In this paper, the eavesdropping model based on eavesdroppers near legitimate users, and the effect of atmospheric channel correlation on the physical layer security (PLS) of the free-space optical (FSO) link are analyzed. According to the joint probability density function (PDF) and cumulative distribution function (CDF) of Gamma-Gamma (G-G) distribution, a new closed-form expression of interception probability is derived. Numerical results show that the interception probability of the FSO system depends on turbulence intensity, channel correlation and radial displacement attenuation of eavesdroppers.
Applications for quanta and space sensing both depend on efficient low-light imaging. To precisely optimize and design image sensor pixels for these applications, it is crucial to analyze the mechanisms behind dark current generation, considering factors such as temperature, trap cross-section and trap concentration. The thresholds for these generating effects are computed using optoelectrical technology computer aided design (TCAD) simulations, and the ensuing changes in pinned photo-diode (PPD) dynamic capacitance are observed. Various generation models along with an interfacial trap model are used to compare PPD capacitance fluctuations during light and dark environments. With the use of this comparison study, current compact models of complementary metal oxide semiconductor (CMOS) image sensors can be modified to accurately capture the impacts of dark current in low-light conditions. The model developed through this study demonstrates a deviation of only 6.85% from the behavior observed in physical devices. These results not only enhance our understanding of dark current generation mechanisms but also offer practical applications by improving the performance and accuracy of image sensors.
In order to effectively prevent and treat heart-based diseases, the study of precise segmentation of heart parts is particularly important. The heart is divided into four parts: the left and right ventricles and the left and right atria, and the left main trunk is more important, thus the left ventricular muscle (LV-MYO), which is located in the middle part of the heart, has become the object of many researches. Deep learning medical image segmentation methods become the main means of image analysis and processing at present, but the deep learning methods based on traditional convolutional neural network (CNN) are not suitable for segmenting organs with few labels and few samples like the heart, while the meta-learning methods are able to solve the above problems and achieve better results in the direction of heart segmentation. Since the LV-MYO is wrapped in the left ventricular blood pool (LV-BP), this paper proposes a new model for heart segmentation: principle component analysis network (PCA-Net). Specifically, we redesign the coding structure of Q-Net and make improvements in threshold extraction. Experimental results confirm that PCA-Net effectively improves the accuracy of segmenting LV-MYO and LV-BP sites on the CMR dataset, and is validated on another publicly available dataset, ABD, where the results outperform other state-of-the-art (SOTA) methods.
The complex refractive index dispersion (CRID) of absorbing materials is very important in many fields, especially in printing industry and medical research. However, due to their strong absorbing, CRID determination is still a challenge. In this study, without diluting treatment or the thickness information, a method is proposed to calculate the CRID of absorbing materials, based merely on the reflectance and transmittance spectra measurements. The method separates the CRID into absorbing part and transparent part based on Kramers-Kronig relations, and it also uses the common Cauchy dispersion formula and Fresnel reflection formula. The CRID of methyl-red-doped poly (methyl methacrylate) (MR-PMMA) (3% mass fraction) and hemoglobin (Hb) solutions (320 g/L) are determined over the spectral range from 400 nm to 750 nm, and the result shows good stability and consistency of the method.
We introduce an all-fiber stationary phase shifter for a fiber-optic gyroscope (FOG) which simultaneously provides phase shifts of opposite signs in different cores of the dual-core optical fiber. We propose a new dual-core fiber-optic gyroscope (DCFOG) in which different cores of the dual-core optical fiber provide independent rotation rate measurements. The device enables implementation of a differential scheme, which ensures the stability of the measured phase shift. As a computer simulation result, the accuracy of the rotation rate sensing is increased by up to 10 times at typical noise levels.
In order to further reduce the cost of manually screening suitable second harmonic signals for curve fitting when detecting methane concentration by tunable diode laser absorption spectroscopy (TDLAS) technology, as well as the influence of certain human factors on the amplitude screening of second harmonic signals, and improve the detection accuracy, a one-dimensional wide atrous convolutional neural network (1D-WACNN) method for methane concentration detection is proposed, and a real-time detection system based on TDLAS technology to acquire signal and Jetson Nano to process signal is built. The results show that the accuracy of this method is 99.96%. Compared with other methods, this method has high accuracy and is suitable for real-time detection of methane concentration.
