Abstract: An encoder operable to filter audio signals into a plurality of frequency band components, generate quantized digital components for each band, identify a potential for pre-echo events within the generated quantized digital components, generate an approximate signal by decoding the quantized digital components using inverse pulse code modulation, generate an error signal by comparing the approximate signal with the sampled audio signal, and process the error signal and quantized digital components. The encoder operable to process the error signal by processing delayed audio signals and Q band values, determining the potential for pre-echo events from the Q band values, and determining scale factors and MDCT block sizes for the potential for pre-echo events.

Abstract: Systems, methods and computer software are disclosed for providing base station and Remote Radio Head (RRH) functionality. In one embodiment, a method is disclosed, the method for providing base station and Remote Radio Head (RRH) functionality in a base station, comprising: providing a baseband card; providing a radio head, the radio head coupled to the baseband card by way of an interface; and switching, under the control of a processor, between use of the baseband card and use of an external baseband unit for controlling the radio head, the external baseband unit used via a Common Public Radio Interface (CPRI) port, thereby providing dual base station and remote radio head functionality.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

Abstract: A spectrophotometric instrument (1) for the quantitative measurement of an optical property of an analyte in a sample cell (4). The spectrophotometric instrument (1) comprises an instrument housing (2a, 2b) for a source of electromagnetic radiation and a detector; an optical pot (3) for accommodating the sample cell (4) having a flexible restraining cradle (6).

Abstract: A positioning device for maintaining positioning of an introducer hub relative to a repositioning sheath or catheter may be provided. The positioning devices may include a first coupler, a second coupler configured to be removably coupled to a portion of a repositioning sheath or catheter that is proximal to the first coupler, and a portion extending between the first coupler and the second coupler, the extension bridge configured to maintain a fixed axial distance between the introducer hub and a proximal end of the repositioning sheath or catheter. A flexible hinge may be incorporated, where a medical device would be configured to pass through the second coupler, the flexible hinge, and the first coupler.

Abstract: Systems and methods for tracking and evaluating fuel consumptions of vehicles are provided. The method can involve operating a telematics device installed in the vehicle to: collect fuel data from at least one fuel data source of the vehicle; track, for each fuel data source, a cumulative amount of fuel consumed by the vehicle; determine a confidence level of each fuel data source; identify an initial fuel data source; generate a plurality of mid-trip fuel records; identify a final fuel data source based on a priority of each fuel data source and each confidence level of each fuel data source; determine whether the plurality of mid-trip fuel records is valid; and transmit the plurality of mid-trip fuel records and an indication of whether the plurality of mid-trip fuel records is valid to a fleet management system.

Abstract: Systems, methods and computer software are disclosed for providing base station and Remote Radio Head (RRH) functionality. In one embodiment, a method is disclosed, the method for providing base station and Remote Radio Head (RRH) functionality in a base station, comprising: providing a baseband card; providing a radio head, the radio head coupled to the baseband card by way of an interface; and switching, under the control of a processor, between use of the baseband card and use of an external baseband unit for controlling the radio head, the external baseband unit used via a Common Public Radio Interface (CPRI) port, thereby providing dual base station and remote radio head functionality.

Abstract: An encoder operable to filter audio signals into a plurality of frequency band components, generate quantized digital components for each band, identify a potential for pre-echo events within the generated quantized digital components, generate an approximate signal by decoding the quantized digital components using inverse pulse code modulation, generate an error signal by comparing the approximate signal with the sampled audio signal, and process the error signal and quantized digital components. The encoder operable to process the error signal by processing delayed audio signals and Q band values, determining the potential for pre-echo events from the Q band values, and determining scale factors and MDCT block sizes for the potential for pre-echo events.

Abstract: A real-time, on-line method and analytical system for determining halohydrocarbons in water which operate by (1) extracting on-line samples; (2) purging volatile halohydrocarbons from the water (e.g., with air or nitrogen); (3) carrying the purge gas containing the analytes of interest over a porous surface where the analytes are adsorbed; (4) recovering the analytes from the porous surface with heat (thermal desorption) or solvent (solvent elution) to drive the analytes into an organic chemical mixture; (5) generating an optical change (e.g., color change) in dependence upon a reaction involving the analytes and a pyridine derivative; and (6) measuring optical characteristics associated with the reaction to quantify the volatile halogenated hydrocarbon concentration.

Abstract: A method may include receiving, via a first computing node, a first pod from a second computing node. The method may also include retrieving a first image file that may include a first set of containers from a registry based on the first pod. The first set of containers may cause a control system to halt operations. The method may then involve generating a first package based on the first set of containers and storing the first package in a filesystem, receiving a second pod from the second computing node, and retrieving a second image file having a second set of containers from the registry. The second pod may include the second set of containers may cause the control system to update software components. The method may also involve generating a second package based on the second set of containers and storing the second package in the filesystem.

Abstract: A method may include receiving, via a first computing node of a cluster of computing nodes in a container orchestration system, a pod from a second computing node in the cluster of computing nodes. The method may also include retrieving an image file comprising one or more containers from a registry, such that the pod may include an indication of a location of the image file in the registry. The one or more containers may include one or more pre-analytic operations for a control system of a plurality of control systems to perform. The method may then involve generating a package based on the one or more containers and storing the package in a filesystem shared with the control system.

Abstract: The disclosure generally provides methods, systems and apparatus for an improved a Physically Unclonable Function (PUF). In one embodiment, the disclosure relates to a method to provide data from a Physically Unclonable Function (PUF) circuit array. The method includes storing a plurality of first data bits into a respective ones of a plurality of first bitcells of the PUF array to form a first dataset; storing a plurality of second data bits into a respective ones of a plurality of second bitcells of the PUF array, the plurality of second data bits defining a helper dataset; reading the first dataset from the plurality of first bitcells to provide a first read dataset; applying an error correction factor to the first read data dataset to form a security key dataset; and outputting the security key dataset from the PUF circuit array.

Abstract: Systems, methods and computer software are disclosed for fronthaul. In one embodiment a method is disclosed, comprising: providing a virtual Radio Access Network (vRAN) having a centralized unit (CU) and a distributed unit (DU); and interconnecting the CU and DU over an Input/Output (I/O) bus using Peripheral Component Interconnect-Express (PCIe); wherein the CU and the DU include a PCI to optical converter and an optical to PCI converter.

Abstract: Systems and methods are described for integrating one or more machine learning models with a client application using Remote Procedure Calls (RPCs). A server deploys a software container associated with a client application, the container comprising executable code corresponding to a machine learning model, a plurality of inputs to the machine learning model, and a plurality of outputs of the machine learning model. The server generates a protocol buffer profile using the inputs and the outputs, the protocol buffer profile defining RPC functions for integrating the client application and the machine learning model. The server receives, from the client application, a request to access the machine learning model using a first RPC function. The server executes the machine learning model to generate a classification value for input provided in the request. The server transmits the classification value to the client application using a second RPC function.