This PDF file contains the front matter associated with SPIE Proceedings Volume 12473 including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

Low frequency ultra-wideband bistatic synthetic aperture radar (UWB BSAR) not only gets the high-resolution image and increase the scatter information, but also has the well ability of the foliage penetrating, which is potential of detecting the concealed target under the vegetation. This paper studies the performance of the back-projection (BP) algorithm in the time domain and range-Doppler (RD) algorithm in the frequency domain for the low frequency UWB BSAR imaging. First, the basic flow of the BP algorithm and RD algorithm for the low frequency UWB BSAR imaging is deduced. Then, the quality and efficiency of two algorithms for the low frequency UWB BSAR imaging are investigated. Finally, the two algorithms are tested based on the low frequency UWB BSAR simulation data, and the imaging performance of the two algorithms is compared and analyzed. The experiment results prove the correctness of the theoretical analysis and the effectiveness of the proposed methods.

Medical image segmentation is a necessary prerequisite for the development of healthcare systems, especially for disease diagnosis and treatment planning. UNet has become the de facto standard in various medical image segmentation tasks with great success. However, because the inherent local nature of convolutional operations makes UNet usually limited in explicitly modeling long-term dependencies, and because the huge parameters and computational complexity of UNet and its variants make UNet and its variants perform poorly for fast image segmentation in medical applications, we propose a new network structure (UNeCt) based on the UNet structure. U-sing a tokenized MLP in the latent space reduces the number of parameters and computational complexity, while being able to produce a better representation to aid segmentation. The network also includes skip connections between encoders and decoders at all levels. The results show that we achieve a good balance between the number of parameters, computational complexity and segmentation performance.

Object spatial positioning in multi-medium is always a difficult problem to overcome. It is not only necessary to realize object detection in complex environment, but also need to overcome camera distortion to achieve spatial positioning, and at the same time ensure the accuracy and speed of detection. In the current space positioning field, there are some problems such as low detection accuracy, many detection restrictions and single environment. In this paper, the light source is taken as the detection target. Firstly, OpenCV is used to correct the light source data set collected by CCD camera to reduce the influence of distortion. Then, based on YOLO series algorithms, an improved YOLOV5 network model is proposed to train the light source training set. The experimental results show that the improved YOLOV5 model can accurately detect the light source after distortion correction with an average accuracy of 96.2%, a transmission rate of 135 f/s and a spatial position error of 7.3526mm.

At present, the development of science and technology in China is accelerating, and it has been deeply integrated with people's daily life and work. Taking computers as an example, it has become an important tool for people's work and entertainment. Based on the above content, this paper studies the application of image processing technology in Chinese calligraphy style feature extraction, with the aim of inheriting excellent traditional culture, analyzes the matters needing attention in the application of image processing technology, and summarizes relevant experience, hoping to provide a reasonable reference for workers in the same field.

Image retrieval is to find out the similar semantic images to the query image, which is an important task in the field of image recognition. It is still an open challenging task due to the semantic gap of image understanding. The traditional image retrieval method is a simple retrieval between the query image and the database. However, only a query image contains weaker category information, so that the traditional image-based retrieval results are not satisfactory. In this paper, we propose a category pattern mining (CPM) strategy to extend an image (point) to an image category (plane). It means the semantic extension is performed from the individual query image to the whole image category. The proposed PTP (point to plane) method mined the category pattern of the query image and enriched the semantic information. The main contribution of the PTP framework is to improve the image retrieval from the traditional image-based retrieval into the new category-based retrieval. Experimental results and evaluations on two databases demonstrate that the proposed PTP method achieves an obvious superiority in the image retrieval tasks.

In order to solve the problems of sky distortion, edge artifact and overall dark of the restored image, a single image dehazing algorithm based on linear transformation was proposed. Firstly, adaptive compression normalize the hazy image, the ratio of the minimum channel to the maximum channel of the image is defined as the anti-saturation of the image, and the model of medium transmission and anti-saturation of the image without haze is established. Then, combining the linear transformation model between haze-free image anti-saturation and hazy image anti-saturation, the initial medium transmission is calculated by linear transformation, the medium transmission obtained by different linear transformation rates are weighted fused through the maximum channel map of image with haze, and the final medium transmission is obtained by fast guided filtering. Finally, atmospheric light values are derived from the quad-tree sub-block search method, and then the restored image is obtained. Both subjective and objective results of experimental simulation show that the algorithm has obvious recovery of details, good effect on the hazy images of different scenes, and good restoration effect on sky region.

Building layout surround-view imaging technology can obtain the internal information of the buildings, which has been widely used in the field of the anti-terrorism and combat. The problems of amplitude attenuation, wall widening, position offset, and multipath ghosts caused by the echoes in the process of penetrating between the building walls will bring the great inconvenience to the reconstruction of the building layouts. In this paper, a fusion imaging algorithm based on the amplitude compensation and correction for the building layout is proposed. First, after obtaining as many and accurate wall positions and wall lengths as possible through wall detection, the original image is segmented and then traversed through the adaptive thresholds. Besides, combined with the gray-scale linear method, the sub-images are enhanced, the clutter is suppressed, and its position has been corrected. Finally, the images obtained from multiple perspectives are fused to obtain the complete building layout image. Simulation results show that the proposed method can effectively compensate the attenuation, correct the wall position, suppress the multipath ghosts, and then finally obtain the high-quality and high-precision building layout image.

The transmission model of underwater photons is established by Monte Carlo simulation to simulate laser detection of objects at a certain distance in the marine environment, and the effects of varying the transmission distance of 10m, 15m, 20m, 25m and the effect of varying the attenuation coefficient of the water body, the asymmetry factor, and the size of the receiving surface on the echo signal at 20m are simulated. The simulation results show that: the larger the transmission distance, the more serious the echo signal distortion and the greater the relative weight of backscattering; the more turbid the seawater, the more unfavorable the effective signal reception; the larger the asymmetry factor is, the better the signal is; increasing the size of the receiving surface can effectively improve the intensity of the echo signal.

in order to realize the purpose of grating electronic subdivision, using a microcomputer calculation method of interpolation. The method is based on single chip real time on the sine and cosine of two grating more fringe signal acquisition and calculation, according to the calculated results to determine the phase of the signal that the segmentation point, then that grating displacement. In this paper, application of SCM about algorithm to finish 64 subdivision of grating, and successfully used in research projects, to achieve satisfactory achievements in scientific research. Calculation method of interpolation by single chip microcomputer and has popularization and application value of the conclusion.

