Crowd Counting Through Walls Using WiFi

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.Comment: 10 pages, 14 figure

PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring

Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semi-supervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.Comment: BuildSys 201

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

2018 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Schedule and abstract book for the Tenth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: October 27-28, 2018Location: UT Conference Center, KnoxvillePlenary Speaker: Holly Gaff, Biological Sciences, Old Dominion Univ.Featured Speaker: Nina Fefferman, Ecology & Evolutionary Biology, Mathematics, Univ. of Tennessee, Knoxvill

Indoor acids and bases.

Numerous acids and bases influence indoor air quality. The most abundant of these species are CO2 (acidic) and NH3 (basic), both emitted by building occupants. Other prominent inorganic acids are HNO3 , HONO, SO2 , H2 SO4 , HCl, and HOCl. Prominent organic acids include formic, acetic, and lactic; nicotine is a noteworthy organic base. Sources of N-, S-, and Cl-containing acids can include ventilation from outdoors, indoor combustion, consumer product use, and chemical reactions. Organic acids are commonly more abundant indoors than outdoors, with indoor sources including occupants, wood, and cooking. Beyond NH3 and nicotine, other noteworthy bases include inorganic and organic amines. Acids and bases partition indoors among the gas-phase, airborne particles, bulk water, and surfaces; relevant thermodynamic parameters governing the partitioning are the acid-dissociation constant (Ka ), Henry's law constant (KH ), and the octanol-air partition coefficient (Koa ). Condensed-phase water strongly influences the fate of indoor acids and bases and is also a medium for chemical interactions. Indoor surfaces can be large reservoirs of acids and bases. This extensive review of the state of knowledge establishes a foundation for future inquiry to better understand how acids and bases influence the suitability of indoor environments for occupants, cultural artifacts, and sensitive equipment