Development of an algorithm for automatic detection and rating of squeak and rattle events

A new algorithm for automatic detection and rating of squeak and rattle (S&R) events was developed. The algorithm utilizes the perceived transient loudness (PTL) that approximates the human perception of a transient noise. At first, instantaneous specific loudness time histories are calculated over 1-24 bark range by applying the analytic wavelet transform and Zwicker loudness transform to the recorded noise. Transient specific loudness time histories are then obtained by removing estimated contributions of the background noise from instantaneous specific loudness time histories. These transient specific loudness time histories are summed to obtain the transient loudness time history. Finally, the PTL time history is obtained by applying Glasberg and Moore temporal integration to the transient loudness time history. Detection of S&R events utilizes the PTL time history obtained by summing only 18-24 barks components to take advantage of high signal-to-noise ratio in the high frequency range. A S&R event is identified when the value of the PTL time history exceeds the detection threshold pre-determined by a jury test. The maximum value of the PTL time history is used for rating of S&R events. Another jury test showed that the method performs much better if the PTL time history obtained by summing all frequency components is used. Therefore, rating of S&R events utilizes this modified PTL time history. Two additional jury tests were conducted to validate the developed detection and rating methods. The algorithm developed in this work will enable automatic detection and rating of S&R events with good accuracy and minimum possibility of false alarm.     

Synchrotron infrared nano-spectroscopy and -imaging

Infrared (IR) spectroscopy has evolved into a powerful analytical technique to probe molecular and lattice vibrations, low-energy electronic excitations and correlations, and related collective surface plasmon, phonon, or other polaritonic resonances. In combination with scanning probe microscopy, near-field infrared nano-spectroscopy and -imaging techniques have recently emerged as a frontier in imaging science, enabling the study of complex heterogeneous materials with simultaneous nanoscale spatial resolution and chemical and quantum state spectroscopic specificity. Here, we describe synchrotron infrared nano-spectroscopy (SINS), which takes advantage of the low-noise, broadband, high spectral irradiance, and coherence of synchrotron infrared radiation for near-field infrared measurements across the mid- to far-infrared with nanometer spatial resolution. This powerful combination provides a qualitatively new form of broadband spatio-spectral analysis of nanoscale, mesoscale, and surface phenomena that were previously difficult to study with IR techniques, or even any form of micro-spectroscopy in general. We review the development of SINS, describe its technical implementations, and highlight selected examples representative of the rapidly growing range of applications in physics, chemistry, biology, materials science, geology, and atmospheric and space sciences.     

Converging Technologies for Improving Human Performance: Integrating From the Nanoscale

In the early decades of the twenty-first century, concentrated efforts can unify science based on the unity of nature, thereby advancing the combination of nanotechnology, biotechnology, information technology, and new humane technologies based in cognitive science. Converging technologies integrated from the nanoscale could determine a tremendous improvement in human abilities and societal outcomes. This is a broad, cross cutting, emerging, and timely opportunity of interest to individuals, society, and humanity in the long term.About eighty scientific leaders, industry experts, and policy makers across a range of fields have contributed to develop a vision for the potential to improve human physical, mental, and social capabilities through the convergence of the four technologies. Six major themes have emerged: (a) The broad potential of converging technologies; (b) Expanding human cognition and communication; (c) Improving human health and physical capabilities; (d) Enhancing group and societal outcomes; (e) National security, and (f) Unifying science and education. This article summarizes the observations, conclusions, and recommendations made in the report (Roco & Bainbridge, eds., 2002. Converging Technologies for Improving Human Performance, NSF-DOC Report, June 2002, Arlington VA, USA).     

Broader Societal Issues of Nanotechnology

Nanoscale science and engineering are providing unprecedented understanding and control over the basic building blocks of matter, leading to increased coherence in knowledge, technology, and education. The main reason for developing nanotechnology is to advance broad societal goals such as improved comprehension of nature, increased productivity, better healthcare, and extending the limits of sustainable development and of human potential. This paper outlines societal implication activities in nanotechnology R&D programs. The US National Nanotechnology Initiative annual investment in research with educational and societal implications is estimated at about $30 million (of which National Science Foundation (NSF) awards about $23 million including contributions to student fellowships), and in nanoscale research with relevance to environment at about $50 million (of which NSF awards about $30 million and EPA about $6 million). An appeal is made to researchers and funding organizations worldwide to take timely and responsible advantage of the new technology for economic and sustainable development, to initiate societal implications studies from the beginning of the nanotechnology programs, and to communicate effectively the goals and potential risks with research users and the public.     

