家用洗碗机能效要求比较分析

该文主要分析和介绍了世界主要发达国家／地区家用洗碗机强制性和自愿性能效要求,包括限值要求、计算和检测方法等,并对主要国家/地区的能效限值要求进行了比较分析,旨在帮助出口生产企业了解发达国家/地区在能效限值方面的要求,提高应对国外技术贸易壁垒的能力。     

PAN/CoFe_2O_4纳米复合物的制备及表征

以硝酸铁、硝酸钴和柠檬酸为原料,通过溶胶-凝胶微波加热自蔓延燃烧法制备出了纯的立方尖晶石型铁酸钴(CoFe2O4)纳米粒子,然后通过原位聚合将聚苯胺(PAN)对所得粒径约20～30nm铁酸钴纳米粒子进行了包覆,制得了PAN/CoFe2O4纳米复合物。通过傅里叶变换红外光谱(FTIR)、透射电子显微镜(TEM)、X射线衍射(XRD)等测试手段对所得产物的形貌和结构进行了表征。结果表明,PAN链段与CoFe2O4纳米粒子之间存在作用力,通过原位聚合制得的PAN包覆在CoFe2O4纳米粒子表面,X射线粉末衍射和红外光谱进一步证实了PAN/CoFe2O4纳米复合物的形成。荧光分析结果表明,引入CoFe2O4纳米粒子后PAN荧光强度增强。     

家庭网络中融合语音通信技术研究

在未来数字家庭中,宽带产品内融合无绳语音服务可以为用户提供更舒适的数字化生活体验。本文比较、分析了数字无绳语音业务融合型家庭宽带产品技术方案,给出了技术方案选择建议,并通过测试进行了验证。     

电磁兼容整改的可行性和有效性探讨

通过对电磁兼容整改措施的有效性和可行性分析,着重讨论了电磁兼容整改措施在产品实际生产过程中得不到有效落实的原因,并从整改方案选择和落实两个方面有针对性地提出了相应的处理对策,对电磁兼容整改和生产过程管理有一定的参考和指导意义。     

LTE无线移动通信系统加密算法验证

一种新的加密算法在提出到最终被采纳用于LTE无线移动通信系统,其间需要进行充分的验证和测试。文章阐述了目前无线移动通信系统所使用的加密算法所应满足的技术要求,并对加密算法、加载了加密算法的网络和设备的评估及验证提出了具体的指标和测试方法。     

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.