移动定位技术的现状与发展趋势

基于位置服务为人们的日常生活带来了极大的方便,同时也推动了移动定位技术行业的快速发展。针对移动定位技术发展状况,分析目前主流定位技术的优劣,并对取得的突破作简单的总结归纳。     

无线体域网高能效竞争接入机制研究

低能耗是无线体域网WBAN通信最重要的指标.构建了IEEE 802.15.6无线体域网CSMA/CA竞争接入的功耗模型,分析了用户优先级、MAC帧负荷尺寸和数据到达量对信道接入能耗的影响,提出了一种通过数据汇聚传输提高能量效率的增强型信道接入方法.仿真结果表明,在低业务量及混合业务场景下,所提方法能够显著减少信道接入的能量消耗.     

基于蝙蝠算法的改进杂草算法研究

提出了一种新型的混合算法并命名为混合杂草蝙蝠算法(Hybridize Invasive Weed Optimization with Bat Algorithm,IWOBA),该算法在杂草算法的基础上利用蝙蝠算法的回声定位来解决每代种子逐步寻优的问题。其原理是利用种群速度和位置的不断更新,增加种群的多样性,从而达到提高种群的全局收敛性。最后利用6个测试函数对该算法和标准杂草算法进行测试比较。仿真结果表明,IWOBA能够有效克服原算法早熟、易陷入局部最优的缺点,可加快算法收敛速度,具有良好的鲁棒性。     

非合作条件下基于责任延伸制的电子废弃物回收主体利益博弈研究

针对由两个制造商、两个回收商、当地政府和消费者构成的物流系统,研究了非合作条件下电子废弃物回收主体的利益博弈.在责任延伸制前提下,以回收商为斯坦伯格主导者,分别构建了非合作条件下的垄断回收模型和竞争回收模型,并采用逆推法求解了模型的纳什均衡条件.最后进行灵敏度分析,探讨了产品替代率和回收率对两种模式下产品销售价格和回收费用的影响.结果表明,对于回收商,垄断回收模式下的平均回收费用总比竞争回收模式高,且回收商需根据临界值选择回收模式;对于制造商,垄断回收模式下设定的价格总比竞争回收模式高,且制造商总是偏好竞争回收模式.研究结果可为决策者对回收模式的选择提供理论依据.     

水源井节能保护及远程监控装置应用研究

为解决油田水源井存在着电潜泵容量大与出水管线小发生节流浪费电能严重,水位无法监测,电潜泵经常干烧和空抽不安全浪费电能,不能远程监控水源井工况等实际问题,研制出水源井节能保护及远程监控装置。该装置的射频导纳测水位部分能自动监测水源井水位,使电潜泵不发生干烧和抽空,安全运行节能;变频恒压供水部分能自动调节供水量,提高泵效;远程监控部分能实现水源井无人值守。该装置已经完成12个水源井工业应用。     

基于广义回归神经网络的情感语音韵律特征预测

韵律特征是语音信号中情感信息的主要表征之一。为了更好地进行情感语音合成的研究,本文通过提取普通话情感语音的韵律特征进行分析,采用广义回归神经网络构建了一个情感语音韵律特征预测模型,并根据所提取的测试集数据文本语境信息进行韵律特征预测,实验获得了相应的结果。实验结果表明,情感语音韵律特征预测效果较好。     

Efficient near-duplicate image detection with a local-based binary representation

Efficient near-duplicate image detection is important for several applications that feature extraction and matching need to be taken online. Most image representations targeting at conventional image retrieval problems are either computationally expensive to extract and match, or limited in robustness. Aiming at this problem, in this paper, we propose an effective and efficient local-based representation method to encode an image as a binary vector, which is called Local-based Binary Representation (LBR). Local regions are extracted densely from the image, and each region is converted to a simple and effective feature describing its texture. A statistical histogram can be calculated over all the local features, and then it is encoded to a binary vector as the holistic image representation. The proposed binary representation jointly utilizes the local region texture and global visual distribution of the image, based on which a similarity measure can be applied to detect near-duplicate image effectively. The binary encoding scheme can not only greatly speed up the online computation, but also reduce memory cost in real applications. In experiments the precision and recall, as well as computational time of the proposed method are compared with other state-of-the-art image representations and LBR shows clear advantages on online near-duplicate image detection and video keyframe detection tasks.     

Arithmetic coding using hierarchical dependency context model for H.264/AVC video coding

In this paper, a hierarchical dependency context model (HDCM) is firstly proposed to exploit the statistical correlations of DCT (Discrete Cosine Transform) coefficients in H.264/AVC video coding standard, in which the number of non-zero coefficients in a DCT block and the scanned position are used to capture the magnitude varying tendency of DCT coefficients. Then a new binary arithmetic coding using hierarchical dependency context model (HDCMBAC) is proposed. HDCMBAC associates HDCM with binary arithmetic coding to code the syntax elements for a DCT block, which consist of the number of non-zero coefficients, significant flag and level information. Experimental results demonstrate that HDCMBAC can achieve similar coding performance as CABAC at low and high QPs (quantization parameter). Meanwhile the context modeling and the arithmetic decoding in HDCMBAC can be carried out in parallel, since the context dependency only exists among different parts of basic syntax elements in HDCM.