Syntheses of NCN and NC3N from ionic precursors in the gas phase and an unusual rearrangement of neutral NC3N : A joint experimental and theoretical study

Neutral NCN is made in a mass spectrometer by charge stripping of NCN-., while neutral dicyanocarbene NCCCN can be formed by neutralization of either the corresponding anionic and cationic species, NCCCN-. and NCCCN+.. Theoretical calculations at the RCCSD(T)/aug-cc-pVTZ//B3LYP/6-31+G(d) level of theory indicate that the (3)Sigma (-)(g) State of NCCCN is 18 kcal mol(-1) more stable than the (1)A(1) state. While the majority of neutrals formed from either NCCCN-. or NCCCN+. correspond to NCCCN, a proportion of the neutral NCCCN molecules have sufficient excess energy to effect rearrangement, as evidenced by a loss of atomic carbon in the neutralization reionization (NR) spectra of either NCCCN+. and NCCCN-.. C-13 labeling studies indicate that loss of carbon occurs statistically following or accompanied by scrambling of all three carbon atoms. A theoretical study at the B3LYP/6-31+G(d)//B3LYP/6-31+G(d) level of theory indicates that C loss is a consequence of the rearrangement sequence NCCCN --> CNCCN --> CNCNC and that C scrambling occurs within singlet CNCCN via the intermediacy of a four-membered C-2v-symmetrical transition structure.

GMM based Bayesian approach to speech enhancement in signal transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

GMM basedbayesian approach to speech enhancement in signal/transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator.The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Joint pitch-analysis formant-synthesis framework for CS recovery of speech

A joint analysis-synthesis framework is developed for the compressive sensing (CS) recovery of speech signals. The signal is assumed to be sparse in the residual domain with the linear prediction filter used as the sparse transformation. Importantly this transform is not known apriori, since estimating the predictor filter requires the knowledge of the signal. Two prediction filters, one comb filter for pitch and another all pole formant filter are needed to induce maximum sparsity. An iterative method is proposed for the estimation of both the prediction filters and the signal itself. Formant prediction filter is used as the synthesis transform, while the pitch filter is used to model the periodicity in the residual excitation signal, in the analysis mode. Significant improvement in the LLR measure is seen over the previously reported formant filter estimation.

Fracture mechanics analysis of cracks in solder joint intermetallic compounds

The trend towards miniaturization of electronic products leads to the need for very small sized solder joints. Therefore, there is a higher reliability risk that too large a fraction of solder joints will transform into Intermetallic Compounds (IMCs) at the solder interface. In this paper, fracture mechanics study of the IMC layer for SnPb and Pb-free solder joints was carried out using finite element numerical computer modelling method. It is assumed that only one crack is present in the IMC layer. Linear Elastic Fracture Mechanics (LEFM) approach is used for parametric study of the Stress Intensity Factors (SIF, KI and KII), at the predefined crack in the IMC layer of solder butt joint tensile sample. Contrary to intuition, it is revealed that a thicker IMC layer in fact increases the reliability of solder joint for a cracked IMC. Value of KI and KII are found to decrease with the location of the crack further away from the solder interfaces while other parameters are constant. Solder thickness and strain rate were also found to have a significant influence on the SIF values. It has been found that soft solder matrix generates non-uniform plastic deformation across the solder-IMC interface near the crack tip that is responsible to obtain higher KI and KII.

Joint turbo equalization and cancelation of nonlinear distortion effects in MC-CDMA signals

In this paper, we consider low-PMEPR (Peak-to-Mean Envelope Power Ratio) MC-CDMA (Multicarrier Coded Division Multiple Access) schemes. We develop frequencydomain turbo equalizers combined with an iterative estimation and cancellation of nonlinear distortion effects. Our receivers have relatively low complexity, since they allow FFT-based (Fast Fourier Transform) implementations. The proposed turbo receivers allow significant performance improvements at low and moderate SNR (Signal-to-Noise Ratio), even when a low-PMEPR MC-CDMA transmission is intended.

Microcalcification enhancement and classification on mammograms using the wavelet transform

This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm. © 2008 IEEE.

Division of labor between M and P visual pathways: different visual pathways minimize joint entropy differently

Visual perception and action are strongly linked with parallel processing channels connecting the retina, the lateral geniculate nucleus, and the input layers of the primary visual cortex. Achromatic vision is provided by at least two of such channels formed by the M and P neurons. These cell pathways are similarly organized in primates having different lifestyles, including species that are diurnal, nocturnal, and which exhibit a variety of color vision phenotypes. We describe the M and P cell properties by 3D Gábor functions and their 3D Fourier transform. The M and P cells occupy different loci in the Gábor information diagram or Fourier Space. This separation allows the M and P pathways to transmit visual signals with distinct 6D joint entropy for space, spatial frequency, time, and temporal frequency. By combining the M and P impacts on the cortical neurons beyond V1 input layers, the cortical pathways are able to process aspects of visual stimuli with a better precision than it would be possible using the M or P pathway alone. This performance fulfils the requirements of different behavioral tasks.

FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications.

Joint Information and Jamming Beamforming for Secrecy Rate Maximization in Cognitive Radio Networks

In this paper, we consider the secure beamforming design for an underlay cognitive radio multiple-input singleoutput broadcast channel in the presence of multiple passive eavesdroppers. Our goal is to design a jamming noise (JN) transmit strategy to maximize the secrecy rate of the secondary system. By utilizing the zero-forcing method to eliminate the interference caused by JN to the secondary user, we study the joint optimization of the information and JN beamforming for secrecy rate maximization of the secondary system while satisfying all the interference power constraints at the primary users, as well as the per-antenna power constraint at the secondary transmitter. For an optimal beamforming design, the original problem is a nonconvex program, which can be reformulated as a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and propose a barrier interior-point method to solve the resulting saddle point problem based on a duality result. To find the global optimal solution, we transform the considered problem into an unconstrained optimization problem. We then employ Broyden-Fletcher-Goldfarb-Shanno (BFGS) method to solve the resulting unconstrained problem which helps reduce the complexity significantly, compared to conventional methods. Simulation results show the fast convergence of the proposed algorithm and substantial performance improvements over existing approaches.