基于信息流动分析的动态社区发现方法

随着社交网络和微博等互联网应用的逐渐流行,其用户规模在迅速膨胀.在这些大规模网络中,社区发现可以为个性化服务推荐和产品推广提供重要依据.不同于传统的网络,这些新型网络的节点之间除了拓扑结构外,还进行频繁的信息交互.信息流动使得这些网络具有方向性和动态性等特征.传统的社区发现方法由于没有考虑到这些新的特征,并不适用于这些新型网络.在传染病动力学理论的基础上,从节点间信息流动的角度,提出一种动态社区发现方法.该方法通过对信息流动的分析来发现联系紧密、兴趣相近的节点集合,以实现动态的社区发现.在真实数据集上的实验结果表明：相对于传统的社区发现方法,所提出的方法能够更准确地发现社区,并且更能体现网络中社区的动态变化.     

Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information (CSI) based indoor localization scheme, C²IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points (APs). C²IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength (RSS) fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C²IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80% time in a very complex indoor environment.     

计算思维导向的计算机程序设计课程教学改革研究

根据程序设计类课程与计算思维之间的内在联系,提出以“案例教学、趣味教学、微课堂”为根本的教学改革,阐述如何在新的人才培养模式下构建教学方法,即通过优化教学内容、创新实践方法等一系列举措,将程序设计的思想与方法转化为学生的创新精神和创新能力。     

玻璃钢筋混凝土的有限元计算与试验

钢筋混凝土广泛应用于土木工程领域，但其结构耐久性因钢筋本身的锈蚀成为现今急待解决的问题。人们一直在寻找对钢筋混凝土进行改良的途径，用玻璃钢筋代替普通钢筋是解决这一问题的一个有效的方法，为此进行了试验和ANSYS有限元分析，结果证明此法具有可行性。     

3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model

Visual tracking techniques based on stereo endoscope are developed to measure tissue motion in robot-assisted minimally invasive surgery. However, accurate 3D tracking of tissue surfaces remains challenging due to complicated deformation, poor imaging conditions, specular reflections and other dynamic effects during surgery. This study employs a robust and efficient 3D tracking scheme with two independent recursive processes, namely kernel-based inter-frame motion estimation and model-based intra-frame 3D matching. In the first process, target region is represented in joint spatial-color space for robust estimation. By defining a probabilistic similarity measure, a mean-shift-based iterative algorithm is derived for location of the target region in a new image. In the second process, the thin-plate spline model is used to fit the 3D shape of tissue surfaces around the target region. An iterative algorithm based on an efficient second-order minimization technique is derived to compute optimal model parameters. The two processes can be computed in parallel. Their outputs are combined to recover 3D information about the target region. The performance of the proposed method is validated using phantom heart videos and in vivo videos acquired by the daVinci^®${\mathrm{daVinci}}^{\circledR }$ surgical robotic platform and a synthesized data set with known ground truth.     

Coupling-and-decoupling: A hierarchical model for occlusion-free object detection

Handling occlusion is a very challenging problem in object detection. This paper presents a method of learning a hierarchical model for X-to-X occlusion-free object detection (e.g., car-to-car and person-to-person occlusions in our experiments). The proposed method is motivated by an intuitive coupling-and-decoupling strategy. In the learning stage, the pair of occluding X?s (e.g., car pairs or person pairs) is represented directly and jointly by a hierarchical And–Or directed acyclic graph (AOG) which accounts for the statistically significant co-occurrence (i.e., coupling). The structure and the parameters of the AOG are learned using the latent structural SVM (LSSVM) framework. In detection, a dynamic programming (DP) algorithm is utilized to find the best parse trees for all sliding windows with detection scores being greater than the learned threshold. Then, the two single X?s are decoupled from the declared detections of X-to-X occluding pairs together with some non-maximum suppression (NMS) post-processing. In experiments, our method is tested on both a roadside-car dataset collected by ourselves (which will be released with this paper) and two public person datasets, the MPII-2Person dataset and the TUD-Crossing dataset. Our method is compared with state-of-the-art deformable part-based methods, and obtains comparable or better detection performance.     

