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We describe a distributed position-based network protocol optimized for minimum energy consumption in mobile wireless networks that support peer-to-peer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each node guarantees strong connectivity of the entire network and attains the global minimum energy solution for stationary networks. Due to its localized nature, this protocol proves to be self-reconfiguring and stays close to the minimum energy solution when applied to mobile networks. Simulation results are used to verify the performance of the protocol.

Abstract-RF wireless interface enables remotely-powered implantable devices. Current studies in wireless power transmission into biological tissue tend to operate below 10 MHz due to tissue absorption loss, which results in large receive antennas. This paper examines the range of frequencies that will optimize the tradeoff between received power and tissue absorption. It first models biological tissue as a dispersive dielectric in a homogeneous medium and performs full-wave analysis to show that the optimal frequency is above 1 GHz for small receive coil and typical transmit-receive separations. Then, it includes the air-tissue interface and models human body as a planarly layered medium. The optimal frequency is shown to remain in the GHz-range. Finally, electromagnetic simulations are performed to include the effect of load impedance and look at the matched power gain. The optimal frequency is in the GHz-range for mm-sized transmit antenna and shifts to the sub-GHz range for cm-sized transmit antenna. The multiple orders of magnitude increase in the operating frequency enables dramatic miniaturization of implantable devices.Index Terms-Implantable medical devices, wireless power transfer.

Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.

SUMMARYDamage detection techniques have been proposed to exploit changes in modal parameters and to identify the extent and location of damage in large structures. Most of such techniques, however, generally neglect the environmental e ects on modal parameters. Such environmental e ects include changes in loads, boundary conditions, temperature, and humidity. In fact, the changes due to environmental e ects can often mask more subtle structural changes caused by damage. This paper examines a linear adaptive model to discriminate the changes of modal parameters due to temperature changes from those caused by structural damage or other environmental e ects. Data from the Alamosa Canyon Bridge in the state of New Mexico were used to demonstrate the e ectiveness of the adaptive ÿlter for this problem. Results indicate that a linear fourinput (two time and two spatial dimensions) ÿlter to temperature can reproduce the natural variability of the frequencies with respect to time of day. Using this simple model, we attempt to establish a conÿdence interval of the frequencies for a new temperature proÿle in order to discriminate the natural variation due to temperature.