To significantly bolster the security of data transmission in an orthogonal frequency division multiplexing passive optical network (OFDM-PON) system,we propose a digital sequence encryption scheme based on multi-chaotic system with multi-layer encryption scheme.By constructing chaotic sequence set with different chaotic system and using them for the various processing of OFDM signals′ generation,the encrypted key space of OFDM-PON system can be improved,which can enhance the confidentiality of system.Firstly,the transmitted data namely plaintexts would be grouped,and then by using the Logistic chaotic system to select the chaotic sequency from the chaotic sequency set to further perform the XOR operations with each set of plaintexts.Consequently,the encrypted data is injected into the 16-QAM (quadrature amplitude modulation) module. Secondly,the Logistic chaotic system is used to select the grouped-QAM signals and determine whether to encrypt the selected signals.Then,the subcarriers of the OFDM signals are grouped,and then are selected by the Logistic chaotic system to realize the encrypted operation.At last,the simulation experiment system is established to provide further validation of the feasibility of our scheme.And,the corresponding results show that,our scheme can present the good confidentiality,and 10540 key spaces can be achieved.

In order to explore new fiber materials that can be used for discrete Raman amplification,first-and second-order Raman amplifiers are designed based on TiO2-doped fiber.A pump parameter configuration scheme with high power conversion efficiency and flat gain is given.The gain characteristics of first-order and second-order Raman amplifiers based on TiO2-doped fiber and second-order Raman amplifiers based on GeO2-doped fiber are compared with the same total pump power.The simulation result shows that in the 60 nm bandwidth range of L-band,32 dBm pump light is injected forward into 6 km long TiO2-doped fiber to amplify 3 dBm signal light. Its power conversion efficiency can reach 41.57%,and the gain flatness is only 1.14 dB.Compared with the second-order Raman amplifier doped with GeO2 fiber,it has a more stable output gain.

Organic light emitting diodes (OLEDs) have a broad prospect for development in future wearable diodes due to its flexible manufacturing and the performance of flexible transparent electrodes (FTEs) affects that of flexible OLED.In this paper,the FTE was fabricated based on the silver nanowires (AgNWs) and poly(3,4-ethylenedioxythiophene)∶poly(styrenesulfonate) (PEDOT∶PSS),which was treated by methanol impregnation,Ar plasma treatment and ultraviolet radiation to optimize the optoelectronic properties of FTE.It was found that methanol impregnation could reduce polymer coating on AgNWs,AgNWs could be welded by Ar plasma treatment and ultraviolet treatment,as well as the synergistic effect of two methods can further optimize the photoelectric properties of FTE.The sheet resistance of the optimal FTE is 14.18 Ω/sq,and the transmittance at 550 nm is more than 84%.The sheet resistance change rate of FTE is less than 15% after 500 bending tests.This work provides a feasible scheme for the preparation and optimization of FTE.

A fiber liquid level temperature sensor based on fiber Mach-Zehnder interferometer (MZI) is designed and fabricated in this paper.The structure is composed of a no-core fiber (NCF) and a multi-mode fiber (MMF) fused between single-mode fibers (SMFs).The change of ambient liquid level and temperature will lead to the change of optical transmission mode of the interferometer, and then cause the movement of interference spectrum fringe.The sensitivity of liquid level and temperature response can be obtained by detecting the drift of wavelength of two valleys in the interference spectrum. The sensitivity coefficient matrix can be used to measure liquid level and temperature simultaneously.The results show that the interference spectrum is red shifted when the liquid level rises.The maximum liquid level response sensitivity is 208.38 pm/mm in the range of 0—36 mm level variation. When the temperature increases,the interference spectrum still keeps red shift.The maximum temperature response sensitivity is 29.67 pm/℃ in the range of 30—70 ℃.The sensor structure has the advantages of high sensitivity,large measuring range,simple manufacturing,low cost and simultaneous measurement of liquid level temperature, etc.,which has the potential application in the sensing field.

