A robust method for estimating respiratory flow using tracheal sounds entropy

The relationship between respiratory sounds and flow is of great interest for researchers and physicians due to its diagnostic potentials. Due to difficulties and inaccuracy of most of the flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibrating the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is novel in terms of being independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. After removing the effects of heart sounds (which distort the low-frequency components of tracheal sounds) on the calculated entropy of the tracheal sounds, the performance of the method at different frequency ranges were investigated. Also, the performance of the proposed method was tested using 6 different segment sizes for entropy calculation and the best segment sizes during inspiration and expiration were found. The method was tested on data of 10 healthy subjects at five different flow rates. The overall estimation error was found to be 8.3 +/- 2.8% and 9.6 +/- 2.8% for inspiration and expiration phases, respectively.     

A split-aperture transmit beamforming technique with phase coherence grating lobe suppression

A small element-to-element pitch (~.5λ) is conventionally required for phased array ultrasound transducers to avoid large grating lobes. This constraint can introduce many fabrication difficulties, particularly in the development of highfrequency phased arrays at operating frequencies greater than 30 MHz. In this paper, a new transmit beamforming technique along with sign coherence factor (SCF) receive beamforming is proposed to suppress grating lobes in large-pitch phased-array transducers. It is based on splitting the transmit aperture (N elements) into N/K transmit elements and receive beamforming on all N elements to reduce the temporal length of the transmit grating lobe signal. Therefore, the use of synthetic aperture beamforming, which can introduce relative phase distortions between the echoes received over many transmit events, can be avoided. After each transmit-receive event, the received signals are weighted by the calculated SCF to suppress the grating lobes. After pulsing all sub-apertures, the RF signals are added to generate one line of the image. Simulated 2-way radiation patterns for different K values show that grating lobes can be suppressed significantly at different steering angles. Grating lobes can be suppressed by approximately 20 dB with K = 2 at steering angles greater than 25° and an element pitch greater than 0.75λ. A technique for determining the optimal transmit sub-apertures has been developed.     

Lead removal from groundwater by granular mixtures of pumice,perlite and lime using permeable reactive barriers

Permeable Reactive Barrier (PRB) is an in situ technology for remediation of contaminated groundwater. This article presents results of studies on three granular mixtures were used for remediation of lead (Pb²⁺) contaminated groundwater using PRBs. The mixtures were composed of pumice, perlite and lime in different proportions. Several column experiments were conducted for evaluation of performance of the mixtures for lead removal. The experiments were carried out for over 50 days to evaluate the long‐term performance of the PRBs. It was found out that pumice‐perlite mixture with a weight proportion of 1 : 1 and pumice‐lime‐perlite mixture with a weight proportion of 2 : 2 : 1 can be used as effective reactive media for lead removal. The removal efficiency of the proposed mixtures was 99.9%. The permeability of the reactive media was relatively constant over 53 days continuous experiments and the results demonstrated that the mentioned mixtures have acceptable performance to maintain hydraulic conductivity of PRBs.     

On modeling center of foot pressure distortion through a medium

The center of foot pressure (COP) is a commonly used output measure of the postural control system as it is indicative of the systems stability. A dense piece of foam, i.e., a sponge, can be used to emulate random environmental conditions that distort the ground reaction forces received and interpreted by the cutaneous sensors in the feet; thus introducing uncertainty into the control system. In this paper, the density and size of the sponge was selected such that a subject's weight did not cause full compression. In general, the COP is measured from the bottom of the sponge. As the sponge is used to distort ground reaction forces, it is reasonable then to assume that the COP signal would also be distorted. The use of other sensory information to identify state of balance, and compute necessary balance adjustments, is therefore required. In addition to a sponge, many different types of specialized footwear and inserts are used for people with peripheral neuropathy, such as diabetics. However, it is difficult to design diabetic footwear without a better understanding of the mechanical and physiological effects that different surfaces typical of outdoor terrains, such as a sponge, which cannot be predicted without the sense of the foot, have on balance. Therefore, the goal of this study was to investigate the change of the COP signal from the top and bottom of the sponge. Portable force sensing mats from Vista Medical were used to obtain the COP from the top and bottom of the sponge. The COP measured on the bottom of the sponge is not the same as the COP measured on the top, particularly in the medial-lateral direction. Several linear and nonlinear models were used to identify the unknown plant; i.e., the sponge. Overall, the nonlinear neural network method had superior performance when compared with the linear models. Thus, the results indicate that the signals from the top and bottom of the sponge are in fact different, and furthermore, they are nonlinearly related. A nonlinear mathematical model is proposed which describes COP distortion through a medium such as a sponge. Although the values for the model parameters determined were for a particular sponge, this study suggests that a neural network plant identification model may be applied to any medium other than the sponge; the information can then be used to determine how the balance control model is affected given the sensory information received.     

