Jeremiah J. Remus

Laser-induced breakdown spectroscopy-based geochemical fingerprinting for the rapid analysis and discrimination of minerals: the example of garnet

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique real-time geochemical analysis that is being developed for portable use outside of the laboratory. In this study, statistical signal processing and classification techniques were applied to single-shot, broadband LIBS spectra, comprising measured plasma light intensities between 200 and 960 nm, for a suite of 157 garnets of different composition from 92 locations worldwide. Partial least squares discriminant analysis was applied to sets of 25 LIBS spectra for each garnet sample and used to classify the garnet samples based on composition and geographic origin. Careful consideration was given to the cross-validation procedure to ensure that the classification algorithm is robust to unseen data. The results indicate that broadband LIBS analysis can be used to discriminate garnets of different composition and has the potential to discern geographic origin.

Archaeological applications of laser-induced breakdown spectroscopy: an example from the Coso Volcanic Field, California, using advanced statistical signal processing analysis

Over the past quarter century, multielement chemical analysis has become a common means for attributing the provenance of archaeological materials. The Coso Volcanic Field (CVF) in California, USA, contains at least 38 high-silica rhyolite domes, many of which contain obsidian glass that has been quarried for tools by the indigenous population for more than 12,000 years. Artifacts made from CVF obsidian are found throughout the southwestern United States and geochemical sourcing of CVF obsidian has been an important tool in understanding prehistoric Native American trading patterns. Laser-induced breakdown spectroscopy (LIBS) is a simple atomic emission spectroscopic technique that has the potential for real-time man-portable chemical analysis in the field. Because LIBS is simultaneously sensitive to all elements, a single laser shot can be used to record the broadband emission spectra, which provides a “chemical fingerprint” of a material. Single-shot broadband LIBS spectra were collected using a commercial benchtop LIBS system for 27 obsidian samples from major sites across the CVF and four additional sites in California and western Nevada outside of CVF. Classification of the samples was performed using partial least-squares discriminant analysis (PLSDA), a common chemometric technique suitable for performing regression on high-dimensional data. Provenance identification for the obsidian samples was evaluated for three separate labeling frameworks. The first framework consisted of a binary classification problem to distinguish CVF samples from non-CVF samples. The second approach focused on the CVF samples with labels that corresponded to the eight separate Coso sites encompassed by the 27 samples. In the third analysis, non-CVF samples were excluded, and the remaining 27 CVF samples were labeled based on groupings defined from previous major and trace element chemical studies, which reduces the number of possible classes from eight to four. Different aspects of the classifier setup considered in this study include the training/testing routine (a 27-fold leave-one-sample-out setup versus a simple split of the data into separate sets for training and evaluation), the number of latent variables used in the regression model, and whether PLSDA operating on the entire broadband LIBS spectrum is superior to that using only a selected subset of LIBS emission lines. The results point to the robustness of the PLSDA technique and suggest that LIBS analysis combined with the appropriate statistical signal processing has the potential to be a useful tool for chemical analysis of archaeological artifacts and geological specimens.

Can the provenance of the conflict minerals columbite and tantalite be ascertained by laser-induced breakdown spectroscopy?

Conflict minerals is a term applied to ores mined in conditions of armed conflict and human rights abuse. Niobium and tantalum are two rare metals whose primary natural occurrence is in the complex oxide minerals columbite and tantalite, the ore of which is commonly referred to as coltan. The illicit export of coltan ore from the Democratic Republic of the Congo is thought to be responsible for financing the ongoing civil conflicts in this region. Determining the chemical composition of an ore is one of the means of ascertaining its provenance. Laser-induced breakdown spectroscopy (LIBS) offers a means of rapidly distinguishing different geographic sources for a mineral because the LIBS plasma emission spectrum provides the complete chemical composition (i.e., “chemical fingerprint”) of any material in real time. To test this idea for columbite–tantalite, three sample sets were analyzed. Partial least squares discriminant analysis (PLSDA) allows correct sample-level geographic discrimination at a success rate exceeding 90%.

Acoustic model investigation of a multiple carrier frequency algorithm for encoding fine frequency structure: Implications for cochlear implants ☆

Current cochlear implants provide frequency resolution through the number of channels. Improving resolution by increasing channels is limited by factors such as the physiological feasibility of increasing the number of electrodes, the inability to increase the number of channels for those already implanted, and the increased possibility of channel interactions reducing channel efficacy. Recent studies have suggested an alternative method: providing a continuum of pitch percepts for each channel based on the frequency content of that channel. This study seeks to determine the frequency resolution necessary for the highest performance gain, which may give some indication of the feasibility for implementation in implants. A discrete set of carrier frequencies, instead of a continuum, are evaluated using an acoustic model to measure speech recognition. Performance increased as the number of available frequencies increased, and substantive improvement was seen with as few as two frequencies per channel. The effect of variable frequency discrimination was also assessed, and the results suggest that frequency modulation can still provide benefits with poor frequency discrimination on some channels. These results suggest that if two or more discriminable frequencies per channel can be generated for cochlear implant subjects then an improvement in speech recognition may be possible.