The space-time complex variables Airy-Laguerre-Gaussian wave packets are the Laguerre-Gaussian wave packets, which is their intensity distribution at the cross section showing as the complex variables Laguerre-Gaussian functions in the space domain, are modulated by the Airy pulses in the time domain. The method of separating variables is a mathematical method to obtain this solution. According to solving the (1+3)D Pxi-axis equations, the analytical solution of the space-time complex variables Airy-Laguerre-Gaussian wave packets in free space has been accessed. When the initial incident power of the space-time complex variables Airy-Laguerre-Gaussian wave packets is not equal to the critical power, the wave packets’ width will fluctuate periodically. Poynting vector reveals the essence of this phenomenon.

The optical structure design and simulation of vehicle rainfall sensor are carried out with ZEMAX. Two groups of six infrared LED for star-shaped layout. The design of incident Angle of infrared pulse light is greater than the critical angle of total reflection when there is no rain and each group of infrared LED works at a certain time sequence. The rainwater on the windshield of the car changes the refractive index of the emitting medium of the pulsed infrared ray, which causes the attenuation of the luminous flux of the receiver's infrared ray and the detection circuit will generate impulse signal. Using fast Fourier transform and fuzzy control technology, the output signal of different windshield rainfall is given, and the output signal is communicated with the body control computer by the built-in LIN bus module of the single-chip microcomputer, so as to realize the automatic control of the windshield wiper in the driving rain and accidental muddy water splashing.

Synthetic aperture lidar is an optical remote sensing method that can theoretically achieve centimeter-level imaging resolution. It faces unprecedented research problems in physical optics, one of which is the single beam modulation mode, which greatly limits its large-scale application. A vortex beam carrying orbital angular momentum can theoretically generate an infinite variety of mutually orthogonal modulation modes. Applying it to imaging detection has the potential to bring richer information freedom while ensuring imaging resolution. In this paper, a vortex beam imaging model based on synthetic aperture method is proposed by combining vortex beam and synthetic aperture technology. Based on the basic principle of synthetic aperture and the theory of vortex beam orbital angular momentum, a synthetic aperture vortex beam imaging radar model is established. The data acquisition process of synthetic aperture vortex beam imaging is also deduced, and the phase angle position relationship in the echo data is analyzed. Finally, the range resolution and azimuth resolution of synthetic aperture vortex beam imaging under specific conditions are solved. Theoretical analysis shows that the rate of change of the topological charge of the vortex beam during the imaging process will have a great impact on the azimuthal resolution, which is consistent with the expected results. This paper lays a foundation for the follow-up research on vortex beam synthetic aperture imaging, and also provides a reference for the development of new radar technology.

In the face of increasing risks caused by high power lasers, nonlinear optical (NLO) materials like porphyrins (Por) have attracted interest because of their outstanding NLO properties. However, the performance of porphyrins is still far from practical application. Here, Carbon dots (CDs) and a series of porphyrins were combined to synthesize the Por-CDs nanohybrids. The nonlinear and photo physical of Por-CDs hybrid materials were investigated. The Por-CDs was confirmed by Fourier Infrared Spectroscopy (FT-IR), Fluorescence spectra and TEM. The nonlinear optical (NLO) performance of Por-CDs was evaluated by the Z-scan test. Nonlinearity present in Por-modified CDs. At same transmission intensity, the Por-CDs composites showed enhanced NLO properties in comparison to the Por and CDs. This research provides a promising route for the design of novel NLO materials.

The prism is one of the most important optical components in optical system, this paper analyzes the difficulties in processing technology. The angle accuracy of prisms can be improved by using slotted flat die, metal optic tooling, glass optic tooling and optic bonding tools. In this paper, the method of obtaining angle precision of prism with different batch and different precision grade is given, it has certain guiding significance for prism machining.

Wireless sensor network, as a popular scientific research project in recent years, plays an increasingly important role in military, medical and social infrastructure construction. However, characteristics like limited node energy capacity and decentralized deployment of wireless sensor network node contribute to short service life of nodes and difficult supplement of node energy which become the urgent problems to be solved in this field. Based on these problems, the Low Energy Adaptive Clustering Hierarchy (LEACH) is proposed to dynamically select cluster heads by clustering which saves node energy. However, the classical LEACH protocols still have some problems. The node energy use is not uniform and cluster head selection is not optimal. In this paper, an Improved LEACH based on Node Residual Energy and Base Station Distance (LEACH-NB) is proposed based on energy and distance to optimize cluster-header selection. So that the node has more remaining energy and be closer to the sink node will be chosen as the cluster-header. Besides, the problem of energy consumption in remote communication between cluster head nodes also needs to be solved. Then on the basis of LEACH-NB and the multi-hop routing protocol, an Improved Multi-Hops Routing Hierarchy among Cluster-heads with Energy and Distance Index based on LEACH-NB (IMRH) protocol is proposed. After that, the IMRH protocol is compared with the classical LEACH protocol, improved protocol EWB I-LEACH protocol as well as SILEACH proposed by other scholars through MATLAB simulation. The analog results illustrate that IMRH protocol has better capacity than other protocols in node demise rate and energy wastage rate.

Aiming at joint diseases such as knee osteoarthritis that seriously troubles middle-aged and elderly people, this device adopts a dual-mode laser rehabilitation treatment scheme that combines pulsed laser array stimulation and continuous laser uniform heating for patients with knee joint lesions. treat. The whole system is mainly composed of pulse drive module, constant current drive module, PID temperature control module, physical therapy temperature control module, main control module and human-computer interaction module.

