Conjugated polymer nanoparticles are a class of nanoparticles with many useful and interesting properties, including very high fluorescence brightness, excellent photostability, and sensing capabilities. They also exhibit interesting and potentially useful phenomena, such as highly efficient energy transfer, anomalous single particle blinking, and twinkling phenomena associated with polaron motion. As little as one dye molecule per nanoparticle can efficiently quench the fluorescence of hundreds of polymer chromophore units. Similarly, loss of a single electron can result in quenching of hundreds of chromophores. These phenomena and properties are dictated by the nature of interactions between chromophores in this dense, nanoscale multichromophoric system, and are characterized as amplified energy transfer or multiple energy transfer. In this review, we summarize the key aspects of conjugated polymer nanoparticles optical properties and phenomena, and discuss the current understanding of exciton dynamics in these and related systems. In particular, our current understanding and theoretical models for amplified or multiple energy transfer based on exciton theory and Förster resonance energy transfer are explored.

Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent postlogin authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66% and False Reject Rate of 0.13% for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users.

Abstract. Advances in embedded systems and low-cost gas sensors are enabling a new wave of low-cost air quality monitoring tools. Our team has been engaged in the development of low-cost, wearable, air quality monitors (M-Pods) using the Arduino platform. These M-Pods house two types of sensors – commercially available metal oxide semiconductor (MOx) sensors used to measure CO, O3, NO2, and total VOCs, and NDIR sensors used to measure CO2. The MOx sensors are low in cost and show high sensitivity near ambient levels; however they display non-linear output signals and have cross-sensitivity effects. Thus, a quantification system was developed to convert the MOx sensor signals into concentrations. We conducted two types of validation studies – first, deployments at a regulatory monitoring station in Denver, Colorado, and second, a user study. In the two deployments (at the regulatory monitoring station), M-Pod concentrations were determined using collocation calibrations and laboratory calibration techniques. M-Pods were placed near regulatory monitors to derive calibration function coefficients using the regulatory monitors as the standard. The form of the calibration function was derived based on laboratory experiments. We discuss various techniques used to estimate measurement uncertainties. The deployments revealed that collocation calibrations provide more accurate concentration estimates than laboratory calibrations. During collocation calibrations, median standard errors ranged between 4.0–6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for CO2. Median signal to noise (S / N) ratios for the M-Pod sensors were higher than the regulatory instruments: for NO2, 3.6 compared to 23.4; for O3, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for CO2, 42.2 compared to 300–500. By contrast, lab calibrations added bias and made it difficult to cover the necessary range of environmental conditions to obtain a good calibration. A separate user study was also conducted to assess uncertainty estimates and sensor variability. In this study, 9 M-Pods were calibrated via collocation multiple times over 4 weeks, and sensor drift was analyzed, with the result being a calibration function that included baseline drift. Three pairs of M-Pods were deployed, while users individually carried the other three. The user study suggested that inter-M-Pod variability between paired units was on the same order as calibration uncertainty; however, it is difficult to make conclusions about the actual personal exposure levels due to the level of user engagement. The user study provided real-world sensor drift data, showing limited CO drift (under −0.05 ppm day−1), and higher for O3 (−2.6 to 2.0 ppb day−1), NO2 (−1.56 to 0.51 ppb day−1), and CO2 (−4.2 to 3.1 ppm day−1). Overall, the user study confirmed the utility of the M-Pod as a low-cost tool to assess personal exposure.

Abstract. Advances in embedded systems and low-cost gas sensors are enabling a new wave of low cost air quality monitoring tools. Our team has been engaged in the development of low-cost wearable air quality monitors (M-Pods) using the Arduino platform. The M-Pods use commercially available metal oxide semiconductor (MOx) sensors to measure CO, O3, NO2, and total VOCs, and NDIR sensors to measure CO2. MOx sensors are low in cost and show high sensitivity near ambient levels; however they display non-linear output signals and have cross sensitivity effects. Thus, a quantification system was developed to convert the MOx sensor signals into concentrations. Two deployments were conducted at a regulatory monitoring station in Denver, Colorado. M-Pod concentrations were determined using laboratory calibration techniques and co-location calibrations, in which we place the M-Pods near regulatory monitors to then derive calibration function coefficients using the regulatory monitors as the standard. The form of the calibration function was derived based on laboratory experiments. We discuss various techniques used to estimate measurement uncertainties. A separate user study was also conducted to assess personal exposure and M-Pod reliability. In this study, 10 M-Pods were calibrated via co-location multiple times over 4 weeks and sensor drift was analyzed with the result being a calibration function that included drift. We found that co-location calibrations perform better than laboratory calibrations. Lab calibrations suffer from bias and difficulty in covering the necessary parameter space. During co-location calibrations, median standard errors ranged between 4.0–6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for CO2. Median signal to noise (S/N) ratios for the M-Pod sensors were higher for M-Pods than the regulatory instruments: for NO2, 3.6 compared to 23.4; for O3, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for CO2, 42.2 compared to 300–500. The user study provided trends and location-specific information on pollutants, and affected change in user behavior. The study demonstrated the utility of the M-Pod as a tool to assess personal exposure.

Abstract-User identification and access control have become a high demand feature on mobile devices because those devices are wildly used by employees in corporations and government agencies for business and store increasing amount of sensitive data. This paper describes SenGuard, a user identification framework that enables continuous and implicit user identification service for smartphone. Different from traditional active user authentication and access control, SenGuard leverages availability of multiple sensors on today's smartphones and passively use sensor inputs as sources of user authentication. It extracts sensor modality dependent user identification features from captured sensor data and performs user identification at background. SenGuard invokes active user authentication when there is a mounting evidence that the phone user has changed. In addition, SenGuard uses a novel virtualization based system architecture as a safeguard to prevent subversion of the background user identification mechanism by moving it into a privileged virtual domain. An initial prototype of SenGuard was created using four sensor modalities including, voice, location, multitouch, and locomotion. Preliminary empirical studies with a set of users indicate that those four modalities are suited as data sources for implicit mobile user identification.