Abstract: Provided are a fibroin film having an electroconductive wiring pattern with high fixability, a sensor device, and a biological sensor device. Specifically, provided is an electroconductive film including: a base material containing fibroin; and electroconductive wiring containing a plurality of electroconductive particles, wherein the electroconductive wiring includes: a permeation portion formed of the electroconductive particles in a permeation layer in which the electroconductive particles have permeated the base material; and a non-permeation portion formed of the electroconductive particles prevented from permeating the base material.

Abstract: Provided are a fibroin aqueous solution excellent in storage stability and also excellent in foamability and a method of producing the same. The fibroin aqueous solution includes fibroin and a compound represented by the general formula (1) or (2).

Abstract: The present disclosure provides an image processing technique for performing high-speed tracking of a detection target in wide area monitoring. An image processing device includes a video acquisition unit to acquire a first image, an object detection processing unit to execute an object detection process on the first image, specify a target object therein, and generate a first object detection result indicating a position of the target object in the first image, a tracking processing unit to acquire a target region image including the target object, generate a resized image by executing a resizing process, execute an object detection process on the resized image, and generate a second object detection result indicating a position of the target object therein, and an integration processing unit to generate a final object detection result by integrating the first and second object detection results.

Abstract: There is provided an image analysis system having an image analysis server which analyzes an input image of a monitoring area and detects a state of a specific monitored object. The image analysis server is configured to divide, in the input image of the monitoring area, a portion of an area in which the monitored object is expected to be present into a plurality of grids, generate a trained model trained by associating “1” with an image of a grid in which the monitored object is present and “0” with an image of a grid in which the monitored object is not present for each grid, calculate, for the input image, a confidence of a presence of the monitored object for each grid using the trained model, determine a presence or absence of the monitored object in the monitoring area based on confidences of the plurality of grids, and detect an abnormality when a confidence value in at least one of the grids is inconsistent with the determination result.

Abstract: Provided is a method for estimating a pulse rate with high accuracy in the case that short-term burst noise is mixed in an estimation interval. Included are a pulse wave signal generating step of generating a pulse wave signal from an image of a pulse rate estimation target; a peak component suppression step of limiting an amplitude value of the pulse wave signal that is larger than a first threshold value to the first threshold value, and outputting a pulse wave analysis signal; and a frequency analysis step of outputting a frequency spectrum of the pulse wave analysis signal.

Abstract: The cloud server holds the main database for storing all the registration data that is handled in the present system, and the edge server that is arranged close to the sensor holds a sub-database for storing part of the registration data. The sub-database in the edge server stores only the registration data having a high probability of being verified in the edge server. In the case where the edge server verifies the detection data that has been acquired by the sensor with the registration data within the sub-database and determines that the registration data that matches the detection data does not exist within the sub-database, the configuration allows the detection data to be transmitted to the cloud server and requests the detection data to be verified with the registration data within the main database.

Abstract: An image analysis system and update method for a machine-learning model are to update a model in operation 111 to a retrained model 121, wherein in an image analysis server 1 which analyzes shot images, a first inference unit 11 performs inference using the trained model in operation 111, a relearning unit 14 performs relearning to generate a retrained model, a second inference unit 12 performs inference using a retrained model 121, and a comparison unit 15 compares the execution results of the first inference unit 11 and the second inference unit 12 to determine whether or not to update the model in operation 111 to the retrained model 121.

Abstract: A technique is provided for detecting the presence or absence of an abnormality with respect to an object appearing in a target image with high accuracy without using AI, even in an environment in which luminance values and colors are likely to fluctuate. An abnormality detection device (230) includes: an image input unit (232) for inputting an input image indicating an abnormality detection target object; a gradient distribution generation unit (234) for dividing the input image into predetermined regions and generating, for each region, a gradient distribution that indicates a distribution of a luminance gradient direction of the region; and an abnormality determination unit (236) for determining the presence or absence of an abnormality by analyzing the gradient distribution generated for each region.

Abstract: An Objective of the present invention is to improve the inter-camera cooperation in a monitoring system comprising multiple cameras each having a function to perform video analysis by use of an AI model. A monitoring system according to the present example comprises: multiple AI cameras 101 each having a function to perform video analysis by use of an AI mode; and an analyzing device 102 that generates the AI model used in the AI cameras 101. Each of the AI cameras 101 detects suspicious behaviors through video analysis using a particular-behavior detection AI model for detecting the suspicious behaviors. When suspicious behaviors are detected by any of the AI cameras 101, the analyzing device 102 generates a particular-person detection AI model for detecting a particular person (suspicious person) having done the suspicious behaviors, and transmits the particular-person detection AI model to each of the AI cameras 101.

Abstract: There is provided an image analysis system having an image analysis server which analyzes an input image of a monitoring area and detects a state of a specific monitored object. The image analysis server is configured to divide, in the input image of the monitoring area, a portion of an area in which the monitored object is expected to be present into a plurality of grids, generate a trained model trained by associating “1” with an image of a grid in which the monitored object is present and “0” with an image of a grid in which the monitored object is not present for each grid, calculate, for the input image, a confidence of a presence of the monitored object for each grid using the trained model, determine a presence or absence of the monitored object in the monitoring area based on confidences of the plurality of grids, and detect an abnormality when a confidence value in at least one of the grids is inconsistent with the determination result.

