Ambient Gestures

We present Ambient Gestures, a novel gesture-based system designed to support ubiquitous ‘in the environment’ interactions with everyday computing technology. Hand gestures and audio feedback allow users to control computer applications without reliance on a graphical user interface, and without having to switch from the context of a non-computer task to the context of the computer. The Ambient Gestures system is composed of a vision recognition software application, a set of gestures to be processed by a scripting application and a navigation and selection application that is controlled by the gestures. This system allows us to explore gestures as the primary means of interaction within a multimodal, multimedia environment. In this paper we describe the Ambient Gestures system, define the gestures and the interactions that can be achieved in this environment and present a formative study of the system. We conclude with a discussion of our findings and future applications of Ambient Gestures in ubiquitous computing

A single antenna ambient noise cancellation method for in-situ radiated EMI measurements in the time-domain

This paper presents a single antenna ambient noise cancellation method for in-situ radiated emissions measurements performed using an entirely time-domain approach and the sliding window Empirical Mode Decomposition. The method requires a pair of successive measurements, an initial one for characterizing the ambient noise and a final one for the EMI measurement in the presence of ambient noise. The method assumes the spectral content of the ambient noise is stable between both measurements. The measured time-domain EMI is decomposed into a finite set of intrinsic mode functions. Some modes contain the ambient noise signals while other modes contain the actual components of the EMI. A brute-force search algorithm determines which mode, or combination of modes, maximize the absolute difference between the magnitude of their spectrum and the ambient noise levels for every frequency bin in the measurement bandwidth. Experimental results show the effectiveness of this method for attenuating several ambient noise signals in the 30 MHz – 1 GHz band.Postprint (published version

Familiarization through Ambient Images Alone

The term “ambient images” has begun to show up in much of the current literature on facial recognition. Ambient images refer to naturally occurring views of a face that captures the idiosyncratic ways in which a target face may vary (Ritchie & Burton, 2017). Much of the literature on ambient images have concluded that exposing people to ambient images of a target face can lead to improved facial recognition for that target face. Some studies have even suggested that familiarity is the result of increased exposure to ambient images of a target face (Burton, Kramer, Ritchie, & Jenkins, 2016). The current study extended the literature on ambient images. Using the face sorting paradigm from Jenkins, White, Van Montfort, and Burton (2011), the current study served three purposes. First, this study captured whether there was an incremental benefit in showing ambient images. Particularly, we observed whether performance improved as participants were shown a low, medium, or high number of ambient images. Next, this study attempted to provide a strong enough manipulation that participant would be able to perform the face sorting task perfectly, after being exposed to a high number (45 total) of ambient images. Lastly, this study introduced time data as a measure of face familiarity. The results found support for one aim of this study and partial support for another aim of this study. Time data were found to be an effective quantitative measure of familiarity. Also, there was some evidence of an incremental benefit of ambient images, but that benefit disappeared after viewing around 15 unique exemplar presentations of a novel identity’s face. Lastly, exposing participants to 45 ambient images alone did not cause them to reach perfect performance. The paper concludes with a discussion on the need to extend past ambient images to understand how to best mimic natural familiarity in a lab setting

Air Temperature Comparison between the MMTS and the USCRN Temperature Systems

A new U.S. Climate Reference Network (USCRN) was officially and nationally commissioned by the Department of Commerce and the National Oceanic and Atmospheric Administration in 2004. During a 1-yr side-by-side field comparison of USCRN temperatures and temperatures measured by a maximum-minimum temperature system (MMTS), analyses of hourly data show that the MMTS temperature performed with biases: 1) a systematic bias–ambient-temperature-dependent bias and 2) an ambient-solar-radiation- and ambient-wind- speed-dependent bias. Magnitudes of these two biases ranged from a few tenths of a degree to over 1C compared to the USCRN temperatures. The hourly average temperatures for the USCRN were the dependent variables in the development of two statistical models that remove the biases due to ambient temperature, ambient solar radiation, and ambient wind speed in the MMTS. The model performance was examined, and the results show that the adjusted MMTS data were substantially improved with respect to both systematic bias and the bias associated with ambient solar radiation and ambient wind speed. In addition, the results indicate that the historical temperature datasets prior to the MMTS era need to be further investigated to produce long-term homogenous times series of area-average temperature

Engineering ambient visual sensors

Visual sensors are an indispensable prerequisite for those AmI environments that require a surveillance component. One practical issue concerns maximizing the operational longevity of such sensors as the operational lifetime of an AmI environment itself is dependent on that of its constituent components. In this paper, the intelligent agent paradigm is considered as a basis for managing a camera collective such that the conflicting demands of power usage optimization and system performance are reconciled