artillery sound recognition methods are lack of generalization ability to arbitrary acoustic propagation environments and detection ranges. In this paper, a novel classification approach is proposed based on the deterministic linear channel model of sound propagation in the atmosphere, which identifies the type of an unknown remote artillery sound by searching the matched source waveform space with the maximum projection energy in a pre-established database. Simulation results show that the proposed method has higher adaptability to the variation of atmospheric propagation channels of the acoustic signal, and provides a feasible idea for improving the ability of long-range artillery sound recognition at any unknown distances.

RFID (Radio Frequency Identification) is expected to provide a new way for the efficient and low-cost control of oilwell downhole tools, and bring huge economic benefits. However, the RF (radio frequency) base station chips on the market could not withstand the high temperature of downhole. In this paper, discrete components are used to build RF front-end circuits to realize the drive and demodulation functions of RF base station chips. The circuit parameters are adjusted and optimized through circuit simulation. Then, the software and hardware design of the downhole reader are accomplished. Finally, experimental results show that the proposed downhole reader based on discrete components can successfully demodulate and read the tag data.

With the explosive increase of wireless communication users and popularization of internet of thing, it is predicted that millions of new device terminals access the wireless communication network in the future. At the same time, the radio resources are not enough to meet users’ communication needs. Thus, it is necessary to develop effective radio resource conservation technologies to improve spectrum resource utilization. This paper focuses on conserving radio resources and specifically describes three typical technologies, which are energy harvesting technology, cognitive radio technology, and D2D(device-to-device) communication technology. Their network structures and corresponding resource allocation schemes are concluded so that their roles on resource conservation are verified and proved. Finally, the paper concludes the facing challenges of these technologies and ongoing researches, which indicate that these technologies still need to be optimized in order to suit for 5G network development.

5G has become the latest mobile communication method and trend in the 2020s and 5G+ or 6G are under development. No matter what types of communication methods, frequency allocation cannot be avoided. The main problems faced by radio regulatory agencies are the scarcity of frequency resources and the huge demand for radio applications. Besides, the contradiction between frequency demand and supply becomes increasingly prominent. At the same time, the frequency utilization is unreasonable and the overall efficiency of frequency utilization is low. The new thinking is that frequency efficiency also depends on the method used. Therefore, several frequency allocation methods will be introduced in this article and compare their efficiency in MATLAB to find the advantages and disadvantages of all kinds of methods and then analyze which allocation method should be used in a certain environment. The results show that tx-ITL owns from 50% to 200% more capacity than the second-efficient one.

The IFR method is known as one of the simplest frequency reuse methods being used in cellular networks. However, it always carries out the frequency reuse progress regardless of the location of users. Thus, FFR method is combined to IFR method to compensate for the defect of IFR method. The research aims to find out the best models using the combination of FFR+IFR methods. The paper will firstly start with the introduction of the two frequency reuse methods, and provide the principles of how they are combined. Then, the ideas of the MATLAB coding for modelling and simulating the models are further introduced in the paper. According to the results of the comparisons among the FFR+IFR models with the frequency reuse factor1, 3, 4, it is found that the FFR+IFR3 model is the most efficient and available. By doing this research, the best model could be used in reality since it is in line with the actual situation to the greatest extent.

For most students and researchers, the actual hardware equipment for studying the classic AM modulation and demodulation is extremely costly and hard to access especially during the pandemic time. Besides, the widely used envelope detector in AM demodulation is typically associated with the problems of distortion and low-quality recovered signal, although it is cheap and simply structured. To comprehensively study AM modulation and demodulation process and to generate better output signal from the demodulator with minimized distortion, this paper presents an approach for students and researchers using the free electric circuit simulator LTspice, along with a combined circuit design strategy of the AM input driver and the envelope detector. The circuit design and the simulation performed by this software show students and researchers a clear and comprehensive transient analysis of the results of this experiment. Meanwhile, by following these parameter setting rules, high-quality AM recovered signal can be obtained with minimized distortion.

Retrieval and matching are important steps in the reasoning of the Scenario Library, which can be used to obtain historical scenarios similar to the target scenarios and their disposal methods, and provide reference and guidance for the rescue work of the target emergencies. Determining an effective retrieval strategy is the prerequisite for the reasoning of the Scenario Library, and then the weights of the scenario elements and attributes are determined through the cloud computing method and the expert scoring method, which lays the foundation for the similarity calculation. Finally, the structure, attributes, and comprehensive similarity are calculated separately to obtain the historical scenario that is most similar to the target scenario, and the Scenario Library retrieval work is completed. The proposed retrieval and matching method can be applied to different types of scenario libraries to help solve different practical problems.

