FPGA-based plasma sterilization device for wound-edge recognition

There is a currently a lack of large-area plasma sterilization devices that can intelligently identify the shape of a wound for automatic steriliza-tion.For this reason,in this work,a plasma sterilization device with wound-edge recognition was developed using afield-programmable gate array(FPGA)and a high-performance image-processing platform to realize intelligent and precise sterilization of wounds.SOLIDWORKS was used to design the mechanical structure of the device,and it was manufactured using 3D printing.The device used an improvement of the traditional Sobel detection algorithm,which extends the detection of edges in only the x and y directions to eight directions(0○,45○,90○,135○,180○,225○,270○,and 315○),completing the wound-edge detection by adaptive thresholding.The device can be controlled according to different shapes of sterilization area to adjust the positioning of a single plasma-jet tube in the horizontal plane for two-dimensional move-ment;the distance between the plasma-jet tube and the surface of the object to be sterilized can be also adjusted in the vertical direction.In this way,motors are used to move the plasma jet and achieve automatic,efficient,and accurate plasma sterilization.It was found that a good sterilization effect could be achieved at both the culture-medium level and the biological-tissue level.The ideal sterilization parameters at the culture-medium level were a speed of 2 mm/s and aflow rate of 0.6 slm,while at the biological-tissue level,these values were 1 mm/s and 0.6 slm,respectively.

Realizing high efficiency and large-area sterilization by a rotating plasma jet device

By tilting a plasma jet and rotating 360°,a large-area can be scanned and sterilized in a short time.Compared with the previous array device,this pipe has the significant advantages of high sterilization uniformity and low gas consumption.Firstly,a rotatable plasma jet device,which can control the swing and rotation of a jet pipe,is designed,and a corresponding theoretical model is established to guide the experiment.Secondly,with Staphylococcus aureus(S.aureus)and Escherichia coli(E.coli)as the target bacteria,the device achieves a short sterilization time of 158 s—the minimum sterilization flow of S.aureus and E.coli is 0.8 slm and 0.6 slm,respectively.The device is compared with an array plasma sterilization device in terms of sterilization speed and gas consumption.The results show that the device is not only better than an array plasma sterilization device with respect to scanning uniformity,but also far less than the array plasma sterilization device in gas consumption of 5 slm.Therefore,the device has great potential in applications involving efficient,large-area sterilization.

Deep Learning Based Signal Detector for OFDM Systems

In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.

火星降落伞开伞过程形态参数辨识与应用

针对柔性目标降落伞开伞过程的形态变化大、光照强度变化大、运动规律性差、存在遮挡等问题,提出了一种基于多算法融合的视觉测量技术的降落伞开伞过程形态参数辨识方法。首先,设计了具有视觉测量靶标功能的降落伞图案,提供了丰富的具有可区分度的标记点,能够准确地对定位点进行跟踪和测量,并开展了双目相机内、外参标定。其次,提出了应用对极几何原理,采用基于暗通道的图像增强技术,提高了图像质量,有效地减轻了各种噪声和过曝光等环境因素的影响;采用稀疏编码超分辨率重建算法,改进了特征点的像素级提取,实现了高精度的亚像素级特征提取;采用特征跟踪扩展卡尔曼滤波算法,提升了特征匹配跟踪的精度和效率。最后,通过全尺寸高空开伞试验的验证,结果表明该方法能够达到较高的辨识精度,具有较好的准确性和鲁棒性。此方法在中国首次火星探测“天问一号”探测器上成功得到了应用,精确地从双目影像中辨识出降落伞开伞过程形态参数,对设计和分析降落伞开伞工况提供了重要的技术参考和数据积累。

Fitting boxes to Manhattan scenes using linear integer programming

We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.