Jont B. Allen

Micromixers are critical components in the lab-on-a-chip or micro total analysis systems technology found in micro-electro-mechanical systems. In general, the mixing performance of the micromixers is determined by characterising the mixing time of a system, for example the time or number of circulations and vibrations guided by tracers (i.e., fluorescent dyes). Our previous study showed that the mixing performance could be detected solely from the electrical measurement. In this paper, we employ electromagnetic micromixers to investigate the correlation between electrical and mechanical behaviours in the mixer system. This work contemplates the “anti-reciprocity” concept by providing a theoretical insight into the measurement of the mixer system; the work explains the data interdependence between the electrical point impedance (voltage per unit current) and the mechanical velocity. This study puts the electromagnetic micromixer theory on a firm theoretical and empirical basis.

Purpose: Although poorer understanding of speech in noise by listeners who are hearing-impaired (HI) is known not to be directly related to audiometric hearing threshold, HT (f), grouping HI listeners with HT (f) is widely practiced. In this article, the relationship between consonant recognition and HT (f) is considered over a range of signal-to-noise ratios (SNRs). Method: Confusion matrices (CMs) from 25 HI ears were generated in response to 16 consonant-vowel syllables presented at 6 different SNRs. Individual differences scaling (INDSCAL) was applied to both feature-based matrices and CMs in order to evaluate the relationship between HT (f) and consonant recognition among HI listeners. Results: The results showed no predictive relationship between the percent error scores ( Pe ) and HT (f) across SNRs. The multiple regression models showed that the HT (f) accounted for 39% of the total variance of the slopes of the Pe . Feature-based INDSCAL analysis showed consistent grouping of listeners across SNRs, but not in terms of HT (f). Systematic relationship between measures was also not defined by CM-based INDSCAL analysis across SNRs. Conclusions: HT (f) did not account for the majority of the variance (39%) in consonant recognition in noise when the complete body of the CM was considered.

Tympanic membrane (TM) and ossicular (OSC) models have been studied for many years. Most are implemented in the frequency‐domain via Kirchhoff’s equations. However, the frequency domain is less intuitive than the time‐domain models and does not apply given non‐linear phenomena. This research presents a human time‐domain TM+OSC implementation, extending our cat model [J. Acoust. Soc. Am. 122, 918–931 (2007)] as a cascade of slightly missmatched transmission lines. The canal plane‐wave volume velocity is uniformly distributed across the canal cross‐section, and updated at ≈1 MHz (to accurately simulate sound propagation). The time‐domain model includes both forward and backward waves, enabling for the first time, the computation of one‐way delays, thus explaining why multi‐modal propagation observed on the TM are not critical. Results are validated with data from normal and pathological ears. Additionally, comparisons between the acoustic impedance in living and dead (temporal bones) ears shows significant temporal‐bone artifacts. Because of ear‐dependent impedance missmatch, canal standing waves are observed. It is frequently assumed that the umbo pressure is the best reference pressure. In fact the “forward‐pressure” P + is a better estimate of the ideal reference, the cochlear pressure [J. Acoust. Soc. Am. 125, 1605–1611 (2009)].

In this paper, we develop a dynamical point process model for how complex sounds are represented by neural spiking in auditory nerve fibers. Although many models have been proposed, our point process model is the first to capture elements of spontaneous rate, refractory effects, frequency selectivity, phase locking at low frequencies, and short-term adaptation, all within a compact parametric approach. Using a generalized linear model for the point process conditional intensity, driven by extrinsic covariates, previous spiking, and an input-dependent charging/discharging capacitor model, our approach robustly captures the aforementioned features on datasets taken at the auditory nerve of chinchilla in response to speech inputs. We confirm the goodness of fit of our approach using the Time-Rescaling Theorem for point processes.