Modelling the propagation of terahertz radiation through a tissue simulating phantom

Terahertz (THz) frequency radiation, 0.1 THz to 20 THz, is being investigated for biomedical imaging applications following the introduction of pulsed THz sources that produce picosecond pulses and function at room temperature. Owing to the broadband nature of the radiation, spectral and temporal information is available from radiation that has interacted with a sample; this information is exploited in the development of biomedical imaging tools and sensors. In this work, models to aid interpretation of broadband THz spectra were developed and evaluated. THz radiation lies on the boundary between regions best considered using a deterministic electromagnetic approach and those better analysed using a stochastic approach incorporating quantum mechanical effects, so two computational models to simulate the propagation of THz radiation in an absorbing medium were compared. The first was a thin film analysis and the second a stochastic Monte Carlo model. The Cole–Cole model was used to predict the variation with frequency of the physical properties of the sample and scattering was neglected. The two models were compared with measurements from a highly absorbing water-based phantom. The Monte Carlo model gave a prediction closer to experiment over 0.1 to 3 THz. Knowledge of the frequency-dependent physical properties, including the scattering characteristics, of the absorbing media is necessary. The thin film model is computationally simple to implement but is restricted by the geometry of the sample it can describe. The Monte Carlo framework, despite being initially more complex, provides greater flexibility to investigate more complicated sample geometries.

A robust hybrid feedback active noise cancellation headset

This paper investigates the robustness of a hybrid analog/digital feedback active noise cancellation (ANC) headset system. The digital ANC systems with the filtered-x least-mean-square (FXLMS) algorithm require accurate estimation of the secondary path for the stability and convergence of the algorithm. This demands a great challenge for the ANC headset design because the secondary path may fluctuate dramatically such as when the user adjusts the position of the ear-cup. In this paper, we analytically show that adding an analog feedback loop into the digital ANC systems can effectively reduce the plant fluctuation, thus achieving a more robust system. The method for designing the analog controller is highlighted. A practical hybrid analog/digital feedback ANC headset has been built and used to conduct experiments, and the experimental results show that the hybrid headset system is more robust under large plant fluctuation, and has achieved satisfactory noise cancellation for both narrowband and broadband noises.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

Association between dietary phyto-oestrogens and bone density in men and postmenopausal women

Phyto-oestrogens have been associated with a decreased risk for osteoporosis, but results from intervention and observational studies in Western countries have been inconsistent. In the present study, we investigated the association between habitual phyto-oestrogen intake and broadband ultrasound attenuation (BUA) of the calcanaeum as a marker of bone density. We collected 7 d records of diet, medical history and demographic and anthropometric data from participants (aged 45–75 years) in the European Prospective Investigation into Cancer-Norfolk study. Phyto-oestrogen (biochanin A, daidzein, formononetin; genistein, glycitein; matairesinol; secoisolariciresinol; enterolactone; equol) intake was determined using a newly developed food composition database. Bone density was assessed using BUA of the calcanaeum. Associations between bone density and phyto-oestrogen intake were investigated in 2580 postmenopausal women who were not on hormone replacement therapy and 4973 men. Median intake of total phyto-oestrogens was 876 (interquartile range 412) μg/d in postmenopausal women and 1212 (interquartile range 604) μg/d in men. The non-soya isoflavones formononetin and biochanin A were marginally significant or significantly associated with BUA in postmenopausal women (β = 1·2; P < 0·1) and men (β = 1·2; P < 0·05), respectively; enterolignans and equol were positively associated with bone density in postmenopausal women, but this association became non-significant when dietary Ca was added to the model. In the lowest quintile of Ca intake, soya isoflavones were positively associated with bone density in postmenopausal women (β = 1·4; P < 0·1). The present results therefore suggest that non-soya isoflavones are associated with bone density independent of Ca, whereas the association with soya or soya isoflavones is affected by dietary Ca.