We propose a low-complexity Distance-Weighted KNN (DW-KNN) algorithm to alleviate the nonlinearity of the visible light orthogonal frequency-division multiplexing (VLC-OFDM) and compare the relationship between peak-to-peak voltage (Vpp) and system performance. Experimental results show that the DW-KNN algorithm can effectively alleviate the nonlinearity in the system. This article improves the general DW-KNN, and the prediction complexity of 64QAM signals is reduced by 86% on the basis of ensuring the performance of the algorithm.

Nowadays, with the huge requirement and fast development of wireless communication, as well as the needs for 5G system, a faster, more efficient, more energy-saving, etc. communication system with higher frequency is pursued. Thus, it is important to optimize the design of the amplifier in communication system. The work presents a review in amplifiers used widely in high-frequency communication system. A common model for communication system is introduced, in which this work especially emphasize the amplifiers. Several important performance measurements are introduced and a unified performance measurement, figures of merits (FOM), is adopted. Basic topologies to design an integrated circuit (IC) are analyzed, especially their performances in high frequency. Some modern IC voltage amplifiers reported in original works based on communication system are selected to calculate their FOM and compare their performances in high frequency communication, such as the voltage gain, bandwidth and power dissipation.

In telecommunication history, frequencies are always a crucial resource and factor. Thus, frequency reusing methods appear, which aim to reduce the costs and increase frequency using efficiency. However, they also cause interference. Therefore, optimizing the methods of the allocation remains critical. This paper shows different methods of frequency reusing. These methods include IFRN, FFR, water-filling, tx-ITL and FLG. From comparing the net-work capacities of different frequency allocation methods and analyzing the real situation, we get the conclusion that when making the frequency allocation plans, not only should we consider the capacity, but we also need to consider the real situation. Only in this way, can we find the best solution.

In this paper, we propose an Adaptive Inductive Network(AINet), whose contributions are mainly manifested in two aspects: First, we propose a routing process evaluation method to reduce noise interference caused by different samples and obtain an accurate representation of the sample class. The second is to introduce a memory iteration mechanism in AINet, which provides a class feature template for the sample induction process to help the model quickly determine the class representation. The experimental results show that AINet can effectively handle the few-shot relationship extraction task, and demonstrate the validity of the class feature modeling method in the few-shot relationship extraction task.

Secondary Users (SUs) can obtain channel use right opportunistically in cognitive radio networks (CRNs) on the basis of not interfering with Primary Users’ (PUs) transmission. In order to meet the diversified transmission requirements of PUs in CRNs, in this paper, we divide PUs into two levels (PU1 and PU2) and assume PU1 packets have higher priority than PU2 packets. In the context of a single-channel CRN, we design a channel allocation mechanism based on the packet transmission actions for one PU1, one PU2 and one SU. A Markov chain model is built and analyzed to derive some performance indexes of SU packets under the classification of PUs. Finally, the system performance of our proposed CRNs with two levels of PUs is compared to the conventional CRNs with a single type of PUs by numerical results. The numerical results show that compared to the system with a single type of PUs, our proposed channel allocation mechanism with two levels of PUs efficiently reduces SU packets’ blocking rate and increases throughput.

Synthetic Aperture Radar (SAR) is faced with complex electromagnetic environment in practical application, so it needs certain anti jamming ability. The SAR micro motion jamming can form false target string in the azimuth direction of SAR image, which makes it difficult to interpret SAR image. Therefore, based on the analysis of the principle of micro motion jamming, this paper proposes a SAR micro motion jamming discrimination method based on dual channel cancellation, and theoretically analyzes the feasibility of the algorithm. Finally, the effectiveness of the algorithm is verified by simulation.

Ionospheric dispersion would change with time and space for Geosynchronous Synthetic Aperture Radar (GEO SAR) systems, which causes a significant de-correlation of radar echoes during the long-time synthetic aperture process and seriously influences the imagery quality in azimuth. To address the issue, a parametric autofocus method by the ionospheric dispersion compensation for GEO SAR systems is proposed in the article. Firstly, the GEO SAR signal model is given, where the phase variations due to the ionospheric dispersion is characterized with the polynomial model. Then, the SAR imaging expression with the Back-Projection (BP) imaging method is utilized to estimate the polynomial parameters for the current GEO SAR echoes based on the Minimum Entropy Criterion of the SAR imagery. Thus, the temporal and spatial phase variations can be properly compensated with the polynomial and the imagery is well focused. Finally, the experimental results based on the ground slide rail indicate the usefulness of the proposed method.

