The common practice of defining operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings is to use fixed set points, which assume occupants have same and static comfort requirements. However, thermal comfort varies from person to person and also changes due to climatic variations or acclimation, making it dynamic. In addition, thermal comfort in transient conditions are different from the steady state conditions, which makes the prediction of thermal comfort more difficult. Thus, thermal comfort has to be monitored over time. In this paper, we present a novel infrared thermography based technique to monitor an individual's thermoregulation performance and thermal comfort levels by measuring the skin temperature on several points on human face, which has a high density of blood vessels and is not usually covered by clothing. Unlike other methods, our method requires no continuous user input or interaction. Our results demonstrate that the monitored facial points behave differently under the heat and cold stresses and it can be explained based on the underlying vascular territories. We define two heuristics to describe the thermoneutral zone based on the observed behaviors and estimate thermal comfort for individuals with 95% confidence level. Considerable variations are observed in the thermoregulation performance and uncomfortably cool conditions metrics between the males and females. Females' thermoregulation system responses are less sensitive to the perception of warm conditions. However, similar behaviors are observed for uncomfortably cool conditions across genders.

Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a fourday long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

Path gain and effective directional gain in urban canyons from actual rooftop base station sites are characterized based on a massive data set of 3000 links on 12 streets in two cities, with over 21 million individual measurements. Large street-to-street path gain variation is found, with median street path gain varying over 35 dB at similar distances. Coverage in the street directly illuminated by a roof edge antenna is found to suffer an average excess loss of 11 dB relative to free space at 200 m, with empirical slope-intercept fit model representing the data with 7.1 dB standard deviation. Offsetting the base antenna 5 m away from roof edge, as is common in macro cellular deployments, introduces an additional average loss of 15 dB at 100 m, but this additional loss reduces with distance. Around the corner loss is well modeled by a diffraction formula with an empirically obtained diffraction coefficient. Effective azimuthal gain degradation due to scatter is limited to 2 dB for 90% of data, supporting effective use of high gain antennas in urban street canyons.

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