Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: An autonomous sensor that includes a snap-through buckling beam, a proof mass, an ultra-low-power microcontroller unit, a wireless transmitter, a power management unit, and a power storage unit. The snap-through buckling beam produces mechanical energy. The proof mass is attached to the snap-through buckling beam. The proof mass transfers mechanical energy to two piezoelectric transducers that convert the mechanical energy into electrical energy and produce an output signal. The ultra-low-power microcontroller unit converts the output signal into output data. The wireless transmitter transfers the output data to an external device. The power management unit provides the electrical energy to the ultra-low-power microcontroller unit and the wireless transmitter. The power storage unit is rechargeable, stores electrical energy from the two piezoelectric transducers, and non-replaceable. The autonomous sensor simultaneously harvests energy and measures vibrations in an external environment.

Abstract: An autonomous sensor that includes a snap-through buckling beam, a proof mass, an ultra-low-power microcontroller unit, a wireless transmitter, a power management unit, and a power storage unit. The snap-through buckling beam produces mechanical energy. The proof mass is attached to the snap-through buckling beam. The proof mass transfers mechanical energy to two piezoelectric transducers that convert the mechanical energy into electrical energy and produce an output signal. The ultra-low-power microcontroller unit converts the output signal into output data. The wireless transmitter transfers the output data to an external device. The power management unit provides the electrical energy to the ultra-low-power microcontroller unit and the wireless transmitter. The power storage unit is rechargeable, stores electrical energy from the two piezoelectric transducers, and non-replaceable. The autonomous sensor simultaneously harvests energy and measures vibrations in an external environment.

Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: A sensor for detecting both magnetic fields and electric fields can include at least one Sawyer-Tower (ST) circuit that can incorporate a multiferroic capacitor. An odd number of ST circuits coupled together in a ring configuration, so that for each ST circuit, the output of one ST circuit is an input to another of the ST circuits. The multiferroic capacitors can include a multiferroic layer that can be deposed on a substrate. For each multiferroic capacitor, the deposition process can cause an inherent amount of impurities in the multiferroic layer. The number of said odd number of ST circuits to be coupled together is chosen according to the amount of impurities, to “forgive” the impurities. The higher the level of impurities in the multiferroic layers, that more ST circuits that are required in the ring to achieve the same sensor sensitivity. BDFO can be chosen for the multiferroic material.

Abstract: A sensor for detecting both magnetic fields and electric fields can include at least one Sawyer-Tower (ST) circuit that can incorporate a multiferroic capacitor. An odd number of ST circuits coupled together in a ring configuration, so that for each ST circuit, the output of one ST circuit is an input to another of the ST circuits. The multiferroic capacitors can include a multiferroic layer that can be deposed on a substrate. For each multiferroic capacitor, the deposition process can cause an inherent amount of impurities in the multiferroic layer. The number of said odd number of ST circuits to be coupled together is chosen according to the amount of impurities, to “forgive” the impurities. The higher the level of impurities in the multiferroic layers, that more ST circuits that are required in the ring to achieve the same sensor sensitivity. BDFO can be chosen for the multiferroic material.

Abstract: A nonlinear dynamic system comprising: a number N of nonlinear components, wherein each nonlinear component experiences intrinsic oscillation when a coupling parameter ? is tuned past a threshold value, and wherein the nonlinear components are unidirectionally coupled together in a ring configuration; and a signal generator configured to generate N coherent locking signals; wherein each locking signal is phase shifted by 2?/N with respect to the other locking signals; and wherein the signal generator is coupled to the nonlinear components such that each locking signal locks a frequency of the intrinsic oscillation of one of the nonlinear components to a frequency of the locking signal.