Methods and systems are disclosed for analyzing one or more images of a textile to determine a presence or absence of defects. In one example, an image of at least a portion of a textile may be obtained and compared to a reference image of a reference textile. Based on the comparison, one or more areas indicative of a height variation between the textile and the reference textile may be determined. An action may be performed based on the one or more areas indicative of the height variation.

Technologies for a fabric acoustic sensor are disclosed. The fabric acoustic sensor includes a conductive thread and a non-conductive thread, which form a diaphragm that vibrates in response to a sound wave. As a result of the vibration, the conductive thread stretches, and a resistance of the conductive thread varies. The change in resistance is measured by a compute device, and the compute device may determine the sound wave based on the change in resistance. In some embodiments, the fabric acoustic sensor may be used to monitor a heart rate, locate an object, and/or provide an input for noise cancellation.

A defect identification device for identifying defects in fabrics comprising a support frame fitted with a crossbar supporting at least one video camera for capturing images of a fabric while it is being woven, movement means for moving the at least one video camera, a processing and control unit programmed to control the movement means to move the video camera automatically in real time along a transverse weft direction (X-X), acquiring in advance the geometry of the fabric to be made and setting at least one theoretical dimensional parameter of comparison and a tolerance limit value for said theoretical dimensional parameter, capturing images of the fabric being formed in real time, processing said images so as to acquire said actual dimensional parameter of the fabric being formed and compare it to the theoretical dimensional parameter, detecting the presence of a weaving error.

Methods and systems are disclosed for analyzing one or more images of a textile to determine a presence or absence of defects. In one example, an image of at least a portion of a textile may be obtained and compared to a reference image of a reference textile. Based on the comparison, one or more areas indicative of a height variation between the textile and the reference textile may be determined. An action may be performed based on the one or more areas indicative of the height variation.

Methods and systems are disclosed for analyzing one or more images of a textile to determine a presence or absence of defects. In one example, an image of at least a portion of a textile may be obtained and compared to a reference image of a reference textile. Based on the comparison, one or more areas indicative of a height variation between the textile and the reference textile may be determined. An action may be performed based on the one or more areas indicative of the height variation.

Computing devices and methods utilizing a joint speaker location/speaker identification neural network are provided. In one example a computing device receives an audio signal of utterances spoken by multiple persons. Magnitude and phase information features are extracted from the signal and inputted into a joint speaker location and speaker identification neural network. The neural network utilizes both the magnitude and phase information features to determine a change in the person speaking. Output comprising the determination of the change is received from the neural network. The output is then used to perform a speaker recognition function, speaker location function, or both.

Methods and systems are disclosed for analyzing one or more images of a textile to determine a presence or absence of defects. In one example, an image of at least a portion of a textile may be obtained and compared to a reference image of a reference textile. Based on the comparison, one or more areas indicative of a height variation between the textile and the reference textile may be determined. An action may be performed based on the one or more areas indicative of the height variation.

An apparatus (100) for optically characterizing a textile sample (106) comprises a presentation subsystem (102) comprising a viewing window (108). A radiation subsystem (114) comprises a radiation source (120) for directing a first, ultraviolet radiation (122) and a second, visible radiation (123) toward the sample (106), and causing the sample (106) to produce a fluorescent radiation (124) and a reflected radiation (125). A sensing subsystem (126) comprises an imager (130) for capturing the fluorescent radiation (124) and the reflected radiation (125) in an array of pixels (408). A control subsystem (132) comprises a processor (136) for controlling the presentation subsystem (102), the radiation subsystem (114), and the sensing subsystem (126), and for creating a fluorescent and reflected radiation image (400) containing both spectral information and spatial information in regard to the fluorescent radiation (124) and the reflected radiation (125).

The current invention concerns determining a fiber composition of a textile sample. The sample is irradiated with near infrared (NIR) light. Reflected NIR light from the sample is captured. A vector of spectral values is determined based on the captured light. The vector is input to a deep neural network (DNN). The DNN comprises a sequence of layers of nodes, in particular an input layer, at least two intermediate layers, and an output layer. The nodes of successive layers of the sequence are interconnected via weighted edges. The DNN outputs for each of a plurality of fiber material types a numerical relative composition amount.

A method of inspecting images on printed products by a computer in a printing machine. Printed products are recorded and digitized by an image sensor of an image inspection system in the course of the image inspection process, and the computer compares them to a digital reference image. If deviations are found, the defective printed products are removed. The computer analyzes the deviations found in the course of the image inspection process together with further data from other system parts and from the machine, determines specific defect classes and the causes thereof based on the defects by machine learning processes, assigns the defects found in the image inspection process to the defect classes in a corresponding way, and displays the classified detected defects with their defect classes and causes to an operator of the machine so that the operator can initiate specific measures to eliminate the defect causes.