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Abstract-Backscatter radio is proposed for sensor networks. In that way, the transmitter for each sensor is simplified to a transistor connected to an antenna and therefore, the cost for each sensor's communicator becomes negligible, while energy used for wireless communication per sensor is minimized. A software-defined transceiver is built to transmit a carrier, receive the reflections from various sensors and extract their transmitted messages. This work presents a thorough model of the backscatter radio link, the system architecture and a set of data extraction techniques for each sensor's information, testing in practice a sensor communicating through backscatter at a range of approximately 15 meters indoors, with 5 milliwatt transmission power at 10 bits per second. This work highlights the idiosyncrasies of the backscatter channel and provides a new communication perspective in the fertile area of scalable sensor networks, especially when low bit-rate, ultra-low cost sensors are required.Index Terms-RFID, fading channel, bit error rate, wireless sensor networks.

We have carried out a series of frequency-of-seeing experiments similar to those performed by Hecht, Shlaer, and Pirenne [J. Gen. Physiol. 25, 819-840 (1942)], using an Ar+ laser operated at 514.5 nm as the source of light. In certain blocks of trials, our subjects were encouraged to report as seen those trials in which the stimulus might have been present. It was determined that sensitivity and reliability were traded against each other over a broad range: for our subjects, the detection of 147 photons at the cornea with 60% frequency of seeing entailed, on the average, a 1% false-positive rate (FPR), whereas the detection of 34 photons at the cornea with 60% frequency of seeing was accompanied by a 33% FPR. A new neural-counting model has been developed in the framework of signal-detection theory. It combines Poisson stimulus fluctuations with additive and multiplicative neural noise, both of which are known to be present in the visual system at threshold. The resulting probability-of-detection curves, derived from the Neyman Type-A counting distribution, are in good accord with our experimental frequency-of-seeing data for sensible values of the model parameters. We deduce that, on the average, our four subjects are able to detect a single photon at the retina with 60% frequency of seeing, at the expense of a 55% FPR. In Part 2 of this set of papers [P.R. Prucnal and M.C. Teich, Biol. Cybern. 43, 87-96 (1982)], we use the normalizing transform, together with probit analysis, to provide improved estimates of threshold parameters, whereas in Part 3 [M.C. Teich, P.R. Prucnal, G. Vannucci, M.E. Breton, and W.J. McGill, submitted to Biol. Cybern.], we consider the effects of non-Poisson quantum fluctuations.

Figure 1: Traditional motion-capture systems excel at recording motions within lab-like environments but struggle with recording outdoor activities such as skiing, biking, and driving. This limitation led us to design a wearable motion-capture system that records human activity in both indoor and outdoor environments. AbstractCommercial motion-capture systems produce excellent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motions almost anywhere. This wearable system gathers ultrasonic time-of-flight and inertial measurements with a set of inexpensive miniature sensors worn on the garment. After recording, the information is combined using an Extended Kalman Filter to reconstruct joint configurations of a body. Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings. Although our prototype does not reliably recover the global transformation, we show that the resulting motions are visually similar to the original ones, and that the combined acoustic and inertial system reduces the drift commonly observed in purely inertial systems. Our final results suggest that this system could become a versatile input device for a variety of augmented-reality applications.