On-Chip Clock Network Skew Measurement using Sub-Sampling

We present a technique for an all-digital on-chip delay measurement system to measure the skews in a clock distribution network. It uses the principle of sub-sampling. Measurements from a prototype fabricated in a 65 nm industrial process, indicate the ability to measure delays with a resolution of 0.5ps and a DNL of 1.2 ps.

Learning from large data : bias, variance, sampling, and learning curves

One of the fundamental machine learning tasks is that of predictive classification. Given that organisations collect an ever increasing amount of data, predictive classification methods must be able to effectively and efficiently handle large amounts of data. However, it is understood that present requirements push existing algorithms to, and sometimes beyond, their limits since many classification prediction algorithms were designed when currently common data set sizes were beyond imagination. This has led to a significant amount of research into ways of making classification learning algorithms more effective and efficient. Although substantial progress has been made, a number of key questions have not been answered. This dissertation investigates two of these key questions. The first is whether different types of algorithms to those currently employed are required when using large data sets. This is answered by analysis of the way in which the bias plus variance decomposition of predictive classification error changes as training set size is increased. Experiments find that larger training sets require different types of algorithms to those currently used. Some insight into the characteristics of suitable algorithms is provided, and this may provide some direction for the development of future classification prediction algorithms which are specifically designed for use with large data sets. The second question investigated is that of the role of sampling in machine learning with large data sets. Sampling has long been used as a means of avoiding the need to scale up algorithms to suit the size of the data set by scaling down the size of the data sets to suit the algorithm. However, the costs of performing sampling have not been widely explored. Two popular sampling methods are compared with learning from all available data in terms of predictive accuracy, model complexity, and execution time. The comparison shows that sub-sampling generally products models with accuracy close to, and sometimes greater than, that obtainable from learning with all available data. This result suggests that it may be possible to develop algorithms that take advantage of the sub-sampling methodology to reduce the time required to infer a model while sacrificing little if any accuracy. Methods of improving effective and efficient learning via sampling are also investigated, and now sampling methodologies proposed. These methodologies include using a varying-proportion of instances to determine the next inference step and using a statistical calculation at each inference step to determine sufficient sample size. Experiments show that using a statistical calculation of sample size can not only substantially reduce execution time but can do so with only a small loss, and occasional gain, in accuracy. One of the common uses of sampling is in the construction of learning curves. Learning curves are often used to attempt to determine the optimal training size which will maximally reduce execution time while nut being detrimental to accuracy. An analysis of the performance of methods for detection of convergence of learning curves is performed, with the focus of the analysis on methods that calculate the gradient, of the tangent to the curve. Given that such methods can be susceptible to local accuracy plateaus, an investigation into the frequency of local plateaus is also performed. It is shown that local accuracy plateaus are a common occurrence, and that ensuring a small loss of accuracy often results in greater computational cost than learning from all available data. These results cast doubt over the applicability of gradient of tangent methods for detecting convergence, and of the viability of learning curves for reducing execution time in general.

A principled experimental design approach to Big Data analysis

Big Datasets are endemic, but they are often notoriously difficult to analyse because of their size, heterogeneity, history and quality. The purpose of this paper is to open a discourse on the use of modern experimental design methods to analyse Big Data in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has wide generality and advantageous inferential and computational properties. In particular, the principled experimental design approach is shown to provide a flexible framework for analysis that, for certain classes of objectives and utility functions, delivers near equivalent answers compared with analyses of the full dataset under a controlled error rate. It can also provide a formalised method for iterative parameter estimation, model checking, identification of data gaps and evaluation of data quality. Finally, it has the potential to add value to other Big Data sampling algorithms, in particular divide-and-conquer strategies, by determining efficient sub-samples.