A sensitive room-temperature metal-semiconductor-metal (MSM) structure is fabricated on high-resistivity silicon substrates (ρ>4 000 Ω·cm) for terahertz (THz) detection by utilizing the photoconductive effect. When radiation is absorbed by the nitrogen-rich niobium nitride, the number of free electrons and electrical conductivity increase. The detector without an attached antenna boasts a voltage responsivity of 7 058 V/W at a frequency of 310 GHz as well as small noise density of 3.5 nV/Hz0.5 for a noise equivalent power of about 0.5 pW/Hz0.5. The device fabricated by the standard silicon processing technology has large potential in high-sensitivity THz remote sensing, communication, and materials detection.
With the deepening of neural network research, object detection has been developed rapidly in recent years, and video object detection methods have gradually attracted the attention of scholars, especially frameworks including multiple object tracking and detection. Most current works prefer to build the paradigm for multiple object tracking and detection by multi-task learning. Different with others, a multi-level temporal feature fusion structure is proposed in this paper to improve the performance of framework by utilizing the constraint of video temporal consistency. For training the temporal network end-to-end, a feature exchange training strategy is put forward for training the temporal feature fusion structure efficiently. The proposed method is tested on several acknowledged benchmarks, and encouraging results are obtained compared with the famous joint detection and tracking framework. The ablation experiment answers the problem of a good position for temporal feature fusion.
Aiming at the problem of low detection accuracy of occluded pedestrian in traffic environments, this paper proposes a key points and visible part fusion network for occluded pedestrian detection. The proposed algorithm constructs two attention modules by introducing human key points and the bounding box of visible parts respectively, which suppresses the occluded parts in the channel features and spatial features of pedestrian features respectively. Experimental results on CityPersons and Caltech datasets demonstrate the effectiveness of the proposed algorithm. The missing rate (MR) is reduced to 40.78 on the Heavy subset of the CityPersons dataset and surpasses many outstanding methods.
In order to explore the effect of unstable resonator stability on laser beam quality, the numerical simulation of mid-infrared laser and visible laser was carried out in GLAD software. The simulation results showed that the existence of defocus aberration, tilt aberration and astigmatic aberration in the unstable resonator can cause the center of the far-field spot of the output annular beam to drift, the number of peripheral diffraction rings to increase, the beam quality to deteriorate, and the degree of effect is different. It is also found that on the basis of the effect of tilt aberration and astigmatism aberration, the introduction of defocus aberration can improve the output laser beam quality to a certain extent. In addition, under the condition of the same aberrations, the effects of different wavelength lasers are roughly the same. However, in terms of the degree of effects, the short-wave laser is much higher than the medium-long-wave laser, which verifies that the resonator debugging of the short-wave laser is more difficult than that of the medium-long-wave laser in the experimental process. The simulation results can provide an important reference for the optimization design of the laser system, the processing of cavity mirror and the formulation of the correction range index of the adaptive optical system.
Integrated optical power splitters are basic but indispensable on-chip devices in silicon photonics. They can be used either for power distribution or monitoring, or as the building blocks for more complex devices or circuits. Although different types of optical power splitters with different architectures have been proposed and demonstrated, devices that could work with arbitrary power splitting ratio in a large bandwidth without polarization dependence are still rare to be seen. In this paper, we propose and investigate an optical power splitter with adiabatically tapered waveguide structures on a thick silicon nitride platform, which could meet the requirement mentioned above. With optimized structural parameters obtained by three-dimensional finite-difference time-domain (3D-FDTD) simulation, the polarization dependence of different power splitting ratio gets almost eliminated for each specific working wavelength. In a broad wavelength range (1 340–1 800 nm), the insertion loss (IL) of the device is below 1 dB, and the variation of the power splitting ratio (PSR) can be controlled within ∼±5% if compared with the targeted design value for 1 550 nm centered wavelength. Simple structure, relaxed critical dimensions, and good fabrication tolerance make this device compatible with the standard fabrication process in commercial silicon photonic foundries.