Aiming at the problem that the Acoustic Diversity Index (ADI) obtained from the marginal area of the nature reserve is easily affected by the noise of human activities outside the area, the advantages of the Differential Microphone Array (DMA) that has low processing complexity and can rapidly form full-band spatial notch are used. Firstly, two sets of backto-back dual-delay DMAs with coprime delay factors are applied to adaptively filter out the anthropogenic noise from a fixed direction. Then, the two sets of spatial filtering outputs are used to suppress the grating lobe effect of the highfrequency pattern of DMAs based on the notch interleaving features in the two-dimensional space consisting of angle and frequency. Finally, the 0-1 detection component of the biotic signal is reconstructed at all time-frequency bins by taking the larger one between the two values, so as to restore the ideal ADI in the whole space without anthropogenic noise. It will provide a feasible idea for robust and accurate monitoring and evaluation of biological acoustic diversity in areas close to human activities.

In recent years, in the process of studying the energy-saving algorithm of wireless sensor networks, wireless sensor networks with low duty cycle appear. In this mode, nodes have their own scheduling table and information table, and periodically change their working state and sleep state. Although certain energy-saving objectives are achieved in this mode, the transmission delay between neighbor nodes will also increase. As a result, the data can not be transmitted timely and effectively. In this paper, the sink node also has a sleep mechanism. After determining the low traffic load sink nodes that need to sleep, it is necessary to select the sensor nodes that sleep with them at the same time to save the unnecessary energy consumption caused by this subnet. Considering the routing planning of load transfer and the heterogeneity of node energy, a load balancing algorithm based on data traffic is proposed, which uses the powerful function of transfer path to perform data emission and realize load balancing so that the sink node can also transfer the load well when sleeping, balance the network energy load and increase the network life cycle. The simulation results show that the algorithm can effectively optimize the topology of the Internet of things, reduce energy consumption, make the energy consumption at a uniform level, maintain high transmission rate, and increase the life cycle of the Internet of things.

As a classic point cloud segmentation network, PointNet is widely used in point cloud segmentation and classification due to its quick speed and stability. But there are still many shortcomings that can be improved. Firstly, the difficulty and numbers of different types in point cloud varies, which lead to poor results on difficult samples. In addition, the PointNet network using the maximum value operation to compress the features of the global point cloud, a lot of detailed information will be lost. Aiming at the problem of segmentation and classification of objects, this paper improves on the original PointNet network and adds a self-encoding network to replace the maximum pooling function to encode global information. Then design a loss function that combines focal loss, self-encoding loss and regullizer loss, which effectively solves the problem of uneven training difficulty. Comparative experiments prove that the method proposed in this paper improves the accuracy of prediction while retaining the speed performance of PointNet.

It is necessary to cohere the full pulse copy-and-forward jamming based on the simultaneous transceiver system with the radar transmitting signal, which can obtain higher pulse compression gain and has both jamming effects of suppression and deception, and is extremely threatening to modern radar. This paper analyzes the generation mechanism of coherent suppression jamming based on the simultaneous transceiver system, studies the generation sequence of jamming signals, establishes relevant mathematical models for simulation, and compares the jamming effects obtained under different parameters. The simulation results show that the expected jamming effect can be achieved by reasonably setting the jamming parameters in practical application.

The exponentially damped harmonics (EDH) wavelet series related to RLC damped system and their generic implementation using simple OTA and grounded capacitor are presented in this work. The EDH wavelet is derived from second-order RLC circuit and generalized to arbitrary order wavelet function, which will be considered as a wavelet base series. Then, wavelet filter is synthesized using a generic method based on the simple OTA-grounded capacitor circuit. The designed wavelet filters with different scale wavelets can be employed to implement EDH wavelet transform. The simulation results demonstrate the proposed approach is efficient. Furthermore, the generic method could be used to implement other wavelet filter and wavelet transform.

5G era has arrived with huge reforms. Rational allocation and utilization of 5G spectrum resources, improvement of signalto-noise ratio, and reduction of interference caused by the frequency allocation are the premise and guarantee of the popularization of 5G communication. Therefore, based on 5G cellular networks and starting from the background application of 5G, new algorithms such as water-filling algorithms and simultaneous iterative water-filling algorithms are given compared with previous algorithms such as integer frequency reuse and fractional frequency reuse, 5G frequency allocation model is elaborated, the selection of the corresponding model under the actual environmental interference like the propagation loss is analyzed, and methods and techniques are constantly put forward to implement corresponding frequency allocation. Ultimately, the relationship between the signal-to-noise ratio and average capacity is obtained, and specific measures concerning how to optimally improve the average capacity to exploit limited spectrum resources with higher efficiency by 5% and lower interference by 3%.

