Magnetic stripe-enabled card applications have been around for over two decades. The use of magnetic and other "chip" smart cards is widespread. for most high security applications, however, downsides remain with current offerings, including the risks of sensitive card information being copied or stolen. Alternatives suing biometrics typically require robust local storage via biometric parameters database or continuous communication with it. Moreover, the biometric parameters associated with such systems are constant and often cannot be modified. To address these problems, researchers at the University of California, Berkeley, have developed a novel approach to storing card information on person without a direct need for a smart cards or biometric recognition features like fingerprint, face, iris, voice, or palmprint.

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 are many who work to build ever-improving legged robots for new and improved applications in military, surveillance, leisure, and education. Animals have a well-defined approach to running at high velocities With respect to small-scale engineered systems, limitations on stride kinematics are common across many dynamic running robots. Kinematic adaptations which would increase the stride length of these robots are possible, but they incur a cost in complexity either in hardware, control, or both. To help solve these challenge, researchers at Berkeley have investigated the efficacy of locomotion strategies in their respective limits, and have developed milliscale and microscale robots using the Smart Composite Microstructures (SCM) process which creates linkages by combining rigid and flexible materials using planar processes. Their latest creation, the X2-VelociRoACH, is made primarily out of cardboard and measures just 10 cm long, yet it can run at stride frequencies up to 45 Hz and velocities up to 4.9 m/s, making it the fastest legged robot relative to size (the X2-VelociRoACH is actually faster than a real roach, which can achieve 1.5 m/s). With the X2-VelociRoACH, the researchers have demonstrated a stride frequency of a legged robotic platform far beyond what an animal of equivalent size would use.

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. 

Thousands of severely disabled patients are unable to communicate due to paralysis, locked-in syndrome, Lou Gehrig’s disease, or other neurological disease. Restoring communication in these patients have proven a major challenge. Prosthetic devices that are operated by electrical signals measured by sensors implanted in the brain are being developed in an effort to address this problem.  Investigators at University of California at Berkeley have responded to this challenge by developing an algorithm to decode speech, including arbitrary words and sentences, using brain recordings from the human cortex.  A computational model is trained and determines how recorded electrical signals at specific brain sites represent different speech features, for example acoustic frequencies.  The trained model then takes as input novel brain recordings and outputs a set of predicted speech features.  Once these steps are accomplished, speech sounds are either directly synthesized or words are identified from the predicted speech features using statistical techniques.  The brain signal decoding algorithm can decode speech solely from brain signals and may permit communication via thought alone.   

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.