A vector polarized beam undergoing a cyclic adiabatic transformation can obtain a Pancharatnam-Berry (PB) phase. This new geometric phase is proportional to light’s total angular momentum (TAM). In this work, experimental evidence is provided for the PB phase evolution on a hybrid-order Poincarésphere. A variety of hybrid-order Poincarésphere beams are directly generated by vortex half-wave plates using a non-interference method, while two TAM converters are used to ensure a closed cycle on the hybrid-order Poincarésphere. By rotating one of the converters, a significant change in PB phase can be observed. The PB phase is related to the topological charge and polarization state of the hybrid-order Poincarésphere bipolar beams and has implications in quantum information science as well as other physical systems such as electron vortex beams.

Achieving safe and comfortable driving is the main target of in-vehicle electronic devices. Infrared rain sensors in vehicles are useful when raining. It works by reflecting harmonious light beams on the windshield. When raindrops land on the windshield, this harmony is disturbed, causing the beam intensity to drop. The system then activates the wipers to operate fully automatically. With the help of the rain sensor, drivers can focus on driving without distraction, thus enjoying a comfortable environment. So far, most of the domestic rain sensors are supplied from abroad, and only a few high-end cars are equipped with rain sensors. In this context, a reliable and inexpensive vehicle-mounted infrared rain sensor has become an urgent need. This paper introduces the basic principle of rain sensors and proposes a simple and reliable detection method. This work is dedicated to analyzing some interference factors that affect the rain sensor work and putting forward a series of solutions. Experiments show that the rain sensor meets the design requirements, and the detection principle and solution are feasible.

Research and development of high-performance flexible broad range photoelectric detectors to meet the needs of the next generation of wearable smart devices will become increasingly important with the rapid development of the information age, based on the Internet of Things, intelligent wearable devices, intelligent households, and wisdom city high and new technology industry. Based on these requirements, a Sb2Se3/SnS2 heterojunction flexible self-driven photodetector was designed and fabricated. In terms of response bandwidth, responsivity, detectivity, and response speed, our device demonstrated exceptional performance. Moreover, our flexible device has good performance even after bending. This study confirms that Sb2Se3/SnS2 photodetectors have great application potential in wireless, unfiltered, and wearable optoelectronic systems. It also lays a foundation for designing and optimizing flexible photodetectors (PDs) with arbitrary heterogeneous combinations.

The detection of ultraviolet signal has attracted more and more attention of researchers. Gallium oxide has attracted much attention because of its particularity. In this paper, the status quo of gallium oxide was investigated, and the working principle of gallium oxide photodetector was introduced in detail. A Ga2O3 MSM UV detector with different parameters was prepared by rf magnetron sputtering and wet etching, and the optimum growth parameters were obtained, which is well prepared for the subsequent research. When the bias voltage is constant, the photoelectric characteristics of the photodetector change with the parameters.

The space-time Airy-Ince-Gaussian Beams are the Ince-Gaussian beams, which is their intensity distribution at the cross-section showing as the Ince functions in the space domain, are modulated by the Airy pulses with initial velocity in the time domain. The solutions of the space-time Airy-Ince-Gaussian functions basing on the initial velocity, the power ratio about the critical power and the input power, and the ellipticity have been accessed according to solving the cylindric coordinates’ (1+3)D Schrödinger equations in Highly Nonlocal Nonlinear Media. Their propagation characteristics are studied in the paper. According to the incident power of the beam is not equal to the critical power, the beam width changes periodically when the beam is propagating. The Poynting vector of the propagating beams at cross section reveals the essence of physics.

In this paper, the combination of spontaneous and stimulated Raman spectroscopy is used to further explore the relationship between OH stretching vibration and hydrogen bond in water under atmospheric pressure and dynamic high pressure and present the change process of hydrogen bond structure in liquid water from a microscopic point of view. The OH spontaneous Raman spectrum of water is deconvoluted into two characteristic peaks, which are 3213 cm-1 and 3436 cm-1 respectively. They belong to the OH stretching vibration under the action of strong hydrogen bond and weak hydrogen bond. In stimulated Raman scattering, under the action of plasma shock wave, the bond length of hydrogen bond is shortened, resulting in the elongation of OH bond length. Corresponding to the enhancement of hydrogen bond, the OH stretching vibration of water is weakened, and the Raman peak moves to the low wave number direction to 3396 cm 1. In the stimulated Raman scattering experiment, due to the exponential enhancement of the output signal, the weaker vibration mode will be masked by the stronger vibration mode. Therefore, only one vibration mode is observed in the spectrum. In this paper, the changes of corresponding hydrogen bond structure under spontaneous and stimulated Raman scattering are compared and analyzed. The research results are helpful to further explore the microphysical mechanism of liquid water molecular interaction and provide a reference for the mechanism research in related fields.

The Airy-Gaussian wave packets are the Airy wave packets modulated by the Gaussian wave packets. Basing on the separating variable method, the analytical solution has been accessed according to solving the paraxial beam equation in cylindric coordinates in free space. The Airy-Gaussian wave packets with complex variables are combing the Airy function with the complex variables and the Gaussian beams. The propagation characteristics of the complex variables Airy-Gaussian wave packets are studied. The complex variables Airy-Gaussian wave packets will rotate with the increase of propagating distance. Poynting vector reveals the essence of this phenomenon.

For high-speed and high-maneuvering target detection, a detection method based on TRT and special GRFT is proposed in this paper. Firstly, the range migration and Doppler migration caused by target velocity and jerk are corrected by TRT. Secondly, estimation of target acceleration can be realized by special GRFT. Then, the estimated acceleration is used to construct a compensation function to correct the range migration and Doppler migration caused by acceleration. Finally, we can achieve coherent accumulation simulation results demonstrate the effectiveness of the proposed method.

There are various problems in the current target tracking of satellite videos, such as the small size of moving targets, few textures, and easy occlusion. To solve the problem of tracking failure caused by occlusion of moving objects, a tracking algorithm based on kernel correlation filtering and trajectory prediction is proposed in this paper. The algorithm in this paper uses the motion characteristics of moving objects in satellite video to predict the trajectory and uses the trajectory prediction algorithm to predict the trajectory of the target when it is occluded. Combined with the kernel correlation filtering algorithm, the target can still be accurately tracked after it is occluded. Experiments on satellite video datasets show that the algorithm in this paper can effectively solve the problem of tracking failure caused by the occlusion of moving objects, and the speed is also very fast.