A mostly-digital analog scan-out chain for low bandwidth voltage measurement for analog IP test

A method of precise measurement of on-chip analog voltages in a mostly-digital manner, with minimal overhead, is presented. A pair of clock signals is routed to the node of an analog voltage. This analog voltage controls the delay between this pair of clock signals, which is then measured in an all-digital manner using the technique of sub-sampling. This sub-sampling technique, having measurement time and accuracy trade-off, is well suited for low bandwidth signals. This concept is validated by designing delay cells, using current starved inverters in UMC 130nm CMOS process. Sub-mV accuracy is demonstrated for a measurement time of few seconds.

胶东地区金矿床成矿时代及其成矿地球动力学背景-兼论壳幔相互作用与成岩成矿

Jiaodong Peninsula is the largest repository of gold in China. Varieties of studies have been involved in the mechanism of metallogenesis. This thesis is a part of the project "Study of basic geology related to the prespecting of the supra-large deposits" which supported by National Climbing Program of China to Prof. Zhou. One of the key scientific problems is to study the age and metallogenic dynamics of ore deposit and to understand how interaction between mantle and crust constrains on metallogenesis and lithogenesis. As Jiaodong Peninsula to be study area, the Rb-Sr, Sm-Nd and Pb isotopic systematics of pyrite and altered rocks are measured to define the age and origin of gold. The elemental and Sr-Nd-Pb isotopic compositions of dikes and granites was studied to implicate the source and lithogenesis of the dike and granite and removal of lithosphere and the interaction between mantle and crust in the Jiaodong Peninsula. Considering the tectonic of Jiaodong Peninsula, basic on the time and space, this thesis gives a metallogenic dynamics of gold mineralization and discusses the constraints of the interaction between mantle and crust on the metallogenesis and lithogenesis. This thesis reports the first direct Rb-Sr dating of pyrites and ores using sub-sampling from lode gold deposit in Linglong, Jiaodong Peninsula and the results demonstrate this as a useful geochronological technique for gold mineralization with poor age constraint. The Rb-Sr data of pyrites yields an isochron age of (121.6-122.7) Ma, whereas, those of ore and ore-pyrite spread in two ranges from 120.0 to 121.8 Ma and 110.0-111.7 Ma. Studies of characteristic of gold deposit, microscopy of pyrite and quartz indicate that the apparent ages of ore and ore-pyrite are not isochron ages, it was only mixed by two end members, i.e., the primitive hydrothermal fluids and wall rocks. However, the isochron age of pyrite samples constrains the age of gold mineralization, i.e., early Cretaceous, which is in good consistence with the published U-Pb ages of zircon by using the SHRIMP technique. The whole rock Rb-Sr isochron age of altered rocks indicates that the age of gold mineralizing in the Xincheng gold deposit is 116.6 ± 5.3 Ma. The Sr, Nd and Pb isotopic compositions of pyrite and altered rocks indicate that the gold and relevant elements were derived from multi-sources, i.e. dikes derived from enriched lithospheric mantle and granites, granodiorites and metamorphic rocks outcropped on the crust. It also shows that the hydrothermal fluids derived from mantle magma degassing had play an important role in the gold mineralizing. The major and trace elements, Sr-Nd-Pb isotopic data of granites and granodiorites suggest that the Linglong Granite and Kunyushan Granite were derived from partial melting of basement rocks in the Jiaodong Peninsula at post-collision of North China Craton with South China Craton. Guojialing Granodiorite was considered to be derived from a mixture source, that is, mixed by magmas derived from an enriched lithospheric mantle and crust during the delamination of lithosphere induced by the subduction of Izanagi Plate and the movement of Tancheng-Lujiang Fault. There are kinds of dikes occurred in the Jiaodong Peninsula, which are accompanying with gold mineralization in time and space. The dikes include gabrro, diabase, pyroxene diorite, gabrrophyre, granite-porphyry, and aplite. The whole rock K-Ar ages give two age intervals: 120-124 Ma for the dikes that erupted at the gold mineralizing stage, and <120 Ma of the dikes that intruded after gold mineralizing. According to the age and the relationship between the dikes and gold mineralizing, the dikes could be divided into two groups: Group I (t = 120-124 Ma) and Group II (t < 120Ma). Group I dikes show the high Mg and K, low Ti contents, negative Nb anomalies and positive Eu anomalies, high ~(87)Sr/~(86)Sr and negative εNd(t) values and an enrichment in light rare earth elements, large ion lithosphile elements and a depletion in high field strength elements. Thus the elemental and isotopic characteristics of the Group I dikes indicate that they were derived from an enriched lithospheric mantle perhaps formed by metasomatism of the melt derived from the recycled crustal materials during the deep subduction of continent. In contrast, the Group II dikes have high Ti, Mg and K contents, no negative Nb anomalies, high ~(87)Sr/~(86)Sr and positive or little negative εNd(t) values, which indicate the derivation from a source like OIB-source. The geochemical features also give the tectonic constraints of dikes, which show that Group I dikes were formed at continental arc setting, whereas Group II dikes were formed within plate background. Considering the tectonic setting of Jiaodong Peninsula during the period of gold mineralizing, the metallogenic dynamics was related to the subduction of Izanagi Plate, movement of Tancheng-Lujiang Fault and removal of lithopheric mantle during Late Mesozoic Era.

