Abstract: Methods are provided for deploying machine learning operations within existing storage devices for streamlining various calibration processes. Machine learning operations are specifically designed to generate inference data as a substitute for various measurements taken during calibration. These operations may be verified through additional sample measurements and rolled back when the results of the machine learning operations are outside of a range of approved values. Storage devices designed to utilize machine learning methods within calibration processes can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing an application stored in the non-volatile memory and receiving a request for measurement data from the application.

Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. A bag of machine learning models is used to reconstruct the data for the untested cylinders. A defect file for the HDD is generated, a classifier model may be applied to the defect file, and one or more neural network models may be applied. If the defects are unsuitable for use by the models, then a scan of the entire HDD is run instead. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges.

Abstract: Methods are provided for tactically deploying machine learning operations within existing storage devices without the need for additional capital investment. Machine learning operations are specifically designed to locate and evaluate multiple types of data to complete an operation, including synthesizing missing data. These operations may be processed within a SoC of a storage device as embedded software. Storage devices designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing a plurality of applications stored in the non-volatile memory, and receiving a request for data, including measurements, from at least one of the applications. The steps can further determine if the requested data is suitable for substitution by an inference and subsequently select at least one machine learning model for generating a suitable inference.

Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. Machine learning modeling is used to reconstruct the data for the untested cylinders. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges. In a test mode, some portion of the cylinders is chosen for testing. Each wedge in the chosen cylinders is tested and labeled defective or non-defective. The test data for each defective wedge is run through a machine learning defect management logic, and inferences are made for the defective/non-defective status of the untested wedges.

Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. Machine learning modeling is used to reconstruct the data for the untested cylinders. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges. In a test mode, some portion of the cylinders is chosen for testing. Each wedge in the chosen cylinders is tested and labeled defective or non-defective. The test data for each defective wedge is run through a machine learning defect management logic, and inferences are made for the defective/non-defective status of the untested wedges.

Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. A bag of machine learning models is used to reconstruct the data for the untested cylinders. A defect file for the HDD is generated, a classifier model may be applied to the defect file, and one or more neural network models may be applied. If the defects are unsuitable for use by the models, then a scan of the entire HDD is run instead. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges.

Abstract: Methods are provided for deploying machine learning operations within existing storage devices for streamlining various calibration processes. Machine learning operations are specifically designed to generate inference data as a substitute for various measurements taken during calibration. These operations may be verified through additional sample measurements and rolled back when the results of the machine learning operations are outside of a range of approved values. Storage devices designed to utilize machine learning methods within calibration processes can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing an application stored in the non-volatile memory and receiving a request for measurement data from the application.

Abstract: Methods are provided for tactically deploying machine learning operations within existing storage devices without the need for additional capital investment. Machine learning operations are specifically designed to locate and evaluate multiple types of data to complete an operation, including synthesizing missing data. These operations may be processed within a SoC of a storage device as embedded software. Storage devices designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing a plurality of applications stored in the non-volatile memory, and receiving a request for data, including measurements, from at least one of the applications. The steps can further determine if the requested data is suitable for substitution by an inference and subsequently select at least one machine learning model for generating a suitable inference.

Abstract: Methods are provided for tactically deploying machine learning operations within existing storage devices without additional capital investment. Machine learning operations can be processed within a SoC of a storage device as embedded software. Storage device designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data and executable instructions and a processor to conduct a variety of steps. The steps can include executing a plurality of applications stored in the non-volatile memory, and receiving a request for data, including measurements, from at least one of the plurality of applications. The steps can further determine if the requested data is suitable for substitution by an inference and subsequently select at least one machine learning model for generating a suitable inference.

Abstract: The present invention relates to a platform that may be utilised in the field of biosensors. According to the present invention there is a method of manufacturing a platform for use in bio-sensing applications comprising the steps of: a) providing a substrate having electrodes thereon; b) performing an overprinting step by overprinting the electrodes with a precursor solution; c) performing a drying step to dry the precursor solution to form a print layer on the electrodes; d) performing a further overprinting step by overprinting the print layer with a precursor solution to increase the print layer thickness; e) performing a transformation step to at least partially transform the print layer from a first substance to a second substance different to the first substance.

