用于并行激光直写的连续深浮雕衍射透镜阵列研究

研究了一种用于并行激光直写的连续深浮雕衍射透镜阵列方法.该方法采用连续浮雕衍射透镜阵列替换传统并行激光直写中的物镜阵列,在兼顾系统分辨力基础上,克服了波带片等衍射透镜阵列衍射效率低的缺点;同时因采用深浮雕结构优化环带宽度,可降低阵列的制作难度.针对并行激光直写系统阵列F/#小的特点,在建立连续深浮雕衍射透镜阵列非旁轴近似聚焦模型基础上,设计、制作和测试了波长为441.6 nm,F/#为7.5的连续深浮雕衍射透镜阵阵列.测试结果表明:该阵列的衍射效率优于70%,远高于波带片阵列的40%.     

Towards dynamic control of magnetic fields to focus magnetic carriers to targets deep inside the body

Magnetic drug delivery has the potential to target therapy to specific regions in the body, improving efficacy and reducing side effects for treatment of cancer, stroke, infection, and other diseases. Using stationary external magnets, which attract the magnetic drug carriers, this treatment is limited to shallow targets (<5 cm below skin depth using the strongest possible, still safe, practical magnetic fields). We consider dynamic magnetic actuation and present initial results that show it is possible to vary magnets one against the other to focus carriers between them on average. The many remaining tasks for deep targeting in-vivo are then briefly noted.     

基于实时样本采集的个性化手写汉字输入系统设计

手写汉字识别是手写汉字输入的基础。目前智能设备中的手写汉字输入法无法根据用户的汉字书写习惯,动态调整识别模型以提升手写汉字的正确识别率。通过对最新深度学习算法及训练模型的研究,提出了一种基于用户手写汉字样本实时采集的个性化手写汉字输入系统的设计方法。该方法将采集用户的手写汉字作为增量样本,通过对服务器端训练生成的手写汉字识别模型的再次训练,使识别模型能够更好地适应该用户的书写习惯,提升手写汉字输入系统的识别率。最后,在该理论方法的基础上,结合新设计的深度残差网络,进行了手写汉字识别的对比实验。实验结果显示,通过引入实时采集样本的再次训练,手写汉字识别模型的识别率有较大幅度的提升,能够更有效的满足用户在智能设备端对手写汉字输入系统的使用需求。     

Numerical simulation of rock fragmentation during cutting by conical picks under confining pressure

In this article, the effect of confining pressure on rock fragmentation process during cutting was investigated by numerical simulation with a discrete element method (DEM). Four kinds of sandstones with different physical properties were simulated in the rock cutting models under different confining pressures. The rock fragmentation process, the cutting force, and the specific energy under different confining pressures were analyzed. With the increase in confining pressure and rock strength, the vertical propagation of cracks was restrained. Rock samples were compacted and strengthened by confining pressure resulting in the increase of the cutting force. The specific energy of rock cutting linearly increased with the increase of the confining pressure ratio.     

Learning to short-time Fourier transform in spectrum sensing

The future wireless communication will come up with a strict requirement on high spectral efficiency, developing novel algorithms for spectrum sensing with deep sensing capability will be more challenging. However, traditional expert feature-based spectrum sensing algorithms are lack of sufficient capability of self-learning and adaptability to unknown environments and complex cognitive tasks. To address this problem, we propose to build up a deep learning network to learn short time-frequency transformation (STFT), a basic entity of traditional spectrum sensing algorithms. Spectrum sensing based on the learning to STFT network is supposed to automatically extract features for communication signals and makes decisions for complex cognitive tasks meanwhile. The feasibility and performances of the designed learning network are verified by classifying signal modulation types in deep spectrum sensing applications.     

深亚微米CMOS器件可靠性机理及模型

随着CMOS集成电路特征尺寸的不断缩小,特别是在其发展到深亚微米阶段之后,CMOS器件面临着负偏置温度的不稳定性、栅氧化层经时击穿、互连系统的电迁移和热载流子注入等可靠性问题。重点对近年来研究得到的深亚微米CMOS器件可靠性机理及其可靠性模型进行了总结。     

Performance of (1 1 0) p-channel SOI-MOSFETs fabricated by deep-amorphization and solid-phase epitaxial regrowth processes

The impact of local deep-amorphization (DA) and subsequent solid-phase epitaxial regrowth (SPER) are studied for the co-integration of devices with hybrid surface orientation. Thin-body p-channel transistors with 20 nm thick film and HfO₂ gate insulator/metal gate along several directions on a (1 1 0) substrate were fabricated and characterized. No deterioration of transconductance or threshold voltage was induced by DA/SPER process. Device co-integration using DA/SPER process is therefore a realistic option. 〈1 1 0〉 channel on (1 1 0) SOI film yields a 200% gain on the current for the (1 0 0) surface orientation. However, the benefit of it decreases with the channel length.     

Effects of ultrasound and temperature on copper electro reduction in Deep Eutectic Solvents (DES)

This paper concerns a preliminary study for a new copper recovery process from ionic solvent. The aim of this work is to study the reduction of copper in Deep Eutectic Solvent (choline chloride–ethylene glycol) and to compare the influence of temperature and the ultrasound effects on kinetic parameters. Solutions were prepared by dissolution of chloride copper salt CuCl₂ (to obtain Copper in oxidation degree II) or CuCl (to obtain Copper in oxidation degree I) and by leaching metallic copper directly in DES. The spectrophotometry UV–visible analysis of the leached solution showed that the copper soluble form obtained is at oxidation degree I (Copper I). Both cyclic voltammetry and linear voltammetry were performed in the three solutions at three temperatures (25, 50 and 80 °C) and under ultrasonic conditions (F = 20 kHz, PT = 5.8 W) to calculate the mass transfer diffusion coefficient kD and the standard rate coefficient k°. These parameters are used to determine that copper reduction is carried out via a mixed kinetic-diffusion control process. Temperature and ultrasound have the same effect on mass transfer for reduction of Cu^II/Cu^I. On the other hand, temperature is more beneficial than ultrasound for mass transfer of Cu^I/Cu. Standard rate constant improvement due to temperature increase is of the same order as that obtained with ultrasound. But, by combining higher temperature and ultrasound (F = 20 kHz, PT = 5.6 W at 50 °C), reduction limiting current is increased by a factor of 10 compared to initial conditions (T = 25 °C, silent), because ultrasonic stirring is more efficient in lower viscosity fluid. These values can be considered as key-parameters in the design of copper recovery in global processes using ultrasound.     

Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance–complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.     

高斯射线束法在深海声源定位中的应用

在深海匹配场(Matched Field Processing-MFP)声源定位中,拷贝场模型的计算精度和速度对声源的定位效果和定位效率有着直接的影响。针对这一问题,本文数值仿真研究了深海匹配场定位中采用高斯射线束理论作为拷贝场计算模型的可行性。高斯射线束理论具有计算速度快,物理意义清晰,并且适合并行化处理等优点。文中利用不同水文环境、不同频域范围的大量仿真实验来证明该模型的适用性。结果表明,高斯射线束模型在深远海的声源定位中有很好的定位精度,并且在较低频段同样有效,可以作为深远海声源定位的拷贝场计算模型。