Background: Speech perception ability depends on auditory and extra-auditory elements. The SIGNAL-to-NOISE RATIO (SNR) is an extra-auditory element that has an effect on the ability to normally follow speech and maintain a conversation. Speech in NOISE perception difficulty is a common complaint of the elderly. In this study, the importance of SNR magnitude as an extra-auditory effect on speech perception in NOISE was examined in the elderly. Methods: The speech perception in NOISE test (SPIN) was conducted on 25 elderly participants who had bilateral low– mid frequency normal hearing thresholds at three SNRs in the presence of ipsilateral white NOISE. These participants were selected by available sampling method. Cognitive screening was done using the Persian Mini Mental State Examination (MMSE) test. Results: Independent T-test, ANNOVA and Pearson Correlation Index were used for statistical analysis. There was a significant difference in word discrimination scores at silence and at three SNRs in both ears (p≤ 0. 047). Moreover, there was a significant difference in word discrimination scores for paired SNRs (0 and +5, 0 and +10, and +5 and +10 (p≤ 0. 04)). No significant correlation was found between age and word recognition scores at silence and at three SNRs in both ears (p≥ 0. 386). Conclusion: Our results revealed that decreasing the SIGNAL level and increasing the competing NOISE considerably reduced the speech perception ability in normal hearing at low– mid thresholds in the elderly. These results support the critical role of SNRs for speech perception ability in the elderly. Furthermore, our results revealed that normal hearing elderly participants required compensatory strategies to maintain normal speech perception in challenging acoustic situations.

Background: Distortion product otoacoustic emissions evaluate the function of outer hair cells in the cochlea. The present study aimed to compare distortion product otoacoustic emissions between normal hearing adult human and rat to better understand their differences. Materials and Methods: In this study, SIGNAL to NOISE RATIO of distortion product otoacoustic emissions was evaluated and the results of normal hearing persons (n=60) and rats (n=30) were evaluated in similar conditions at of 2, 3, 4, and 5 KHz. Results: The difference between the two groups was significant in most frequencies except 3 KHz. In rats, a higher SIGNAL to NOISE RATIO was observed at 4 and 5 KHz and a lower RATIO at 2 KHz. There was no significant difference among different frequencies in human samples. Conclusion: Significant differences in distortion product otoacoustic emissions in the two groups may demonstrate the different mechanisms for cochlear function in human and rat. This result may indicate the need for extra cautions in using rats as an animal model.

Purpose: Recruiting the Pharmacokinetic (PK) parameters estimated from non-invasive methods such as Dynamic Contrast Enhanced MRI (DCE-MRI) to evaluate or plan treatment procedure is widely investigated in clinical practices. Interpretation of the DCE-MRI data is highly dependent to precision and accuracy of the estimated parameters. One of the most effective factors on the DCE-MR images and on the contrast concentRATIOn profile is the SIGNAL to NOISE RATIO (SNR). This work focuses on the analytical evaluation of the NOISE effect on accuracy of the estimated PK parameters in DCE-MRI studies. Materials and Methods: Tofts model as a popular pharmacokinetic model and model selection technique was used to simulate 3470 time curves of contrast concentRATIOn. Maximum likelihood estimator as a minimum variance unbiased estimator was recruited to estimate the PK parameters. Eleven levels of SIGNAL to NOISE RATIOs (SNR= 5, 8, 10, 13, 15, 20, 25, 30, 35, 50, NOISEless) were added to the simulated CA concentRATIOn profiles. The PK parameters were estimated for 11 series data and then Mean Percentage Error (MPE) was calculated for estimated parameters. Results: The results indicate that the most sensitive parameter to the SNR of the DCE-MR images is inverse transfer constant. A SNR greater than 25 was found to ensure a reasonable error (MPE <5%) in all models parameters. Conclusion: Clinical decision based on the DCE-MRI data analysis and estimated PK parameters needs a good image quality (SNR>25), an accurate and robust estimator and correct pharmacokinetic model selection.