Leaking data through screen-shooting has become the main way of modern leaks. Digital watermarking technology can trace the leaker through the watermark information after the data is leaked. The current screen-shooting watermarking scheme can resist part of the distortion in the screen-shooting process, but it faces two problems. On the one hand, the watermark capacity is small. On the other hand, when the shot watermarked image is incomplete, high watermark extraction accuracy cannot be guaranteed. Based on the above problems, we propose a resistant to incomplete shooting watermarking (RiSw) scheme. Specifically, we design a set of codecs that can embed binary images as watermarks into carrier images and extract them, which not only ensures good visual effects of watermarked image, but also greatly increases watermark capacity. To resist incomplete shooting, we propose an incomplete shooting layer to simulate the situation of incomplete shooting in the screen-shooting process. Robustness to incomplete shooting can be achieved through end-to-end training. Extensive experiments show that the scheme proposed in this paper has superior performance. Even if the watermarked image lacks 50% pixels, it can still maintain a stable extraction accuracy.
In order to mitigate the nonlinear effects of Mach-Zehnder modulator (MZM) on optical transmission signals in intensity modulation and direct detection (IM-DD) systems, a combined approach utilizing sinusoidal subcarrier modulation (SSM) and the Levenberg-Marquardt back propagation (LM-BP) neural network is proposed in this paper. The method employs a sine wave as the subcarrier to carry the 4 pulse amplitude modulation (PAM4) signals, aiming to equalize the distorted signals after MZM modulation. Subsequently, the LM-BP algorithm eliminates any remaining inter-symbol interference (ISI). This scheme uses sine wave modulation to solve the problem of additional ISI caused by triangular wave modulation. Furthermore, this combined approach simplifies the algorithm complexity compared to solely relying on a neural network equalizer. In this paper, the performance of SSM-LM-BP scheme is simulated and analyzed in IM-DD system. The results show that the joint scheme outperforms the triangular wave modulation scheme as well as the neural network algorithm after transmitting 50 Gbit/s PAM4 signals for 80 km without relays under the conditions of dispersion compensation, and the symbol error rate (SER) can be as low as 10−5.
A temperature and refractive index sensor based on fiber Bragg grating (FBG) end surface cascade open Fabry-Pérot (FP) cavity has been designed and demonstrated experimentally. The open FP cavity has been fabricated on the end face of an FBG by dislocation fusion in this work, the open FP cavity could be used for refractive index sensing, and the temperature is measured by the FBG. The working principle of the sensor and the method of improving the sensitivity are analyzed by theoretical simulation. The refractive index sensitivity of the sensor is 1 108.4 nm/RIU, while the maximum fluctuation of the sensor stability experiment detection is 0.005 nm. The results show that it has satisfactory characteristics. The sensor is a compact all-fiber structure, so it has potential applications in the field of temperature refractive index sensing, such as biomedical and capacitor electrolyte detection.
Within the fields of underwater robotics and ocean information processing, computer vision-based underwater target detection is an important area of research. Underwater target detection is made more difficult by a number of problems with underwater imagery, such as low contrast, color distortion, fuzzy texture features, and noise interference, which are caused by the limitations of the unique underwater imaging environment. In order to solve the above challenges, this paper proposes a multi-color space residual you only look once (MCR-YOLO) model for underwater target detection. First, the RGB image is converted into YCbCr space, and the brightness channel Y is used to extract the non-color features of color-biased images based on improved ResNet50. Then, the output features of three scales are combined between adjacent scales to exchange information. At the same time, the image features integrated with low-frequency information are obtained via the low-frequency feature extraction branch and the three-channel RGB image, and the features from the three scales of the two branches are fused at the corresponding scales. Finally, multi-scale fusion and target detection are accomplished utilizing the path aggregation network (PANet) framework. Experiments on relevant datasets demonstrate that the method can improve feature extraction of critical targets in underwater environments and achieve good detection accuracy.