A 3D cloud-construction algorithm for the EarthCARE satellite mission

This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active–passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed. The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of theatmosphere(TOA)for (21 km)2 domains constructed from MODIS, CloudSat andCloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth’s Radiant Energy System (CERES) radiances: differences betweenmodelled and measured reflected shortwave fluxes are within±10Wm−2 for∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation∼65% are within ±10Wm−2.

Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memory

Stratospheric water vapor and climate: sensitivity to the representation in radiation codes

There has been considerable interest in the climate impact of trends in stratospheric water vapor (SWV). However, the representation of the radiative properties of water vapor under stratospheric conditions remains poorly constrained across different radiation codes. This study examines the sensitivity of a detailed line-by-line (LBL) code, a Malkmus narrow-band model and two broadband GCM radiation codes to a uniform perturbation in SWV in the longwave spectral region. The choice of sampling rate in wave number space (Δν) in the LBL code is shown to be important for calculations of the instantaneous change in heating rate (ΔQ) and the instantaneous longwave radiative forcing (ΔFtrop). ΔQ varies by up to 50% for values of Δν spanning 5 orders of magnitude, and ΔFtrop varies by up to 10%. In the three less detailed codes, ΔQ differs by up to 45% at 100 hPa and 50% at 1 hPa compared to a LBL calculation. This causes differences of up to 70% in the equilibrium fixed dynamical heating temperature change due to the SWV perturbation. The stratosphere-adjusted radiative forcing differs by up to 96% across the less detailed codes. The results highlight an important source of uncertainty in quantifying and modeling the links between SWV trends and climate.

Absolute high spectral resolution measurements of surface solar radiation for detection of water vapour continuum absorption

Solar-pointing Fourier transform infrared (FTIR) spectroscopy offers the capability to measure both the fine scale and broadband spectral structure of atmospheric transmission simultaneously across wide spectral regions. It is therefore suited to the study of both water vapour monomer and continuum absorption behaviours. However, in order to properly address this issue, it is necessary to radiatively calibrate the FTIR instrument response. A solar-pointing high-resolution FTIR spectrometer was deployed as part of the ‘Continuum Absorption by Visible and Infrared radiation and its Atmospheric Relevance’ (CAVIAR) consortium project. This paper describes the radiative calibration process using an ultra-high-temperature blackbody and the consideration of the related influence factors. The result is a radiatively calibrated measurement of the solar irradiation at the ground across the IR region from 2000 to 10 000 cm−1 with an uncertainty of between 3.3 and 5.9 per cent. This measurement is shown to be in good general agreement with a radiative-transfer model. The results from the CAVIAR field measurements are being used in ongoing studies of atmospheric absorbers, in particular the water vapour continuum.

Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

Femtosecond pulse shaping using differential evolutionary algorithm and wavelet operators

Evolutionary meta-algorithms for pulse shaping of broadband femtosecond duration laser pulses are proposed. The genetic algorithm searching the evolutionary landscape for desired pulse shapes consists of a population of waveforms (genes), each made from two concatenated vectors, specifying phases and magnitudes, respectively, over a range of frequencies. Frequency domain operators such as mutation, two-point crossover average crossover, polynomial phase mutation, creep and three-point smoothing as well as a time-domain crossover are combined to produce fitter offsprings at each iteration step. The algorithm applies roulette wheel selection; elitists and linear fitness scaling to the gene population. A differential evolution (DE) operator that provides a source of directed mutation and new wavelet operators are proposed. Using properly tuned parameters for DE, the meta-algorithm is used to solve a waveform matching problem. Tuning allows either a greedy directed search near the best known solution or a robust search across the entire parameter space.

MICPA: a client-assisted channel assignment scheme for throughput enhancement in WLANs

The emergence of high-density wireless local area network (WLAN) deployments in recent years is a testament to the insatiable demands for wireless broadband services. The increased density of WLAN deployments brings with it the potential of increased capacity, extended coverage, and exciting new applications. However, the corresponding increase in contention and interference can significantly degrade throughputs, unless new challenges in channel assignment are effectively addressed. In this paper, a client-assisted channel assignment scheme that can provide enhanced throughput is proposed. A study on the impact of interference on throughput with multiple access points (APs)is first undertaken using a novel approach that determines the possibility of parallel transmissions. A metric with a good correlation to the throughput, i.e., the number of conflict pairs, is used in the client-assisted minimum conflict pairs (MICPA) scheme. In this scheme, measurements from clients are used to assist the AP in determining the channel with the minimum number of conflict pairs to maximize its expected throughput. Simulation results show that the client-assisted MICPA scheme can provide meaningful throughput improvements over other schemes that only utilize the AP’s measurements.

