压力传感器静态检定装置的测量不确定度

本文根据压力传感器（静态）国家计量检定规程，对压力传感器静态检定装置进行误差分析，得出该装置的测量不确定度。最后，通过实例说明实际测量不确定度与理论分析相符合。     

关于智能电度表的误差分析与修正

本文详细介绍了多功能智能电度表在研制过程中，分析与修正了由计算机采样与计算所引起的电量值误差的原因，并由此研制出一种多功能智能电度表，以及一种简单易行的误差修正的编程算法。     

SDH中泊松分布下误块率与误码率的关系分析

本文较详细地介绍了ＳＤＨ中的ＢＩＰ误码校验方式，推导了泊松分布下，ＢＩＰ误块率与误码率的一般关系式，给出了ＳＤＨ中几种典型的误块率与误码率的关系曲线，并做了计算机模拟验证     

VSP旅行时反演应用于VSP—CDP转换

非零偏移距垂直地震剖面(VSP)模型的制作是非零偏移距VSP资料处理的关键环节之一,是VSP-CDP转换的基础。本文试用旅行时反演修正非零偏移距VSP的模型,以加速模型的制作过程,提高非零偏移距VSP的处理速度。用合成VSP资料试算表明:旅行时的随机误差对模型的制作影响不大;只要能较准确地确定界面位置,用旅行时反演修改制作非零偏移VSP模型是可行的。本文还探讨了初始模型的不同选择方法,并将旅行时反演用于实际VSP资料的处理。结果表明,大大地节省了非零偏移距VSP资料的处理时间。     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

迫零对Turbo码的性能影响

从距离谱的角度对迫零在Turbo码中的作用进行了理论分析。通过仿真，研究了迫零对Turbo码性能的影响。仿真结果表明，Turbo码编码器迫零处理有助于改善Turbo码的性能。     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.