Decoding of a first message is disclosed, wherein first and second messages are encoded by a code (represented by a state machine) to produce first and second code words, which are received over a communication channel. A plurality of differences (each corresponding to a hypothesized value of a part of the first message) between the first and second messages are hypothesized. An initial code word segment is selected having, as associated previous states, a plurality of initial states (each associated with a hypothesized difference and uniquely defined by the hypothesized value of the part of the first message). The first message is decoded by (for each code word segment, starting with the initial code word segment): determining first and second metrics associated with respective probabilities that the code word segment of the first and second code word (respectively) corresponds to a first message segment content, the probability of the second metric being conditional on the hypothesized difference of the initial state associated with the previous state of the state transition corresponding to the first message segment content, determining a decision metric by combining the first and second metrics, and selecting (for the first message) the first message segment content or a second message segment content based on the decision metric. If the first message segment content is selected, the subsequent state of the state transition corresponding to the first message segment content is associated with the initial state associated with the previous state of the state transition.

A method of compression of the color data of point clouds is described herein. A palette of colors that best represent the colors existing in the cloud is generated. Clustering is utilized for generating the palette. Once the palette is generated, an index to the palette is found for each point in the cloud. The indexes are coded using an entropy coder afterwards. A decoding process is then able to be used to reconstruct the point clouds.

A signal translating method may include, according to one aspect of the present application, receiving a source signal of a first domain; identifying erroneous features and effective features from the source signal; translating the source signal of the first domain into a first virtual signal of a second domain, the first virtual signal is that in which erroneous features included in the source signal has been removed; and outputting the first virtual signal. Therefore, the virtual signal of the second domain in which the erroneous features removed may be output

Methods and apparatuses are described for automated analysis of customer interaction text to generate customer intent information and a hierarchy of customer issues. A server captures computer text segments including a first portion comprising a transcript of an interaction and a second portion comprising notes about the interaction. The server generates interaction embeddings corresponding to the first portion of the computer text segment for a trained neural network. The server executes the neural network using the interaction embeddings to generate an interaction summary for each computer text segment. The server converts each interaction summary into a multidimensional vector and aggregates the multidimensional vectors into clusters based upon a similarity measure. The server aligns the clusters of vectors with attributes of the interaction summaries to generate a hierarchical mapping of customer issues.

A method for monitoring operation of a controller area network (CAN) comprising a plurality of nodes. The method comprises measuring a voltage associated with a CAN message transmitted on the network, determining a message signature in dependence on the measured voltage, and comparing the message signature with a node signature to determine the authenticity of the CAN message. One or more actions may be taken in dependence on the determined authenticity.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection and representation in images. In one aspect, a method includes detecting occurrences of objects of a particular type in images captured within a first duration of time, and iteratively training an image embedding function to produce as output representations of features of the input images depicting occurrences of objects of the particular type, where similar representations of features are generated for images that depict the same instance of an object of a particular type captured within a specified duration of time, and dissimilar representations of features are generated for images that depict different instances of objects of the particular type.

An apparatus and methods for processing monitored biosignals are provided that are particularly suited for reducing noise and artifacts in continuously monitored quasi-periodic biosignals without prior knowledge of the noise distribution. The framework trains a subspace manifold with reference signals. Subsequent signals are successively projected onto the trained manifold and adjusted based on the nearest neighbors of the state of the sample being projected as well as the state of the sample at the previous time point. A denoised or modified output is obtained with inverse mapping. The reference signals may optionally be labeled during manifold training with clinical events/variables or measurable diseases/injuries from a library of relevant labels. During reconstruction, the label of the estimated state in the manifold can be obtained from the label corresponding to the estimated state.

LK-SURF, Robust Kalman Filter, HAR-SLAM, and Landmark Promotion SLAM methods are disclosed. LK-SURF is an image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking using stereo images to produce 3D features can be tracked and identified. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis and the X84 outlier rejection rule. Hierarchical Active Ripple SLAM is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple tracked objects, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of tracked objects, landmarks, and estimated object locations. In Landmark Promotion SLAM, only reliable mapped landmarks are promoted through various layers of SLAM to generate larger maps.

LK-SURF, Robust Kalman Filter, HAR-SLAM, and Landmark Promotion SLAM methods are disclosed. LK-SURF is an image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking using stereo images to produce 3D features can be tracked and identified. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis and the X84 outlier rejection rule. Hierarchical Active Ripple SLAM is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple tracked objects, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of tracked objects, landmarks, and estimated object locations. In Landmark Promotion SLAM, only reliable mapped landmarks are promoted through various layers of SLAM to generate larger maps.

A system is described which accepts corporate title and employee data associated with that corporate title data at a first company, putting the corporate title and employee data through a configured network and generating a vector of terms and a set of coefficients associated with that title. Information about an employee is put through a second network using those terms and coefficients to determine if the employee would have the same or similar title at the first company.