Abstract: A machine learning model fraud detection system and fraud detection method wherein a license/model management apparatus: generates a test data-trained model by inputting a pre-trained model and test data associated therewith from a licensor apparatus, carrying out learning using the test data on the pre-trained model; stores the test data-trained model in association with the output values obtained when the test data is executed in the test data-trained model; inputs the associated test data into a user model, executes the model when the user model is inputted from a user apparatus using the test data-trained model; compares the output data from the user model with the stored output values from the test data-trained model and detects the fraud if the resulting error is outside tolerance limits.

Abstract: Provided is a method for estimating a pulse rate with high accuracy in the case that short-term burst noise is mixed in an estimation interval. Included are a pulse wave signal generating step of generating a pulse wave signal from an image of a pulse rate estimation target; a peak component suppression step of limiting an amplitude value of the pulse wave signal that is larger than a first threshold value to the first threshold value, and outputting a pulse wave analysis signal; and a frequency analysis step of outputting a frequency spectrum of the pulse wave analysis signal.

Abstract: Provided are a spectacle lens and a technique associated therewith, the spectacle lens including: an object-side surface and an eyeball-side surface; a first refractive region having a first refractive power based on a prescription for correcting a refractive error of an eye; and a second refractive region having a refractive power different from the first refractive power, the second refractive region having a function of forming a focal point at a position other than a retina of the eye so as to suppress progression of the refractive error of the eye, wherein an interface portion is disposed inside the spectacle lens, the interface portion includes a base portion, and convex portions protruding from the base portion, or concave portions recessed from the base portion, the function of the second refractive region is exerted by using mutually different respective refractive indexes of two types of members sandwiching the interface portion therebetween, and the convex portions or the concave portions, and an o

Abstract: The cloud server holds the main database for storing all the registration data that is handled in the present system, and the edge server that is arranged close to the sensor holds a sub-database for storing part of the registration data. The sub-database in the edge server stores only the registration data having a high probability of being verified in the edge server. In the case where the edge server verifies the detection data that has been acquired by the sensor with the registration data within the sub-database and determines that the registration data that matches the detection data does not exist within the sub-database, the configuration allows the detection data to be transmitted to the cloud server and requests the detection data to be verified with the registration data within the main database.

Abstract: A plurality of terminal stations that share the same access point are controlled to have communication opportunities on the basis of terminal station groups (e.g., operators). A radio communication system, in which the plurality of terminal stations 102 to 106 is connected to a network via an access point 101, includes an access manager 107 connected to the access point. The access manager includes a user/group manager that manages the plurality of terminal stations in groups, and a radio resource manager that determines the necessity of bandwidth limit on the basis of the communication status of each of the groups and transmit from the access point a control message that allows communication of a particular group among the groups and stops communication of other groups when it is determined that the bandwidth limit is necessary.

Abstract: A plurality of terminal stations that share the same access point are controlled to have communication opportunities on the basis of terminal station groups (e.g., operators). A radio communication system, in which the plurality of terminal stations 102 to 106 is connected to a network via an access point 101, includes an access manager 107 connected to the access point. The access manager includes a user/group manager that manages the plurality of terminal stations in groups, and a radio resource manager that determines the necessity of bandwidth limit on the basis of the communication status of each of the groups and transmit from the access point a control message that allows communication of a particular group among the groups and stops communication of other groups when it is determined that the bandwidth limit is necessary.

Abstract: A machine learning model fraud detection system and fraud detection method wherein a license/model management apparatus: generates a test data-trained model by inputting a pre-trained model and test data associated therewith from a licensor apparatus, carrying out learning using the test data on the pre-trained model; stores the test data-trained model in association with the output values obtained when the test data is executed in the test data-trained model; inputs the associated test data into a user model, executes the model when the user model is inputted from a user apparatus using the test data-trained model; compares the output data from the user model with the stored output values from the test data-trained model and detects the fraud if the resulting error is outside tolerance limits.

Abstract: A wireless communication system comprises a mobile communication apparatus, and a terminal device. The mobile communication apparatus comprises a first wireless unit that wirelessly communicates control information to and from the terminal device in a first communication method, and a second wireless unit that wirelessly communicates user information to and from the terminal device in a second communication method that uses a higher frequency band than the first communication method. The terminal device comprises a third wireless unit that performs wireless communication to and from the first wireless unit, and a fourth wireless unit that performs wireless communication to and from the second wireless unit. The mobile communication apparatus or the terminal device determines a timing of wireless communication to be performed between the second wireless unit and the fourth wireless unit by performing wireless communication between the first wireless unit and the third wireless unit.

Abstract: A method for adjusting a transmission timing of a terminal station, which is performed at a base station in a Time-Division Duplex communication system, is disclosed. The method includes (a) receiving ranging signals or other reference signals, transmitted from a terminal station; (b) determining whether transmission timing at the terminal station is appropriate or not, by referring to the ranging signals transmitted from the terminal station; and (c) transmitting a value for adjusting the transmission timing at the terminal station. The value of the transmission timing is a value of the integral multiples of the timing offset detection capability of the ranging signal, or a value calculated by referring to the received reference signals, which have assigned with a different interval from an interval of the ranging signals.

Abstract: A wireless communication system comprises a mobile communication apparatus, and a terminal device. The mobile communication apparatus comprises a first wireless unit that wirelessly communicates control information to and from the terminal device in a first communication method, and a second wireless unit that wirelessly communicates user information to and from the terminal device in a second communication method that uses a higher frequency band than the first communication method. The terminal device comprises a third wireless unit that performs wireless communication to and from the first wireless unit, and a fourth wireless unit that performs wireless communication to and from the second wireless unit. The mobile communication apparatus or the terminal device determines a timing of wireless communication to be performed between the second wireless unit and the fourth wireless unit by performing wireless communication between the first wireless unit and the third wireless unit.