Quantitative analysis of infrared contrast enhancement algorithms

This thesis examines a quantitative analysis of infrared contrast enhancement algorithms found in literature and developed by the author. Four algorithms were studied, three of which were found in literature and one developed by the author: tail-less plateau equalization (TPE), adaptive plateau equalization (APE), the method according to Aare Mallo (MEAM), and infrared multi-scale retinex (IMSR). Engineering code was developed for each algorithm. From this engineering code, a rate of growth analysis was conducted to determine each algorithm’s computational load. From the analysis, it was found that all algorithms with the exception of IMSR have a desirable linear nature. Once the rate of growth analysis was complete, sample infrared imagery was collected. Three scenes were collected for experimentation: a low-to-high thermal variation scene, a low-to-mid thermal variation scene, and a natural scene. After collecting sample imagery and processing it with the engineering code, a paired comparison psychophysical trial was executed using local firefighters, common users of the infrared imaging system. From this trial, two metrics were formed: an average rank and an interval scale. From analysis of both metrics plus an analysis of the rate of growth, MEAM was declared to be the best algorithm overall

Information provision measures for voice agent product recommendations— The effect of process explanations and process visualizations on fairness perceptions

While voice agent product recommendations (VAPR) can be convenient for users, their underlying artificial intelligence (AI) components are subject to recommendation engine opacities and audio-based constraints, which limit users’ information level when conducting purchase decisions. As a result, users might feel as if they are being treated unfairly, which can lead to negative consequences for retailers. Drawing from the information processing and stimulus-organism-response theory, we investigate through two experimental between subjects studies how process explanations and process visualizations—as additional information provision measures—affect users’ perceived fairness and behavioral responses to VAPRs. We find that process explanations have a positive effect on fairness perceptions, whereas process visualizations do not. Process explanations based on users’ profiles and their purchase behavior show the strongest effects in improving fairness perceptions. We contribute to the literature on fair and explainable AI by extending the rather algorithm-centered perspectives by considering audio-based VAPR constraints and directly linking them to users’ perceptions and responses. We inform practitioners how they can use information provision measures to avoid unjustified perceptions of unfairness and adverse behavioral responses

A correlation based method for measuring and monitoring the impulse response of analog LTI systems with low realization cost

This article deals with the problem of measuring the impulse response of a linear time invariant (LTI) analog network. At the beginning three known measurement principles are discussed and compared with respect to their advantages and disadvantages. It is shown that good results can only be attained at the cost of measurement expense. Subsequently a new correlation based method is presented where low complexity and high quality results have not to be a contradiction. The measurement circuit basically consists of two m-sequence generators and an analog low pass filter. The characteristics of the proposed measurement circuit are examined and explained by two examples

When Do Customers Perceive Artificial Intelligence as Fair? An Assessment of AI-based B2C E-Commerce

Artificial intelligence (AI) enables new opportunities for business-to-consumer (B2C) e-commerce services, but it can also lead to customer dissatisfaction if customers perceive the implemented service not to be fair. While we have a broad understanding of the concept of fair AI, a concrete assessment of fair AI from a customer-centric perspective is lacking. Based on systemic service fairness, we conducted 20 in-depth semi-structured customer interviews in the context of B2C e-commerce services. We identified 19 AI fairness rules along four interrelated fairness dimensions: procedural, distributive, interpersonal, and informational. By providing a comprehensive set of AI fairness rules, our research contributes to the information systems (IS) literature on fair AI, service design, and human-computer interaction. Practitioners can leverage these rules for the development and configuration of AI-based B2C e-commerce services

When Do Customers Perceive Artificial Intelligence as Fair? An Assessment of AI-based B2C E-Commerce

Artificial intelligence (AI) enables new opportunities for business-to-consumer (B2C) e-commerce services, but it can also lead to customer dissatisfaction if customers perceive the implemented service not to be fair. While we have a broad understanding of the concept of fair AI, a concrete assessment of fair AI from a customer-centric perspective is lacking. Based on systemic service fairness, we conducted 20 in-depth semi-structured customer interviews in the context of B2C e-commerce services. We identified 19 AI fairness rules along four interrelated fairness dimensions: procedural, distributive, interpersonal, and informational. By providing a comprehensive set of AI fairness rules, our research contributes to the information systems (IS) literature on fair AI, service design, and human-computer interaction. Practitioners can leverage these rules for the development and configuration of AI-based B2C e-commerce services

Folate catabolites in spot urine as non-invasive biomarkers of folate status during habitual intake and folic acid supplementation.

Folate status, as reflected by red blood cell (RCF) and plasma folates (PF), is related to health and disease risk. Folate degradation products para-aminobenzoylglutamate (pABG) and para-acetamidobenzoylglutamate (apABG) in 24 hour urine have recently been shown to correlate with blood folate. Since blood sampling and collection of 24 hour urine are cumbersome, we investigated whether the determination of urinary folate catabolites in fasted spot urine is a suitable non-invasive biomarker for folate status in subjects before and during folic acid supplementation. Immediate effects of oral folic acid bolus intake on urinary folate catabolites were assessed in a short-term pre-study. In the main study we included 53 healthy men. Of these, 29 were selected for a 12 week folic acid supplementation (400 µg). Blood, 24 hour and spot urine were collected at baseline and after 6 and 12 weeks and PF, RCF, urinary apABG and pABG were determined. Intake of a 400 µg folic acid bolus resulted in immediate increase of urinary catabolites. In the main study pABG and apABG concentrations in spot urine correlated well with their excretion in 24 hour urine. In healthy men consuming habitual diet, pABG showed closer correlation with PF (rs = 0.676) and RCF (rs = 0.649) than apABG (rs = 0.264, ns and 0.543). Supplementation led to significantly increased folate in plasma and red cells as well as elevated urinary folate catabolites, while only pABG correlated significantly with PF (rs = 0.574) after 12 weeks. Quantification of folate catabolites in fasted spot urine seems suitable as a non-invasive alternative to blood or 24 hour urine analysis for evaluation of folate status in populations consuming habitual diet. In non-steady-state conditions (folic acid supplementation) correlations between folate marker (RCF, PF, urinary catabolites) decrease due to differing kinetics