Enhancing signal-to-noise ratio and resolution in low-field NMR relaxation measurements using post-acquisition digital filters

The traditional way to enhance signal-to-noise ratio (SNR) of nuclear magnetic resonance (NMR) signals is to increase the number of scans. However, this procedure increases the measuring time that can be prohibitive for some applications. Therefore, we have tested the use of several post-acquisition digital filters to enhance SNR up to one order of magnitude in time domain NMR (TD-NMR) relaxation measurements. The procedures were studied using continuous wave free precession (CWFP-T₁) signals, acquired with very low flip angles that contain six times more noise than the Carr–Purcell–Meiboom–Gill (CPMG) signal of the same sample and experimental time. Linear (LI) and logarithmic (LO) data compression, low-pass infinity impulse response (LP), Savitzky–Golay (SG), and wavelet transform (WA) post-acquisition filters enhanced the SNR of the CWFP-T₁ signals by at least six times. The best filters were LO, SG, and WA that have high enhancement in SNR without significant distortions in the ILT relaxation distribution data. Therefore, it was demonstrated that these post-acquisition digital filters could be a useful way to denoise CWFP-T₁, as well as CPMG noisy signals, and consequently reducing the experimental time. It was also demonstrated that filtered CWFP-T₁ method has the potential to be a rapid and nondestructive method to measure fat content in beef and certainly in other meat samples.     

双折射滤光片的色散特性研究

研究了由理想线偏振片和单轴双折射晶体波片组成的双折射滤光片倾斜放置时的二阶色散和 三阶色散特性，包括波片厚度、光线入射角和光轴旋转角的变化对二阶和三阶色散的影响. 还给出了双折射滤光片的群延迟表达式，并就色散特性与GT干涉仪进行了详细比较. 关键词： 双折射滤光片 群延迟 二阶色散 三阶色散     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

基于进化人工神经网络的组合系统滤波技术

在组合系统运用Kalman滤波器技术时,准确的系统模型和可靠的观测数据是保证其性能的重要因素,否则将大大降低Kalman滤波器的估计精度,甚至导致滤波器发散.为解决上述Kalman应用中的实际问题,提出了一种新颖的基于进化人工神经网络技术的自适应Kalman滤波器.仿真试验表明该算法可以在系统模型不准确时、甚至外部观测数据短暂中断时,仍能保证Kalman滤波器的性能.     

Ground-state complex formation of perylene with pyromellitic dianhydride studied by static fluorescence quenching

The association of complex formation with static quenching in CT systems was investigated. Evaluation of the data made evident that the inner filter effect must be allowed for. Time-resolved and temperature-dependent stationary measurements of fluorescence led to the separation of dynamic and static quenching components. The static quenching constant is discussed with respect to the equilibrium constant of complex formation determined by absorption spectroscopy.     

Estimating Sampling Errors in REA Survey: A Model Approach for the Synthetic Presentation of Results for Main Variables Involved in Economic Analysis

A new sample business survey for agriculture, the REA survey, and a project of integration with the FADN network (RICA in Italy) have significantly changed the production of statistical information nowadays available with reference to the agricultural sector. On the basis of this relevant information, new economic analyses are being developed on farms’ performance, agricultural households’ income and the Common Agricultural Policy (CAP). In this paper the authors estimate the relationship between the levels of variables of interest and their sampling errors using models in order to improve the accessibility of the information on estimates accuracy to the final users (agricultural analysts, policy makers). The paper is the result of a joint research of the three authors. Sections 1–3 and 6 by Pizzoli, Sects. 4 and 5 by Rondinelli, Sect. 7 by Filiberti, conclusions joint to the three authors.     

互补型自适应滤波器在心磁信号处理中的应用

将心磁信号从干扰噪声中加以提取并有效地消除噪声干扰是心磁信号处理中尤为重要的环节 .从改进算法的角度出发，提出互补型自适应滤波器结构以实现心磁信号的消噪处理.该滤波器针对心磁这类非平稳信号进行设计，有效地解决了常规自适应滤波器应用于心磁信号处理时收敛速度和稳态误差的矛盾.通过仿真实验和心磁实验结果表明，该算法能有效地消除心磁信号的背景噪声和工频干扰噪声.同时该算法也可用于其他非平稳信号的消噪处理. 关键词： 自适应滤波 心磁图 最小均方误差     

四种多变量校准方法在FTIR多组分分析中的性能比较

本文对四种多变量校准方法－－经典最小二乘法（CLS），偏最小二乘法（PLS），卡尔曼滤波法（KFM）以及人工神经网络法（ANN）－－在多组分浓度分析方面的性能进行了比较。选择五种红外谱图严重混叠的大气有机毒物－－1，3－丁二烯，苯，邻二甲苯，氯苯和丙烯醛－－作为分析对象。分别计算各种方法对该5组分体系的平均预测误差MPE和平均相对误差MRE进行比较。结果表明，偏最小二乘法在处理这类问题中是最稳健的方法。     

A notch filter for 140 GHz microwave radiation

The construction of a notch-filter for 140 GHz with very low passband insertion loss is described. It is based on a single-mode rectangular waveguide and cylindrical cavities matched to it. The typical transmission characteristic is also presented.     

A New Method for Rotation and Brightness Invariant Pattern Recognition Based on Modified Multiple Circular Harmonic Expansions

A new method for rotation and brightness invariant pattern recognition was proposed by applying multiple circular harmonic expansions to the joint transform correlator. The amplitudes of the multiple orders of circular harmonic expansions made from a detecting image were synthetically modified to respond to the same auto-correlation peaks. These modified circular harmonic expansions were arranged in the input plane as reference patterns together with an arbitrary target pattern, and the correlation signals between them were calculated in the subtracted joint transform correlator. The fraction of the correlation-peak intensities between the target and the references were extracted as a new discrimination parameter. This new parameter performs pattern recognition under rotation and brightness invariance with good discriminability. Its high discriminability has been proved in computer simulations using the face image patterns of many individuals.