Abstract: A central management system polls a lighting node in a mesh lighting network, sending a command message for the node to measure a link quality indication (LQI) of the received command message. The node returns a status message, including in its payload the measured LQI. The management system computes from the node's status messages, a statistical characterization of the link quality of the node and compares it with a threshold quality value. If the comparison indicates sufficient degradation in link quality, the management system automatically transmits a disconnect command to the node. In response, the node disconnects and enters a discovery mode to find a better quality link to rejoin the network. In example embodiments, the lighting node may also report its illumination and operational status in the payload of the status message, along with the LQI, enabling the management system to determine whether illumination by the node is failing.

Abstract: A central management system polls a lighting node in a mesh lighting network, sending a command message for the node to measure a link quality indication (LQI) of the received command message. The node returns a status message, including in its payload the measured LQI. The management system computes from the node's status messages, a statistical characterization of the link quality of the node and compares it with a threshold quality value. If the comparison indicates sufficient degradation in link quality, the management system automatically transmits a disconnect command to the node. In response, the node disconnects and enters a discovery mode to find a better quality link to rejoin the network. In example embodiments, the lighting node may also report its illumination and operational status in the payload of the status message, along with the LQI, enabling the management system to determine whether illumination by the node is failing.

Abstract: Example embodiments of the invention efficiently organize, control and prioritize a distributed lighting network. A mesh lighting network is organized from the bottom up into groups of lighting devices with control signal repeaters fanning out control signals to each group. Each lighting device is capable of forwarding control signals to additional lighting devices. During build-out of the mesh network, as new lighting devices are installed, the control network automatically selects a device whose repeater function will provide the most reliable and efficient distribution of control signals to the new group of devices being installed.

Abstract: Example embodiments of an invention to efficiently organize and control a distributed lighting network. A process supports message flows and an interface to facilitate the organization and control a very large array of intelligent lighting nodes. Each newly installed lighting node broadcasts an initial contact message that includes fields specifying requests, such as lighting schedule, intensity, color, aiming direction, flood lighting, etc. A central controller receives the message at a discovery message and provisioning request interface, commissions the lighting node, and processes the provisioning requests.

Abstract: A microneedle device (200) including at least one microneedle (1) having one or more nanowires (203) on a surface of said at least one microneedle. The microneedle device is typically used in a sensor such as a sensor for monitoring glucose levels in the body and the nanowires may have a membrane (207) covering at least part of the nanowires.

Abstract: In a method for the production of a membrane electrode assembly comprising a membrane, electrodes and a catalyst, the catalyst is pressed into the membrane material, e.g. when forming the material in situ.

Abstract: A membrane-electrode assembly includes a grid for controlling ion flow, separate layers of membrane material and/or an ionically inactive material for the transmission of a liquid or gaseous reaction component to end/or form at least one of the electrodes.

Abstract: A method for the photoelectrolysis of a liquid or gaseous species, comprises irradiating an ion exchange membrane of a membrane electrode assembly, wherein the membrane is an optically transparent material and comprises the species.

Abstract: This invention relates to a metal ion specific capacitance sensor with exceptional sensitivity and wide operating range. It is versatile because different kinds of recognition elements can be immobilized directly in a self-assembling monolayer substantially completely covering the surface of the measuring noble metal electrode. The electrode then becomes selective to those metal ions in the solution that show affinity to the recognition element on the surface. Compared to previously described electrochemical sensors, the sensor according to the present invention shows many orders of magnitude better sensitivity because of the unique measuring principle.

Abstract: A structured packing (which may or may not include a catalyst) formed from a mesh material having pore openings of less than 50 microns wherein the packing is provided with turbulence generators to promote flow of fluid through the pore openings and may be further provided with additional openings larger than the pores to improve bulk mixing.