Abstract: A recognition method executed by a processor, includes: acquiring positional relation between a plurality of sensors each of which senses a distance to an object; provisionally classifying an orientation of the object relative to each individual sensor included in the sensors into one of a plurality of classifications based on sensing data acquired by the individual sensor; calculating likelihood of each combination corresponding to the positional relation between the sensors based on a result of provisional classification of the orientation of the object relative to the individual sensor; and classifying the orientation of the object corresponding to each individual sensor in accordance with the calculated likelihood of each combination.

Abstract: A motion recognition method for a computer to execute a process includes acquiring skeleton information in time series based on positional information of each of a plurality of joints that includes a certain joint of a subject who makes a motion; estimating a region where the certain joint is positioned among a plurality of regions obtained by dividing a region of an object used for the motion based on the positional information; recognizing the motion of the subject by using the skeleton information and the estimated region; and outputting the recognized motion of the subject.

Abstract: A learning device generates a plurality of learning images in which a distance image representing a distance from a reference position to each position of a human body or each position of an object and a part image for identifying each part of the human body or a part of the object are associated with each other. The learning device corrects, based on a distance image and a part image of the learning image, a value of a region corresponding to a part of the object among regions of the distance image. The learning device learns, based on a plurality of learning images including a corrected distance image, an identifier in which characteristics of the distance image and a part of the human body or a part of the object are associated with each other.

Abstract: A posture recognition system includes a sensor and a recognition apparatus, wherein the sensor captures a distance image including information on a distance to a subject, at an angular field controlled based on the distance to the subject, and the recognition apparatus includes a memory, and a processor coupled to the memory and configured to acquire the distance image and the angular field used when the sensor captures the distance image, from the sensor, and correct the acquired distance image based on a ratio between a first resolution corresponding to another angular field used when a training distance image is captured, and a second resolution corresponding to the acquired angular field, the training distance image being used for generating a learning model which recognizes a posture of the subject.

Abstract: An estimation method in which a computer executes processing includes: acquiring a first distance image that includes information regarding a distance from a sensor to a first subject; estimating three-axis polar coordinates data of the first subject from an acquired first distance image using a prediction model for posture recognition that has learned three-axis polar coordinates data based on a spine vector that corresponds to a spine of a second subject and a shoulder vector that corresponds to a line that connects both shoulders of the second subject that are generated on the basis of coordinate data that represents a position of the second subject and a second distance image based on the coordinate data of the second subject and the distance from the sensor; and estimating a posture of the first subject on the basis of the three-axis polar coordinates data of the first subject.

Abstract: A depth-image processing device includes: a memory; and a processor coupled to the memory and configured to: generate, cased on a synthetic model in which a three-dimensional model of a human body and a three-dimensional model of an object are combined, a plurality of learning images in which a depth image that indicates a distance from a reference position to respective positions on the human body or to respective positions on the object, and a part image to identify any one of respective parts of the human body and a cart of the object are associated with each other, and learn an identifier in which a feature of the depth image and any one of a part of the human body and a part of the object are associated with each other, based on the learning images.

Abstract: A recognition method in which a computer executes processing includes: generating posture information used to specify a posture of a subject on the basis of a distance image that includes the subject; inputting the distance image and the posture information to a learned model that is learned to recognize a skeleton of the subject; and specifying the skeleton of the subject using an output result of the learned model.

Abstract: An apparatus includes a memory; a processor coupled to the memory, wherein the processor acquires three-dimensional information of observation points on an object from a sensor, specifies a first area and second areas adjacent to the first area from areas of the object based on the three-dimensional information, and specifies positions of feature points included in the second areas; sets links based on the feature points included in the second areas to estimate positions of skeletons included in the second areas; and uses end points of the skeletons and lengths from the end points of the skeletons to a position of a part included in the first area among parameters used to specify the position of the part, specifies the position of the part included in the first area, and estimates a position of a skeleton included in the first area based on the specified position of the part.

Abstract: A recognition method executed by a processor, includes: acquiring positional relation between a plurality of sensors each of which senses a distance to an object; provisionally classifying an orientation of the object relative to each individual sensor included in the sensors into one of a plurality of classifications based on sensing data acquired by the individual sensor; calculating likelihood of each combination corresponding to the positional relation between the sensors based on a result of provisional classification of the orientation of the object relative to the individual sensor; and classifying the orientation of the object corresponding to each individual sensor in accordance with the calculated likelihood of each combination.

Abstract: A posture recognition system includes a sensor and a recognition apparatus, wherein the sensor captures a distance image including information on a distance to a subject, at an angular field controlled based on the distance to the subject, and the recognition apparatus includes a memory, and a processor coupled to the memory and configured to acquire the distance image and the angular field used when the sensor captures the distance image, from the sensor, and correct the acquired distance image based on a ratio between a first resolution corresponding to another angular field used when a training distance image is captured, and a second resolution corresponding to the acquired angular field, the training distance image being used for generating a learning model which recognizes a posture of the subject.