The rapid development of the civil aviation field has made people pay more attention to aviation safety. This paper discusses the application of target detection technology in airport airspace control. Airport airspace control includes many aspects, and this article focuses on bird strike prevention. For the target detection technology, we selected three target detection technologies, HOG+SVM, RCNN and YOLO for discussion and comparison. Considering the application scenarios and real-time requirements of bird strike prevention, YOLO was finally selected for feasibility experiment. Experiments with YOLO got pretty good results, YOLO can successfully detect flying birds in the airport scene. This proves the feasibility of the application of object detection technology in airport airspace control.

Image super-resolution reconstruction technology in remote sensing can improve the spatial resolution of remote sensing images with the breakthrough of physical hardware limitations. With the development of deep learning technology, more and more algorithms proposed in the field of natural images are applied to the field of remote sensing super-resolution. Due to the large difference in the size of the objects in remote sensing images and the high complexity of the image, the reconstructed image will be blurred when the algorithm in the field of natural images is directly used. To address this problem, this paper proposes a shallow feature extraction feature fusion with multiple convolutions, followed by the extraction of high-frequency information using the Swin Transformer module with a fusion attention mechanism. The edge details of the image are extracted using the gradient of the image in the final reconstruction process, and complementary fusion is performed at the end of the network, which can effectively supplement the lack of shallow features caused by the deep network. Finally, experiments show that the proposed model obtains satisfactory reconstruction results of remote sensing images.

In order to improve the detection rate of smoke recognition and reduce the false positive and error rates, a new local binary pattern (Zigzag Local Binary Pattern, ZLBP) is proposed. In ZLBP, we first rearrange the pixels in the local area into four linear areas by four zigzags with four directions, and then design two coding methods for the linear areas. For four linear areas, we can get four feature vectors, each of which is computed based on two codes of the same linear area. Finally, we concatenate the four feature vectors to generate ZLBP feature. Experimental results show that the new proposed pattern is effective and suitable for smoke identification.

In the field of remote sensing images, due to the limitations of hardware equipment, image transmission, natural environment and other reasons, the resolution of the obtained remote sensing images cannot reach the desired resolution. The emergence of image super-resolution reconstruction technology can improve the resolution of remote sensing images without increasing the high cost. Image super-resolution reconstruction refers to the fact that low-resolution images can obtain high-resolution images through certain algorithmic techniques. With the rapid development of deep learning ideas, researchers have applied it to the field of image super-resolution reconstruction and achieved good results. Image super-resolution reconstruction also shifts from traditional reconstruction methods to deep learning-based methods. The emergence of the idea of Generative Adversarial Networks has further advanced the field of image super-resolution reconstruction. By using the idea of Generative Adversarial Network (GAN), researchers can obtain more realistic high-resolution images. This paper mainly uses the SRGAN model, the image dataset DIV2K for super-resolution reconstruction, and uses a dense residual structure in the generator network to obtain more image information, so that the effect of image reconstruction is more realistic. Through the experimental verification on the SIRI-WHU remote sensing test data set, the two evaluation indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are compared, and the effect is improved. Better generation results can also be observed through subjective human vision.

Low frequency ultra-wideband synthetic aperture radar (LF UWB SAR) not only obtains the high-resolution image, but also has the well capability of the foliage penetrating, which is potential of detecting the concealed target under the vegetation. This paper studies the target change detection based on the Edgeworth statistical distribution features in the LF UWB SAR images. First, the Edgeworth expansion is used to estimate the probability density function of the pixel neighborhood, and then the K-L divergence has been used as the standard to evaluate the difference between the probability density functions, to realize the target change detection in the multi-temporal SAR images. Finally, the proposed algorithm is tested based on the LF UWB BSAR data, and then the detection performance is shown and analyzed. The experiment results prove the correctness of the theoretical analysis and the effectiveness of the proposed method.

Aiming at the problems of unclear, difficult, and inefficient classification of traditional manual waste, and the difficulty of deploying large existing garbage classification network models, a lightweight garbage detection and classification network S-YOLOv5 is designed based on YOLOv5s. First, a garbage dataset containing 18 types of common household garbage is constructed and labeled according to the principles of garbage classification; secondly, a module combining shufflenetv2 and CoordAttention was introduced to replace the YOLOv5s backbone network, and the ReLU activation function in the shufflenet module was substituted by FReLU; finally the PANet structure was replaced by the BiFPN structure, so as to reduce the model complexity and achieve lightweight while maintaining a high mAP. The experimental results show that the size of S-YOLOv5 is only 2.6MB, which is about 1/6 of the original network size, and the mAP is 80.2%. The size of the proposed network is reduced while maintaining high accuracy, making it more suitable for deployment in smart devices.

Crack detection of industrial defect target detection is one of the most critical aspects of industrial product quality control, and to address the problems of false detection, missed detection and insufficient feature extraction for fine cracks in target detection, this paper introduces a hybrid attention mechanism based on the original YOLOv5, which improves the accuracy of the backbone feature extraction network for fine crack detection. The experimental results show that the target loss of the validation set of the improved YOLOv5s model converges significantly, the model training results are accurate, there is no overfitting or underfitting phenomenon, and the average accuracy mean value is improved by 3.8% compared with the original YOLOv5s model. The improved YOLOv5s model can identify and detect fine cracks under both illumination or dim conditions, and the model generalization ability is good enough to meet the relevant requirements in industrial production processes.

LEACH algorithm is a classic wireless sensor network protocol. Prolonging the network lifetime has always been its core proposition. In the light of the randomness of cluster head election in LEACH protocol, LEACH-OR protocol adds weight factor Q with distance weight and energy weight to the original threshold formula to extend the network lifetime. This paper analyzes the shortcomings of classic LEACH protocol and proposes an improved protocol LEACH-IMPROVED based on LEACH-OR. The author turns weight factor Q into function to balance the distance weight and energy weight so that nodes with much more energy and a suitable distance can be selected as cluster heads, and the distance weight and energy weight are changing all the time according to the residual energy. The simulation shows that the lifetime of LEACH IMPROVED protocol is extended by 30%~40% when compared with LEACH algorithm, and the energy efficiency is improved.

