METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.
RESULTS AND CONCLUSION: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.
METHODOLOGY: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals.
RESULTS: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased.
DISCUSSION: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.
METHODS: Fifty-five cases of CCTA were collected retrospectively and all images including reformatted axial images at systolic and diastolic phases as well as images with curved multi planar reformation (cMPR) were obtained. Quantitative image quality including signal intensity, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of right coronary artery (RCA), left anterior descending artery (LAD), left circumflex artery (LCx) and left main artery (LM) were quantified using Analyze 12.0 software.
RESULTS: Six hundred and fifty-seven coronary arteries were evaluated. There were no significant differences in any quantitative image quality parameters between genders. 100 kilovoltage peak (kVp) scanning protocol produced images with significantly higher signal intensity compared to 120 kVp scanning protocol (P<0.001) in all coronary arteries in all types of images. Higher SNR was also observed in 100 kVp scan protocol in all coronary arteries except in LCx where 120 kVp showed better SNR than 100 kVp.
CONCLUSIONS: There were no significant differences in image quality of CCTA between genders and different tube voltages. Lower tube voltage (100 kVp) scanning protocol is recommended in clinical practice to reduce the radiation dose to patient.
METHODS: Speech-and-noise signals were presented to, and recorded from, six hearing aids mounted on a head and torso simulator. Test stimuli were nonsense words mixed with pink, cafeteria, or speech-modulated noise at 0 dB SNR. Fricatives /s, z/ were extracted from the recordings for analysis.
RESULTS: Analysis of the noise confirmed that MBNR in all hearing aids was activated for the recordings. More than 1.0 dB of acoustic change occurred to /s, z/ when MBNR was turned on in four out of the six hearing aids in the pink and cafeteria noise conditions. The acoustics of /s, z/ by female talkers were affected more than male talkers. Significant relationships between amount of noise reduction and acoustic change of /s, z/ were found. Amount of noise reduction accounts for 42.8% and 16.8% of the variability in acoustic change for /s/ and /z/ respectively.
CONCLUSION: Some clinically-available implementations of MBNR have measurable effects on the acoustics of fricatives. Possible implications for speech perception are discussed.
METHOD: Articles published between 2000 and 2016 were searched in PUBMED and EBSCO databases.
RESULTS: Thirty-two articles were included in the final review. Most studies with adult participants showed that SMNR has no effect on speech intelligibility. Positive results were reported for acceptance of background noise, preference, and listening effort. Studies of school-aged children were consistent with the findings of adult studies. No study with infants or young children of under 5 years old was found. Recent studies on noise-reduction systems not yet available in wearable hearing aids have documented benefits of noise reduction on memory for speech processing for older adults.
CONCLUSIONS: This evidence supports the use of SMNR for adults and school-aged children when the aim is to improve listening comfort or reduce listening effort. Future research should test SMNR with infants and children who are younger than 5 years of age. Further development, testing, and clinical trials should be carried out on algorithms not yet available in wearable hearing aids. Testing higher cognitive level for speech processing and learning of novel sounds or words could show benefits of advanced signal processing features. These approaches should be expanded to other populations such as children and younger adults. Implications for rehabilitation The review provides a quick reference for students and clinicians regarding the efficacy and effectiveness of SMNR in wearable hearing aids. This information is useful during counseling session to build a realistic expectation among hearing aid users. Most studies in the adult population suggest that SMNR may provide some benefits to adult listeners in terms of listening comfort, acceptance of background noise, and release of cognitive load in a complex listening condition. However, it does not improve speech intelligibility. Studies that examined SMNR in the paediatric population suggest that SMNR may benefit older school-aged children, aged between 10 and 12 years old. The evidence supports the use of SMNR for adults and school-aged children when the aim is to improve listening comfort or reduce listening effort.