A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

Abdominal tuberculosis: a presumptive diagnosis

BACKGROUND: Abdominal tuberculosis (TB) is common. But the diagnosis of abdominal TB is fraught with difficulties as it is often not possible to get a microbiological confirmation of the infection. We therefore undertook this study to highlight those pertinent clinical and laboratory features which would enable one to make a provisional diagnosis of abdominal TB early, to pave way for a trial of anti-tuberculosis chemotherapy. METHOD: This is a retrospective study of 12 patients treated for abdominal TB in our department over a period of 2 years. FINDINGS: Seven of the patients suffered from chronic diarrhoea for periods ranging from 4 weeks to 12 months. Four patients had progressive abdominal distension (ascites). The last patient came in with multiple abdominal swellings. Seven patients had clinical and biochemical features of malabsorption. Another 9 patients also had persistent pyrexia. The ascitic fluid was exudative in the 4 patients mentioned earlier. A definitive diagnosis could not be established in any of these patients. The diagnosis of abdominal TB was thus one of exclusion in these patients who showed prompt response to anti-tuberculosis therapy. CONCLUSION: Our study justifies a trial of anti-TB chemotherapy in TB endemic areas in the following clinical situations: (a) patients with chronic diarrhoea of unknown aetiology and (b) patients with exudative ascitic fluid, after all other possible causes, have been excluded. A prompt response to anti-TB therapy should be accepted as sufficient ground for the diagnosis of abdominal TB even when histopathological or microbiological confirmation of the disease is not possible. Our study reflects the experience of other workers from Third World countries.     

Effect of Rare-Earth Additives on Electromechanical Properties of Modified Lead Titanate Ceramics

The influence of rare-earth additives, such as La, Nd, Sm, and Gd, and poling conditions on the electromechanical properties of (Pb_{1−3 x /2}Ln _x )(Ti_0.98Mn_0.02)O₃ compositions, x = 0.04–0.12, were investigated. The type and amount of additive were found to affect the lattice anisotropy, dielectric constant, and electromechanical properties. A large electromechanical anisotropy ( k _t / k _p ) could be obtained in 10 mol% Sm-modified and 8 mol% Gd-modified lead titanate ceramics, and seemed to correlate to a low Poisson's ratio.     

Fluorescent dye labeled bacteriophages--a new tracer for the investigation of viral transport in porous media: 1. Introduction and characterization

A new method for the study of pathogen transport in porous media is presented. The method is based on conjugation of fluorescent dyes to target bacteriophages and application of the modified bacteriophages for tracer studies. We demonstrate that the relevant transport determining properties of Rhodamine and several fluorescein-labeled phages are practically identical to those of the native bacteriophages. The advantages of the proposed method relative to direct enumeration of bacteriophages by plaque forming unit method, turbidity, fluorescent microspheres, and other alternative tracers are discussed. Notable advantages include simple quantitation by optical methods, unbiased signals even when virus aggregates are formed, and the ability to decouple inactivation kinetics from transport phenomena. Additionally, the signal reflects the removal and transport of the studied microorganism and not a surrogate.     

Development of an interferometer for measurement of the diffusion coefficient of miscible liquids

A common-path interferometer (CPI) system was developed to measure the diffusivity of transparent liquid pairs by real-time visualization of the concentration gradient profile. The CPI is an optical technique that can be used to measure changes in the gradient of the refractive index of transparent materials. The CPI is a shearing interferometer that shares the same optical path from a laser light source to the final imaging plane. Molecular diffusivity of liquids can be determined by use of physical relations between changes in the optical path length and the liquid phase properties. The data obtained by this interferometer are compared with similar results from other techniques. This demonstrates that the instrument is reliable for measurement of the diffusivity of miscible liquids and allows the system to be compact and robust. It can also be useful for studies in interface dynamics as well as other applications in a low-gravity environment.     

A joint compression-discrimination neural transformation applied to target detection.

Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.     

Sol-gel derived PbTio3-polymer piezoelectric composites

Amorphous lead titanate powder was prepared using a sol-gel process. The powder was fired to temperatures ranging from 500°C to 1200°C and soaked for various lengths of time. X-ray diffraction confirmed that the fired powders were tetragonal PbTiO₃. Surface area measurements indicated that the specific surface area of the powder decreased with increasing heat treatment. The powders were mixed with Eccogel polymer to make composites in which the ceramic phase constituted 60 percent by volume. Piezoelectric coefficients were found to be greatest for those composites containing powder fired to 1100°C and soaked for 1 hour. The d_hg_h value obtained for this heat treatment was 540×10^-15m²N^-1.     

Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms

Estevez, L., Kehtarnavaz, N., and Wendt, R. III, Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms,Digital Signal Processing6(1996), 224–232.This paper presents a clustering algorithm, called interactive selective and adaptive clustering (Isaac), to assist radiologists in looking for small clusters of microcalcifications in mammograms. Isaac is developed to identify suspicious microcalcification regions which are missed by other classification techniques due to false positive samples in the feature space. It comprises two parts: (i) selective clustering and (ii) interactive adaptation. The first part reduces the number of false positives by identifying the microcalcification subspace or domains in the feature space. The second part allows the radiologist to improve results by interactively identifying additional false positive or true negative samples. Clinical evaluations of mammograms indicate the potential of using this algorithm as an effective tool to bring microcalcification areas to the attention of the radiologist during a routine reading session of mammograms.