Investigation of hearing aid users'speech understanding in noise and their spectral-temporal resolution skills

Purpose:Our study aims to compare speech understanding in noise and spectral-temporal resolution skills with regard to the degree of hearing loss,age,hearing aid use experience and gender of hearing aid users.Methods:Our study included sixty-eight hearing aid users aged between 40-70 years,with bilateral mild and moderate symmetrical sensorineural hearing loss.Random gap detection test,Turkish matrix test and spectral-temporally modulated ripple test were implemented on the participants with bilateral hearing aids.The test results acquired were compared statistically according to different variables and the correlations were examined.Results:No statistically significant differences were observed for speech-in-noise recognition,spectraltemporal resolution among older and younger adults in hearing aid users(p>0.05).There wasn’t found a statistically significant difference among test outcomes as regards different hearing loss degrees(p>0.05).Higher performances were obtained in terms of temporal resolution in male participants and participants with more hearing aid use experience(p<0.05).Significant correlations were obtained between the results of speech-in-noise recognition,temporal resolution and spectral resolution tests performed with hearing aids(p<0.05).Conclusion:Our study findings emphasized the importance of regular hearing aid use and it showed that some auditory skills can be improved with hearing aids.Observation of correlations among the speechin-noise recognition,temporal resolution and spectral resolution tests have revealed that these skills should be evaluated as a whole to maximize the patient’s communication abilities.

An Algorithm for Detecting Ice Cloud at Different Altitudes by Combining Dual CrIS Full Spectrum Resolution CO2 Channels

Using infrared sensors to detect ice clouds in different atmospheric layers is still a challenge.The different scattering and absorption properties of longwave and shortwave infrared channels can be utilized to fulfill this purpose.In this study,the release of Suomi-NPP Cross-track Infrared Sounder(Cr IS)full spectrum resolution is used to select and pair channels from longwave(~15μm)and shortwave(~4.3μm)CO2 absorption bands under stricter conditions,so as to better detect ice clouds.Besides,the differences of the weighting function peaks and cloud insensitive level altitudes of the paired channels are both within 50 h Pa so that the variances due to atmospheric conditions can be minimized.The training data of clear sky are determined by Visible Infrared Imaging Radiometer Suite(VIIRS)cloud mask product and used to find the linear relationship between the paired longwave and shortwave CO2 absorption channels.From the linear relationship,the so-called cloud emission and scattering index(CESI)is derived to detect ice clouds.CESI clearly captures the center and the ice cloud features of the Super Typhoon Hato located above 415 h Pa.Moreover,the CESI distributions agree with cloud top pressure from the VIIRS in both daytime and nighttime in different atmospheric layers.

NASA’s Mission ACTIVATE: Objectives, Strategies, and Limitations

The primary goal of this report is to describe the operational concepts of NASA’s ACTIVATE mission. ACTIVATE hopes to improve the understanding of aerosol dispersion and models, provide accurate data for aerosols’ characterization and ozone profiles, and establish knowledge of the relationships between aerosols and water. ACTIVATE’s science objectives are to quantify Na-CCN-Nd relationships and reduce uncertainty in model cloud droplet activation parameterizations, improve process-level understanding and model representation of factors governing cloud micro/macro-physical properties and how they couple with cloud effects on aerosol, plus assess advanced remote sensing capabilities for retrieving aerosol and cloud properties related to aerosol-cloud interactions. ACTIVATE utilizes the fixed-wing B-200 King Air to collect data. Data collected by ACTIVATE is highly relevant for meteorologists and environmental scientists looking to understand more about aerosol-cloud formations. Finally, ACTIVATE is a 5-year mission spanning from January 2019 to December 2023 and has used, and will continue to use, instruments such as the High Spectral Resolution Lidar-2 (HSRL-2), the Research Scanning Polarimeter (RSP), and the Diode Laser Hygrometer (DLH).

Evaluation of the Minimum Size of a Window for Harmonics Signals

Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.

Vegetation Products Derived from Fengyun-3D Medium Resolution Spectral Imager-Ⅱ

The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.

Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-Ⅱ Imagery by Using a Deep Belief Network

Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention.In this study, we constructed an SM inversion model by using a deep belief network(DBN) to extract SM data from Fengyun-3 D(FY-3 D) Medium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) imagery;we named this model SM-DBN.The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3 D MERSI-Ⅱ imagery, which are relevant to temperature, were used as inputs while land surface temperatures(LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index(NDVI), and enhanced vegetation index(EVI);and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-Ⅱ images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression(LR) and back propagation(BP) neural network models. The overall accuracy of these models was measured by using the root mean square error(RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models.

Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval

The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors(i.e.,spectral resolution,signal-to-noise ratio,plant growth stages,and treatments).This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll(Chl)content(Cab).Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions,noise levels,growth stages,and nitrogen treatments.The Chl content was also measured destructively by using a microplate spectrophotometer.The performance of the PROSPECT model was compared with a commonly used random forest(RF)model.The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm.The ranges of the relative root mean square errors(rRMSE)of the PROSPECT and RF models were 12%-13%and 11.70%-12.86%,respectively.However,the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37%and 12.04%-15.80%for PROSPECT and RF,respectively.For different growth stages,the PROSPECT model had similar prediction accuracies(rRMSE=9.26%-12.41%)to the RF model(rRMSE=9.17%-12.70%).Furthermore,the superiority of the PROSPECT model(rRMSE=10.10%-12.82%)over the RF model(rRMSE=11.81%-15.47%)was prominently observed when tested with plants growth at different nitrogen treatment levels.The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.