In the field of 5G communication, how to improve communication quality and communication speed has always been the important topic of research in the industry. At present, the widely used cellular base station has high requirements for the power allocation and frequency allocation of the base station due to the different number of users and service locations. The research shows that using the power allocation method based on water-filling algorithm in densely populated areas can improve the channel capacity and signal quality. Therefore, this paper will deeply study the base station power allocation in densely populated areas based on 19-cell structure.

Due to the interference caused by the superimposed transmission of data streams to users and the constraint on the number of reusable users in a beam, the problem of users' power allocation that affects the actual system performance in mmWaveNOMA system is NP-hard. The common approach in the literature for this problem is to solve power allocation and user scheduling respectively to obtain suboptimal results. In this paper, we proposed an efficient heuristic scheme named EHS which joint user scheduling and power allocation to maximize the weighted sum-rate (WSR) as the system performance objective. Firstly, we introduce a series of auxiliary functions to transform the non-convex objective equation into a convex function under certain conditions. Secondly, we divided the NP-hard problem into two resoluble sub-problems. The single beam user scheduling algorithm based on dynamic programming (SBUS) and the multi-beam power control (MBPC) algorithm based on polynomial knap-bag problem are proposed to solve the two sub-problems respectively. Finally, the SBUS algorithm and the MBPC algorithm are combined to optimize the ultimate goal. Numerical results show that the total rate of the system achieved by EHS is 13.6% higher than existing Fractional transmit power control algorithm (FTPC).

This paper discusses the shortest length of the total cable between nodes in an indoor communication network for the design of an indoor distribution system.Using the distribution particle group algorithm, with the shortest between the node cable length as the goal, realize the system optimization design, the cable connection path between the node abstract for multi-branch no return traveler problem, based on the particle group intelligent optimization algorithm, optimize the layout of the indoor communication network nodes, so as to achieve the lightweight design and the optimal cost of the whole communication system.

In deep space exploration, signal of very far-away spacecraft is usually highly dynamic and weak. During the operation of spacecraft maneuver, usually omnidirectional antenna with low power is adopted, which makes it difficult to achieve reliable telemetry reception and decoding. In this paper, we present a new method of monitoring the status of spacecraft maneuver with radiometric doppler tracking, the method only depends on the carrier without decoding telemetry signal. The basic theorem and algorithm are discussed in detail, and experiment demonstration could testify the effectiveness of the method presented in the paper.

In order to improve the performance of beamforming of uniform planar array (UPA) against jammer motion, an improved null broadening beamforming approach of UPA is introduced based on covariance matrix expansion. The linear constraint sector suppressed (LCSS) beamforming is further used to form broad nulls in case of UPA, by which jammer motion can be inhibited. Then, the covariance matrix expansion (CME) and spatial smoothing techniques are combined. Virtual arrays are constructed by the CME technique, so the performance of null broadening can be greatly improved. The spatial smoothing technique is combined, by which the coherent interference can be candled. Simulation results show that, broad nulls can be formed by the improved approach, and coherent interference can be suppressed effectively.

With the development of the 5G communication systems, mobile data traffic demand and users are greatly increased. Since D2D technology has been one of the widely used technology of 5G communication systems, the frequency reuse methods should be used to solve the shortage of the frequency channels of the D2D communication systems. Fractional Frequency Reuse(FFR) is a promising scheme to improve the utilization of the frequency spectrum. However, the capacity of different reuse method used in cell center and cell edge are totally different. This paper is trying to find out which frequency method used in cell edges and cell centers when considering the path loss of the communication and the shadow. And in this paper, different number of users and different percentage of the D2D pairs are also considered. The results show that the IFR1+txITL FFR method is the best one and number of users of the system doesn’t influence the capacity of the system a lot but the percentage of the D2D pairs really influence the capacity of the system.

This paper mainly studies the frequency allocation for 5G+, the magic of mobile communications. The effectiveness of these methods and practicability of the algorithm are analyzed. We first introduce the structure of the model we use, which is cellular network consists of 19 cells. The model covers the following five approaches: IFR3+FFR, SWF, tx_ITL, selfoptimizing tx_ITL and FLG. In order to elaborate the difference of the 5 models in this paper, we describe the algorithms of each model we use by giving pseudo code and flowchart in the method part. The FFR algorithm uses IFR1 and IFR3 at the same time. In the cell center, we use IFR1 algorithm, while in cell edge we use IFR3. Partitioning a cell into a cell center and a cell edge. The core of the other four methods is the simultaneous water-filling algorithm, which is a smarter frequency allocation with a self-optimizing algorithm and distributed intelligence for every user to decide on which frequency should be selected. The basic principle of water-filling algorithm is based on the Shannon formula and boundary conditions. The other three methods are developed from the water-filling algorithm. By comparing the average capacity of each network, we reach the conclusion under different conditions.

