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The interplay between Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HDVs) in mixed traffic environments is often presumed to influence the behavior of the other, and the dynamic impacts of such interplay on traffic flows is a critical aspect that is absent in most existing studies. This study employs a data-driven optimization approach to model the driving behavior of Connected Automated Vehicles (CAV) in mixed traffic and investigates the impact of CAVs on overall traffic performance. Specifically, considering a scenario of a gradual increase in the penetration of CAVs in the conventional traffic stream, currently dominated by Human-driven vehicles (HDV), four possible car-following configurations are identified where a CAV has to behave differently. Regarding such configurations, existing car-following and lane-changing models of CAVs are tuned using a Lipschitzian optimization algorithm and a local search method with data obtained from the WAYMO Open Dataset. The developed driving model of CAVs is used to simulate mixed traffic on a freeway section attached to an on-ramp, which often induces traffic bottlenecks. Under varying market penetration of CAVs, traffic performances, including travel time, throughput, and string stability, are compared with conventional traffic. The findings suggest significant improvements at a network level, for example, by delaying and dampening shockwaves. However, on an individual level, CAVs feel hindered by the slower-moving HDVs.

The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patient-specific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective model-based monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patient-ventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, real-time collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.

This article introduces a nonlinear observer to reconstruct faults in a class of nonlinear descriptor systems affected by disturbances, in both the state and output equations. The fault enters the system through nonlinear functions in both its state and output equations. The original system is first transformed such that design freedom in its structure is easier to exploit, and then a nonlinear observer is designed based on the transformed system to reconstruct the fault. Linear matrix inequalities are used to determine the gains of the observer such that the effect of the disturbances on the fault reconstruction is bounded. Finally, three simulation examples are carried out to verify the effectiveness of the scheme.

There exists a need for the design of light emitting diode (LED) luminaires which can deliver both visual and non-visual benefits of light to humans. In this work, we introduce an optimization approach based on spectral shaping for a minimalistic and practical design of a circadian-tunable multi-channel luminaire which also outputs white light with high quality and luminous efficacy of radiation (LER). The spectral optimization approach utilizes Multi-objective Genetic Algorithm to maximize circadian tunability, light quality and LER while minimizing the number of channels. Solution sets are constrained using the non-visual quality metric, Melanopic Efficacy of Luminous Radiation (MELR) from the Melanopic Equivalent Daylight Illuminance (MEDI) approach and the more stringent visual quality metric TM-30 in addition to conventional Color Rendering Index (CRI). By matching theoretically optimized LED parameters to commercially available LED parameters for commercialization purposes, we establish the maximum MELR tunability that is achievable with 4 and 5 LED channels and the resulting trade-off in efficacy and light quality. Based on the results and analysis in this work, we detail a spectral optimization approach to propel the field of indoor lighting towards human-centric lighting.

Smart homes and Internet of Things are emerging concepts in modern society, with intelligent lighting being an important part of it. Besides providing visual satisfaction through its color-rendering properties, lighting also has other effects on human well-being. In order to exploit the full potential of a smartly lit home, lighting systems need to be equipped with accurate controllers that can control the spectrum and color characteristics of light in addition to conventional ON-OFF and dimming control. However, current commercial smart lighting products with such capabilities need to employ expensive sensors which are still lacking in terms of closed-loop feedback which is imperative for accurate color control of light-emitting diode (LED)-based luminaires. This paper presents a novel approach that uses the camera available on modern smartphones to perform closed-loop color control for lighting systems in smart homes. The algorithm is able to perform multi-channel mixing for any color and also white light at a desired correlated color temperature with high color-rendering index. This approach proves to be very economical and convenient as no external sensors are required and can be performed using any Android smartphone on a compatible LED-based luminaire.