Effects of Stimulus Level on Speech Perception with Cochlear Prostheses

This study is one of a series that examines stimulus features important for cochlear implant function. Here, we examine effects of stimulus level. In subjects with cochlear implants, a number of psychophysical tests of temporal discrimination (pulse rate discrimination, gap detection, etc.) show marked improvement as a function of stimulus level through most or all of the dynamic range, while electrode-place discrimination can improve or degrade as a function of level. In this study, effects of these combined potential influences were studied by examining the effects of stimulus level on syllable identification. We tested two hypotheses: that syllable identification varies as a function of stimulus level and that level and electrode configuration interact in affecting syllable identification. We examined vowel and consonant identification as a function of stimulus level for bipolar and monopolar electrode configurations. We used experimental processor maps where upper and lower stimulation limits of each electrode pair were equated to eliminate confounding effects of dynamic range, which varies across subjects and electrodes. For each channel, stimulation amplitude was set to a fixed percentage of its dynamic range. Eight adult subjects with Nucleus CI24M implants were tested using the SPEAK processing strategy. With each electrode configuration, stimulus levels were tested from 0% to 90% of the dynamic range in nine steps. The effects on consonant and vowel identification were similar. Phoneme identification was usually better for monopolar than for bipolar stimulation. In the lower half of the dynamic range, syllable identification usually increased as a function of stimulus level. In the upper half of the dynamic range, syllable identification continued to increase as a function of level to 90% of the dynamic range for some subjects, while for others there was no appreciable change or a decrease as a function of level. Decreases in performance at high levels were more common with monopolar than bipolar stimulation. These results suggest that if speech processors are programmed to optimize level for each individual, speech perception performance could be improved.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42440/1/30040049.pd

Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations

Viscoelastic material properties at high strain rates are needed to model many biological and medical systems. Bubble cavitation can induce such strain rates, and the resulting bubble dynamics are sensitive to the material properties. Thus, in principle, these properties can be inferred via measurements of the bubble dynamics. Estrada et al. (2018) demonstrated such bubble-dynamic high-strain-rate rheometry by using least-squares shooting to minimize the difference between simulated and experimental bubble radius histories. We generalize their technique to account for additional uncertainties in the model, initial conditions, and material properties needed to uniquely simulate the bubble dynamics. Ensemble-based data assimilation minimizes the computational expense associated with the bubble cavitation model , providing a more efficient and scalable numerical framework for bubble-collapse rheometry. We test an ensemble Kalman filter (EnKF), an iterative ensemble Kalman smoother (IEnKS), and a hybrid ensemble-based 4D-Var method (En4D-Var) on synthetic data, assessing their estimations of the viscosity and shear modulus of a Kelvin–Voigt material. Results show that En4D-Var and IEnKS provide better moduli estimates than EnKF. Applying these methods to the experimental data of Estrada et al. (2018) yields similar material property estimates to those they obtained, but provides additional information about uncertainties. In particular, the En4D-Var yields lower viscosity estimates for some experiments, and the dynamic estimators reveal a potential mechanism that is unaccounted for in the model, whereby the apparent viscosity is reduced in some cases due to inelastic behavior, possibly in the form of material damage occurring at bubble collapse

Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations

Viscoelastic material properties at high strain rates are needed to model many biological and medical systems. Bubble cavitation can induce such strain rates, and the resulting bubble dynamics are sensitive to the material properties. Thus, in principle, these properties can be inferred via measurements of the bubble dynamics. Estrada et al. (2018) demonstrated such bubble-dynamic high-strain-rate rheometry by using least-squares shooting to minimize the difference between simulated and experimental bubble radius histories. We generalize their technique to account for additional uncertainties in the model, initial conditions, and material properties needed to uniquely simulate the bubble dynamics. Ensemble-based data assimilation minimizes the computational expense associated with the bubble cavitation model. We test an ensemble Kalman filter (EnKF), an iterative ensemble Kalman smoother (IEnKS), and a hybrid ensemble-based 4D--Var method (En4D--Var) on synthetic data, assessing their estimations of the viscosity and shear modulus of a Kelvin--Voigt material. Results show that En4D--Var and IEnKS provide better moduli estimates than EnKF. Applying these methods to the experimental data of Estrada et al. (2018) yields similar material property estimates to those they obtained, but provides additional information about uncertainties. In particular, the En4D--Var yields lower viscosity estimates for some experiments, and the dynamic estimators reveal a potential mechanism that is unaccounted for in the model, whereby the viscosity is reduced in some cases due to material damage occurring at bubble collapse

DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data

The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge\u27s main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability

Effects of Electrode Configuration and Place of Stimulation on Speech Perception with Cochlear Prostheses

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42437/1/10162-2-2-87_10020087.pd

Multifunctional energy storage and piezoelectric properties of 0.65Pb(Mg1/3Nb2/3)O3–0.35PbTiO3 thick films on stainless-steel substrates

AbstractThe miniaturization of electronic devices and power systems requires the fabrication of functional components in the form of micrometer-sized thick films. A major challenge is the integration of functional ceramics with metals, which are considered incompatible with high-temperature ceramic processing. To overcome the integration barrier, an aerosol deposition (AD) spray-coating method based on room temperature deposition can be used. By employing the AD method, we were able to deposit relaxor-ferroelectric 0.65Pb(Mg1/3Nb2/3)O3–0.35PbTiO3 ceramic thick films on low-cost stainless-steel substrates. The as-deposited films were dense, with ∼97% of the theoretical density. Moreover, the post-deposition annealing at 500 °C did not result in any microstructural changes. Compared to the as-deposited films, the annealed films exhibit improved energy storage and electromechanical properties. The annealed thick films achieve a recoverable energy density of 15.1 J⋅cm−3 at an electric field of 1350 kV⋅cm−1 and an electric-field cycling stability of 5 million cycles. A piezoelectric response was detected through the entire film thickness by piezoelectric force microscopy. Macroscopic displacement measurements revealed a maximum relative strain of 0.38% at 1000 kV⋅cm−1, corresponding to inverse effective piezoelectric coefficient of ∼40 pm⋅V−1. In this study, we overcame the integration challenges and demonstrated the multifunctionalization of future ceramic-metal structures, as the deposited thick films on stainless steel exhibit energy storage capability and piezoelectric properties

Franck-Condon blockade in suspended carbon nanotube quantum dots

Understanding the influence of vibrational motion of the atoms on electronic transitions in molecules constitutes a cornerstone of quantum physics, as epitomized by the Franck-Condon principle of spectroscopy. Recent advances in building molecular-electronics devices and nanoelectromechanical systems open a new arena for studying the interaction between mechanical and electronic degrees of freedom in transport at the single-molecule level. The tunneling of electrons through molecules or suspended quantum dots has been shown to excite vibrational modes, or vibrons. Beyond this effect, theory predicts that strong electron-vibron coupling dramatically suppresses the current flow at low biases, a collective behaviour known as Franck-Condon blockade. Here we show measurements on quantum dots formed in suspended single-wall carbon nanotubes revealing a remarkably large electron-vibron coupling and, due to the high quality and unprecedented tunability of our samples, admit a quantitative analysis of vibron-mediated electronic transport in the regime of strong electron-vibron coupling. This allows us to unambiguously demonstrate the Franck-Condon blockade in a suspended nanostructure. The large observed electron-vibron coupling could ultimately be a key ingredient for the detection of quantized mechanical motion. It also emphasizes the unique potential for nanoelectromechanical device applications based on suspended graphene sheets and carbon nanotubes.Comment: 7 pages, 3 figure