Target detection based on a dynamic subspace

For hyperspectral target detection, it is usually the case that only part of the targets pixels can be used as target signatures, so can we use them to construct the most proper background subspace for detecting all the probable targets? In this paper, a dynamic subspace detection (DSD) method which establishes a multiple detection framework is proposed. In each detection procedure, blocks of pixels are calculated by the random selection and the succeeding detection performance distribution analysis. Manifold analysis is further used to eliminate the probable anomalous pixels and purify the subspace datasets, and the remaining pixels construct the subspace for each detection procedure. The final detection results are then enhanced by the fusion of target occurrence frequencies in all the detection procedures. Experiments with both synthetic and real hyperspectral images (HSI) evaluate the validation of our proposed DSD method by using several different state-of-the-art methods as the basic detectors. With several other single detectors and multiple detection methods as comparable methods, improved receiver operating characteristic curves and better separability between targets and backgrounds by the DSD methods are illustrated. The DSD methods also perform well with the covariance-based detectors, showing their efficiency in selecting covariance information for detection.     

Phase Assemblages of Pr α-sialon Derived from SHS-ed Powders and TEM Study on the Nucleation of Pr α-sialon

The Pr α-sialon powders prepared by self-propagating high-temperature synthesis (SHS), consisting of 55 wt% Pr α-sialon and 45 wt% of β-sialon (abbreviated as α' and β'), were hot-pressed at 1800°C for 1 h. The results showed that Pr α' phase would transfer to β' with the appearance of JEM phase (Pr(Si_{6− z} Al _z )(N_{10− z} O _z )) after sintering, thus resulting in the increase of β' phase to 86 wt%. The addition of Y₂O₃ into SHS-ed Pr α' powders as the starting materials restrains the transformation of α' to β' and prevents the formation of JEM phase as well. The nucleation mechanism of Pr α' grain during hot-pressing was investigated in terms of transmission electron microscope and energy-dispersive spectrometer analysis. Two nucleation modes of Pr α' grains were found, i.e., nucleating on the undissolved Pr α' grains and on the nuclei of (Pr, Y) α' grains precipitated from liquid phase.     

Flow regime, hydrodynamics, floc size distribution and sludge properties in activated sludge bubble column, air-lift and aerated stirred reactors

This paper presents a comparative study how reactor configuration, sludge loading and air flowrate affect flow regimes, hydrodynamics, floc size distribution and sludge solids-liquid separation properties. Three reactor configurations were studied in bench scale activated sludge bubble column reactor (BCR), air-lift reactor (ALR) and aerated stirred reactor (ASR). The ASR demonstrated the highest capacity of gas holdup and resistance, and homogeneity in flow regimes and shearing forces, resulting in producing large numbers of small and compact flocs. The fluid dynamics in the ALR created regularly directed recirculation forces to enhance the gas holdup and sludge flocculation. The BCR distributed a high turbulent flow regime and non-homogeneity in gas holdup and mixing, and generated large numbers of larger and looser flocs. The sludge size distributions, compressibility and settleability were significantly influenced by the reactor configurations associated with the flow regimes and hydrodynamics.     

Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC

In this paper, an optimal entropy-constrained non-uniform scalar quantizer is proposed for the pixel domain DVC. The uniform quantizer is efficient for the hybrid video coding since the residual signals conforming to a single-variance Laplacian distribution. However, the uniform quantizer is not optimal for pixel domain distributed video coding (DVC). This is because the uniform quantizer is not adaptive to the joint distribution of the source and the SI, especially for low level quantization. The signal distribution of pixel domain DVC conforms to the mixture model with multi-variance. The optimal non-uniform quantizer is designed according to the joint distribution, the error between the source and the SI can be decreased. As a result, the bit rate can be saved and the video quality won’t sacrifice too much. Accordingly, a better R-D trade-off can be achieved. First, the quantization level is fixed and the optimal RD trade-off is achieved by using a Lagrangian function J(Q). The rate and distortion components is designed based on P(Y|Q). The conditional probability density function of SI Y depend on quantization partitions Q, P(Y|Q), is approximated by a Guassian mixture model at encocder. Since the SI can not be accessed at encoder, an estimation of P(Y|Q) based on the distribution of the source is proposed. Next, J(Q) is optimized by an iterative Lloyd-Max algorithm with a novel quantization partition updating algorithm. To guarantee the convergence of J(Q), the monotonicity of the interval in which the endpoints of the quantizer lie must be satisfied. Then, a quantizer partition updating algorithm which considers the extreme points of the histogram of the source is proposed. Consequently, the entropy-constrained optimal non-uniform quantization partitions are derived and a better RD trade-off is achieved by applying them. Experiment results show that the proposed scheme can improve the performance by 0.5 dB averagely compared to the uniform scalar quantization.