The Tag map is constructed by TagSLAM framework and the camera track is located, meanwhile, the trajectory of the robot is calculated by integrating the obtained wheel tachometer data. The internal and external parameters of the camera and wheel tachometer are obtained by solving the least square problem after the camera and wheel tachometer tracks are aligned. This method can calibrate the internal and external parameters of camera wheel tachometer without the robot moving along a specific trajectory. Experimental results show that the method is accurate and the calibration results are very close to the standard values of internal and external parameters of camera wheel speedometer.

Color light field images encryption algorithm based on frequency domain DNA coding and integer wavelet transform (IWT) is introduced. First, the light field image is decomposed into sub-view images in the angular domain plane. Subsequently, a low-frequency coefficient matrix and three high-frequency coefficient matrices are obtained in the frequency domain using the two-dimensional IWT. The low-frequency coefficient matrix is encrypted using DNA coding and a self-controlled chaotic system based on PWLCM. The high-frequency coefficient matrix is scrambled, and then the reconstructed intermediate encrypted image is obtained by a two-dimensional integer wavelet inverse transform. Finally, in the spatial domain, a diffusion operation is performed to obtain the final encrypted image. The simulation shows that this algorithm has the advantages of large key space, high operation efficiency, good encryption security and strong resistance to various attacks.

With the rapid development of the Internet of things, the problem of access control has become increasingly prominent. In order to solve the problems of high redundancy and low integrity caused by the traditional Role-Based Access Control model, this paper proposes an access control model of Internet of things based on attribute exploration. Firstly, the decision model is established based on attribute exploration algorithm, and finally merged into a complete decision model. The model has the function of dynamically modifying access rights in the process of building decision model. It is a new exploration in the field of access control model.

Monocular pose estimation is a basic element in computer vision, and monocular pose estimation based on point features, as one of the important branches of monocular pose estimation, is widely used in many fields such as robot positioning, virtual reality, and image precision measurement. When solving the monocular pose based on point features, the number of point features and image noise has a great influence on the estimation accuracy. Therefore, this paper proposes a robust orthogonal iterative pose measurement algorithm that introduces an intermediate coordinate system between the camera coordinate system and the world coordinate system to optimize estimation constraints. Then, the method that uses the least square method to solve the error problem is applied to solving the distance between the characteristic point and the camera's optical center. Moreover, the calculation of the initial value of the camera pose is simplified through the intermediate coordinate system. Finally, the camera pose is optimized by orthogonal iteration. Experiments show that, compared with existing algorithms, the algorithm proposed in this paper is more robust to the number of point features and image noise, and the overall solution accuracy is better.

Aiming at the hidden danger caused by the extensive application of automotive radar, an improved single maneuvering target tracking algorithm of automotive radar is proposed in this paper, and its research and simulation are carried out. In automotive radar applications, the movement of a single model does not accurately describe the motion of target relative to the radar, in order to improve the accuracy of the description of target motion state, this article put forward the Interactive Multiple Model Strong Tracking Unscented Kalman Filter (IMM - STUKF) algorithm, this algorithm not only solves the problem of imprecise description of the actual motion state of the target, it also makes up for the poor robustness of traditional UKF algorithm and ensures the tracking accuracy of single maneuvering target. Experimental simulation results show that the proposed algorithm can reduce tracking error more effectively than the traditional IMM-UKF algorithm.

An important goal of satellite communications in the future is to meet the needs of multimedia and high data rate Internet. How to provide users with satisfactory services while using resources reasonably is an important topic. QoE (Quality of Experience) is the subjective evaluation of users' satisfaction with the provided services, and determines the user's service provider selection behavior. QoE can be qualitatively analyzed by quality of service (QoS) parameters. At present, there are existing studies that use artificial intelligence and big data to map QoS parameters to QoE model training. However, QoS parameter collection is time-consuming and laborious, and the data is unevenly distributed. Traditional machine learning frameworks need to process data uniformly, which will undoubtedly cause data leakage. Federated learning is a popular distributed learning framework, which solves the problem of data islands and protects user privacy. However, federated learning requires a central server to coordinate various data holders for model aggregation, which brings a single point of failure and data security issues. In addition, if one or more data holders submit malicious parameters to deliberately hinder model aggregation, it will affect system performance. Therefore, this paper proposes a distributed learning framework based on the alliance chain, and uses a reputation mechanism to adjust the weight of each participant’s local parameters in the model aggregation process, so as to resist single points of failure, sybil attacks and collusion attacks, and improve the credibility of the system.

