Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

A BiCMOS dynamic multiplier using Wallace tree reductionarchitecture and 1.5-V full-swing BiCMOS dynamic logic circuit

The authors present a BiCMOS dynamic multiplier, which is free from race and charge-sharing problems, using Wallace tree reduction architecture and 1.5-V full-swing BiCMOS dynamic logic circuit. Based on a 1-μm BiCMOS technology, a 1.5-V 8×8 multiplier designed, shows a 2.3× improvement in speed as compared to the CMOS static one     

4H-SiC金属-半导体-金属结构紫外探测器的模拟与分析

用MEDICI软件对金属-半导体-金属(MSM)结构4H-SiC紫外(UV)探测器的I-V特性以及光谱响应等特性进行了模拟与分析,并探讨了金属电极的宽度、电极间距以及外延层厚度对探测器响应度的影响.结果表明,室温下该探测器的暗电流线性密度达到10-13A/μm,且在不同电压下光电流至少比暗电流大两个数量级;探测器的光谱响应范围为200~400 nm,在347 nm处响应度达到极大值;增大指宽或者减小指间距可以提高探测器的响应度;当波长小于峰值波长时外延层厚度对探测器的响应度基本没影响,而当波长大于峰值波长时随着外延层厚度的增大探测器的响应度有所增大.     

一种高效率绿色模式开关电源控制器的研究

提出了一种高效率绿色模式降压型开关电源控制器芯片的设计,特点是采用PWM/Burst多模式控制策略提高全负载条件下的电源转换效率. 由于降低了低负载和待机条件下的电源功耗,可减小由电池供电的现代便携式设备的静态功耗,延长设备的待机时间和电池的寿命. 芯片还实现了模式转换过程中的平滑过渡以及过冲电压的抑制. 此外,还引入一种高精度、高效率的片上电流检测技术,进一步降低了功耗. 该芯片在1.5μm BCD (bipolar-CMOS-DMOS）工艺下设计和制造,测试结果表明芯片已达到预期的性能要求.     

Gold Nanoparticles and g‐C3N4‐Intercalated Graphene Oxide Membrane for Recyclable Surface Enhanced Raman Scattering

Toxic organic pollutants in the aquatic environment cause severe threats to both humans and the global environment. Thus, the development of robust strategies for detection and removal of these organic pollutants is essential. For this purpose, a multifunctional and recyclable membrane by intercalating gold nanoparticles and graphitic carbon nitride into graphene oxide (GNPs/g‐C₃N₄/GO) is fabricated. The membranes exhibit not only superior surface enhanced Raman scattering (SERS) activity attributed to high preconcentration ability to analytes through π–π and electrostatic interactions, but also excellent catalytic activity due to the enhanced electron–hole separation efficiency. These outstanding properties allow the membrane to be used for highly sensitive detection of rhodamine 6G with a limit of detection of 5.0 × 10^?14m and self‐cleaning by photocatalytic degradation of the adsorbed analytes into inorganic small molecules, thus achieving recyclable SERS application. Furthermore, the excellent SERS activity of the membrane is demonstrated by detection of 4‐chlorophenol at less than nanomolar level and no significant SERS or catalytic activity loss was observed when reusability is tested. These results suggest that the GNPs/g‐C₃N₄/GO membrane provides a new strategy for eliminating traditional, single‐use SERS substrates, and expands practical SERS application to simultaneous detection and removal of environmental pollutants.     

放电泵浦XeCl激光的理论模型

本文对传输线作电源的自持放电XeCl激光进行动力学分析,给出电子能量分布玻尔兹曼方程、能量平衡方程、电子线路方程及激光振荡方程的数值解,并与实验结果作了比较。     

Comparing Through-Silicon-Via (TSV) Void/Pinhole Defect Self-Test Methods

Three methods have been proposed to test Through-Silicon-Vias (TSV) electrically prior to 3D integration. These test methods are (1) sense amplification; (2) leakage current monitor; and (3) capacitance bridge methods. These tests are aimed at detecting one or both of two failure types, pin-holes and voids. The test circuits measure capacitance and leakage current of the TSVs, and generate a 1 bit pass/fail signal. The outputs are streamed out through a scan chain. The test time is 10 μs for the leakage test and the sense amplification methods, and is 15 μs for the capacitive bridge method. All these methods can be implemented for test-before-stacking, which will increase assembled yield. Resolution, power and area of these TSV test circuits were compared. The performance of each circuit was studied at PVT corners. The IMEC TSV technology was assumed, and the designs were simulated using the 32 nm predicted device model. Without any failure, the TSV capacitance’s mean value is 37 fF, and its leakage resistance is higher than 850 MΩ. With respect to 37 fF standard capacitance, resolution for the sense amplification method is 3.3 fF (8.9%); it is 0.16 fF (0.4%) for the capacitance bridge method. Although the capacitance bridge method has relatively better resolution, it takes 4x area and 10x power than the other two, and is also more sensitive to PVT variation. Resolution of the leakage current monitor method is 10 MΩ (1.1%) with respect to its threshold 850 MΩ, and use 42.5aJ power in normal case. Sense amplification circuit can be modified to detect equivalent leakage resistance under 2KΩ.     

A network traffic prediction model of smart substation based on IGSA‐WNN

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA‐WNN. A comparative analysis of the experimental results shows that the performance of the IGSA‐WNN‐based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.     

一种动态利用码资源的CDMA系统性能研究

本文提出了一种基于随机预约ALOHA访问方式,能支持话音和数据业务的动态使用码资源的码分多址访问协议。在该协议中,话音终端采用预约请求排队访问方式。数据终端采用时隙ALOHA方式传输数据分组。理论分析和计算机仿真结果表明,该协议能有效地提高系统码资源的利用率。在系统处于重负载情况下该协议能优先保证话音业务服务质量,而处于轻负载下系统码资源能为数据业务充分使用。     

提取光栅耦合的高电子迁移率晶体管中的太赫兹辐射

借助于傅立叶光谱仪,在具有耦合光栅的高电子迁移率晶体管中观察到了波长在400um左右的微弱的太赫兹辐射。同时使用了一种调制探测技术,从而使得太赫兹辐射的绝对功率能够被提取计算出来。并且发现器件的太赫兹辐射功率正比于源漏电流,而黑体辐射功率则与器件电功率有密切关系。此外,太赫兹辐射对于源漏偏置电压及栅压的依赖性,说明太赫兹辐射是由加速电子与光栅的相互作用而引起的。