Abstract: A depth-image processing device includes: a memory; and a processor coupled to the memory and configured to: generate, cased on a synthetic model in which a three-dimensional model of a human body and a three-dimensional model of an object are combined, a plurality of learning images in which a depth image that indicates a distance from a reference position to respective positions on the human body or to respective positions on the object, and a part image to identify any one of respective parts of the human body and a cart of the object are associated with each other, and learn an identifier in which a feature of the depth image and any one of a part of the human body and a part of the object are associated with each other, based on the learning images.

Abstract: A learning device generates a plurality of leaning images in which a distance image representing a distance from a reference position to each position of a human body or each position of an object and a part image for identifying each part of the human body or a part of the object are associated with each other. The learning device corrects, based on a distance image and a part image of the learning image, a value of a region corresponding to a part of the object among regions of the distance image. The learning device learns, based on a plurality of learning images including a corrected distance image, an identifier in which characteristics of the distance image and a part of the human body or a part of the object are associated with each other.

Abstract: A sensor device includes: a first portion that is formed of an inorganic semiconductor material, includes a control module, and is reusable; a second portion that is formed of an organic material and separably coupled to the first portion; and a sensor that is disposed in at least one of the first and second portions.

Abstract: An apparatus includes a memory; a processor coupled to the memory, wherein the processor acquires three-dimensional information of observation points on an object from a sensor, specifies a first area and second areas adjacent to the first area from areas of the object based on the three-dimensional information, and specifies positions of feature points included in the second areas; sets links based on the feature points included in the second areas to estimate positions of skeletons included in the second areas; and uses end points of the skeletons and lengths from the end points of the skeletons to a position of a part included in the first area among parameters used to specify the position of the part, specifies the position of the part included in the first area, and estimates a position of a skeleton included in the first area based on the specified position of the part.

Abstract: A sensor device includes: a first portion that is formed of an inorganic semiconductor material, includes a control module, and is reusable; a second portion that is formed of an organic material and separably coupled to the first portion; and a sensor that is disposed in at least one of the first and second portions.

Abstract: A semiconductor integrated circuit includes a first transmission power mode configured to transmit by a first power, and a second transmission power mode configured to transmit by a second power smaller than the first power, the semiconductor integrated circuit. The semiconductor integrated circuit includes a first transistor configured to receive and amplify a transmission signal in the second transmission power mode, and an attenuator including a resistor element and a switching element, provided between an output of the first transistor and an output terminal, configured to control attenuation of an output signal of the first transistor.

Abstract: A transmitter includes a phase control circuit configured to receive a first and a second modulation signals, and a power amplifier configured to receive a third modulation signal. The phase control circuit includes a variable frequency divider, a frequency division ratio being controlled by the first modulation signal; a frequency modulation D/A converter configured to modulate the frequency by the second modulation signal; and a voltage controlled oscillator, including a varactor, configured to receive a first control voltage based on the first modulation signal and a second control voltage based on the second modulation signal. At least one of a capacitance value of the varactor of the voltage controlled oscillator, a control bit number of the frequency modulation D/A converter, and a bias current value of the frequency modulation D/A converter is changed based on a data transfer rate.

Abstract: A semiconductor integrated circuit includes a first transmission power mode configured to transmit by a first power, and a second transmission power mode configured to transmit by a second power smaller than the first power, the semiconductor integrated circuit. The semiconductor integrated circuit includes a first transistor configured to receive and amplify a transmission signal in the second transmission power mode, and an attenuator including a resistor element and a switching element, provided between an output of the first transistor and an output terminal, configured to control attenuation of an output signal of the first transistor.

Abstract: A band pass filter has a high pass filter into which an input signal is input; an amplifier having an inverting input terminal into which an output of the high pass filter is input, wherein the amplifier amplifies an input voltage between the inverting input terminal and a non-inverting input terminal and outputs an output signal to an output terminal; a first resistor connected between a non-inverting output terminal and the inverting input terminal of the amplifier; a first capacitor having a first terminal that is connected to the inverting input terminal; and an inverting amplifier that inverts a polarity of an output signal from the non-inverting output terminal of the amplifier and outputs the inverted signal to a second terminal of the capacitor.

Abstract: A band pass filter has a high pass filter into which an input signal is input; an amplifier having an inverting input terminal into which an output of the high pass filter is input, wherein the amplifier amplifies an input voltage between the inverting input terminal and a non-inverting input terminal and outputs an output signal to an output terminal; a first resistor connected between a non-inverting output terminal and the inverting input terminal of the amplifier; a first capacitor having a first terminal that is connected to the inverting input terminal; and an inverting amplifier that inverts a polarity of an output signal from the non-inverting output terminal of the amplifier and outputs the inverted signal to a second terminal of the capacitor.