International strategy for Nanotechnology Research

The worldwide nanotechnology research and development (R&D) investment reported by government organizations has increased by a factor of 3.5 between 1997 and 2001, and the highest rate of 90% is in 2001. At least 30 countries have initiated or are beginning national activities in this field. Scientists have opened a broad net of discoveries that does not leave any major research area untouched in physical, biological, and engineering sciences. Industry has gained confidence that nanotechnology will bring competitive advantages. The worldwide annual industrial production is estimated to exceed $1 trillion in 10–15 years from now, which would require about 2 million nanotechnology workers. U.S. has initiated a multidisciplinary strategy for development of science and engineering fundamentals through the National Nanotechnology Initiative. Japan and Europe have broad programs, and their current plans look ahead to four to five years. Other countries have encouraged their own areas of strength, several of them focusing on fields of the potential markets. Differences among countries are observed in the research domain they are aiming for, the level of program integration into various industrial sectors, and in the time scale of their R & D targets. Nanotechnology is growing in an environment where international interactions accelerate in science, education and industrial R & D. A global strategy of mutual interest is envisioned by connecting individual programs of contributing countries, professional communities, and international organizations.     

Anomalous scaling of stock price dynamics within ARCH-models

We show that autoregressive-conditional-heteroskedasticity (ARCH) models can encompass the observed anomalous scaling properties of stock price dynamics remarkably well. We find that with a suitable choice of parameters, simple ARCH models can reproduce the non-standard scaling behavior of the central part of the probability distribution functions of stock prices at different time horizons, as empirically found for the Standard & Poors 500 (S&P 500) index data, but fail to reproduce the shape of the S&P 500 distribution, in particular at the smallest time horizon (1 min). A linear version of ARCH processes, denoted here as LARCH models, still preserving the anomalies observed, permits to fit the 1 min S&P 500 distribution more accurately. Received 12 October 2000 and Received in final form 5 February 2001     

Rényi’s information transfer between financial time series

In this paper, we quantify the statistical coherence between financial time series by means of the Rényi entropy. With the help of Campbell’s coding theorem, we show that the Rényi entropy selectively emphasizes only certain sectors of the underlying empirical distribution while strongly suppressing others. This accentuation is controlled with Rényi’s parameter q$q$. To tackle the issue of the information flow between time series, we formulate the concept of Rényi’s transfer entropy as a measure of information that is transferred only between certain parts of underlying distributions. This is particularly pertinent in financial time series, where the knowledge of marginal events such as spikes or sudden jumps is of a crucial importance. We apply the Rényian information flow to stock market time series from 11 world stock indices as sampled at a daily rate in the time period 02.01.1990–31.12.2009. Corresponding heat maps and net information flows are represented graphically. A detailed discussion of the transfer entropy between the DAX and S&P500 indices based on minute tick data gathered in the period 02.04.2008–11.09.2009 is also provided. Our analysis shows that the bivariate information flow between world markets is strongly asymmetric with a distinct information surplus flowing from the Asia–Pacific region to both European and US markets. An important yet less dramatic excess of information also flows from Europe to the US. This is particularly clearly seen from a careful analysis of Rényi information flow between the DAX and S&P500 indices.     

The emergence and policy implications of converging new technologies integrated from the nanoscale

Science based on the unified concepts on matter at the nanoscale provides a new foundation for knowledge creation, innovation, and technology integration. Convergent new technologies refers to the synergistic combination of nanotechnology, biotechnology, information technology and cognitive sciences (NBIC), each of which is currently progressing at a rapid rate, experiencing qualitative advancements, and interacting with the more established fields such as mathematics and environmental technologies (Roco & Bainbridge, 2002). It is expected that converging technologies will bring about tremendous improvements in transforming tools, new products and services, enable human personal abilities and social achievements, and reshape societal relationships.After a brief overview of the general implications of converging new technologies, this paper focuses on its effects on R&D policies and business models as part of changing societal relationships. These R&D policies will have implications on investments in research and industry, with the main goal of taking advantage of the transformative development of NBIC. Introduction of converging technologies must be done with respect of immediate concerns (privacy, toxicity of new materials, etc.) and longer-term concerns including human integrity, dignity and welfare. The efficient introduction and development of converging new technologies will require new organizations and business models, as well as solutions for preparing the economy, such as multifunctional research facilities, integrative technology platforms, and global risk governance.     

基于金刚石体系中氮-空位色心的固态量子传感

在室温下,金刚石中的氮-空位(NV)色心具有荧光强度稳定、电子自旋相干时间长以及与生俱来的原子尺寸的特点,是优良的纳米量子传感器.在成像领域中,将各种超分辨成像显微技术应用于NV色心体系,发展出多种高空间纳米分辨率的成像方法.此外,NV色心作为固态量子比特可以通过光学方法对其进行初始化和读取.NV色心电子自旋量子态还可以与电磁场、应力等进行相干耦合.基于这些耦合,科研人员在实验上实现了对相关物理量纳米级空间分辨率的高灵敏表征.目前这些量子传感技术可以应用在新材料、单个蛋白质核自旋、活体神经元等方面的测量中.本综述主要介绍金刚石中NV色心纳米量子传感器件的工作原理、实验实现和优化以及在相关领域的应用.     

