Uniqueness of Iris Pattern Based on AR Model

The assessment of iris uniqueness plays a crucial role in analyzing the capabilities and limitations of iris recognition systems. Among the various methodologies proposed, Daugman's approach to iris uniqueness stands out as one of the most widely accepted. According to Daugman, uniqueness refers to the iris recognition system's ability to enroll an increasing number of classes while maintaining a near-zero probability of collision between new and enrolled classes. Daugman's approach involves creating distinct IrisCode templates for each iris class within the system and evaluating the sustainable population under a fixed Hamming distance between codewords. In our previous work [23], we utilized Rate-Distortion Theory (as it pertains to the limits of error-correction codes) to establish boundaries for the maximum possible population of iris classes supported by Daugman's IrisCode, given the constraint of a fixed Hamming distance between codewords. Building upon that research, we propose a novel methodology to evaluate the scalability of an iris recognition system, while also measuring iris quality. We achieve this by employing a sphere-packing bound for Gaussian codewords and adopting a approach similar to Daugman's, which utilizes relative entropy as a distance measure between iris classes. To demonstrate the efficacy of our methodology, we illustrate its application on two small datasets of iris images. We determine the sustainable maximum population for each dataset based on the quality of the images. By providing these illustrations, we aim to assist researchers in comprehending the limitations inherent in their recognition systems, depending on the quality of their iris databases

Amperometric Bioelectronic Tongue for glucose determination

An amperometric Bioelectronic Tongue is reported for glucose determination that contains eight sensor electrodes constructed using different metal electrodes (Pt, Au), oxidoreductase enzymes (glucose oxidase, ascorbate oxidase, uricase), and membrane coatings (Nafion, chitosan). The response to varying concentrations of glucose, ascorbic acid, uric acid, and acetaminophen was tested for two models, concentration determination by current density measurements at individual electrodes and concentration determination by a linear regression model for the entire electrode array. The reduced chi-squared for the full array model was found to be about one order of magnitude lower than that for the individual-electrode model. Discrimination of glucose from chemical interference by the other three species is accomplished through a combination of enzyme catalysis, metal electrocatalysis, and membrane surface charge. The benefit of incorporating enzyme electrodes into the sensor array is illustrated by the lower correlation coefficients between different enzyme electrodes relative to non-enzyme coated electrodes. This approach can be more generally applied to detection of other substrates of oxidoreductase enzymes

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32–64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks

Energy intake estimation from counts of chews and swallows

Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, time-consuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal). Energy intake was estimated by four different methods: weighed food records (gold standard), diet diaries, photographic food records, and CCS models. Counts of chews and swallows were measured using wearable sensors and video analysis. Results for the training meals demonstrated that CCS models presented the lowest reporting bias and a lower error as compared to diet diaries. For the validation meal, CCS models showed reporting errors that were not different from the diary or the photographic method. The increase in error for the validation meal may be attributed to differences in the physical properties of foods consumed during training and validation meals. However, this may be potentially compensated for by including correction factors into the models. This study suggests that estimation of energy intake from CCS may offer a promising alternative to overcome limitations of self-report.Fil: Fontana, Juan Manuel. University of Alabama; Estados Unidos. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Higgins, Janine A.. University of Colorado Anschutz Medical Center; Estados UnidosFil: Schuckers, Stephanie C.. Clarkson University; Estados UnidosFil: Bellisle, France. Universite de Paris 13-Nord; FranciaFil: Pan, Zhaoxing. University of Colorado Anschutz Medical Campus; Estados UnidosFil: Melanson, Edward L.. University of Colorado Anschutz Medical Campus; Estados UnidosFil: Neuman, Michael R.. Michigan Technological University; Estados UnidosFil: Sazonov, Edward. University of Alabama; Estados Unido