The Effect of Changes to the Method of Estimating the Pollen Count from Aerobiological Samples

Pollen data have been recorded at Novi Sad in Serbia since 2000. The adopted method of producing pollen counts has been the use of five longitudinal transects that examine 19.64% of total sample surface. However, counting five transects is time consuming and so the main objective of this study is to investigate whether reducing the number to three or even two transects would have a significant effect on daily average and bi-hourly pollen concentrations, as well as the main characteristics of the pollen season and long-term trends. This study has shown that there is a loss of accuracy in daily average and bi-hourly pollen concentrations (an increase in % ERROR) as the sub-sampling area is reduced from five to three or two longitudinal transects. However, this loss of accuracy does not impact on the main characteristics of the season or long-term trends. As a result, this study can be used to justify changing the sub-sampling method used at Novi Sad from five to three longitudinal transects. The use of two longitudinal transects has been ruled out because, although quicker, the counts produced: (a) had the greatest amount of % ERROR, (b) altered the amount of influence of the independent variable on the dependent variable (the slope in regression analysis) and (c) the total sampled surface (7.86%) was less than the minimum requirement recommended by the European Aerobiology Society working group on Quality Control (at least 10% of total slide area).

Fingerprints of changes in annual and seasonal precipitation from CMIP5 models over land and ocean

By comparing annual and seasonal changes in precipitation over land and ocean since 1950 simulated by the CMIP5 (Coupled Model Intercomparison Project, phase 5) climate models in which natural and anthropogenic forcings have been included, we find that clear global-scale and regional-scale changes due to human influence are expected to have occurred over both land and ocean. These include moistening over northern high latitude land and ocean throughout all seasons and over the northern subtropical oceans during boreal winter. However we show that this signal of human influence is less distinct when considered over the relatively small area of land for which there are adequate observations to make assessments of multi-decadal scale trends. These results imply that extensive and significant changes in precipitation over the land and ocean may have already happened, even though, inadequacies in observations in some parts of the world make it difficult to identify conclusively such a human fingerprint on the global water cycle. In some regions and seasons, due to aliasing of different kinds of variability as a result of sub sampling by the sparse and changing observational coverage, observed trends appear to have been increased, underscoring the difficulties of interpreting the apparent magnitude of observed changes in precipitation.

