Objective: This study aims to quantitatively evaluate the effect of acoustic radiation force impulse imaging (ARFI) in neonatal brain development. Materials and Methods: The authors observed 41 neonatal brain different tissues by using traditional two-dimensional gray scale ultrasound and color Doppler flow imaging and frequency spectrum ultrasound. After that they used ARFI to quantitative evaluate white and gray matter of neonatal different tissues in brain with different gestational ages. They also used new technical index: virtual touch tissue quantification (VTQ) to evaluate elastic changes of brain tissues. Results: Different tissues in brain had different elastic numerical values. Neonatal with different gestational ages had different elastic numerical values. The more gestational ages were, the more the elastic numerical values. Elastic numerical values between preterm and full-term infants were different. Elastic numerical values of full-term infants were higher than preterm infants. Conclusion: ARFI provides a new quantitative index to evaluate neonatal brain development. It increases objectivity and reliability of clinical analysis. Ultrasound was a noninvasive examination method, safe, simple, and convenient, and it has more usefulness of ARFI in quantitative evaluation of neonatal brain development.
This paper introduces a novel hearing aid named artificial ultrasonic bone conduction hearing device, which is different from the traditional hearing aid in two sides: 1) sound conduction manner, 2) human perceptive principles. We focus on discussing the structure of the hearing aid and the research of frequency transposition algorithm. In addition, we design algorithm experimental platform and some sample electromechanical transducers.
Based on software radio theory, this paper focuses on researching a direct conversion structure for the radio frequency (RF) receiver front-end and tries to apply this flexible receiver front-end in MRI receiving system. In particular, we look at the conventional architecture of RF receiver front-ends of MRI; present architecture of a direct conversion for RF receiver front-end of MRI; outline the key aspects of designing such multi-channel and multi-mode front-end systems. In addition, based on this architecture, a practical RF front-end receiving system of MRI is given. Two parts are included in this system; one is the conventional receiver front-end the other is the direct conversion design for RF receiver front-end of MRI.
Six Chinese vowels /a/, /o/, /e/, /i/, /u/, and / / are recognized based on the one-channel detected facial myoelectric signal (MES). Zygomaticus major and anterior belly of the digastric are carefully selected as the electrodes sites of MES detected. Over-sampling technology and four-layer wavelet decomposition are used to reduce noise in MES records. Digital down converter down converts the original sampling rate to Nyquist frequency. By anterior methods, clean MES is gotten with no signal distortion. For MES is not voice signal, MFCC is not selected as the feature set. According to MES characteristic, ten-order AR model is set up. The coefficients of AR model, cepstral coefficients, and amplitude of MES are chosen to form the original feature set for recognition. Principal components analysis (PCA) reduces the dimension of original feature set before the proposed BP networks. Combining two BP network classifier, an efficient classifier is proposed. The result of experiment shows that Chinese vowel / /, /and /i/ have good classification rate (more than 90%) based on one-channel facial myoelectric signal. Index Terms -unvoice speech, feature extraction, BP network, endpoint detection, wavelet decomposition.
In order to make the artificial forearm controller easier be trained and have higher robust, an adaptive controller for myoelectric signal (MES) is proposed. The control signal, MES, is derived from natural contraction patterns, which can be produced reliably with no subject training. To find features of MES, twenty-five filters with different center frequency and same bandwidth are designed and the feature curves of MES are shown in this paper. Based on the wavelet transform (WT) and the proposed feature curves, an ensemble of gate crossings based representations of MES is proposed. The gate for counting the gate crossings can change along with contractions, and this makes feature extraction adaptive to dissimilar contraction levels. Two-layer perceptron neural networks are used to classify a single site MES based on two features, specifically the gate crossings between 0 and 31.25Hz and the gate crossings between 31.25 and 62.5Hz. Based on the proposed controller scheme, the simulation result displays high accuracy rate.
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