The passenger boot system of the subway station is an important part of the subway station and is an important measure in the quality assurance of subway station. Modern subway stations often choose construction in urban underground, and the space environment is more complicated, and there is a decline in the spatial direction of the underground, and passengers can be lost in the station. Therefore, how to conduct scientific and effective optimization upgrades to the pedestrian boot system of the subway station, and enhance passengers' travel efficiency in the subway station. This paper is based on the VRP(Virtual Reality Platform) virtual reality platform, using software such as 3DSMAX, Photoshop, VRP-Builder, and simulates passengers from the process of entering the station. To achieve the ride process visualization, the passengers can easily and easily understand the ride process, improve travel efficiency, which is convenient for life. It also enables the subway staff to understand the passengers to the subway station's ride process, which is convenient for follow-up to optimize upgrade, and enhance the quality of the subway station.

Underwater wireless optical communication can realize short-distance, high-speed communication. It effectively supplements the vacancy of optical cable communication, radio communication and underwater acoustic communication in underwater networking. Using Orthogonal Frequency Division Multiplexing (OFDM) technology in underwater wireless optical communication can effectively reduce the influence of multipath effects in the channel and improve system communication performance. The error correction ability of Reed-solomon (RS) code under medium code length is close to the theoretical value. This letter analyzes the underwater optical channel model, simulates the underwater optical communication system based on RS coding and OFMD, and studies the influence of communication distance and signal-to-noise ratio (SNR) on bit error rate (BER), as well as the error code distribution in the presence of Doppler shift and the impact of interleaving on system performance. The simulation results show that the BER of received signal increases with the increase of communication distance and the decrease of SNR; When Doppler shift exists, error codes appear in concentrated positions. Deep interleaving can effectively utilize the error correction ability of RS coding, reduce the influence of Doppler shift, and improve the communication quality of the system.

With the rapid development of new energy technology, the massive access to related equipment such as distributed photovoltaic devices and energy storage devices performs a huge impact on the quality, safety, and stability of the operation of the power grid. In the current stage, the Broadband Power Line Carrier HPLC technology has been widely applied for the access of distributed photovoltaic devices to perform data collection and transmission functions. However, due to the limitation of the HPLC networking method and communication mode, the current communication system exists problems such as long-distance node communication could not be covered, power line communication is susceptible to interference, and could not be safely and accurately controlled in real-time, which is difficult to achieve broad and reliable access of devices. Therefore, this paper proposes a novel concept that is using 4G technology to replace HPLC technology to help realize local communication in the smart distribution network. According to the practical test results, the current design could operate stably and efficiently which provides a practical reference for relevant research.

In this work, coherent modulation Terahertz communication link with pointing errors are analyzed for the Gamma-Gamma turbulence channels. The high-precision bit error rate and outage probability expressions for the Gamma-Gamma turbulent channel using coherent quadrature phase-shift keying signal are derived, and the bit error rate and outage probability are analyzed for its asymptotical property. The results show that our series error rate expression can accurately evaluate the system performance. It has high precision in estimating and predicting actual error rates.

In active detection, the Doppler of moving target leads to frequency shift of active echo signal, which is reflected in time dimension as time delay of echo. Due to the good resistance of PTFM (pulse train of frequency modulated) to the time-domain fading characteristics of ocean channel, it is widely used. PTFM is composed of multiple LFM, and the Doppler motion has a complex influence on it. Many scholars simply think that the interval of LFM echo in the moving target is still the pulse width T, but in fact it has changed. This paper gives the influence of Doppler motion on signal quantitatively through formula derivation, and gives the subsequent signal processing method, which has good application value for engineering application.

It is suggested and designed to use Terfenol-D magnetostrictive material in a fiber extrinsic F-P interferometric magnetic field sensor. The two reflective end faces of the F-P cavity are, respectively, the copper reflective layer and the fiber end face. As a result, changes of the magnetic field have an impact on the F-P cavity's cavity length, and the environmental magnetic field can be detected by measuring the variation in cavity length. The typical relationship between magnetic field and spectral drift of the sensor is theoretically analyzed. The sensitivity is measured experimentally to be 0.82 nm/mT for a magnetic field intensity of 0 mT to 70 mT at room temperature. The sensor has a compact structure, and the complexity of the low-level interference signal demodulation makes it suitable for use in engineering.

In view of the current higher requirements for comfort issues such as adaptive car seat adjustment, human size measurement technology is gradually improving in China. In this paper, combined with the non-contact measurement technology method, a body size measurement system based on image processing is proposed. It combines image processing methods such as open pose human pose estimation and edge detection, and achieves the advantages of simple algorithm, fast processing speed and high accuracy, the error rate is 0.19125, to prepare for the self-adaptive car seat adjustment system, and also to provide a reliable reference for further improving the measurement method of human body size.

Based on the development of wireless communication technology, wireless sensor network (WSN) composed of micro sensors has been widely used. Aiming at the problems of uneven node distribution and energy consumption in wireless sensor networks, A PSO-ABC hybrid algorithm based on accurate perception model is proposed in this paper. The algorithm takes network coverage and energy consumption of sensor node deployment as objective optimization functions, introduces virtual force -- Molecular force, and adaptively adjusts the inertia weight of the algorithm, which can significantly improve the diversity of particles and network coverage. The communication of information by PSO-ABC makes it as efficient as possible to jump out of the local optimality and improves the convergence accuracy of the algorithm. Information exchange mechanism is used to exchange search information to avoid local optimization. On this basis, our objective optimization is to improve network coverage and reduce energy costs. By using the proposed PSO-ABC hybrid algorithm to search for the optimal deployment scheme of nodes, coverage blind areas and coverage redundancy in the network can be significantly avoided.