A study on quality-adjusted impact of time lapse on iris recognition

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

The effects of noise on speech recognition in cochlear implant subjects: predictions and analysis using acoustic models

Cochlear implants can provide partial restoration of hearing, even with limited spectral resolution and loss of fine temporal structure, to severely deafened individuals. Studies have indicated that background noise has significant deleterious effects on the speech recognition performance of cochlear implant patients. This study investigates the effects of noise on speech recognition using acoustic models of two cochlear implant speech processors and several predictive signal-processing-based analyses. The results of a listening test for vowel and consonant recognition in noise are presented and analyzed using the rate of phonemic feature transmission for each acoustic model. Three methods for predicting patterns of consonant and vowel confusion that are based on signal processing techniques calculating a quantitative difference between speech tokens are developed and tested using the listening test results. Results of the listening test and confusion predictions are discussed in terms of comparisons between acoustic models and confusion prediction performance.

A comparison of adaptive psychometric procedures based on the theory of optimal experiments and Bayesian techniques: Implications for cochlear implant testing

Numerous previous studies have focused on the development of quick and efficient adaptive psychometric procedures. In psychophysics, there is often a model of the psychometric function supported by previous studies for the task of interest. The theory of optimal experiments provides a framework for utilizing a model of the process to develop quick and efficient sequential-testing strategies for estimating model parameters, making it appropriate for developing adaptive psychophysical-testing methods. In this study, we investigated the application of sequential parameter search strategies based on the theory of optimal experiments and Bayesian adaptive procedures for measuring psychophysical variables. The results presented in this article suggest that more sophisticated psychometric procedures can expedite the measurement of psychophysical variables. Such techniques for quickly collecting psychophysical data may be particularly useful in cochlear implant research, where a large set of psychophysical variables are useful for characterizing the performance of an implanted device. It is to be hoped that further development of these techniques will make psychophysical measurements available to clinicians for tuning and optimizing the speech processors of individual cochlear implant patients.

Comparison of a distance-based likelihood ratio test and k-nearest neighbor classification methods

Several studies of the k-nearest neighbor (KNN) classifier have proposed the use of non-uniform weighting on the k neighbors. It has been suggested that the distance to each neighbor can be used to calculate the individual weights in a weighted KNN approach; however, a consensus has not yet been reached on the best method or framework for calculating weights using the distances. In this paper, a distance likelihood ratio test was discussed and evaluated using simulated data. The distance likelihood ratio test (DLRT) shares several characteristics with the distance-weighted k-nearest neighbor methods but approaches the use of distance from a different perspective. Results illustrate the ability of the distance likelihood ratio test to approximate the likelihood ratio and compare the DLRT to two other k-neighborhood classification rules that utilize distance-weighting. The DLRT performs favorably in comparisons of the classification performance using the simulated data and provides an alternative non-parametric classification method for consideration when designing a distance-weighted KNN classification rule.

Comparison of adaptive psychometric procedures motivated by the Theory of Optimal Experiments: Simulated and experimental results

The wide use of psychometric assessments and the time necessary to conduct comprehensive psychometric tests has motivated significant research into the development of psychometric testing procedures that will provide accurate and efficient estimates of the parameters of interest. One potential framework for developing adaptive psychometric procedures is the Theory of Optimal Experiments. The Theory of Optimal Experiments provides several metrics for determining informative stimulus values based on a model of the psychometric function to be provided by the investigator. In this study, two methods based on a previous implementation of the Theory of Optimal Experiments are presented for comparison to two fixed step size staircase methods and also an existing adaptive method that utilizes a Bayesian framework. The psychometric procedures were used to measure detection thresholds and discrimination limens on two separate psychoacoustic tasks with normal-hearing subjects. Computer simulations were performed based on the outcomes of the experimental psychoacoustic detection task to analyze performance over a large sample size in the case of known truth. Results suggest that the proposed stimulus selection rules motivated by the Theory of Optimal Experiments perform better than previous techniques and also extend estimation to multiple parameters.

Vowel and consonant confusion in noise by cochlear implant subjects: predicting performance using signal processing techniques

Cochlear implants are able to restore some degree of hearing to deafened individuals; however implant users are particularly susceptible to background noise. The effect of noise can be assessed using vowel and consonant confusions measured in listening experiments. The paper presents three signal processing methods developed to predict patterns in vowel and consonant confusion in noise for cochlear implant users. Prediction performance is tested using the results of a listening experiment conducted with acoustic models of two cochlear implant speech processors and normal hearing subjects. Confusion prediction is based on prediction metrics calculated using each methods unique representation of the speech tokens.