The detection of water source microorganisms is of great significance to the biosafety of water source and so on. However, the traditional methods such as microscopic observation are inefficient and need professional personnel. Therefore, an automatic recognition method of micro-organisms in water source is proposed. Water samples were collected and a microorganisms image set was made. Automatic and semi-automatic image segmentation algorithms were proposed to extract the target microorganisms area, and 6 features were extracted. The model optimization problem of water microorganisms classification process was studied. First, the parameters of a few features were optimized. Then, all the features were fused, and a microorganisms recognition model of support vector machine optimized by particle swarm optimization (PSO-SVM) was established and compared with other recognition algorithms. The results show that, compared with the other 3 recognition algorithms, PSO-SVM can recognize different kinds of microorganisms more effectively, with an average recognition rate of 97.08%.

Based on the application of distributed optical fiber sensing system in the field of perimeter security monitoring, there are problems such as slow response speed and low recognition rate. Although the recognition rate of the traditional convolutional neural network is very high, its huge amount of parameters makes industrial deployment difficult and the recognition response speed is slow. This paper introduces the lightweight convolutional neural network MobileNet, which uses depth-separable convolution to replace the traditional convolution, which greatly reduces the amount of model parameters. This paper uses MobileNet as the benchmark network to implement a one-dimensional lightweight network based on MobileNet-18 Φ-OTDR perimeter intrusion event recognition, compared the network recognition rate and recognition speed under different structures through experiments, and selected MobileNet-18 as the best model under the condition that the accuracy of the model would not be greatly reduced. In the experiment, six perimeter fiber intrusion signals of climbing, cutting, wind blowing, lifting, pulling and walking were collected. Among the six types of fiber intrusion signal recognition, MobileNet-18 achieved a recognition rate of 98.33% and a response time of 9.27 ms.

For the daily essential food of people, automatic damage detection and automatic classification are essential for the increasing consumption of fruit. In view of this demand, automatic detection of fruit damage has become a hot topic in recent years. In this paper, the application of convolutional neural network, an existing deep learning technology, in fruit feature extraction and classification was discussed. A method based on ResNet34 as the backbone network and the introduction of attention mechanism SE and CBAM module was proposed to realize the detection and basic classification of fruit damage. The method was verified on fruit fresh and rotten for classification data set, and compared with VGG16, GoogLeNet, MobileNetV2 and other common networks. The accuracy of fruit damage detection and classification is improved. The classification accuracy reaches 98.8%. By adding the new apple data set, the performance of the model is further improved, compared with the original network ResNet34, and the generalization of the model is effectively improved, which provides a reference for the complex multi-feature classification of actual fruit images.

The automobile wheel speed sensor is one of the key components of anti-lock braking system (ABS), and its performance directly determines the level of ABS performance. The output of the magnetoelectric ABS speed sensor is approximately a sinusoidal signal, and the performance of the sensor can be obtained by analyzing the amplitude, frequency and effective value of the sinusoidal signal. This work takes STM32F407 as the main control board and uses fast Fourier transform as the main tool to design a digitally controlled sinusoidal signal measurement device to measure various parameters. It has been verified by experiments that the device can accurately measure the frequency, effective value, peak value and distortion of the input signal within 5% error and restore the signal waveform. At the same time, in order to facilitate observation, this design uses the bluetooth communication module to synchronously upload the measurement data to the mobile phone.

Image semantic segmentation requires fine detail information and rich semantic information, but in the stage of feature extraction, continuous down-sampling operation will lead to the loss of spatial details of objects in the image. To solve this problem, a semantic segmentation algorithm based on double-branch structure is proposed, which can obtain rich semantic information effectively and reduce the loss of object details in feature extraction stage. One branch of the algorithm uses shallow network to retain high-resolution detail information which is helpful for object edge segmentation, and the other branch uses deep network for downsampling to obtain semantic information which is helpful for object category recognition, and then the effective fusion of the two kinds of information can generate accurate pixel prediction. Experimental results on Cityscapes and CamVid datasets show that the proposed algorithm achieves better segmentation performance under fewer parameters than existing semantic segmentation algorithms.