In view of the problems existing in the current electronic mine pressure monitoring equipment, such as distorted interference data, many transmission cables, short power supply distance and so on, the optical fiber mine pressure monitoring equipment has the problems of small capacity, high cost, monitoring blind areas and so on. Through the research of key technologies such as distributed grating array stress sensing technology, distributed grating array sensing multiplexing and network technology, distributed grating array sensing demodulation technology, combined with the mine pressure theory, optical fiber communication technology and computer technology, the project develops the grating array stress sensing device and grating array sensing demodulation substation suitable for the requirements of coal and rock stress measurement. Combined with the monitoring and early warning platform software, a real-time, remote and online distributed grating array stress monitoring system is formed.

Basing on morphological characteristics of retinal vascular structure and its changing, in order to realize the early diagnosis and quantitative analysis of the severity of diabetes, cardiovascular disease, and fundus disease ect, we propose a retinal vascular image multi-scale segmentation method based on hybrid model in this paper. First, combing the statistical principle and image enhancement method, the retinal vascular image was segmented and extracted. Second, a hybrid model consisting of a Gaussian model and two exponential models for vascular fitting was developed. Then, the K-means clustering method is used to estimate initial parameters, and the estimated parameters are iteratively processed to solve model parameters; Finally, the retinal vascular image is segmented according to the maximum a posteriori criterion to extract vessels. The experimental results on DRIVE database show that our proposed segmentation method can extract retinal vascular network effectively, and the segmentation accuracy is 94.62%. The proposed segmentation method can thus help the ophthalmologists in efficient retinal image analysis and fruitful treatment to the patient community.

3D human pose estimation is a hot research topic at present, and it also has a wide application potential. The inherent uncertainty and multiple solutions of 2D to 3D mapping based on a single image limit the accuracy of 3D human pose estimation. Considering that human posture is affected by physiological features and motion states, the network design in this paper uses physiological and motion features to provide constraints for posture estimation, in order to achieve better accuracy. Specifically, in the network design of this paper, three auxiliary judgment networks, namely gender, motion type and true false judgment, are used to further constrain the generated posture. Moreover, experiments on Human3.6M dataset show that the accuracy of mapping 2D joint coordinates to 3D pose coordinates can be effectively improved by introducing constraints of physiological features and motion states.

In recent years, the accuracy requirement of vehicle navigation and positioning is higher and higher. Since some obvious disadvantages emerge in the integration of various traditional technologies, many studies have begun to apply machine learning to vehicle navigation and positioning, which utilize the powerful self-learning ability of machine learning algorithms. The main advantages of machine learning methods include solving the problem of narrow application scope of traditional information fusion algorithms. Solve the problems of low navigation and positioning accuracy and poor anti-interference ability. In this paper, the applications of machine learning related algorithms in vehicle navigation and localization are overviewed in detail, including support vector machines, neural networks and random forests. Meanwhile, the application research status of machine learning technology in vehicle navigation and positioning is summarized, and the future research directions are prospected.

In smart construction site, multi objects in real time monitoring streams are often needed to be detected at the same time. If YOLOv4 models are not accelerated, higher inference delay will be occurred, so that the purpose of real time detection can’t be achieved. The features of YOLOv4 model are firstly introduced in this paper, and then we discuss how to use YOLOv4-tiny-3l and TensorRT to accelerate the inference process of YOLOv4 model in detail. The experiments show that YOLOv4-tiny-3l models can be used to detection objects in multi real time streams smoothly, but the accuracy is pretty poor, so that the models can’t be used in practices. When adopting TensorRT toolkit to quantize YOLOv4 models with FP16 precision, the accelerated models can be used to detect objects in multi real time streams smoothly with a small loss of accuracy.

Hot spot detection is a very important aspect in the field of PV plant inspection, and with the development of UAV technology, PV hot spot detection by UAV based on image processing has gradually emerged. To solve this problem, this paper introduces a deep learning model for coarse detection of PV regions to remove background interference, and uses image preprocessing, convolutional operations, morphological operations and Hough line transformation to finally achieve component-level segmentation of PV arrays. The experimental results show that the algorithm of this paper can segment the PV array accurately and quickly, and the effect is better than the traditional segmentation algorithm.

In order to realize the rapid perception of complex scenes, the traditional scene complexity assessment algorithm has strong limitations in feature representation and scope of application, and it is difficult to deal with complex scenes. However, the existing deep network methods are lack of the consideration of the correlation between the underlying features of gray image and the complexity level, and the amount of parameters is too high to meet the needs of rapid response in practical applications. Based on the deep separable convolution module and residual connection structure, this paper designs a lightweight complexity assessment network X-CENet with stronger feature expression ability. A dense connection module which makes full use of multi-level features is introduced to improve the feature expression ability of the network for scene images. The underlying information such as image texture is particularly important for the assessment of complexity, so the feature cascade layer of the head and tail of the main modules is added to strengthen the utilization of the underlying feature information in the network. Experiments show that compared with other deep networks, this method can obtain higher assessment accuracy in the dimensions of image characteristics and detection performance with smaller parameters. Compared with the Inception V3 with similar parameter amount, this method improves the LCC index by 2.849% and the SRCC index by 3.338%.

Multi-object tracking (MOT) system usually consists of two tasks, object detection and re-identification (ReID). Current MOT methods tend to join detection and ReID in a single network to enhance inference speed. Such one-shot models allow joint optimization of detection and Re-ID via a shared backbone, reducing computation cost. However, the different demands of features between the two tasks in one-shot systems lead to competition in the optimization procedure. The detection task needs the features of the instances with the same class to be similar, while the ReID task needs the features of different instances to be distinguishable. Existing methods address the contradiction by disentangling the features into detection-specific and ReID-specific features. But these methods neglect the discussion of semantic interpretation of disentangling modules. In this paper, we propose a feature decoupling module, Global and Local Context-based Decoupling Module (GLCD), to disentangle features extracted by the backbone into two task-specific features. By extracting global and local contexts, the two tasks can choose different contexts by learnable parameters to enforce each self. We conduct our decoupling module into SOTA one-shot MOT method and experiments show performance improvement.

This paper proposes a data mining method for information systems based on decision tree algorithm, establishes a more effective data mining model, and improves the accuracy and efficiency of hospital data mining. Based on the C4.5 decision tree algorithm, the model adds methods such as cosine similarity judgment, which reduces resource consumption, greatly improves the accuracy, and takes the clinical diagnosis data of 5 common diseases in respiratory medicine as a sample and obtains more efficient and accurate data results through simulation testing.

