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81.
Tagging, tracking, or validation of products are often facilitated by inkjet-printed optical information labels. However, this requires thorough substrate pretreatment, ink optimization, and often lacks in printing precision/resolution. Herein, a printing method based on laser-driven deposition of solid polymer ink that allows for printing on various substrates without pretreatment is demonstrated. Since the deposition process has a precision of <1 µm, it can introduce the concept of sub-positions with overlapping spots. This enables high-resolution fluorescent labels with comparable spot-to-spot distance of down to 15 µm (444,444 spots cm−2) and rapid machine learning-supported readout based on low-resolution fluorescence imaging. Furthermore, the defined thickness of the printed polymer ink spots can be used to fabricate multi-channel information labels. Additional information can be stored in different fluorescence channels or in a hidden topography channel of the label that is independent of the fluorescence.  相似文献   
82.
Prior studies on carbon-filler based, conductive polymer composites have mainly investigated how conductive filler morphology and concentration can tailor a material's electrical conductivity and overlooks the effects of filler alignment due to the difficulty to control and quickly quantify the filler alignment. Here, direct ink write 3D printing's unique ability is utilized to control carbon fiber alignment with a single process parameter, velocity ratio, to instantaneously activate or deactivate the electrical network in composites. Maximum electrical conductivity is achieved by randomly aligning carbon fibers that enhances the chance of direct fiber-to-fiber contact and, thus, activating the electrical network. However, aligning the fibers by increasing the velocity ratio disrupts the electrical network by minimizing fiber-to-fiber contact that resulted in a drastic decrease in electrical conductivity by as much as five orders of magnitude in both short and long carbon fiber composites. With this study, this study demonstrates that electrically conductive or insulative composites can be fabricated sequentially with a single ink. This novel ability to instantaneously control the electrical conductivity of carbon fiber reinforced composites allow to directly embed conductive pathways into designs to 3D print multifunctional composites that are capable of localized heating and self-sensing.  相似文献   
83.
为解决雷达实时信号处理功能及性能有效验证问题,文中设计并实现了一种宽窄带一体化、多通道雷达数据回放系统。该系统按雷达工作的时序和数据率将雷达回波数据发送至实时信号处理系统,从而对目标跟踪场景进行复现,实现雷达实时信号处理功能及性能的有效验证。系统硬件平台由磁盘阵列服务器与两块PCIe光纤板组成。基于Xilinx系列产品,结合系统特点对PCIe DMA控制流程及响应机制做进一步调整,实现了服务器到光纤板的雷达回波数据高速批量传输。根据雷达工作机制及回波数据结构,设计FPGA回放控制状态机及相关功能模块,严格按照雷达工作的时序和数据率使雷达回波数据由光纤板传输至实时处理系统。分别设计软件同步控制方法和硬件同步控制方法,保证多板卡数据回放的同步性。测试结果表明,该系统实现了宽、窄带雷达回波数据的板间同步回放,各数据类型分别支持6路并行数据通道,数据回放峰值速率可达1 920 MB·s-1。文中系统可依托雷达原有硬件平台实现,无需增设板卡和线缆,具有良好的集成性与通用性,目前已被应用于某多功能相控阵雷达中。  相似文献   
84.
卢东祥 《电子科技》2023,36(3):81-86
为了进一步提高城市道路交通网络的通行效率,粒子群优化和神经网络等多种智能优化算法受到越来越多的关注。近年来,深度学习技术的普及与应用大幅提升了城市交通网络的节点识别效率,而交通网络的节点调度又扩展了深度学习技术的应用。文中详细分析了交通节点调度所面临的关键问题,归纳并总结了相关网络节点分配的研究现状。在此基础上,深入研讨了城市交通网络节点调度与深度学习的应用前景,并对交通网络节点分配优化策略的未来研究方向进行了展望。  相似文献   
85.