PVC membrane and coated graphite potentiometric sensors based on Et4todit for selective determination of samarium(III)

Solution studies on the binding properties of 4,5,6,7-tetrathiocino[1,2-b:3,4-b']diimidazolyl-1,3,8,10-tetraethyl-2,9-dithione (Et(4)todit) toward a number of cationic species including some lanthanide ions revealed the occurrence of a selective 1:1 complexation of the ligand with Sm(3+) ion. Consequently, Et(4)todit was used as a suitable neutral ionophore for the preparation of novel polymeric membrane (PME) and coated graphite (CGE) Sm(3+)-selective electrodes. The electrodes exhibit a Nernstian behavior for Sm(3+) ions over wide concentration ranges (1.0 x 10(-5)-1.0 x 10(-1) M for PME and 1.0 x 10(-7)-1.0 x 10(-1) M for CGE) and very low limits of detection (8.0 x 10(-6) M for PME and 1.6 x 10(-8) M for CGE). The proposed potentiometric sensors manifest advantages of relatively fast response, and, most importantly, good selectivities relative to wide variety of other cations, including other lanthanide ions. The selectivity behavior of the proposed Sm(3+)-selective electrodes revealed a great improvement compared to the best previously reported electrode for samarium(III) ion. The potentiometric responses of the electrodes are independent of the pH of the test solution in the pH range 4.0-6.5. The electrodes were successfully applied to the recovery of Sm(3+) ion from tap water samples and also, as an indicator electrode, in potentiometric titration of samarium(III) ions.     

Quantitative Evaluation of Different Dosage Forms of Ampicillin

The bioavailability of five different commercial brands of ampicillin was examined. The absorption of each dosage form was compared in a crossover study of twelve healthy volunteers (6 males and 6 females). Urinary excretion rates were also employed to evaluate the absorption process. Statistical analysis of the results was carried out to evaluate the significance of differences between dosage forms and subjects. The statistical analysis indicated no significant differences between different tested brands of ampicillin, while the differences between subjects were significant. Comparison between the two different genders indicated no significant differences between the male and female subjects.     

Screening of vibroarthrographic signals via adaptive segmentationand linear prediction modeling

This paper proposes a noninvasive method to diagnose chondromalacia patella at its early stages by recording knee vibration signals (also known as vibroarthrographic or VAG signals) over the mid-patella during normal movement. An adaptive segmentation method was developed to segment the nonstationary VAG signals. The least squares modeling method was used to reduce the number of data samples to a few model parameters. Model parameters along with a few clinical parameters and a signal variability parameter were then used as discriminant features for screening VAG signals by applying logistic and discriminant algorithms. The system was trained using ten normal and eight abnormal signals. It correctly screened a separate test set of ten normal and eight abnormal signals except for one normal signal. The proposed method should find use as an alternative technique for diagnosis of knee joint pathology or as a test before arthroscopy or major knee surgery     

Engineering Microneedle Patches for Improved Penetration:Analysis,Skin Models and Factors Affecting Needle Insertion

Transdermal microneedle(MN)patches are a promising tool used to transport a wide variety of active compounds into the skin.To serve as a substitute for common h...     

Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACT

A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.