Assessing species’ habitat associations from occurrence records, standardised monitoring data and expert opinion: a test with British butterflies

Accurate knowledge of species’ habitat associations is important for conservation planning and policy. Assessing habitat associations is a vital precursor to selecting appropriate indicator species for prioritising sites for conservation or assessing trends in habitat quality. However, much existing knowledge is based on qualitative expert opinion or local scale studies, and may not remain accurate across different spatial scales or geographic locations. Data from biological recording schemes have the potential to provide objective measures of habitat association, with the ability to account for spatial variation. We used data on 50 British butterfly species as a test case to investigate the correspondence of data-derived measures of habitat association with expert opinion, from two different butterfly recording schemes. One scheme collected large quantities of occurrence data (c. 3 million records) and the other, lower quantities of standardised monitoring data (c. 1400 sites). We used general linear mixed effects models to derive scores of association with broad-leaf woodland for both datasets and compared them with scores canvassed from experts. Scores derived from occurrence and abundance data both showed strongly positive correlations with expert opinion. However, only for occurrence data did these fell within the range of correlations between experts. Data-derived scores showed regional spatial variation in the strength of butterfly associations with broad-leaf woodland, with a significant latitudinal trend in 26% of species. Sub-sampling of the data suggested a mean sample size of 5000 occurrence records per species to gain an accurate estimation of habitat association, although habitat specialists are likely to be readily detected using several hundred records. Occurrence data from recording schemes can thus provide easily obtained, objective, quantitative measures of habitat association.

Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp RFLP/PCR data set

The taxonomy of the N(2)-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradryrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses Clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster. (C) 2008 Elsevier B.V. All rights reserved.

Determinación de nitratos en vegetales : comparación de cuatro métodos analíticos

Para la cuantificación de nitratos hay numerosas técnicas y no existe entre los analistas unanimidad en la selección de la más adecuada. Por tal motivo, se compararon cuatro métodos para la determinación de nitratos en muestras vegetales con el fin de evaluar la correlación entre los mismos y establecer pautas para su utilización. Se utilizaron 690 muestras de lechuga (Lactuca sativa L. var. capitata), pertenecientes a los tipos arrepollado y mantecoso, recolectadas a lo largo de un año en el Mercado Cooperativo de Guaymallén (Mendoza, Argentina). Según los tenores de nitratos encontrados en la población estudiada se efectuó un sub-muestreo aleatorio estratificado proporcional para lograr un número de muestras que representaran la variabilidad del total de la población. Se utilizaron cuatro métodos para la determinación de nitratos: 1. destilación por arrastre con vapor, considerado como método de referencia 2. colorimetría por nitración con ácido salicílico 3. colorimetría modificada 4. potenciometría con electrodo selectivo Se probaron diferentes modelos de regresión entre el método de referencia y los otros tres, siendo el lineal el que mejor se ajustó en todos los casos. Los métodos estudiados tuvieron comportamiento semejante. La mayor correlación (r2 = 93 %) se observó entre la destilación por arrastre con vapor y la potenciometría; no obstante, los restantes también presentaron alta correlación. Consecuentemente, la elección del procedimiento analítico dependerá principalmente del número de muestras a analizar, del tiempo requerido por el análisis y del costo del mismo.

Development of error correction techniques for nitrate-N load estimation models

Excess nutrient loads carried by streams and rivers are a great concern for environmental resource managers. In agricultural regions, excess loads are transported downstream to receiving water bodies, potentially causing algal blooms, which could lead to numerous ecological problems. To better understand nutrient load transport, and to develop appropriate water management plans, it is important to have accurate estimates of annual nutrient loads. This study used a Monte Carlo sub-sampling method and error-corrected statistical models to estimate annual nitrate-N loads from two watersheds in central Illinois. The performance of three load estimation methods (the seven-parameter log-linear model, the ratio estimator, and the flow-weighted averaging estimator) applied at one-, two-, four-, six-, and eight-week sampling frequencies were compared. Five error correction techniques; the existing composite method, and four new error correction techniques developed in this study; were applied to each combination of sampling frequency and load estimation method. On average, the most accurate error reduction technique, (proportional rectangular) resulted in 15% and 30% more accurate load estimates when compared to the most accurate uncorrected load estimation method (ratio estimator) for the two watersheds. Using error correction methods, it is possible to design more cost-effective monitoring plans by achieving the same load estimation accuracy with fewer observations. Finally, the optimum combinations of monitoring threshold and sampling frequency that minimizes the number of samples required to achieve specified levels of accuracy in load estimation were determined. For one- to three-weeks sampling frequencies, combined threshold/fixed-interval monitoring approaches produced the best outcomes, while fixed-interval-only approaches produced the most accurate results for four- to eight-weeks sampling frequencies.

