5V转换成-10V的简单电路

典型开关式电容器充电泵不需要电感器，因此容易设计．且能将正电压加倍及将正电压转换成一个等效负电压。但在某些应用中．只有正电源可用．且电源系统必须产生一个幅度比正电源电压幅度更大的负电压。图1所示电路可将其输入电压反相的同时将所得负电压加倍。     

100 Å-thick tunnel junction by double impurity doping liquid phase epitaxy for V-band TUNNETT oscillation

The tunnel injection transit time (TUNNETT) diodes with p⁺p⁺n⁺n⁻n⁺ structure were fabricated by liquid phase epitaxy (LPE). About 100 Å tunnel junction (p⁺n⁺) was successfully prepared by the double impurity diffusion of Ge and S during LPE growth. Continuous wave (CW) oscillation was realized at 51.520 GHz in the V-band cavity with the phase noise of −60 dBc/Hz at 1 kHz bandwidth.     

Properties of Bi-doped PbTe layers grown by liquid phase epitaxy under controlled Te vapor pressure

Effects of Bi doping in PbTe liquid-phase epitaxial layers grown by the temperature difference method under controlled vapor pressure (TDM-CVP) are investigated. For Bi concentrations in the solution, x_Bi, lower than 0.2 at.%, an excess deep-donor level (activation energy E_d≈0.03–0.04 eV) appears, and Hall mobility is low. In contrast, for x_Bi>0.2 at.%, Hall mobility becomes very high, while carrier concentration is in the range of 10¹⁷ cm⁻³. Inductive coupled plasma (ICP) emission analysis shows that, for x_Bi=1 at.%, Bi concentration in the epitaxial layer is as high as N_Bi=2.3–2.7 × 10¹⁹ cm⁻³. These results indicate that Bi behaves not only as a donor but also as an acceptor, and the nearest neighbor or very near donor-acceptor (D-A) pairs are formed, so that strong self-compensation of Bi takes place. Carrier concentration for highly Bi-doped layers shows a minimum at a Te vapor pressure of 2.2 × 10⁻⁵ torr for growth temperature 470°C, which is coincident with that of the undoped PbTe.     

Microfluidic Spinning of Cell‐Responsive Grooved Microfibers

Engineering living tissues that simulate their natural counterparts is a dynamic area of research. Among the various models of biological tissues being developed, fiber‐shaped cellular architectures, which can be used as artificial blood vessels or muscle fibers, have drawn particular attention. However, the fabrication of continuous microfiber substrates for culturing cells is still limited to a restricted number of polymers (e.g., alginate) having easy processability but poor cell–material interaction properties. Moreover, the typical smooth surface of a synthetic fiber does not replicate the micro‐ and nanofeatures observed in vivo, which guide and regulate cell behavior. In this study, a method to fabricate photocrosslinkable cell‐responsive methacrylamide‐modified gelatin (GelMA) fibers with exquisite microstructured surfaces by using a microfluidic device is developed. These hydrogel fibers with microgrooved surfaces efficiently promote cell encapsulation and adhesion. GelMA fibers significantly promote the viability of cells encapsulated in/or grown on the fibers compared with similar grooved alginate fibers used as controls. Importantly, the grooves engraved on the GelMA fibers induce cell alignment. Furthermore, the GelMA fibers exhibit excellent processability and could be wound into various shapes. These microstructured GelMA fibers have great potential as templates for the creation of fiber‐shaped tissues or tissue microstructures.     

Approximate Graphic Method for Solving Fermi Level and Majority Carrier Density of Semiconductors with Multiple Donors and Multiple Acceptors

In this work we present a generic approximate graphic method for determining the equilibrium Fermi level and majority carrier density of a semiconductor with multiple donors and multiple acceptors compensating each other. Simple and easy-to-follow procedures of the graphic method are described. By graphically plotting two wrapping step functions facing each other, one for the positive hole-acceptor and one for the negative electron-acceptor, we have the crossing point that renders the Fermi level and majority carrier density. Using the graphic method, new equations are derived, such as the carrier compensation proportional to NA/ND, not the widely quoted NA – ND. Visual insight is offered to view not only the result of graphic determination of Fermi level and majority carrier density, but also the dominant and critical pair of donors and acceptors in compensation. The graphic method presented in the work will help guide the design, adjustment, and improvement of the multiply doped semiconductors. Comparison of this approximate graphic method with previous work on compensation, and with some experimental results is made. Future work in the field is proposed.     

水平井电缆牵引器滚轮与管壁压力的测量

水平井电缆牵引器滚轮与管壁之间的压力是进行牵引器结构设计和作业控制的一项关键参数。如果压力施加不当,滚轮将出现打滑或堵转。由于套管细长,滚轮与管壁的接触位置又不断在发生变化,因此压力测量的难度很大。笔者提出在套管上开窗口并用球头立柱支撑压板,使立柱与压板始终处于单面点接触状态,确保压板发生变形时立柱只受到压力。为防止压板从窗口掉入套管,将压板的两个侧立面加工为楔形,与窗口侧立面具有相同的锥度。研究介绍了测力系统的设计和工作原理,提出了有无预紧力2种情况下支撑立柱的压力计算模型,建立了测试系统,并对牵引器在套管内爬行时的压力进行了测量。根据得到的16根支撑立柱的应变值,计算和分析了压板和立柱上的压力分配。     

Efficient implementation of 3D filter for moving object extraction

In this paper the design and implementation of Multi-Dimensional (MD) filter, particularly 3-Dimensional (3D) filter, are presented. Digital (discrete domain) filters applied to image and video signal processing using the novel 3D multirate algorithms for efficient implementation of moving object extraction are engineered with an example. The multirate (decimation and/or interpolation) signal processing algorithms can achieve significant savings in computation and memory usage. The proposed algorithm uses the mapping relations of z-transfer functions between non-multirate and multirate mathematical expressions in terms of time-varying coefficient instead of traditional polyphase de- composition counterparts. The mapping properties can be readily used to efficiently analyze and synthesize MD multirate filters.     

Aggregate volumetric estimation based on PCA and momentum-enhanced BP neural network

This paper proposes a Back Propagation (BP) neural network with momentum enhance-ment aiming to achieving the smooth convergence for aggregate volumetric estimation purpose. Net-work inputs are first selected by optically measuring the eight geometry-related parameters from the given particle image. To simplify the network structure, principal component analysis technique is applied to reduce the input dimension. The specific network structure is finalized based on both em-pirical expertise and analysis on selecting the appropriate number of neurons in hidden layer. The network is trained using the finite number of randomly-picked particles. The training and test results suggest that, compared to the generic BP network, the training duration of the proposed neural network is greatly attenuated, the complexity of the network structure is largely reduced, and the es-timation precision is within 2%, being sufficiently up to technical satisfaction.