A new direction-of-arrival estimation method exploiting signal structure information

A new method is proposed to estimate the direction-of-arrival (DOA) based on uniform linear array sampling and named as sparsity and temporal correlation exploiting (SaTC-E). By exploiting the structure information of source signals, including spatial sparsity and temporal correlation of sources, SaTC-E accomplishes DOA estimation with superior performance via block sparse bayesian learning methodology and grid refined strategy. SaTC-E is applicable into time-varying manifold scenario, such as wideband sources, time-varying array, provided that the array manifold matrix is determinable. It has improved performance with some other merits, including superior resolution, requirement for a few snapshots, no knowledge of source number, and applicability to spatially and temporally corrected sources. Real data tests and numerical simulations are carried out to demonstrate the advantages of SaTC-E.     

Sum and difference coarray based MIMO radar array optimization with its application for DOA estimation

Based on the sum and difference coarrays, multiple-input multiple-output (MIMO) radar with minimum redundancy (MR) concept, referred to as MR MIMO, can considerably increase the spatial degrees of freedom (DOFs). However, traditional MR MIMO needs computational search to determine the position of each element. In this paper, a modified MR monostatic MIMO configuration is proposed, referred to as MMRM MIMO. In the proposed system, the MMRM MIMO radar is consisted of several levels of uniform linear array, which brings the advantage that the position of each element can be determined without computational search. Furthermore, it offers more than \(N^{2}\) DOFs for an N-elemental array. In order to utilize the extended DOFs of MMRM MIMO radar for direction-of-arrival (DOA) estimation, an average Toeplitz approximation method (TAM) is employed, which achieves robust performance even under low signal-to-noise ratio, few snapshots and array error. Numerous simulation results are provided to demonstrate the effectiveness of the proposed method for DOA estimation.     

Robotic grasping recognition using multi-modal deep extreme learning machine

Recognizing which part of an object is graspable or not is important for intelligent robot to perform some complicated tasks. In order to obtain good grasping performance, learning rich representations efficiently from multi-modal RGB-D images is crucial. To address this problem, in this paper, we propose an effective multi-modal deep extreme learning machine structure. In this structure, unsupervised hierarchical extreme learning machine (ELM) is conducted for feature extraction for RGB and depth modalities separately. Then, the shared layer is developed by combining both RGB and depth features. Finally, the ELM is used as supervised feature classifier for final decision. Experimental validation on Cornell grasping dataset illustrates that the proposed multiple modality fusion method achieves better grasp recognition performance.     

Weighted Graph Based Clustering and Local Mobility Management for Dense Small Cell Network with X2 Interface

Along with the surge in mobile data, dense small cell network has become an effective method to improve system capacity and spectrum efficiency. However, because more small cells are deployed, the interference among dense small cells exacerbates. It also makes frequent handover for mobile users (UEs), which brings a great deal of signaling overhead to the core network. In order to solve the problems of interference and frequent handover, a novel clustering scheme for dense small cell network is proposed in this paper. The scheme is based on the weighted graph. First, we present a dense small cell clustering model based on X2 interface to minimize core network signaling overhead. To improve the usability of the model, we model the system as an undirected weighted graph. Then we propose the maximum benefit merging algorithm to reduce the complexity. This method enables adjacent small cells to cooperate and form virtual cellular cluster according to handover statistics information. Then we select cluster head (CH) according to certain rule in each cluster. Cluster head acts as the mobility anchor, managing the handovers between cluster members. This can reduce core network signaling overhead and the interference among small cells effectively. Compared with the 3GPP handover algorithm, the proposed clustering model in this paper can reduce the signaling overhead more than 70%. The simulation results show that the proposed clustering model can effectively cluster the dense small cell.     

Retrospective analysis of time series for frame selection in surveillance video summarization

In recent days, we have witnessed a dramatical growth of videos in various real-life scenarios. In this paper, we address the problem of surveillance video summarization. We present a new method of key-frame selection for this task: By virtue of retrospective analysis of time series, temporal cuts are first detected by sequentially measuring dissimilarities on a given video with threshold-based decision making; then, with the detected cuts, the video is segmented into a number of consecutive clips containing similar video contents; key frames are last selected by performing a typical clustering procedure in each resulted clip for final video summary. We have conducted extensive experiments on the benchmarking ViSOR dataset and the publicly available IVY LAB dataset. Excellent performances outperforming state-of-the-art competitors were demonstrated on key-frame selection for surveillance video summarization, which suggests good potentials of the proposed method in real-world applications.     

超宽带发射电路的设计研究

介绍了超宽带通信系统的关键电路的原理与设计,提出了超宽带发射与接收系统的原理与具体实施方案,以及UWB发射电路,接收等关键电路的设计方法,对于超宽带高速无线接入的实现,起到了关键的作用。     

视频处理器中的一种新的彩色图像增强方法

LED显示屏显示图像之前,视频图像通常用视频处理器进行图像增强等操作。本文考虑到视频处理器在RGB彩色图像变换时可能产生的彩色失真情况,提出一种自适应的灰度变换算法,此算法将原始的RGB彩色图像和经过灰度变换后的RGB彩色图像转换到HSB彩色空间,然后将两者的色调与亮度分量进行重组,再对其饱和度进行补偿。这种方法的优点是既保证了原始彩色图像在进行灰度变换后的灰度细节,又能保证其彩色细节,同时提高了原始彩色图像的对比度。另外,此算法计算量小,利于硬件的实时实现。     

羟基红花黄色素A注射剂、阿司匹林及波立维对家兔血小板聚集及超微结构的影响

目的：观察羟基红花黄色素A（HSYA）注射剂、阿司匹林和波立维对家兔血小板聚集功能及超微结构的影响。方法：采用体内实验法,观察羟基红花黄色素A注射剂、阿司匹林和波立维对由花生四烯酸（从）、二磷酸腺苷（ADP）和血小板活化因子（PAF）诱导的家兔血小板聚集作用的影响,以及血小板超微结构的变化。结果：羟基红花黄色素A注射剂能抑制AA、PAF诱导的家兔血小板聚集;扫描电镜显示：羟基红花黄色素A注射剂能减少AA和PAF诱导后的聚集型血小板数量并使树突型血小板突起变少变短。结论：羟基红花黄色素A注射剂具有抗PAF诱导的血小板聚集作用,抑制血小板的活化,从而为临床抗血小板聚集药物的使用提供更多选择。     

单级静态高峰值功率灯抽运脉冲Nd∶YAG固体激光器

报道了采用对称平面平行腔结构实现单级静态输出30 kW高峰值功率灯抽运Nd∶YAG固体激光器的研究结果。从速率方程出发,推导出脉冲Nd∶YAG固体激光器的单脉冲能量表达式,模拟出输出镜最佳透过率及最大输出能量。通过实验选取激光器工作的最佳参数,研制出一台高峰值功率灯抽运脉冲Nd∶YAG激光器,理论模拟和最佳实验结果基本一致。激光器在最大输入电压为800 V,脉宽为2 ms时,输出最大单脉冲能量60 J,最大峰值功率30 kW,光束质量M2为5.9,总体电光转换效率3.3%。在最大输入电压为800 V,脉宽为1.5 ms时最大平均功率405 W。采用该激光器切割6 mm低碳钢和4 mm不锈钢,在脉宽为2 ms,频率为6 Hz,峰值功率为30 kW时,切割4 mm不锈钢速度为1 mm/s,切割6 mm低碳钢速度为1.5~2 mm/s。