Blood cell counting based on U-Net++ and YOLOv5

Clinical information about a variety of disorders is available through blood cell counting, which is usually done by manual methods. However, manual methods are complex, time-consuming and susceptible to the subjective experience of inspectors. Although many efforts have been made to develop automated blood cell counting algorithms, the complexity of blood cell distribution and the highly overlapping nature of some red blood cells (RBCs) remain significant challenges that limit the improvement of analytical accuracy. Here, we proposed an end-to-end method for blood cell counting based on deep learning. Firstly, U-Net++ was used to segment the whole blood cell image into several regions of interest (ROI), and each ROI contains only one single cell or multiple overlapping cells. Subsequently, YOLOv5 was used to detect blood cells in each ROI. Specifically, we proposed several strategies, including fine classification of RBCs, adaptive adjustment for non-maximal suppression (NMS) threshold and blood cell morphology constraints to improve the accuracy of detection. Finally, the detection outcomes for each ROI were combined and superimposed. The results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells, with a 98.18% accuracy rate for blood cell counting.     

Balancing MXene Surface Termination and Interlayer Spacing Enables Superior Microwave Absorption

Surface chemistry and interlayer engineering determines the electrical properties of 2D MXene. However, it remains challenging to regulate the surface and interfacial chemistry of MXene simultaneously. Herein, simultaneous modulation of Ti₃C₂T_x MXene surface termination and layer spacing by alkali treatment are achieved. The electrical and electromagnetic properties of Ti₃C₂T_x are investigated in detail with respect to KOH and ammonia concentration dependence. A high concentration of KOH caused the Ti₃C₂T_x layer spacing to expand to 13.7 Å and the surface O/F ratio to increase to 33.84. Because of its weaker ionization effect, ammonia provides finer tuning compared to the drastic intercalation of KOH with a thorough sweeping of the F-containing groups. Ti₃C₂T_x is enriched with conductive -OH termination after ammonia treatment, which achieves an effective balance with the increased interlayer resistance. Therefore, NH₃H₂O-Ti₃C₂T_x achieves broad-band impedance matching and exhibits an efficient microwave loss of −49.1 dB at a low thickness of 1.7 mm, with an effective frequency bandwidth of 3.9 GHz. The results herein optimize the electrical properties of Ti₃C₂T_x using surface and interfacial chemistry to achieve broad microwave absorption, providing a framework for enhancing the electromagnetic wave loss of intrinsic MXene.     

Weak signal detection and adaptive synchronous stability of a novel fifth-order memristive circuit system

This paper is devoted to introduce a novel complex fifth-order memristive circuit system and its applications in synchronous stability and weak signal detection. Firstly, the typical dynamical behaviors of the memristive system are discussed by chaotic phase portrait, complexity analysis, one-parameter bifurcation and Lyapunov exponent spectrum. Secondly, the adaptive control method is applied to realize the synchronization between the drive memristive system (DMS) and the response memristive system (RMS). The results indicate that the synchronization method has strong robustness and anti-interference ability. Thirdly, the weak signal detection of the novel five-dimensional memristive system is realized by using the extreme sensitivity of chaotic system to initial values. Finally, the fifth-order memristive circuit is designed by using basic electronic elements and simulated by Multisim software. And the anti-interference ability and sensitivity of the fifth-order memristive circuit are further verified by adding different weak disturbance signals at different positions of the circuit.     

利用双载频方向图相乘的稀疏均匀阵栅瓣抑制方法

针对稀疏阵列天线间距远大于信号波长导致阵列方向图出现大量栅瓣的问题,该文基于不同载频阵列方向图主瓣与栅瓣相对位置关系存在差异的特性提出一种新型的栅瓣抑制算法。该算法充分利用不同载频回波信息,避免了大规模搜索,有效降低了计算量。首先根据算法原理确定了影响该栅瓣抑制算法性能的因素,然后进一步对影响栅瓣抑制性能的关键参数进行了定量分析,推导得出了栅瓣抑制后峰值旁瓣比(PSLR)与频率差的关系表达式。该表达式为栅瓣抑制快速选择最优频率差提供了理论依据。最后,通过计算机仿真验证了该算法对栅瓣抑制的有效性以及该文所推导的峰值旁瓣比与频率差关系表达式的正确性。     