The emergence and policy implications of converging new technologies integrated from the nanoscale

Science based on the unified concepts on matter at the nanoscale provides a new foundation for knowledge creation, innovation, and technology integration. Convergent new technologies refers to the synergistic combination of nanotechnology, biotechnology, information technology and cognitive sciences (NBIC), each of which is currently progressing at a rapid rate, experiencing qualitative advancements, and interacting with the more established fields such as mathematics and environmental technologies (Roco & Bainbridge, 2002). It is expected that converging technologies will bring about tremendous improvements in transforming tools, new products and services, enable human personal abilities and social achievements, and reshape societal relationships.After a brief overview of the general implications of converging new technologies, this paper focuses on its effects on R&D policies and business models as part of changing societal relationships. These R&D policies will have implications on investments in research and industry, with the main goal of taking advantage of the transformative development of NBIC. Introduction of converging technologies must be done with respect of immediate concerns (privacy, toxicity of new materials, etc.) and longer-term concerns including human integrity, dignity and welfare. The efficient introduction and development of converging new technologies will require new organizations and business models, as well as solutions for preparing the economy, such as multifunctional research facilities, integrative technology platforms, and global risk governance.(*) This is an extension of the presentation made at the Converging Technologies Conference, February 26, 2004, New York.This revised version was published online in August 2005 with a corrected issue number.     

Turning the Ship: The Transformation of DESY, 1993–2009

This article chronicles the most recent history of the Deutsches Elektronen-Synchrotron (DESY) located in Hamburg, Germany, with particular emphasis on how this national laboratory founded for accelerator-based particle physics shifted its research program toward multi-disciplinary photon science. Synchrotron radiation became DESY’s central experimental research program through a series of changes in its organizational, scientific, and infrastructural setup and the science policy context. Furthermore, the turn toward photon science is part of a broader transformation in the late twentieth century in which nuclear and particle physics, once the dominating fields in national and international science budgets, gave way to increasing investment in the materials sciences and life sciences. Synchrotron radiation research took a lead position on the experimental side of these growing fields and became a new form of big science, generously funded by governments and with user communities expanding across both academia and industry.     

A multifractal detrended fluctuation analysis of trading behavior of individual and institutional traders in Tehran stock market

Employing the multifractal detrended fluctuation analysis (MF-DFA), the multifractal properties of trading behavior of individual and institutional traders in the Tehran Stock Exchange (TSE) are numerically investigated. Using daily trading volume time series of these two categories of traders, the scaling exponents, generalized Hurst exponents, generalized fractal dimensions and singularity spectrum are derived. Furthermore, two main sources of multifractality, i.e. temporal correlations and fat-tailed probability distributions are also examined. We also compare our results with data of S&P 500. Results of this paper suggest that for both classes of investors in TSE, multifractality is mainly due to long-range correlation while for S&P 500, the fat-tailed probability distribution is the main source of multifractality.     

A new test track for automotive squeak and rattle (S&R) detection

The perceived quality of interior sounds is of increasing importance in the automotive industry since it is important for the customer perception of vehicle quality. Squeak and rattle (S&R) is a group of intermittent interior noise that reduce the sense of quality dramatically. To identify and solve S&R problems the car manufacturers do both simulations and tests in laboratory of complete vehicles as well as subsystems. As a complement, to laboratory testing and for verification, complete vehicle tests at proving grounds are done. In order to systematically test for vehicle S&R noise at proving grounds there is a need for a new type of test track that in a controlled and repetitive ways excite vehicles at different frequencies. This paper describes such a new test track, called the Frequency Sweep Test Track (FSTT). The test track is based on sweep excitation and improves the precision when detecting and solving S&R issues. Different design considerations such as sweep waveform, frequency range and sweep rate are discussed. The track design is evaluated using a quarter-car model including a tandem ellipsoid tyre model. In a case study a FSTT was built and the excitation of a car was measured. The track excited the expected frequency range and the track operated well in detecting a rattle in the dashboard of an executive production car and at which frequency the rattle occurred.     

The Grand Challenges of Nanotechnology

Amazing breakthroughs and advances continue to be made in nanoscale science and engineering and the rapidly emerging field of nanotechnology, including near-commercial applications in biomedicine, computing and environmental protection. The National Nanotechnology Initiative, begun by the Clinton Administration has placed nanoscale research on a new funding trajectory. But, many grand challenges must be overcome, technical ones as well as those related to funding, science and technology workforce, and the need for stronger collaboration across discipline, organizations, government agencies and with other countries.     