In order to solve the problem of damage identification of oil derrick steel structure under noise background, a damage identification method based on energy entropy difference is proposed. First, the signal is denoised by stochastic resonance, then the denoised signal is decomposed according to Hilbert Vibration Decomposition (HVD), and the amplitude spectrum energy entropy difference is calculated and used as the damage index; taking the ZJ70 oil derrick steel structure laboratory model as an example to carry out experimental analysis. The results show that this method can identify the damage location of oil derrick steel structure effectively and accurately.

To address the shortcomings of the water circulation algorithm, which is prone to fall into local optimum and slow convergence, an improved adaptive water circulation algorithm based on chaotic is proposed. Firstly, introducing cubic mapping into the initialization and rainfall process of the water cycle algorithm to improve the quality and population diversity of the initial solution.Secondly, replacing the original fixed step size with an adaptive step size to balance the relationship between exploitation and exploration and to improve the convergence speed of the algorithm.Finally, the improved algorithm is tested against the original algorithm by 10 benchmark functions. The simulation shows that the improved algorithm has a strong ability to jump out of the local optimum in high-dimensional functions, and has a fast convergence speed, high accuracy of the algorithm and good robustness.

At present, in smart city and other applications, message middleware Kafka is usually used to distribute massive image terminal data to business components such as data analysis. However, in practical business applications, it is found that the traditional Kafka message distribution mode is usually difficult to apply to high real-time massive data distribution scenarios. To solve this problem, the message distribution mode of the system is optimized, and a mode of separating normal real-time distribution from abnormal reissue is proposed. In this distribution mode, when the subscriber is normal, the message is directly distributed to the subscriber in real time. When the subscriber is abnormal, Kafka is responsible for reissuing the message after fault recovery. The experimental results show that the delay and resource consumption of the distribution mode are significantly reduced, the delay is reduced by 60% ~ 70%, and the CPU and memory utilization are reduced by 45% and 43% respectively.

Aiming at the problem of no standard for real-time data hotspot classification, this paper proposes a method based on SVD-PCA microblog hotspot event prediction, based on the TF-IDF algorithm to calculate keyword weights, construct a text-keyword matrix, and use SVD to generate covariance matrix, and use PCA method to reduce dimensionality to alleviate sparsity, determine the evaluation criteria of hot topics based on the amount of reading and discussion, and use logistic regression to predict. Finally, through experiments on the news data set of a news website from July 2020 to July 2021, the prediction method proposed in this paper can effectively improve the accuracy of the prediction of hot events on microblog.

The intrusion of foreign objects such as pedestrians and vehicles into the boundary of the railway has seriously threatened the safety of pedestrians and the safety of railway traffic. Aiming at the low utilization rate of the classic Faster R-CNN model for inter-channel information, in the feature extraction stage of the model, combined with the inter-channel attention module of SENet, the SE-Faster R-CNN model is proposed and used on this basis A more balanced loss function and adjustment of the size of the anchor improve the detection accuracy of the model. When the number of railway foreign body images is insufficient, first use VOC2007 to train the model, and then use part of the KITTI data set and a small amount of high-speed rail platform monitoring data and railway foreign body data to fine-tune the model, making the model better in the railway scene Performance. The experimental results show that although the algorithm speed has dropped slightly, the mAP of the improved Faster R-CNN model on the VOC data set has reached 80.3%, which is an increase of 6.9% compared to the original model. The image data on the railway platform and the railway foreign body The average accuracy on the image data reached 87%, especially for pedestrians and trains in the railway, the detection accuracy reached 89.61% and 90.9%, respectively. Studies have shown that the introduction of the SEnet structure into Faster R-CNN improves the feature extraction capabilities of the original model, which makes the model's detection accuracy for targets greatly improved, and can well complete the task of railway foreign body detection.

The study aimed to investigate whether distinct cellular network structures would result in different performances for specific spectrum distribution methodology and find the optimal structure for a 19-cell wireless system. Based on the hybrid spectrum allocation involving Simultaneous Water-Filling (SWF) and Fraction Frequency Reuse (FFR), we proposed four structures. Furthermore, the comparison of their channel capacity was made based on the hybrid spectrum allocation involving SWF and FFR algorithm. The consistency of the methodology was maintained by considering the distance-based path loss and the intercell interference for channel transmission. Both numerical and analytic results were validated to prove that the most cell-scattered network structure had the best performance, with a maximum 30% improvement to the others. In addition, the research also certified that the FFR algorithm could use the spectral resource thoroughly to optimize the channel capacity to a large extent.

