Abstract: Some embodiments relate to a method and system of optimizing sensor data collection for centralized training of machine learning models. The method comprises identifying a data prioritization technique; collecting sensor data and implementing the identified data prioritization technique to prioritize the data for storage and transmission to a centralized repository; transmitting the data to the centralized repository; training the machine learning model on the centralized repository using the transmitted data to predict or identify events, such as failure in vehicles and other machinery; evaluating the data prioritization technique using a forward pass of an autoencoder to create an output; and determining an adjustment to the data prioritization technique based on the output.

Abstract: It is known for a product to be mass produced by way of a manufacturing process. Typically, a quality control step is used in a manufacturing process to monitor the quality of manufactured products. However, quality control procedures in manufacturing are typically labour intensive. A technician or other person must inspect the product and carry out any necessary tests. The present disclosure provides a surface roughness measurement system and method for determining a surface roughness of a product with an imaging system, a coherent light source, a light sensor and several trained machine learning algorithms.

Abstract: Some embodiments relate to a method of controlling a sensor apparatus for an electrical circuit. The method comprises: applying a clustering algorithm to a database of sensor measurements acquired from the sensor, the database of sensor measurements comprising sensor measurements indicative of the power supply during one or more events associated with the operation of one or more of the electrical loads and sensor measurements indicative of noise measurements during a baseline condition of the electrical circuit, the clustering algorithm being applied to the database of sensor measurements to determine a set of measurement clusters; identifying a cluster, from the set of measurement clusters, indicative of noise measurements; determining a noise threshold for the sensor based on the cluster indicative of noise measurements; and controlling one or more feedback actions of the sensor apparatus in dependence on the determined noise threshold.

Abstract: Some embodiments relate to a method of determining authenticity of a product, such as an electrical product. The method includes acquiring image sensor data indicative of an image including a label including information related to the product. The method includes identifying regions of the label that include information related to the product, and classifying each identified region based on a determined form of information included in the region, by using a trained machine learning model based on the acquired image data. For each classified region of the label, the method involves identifying attributes of the classified region based on the acquired image data, and comparing the identified attributes against respective expected attributes for a label region of an authentic product that includes the determined form of information of the classified region. The method involves providing an indication of authenticity of the product determined based on the comparison.

Abstract: A grip adjustment system comprising: a sleeve positionable, in use, on an object configured to be gripped by a user. The system also comprises a distributed array of actuators, each actuator being arranged to actuate a respective portion of the sleeve between a first position and a second position in response to an actuation signal; and a processor. The processor is operable to: receive a pressure distribution and an event quality indicator corresponding to an event of interest; determine an optimal grip based on the pressure distribution and the event quality indicator; select an actuator, of the distributed array of actuators, to be actuated based on the optimal grip; transmit the actuation signal to the actuator such that the shape of the sleeve is changed and the grip of the user is adjusted.

Abstract: It is known for a product to be mass produced by way of a manufacturing process. Typically, a quality control step is used in a manufacturing process to monitor the quality of manufactured products. However, quality control procedures in manufacturing are typically labour intensive. A technician or other person must inspect the product and carry out any necessary tests. The present disclosure provides a surface roughness measurement system and method for determining a surface roughness of a product with an imaging system, a coherent light source, a light sensor and several trained machine learning algorithms.