3D conformable electronic devices on freeform surfaces show superior performance to the conventional, planar ones. They represent a trend of future electronics and have witnessed exponential growth in various applications. However, their potential is largely limited by a lack of sophisticated fabrication techniques. To tackle this challenge, a new direct freeform laser (DFL) fabrication method enabled by a 5-axis laser processing platform for directly fabricating 3D conformable electronics on targeted arbitrary surfaces is reported. Accordingly, representative laser-induced graphene (LIG), metals, and metal oxides are successfully fabricated as high-performance sensing and electrode materials from different material precursors on various types of substrates for applications in temperature/light/gas sensing, energy storage, and printed circuit board for circuit. Last but not the least, to demonstrate an application in smart homes, LIG-based conformable strain sensors are fabricated and distributed in designated locations of an artificial tree. The distributed sensors have the capability of monitoring the wind speed and direction with the assistance of well-trained machine-learning models. This novel process will pave a new and general route to fabricating 3D conformable electronic devices, thus creating new opportunities in robotics, biomedical sensing, structural health, environmental monitoring, and Internet of Things applications.  相似文献   
86.
This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.  相似文献   
87.
陆上集群无线电(Terrestrial Trunked Radio,TETRA)数字集群通信系统因其开放性易受到内外部电磁干扰。干扰信号的类型多种多样,针对不同的干扰样式,采取的抗干扰措施也各不相同,因此干扰信号的识别具有重大意义。基于此,提出了一种干扰信号智能识别技术。该技术首先对受到不同干扰后的TETRA音频数据进行特征提取,并筛选出具有分类能力的特征,其次使用决策树、支持向量机和随机森林3种分类模型对特征提取后的待测试信号进行智能分类识别。实验结果表明,使用的这3种模型能够有效判断TETRA系统中的信号是否受到干扰,以及受到何种样式的干扰,可为后续TETRA系统中的信号干扰识别提供参考。  相似文献   
88.
Teleoperation during a catastrophic event requires an interface that can perform under frequently changing circumstances caused by unpredictable and dangerous conditions. Thus, teleoperation interfaces are under active development to provide both visual and haptic feedback to the fingers. However, studies of teleoperation systems with finger haptic feedback based on force profiles are difficult to conduct because of interface limitations. Therefore, in this paper, we introduce an intuitive teleoperation interface, an anthropomorphic teleoperated robot, and a hand-wearable force-feedback system that provides various feedbacks to the fingers. We combined these systems to compare and evaluated the performance of tactile and kinesthetic finger feedback using two experiments: maintaining appropriate grip force for variably fragile objects and following a force trajectory that changed in real time. Ten subjects participated in the experiments. The results were analyzed using repeated measures analysis of variance. Feedback factors differed significantly. Provision of force feedback to the user’s finger was most effective in both teleoperation experiments.  相似文献   
89.
While malicious samples are widely found in many application fields of machine learning, suitable countermeasures have been investigated in the field of adversarial machine learning. Due to the importance and popularity of Support Vector Machines (SVMs), we first describe the evasion attack against SVM classification and then propose a defense strategy in this paper. The evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear classifier. Specially, we propose what is called a vulnerability function to measure the vulnerability of the SVM classifiers. Utilizing this vulnerability function, we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion attack. Our defense method is verified to be very effective on the benchmark datasets, and the SVM classifier becomes more robust after using our kernel optimization scheme.  相似文献   
90.
铁路接触网绝缘子状态检测对铁路行车安全有着 重大的意义,为解决目前人工对绝缘 子图像检测结果的不确定性,提出一种深度学习结合灰度纹理特征的检测方法。首先使用 Faster R-CNN (faster region-based convolutional neural network)目标检测算法对图像中绝缘子精确识别,再通过灰度共生矩阵对绝缘子纹理 特征进行分析提取,之后结合支持向量机将绝缘子分为正常绝缘子和异常绝缘子,实验数 据结果证明使用能量、熵、相关度3种纹理特征进行绝缘子状态分类时对实验数据中的正 常状态绝缘子的分类精度可达100%,异常状态绝缘子的分类精度达97.5%,最后依据绝缘 子图像灰度分布的周期性特点,利用灰度积分投影将异常绝缘子分为破损绝缘子和夹杂异 物绝缘子。实验结果表明所提方法可以有效对绝缘子状态进行检测分类。  相似文献   
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