Characterization of indoor wireless channel at 5 GHz U-NII bands

The paper reports on the experiments undertaken at the University of Wollongong to characterise fading profiles and delay parameters of an indoor wireless channels at 5 GHz U-NII bands. The measurements were undertaken at different locations around the campus with results recorded for a post-processing to calculate the Rician K-factor, the level crossing rate and the average fade duration as well as mean excess delay, rms delay spread, and the coherence bandwidth of the channel. The presented measurement results can be useful in developing a Markov chain based model of the transport channel for IEEE802.11a or HYPRLAN-2 networks. The results also indicate scenarios where the coherence bandwidth of the channel is smaller than the width of the sub-carrier OFDM channels in either of the mentioned systems.     

A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model. This revised version was published online in June 2006 with corrections to the Cover Date.     

The evidence for neural information processing with precise spike-times: A survey

This paper surveys recent findings in neuroscience regarding the behavioral relevancy of the precise timing with which real spiking neurons emit spikes. The literature suggests that in almost any system where the processing-speed of a neural (sub)-system is required to be high, the timing of single spikes can be very precise and reliable. Additionally, new, more refined methods are finding precisely timed spikes where previously none where found. This line of evidence thus provides additional motivation for researching the computational properties of networks of artificial spiking neurons that compute with more precisely timed spikes. This revised version was published online in June 2006 with corrections to the Cover Date.     

The super-connected property of recursive circulant graphs

In a graph G, a k-container C_k(u,v) is a set of k disjoint paths joining u and v. A k-container C_k(u,v) is k∗-container if every vertex of G is passed by some path in C_k(u,v). A graph G is k∗-connected if there exists a k∗-container between any two vertices. An m-regular graph G is super-connected if G is k∗-connected for any k with 1?k?m. In this paper, we prove that the recursive circulant graphs G(2^m,4), proposed by Park and Chwa [Theoret. Comput. Sci. 244 (2000) 35-62], are super-connected if and only if m≠2.     

拱坝近似优化的人工神经网络方法

以国内已建拱坝体形参数为基础,以ＢＰ神经网络方法为手段,建立了拱坝体形参数近似优化的人工神经网络模型．借助该模型不仅可以为拱坝体形优化快速提供高质量的初始可行解,而且可为中、小拱坝体形设计直接提供近似最优解．     

神经元网络——它的背景、进展和问题

近年来神经元网络研究的进展,在国际学术界引起了强烈的轰动,成了各界学人的热门话题.本文分析了神经元网络研究的来龙去脉,简要介绍了若干代表性的进展,指出了这一研究的意义、问题和进一步研究的方向,希望引起各界同仁的关注.     

神经网络在结构损伤模拟中的应用

识别一个结构在震动状态下的变化，在结构监测中是十分重要的，神经网络就非常适用于这种目的。本文研究了使用可分析的学习样本来训练神经网络的可行性问题。神经网络从损伤状态中训练产生，然后用于诊断一个五层钢框架在一系列震动模拟中的状态。结果表明，使用神经网络可使在线结构诊断更加可行。     

Combination of interference alignment and beamforming in cellular networks

In this paper, we propose two joint transmit-receive iterative algorithms without the cooperation between different base stations based on the idea of interference alignment (IA) to improve the throughput of relay backhaul links in cellular networks for the case of imperfect channel knowledge,which can be implemented with small changes to existing TD-LTE standards. Unlike the previous interference alignment algorithms’ only reducing the sum interference to the other receivers at the transmitter or the sum received Multi-user interference (MUI) at the receiver, our algorithm shapes the transmission of each data stream at transmitters in order not only to minimize interference to the other users, but also to minimize the interference between different streams objected to the same user, suppressing the MUI and Multi-stream interference (MSI) at receivers. The proposed algorithm I is to maximize the SINR at receivers. But the complexity is relatively high. Algorithm II only needs linear operations and sacrifices a little performance for much lower complexity compared to the Maximize SINR iterative algorithm which needs the inversion operation of matrix. It is also proved that the algorithm converges monotonically. The simulation results show that the techniques have considerable performance gain compared with the previous algorithms. Further research about power allocation is also discussed.     

上海市生活垃圾废塑料回收再利用现状及优化策略

在生活垃圾分类背景下上海市可回收物回收体系逐步完善,生活源废塑料回收再利用量获得极大提升。通过现场调研与统计资料分析,对当前上海市生活垃圾废塑料分类回收现状、再利用技术进行分析梳理,结果显示当前生活垃圾废塑料回收类别以PET、PP为主,分别占总回收量的45.3%和30.7%;另有近30%具有回收价值的废塑料未被回收,主要以PP和PET为主。在此基础上,提出需进一步完善分类回收标准与补贴政策、推动PET同级利用等回收技术创新、强化宣传教育与主体监管等优化策略,以期促进废塑料回收与再利用行业进一步发展。     

EnGAN：医学图像分割中的增强生成对抗网络

原始采集的医学图像普遍存在对比度不足、细节模糊以及噪声干扰等质量问题,使得现有医学图像分割技术的精度很难达到新的突破。针对医学图像数据增强技术进行研究,在不明显改变图像外观的前提下,通过添加特定的像素补偿和进行细微的图像调整来改善原始图像质量问题,从而提高图像分割准确率。首先,设计引入了一个新的优化器模块,以产生一个连续分布的空间作为迁移的目标域,该优化器模块接受数据集的标签作为输入,并将离散的标签数据映射到连续分布的医学图像中;其次,提出了一个基于对抗生成网络的EnGAN模型,并将优化器模块产生的迁移目标域用来指导对抗网络的目标生成,从而将改善的医学图像质量知识植入模型中实现图像增强。基于COVID-19数据集,实验中使用U-Net、U-Net+ResNet34、U-Net+Attn Res U-Net等卷积神经网络作为骨干网络,Dice系数和交并比分别达到了73.5%和69.3%、75.1%和70.5%,以及75.2%和70.3%。实验的结果表明,提出的医学图像质量增强技术在最大限度保留原始特征的条件下,有效地提高了分割的准确率,为后续的医学图像处理研究提供了一个更为稳健和高效的解决方案。