大数据背景下计算机专业SPOC混合式教学模式的构建

随着信息化技术的发展和创新,社会对计算机专业技术人员的素质也提出了更高的要求。因此,应对高校信息技术教育进行变革,以促进高等院校信息化建设的深入发展。基于此,文中通过对高校计算机课程体系现状的剖析,并根据当今的发展潮流,简要概述了SPOC混合式教学模式的定义,继而总结出SPOC混合式教学模式的设计原则,最后深入分析了SPOC混合式教学模式的构建途径。     

第二代北斗客户端用射频声表面波滤波器的研制

介绍了一种可满足第二代北斗客户端用B3波段载频滤波的声表面波(SAW)射频滤波器。该滤波器采用“5换能器纵向耦合结构+谐振器”的混合型设计方案,在充分考虑了SMD3030A封装及键合引线寄生影响后,成功研制出-1 dB带宽35 MHz、插入损耗2.2 dB、矩形系数1.9的滤波器产品。结果表明,仿真与实测吻合性较好,证明了该设计方案的可行性。     

基于太赫兹布拉格光纤的表面生物传感器

空心布拉格光纤在传感领域应用极为广泛。本文提出了一种基于带有缺陷层的太赫 兹(terahertz,THz)空心布拉格光纤的表面生物传感器,并对该传感器在太赫兹频段的细菌种类检测性能进行 分析。在太赫兹布拉格光纤中,高折射率光敏树脂层和低折射率空气层以纤芯为中心周期性 排列,在纤芯内壁沉积分析物引入缺陷模式。本文对提出的传感器性能进行了数值研究。仿 真结果表明,该传感器有很高的纤芯功率比,限制损耗在0.3 THz和0.45 TH z频 段有损耗峰,能得到可识别的特征频率,此特征频率用于细菌种类检测。当频率大于0.7 TH z时,该传感器可实现厚度无关的表面生物传感。本文所提出的基于太赫兹空心布拉格光纤 的 表面生物传感器可用于检测细菌种类,在微生物检测方面有很大的应用潜力。     

基于40 nm CMOS工艺的高效率功率放大器设计

针对硅基毫米波功率放大器存在的饱和输出功率较低、增益不足和效率不高的问题,基于TSMC 40nm CMOS工艺,设计了一款工作在28GHz的高效率和高增益连续F类功率放大器。提出的功率放大器由驱动级和功率级组成。针对功率级设计了一款基于变压器的谐波控制网络来实现功率合成和谐波控制,有效地提高了功率放大器的饱和输出功率和功率附加效率。采用PMOS管电容抵消功率级的栅源电容,进一步提高线性度和增益。电路后仿真结果表明,设计的功率放大器在饱和输出功率为20.5dBm处的峰值功率附加效率54%,1dB压缩点为19dBm,功率增益为27dB,在24GHz~32GHz频率处的功率附加效率大于40%。     

大口径空间相机反射镜高反膜研究进展

大口径反射镜高反膜是大口径空间相机光学系统中的重要光学元件。为了观测到更多的目标、获取更多的地球以及空间观测信息,对大口径空间相机的反向射膜提出了更高要求,即要求有更宽的工作谱段和更高的反射率,还要提升基底光学性能,降低空间环境对高反膜性能的影响以及降低薄膜应力等等。对近年国内外天基大口径空间相机反射镜高反膜研究进行了综述,概述了针对以上要求所取得的成果。尽管高反膜的设计制造仍面临许多困难,但随着研究的深入与新方法的提出,这几方面难点已基本找到解决的途径。     

An improved U-Net for cell confluence estimation

Cell confluence is an important metric to determine the growth and the best harvest time of adherent cells. At present, the evaluation of cell confluence mainly relies on experienced labor, and thus it is not conducive to the automated cell culture. In this paper, we proposed an improved U-Net algorithm (called DU-Net) for the segmentation of adherent cells. First, the general convolution was replaced by the dilated convolution to expand the receptive fields for feature extraction. Then, the convolutional layers were combined with the batch normalization layers to reduce the dependence of the network on initialization. As a result, the segmentation accuracy and F1-score of the proposed DU-Net for adherent cells with low confluence (<50%) reached 96.94% and 93.87%, respectively, and for those with high confluence (≥50%), they reached 98.63% and 98.98%, respectively. Further, the paired t-test results showed that the proposed DU-Net was statistically superior to the traditional U-Net algorithm.