Background and Aim: The output SIGNAL-to-NOISE RATIO (SNR) is one of the essential factors in hearing aid benefits. There is limited evidence regarding SNR improvement by the Channel-Free (CFHA) and Multi-Channel Hearing Aid (MCHA) and the speech understanding in NOISE through them. This study aimed to investigate the extent to which output SNR was modified by CFHA and MCHA processing and the variation in aided speech recognition abilities with a change in output SNR. Methods: Thirty-six participants aged 50–65 years were included. A chosen CFHA and MCHA were used to obtain the output SNR and sentence recognition in NOISE in four different processing algorithms (linear, linear+NOISE reduction, WDRC, WDRC+NOISE reduction). Hagerman’s phase inversion technique was used to measure the attenuation of NOISE and, in turn, to obtain the output SNR of the hearing aid. Results: In all hearing aid processing algorithms among those with normal hearing and people with hearing loss, the output of CFHA revealed higher attenuation values than that of MCHA. There was a significant effect of the hearing aids and processing algorithms in both normal and individuals with hearing impairment on the mean SNR. Further, multiple linear regression analysis results showed that whether the hearing is channel-free or multichannel significantly predicted speech recognition scores, while output SNR and processing algorithms did not. Conclusion: The SIGNAL processing algorithms in CFHA had greater NOISE attenuation values, better output SNR, and speech recognition scores, showing an advantage over the modern MCHA among individuals with hearing impairment.

This paper presents a new estimator for the speech enhancement using codebook. Codebook-based speech enhancement method separates the NOISE and speech from each other and synthesizes the enhanced speech SIGNAL by optimally selecting the speech codebook indexes. This method can enhance the noisy speech with SIGNAL to NOISE RATIO of less than zero decibel. In this method it is very important to select the correct codebook indexes. Therefore, in this paper, the maximum likelihood estimator is proposed for speech and NOISE by applying auditory quality-enhancing weights. The relation of this estimator is also used as a distance function in the design of codebooks. This method is simulated for different speakers and NOISEs. The results show the proposed maximum likelihood estimator leads to better speech enhancement than the euclidean distance estimator. The proposed method is also more successful in dealing with non-stationary or stationary NOISEs and negative or positive SNRs than other methods. The cost of the superior quality enhancement in this method is the requirement to a relatively time-consuming SIGNAL processing.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective NOISE reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its RATIO to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the deNOISEd SIGNAL. Finally, by employing inverse WT the deNOISEd SIGNAL is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other NOISE reduction methods and the achieved results verify superior improvement than other methods.

In this paper, the SIGNAL-to-NOISE RATIO (SNR) of dynamic communication channels, using a radial basis function (RBF) neural network with time-delay structure, is estimated. The exactitude of the estimation is sufficient for systems which are based on link adaptation techniques. The proposed system for estimating SNR does not demand on having any prior knowledge about transmitted symbols. This feature of the proposed model results to save system resources. This saving is one of the benefits of proposed estimator as compared to transmitted data aided (TxDA) estimators. The performance index of the system, in terms of normalized mean squared error (NMSE) criterion, is achieved less than 0.001 for practical applications.

Introduction: The aim of this study was to determine the correlation of the SIGNAL-to-NOISE RATIO (SNR) value on distortion product otoacoustic emissions (DPOAE) examination with malondialdehyde (MDA) levels in a diabetic rat model. Materials and Methods: The subjects of this study were 25 rats. The samples were divided into 5 groups (days of confirmed diabetes): group 1 (control/non-treatment),group 2 (3 days),group 3 (6 days),group 4 (9 days),and group 5 (12 days). Samples that confirmed diabetes were assessed by DPOAE examination and subjected to MDA-level examination. The data were processed using SPSS and considered significant if p <0. 05. Results: The study showed a decrease in SNR values and an increase in MDA levels for the rats, which was confirmed by diabetes. The most significant result was shown by group 5, which compared to the other diabetes groups. A post hoc test showed the significant difference SNR value in each group (p<0. 05),except for groups 1 and 2, the MDA levels showed significant differences for all groups. The Pearson correlation test showed a negative correlation between SNR values and MDA levels. A significant correlation between SNR values and MDA levels was found in group 5. Conclusions: The study showed a correlation of SNR values from DPOAE examination to MDA levels in diabetes rats, indicating that there has been tissue damage (cochlea), which is characterized by a decrease in the SNR value.