An ultra-high sensitivity refractive index sensor based on double biased fiber Mach-Zehnder interferometer (MZI) is proposed.The interference mechanism of biased MZI is analyzed theoretically.The refractive index sensing characteristics of the multimodal biased fiber,the variation of refractive index sensitivity in different biased lengths and refractive index ranges are simulated and analyzed by beam propagation method (BPM),and the sensing characteristics of single-biased and double-biased are compared.The simulation results show that the refractive index sensitivity of single-biased MZI is -5 557 nm/RIU when the refractive index range is 1.333 0—1.334 0,and the refractive index sensitivity of double-biased MZI is -14 071 nm/RIU,which provides a import theoretical basis for high sensitivity measurement of liquid refractive index.The sensor has a compact structure and the overall length is only 1 200 μm,which has important application value in the field of liquid refractive index measurement.

To address the polarization dependence or complex structure of common optical modulators,this paper proposes a controllable optical pulse modulator based on optofluidics.The continuous light is converted into digital pulse signal by using a microfluidic optical switch,which based on the discrete droplets generated by T-junction and the principle of total internal reflection,and the width and frequency of the optical pulse can be modulated by adjusting the length and generation rate of the droplets.The proposed microfluidic optical pulse modulator has the advantages of simple structure,low insertion loss,high extinction ratio and easy integration.The flow field and optical field characteristics of the microfluidic optical pulse modulator are analyzed and its structure is optimized. The results show that the extinction ratio of the optical pulse modulator is 17.74 dB and the insertion loss is 0.49 dB.

With the popularity of digital video,high efficiency video coding (HEVC) video steganography has received more and more attention.Residual coefficient domain steganography has high security and low bit rate increase.However,during the process of video encoding,the reconstructed coefficient does not meet the independence between blocks,resulting in the modification of video residual coefficients often lead to more serious distortion drift.In this paper,an adaptive steganography algorithm for HEVC video without intra frame distortion drift is proposed.Firstly,the appropriate carrier is selected according to the intra frame prediction modes and the multi-coefficient modification method so as to avoid the distortion drift caused by the coefficient modification.Secondly,the distortion function is designed which contains two factors,i.e.,intra block distortion and modified carrier coefficient distribution.It is employed to guide the syndrome trellis codes (STC) to modify the carrier with low embedded distortion.Finally,according to the minimum distortion cost,the message is embedded into quantized discrete sine transform (QDST) coefficient of the 4×4 luminance block,which meets specific conditions.Experimental results show that the proposed video steganography algorithm can effectively avoid intra frame distortion drift and ensure the security of the steganography algorithm while achieving good visual quality.

Fall detection mostly depends on sensor equipment.The method is highly influenced by equipment and environmental factors,and often can not work well.In addition,vision-based methods are often not effective in terms of real-time and robust. In order to solve these problems,a lightweight fall detection algorithm is proposed with strong robustness and convenient deployment in embedded devices.Taking YOLOv5 as the benchmark model,the lightweight attention mechanism module is firstly integrated to make the network focus on the target area to be identified and enhance the recognition accuracy of the network.Secondly,the model is pruned by the model compression method,which reduces the volume and calculation.Therefore it makes the model lightweight,so as to improve the reasoning speed and facilitate deployment in embedded devices.Finally,knowledge distillation is carried out on the pruned model,which can improve the detection accuracy without increasing the complexity of the model.The experimental results show that compared with the benchmark model, the mAP of this model is increased by 1.7%,the recall is increased by 1.2%,the model volume is reduced by 79.1%,and the floating-point operation is reduced by 70.9%.The proposed model is deployed on the embedded device Jetson Nano,and the detection speed is up to 13.2 frame/s,which basically meets the requirements of real-time fall detection.

Aiming at the obvious block effect,color distortion and low brightness of the traditional dark channel prior algorithm in processing foggy images with large areas of sky,a dehazing algorithm combining regional growth and tolerance mechanism is proposed.Firstly,the dark channel is obtained by gray scale image etching;Secondly,the sky region is segmented by the seed region growth method,and the average gray value of the sky region is estimated as the atmospheric light value;Then,combined with the atmospheric scattering model,the rough transmittance is obtained,and the improved tolerance mechanism and guided filtering are used to modify and refine the transmittance;Finally,Retinex method is introduced to post-process the image to further adjust the color and brightness.The experimental results show that the dehazing algorithm proposed in this paper has obvious dehazing effect when processing the image with sky region,the color of the sky region has been significantly improved,and the image is clear and bright as a whole.

