YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

Compiler compatible 5.66 Mb/mm2 8T 1R1W register file in 14 nm FinFET technology

1-read/1-write (1R1W) register file (RF) is a popular memory configuration in modern feature rich SoCs requiring significant amount of embedded memory. A memory compiler is constructed using the 8T RF bitcell spanning a range of instances from 32 b to 72 Kb. An 8T low-leakage bitcell of 0.106 μm² is used in a 14 nm FinFET technology with a 70 nm contacted gate pitch for high-density (HD) two-port (TP) RF memory compiler which achieves 5.66 Mb/mm² array density for a 72 Kb array which is the highest reported density in 14 nm FinFET technology. The density improvement is achieved by using techniques such as leaf-cell optimization (eliminating transistors), better architectural planning, top level connectivity through leaf-cell abutment and minimizing the number of unique leaf-cells. These techniques are fully compatible with memory compiler usage over the required span. Leakage power is minimized by using power-switches without degrading the density mentioned above. Self-induced supply voltage collapse technique is applied for write and a four stack static keeper is used for read Vmin improvement. Fabricated test chips using 14 nm process have demonstrated 2.33 GHz performance at 1.1 V/25 °C operation. Overall V_min of 550 mV is achieved with this design at 25 °C. The inbuilt power-switch improves leakage power by 12x in simulation. Approximately 8% die area of a leading 14 nm SoC in commercialization is occupied by these compiled RF instances.     

小卫星及多星测控的技术发展

本文从小卫星、星座飞速发展以及对测控通信系统新的要求出发，介绍了综合基带、连续波多波束相控阵天线的功能、特点及工作原理，提出了建立新型多功能智能化测控通信站的设想。     

一种漂移室定位子性能反常现象的研究

研究了一种漂移室定位子性能的反常现象,它关系到定位子的使用寿命．着重测量了暗电流和随机噪声与时间、高压及温度的关系.     

液体可视图象序列中杂质小目标的检测方法

在分析红外图象和视图象差异的基础上，研究了一种适用于可视图像序列的运动小目标检测算法。受目标影响的象素点具有较弱的时间相关性，即相对普通象素点有较大的时间方差，根据此特征，用管道更新的方法产生新方差图象序列，再用管道更新的方法累积，增加信噪比，然后用基于统计均值的自适应门限方法分割出目标，最后将原图象的梯度倒数作为象素点强度的加权，有效地抑制灰度突变边界的传感器噪音，保存目标点。     

一种新的HEMT小信号模型参数提取方法

介绍了一种高电子迁移率晶体管(HEMT)的小信号参数提取方法——综合多偏置点优化参数提取法。该文首先推导并提出了器件的模型、确定外部参数和内部参数，其次介绍了多偏置点优化算法。最后，以PHEMT器件为例进行鲁棒性和精确性测试，实验采用一系列随机起始值，结果表明，提取的参数值与经验值相差小于1％。     

基于数学形态学的红外图像小目标检测

针对红外成像跟踪系统的低信噪比、背景和噪声干扰严重的小目标图像,以及后续的目标识别处理需要目标的灰度信息的特点,设计了一种基于数学形态学的红外灰度图像小目标检测算法,并用FPGA硬件实现。实验表明,数学形态学滤波能够大大提高目标的信噪比,去除背景和噪声干扰,保留目标的灰度信息,满足系统实时性要求,是一种有效的红外灰度图像小目标检测算法。     

UPML formulation for truncating conductive media in curvilinear coordinates

This work presents a formulation based on UPML for truncating conductive media by using a local and non-orthogonal coordinate system to solve Maxwell’s equations by the FDTD method. The detailed procedure for obtaining the UPML equations for this case is shown and the complete equation set is provided.     

X-ray metrology for advanced silicon processes

X-ray reflectivity (XRR), X-ray fluorescence (XRF) and small angle X-ray scattering (SAXS) techniques are used to the monitoring of Cu/porous low κ processes, which are developed for the next generation (≤65 nm) integrated circuits. Sensitivity of XRR and XRF is sufficient to detect drifts of the copper barrier layer, copper seed layer and Cu CMP (chemical-mechanical polishing) processes. Their metrology key parameters comply with production requirements. SAXS allows determining the pore structure of low κ films: average pore size and pore size distribution.     

乳液聚合阶段Ⅱ动力学研究的进展

本文简述了乳浓聚合动力学研究的进展,着重于澳大利亚Sydney大学Gilbert等在乳液聚合阶段Ⅱ动力学方面的研究概况。介绍了不同水溶性单体的小尺寸种子乳液体系的SmithEwart递推方程的求解方法及其解析解形式和乳液聚合动力学数据的处理。同时讨论了该研究的局限性。