P ervasive networks of wireless sensor and communication nodes have the potential to significantly impact society and create large market opportunities. For such networks to achieve their full potential, however, we must develop practical solutions for self-powering these autonomous electronic devices.Fixed-energy alternatives, such as batteries and fuel cells, are impractical for wireless devices with an expected lifetime of more than 10 years because the applications and environments in which these devices are deployed usually preclude changing or re-charging of batteries. There are several power-generating options for scavenging ambient environment energy, including solar energy, thermal gradients, and vibration-based devices. However, it's unlikely that any single solution will satisfy all application spaces, as each method has its own constraints: solar methods require sufficient light energy, thermal gradients need sufficient temperature variation, and vibration-based systems need sufficient vibration sources. Vibration sources are generally more ubiquitous, however, and can be readily found in inaccessible locations such as air ducts and building structures.We've modeled, designed, and built small cantilever-based devices using piezoelectric materials that can scavenge power from low-level ambient vibration sources. Given appropriate power conditioning and capacitive storage, the resulting power source is sufficient to support networks of ultra-low-power, peer-to-peer wireless nodes. These devices have a fixed geometry and-to maximize power output-we've individually designed them to operate as close as possible to the frequency of the driving surface on which they're mounted. Here, we describe these devices and present some new designs that can be tuned to the frequency of the host surface, thereby expanding the method's flexibility. We also discuss piezoelectric designs that use new geometries, some of which are microscale (approximately hundreds of microns). Problem overviewWe first analyze the wireless sensor nodes' power requirements, and then investigate the various sources that can fill those demands. Power demandAssuming an average distance between wireless sensor nodes of approximately 10 meters-which means that the radio transmitter should operate at approximately 0 dBm (decibels above or below 1 milliwatt)-the radio transmitter's peak power consumption will be around 2 to 3 mW, depending on its efficiency. Using ultra-low-power techniques, 1 the receiver should consume less than 1 mW. Including the dissipation of the sensors and Given appropriate power conditioning and capacitive storage, devices made from piezoelectric materials can scavenge power from low-level ambient sources to effectively support networks of ultra-low-power, peerto-peer wireless nodes.

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This article discusses the use of a brain-computer interface (BCI) to obtain emotional feedback from a human in response to the motion of humanoid robots in collaborative environments. The purpose of this study is to detect the human satisfaction level and use it as a feedback for correcting and improving the behavior of the robot to maximize human satisfaction. This article describes experiments and algorithms that use human brains activity collected through BCI in order to estimate the level of satisfaction. Users wear an electroencephalogram (EEG) headset and control the movement of the robot by mental imagination. The robots responds to the mental imagination may not be the same as human mental command and this will affect the emotional satisfaction level. The headset records brain activity from 14 locations on the scalp. Power spectral density of each EEG frequency band and four largest Lyapunov exponents of each EEG signal form the feature vector. The Mann-Whitney-Wilcoxon test is then used to rank all the features. The highest rank features are then selected to train a linear discriminant classifier (LDC) to determine the satisfaction level. Our experimental results show an accuracy of 79.2% in detecting the human satisfaction level.