The fundamental vibration-rotation bands of 13C16O and 12C18O

Rotation lines in the fundamental vibration bands of 13C16O and 12C180 have been measured, using very high resolving power and more accurate wavelength calibrations than previously. The molecular rotational and vibrational constants have been deduced and compared in relation to the mass differences between these molecules and the main species 12C160.

The infrared spectrum of the ν2 and ν3 bands of H16OF, H18OF, and D16OF

Fourier transform IR spectra in the ν2 and ν3 regions between 800 and 1500 cm−1 have been measured of H16OF with a resolution of 0.007 cm−1 and of H18OF and DOF with a resolution of 0.040 cm−1. Ground state constants have been improved for H16OF and have been obtained for the first time for H18OF. Parameters of the v2 = 1 and v3 = 1 excited states have been determined from rovibrational analyses of ca. 1000 ν2/ν3 lines which were fitted with σ 0.36, 4.5, and 7.6 × 10−3 cm−1 for H16OF, H18OF, and D16OF, respectively. Band centers of ν2/ν3 are 1353.40466(5)/889.07974(6), 1350.3976(5)/862.2967(7), and 1002.0083(9)/891.0014(15) cm−1, respectively, for the three isotopic species. While ν2 and ν3 are sufficiently separated in HOF to be treated independently, a Coriolis resonance is evident in DOF, the interaction constant ξ23c = 0.19073(16) cm−1 being in agreement with the prediction from the harmonic force field.

Frequency of word-use predicts rates of lexical evolution throughout Indo-European history

Greek speakers say "ovpa", Germans "schwanz'' and the French "queue'' to describe what English speakers call a 'tail', but all of these languages use a related form of 'two' to describe the number after one. Among more than 100 Indo-European languages and dialects, the words for some meanings (such as 'tail') evolve rapidly, being expressed across languages by dozens of unrelated words, while others evolve much more slowly-such as the number 'two', for which all Indo-European language speakers use the same related word-form(1). No general linguistic mechanism has been advanced to explain this striking variation in rates of lexical replacement among meanings. Here we use four large and divergent language corpora (English(2), Spanish(3), Russian(4) and Greek(5)) and a comparative database of 200 fundamental vocabulary meanings in 87 Indo-European languages(6) to show that the frequency with which these words are used in modern language predicts their rate of replacement over thousands of years of Indo-European language evolution. Across all 200 meanings, frequently used words evolve at slower rates and infrequently used words evolve more rapidly. This relationship holds separately and identically across parts of speech for each of the four language corpora, and accounts for approximately 50% of the variation in historical rates of lexical replacement. We propose that the frequency with which specific words are used in everyday language exerts a general and law-like influence on their rates of evolution. Our findings are consistent with social models of word change that emphasize the role of selection, and suggest that owing to the ways that humans use language, some words will evolve slowly and others rapidly across all languages.

The distinct contributions of age of acquisition and word frequency in auditory word perception

We report two studies of the distinct effects that a word's age of acquisition (AoA) and frequency have on the mental lexicon. In the first study, a purely statistical analysis, we show that AoA and frequency are related in different ways to the phonological form and imageability of different words. In the second study, three groups of participants (34 seven-year-olds, 30 ten-year-olds, and 17 adults) took part in an auditory lexical decision task, with stimuli varying in AoA, frequency, length, neighbourhood density, and imageability. The principal result is that the influence of these different variables changes as a function of AoA: Neighbourhood density effects are apparent for early and late AoA words, but not for intermediate AoA, whereas imageability effects are apparent for intermediate AoA words but not for early or late AoA. These results are discussed from the perspective that AoA affects a word's representation, but frequency affects processing biases.

An analysis of real time implementation of Fourier transform-based frequency recognition algorithms

Frequency recognition is an important task in many engineering fields such as audio signal processing and telecommunications engineering, for example in applications like Dual-Tone Multi-Frequency (DTMF) detection or the recognition of the carrier frequency of a Global Positioning, System (GPS) signal. This paper will present results of investigations on several common Fourier Transform-based frequency recognition algorithms implemented in real time on a Texas Instruments (TI) TMS320C6713 Digital Signal Processor (DSP) core. In addition, suitable metrics are going to be evaluated in order to ascertain which of these selected algorithms is appropriate for audio signal processing(1).

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

Radio frequency energy harvesting in wireless sensor networks

As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks (WSNs) has been paid much attention to. This article presents a new approach to making use of electromagnetic energy from useless radio frequency (RF) signals transmitted in WSNs, with a quantitative analysis showing its feasibility. A mechanism to harvest the energy either passively or actively is proposed.

A low clock frequency FFT core implementation for multiband full-rate ultra-wideband (UWB) receivers

This paper discusses the design, implementation and synthesis of an FFT module that has been specifically optimized for use in the OFDM based Multiband UWB system, although the work is generally applicable to many other OFDM based receiver systems. Previous work has detailed the requirements for the receiver FFT module within the Multiband UWB ODFM based system and this paper draws on those requirements coupled with modern digital architecture principles and low power design criteria to converge on our optimized solution particularly aimed at a low-clock rate implementation. The FFT design obtained in this paper is also applicable for implementation of the transmitter IFFT module therefore only needing one FFT module in the device for half-duplex operation. The results from this paper enable the baseband designers of the 200Mbit/sec variant of Multiband UWB systems (and indeed other OFDM based receivers) using System-on-Chip (SoC), FPGA and ASIC technology to create cost effective and low power consumer electronics product solutions biased toward the very competitive market.

Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.