X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out wo...X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.展开更多
Cognitive radio (CR) is a technology that provides a promising new way to improve the efficiency of the use of the electromagnetic spectrum that available. Spectrum sensing helps in the detection of spectrum holes (un...Cognitive radio (CR) is a technology that provides a promising new way to improve the efficiency of the use of the electromagnetic spectrum that available. Spectrum sensing helps in the detection of spectrum holes (unused channels of the band), and instantly move into vacant channels while avoiding occupied ones. An energy detector with baseband sampling for CR is presented with mathematical analyses for an additive white Gaussian noise (AWGN) channels. A brief overview of the energy detection based spectrum sensing for CR technology is introduced. Practical implementation issues on Texas Instruments TMS320C6713 floating point DSP board are presented. Novelties of this work came from a derivation of probability of detection and probability of false alarm for the baseband energy detector without including the sampling theorems and the associated approximation.展开更多
目的探索利用实时能量代谢检测技术检测单采血小板(AP)中线粒体呼吸功能(MRF)的最佳条件参数,并研究AP在贮存过程中MRF的变化。方法收集AP标本,利用Seahorse XF检测其中MRF,通过调整AP的上样浓度、洗涤条件、线粒体呼吸调节药物浓度等...目的探索利用实时能量代谢检测技术检测单采血小板(AP)中线粒体呼吸功能(MRF)的最佳条件参数,并研究AP在贮存过程中MRF的变化。方法收集AP标本,利用Seahorse XF检测其中MRF,通过调整AP的上样浓度、洗涤条件、线粒体呼吸调节药物浓度等多项参数,最后分别研究贮存1 d和5 d的AP中MRF。结果AP上样浓度为5×10^(7)/100μL时,基础呼吸值316±65为最优;洗涤组的基础呼吸值329±120比未洗涤组的57±7更优(P<0.05);FCCP浓度以2μmol/L为最优。贮存1 d AP的基础呼吸值为683±161,而贮存5 d AP的基础呼吸值为140±23(P<0.05);贮存1 d AP的最大呼吸值为1044±82,而贮存5 d AP最大呼吸值<200(P<0.05)。2组的MRF动力曲线有明显差异。结论实时能量代谢检测技术可以实现对AP中MRF的检测。AP在贮存过程中MRF明显受损,提示这可能是血小板贮存损伤的重要新机制。展开更多
A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the di...A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour. An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the nega- tive influence of image noise to obtain an accurate foreground map than other foreground detection algo- rithms. Most shadow pixels are also eliminated by this method.展开更多
文摘X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.
文摘Cognitive radio (CR) is a technology that provides a promising new way to improve the efficiency of the use of the electromagnetic spectrum that available. Spectrum sensing helps in the detection of spectrum holes (unused channels of the band), and instantly move into vacant channels while avoiding occupied ones. An energy detector with baseband sampling for CR is presented with mathematical analyses for an additive white Gaussian noise (AWGN) channels. A brief overview of the energy detection based spectrum sensing for CR technology is introduced. Practical implementation issues on Texas Instruments TMS320C6713 floating point DSP board are presented. Novelties of this work came from a derivation of probability of detection and probability of false alarm for the baseband energy detector without including the sampling theorems and the associated approximation.
文摘目的探索利用实时能量代谢检测技术检测单采血小板(AP)中线粒体呼吸功能(MRF)的最佳条件参数,并研究AP在贮存过程中MRF的变化。方法收集AP标本,利用Seahorse XF检测其中MRF,通过调整AP的上样浓度、洗涤条件、线粒体呼吸调节药物浓度等多项参数,最后分别研究贮存1 d和5 d的AP中MRF。结果AP上样浓度为5×10^(7)/100μL时,基础呼吸值316±65为最优;洗涤组的基础呼吸值329±120比未洗涤组的57±7更优(P<0.05);FCCP浓度以2μmol/L为最优。贮存1 d AP的基础呼吸值为683±161,而贮存5 d AP的基础呼吸值为140±23(P<0.05);贮存1 d AP的最大呼吸值为1044±82,而贮存5 d AP最大呼吸值<200(P<0.05)。2组的MRF动力曲线有明显差异。结论实时能量代谢检测技术可以实现对AP中MRF的检测。AP在贮存过程中MRF明显受损,提示这可能是血小板贮存损伤的重要新机制。
文摘A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour. An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the nega- tive influence of image noise to obtain an accurate foreground map than other foreground detection algo- rithms. Most shadow pixels are also eliminated by this method.
