In our electronically connected society, human identification systems are critical to secure authentication, and also enabling for tailored services to individuals. Conventional human identification systems, such as biometric-based or vision-based approaches, require either the deployment of dedicated infrastructure, or the active cooperation of users to carry devices. Consequently, pervasive implementation of conventional human identification systems is expensive, inconvenient, or intrusive to privacy. Recently, WiFi infrastructure, and associated WiFi-enabled mobile and IoT devices have become ubiquitous, and correspondingly, have enabled many context-aware and location-based services. To address the challenges of human identification systems and take advantage of the popularity of WiFi, researchers at UC Berkeley developed a human identification system based on analyzing signals from existing WiFi-enabled devices. This novel device-free approach uses WiFi signal analysis to reveal the unique, fine-grained gait patterns of individuals as the "fingerprint" for human identification.

Wireless sensors and the Internet of Things (IoT) have the potential to greatly impact society. Millimeter-scale wireless microsystems are the foundation of this vision. Accordingly, to realize this potential, these microsystems must be extremely low-cost and energy autonomous. Integrating wireless sensing systems on a single silicon chip with zero external components is a key advancement toward achieving those cost and energy requirements.  Almost all commercial microsystems today use off-chip quartz technology for precise timing and frequency reference. The quartz crystal (XTAL) is a bulky off-chip component that puts a size limitation on miniaturization and adds to the cost of the microsystem. Alternatively, MEMS technology is showing promising results for replacing the XTAL in space-constrained applications. However, the MEMS approach still requires an off-chip frequency reference and the resulting packaging adds to the cost of the microsystem.  To achieve a single-chip solution, researchers at UC Berkeley developed: (1) an approach to calibrating the frequency of an on-chip inaccurate relaxation oscillator such that it can be used as an accurate frequency reference for low-power, crystal-free wireless communications; and (2) a novel ultra-low power radio architecture that leverages the inaccurate on-chip oscillator, operates on energy harvesting, and meets the 1% packet error rate specification of the IEEE 802.15.4 standard. 

Earthquakes are unpredictable disasters. Earthquake early warning (EEW) systems have the potential to mitigate this unpredictability by providing seconds to minutes of warning. This warning could enable people to move to safe zones, and machinery (such as mass transit trains) to be slowed or shutdown. The several EEW systems operating around the world use conventional seismic and geodetic network infrastructure – that only exist in a few nations. However, the proliferation of smartphones – which contain accelerometers that could potentially detect earthquakes – offers an opportunity to create EEW systems without the need to build expensive infrastructure. To take advantage of this smartphone opportunity, researchers at the University of California, Berkeley have developed a technology to allow earthquake alerts to be issued based on detecting earthquakes underway using the sensors in smartphones. Called MyShake, this EEW system has been shown to record magnitude 5 earthquakes at distances of 10 km or less. MyShake incorporates an on-phone detection capability to distinguish earthquakes from every-day shakes. The UC Berkeley technology also collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is underway as well as estimates the location and magnitude in real-time. This information can then be used to issue an alert of forthcoming ground shaking. Additionally, the seismic waveforms recorded by MyShake could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics.

Energy Isolation Lock out Tag out (“LOTO”) is a series of CalOSHA and FedOSHA code compliance requirements and is the primary means by which equipment must be rendered “safe” prior to allowing personnel to work on the equipment.  LOTO codes require equipment-specific written procedures identifying all types of energy sources needed to operate the equipment as well as the energy-isolation methods and locations of utility disconnects, stored energy, etc. In addition, every LOTO procedure must be annually verified to confirm the written procedure is still accurate to the equipment.   Whereas current LOTO procedures are typically hand-written or using other time-consuming processes, UC Berkeley authors have created software allowing users to retrieve LOTO procedures in real-time guiding the end-user through a logical thought process to allow them to identify all energy sources and safety processes, and equipment needed.  

To-date, plasmon detection methods have been utilized in the life sciences, electrochemistry, chemical vapor detection, and food safety. While passive surface plasmon resonators have lead to high-sensitivity detection in real time without further contaminating the environment with labels. Unfortunately, because these systems are passively excited, they are intrinsically limited by a loss of metal, which leads to decreased sensitivity. Researchers at the University of California, Berkeley have developed a novel method to detect distinct molecules in air under normal conditions to achieve sub-parts per billion detection limits, the lowest limit reported. This device can be used detecting a wide array of molecules including explosives or bio molecular diagnostics utilizing the first instance of active plasmon sensor, free of metal losses and operating deep below the diffraction limit for visible light.  This novel detection method has been shown to have superior performance than monitoring the wavelength shift, which is widely used in passive surface plasmon sensors. 

There is a growing interest in point-of-care testing (POCT) where testing is done at or near the site of patient care, since POCT has a short therapeutic turnaround time, decreased process steps where errors can occur and only a small sample volume is required to perform a test.    UC Berkeley researchers have developed a mobile molecular diagnostics system that leverages efficient and dependable blood sampling, automated sample preparation, rapid optical detection of multi-analyte nucleic acids and proteins, and user-friendly systems integration with wireless communication.  The system includes a hand-held automated device with an adaptive sample control module, an optical signal transduction module, and an interface to a smartphone making this a reliable and field-applicable system for point-of-care and on-demand diagnostics. 

On-chip capacitively transduced vibrating polysilicon micromechanical resonators have achieved quality factor Q's over 160,000 at 61 MHz and larger than 14,000 at about 1.5 GHz -- making them suitable for on-chip frequency selecting and setting elements for filters and oscillators in wireless communication applications. However, there are applications -- such as software-defined cognitive radio, that require even higher Q's at RF to enable low-loss selection of single channels (instead of bands) to reduce power consumption down to levels conducive to battery-powered handheld devices. To address those higher Q RF applications, researchers at UC Berkeley have invented design improvements to MEMS resonators that reduce energy loss and in turn increase resonator Q. In reducing energy loss to the substrate while supporting all-polysilicon UHF MEMS disk resonators, the Berkeley design improvements enable quality factors as high as 56,061 at 329 MHz and 93,231 at 178 MHz -- that are values in the same range as previous disk resonators using multiple materials with more complex fabrication processes. Measurements confirm Q improvements of 2.6X for contour modes at 154 MHz, and 2.9X for wine glass modes around 112 MHz over values achieved by all-polysilicon resonators with identical dimensions. The results not only demonstrate an effective Q-enhancement method with minimal increase in fabrication complexity, but also provide insights into energy loss mechanisms that have been largely responsible for limiting Q's attainable by all-polysilicon capacitively transduced MEMS resonators.