The vibration sensing technology based on optical frequency domain reflectometry (OFDR) has attracted a lot of research due to its superior electromagnetic interference resistance, good corrosion resistance, convenient installation and accurate positioning capability. Phase demodulation algorithm is the key technology of vibration sensing based on OFDR. There are many phase demodulation algorithms applied to OFDR vibration sensing, but they are more or less affected by modulation depth or light intensity. In order to reduce the impact of these two factors on the demodulation results at the same time, this paper proposes an improved phase generation carrier algorithm, which eliminates the nonlinear terms of the light intensity term and modulation depth term in the demodulation signal through mathematical calculation. Compared with the traditional algorithm from both theoretical and simulation aspects, the performance of the improved algorithm is verified, which can reduce the impact of these two factors on the demodulation results at the same time. The work of this paper can be used to improve the stability of the vibration sensing system.

In order to further study the shape detection of optical frequency domain reflectometry(OFDR), and effectively calculate the shape and position of optical fiber in application scenarios such as deformation monitoring, this paper designed an algorithm to reconstruct three-dimensional shape using the optical fiber strain data measured by distributed optical frequency domain reflection technology. Compared with the existing algorithm, spline interpolation was added to the data processing, which improved the precision of shape reduction, and the simulation experiments were used to verify the algorithm. Firstly, a calculation method was designed to obtain the sampling values of curvature and bending direction of three-core optical fiber from the strain data, and the curve equation was constructed by combining the Frenet-Serret formula. Then the finite element analysis software was used to model and extract the S-shape strain data and the S-shape fiber was reconstructed in the three-dimensional space. The results show that the position error increases with the increase of fiber length. The root mean square error is 0.996 mm, and the maximum error per unit length is 0.0824%. The results show that the algorithm can recover the original curve well.

With a certain intensity, a femtosecond laser focusing in the air will generate an air plasma and induce a shock wave. In order to study the propagation process of the shock wave, an ultrafast time-resolved vortex filtering imaging technique is introduced in this paper, and the observed dynamic process of the shock wave is analyzed. The shock wave from air plasma was observed when pumped by a 1.5 mJ femtosecond laser focused into the air through a lens, and the dynamic evolution of the shock wave in the period from 3-15 microseconds was analyzed. The results show that the femtosecond laser plasma air shock wave diffuses outwards in an asymmetrical spherical shape, and the propagation velocity is different along the laser propagation direction and behind the laser propagation direction, which are 372 m/s and 341 m/s, respectively. This observation is different from the traditional point explosion model, namely the symmetric case. We give a reasonable explanation for this asymmetric dynamic process.

Liquid crystal phased array is a kind of electronically controlled programmable optical phased array device based on liquid crystal, which has the advantages of small size, light weight, low power consumption and easy control. The device can precisely control the beam steering by modulating the phase of the incident beam, which not only realizes the performance of fast and flexible the spatial scanning for automatically acquisition, tracking and aiming at the moving targets, but also improves the system integration, and reduces the manufacturing cost. Liquid crystal phased array can be widely used in the fields of radar and space optical communication and other fields. This paper summarizes the research progress and key technologies of liquid crystal phased array at home and abroad, mainly including beam control, system analysis and modeling, and system performance optimization. The application and research progress of liquid crystal phased array in high-energy laser, space optical communication and Lidar are also introduced.

Border and coastal defense is an important part of national security. The distributed or quasi-distributed optical fiber vibration alarm system with long-distance and real-time alarm capability can provide important technical support for real-time intrusion event detection and alarm of the long land border and coastline of PRC. This paper focuses on different types of optical fiber vibration alarm system, such as scattering, interference, fiber Bragg grating and combined types. The principle, characteristics and defects of these technologies are analyzed. As for the technologies mentioned above, their specific application scenario and installation methods are introduced. Finally, the application prospect and development direction of optical fiber vibration alarm system in border and coastal defense are proposed.

It was necessary to obtain and process the relationship between the physical input and digital output of digital imaging equipment for its proper application in optical experiments. Based on ISO 14524:2009 standards, the focal plane opto-electronic conversion functions of digital single lens reflex camera Canon EOS 500D were measured and the data was analyzed. By comparing the effects of different pixel area sizes and different picture formats on the measurement results, it was found that the measurement deviation of the focal plane opto-electronic conversion function increased with the decrease of the pixel area size, and the relationship between the digital output value in RAW formats and the focal plane exposure was linear, which was different from the nonlinear relationship between the digital output value in JPEG formats and the focal plane exposure. When the DSLR camera was used as an instrument to record and measure light distribution, the RAW format that directly reflected the irradiance information received by the sensor can be used to obtain a large dynamic range, theoretically avoid the impact of subsequent processing of camera such as nonlinear transformation, and represent the scene information more accurately after necessary corrections.