A first magnetic field generator generates a magnetic field intersecting a detection object being transported along a transport path. A second magnetic field generator opposite to the first magnetic field generator with respect to the transport path generates a magnetic field intersecting the detection object. A first magnetoresistive element between the first magnetic field generator and the transport path outputs, as a change in resistance, a change in magnetic flux density produced by transport of the detection object. The first and second magnetic field generators are different in a magnetic pole facing the transport path and are arranged with a center of the first magnetic field generator in a transport direction of the detection object and a center of the second magnetic field generator in the transport direction are located at mutually different positions. The first magnetoresistive element includes a first resistor and a second resistor arranged with spacing therebetween.

A magnetic sensor includes a substrate having a first surface and a second surface, which is opposite the first surface, and a detection unit provided on the first surface. The detection unit includes a magnetoresistive effect element, the resistance value of which changes in accordance with an input magnetic field, provided on the first surface, and a protective layer that covers at least the magnetoresistive effect element. The magnetoresistive effect element is configured in a linear shape extending in a first direction on the first surface. The detection unit has a first width, which is a length in a second direction, orthogonal to the first direction, and a second length, which is greater than the first width. The first width is the length of the detection unit on the first surface, and the second width is the length of the top surface of the detection unit.

Discussed is a secondary battery and a method for detecting lithium dendrite of the secondary battery. The secondary battery includes: a positive electrode and a negative electrode; a battery case configured to accommodate the positive electrode and the negative electrode; and a lithium dendrite detector provided at a tab of the positive electrode at a side of the battery case to detect lithium dendrite.

A sensor and/or detector having been optimized to produce a rapid rate of change in capacitive reactance and or inductive reactance such that changes in material composition or signal withing the electromagnetic field of the sensor or detecting means will produce a high rate of change in the output signal of the sensing or detecting means.

A control device controls a magnetic field generated by a magnetic field generator, and includes a magnetic field controller that controls the magnetic field generator based on a detected value by a magnetic field sensor. The magnetic field controller receives a command value generated by the magnetic field generator, and the detected value. The magnetic field controller generates an error signal based on an error between the command value and the detected value and outputs to the magnetic field generator a control signal amplified by a control gain against the error. The control gain includes: a first gain that becomes smaller as a frequency of the error signal gets higher; and a second gain that gets larger as an amplitude of the error signal gets larger.

Example embodiments of the present invention provide a magnetic relaxometry measurement apparatus, comprising: a magnetizing system configured to supply a pulsed magnetic fields to a sample; a sensor system configured to detect magnetic fields produced by induced magnetization of the sample after a magnetic field pulse from the magnetizing system; one or more compensating coils configured to suppress generation of eddy currents in an environment surrounding the apparatus due to the pulsed magnetic fields.

A magnetization measurement device includes: a current supply part supplying a periodically changing current to a sample made of a soft magnetic material with uniaxial magnetic anisotropy in a first direction and a bias magnetic field applied in a second direction crossing the first direction; a light irradiation part irradiating a surface of the sample with linearly polarized pulse light having a predetermined delay time with respect to the current and having a predetermined polarized surface; and a measurement part measuring magnetization of the sample at the delay time based on reflected light of the pulse light reflected by the surface of the sample. These enable the measurement of the change in the magnetization of the sample over time, which corresponds to supply of the periodically changing current.

A magnetization measurement device includes: a current supply part supplying a periodically changing current to a sample made of a soft magnetic material with uniaxial magnetic anisotropy in a first direction and a bias magnetic field applied in a second direction crossing the first direction; a light irradiation part irradiating a surface of the sample with linearly polarized pulse light having a predetermined delay time with respect to the current and having a predetermined polarized surface; and a measurement part measuring magnetization of the sample at the delay time based on reflected light of the pulse light reflected by the surface of the sample. These enable the measurement of the change in the magnetization of the sample over time, which corresponds to supply of the periodically changing current.

A system may transform sensor data from a sensor domain to an image domain using data-driven manifold learning techniques which may, for example, be implemented using neural networks. The sensor data may be generated by an image sensor, which may be part of an imaging system. Fully connected layers of a neural network in the system may be applied to the sensor data to apply an activation function to the sensor data. The activation function may be a hyperbolic tangent activation function. Convolutional layers may then be applied that convolve the output of the fully connected layers for high level feature extraction. An output layer may be applied to the output of the convolutional layers to deconvolve the output and produce image data in the image domain.

A system may transform sensor data from a sensor domain to an image domain using data-driven manifold learning techniques which may, for example, be implemented using neural networks. The sensor data may be generated by an image sensor, which may be part of an imaging system. Fully connected layers of a neural network in the system may be applied to the sensor data to apply an activation function to the sensor data. The activation function may be a hyperbolic tangent activation function. Convolutional layers may then be applied that convolve the output of the fully connected layers for high level feature extraction. An output layer may be applied to the output of the convolutional layers to deconvolve the output and produce image data in the image domain.