Establishing precise estimates of abundance in patchy habitats of the marine nearshore

Exploratory investigations of optimal sampling designs are a critical component of the decision-making process in ecology where inherent natural variation can lead to erroneous conclusions if left unexamined. Pilot studies and exploratory analyses that investigate the precision of sampling regimes may reduce the chances of erroneous results and can be used to optimise processing time in larger ecological research programs. In our study, we calculated optimal precision estimates for sampling macroinvertebrates and ichthyofauna in surf-zone wrack accumulations by investigating the precision of the mean for sub-samples of seine nets and also for the number of replicate seine nets to guide future sampling regimes. We discovered that the processing time for individual seine net samples could be reduced by 50% using sub-sampling and that time to process replicate seine net samples could be reduced by 25% while maintaining acceptable precision. In future, we suggest that the use of pilot studies with similar exploratory approaches should be less of an exception and more a critical component of ecological investigations, particularly in under-studied or newly-developing areas of research. Further, these types of exploratory approaches are crucially important in a variety of extremely patchy environments where variability is likely to be high.

A Sub-Nyquist Sampling Method For Computing The Level-crossing-times Of An Analog Signal: Theory And Applications

We address the problem of computing the level-crossings of an analog signal from samples measured on a uniform grid. Such a problem is important, for example, in multilevel analog-to-digital (A/D) converters. The first operation in such sampling modalities is a comparator, which gives rise to a bilevel waveform. Since bilevel signals are not bandlimited, measuring the level-crossing times exactly becomes impractical within the conventional framework of Shannon sampling. In this paper, we propose a novel sub-Nyquist sampling technique for making measurements on a uniform grid and thereby for exactly computing the level-crossing times from those samples. The computational complexity of the technique is low and comprises simple arithmetic operations. We also present a finite-rate-of-innovation sampling perspective of the proposed approach and also show how exponential splines fit in naturally into the proposed sampling framework. We also discuss some concrete practical applications of the sampling technique.

Group testing based spectrum hole search using a simple sub-nyquist sampling scheme

In this paper, we consider the problem of finding a spectrum hole of a specified bandwidth in a given wide band of interest. We propose a new, simple and easily implementable sub-Nyquist sampling scheme for signal acquisition and a spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy in the frequency domain by testing a group of adjacent subbands in a single test. The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent sub-bands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes. We extend this framework to a multi-stage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Further, we provide the analytical means to optimize the hypothesis tests with respect to the detection thresholds, number of samples and group size to minimize the detection delay under a given error rate constraint. Depending on the sparsity and SNR, the proposed algorithms can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme; the latter is in fact a special case of the group test when the group size is set to 1. We validate our analytical results via Monte Carlo simulations.

Non-compressive wideband spectrum sensing with sub-Nyquist sampling rates

In this paper, we propose a low complexity and reliable wideband spectrum sensing technique that operates at sub-Nyquist sampling rates. Unlike the majority of other sub-Nyquist spectrum sensing algorithms that rely on the Compressive Sensing (CS) methodology, the introduced method does not entail solving an optimisation problem. It is characterised by simplicity and low computational complexity without compromising the system performance and yet delivers substantial reductions on the operational sampling rates. The reliability guidelines of the devised non-compressive sensing approach are provided and simulations are presented to illustrate its superior performance. © 2013 IEEE.