To improve the detection performance of aircraft swarms in remote sensing images with characteristics of small target, scale diversity and dense distribution, a multi-scale-feature-fused attention mechanism is proposed and used for deep learning networks in this paper. Based on the fundamental YOLOv5 network, an enhanced multi-scale CBAM attention module that combines the channel attention and the spatial attention is performed on the fused feature maps at various stages and scales. Consequently, more detailed attention information can be obtained. Experimental results demonstrate that the proposed method can effectively improve the detection accuracy of aircraft swarm targets compared with some traditional methods. In detail, the proposed method can reach 9.2% higher recall than the original YOLOv5 and 3.4% higher recall than the YOLOv5 integrated with traditional CBAM modules while the computational cost is similar

As a typical linear representation method, collaborative representation has become an important research direction in the field of power image classification. Traditional cooperative representation algorithms often ignore the competitiveness and distinguish ability of each kind of samples, which affects the performance of power image classification. In order to further improve the accuracy of power equipment image recognition, this paper proposes an image classification algorithm based on improved cooperative representation, which makes full use of the competition between each kind of samples and the local geometric structure characteristics of samples. Experiments on power image data sets with and without noise show that the proposed algorithm has good classification performance.

The cost of operation and maintenance(O&M) has been one of the central budgets in the wind turbines' life cycle. In the final stage of the O&M work, the administrators must manually review the on-site photos to ensure the O&M is qualified, which is time-consuming and ineffective. To improve the efficiency and quality of O&M reviewing work while reducing its costs, we propose an auxiliary reviewing system to optimize the reviewing process. Our system architecture consists of data collection, analysis, and presentation modules. During day times, the data collection module will handle storing and organizing the photos of O&M. The data analysis module will perform duplicate image detection using the MessageDigest Algorithm 5(MD5) and missed image detections through the pre-trained ResNet50 deep learning model. The review results from the analysis module will be fully updated to the database after the analyzing process and rendered into graphs or tables on the webpage when required by the reviewers. Analyses are done in this paper to visualize the functions of each module of the proposed system. The experiment evaluates the performance of three deep learning models, including AlexNet, VGG16, and ResNet50, based on authentic on-site inspection images data from our "first annual inspection" dataset to determine which model yields the best image classification performance. The experimental result reveals that ResNet50 can reach the highest accuracy at 96.2% on the test dataset. Thus, we choose ResNet50 to train the image classifier of our data analysis module.

Cardiovascular disease (CVD) is a common disease related to the heart and blood vessels. Due to the lack of research on the characteristics and effect of cardiovascular calcification on hemodialysis patients in China, it is almost impossible to accurately extract, segment, and measure the cardiovascular calcified regions from the cardiovascular calcification image. This article proposed an algorithm to extract and segment the calcified regions based on image characteristics. Firstly, the dome method was used to obtain the approximate region of calcification and the basic extraction and segmentation algorithm was used to preprocess the calcified region. Then, the region of calcification was enhanced using the image enhancement method before being further processed by the basic algorithm. After that, the preprocessed segmented image was compared back to the original image and only the initial grey value of the common area was kept in the original image. Finally, the basic segmentation algorithm was utilized again to process the original image before the threshold division and binarization being performed to obtain the final segmentation results. The results indicated that our method can segment the calcified region more accurately and thus more accurate distinguishment of the calcified regions from the non-calcified regions can be achieved.

Based on binocular vision and image processing algorithm, the recognition and location of high voltage side and low voltage side of transformer were completed for transformer automatic wiring positioning platform. On the basis of binocular vision calibration and 3D point cloud reconstruction, P3P algorithm was used to complete the positioning of transformer operation space coordinate system. Based on the image threshold segmentation algorithm, the position of transformer high voltage terminal, low voltage terminal, and transformer rotation angle were identified on the basis of 3d point cloud data. The target position information was provided for transformer automatic wiring device.

Three-dimensional scanning technology, graphic image processing, virtual reality and other technologies are an important and inevitable way to protect cultural relics. With the passage of time, most of the cultural relics have been removed. Aiming at the main process of fragmenting cultural relics restoration based on point cloud processing, this paper summarizes the algorithms of 3d data denoising and simplification, model feature extraction, model classification and model assembling in virtual restoration of fragmenting cultural relics.

In this paper, a multi-feature fusion probabilistic topic model, called MFF-PTM, is proposed to realize unsupervised 3D point cloud classification. Our MFF-PTM consists of three key stages: 1) a novel multi-feature descriptor is designed to characterize different 3D point clouds by the combination of statistical, morphological and histogram features; 2) a Rsphere clustering algorithm is proposed to construct 3D visual vocabulary and generate the co-occurrence matrix, which can effectively avoid the initialization problem of category; 3) PTM employs the co-occurrence matrix to predict the probability distribution of a certain point cloud belonging to different category topics. The experimental results have clearly shown that the proposed MFF-PTM model can outperform the traditional PTM models with single feature description for 3D point cloud classification.

This article mainly introduces the principle and implementation of the system of the “in-transit” materials’ information management visualization system by using Beidou, inertial navigation, geographic information and network technology. It can provide more real-time, intuitive, scientific and efficient information assurance of materials and geography for providing efficient auxiliary decision-making information for material support decision-making.

Digital watermarking is a new kind of protection technology of anti-counterfeiting and anti-theft that is born because it can not protect its copyright well in the process of multimedia information transmission. The characteristics of chaotic encryption model play an important role in the field of secure communication and image encryption, in this paper, according to its characteristics, the application of chaos model in digital watermarking algorithm is studied, it provides a research method for digital watermarking security rights protection.

