DSSS Signal Detection Based on CNN.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

Period Estimation of Spread Spectrum Codes Based on ResNet.

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.

Enhancement of interaction of L-929 cells with functionalized graphene via COOH+ ion implantation vs. chemical method.

Low hydrophilicity of graphene is one of the major obstacles for biomaterials application. To create some hydrophilic groups on graphene is addressed this issue. Herein, COOH+ ion implantation modified graphene (COOH+/graphene) and COOH functionalized graphene were designed by physical ion implantation and chemical methods, respectively. The structure and surface properties of COOH+/graphene and COOH functionalized graphene were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and contact angle measurement. Compared with graphene, COOH+/graphene and COOH functionalized graphene revealed improvement of cytocompatibility, including in vitro cell viability and morphology. More importantly, COOH+/graphene exhibited better improvement effects than functionalized graphene. For instance, COOH+/graphene with 1 × 1018 ions/cm2 showed the best cell-viability, proliferation and stretching. This study demonstrated that ion implantation can better improve the cytocompatibility of the graphene.

Study on the surface properties of surface modified silicone intraocular lenses.

AIM: To prepare a new-type soft intraocular lens (IOL) that silicone intraocular lenses (IOLs) were modified by surface modification technique to assess IOLs biocompatibility. METHODS: With the technique of ion beam combined with low temperature and low pressure plasma, the surface characteristics of the IOLs including physical and optical properties were determined by the instruments of IOLs resolution, UV/VIS scanning spectrophotometer, contact angle measurement system, electron spectroscopy for chemical analysis (ESCA) and scanning electron microscope (SEM). RESULTS: The color of titanium (Ti) modified IOLs was light yellow and that of titanium nitride (TiN) modified IOLs was light brown. The absorptive degree of ultraviolet rays and the hydrophilicity of the surfaces of modified IOLs were increased, and appeared suitable chemical compositions. The resolution of unmodified and modified IOLs reached normal standard. The surfaces of unmodified and Ti-modified IOLs appeared uniform. The surfaces of TiN-modified IOLs presented fine porcelain structure. CONCLUSION: The optical properties of all IOLs and the surface morphology of the modified IOLs were not affected by modification processes. The surface properties of the modified IOLs were improved.

F-heparin modified intraocular lenses in Rhesus monkeys.

AIM: In order to improve the biocompatibility of intraocular lenses (IOL), the polymethylmethacrylate (PMMA) IOL was modified with F-heparin. METHODS: The PMMA IOL was modified with F ions and heparin by the technique of ion beam combined with low temperature and low pressure plasma. The monkeys (20 eyes) with cataract partly were randomly classified into 2 groups and implanted with PMMA IOL and modified IOL respectively for 180 days. All of the eyes were examined by slit-lamp microscope at postoperative 15, 30, 60, 90, 180 days. The extracted IOL was analyzed with computer image analysis, light microscope (LM) and scanning electron microscope (SEM) at postoperative 180 days. RESULTS: The early inflammatory reactions postoperatively include anterior chamber exudation and aqueous cell count. The modified IOL group showed less than the non-modified IOL group. The late foreign body cell reaction that adhered to the surface of non-modified IOL was more predominant. The morphologic and pathological changes of posterior capsule opacification (PCO) in monkeys' eyes included fibrosis-type, pearl-type and soemmerring's ring. There was a significant difference between the two groups. CONCLUSION: F-heparin modified IOL has good uveal and capsular biocompatibility.