Deeply fusing multi-model quality-aware features for sophisticated human activity understanding

Detecting and understanding human action under sophisticated lighting condition and backgrounds, also known as human action recognition in real-world context, is an indispensable component in modern intelligent systems and has becoming a hot research topic currently. Nowadays, human action recognition is still a tough challenge due to intra-class and inter-class, environment and temporal-level differences of the same action. Algorithms based on the single visual channel cannot achieve satisfactory performance. Thus, in this paper, we propose a novel action recognition framework towards sophisticated activity understanding, focusing on intelligently combining multimodel quality-related action features. Specifically, we first design a multi-channel feature fusion (MCFF) algorithm to capture visual appearance, motion and acoustic patterns from each video frame, where image-level labels are characterized by choosing high quality multimodel features. Subsequently, we design an adaptive key frame selection algorithm that can be applied to characterize human action from human action video stream. Thereafter, we engineer a multimodel feature based on an auxiliary human action retrieval system to achieve sophisticated activity understanding. Extensive experimental evaluations have demonstrated that the effectiveness and robustness of our proposed method.     

利用声纳基阵实现多通道分集与均衡

在水下电报、远程控制、语音和图像传递等水声通信技术中,由于海洋水声信道是一个时变、频变、空变的非平稳随参信道,使得水声信道的传输速率低、频率资源利用率有限、码间干扰严重、误码率高。针对海洋水声信道的特点,描述如何利用基阵声纳的多通道结构,来实现多通道分集与联合均衡,从而减小多途效应引起的水声通信中的码间干扰,使得陆地移动通信系统中的一些先进技术能够在此基础上得到应用,并为现有的目标探测声纳增加水声通信功能提供参考。     

多通道相位差可调正弦波信号发生器的设计

在实验基础上提出了一种多通道正弦波信号发生器的设计模型，此信号发生器产生的信号频率和各通道相位差可根据实际需要进行调整，在信号测试等方面具有广泛用途，另外结合实例给出了系统各部分的硬件原理图、波形误差分析以及实验结果。     

A novel fusion paradigm for multi-channel image denoising

Multi-channel and single-channel image denoising are on two important development fronts. Integrating multi-channel and single-channel image denoisers for further improvement is a valuable research direction. A natural assumption is that using more useful information is helpful to the output results. In this paper, a novel multi-channel and single-channel fusion paradigm (MSF) is proposed. The proposed MSF works by fusing the estimates of a multi-channel image denoiser and a single-channel image denoiser. The performance of recent multi-channel image denoising methods involved in the proposed MSF can be further improved at low additional time-consuming cost. Specifically, the validity principle of the proposed MSF is that the fused single-channel image denoiser can produce auxiliary estimate for the involved multi-channel image denoiser in a designed underdetermined transform domain. Based on the underdetermined transformation, we create a corresponding orthogonal transformation for fusion and better restore the multi-channel images. The quantitative and visual comparison results demonstrate that the proposed MSF can be effectively applied to several state-of-the-art multi-channel image denoising methods.     

波分复用系统中的偏振模散补偿研究

本文阐述了波分复用(WDM)系统进行偏振模散(PMD)补偿的背景和理论基础,首次通过考虑偏振主态(PSP)带宽所能容纳的信道数目提出了两种基于已有单信道补偿方案的满足特定条件的WDM系统中的PMD补偿方案。     

Throughput and delay analysis of multi-channel wireless infrastructure networks

Wireless infrastructure networks (WINs) provide ubiquitous connectivity to mobile nodes in metro areas. The nodes in such backbone networks are often equipped with multiple transceivers to allow for concurrent transmissions in multiple orthogonal channels. In this study, we develop an analytical model for the estimation of the delay and throughput performance of wireless infrastructure networks employing slotted ALOHA channel access and slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) over multiple channels. The analytical model, which takes into account the correlation due to multi-hop transmissions, approximates the performance observed through simulations accurately.     

方位多通道合成孔径雷达数据重建方法

多通道合成孔径雷达可以突破传统SAR不能同时实现高分辨宽测绘带的限制,是SAR发展的必然趋势,在成像处理时,它必须重建方位均匀采样数据。该文提出了一种基于降升调频斜率技术的处理方法,利用线性调频信号特性对方位非均匀数据进行解线性调频处理,然后采用升采样参考函数进行逆解线性调频处理,即完成方位数据的均匀重建。文中给出了信号处理流程,并与重建滤波器组法进行了比较。仿真结果证明了理论分析的正确性。     

基于色域划分的多通道喷墨打印系统分色模型研究

随着颜色复制精度的提高,含有浅色墨的多通道喷墨打印系统使用范围越来越广,但是含浅色墨的喷墨打印系统墨色维度高,光谱冗余严重,分色数据量较大、灵活性不高,且由于同色油墨间的相关性,映射精度对分色精度影响较大。针对这些问题,本研究在色域划分思想的基础上,基于分区纽介堡方程理论,结合灰成分替代原理以及两种色墨替代原理,建立了CMYKcmk两种色墨喷墨打印系统的分色计算模型。结果表明本方法能够减少分色计算量,提高分色可调整性,较好地实现两种色墨喷墨打印分色。     

Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal

For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei-Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method.     

Fast algorithm for non-uniformly sampled signal spectrum reconstruction

In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).