With the advent and continuous development of the Internet of Things era, a large number of emerging applications, such as driverless, have emerged. Usually such applications need to consume a lot of computing resources and have high demand for low latency and data processing. However, the limited computing power of the devices themselves cannot meet the low latency and other needs of emerging applications, which limits the further development of IoT. Mobile edge computing (MEC) is an emerging computing paradigm where mobile devices interact directly with MEC platforms at the network edge and offload computational tasks to MEC servers to solve the problem of limited device resources. In this paper, we conduct an in-depth study of the computational offloading problem in mobile edge computing, and provide an in-depth and comprehensive summary of the current status and results of the research on MEC-oriented computational offloading strategies. First, the MEC technology and its typical application scenarios are introduced; then, the computation offloading strategy with optimized time, energy consumption, and hybrid goals for system performance gain is introduced from two aspects: single-user computation offloading and multi-user computation offloading.

In the field of military wireless communication, digital signals will generate bit errors due to various interferences in the process of channel transmission. In order to improve the communication quality and ensure the correctness and reliability of the communication, the method of error control is usually used for error correction. Error control often adopts the technology of error correction code, which improves the reliability of information transmission by adding redundant information to the effective information code stream. RS code is a typical error correction code with strong error correction ability, which can correct both random errors and burst errors. On this basis, this paper studies the basic rules of RS code construction, designs a new type of codec system circuit for RS (35,27) code, and finally uses hardware description language to design the encoder and decoder, implementation and simulation verification. It has been verified that the RS encoding, and decoding system is stable and reliable, has excellent performance, strong error correction ability, strong scalability, and has a broad application space.

Recently, the whole society has had higher and higher requirements for the stable operation of the power system, and the safety of power production is directly related to people's daily life order and the development of the national economy. How to build a power system security framework to repair problems in the power system in a timely manner, and how to improve the accuracy of remote live work while improving work efficiency have become the main needs of the development of the power industry. In the practical application scenario of power repair, this paper obtains the target coordinates through the line laser scanning binocular stereo vision technology of the interactive channel and completes the preparations for the binocular stereo vision including image preprocessing, camera calibration, and stereo correction. The stereo vision algorithm is used to improve the efficiency of electric power intelligent emergency repair operations, and the advantages of binocular stereo vision algorithm in the application of electric power emergency repair are illustrated by comparing the accuracy of different algorithms through experiments.

In this paper, a fast recognition system is designed to realize the fast recognition of high speed moving targets through effective analysis of the tail flame spectrum characteristics. The system is mainly composed of tracking imaging for target alignment and spectral analysis for target identification. The discriminating factor recognition algorithm is studied to realize the fast recognition of high-speed targets. The multi-band peak mean and valley mean ratio relations are studied and the effective discriminating interval is set to ensure the fast recognition of high-speed targets. The influence of the target velocity on the acquired spectrum is analyzed and the function relation between tangential velocity and radial velocity which determines the spectral shift is given. The experimental detection was carried out at a range of 0.5/1.0/2.0/4.0 km and a speed of 10-100 m/s on a small number of rocket tail flares. The results show that the larger the attenuation degree is, the larger the spectral amplitude difference is and the smaller the spectral morphology change is. In the same sampling period, the spectral distribution of different velocities showed obvious deviation, but the distribution morphology showed no change. By calculating the range of valley mean and peak mean and the inclusion relation among factors, the high speed target can be recognized effectively

To solve the problem of inaccurate accuracy and large noise signal of traditional RSSI location algorithm, especially in harsh environment with many obstacles and interference factors, we put forward a design and implementation of RSSI location algorithm based on Kalman filter is presented. Firstly, Kalman filter is used to filter the collected RSSI value signal as a whole to alleviate the problem of signal drift and impact and improve the state accuracy. Finally, an improved weighted quadrilateral ranging positioning algorithm is used to correct the filtered signal again to make the positioning of the nodes to be measured more accurate. The simulation results show that the trajectory after Kalman filtering is closer to the actual trajectory than that before filtering. The algorithm in this paper is compared with the traditional trilateral and quadrilateral ranging positioning algorithm. To a certain extent, the positioning error is smaller and more stable with the increase of the number of experiments.

The integrity of barrier coverage has an important influence on the communication quality of wireless sensor networks. Therefore, calculating the success rate of barrier coverage is of great significance in the study of wireless sensor network coverage. Due to the influence of environmental factors, wireless sensor nodes will be offset accordingly during the process of throwing. This paper mainly focuses on the success rate of line-based deployed barrier coverage under right-angle and polar coordinate systems that obey normal distribution and compares the success rate of barrier coverage under right-angle and polar coordinate systems through simulation. The experimental results show that the success rate of the line-based deployed barrier coverage increases as the offset decreases and the simulated success rate is almost lower than the analogue success rate, both in the Cartesian and polar coordinate systems when the experimental data meet the constraints. The simulated values are more consistent with the analogue values in the Cartesian coordinate system, but the deviations in the Polar coordinate system are larger.

Aiming to solve the problems of the atmospheric turbulence in free-space optical communications which cause the increase of the system bit error and influence the communication stability, an algorithm is proposed to suppress the influence of atmospheric turbulence. This algorithm combines traditional grouped matrix interleaver and random interleaver design methods to construct a new type of interleaving matrix to solve the turbulence effect under different atmospheric conditions. A field programmable gate array (FPGA) board with external DDR3 is used to implement the algorithm and optimize the delay. The results show that the algorithm is accurate and reliable in different turbulence models. At the same time, the impact of turbulence effects on link performance can be reduced to a certain extent.

Body temperature and heart rate are two important parameters of human vital signs. Real-time monitoring of them can provide effective treatment schemes for medical staff. Flexible wearable medical devices have attracted much attention because of their light weight, portability, excellent electrical performance and high integration. This paper analyzes and studies the realization of body temperature and heart rate monitoring by flexible wearable medical devices, gives the principle of measuring body temperature with commonly used flexible thermistor materials and the development of ECG technology for measuring heart rate, points out the challenges faced by accurate measurement, and finally prospects its development trend in the future.

With the development of society, gestures are used in many aspects, but the computer's functionality for gesture recognition is still to be improved. This article is mainly a preliminary idea of a basic gesture recognition system built based on the existing Google deep learning framework TensorFlow and gesture recognition components in MediaPipe and OpenCv machine vision open-source library. The training dataset is first subjected to skeleton key point coordinate extraction, then the pre-processed dataset is used to train the neural network and constitute the preliminary model, and finally the model is corrected and changed in the end.