Aiming at the problem of low visibility of hazy images and dim brightness of dehazed image,a two-stage dehazing algorithm based on atmospheric scattering model was proposed.Firstly,the minimum channel of hazy-free image was derived by linearly transforming of brightness of hazy image and stretching saturation of hazy image,which was used to calculate rough transmittance combining with the minimum channel of the hazy image.The rough transmittance were optimized separately by double gradient cost function or guided filtering in different stages.Finally,the hazy-free image was restored by the atmospheric scattering model that would be used again to improve brightness for dehazed image.The experimental results show that the restored image become clearer and brighter after hazy removal;The average of objective indicators such as image comprehensive quality, peak signal-to-noise ratio and running time are superior to compared algorithms, in which the image comprehensive quality is improved by at least 1.55 times and the running speed is accelerated by at least 1.50 times on average.The proposed algorithm effectively improves the visibility and brightness of hazy image.

In order to improve the detection effect of the vehicle assisted driving system on the vehicle ahead,and further obtain accurate distance information,this paper proposes an improved you only look once v5s (YOLOv5s) target vehicle detection algorithm,and uses binocular to measure the distance of the vehicle ahead.Based on the YOLOv5s detection network,firstly,the convolutional block attention module (CBAM) is introduced into the network to effectively extract the contour features of the detection target;secondly,the PANet network in Neck is replaced with BiFPN to improve the feature fusion ability,and DIoU is used to optimize the loss function to enhance the accuracy of vehicle detection.The SURF algorithm is used for stereo matching,and the feature matching points are constrained to obtain the optimal disparity value.Finally,the distance information of the preceding vehicle is obtained through the principle of binocular vision ranging.The test shows that within a distance of 20 m,the accuracy of vehicle recognition rate is 92.1%,increased by 1.54%,and the average error rate of ranging is 2.75%.

Long-term monitoring of heart rate is of great significance for the prevention and treatment of cardiovascular diseases.Commonly used equipment for this purpose includes patient monitor,electrocardiograph,smartwatch,sports bracelet,et al.They are all contact devices.Thus,skin indentation and itch are inevitable problems for long-term wearing.The technic named remote photoplethysmography (rPPG) can acquire heart rate from facial videos,which makes it a promising method for long-term heart rate monitoring.However,the data analyses of most rPPG researches rely on computer,which is a stumbling block to the commercialization and expansion of rPPG technic.To solve this problem,this paper attempts to realize heart-rate monitoring based on rPPG technic on the embedded platform.The monitoring system is mainly composed of Raspberry PI 4B development board,camera and touch screen.AdaBoost algorithm was used to realize face recognition and tracking.Forehead and cheek were selected as ROI.Butterworth bandpass filter was used to de-noise.BVP waveform was extracted according to POS model.The final pulse wave was obtained by blind source separation of multiple BVP waveforms from different ROIs.Heart rate was calculated by energy spectrum analysis.Experimental results show that our system has the same heart rate detection accuracy and robustness as PC terminal.The achievements of this paper should be useful for the miniaturization and popularization of long-term heart rate monitoring equipment,and also provide a strong guarantee for remote monitoring treatment in smart medical care.

In the examination,comparison and analysis of the scientific material evidence in the court,infrared spectroscopy technology has its own advantages,which can be used to determine the type and composition of substances and to analyze the internal structure of substances and their physical and chemical properties.This paper will give a brief classification and overview of the many common physical evidence involved in forensic science according to the different attributes of material sources,based on the relevant knowledge of infrared spectroscopy technology,further elaborate the current application status and the latest progress of infrared spectroscopy technology in forensic scientific biological evidence and non-biological evidence,and to look forward to the future,in order to promote the deeper research and development of infrared spectroscopy technology in the field of forensic science.