With the continuous development of education, online education is becoming more and more popular with learners because of its convenience. However, online education has the problem of insufficient interaction with students. Students still need to rely on the students themselves to judge the lack of knowledge in practice tasks, which will greatly affect the learning efficiency. In this paper, we propose a method based on formal concept analysis to analyze the knowledge that may not be mastered, and a method of intensive practice for students. First of all, establishing the formal context of student error test questions, construct the concept lattice to calculate the degree of importance of the knowledge concept that may not be mastered, judge the student’s mastery of the knowledge concept, and give feedback. Secondly, construct the concept lattice of the unpractised test set, and calculate the similarity of the concept extension set that meets the threshold, find similar topics and push them, and realize targeted training for students who have not mastered the knowledge concepts.

Aiming at the problems of my country's current camellia fruit picking mainly relying on manual picking, large labor and low efficiency, the development and promotion of camellia fruit picking equipment and technology are discussed, and technical analysis is carried out for the design of camellia fruit picking robots. This article analyzes the characteristics and existing problems of camellia fruit picking, and analyzes the four major components of the picking robot: "end effector", "manipulator motion control system", "visual recognition positioning system" and "driving walking device". In summary, summarize the problems that have been solved, the results achieved, and the difficulties that still exist, so as to provide enlightenment for the subsequent exploration of the subject research of "camellia fruit picking robot".

The paper proposes a new method of the turbo decoding algorithm with a low error floor based on source information redundancy. The method uses the information after frame synchronization and turbo decoding to make statistics of the occurrence probability of information symbols, which is used as the extrinsic information of the input component decoder. Simulation results show that the proposed method can further reduce the bit error rate compared with the traditional turbo decoding method, decrease the threshold value of error floor effectively and improve the decoding performance without increasing the consumption of additional hardware resources and computational complexity.

An ideal antenna layout design scheme can effectively increase the isolation degree between antennas in a limited area and significantly reduce the interference between cosite radio stations. In this paper, a new objective function is adopted to make full use of horizontal and vertical isolation of antennas in three-dimensional space according to the characteristics of the frequency band and antenna in V/UHF ground-to-air communication, and the GA/SA algorithm is proposed to optimize three-dimensional antenna layout based on the new mathematical models and constraints of antenna layout optimization. The calculation results show that this method can achieve better antenna layout optimization in complex multi-antenna systems with multiple constraints.

Aiming at the problem of short-range high-speed bullet trajectory measurement under clutter background, a new signal processing method used for practical radar system is proposed. Firstly, an introduction of the high-speed bullet target detection system is described. Secondly, the high-speed moving target echo is derived and analyzed. Then the processing algorithm is presented, in which a parallel multi-channel search processing and an iterative high-speed target matching processing are designed. Finally, the effectiveness of the designed processing algorithm is verified using a ground experimental radar system.

Large array has been widely used in underwater acoustic target detection, marine resources exploration and other fields due to its long detection distance and high azimuth resolution. Considering using steered minimum variance (STMV) algorithm in large array increases computational cost, and restricts its real-time implementation, an iterative algorithm of sub-array STMV (ISSMV) is proposed to permit real-time implementation on the limited computational resources. Theoretical analysis demonstrates that the proposed method can reduce the computational cost and eliminate the influence of strong coherent interference in weak target detection. The sea trial results show that the ISSMV algorithm maintains the high azimuth resolution of STMV and improves the ability of weak target detection while the computational cost of the proposed algorithm reduced sharply.

Generally, autonomous parking is divided into two parts: path planning and path tracking. Path planning algorithms such as A*, ant colony algorithm, etc., all have the problem of insufficient trajectory points. Meantime, path tracking algorithms: the proportional integral derivative (PID) and model predictive control (MPC) are used wisely. PID cannot eliminate errors, control lag, and easily cause overshoot. Therefore, this paper uses A* correction and MPC to solve these problems. First, the A* algorithm is used to obtain the planned trajectory of the vehicle, considering the slot boundary and the road boundary. Second, the algorithm selects the appropriate Bezier curve to reduce the jitter at the path joints. Finally, the path tracking is realized based on MPC. Then, using MATLAB and PreScan (a vehicle simulation software) to simulate and implement the autonomous parking algorithm. The results show that the algorithm has certain reliability and stability for vertical parking spaces.

In coherent optical communication QPSK system, an improved two-stage phase estimation algorithm is proposed to improve the estimation accuracy of carrier phase recovery, The BER simulation results compared with other schemes verify the feasibility of the algorithm. FPGA design adopts parallel design scheme. Simulation results compared with MATLAB verify the feasibility of the parallel design

In modern communication network it’s always vital to deliver high quality and reasonable services to users as well as having the ability to adjust system parameters according to the environmental change, which is called cognitive radio. Currently there are many power allocation algorithms that have this ability, such as Transmit Interference Temperature Limit(tx-ITL). In this paper, firstly, the advantages and drawbacks of existing algorithms will be discussed . Secondly, an improved algorithm based on tx-ITL is proposed to try to fix the drawbacks of previous algorithms in fiercely changing environment. Finally, the simulation results of proposed algorithm show an improvement in robustness and fairness of network while still reaching the satisfying capacity. This improved method may help communication network to operate in a smarter way.