The long view of nanotechnology development: the National Nanotechnology Initiative at 10 years

A global scientific and societal endeavor was set in motion by the nanotechnology vision formulated in 1999 that inspired the National Nanotechnology Initiative (NNI) and other national and international R&D programs. Establishing foundational knowledge at the nanoscale has been the main focus of the nanotechnology research community in the first decade. As of 2009, this new knowledge underpinned about a quarter of a trillion dollars worldwide market, of which about $91 billion was in US products that incorporate nanoscale components. Nanotechnology is already evolving toward becoming a general-purpose technology by 2020, encompassing four generations of products with increasing structural and dynamic complexity: (1) passive nanostructures, (2) active nanostructures, (3) nanosystems, and (4) molecular nanosystems. By 2020, the increasing integration of nanoscale science and engineering knowledge and of nanosystems promises mass applications of nanotechnology in industry, medicine, and computing, and in better comprehension and conservation of nature. Nanotechnology’s rapid development worldwide is a testimony to the transformative power of identifying a concept or trend and laying out a vision at the synergistic confluence of diverse scientific research areas. This chapter provides a brief perspective on the development of the NNI since 2000 in the international context, the main outcomes of the R&D programs after 10 years, the governance aspects specific to this emerging field, lessons learned, and most importantly, how the nanotechnology community should prepare for the future.     

Comparison between volatility return intervals of the S&;P 500 index and two common models

We analyze the S&P 500 index data for the 13-year period, from January 1, 1984 to December 31, 1996, with one data point every 10 min. For this database, we study the distribution and clustering of volatility return intervals, which are defined as the time intervals between successive volatilities above a certain threshold q. We find that the long memory in the volatility leads to a clustering of above-median as well as below-median return intervals. In addition, it turns out that the short return intervals form larger clusters compared to the long return intervals. When comparing the empirical results to the ARMA-FIGARCH and fBm models for volatility, we find that the fBm model predicts scaling better than the ARMA-FIGARCH model, which is consistent with the argument that both ARMA-FIGARCH and fBm capture the long-term dependence in return intervals to a certain extent, but only fBm accounts for the scaling. We perform the Student's t-test to compare the empirical data with the shuffled records, ARMA-FIGARCH and fBm. We analyze separately the clusters of above-median return intervals and the clusters of below-median return intervals for different thresholds q. We find that the empirical data are statistically different from the shuffled data for all thresholds q. Our results also suggest that the ARMA-FIGARCH model is statistically different from the S&P 500 for intermediate q for both above-median and below-median clusters, while fBm is statistically different from S&P 500 for small and large q for above-median clusters and for small q for below-median clusters. Neither model can fully explain the entire regime of q studied.     

Electronic and optoelectronic nano-devices based on carbon nanotubes

The discovery and understanding of nanoscale phenomena and the assembly of nanostructures into different devices are among the most promising fields of material science research. In this scenario, carbon nanostructures have a special role since, in having only one chemical element, they allow physical properties to be calculated with high precision for comparison with experiment. Carbon nanostructures, and carbon nanotubes (CNTs) in particular, have such remarkable electronic and structural properties that they are used as active building blocks for a large variety of nanoscale devices. We review here the latest advances in research involving carbon nanotubes as active components in electronic and optoelectronic nano-devices. Opportunities for future research are also identified.     

Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition

Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity “dendrophilia”, and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology.     

真空紫外成像光谱仪在空间科学中的应用与展望

真空紫外成像光谱仪是空间科学研究中重要的数据获取工具,通过对不同天体目标真空紫外辐射的观测,可以反演出天体中主要物质的含量和变化规律,从而为空间天气、宇宙起源等许多前沿科学提供研究资料.分析了真空紫外成像光谱仪在空间科学研究中的优势,介绍了国外发展概况,列举了日地空间环境观测、地外行星体观测和宇宙空间观测三个应用领域中...     

Dynamics of Self-Organized and Self-Assembled Structures,by Rashmi C. Desai and Raymond Kapral

The arrival of the first hard X-ray free electron laser facilities promises new advances in structural dynamics and nanoscale imaging that will have impact across the sciences. This introductory review is intended to cover the basic physics behind this potential and illustrate the current state-of-the-art by discussing a number of recent findings from the LCLS facility at the Stanford Linear Accelerator Centre (SLAC). We concentrate on the new science using these light sources rather than the new light source technology itself, although a brief introduction to the operation of LCLS is given. Emphasis is placed upon the new regime of high intensity X-ray matter interaction physics with ultrashort X-ray pulses. We discuss how the unique combination of X-ray parameters will open new opportunities for time resolved structural studies and how the high brightness enables a new class of coherent diffraction X-ray imaging. The potential importance of this new imaging method in the study of nanostructures and biological systems at the sub-cellular and molecular level will be outlined.