The disclosure describes new apparatus, systems and methods utilizing magnetoelectric neural stimulators with tunable amplitude and waveform. Specific embodiments of the present disclosure include a magnetoelectric film, a magnetic field generator and an electrical circuit coupled to the magnetoelectric film, in particular embodiments, the electrical circuit comprises components configured modify an electrical output signal produced by the magnetoelectric film. In certain embodiments, the electrical circuit is configured to modify the electric signal to charge a charge storage element, to transmit data to an implantable wireless neural stimulator, and to provide a stimulation output to electrodes.

An active cooling system and method for using the active cooling system are described. The active cooling system includes a cooling element having a first side and a second side. The first side of the cooling element is distal to a heat-generating structure and in communication with a fluid. The second side of the cooling element is proximal to the heat-generating structure. The cooling element is configured to direct the fluid using a vibrational motion from the first side of the cooling element to the second side such that the fluid moves in a direction that is incident on a surface of the heat-generating structure at a substantially perpendicular angle and then is deflected to move along the surface of the heat-generating structure to extract heat from the heat-generating structure.

A system for cooling a mobile phone and method for using the system are described. The system includes an active piezoelectric cooling system, a controller and an interface. The active piezoelectric cooling system is configured to be disposed in a rear portion of the mobile phone distal from a front screen of the mobile phone. The controller is configured to activate the active piezoelectric cooling system in response to heat generated by heat-generating structures of the mobile phone. The interface is configured to receive power from a mobile phone power source when the active piezoelectric cooling system is activated.

A magneto-electric converter capable of converting a variation in magnetic field into a potential difference between two electrical terminals includes a support layer comprising two electrical terminals; a stack disposed on the support layer of a first layer made from a magnetostrictive material defining the reference plane and of a second layer made from a piezoelectric material having a polarization axis in the plane defined by the second layer, parallel to the reference plane; the second layer comprising electrodes; and a means for electrical connection of the electrodes to the electrical terminals.

A bilayer vibrotactile actuator adapted to provide a vibrational force. The bilayer vibrotactile actuator contains two metal coil layers separated from each other; and a magnet adapted to vibrate as a mass. Each of the two metal coil layers is adapted to be connected to an external power supply to generate an electromagnetic field, causing the magnet to vibrate. A wearable haptic feedback device containing the bilayer vibrotactile actuator is also disclosed. With two metal coil layers effecting on the magnet, the bilayer vibrotactile actuator can generate a more powerful overall magnetic field providing an enhanced haptic sensation to users.

A bilayer vibrotactile actuator adapted to provide a vibrational force. The bilayer vibrotactile actuator contains two metal coil layers separated from each other; and a magnet adapted to vibrate as a mass. Each of the two metal coil layers is adapted to be connected to an external power supply to generate an electromagnetic field, causing the magnet to vibrate. A wearable haptic feedback device containing the bilayer vibrotactile actuator is also disclosed. With two metal coil layers effecting on the magnet, the bilayer vibrotactile actuator can generate a more powerful overall magnetic field providing an enhanced haptic sensation to users.

A cooling system and method for using the cooling system are described. The cooling system includes an array of cooling elements and a controller. The array of cooling elements corresponds to regions of the heat-generating structure where heat is generated in response to operation of the semiconductor. The controller is configured to activate portions of the array of cooling elements based on a determination that operation of the heat-generating structure is likely to generate heat in a given region of the heat-generating structure.

A cooling system and method for using the cooling system are described. The cooling system includes an array of cooling elements and a controller. The array of cooling elements corresponds to regions of the heat-generating structure where heat is generated in response to operation of the semiconductor. The controller is configured to activate portions of the array of cooling elements based on a determination that operation of the heat-generating structure is likely to generate heat in a given region of the heat-generating structure.

A matching control method for mechanical impedance of a magnetostrictive precision transducer includes developing a three-layer neural network model corresponding to a Young's modulus of a Terfenol-D material; acquiring sample data to form a training sample set and a testing sample set; training the model using a Bayesian regularization training algorithm, and optimizing connection weights and thresholds among layers of the tested model, so as to obtain a final three-layer neural network model; based on the final model, building an inverse model of mechanical impedance of the magnetostrictive precision transducer; using a current level of impedance of a load as an input of the inverse model to obtain a bias magnetic field, and changing a level of the bias magnetic field by changing a bias current in an excitation coil of the transducer, thereby achieving adaptive matching between the mechanical impedance of the transducer and the impedance of the load.

A matching control method for mechanical impedance of a magnetostrictive precision transducer includes developing a three-layer neural network model corresponding to a Young's modulus of a Terfenol-D material; acquiring sample data to form a training sample set and a testing sample set; training the model using a Bayesian regularization training algorithm, and optimizing connection weights and thresholds among layers of the tested model, so as to obtain a final three-layer neural network model; based on the final model, building an inverse model of mechanical impedance of the magnetostrictive precision transducer; using a current level of impedance of a load as an input of the inverse model to obtain a bias magnetic field, and changing a level of the bias magnetic field by changing a bias current in an excitation coil of the transducer, thereby achieving adaptive matching between the mechanical impedance of the transducer and the impedance of the load.