Terahertz pulse imaging of stratified architectural materials for cultural heritage studies

Terahertz pulse imaging (TPI) is a novel noncontact, nondestructive technique for the examination of cultural heritage artifacts. It has the advantage of broadband spectral range, time-of-flight depth resolution, and penetration through optically opaque materials. Fiber-coupled, portable, time-domain terahertz systems have enabled this technique to move out of the laboratory and into the field. Much like the rings of a tree, stratified architectural materials give the chronology of their environmental and aesthetic history. This work concentrates on laboratory models of stratified mosaics and fresco paintings, specimens extracted from a neolithic excavation site in Catalhoyuk, Turkey, and specimens measured at the medieval Eglise de Saint Jean-Baptiste in Vif, France. Preparatory spectroscopic studies of various composite materials, including lime, gypsum and clay plasters are presented to enhance the interpretation of results and with the intent to aid future computer simulations of the TPI of stratified architectural material. The breadth of the sample range is a demonstration of the cultural demand and public interest in the life history of buildings. The results are an illustration of the potential role of TPI in providing both a chronological history of buildings and in the visualization of obscured wall paintings and mosaics.

Femto-chemometrics: the signal processing of fast pulse transients

Traditional chemometrics techniques are augmented with algorithms tailored specifically for the de-noising and analysis of femtosecond duration pulse datasets. The new algorithms provide additional insights on sample responses to broadband excitation and multi-moded propagation phenomena.

Multiple-edge noise barriers

A numerical model using boundary element techniques is discussed which enables the insertion loss for various noise barriers of complex profile and surface cover to be calculated. The model is applied to single-foundation noise barriers to which additional side-panels are added to create fork-like profiles. Spectra of insertion loss and mean insertion loss results over a range of receiver positions for a broadband source are presented. It is concluded that ‘multiple-edged’ barriers show a significant increase in acoustic-efficiency over a simple vertical screen. Adding lightweight side-panels would be a relatively inexpensive measure, and one which could be applied to barriers already in existence. This type of barrier would also allow the height of the construction to be kept to a minimum.

Analysis and compensation of power amplifier nonlinearity in MIMO transmit diversity systems

The nonlinearity of high-power amplifiers (HPAs) has a crucial effect on the performance of multiple-input-multiple-output (MIMO) systems. In this paper, we investigate the performance of MIMO orthogonal space-time block coding (OSTBC) systems in the presence of nonlinear HPAs. Specifically, we propose a constellation-based compensation method for HPA nonlinearity in the case with knowledge of the HPA parameters at the transmitter and receiver, where the constellation and decision regions of the distorted transmitted signal are derived in advance. Furthermore, in the scenario without knowledge of the HPA parameters, a sequential Monte Carlo (SMC)-based compensation method for the HPA nonlinearity is proposed, which first estimates the channel-gain matrix by means of the SMC method and then uses the SMC-based algorithm to detect the desired signal. The performance of the MIMO-OSTBC system under study is evaluated in terms of average symbol error probability (SEP), total degradation (TD) and system capacity, in uncorrelated Nakagami-m fading channels. Numerical and simulation results are provided and show the effects on performance of several system parameters, such as the parameters of the HPA model, output back-off (OBO) of nonlinear HPA, numbers of transmit and receive antennas, modulation order of quadrature amplitude modulation (QAM), and number of SMC samples. In particular, it is shown that the constellation-based compensation method can efficiently mitigate the effect of HPA nonlinearity with low complexity and that the SMC-based detection scheme is efficient to compensate for HPA nonlinearity in the case without knowledge of the HPA parameters.