一种战场士兵多生命体征监测头带设计

针对战场环境,从便携式角度出发,采用可穿戴技术,设计了一种集成心电、脑电、脉搏波、体温以及人体姿态测量的战场士兵多生命体征监测头带。其中,以超低功耗的MSP430单片机为主控芯片,以低功耗的ADS1292R作为双电极心电和脑电采集芯片,以反射式光频转换器采集人体光电容积脉搏波,以医疗专用负温度系数（ NTC）热敏电阻器作为体温传感器,以MMA7660数字三轴加速度计作为姿态测量传感器。通过蓝牙将所采集信息传送至单兵PDA,实现了多生命体征的监测,具备体积小、重量轻、功耗低、成本低、便携舒适等特点。     

基于误差四元数的单兵导航系统算法

MEMS传感器在尺寸、价格和功耗方面的进步促进了单兵导航系统的发展.对基于MEMS的单兵导航系统,航向估计是GPS信号丢失情况下的一个基础性难题,准确的航向估计能有效抑制导航误差的漂移.为获得准确的航向信息,减小单兵定位误差,提出了基于误差四元数的数据融合算法,利用卡尔曼滤波算法估计误差四元数,并进行了场地实验以验证算法的有效性.结论证明:当步行距离为80 m时,定位误差小于总距离的3％.     

一种基于条件证据网络的多源异类知识融合识别方法

针对融合识别领域中不同框架下多源异类传感器的不确定证据信息无法有效融合的问题, 提出一种基于条件证据网络的多源异类知识融合识别方法. 该方法将战场协同作战中不同框架下多源异类传感器的领域知识统一在证据网络的结构下, 形成多源异类知识融合识别模型, 对多源异类传感器的不确定性证据信息进行基于条件证据网络的融合推理, 得到识别结果. 仿真实例验证了所提出方法的优越性.

     

基于神经二部分裂结构的多智能体WSNs数据融合算法?

为进一步提高无线传感器网络WSNs( Wireless Sensor Networks)使用寿命,从提高算法数据融合效率角度出发,提出一种神经二部分裂结构的多智能体WSNs数据融合算法。首先,根据神经结构的主干与枝干承载信息量不同的原理,选取主干、枝干通讯链路并赋予较大能量,并给出主、辅中心节点选取方法;其次,设计了基于LMS的自适应加权融合算法,分别针对节点层级、枝干中心层级和主干中心层级进行逐层处理,实现了对神经二部分裂结构的数据融合;最后,通过与两种已有算法进行仿真对比,显示本文算法在Sink节点接收数据包,能耗等指标上均具有优势,验证了算法有效性。     

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.     

Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

The information fusion estimation problems are investigated for multi-sensor stochastic uncertain systems with correlated noises. The stochastic uncertainties caused by correlated multiplicative noises exist in the state and observation matrices. The process noise and the observation noises are one-step auto-correlated and two-step cross-correlated, respectively. While the observation noises of different sensors are one-step cross-correlated. The optimal centralized fusion filter, predictor and smoother are proposed in the linear minimum variance sense via an innovative analysis approach. To enhance the robustness and flexibility, a distributed fusion filter is put forward, which requires the calculation of filtering error cross-covariance matrices between any two local filters. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection (CI) fusion algorithm, which can reduce the computational cost. A simulation example is given to show the effectiveness of the proposed algorithms.     

基于SAM-CS和SOFM的胃上皮肿瘤细胞图像识别

针对胃上皮肿瘤细胞图像(以下简称肿瘤细胞图像)黏结严重和信息冗余的特点,提出了一种将自适应观测矩阵的压缩感知(SAM-CS)和自组织特征映射(SOFM)神经网络相结合的算法。该算法将肿瘤细胞图像拉成列向量,然后利用通过自适应过程产生的观测矩阵,基于压缩感知理论对图像信息进行观测,产生线性观测向量,最后利用SOFM神经网络的学习算法对观测向量进行训练和分类,实现对肿瘤细胞图像的识别。实验表明,相比常用算法,该算法至少提高了4.2%的识别准确率和5.7%的运算速度。     

SAP与物采系统融合衔接流程设计

本文主要介绍了物采系统,重点分析了SAP与物采系统融合衔接中的问题,针对这些问题提出了相应的融合衔接设计了,解决了SAP与物采系统数据传递,从而形成了物资采购从谈价议价、招标、反向拍卖到产生采购订单付款一个完整科学管理信息流程,满足了物资采购管理和采购交易为一体的业务管理需求,为以后其他系统与SAP系统融合提供了借鉴意义。     

SAP与FMIS融合系统的设计应用研究

文章主要通过SAP与FMIS系统融合问题,重点分析介绍了融合系统的设计方案和实施,解决了SAP与FMIS系统数据传递、财务数据差异等问题。为以后其他系统与SAP系统融合提供了重要借鉴意义。     

Optical complexities of living cytoplasm--implications for live cell imaging and photo-micromanipulation techniques

The ability to manipulate the intracellular environment within living cells and to monitor the cytosolic chemical changes which occur during cell stimulation has lead to major advances in our understanding of how cells read and respond to their environment. Perhaps the most powerful suite of techniques for achieving these dual objectives is based on the use of light (photons). Because cells are 'transparent', light has been used to both interrogate and manipulate the chemistry inside living cells, exploiting technical advances in both the physical and biochemical sciences. However, cells are neither transparent nor homogeneous with respect to their optical properties. The interface between light and the living cell cytoplasm thus represent an important, yet largely ignored, interface. There has been no review of the optical properties of cytoplasm and little discussion about how the optical properties of living cytoplasm influence the outcome of such measurements and manipulations. In this short review, we discuss the importance of understanding the optical properties of cytoplasm for such techniques and how imperfections in experimental interpretation can arise.