Distributed motion platforms require high-precision attitude measurement. In this paper, we study the high-precision attitude control and simulation technology of the Unmanned Aerial Vehicle (UAV) motion platform, which based on the combined measurement and adaptive filtering of single-antenna GPS and accelerometer. A new high-precision attitude measurement algorithm for UAVs has been achieved, which has been verified by digital simulation. Specifically, in the MATLAB/Simulink simulation environment, we achieved an average measurement accuracy of 0.01° or less for the three attitude angles of the UAV carrier: pitch angle, heading angle, and roll angle within 1000s of simulation time.

In the guiding captive test, the data transmission and information flow design of the test is vitally important. The test can verify the precision closing ability of the missile and the interception and tracking ability of the missile to the target under realistic conditions. The communication scheme and data transmission path of dual aircraft guiding captive test are studied. The principle of the test system is discussed. The typical composition of the system, information flow transmission and application process are analyzed. The missile used for the guiding captive test and the onboard information communication interaction sequence conforming to the guiding captive test are designed. The parameter design and flight route of guiding captive test under typical conditions are given. The test data analysis and evaluation methods are discussed.

Aiming at the problems of low accuracy of bird and drone about radar samples, lack of relevant data, a doppler spectrum recognition method of bird and drone based on one-dimensional deep neural network is proposed. First, take Fourier transform on measured radar echo to acquire the doppler specturm vector of the target, to construct doppler specturm dataset.Then based on the characteristics of the doppler spectrum of bird and drone, design the network structure for doppler spectrum vector of target. To reduce the influence of target flight direction and SNR on accuracy, speed up the training and feature enhancing, the first two layers of network add modulo layer and normalization layer. Then connect the improved one-dimensional ResNet18 to build the entire networks. By training the target doppler specturm samples to get and optimize the final model. Experimental results show that this method can achieve excellent results on bird and drone doppler dataset, with accuracy over 97%.

With the growing demand for multimedia services and limited spectrum resources, it is critical to effectively use such resources and optimise the power distribution in cellular networks to reach higher capacity. This research aimed to discover a Fractional Frequency Reuse method that provides more network capacity than previous allocation methods. We looked at the capacity of each algorithm in an M-cell network using a mix of Integer Frequency Reuse (IFR), simultaneous waterfilling (SWF), and forward-looking game water-filling (FLG). After that, simulations are applied to determine which approach achieves the maximum capacity among all of them. The capacity of the FLG+IFR3 was somewhat more significant than that of the SWF+IFR3 and significantly higher than that of the IFR1+IFR3. In the FLG+IFR3 scenario, we also find out that raising the power budget for each user can not always result in satisfactory results. Therefore, we proposed an optimised power allocation method that helps reach a relatively high capacity for the entire network. The findings revealed fascinating theoretical and technological insights into 24-cell frequency allocation behaviour, which might be helpful in future frequency allocation design and implementation.

In order to improve the professionalism and accuracy of machine translation in the field of notice to air navigation, a twoway machine translation model of notice to air navigation based on transfer learning and back translation is proposed to solve the problem of the parallel corpus of machine translation in the field of air notice. On the basis of training the machine translation of People’s Daily, the parameters obtained from the training are transferred to the encoding end and the decoding end of the machine translation model of the partly back-translated announcements to initialize the parameters at both ends, and adjust the parameters at both ends through experiments. Obtain the two-way machine translation model of the notice of air travel. Experiments show that the introduction of the data enhancement strategy of transfer learning and partial back translation improves the robustness of the model and improves the translation quality. The BLEU value of the translation evaluation index of the Chinese-to-English model has increased by 2.08%. The BLEU value of the translation evaluation index increased by 3.12 percentage points

In the context of China's vigorously promoting the construction of digital transportation, we first referred to the relevant experience of western governments in building data governance institutions, and summarized the classic data governance organization models, such as decentralized operation model, centralized operation model, federal operation model, etc. Secondly, we investigated the development status of digital transportation in China and summarized the opportunities and challenges faced by the establishment of a digital transportation data governance organization. Finally, we took Shandong province as an example and put forward a new organizational framework and operation mode with personnel division suitable for data governance in combination with the existing organizational structure of Shandong Province. It is expected to provide explicit instruction for the construction of domestic traffic data governance organizations, improve the data governance ability of traffic management departments, and better support the digital development of the transportation industry.