减振降噪阻尼材料及其应用

介绍国内外减振材料的发展概况、着重阐述自行研制的S2复合阻尼钢板的各种性能及在多种结构上的抑振降噪效果，并对S2复合阻尼钢板的抑振降噪机理进行了初步的探讨。实践证明，S2复合阻尼钢板是一种抑振降噪的有效材料。     

Object tracking based on an online learning network with total error rate minimization

This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms.     

基于纹理特征与随机森林的生态声音识别

针对真实环境中普遍存在的非平稳噪声,提出一种基于纹理特征与随机森林的生态声音识别方法。该方法首先使用一种基于噪声估计的音频增强算法,即短时谱估计对输入端声音信号进行前端处理,得到增强信号功率谱;然后根据得到的增强信号功率谱图的纹理信息,采用和差统计法对其进行纹理特征提取;最后,利用基于决策树的组合分类器,即随机森林进行识别和分类。设计了两组对比实验,结果表明该方法不仅有良好的识别性能,而且具有噪声鲁棒性。     

基于集成学习的离子通道药物靶点预测

新药研制成功的关键在于药物靶点的发现和准确定位.在已知的药物靶点中,离子通道蛋白是一类广受欢迎的靶点,它与免疫系统、心血管等疾病密切相关.对于靶点的发现,传统生物方法成本高、耗时久.因此,探讨了基于机器学习的离子通道蛋白药物靶点的挖掘,以加快药物靶点发现过程,节约经费.由于药物靶点相关序列的长度不一致,考虑了蛋白质序列编码的13种特征,它们能将不等长的蛋白质序列转化成等长序列.通过数值实验筛选能够较好地区分靶点和非靶点的特征子集,并采用集成学习的方法整合特征得到预测模型.通过与已有工作的比较表明,提出的集成模型能得到较高的准确率,具有很好的应用前景.     

随机背包公钥密码的分析与改进

针对随机背包公钥密码方案,提出一种私钥恢复攻击方法.发现Wang等人所构造的随机背包公钥方案实际上是隐含使用了一个特殊的超递增背包.通过使用普通超递增背包代替该特殊超递增背包,将超递增背包隐藏在随机选择的背包中,对原方案进行了改进,提出一种新的基于中国剩余定理的背包公钥密码方案.改进后的方案消除了原方案存在的设计缺陷,能够抵抗针对原方案提出的格规约攻击、低密度攻击以及shamir攻击.     

一种基于像素梯度信息的背景减除法

讨论了背景模型的更新参数与模型精度的关系。通过精确的梯度背景模型值间接估计当前帧中背景像素理论上的期望梯度值。以高斯模型为基础,将当前帧背景像素的实际梯度值与其理论上的期望值进行比较,计算偏差概率,以此为基础,形成不依赖于局部纹理的梯度特征的相似性度量方法。再用梯度特征的相似度量化地调整差分图像在各像素点处的二值化阈值,实现像素值信息与梯度信息的融合使用。实验表明,本方法对前景分割有一定的改善效果。     

Joint underwater target detection and tracking with the Bernoulli filter using an acoustic vector sensor

In this paper, we study the problem of joint underwater target detection and tracking using an acoustic vector sensor (AVS). For this challenging problem, first a realistic frequency domain simulation is set up. The outputs of this simulation generate the two dimensional FRequency–AZimuth (FRAZ) image. On this image, the random finite set (RFS) framework is employed to characterize the target state and sensor measurements. We propose to use the Bernoulli filter, which is the optimal Bayes filter emerged from the RFS framework for randomly on/off switching single dynamic systems. Moreover, to increase the performance of detection and azimuth tracking in low signal-to-noise ratio (SNR) scenarios, a track-before-detect (TBD) measurement model for AVS is proposed to be used with the Bernoulli filter. Sequential Monte Carlo (SMC) implementation is preferred for the Bernoulli filter recursions. Extensive simulation results prove the performance gain obtained by the proposed approach both in estimation accuracy and detection range of the system.     

The frequency partitioned block modified filtered-x NLMS with orthogonal correction factors for multichannel Active Noise Control

The Normalized Least Mean Square (NLMS) algorithm with a filtered-x structure (FxNLMS) is a widely used adaptive algorithm for Active Noise Control (ANC) due to its simplicity and ease of implementation. One of the major drawbacks is its slow convergence. A modified filtered-x structure (MFxNLMS) can be used to moderately improve the speed of convergence, but it does not offer a huge improvement. A greater increase in the speed of convergence can be obtained by using the MFxNLMS algorithm with orthogonal correction factors (M-OCF), but the usage of orthogonal correction factors also increases the computational complexity and limits the usage of the M-OCF in massive real-time applications. However, Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform to ameliorate this computational drawback. In this paper, we propose to derive the M-OCF algorithm to a partitioned block-based version in the frequency domain (FPM-OCF) for multichannel ANC systems in order to better exploit the parallel capabilities of the GPUs. The results show improvements in the convergence rate of the FPM-OCF algorithm in comparison to other NLMS-type algorithms and the usefulness of GPU devices for developing versatile, scalable, and low-cost multichannel ANC systems.     

Multi-target tracking on confidence maps: An application to people tracking

We propose a generic online multi-target track-before-detect (MT-TBD) that is applicable on confidence maps used as observations. The proposed tracker is based on particle filtering and automatically initializes tracks. The main novelty is the inclusion of the target ID in the particle state, enabling the algorithm to deal with unknown and large number of targets. To overcome the problem of mixing IDs of targets close to each other, we propose a probabilistic model of target birth and death based on a Markov Random Field (MRF) applied to the particle IDs. Each particle ID is managed using the information carried by neighboring particles. The assignment of the IDs to the targets is performed using Mean-Shift clustering and supported by a Gaussian Mixture Model. We also show that the computational complexity of MT-TBD is proportional only to the number of particles. To compare our method with recent state-of-the-art works, we include a postprocessing stage suited for multi-person tracking. We validate the method on real-world and crowded scenarios, and demonstrate its robustness in scenes presenting different perspective views and targets very close to each other.     

Hypermedia Support for Software Development: A Retrospective Assessment

Hypermedia technologies have been available for about a decade. Beginning with Douglas Engelbart, researchers have become interested in applying hypermedia concepts and technologies to software development. This paper assesses this symbiosis between hypermedia and software development as described by research contributions; by our count, more than one hundred articles. To date, no collection of, or guide to, this literature has been published. A number of significant contributions are classified, described, and appraised — providing a guide into what has been a rich but perhaps under-reported research area. Hypermedia has made significant contributions to software development in three primary areas: coping with massive amounts of information associated with software development projects; establishing and maintaining linkages between various types of software engineering documents; and enabling development engineers to record, document, and preserve knowledge about development thoughts, processes and rationale. These contributions may help direct the focus of future research towards building on, combining and assessing the resulting contributions. A research agenda is proposed along these lines.