Abstract: Single loop inductive sensors are widely deployed in infrastructure for traffic data collection, however, these loops currently provide little more than vehicle detection. A system and method are provided that enable single loop inductive sensors to be used for vehicle classification (e.g., identification as motorcycle, passenger car, bus, etc.). Classification may be done using the Federal Highway Administration's 13 class system. Initially a signature library is built from vehicle signatures with known classifications. Vehicle signature waveforms of unknown classification obtained from inductive loop sensors are analyzed to identify specific features in the waveform including the number of “peaks”, the first peak location and its magnitude. A classifier (e.g., K-nearest neighbor) uses a representation of the vehicle signature and the features to determine from the signature library the classification of the vehicle.

Abstract: A data condenser and method provides lossless condensation of numbers, letters, words, phrases, and other indicia to data object values which results in reduction of file size. The data condenser and method classifies data as individual data objects or groups of data objects and distinguishes terms which repeat (e.g. recur). A reference library is optimized according to the quantity of classified data to minimize storage requirements. The classified data is assigned a unique value which populates the reference file. An output file is created by the data condenser using the reference library to achieve optimal lossless condensation. A data reverter and method provides for reversion of condensed data objects such as numbers, letters, words, phrases and other indicia to uncondensed data objects for efficient and accurate use without loss of data objects.

Abstract: System and method for tracking a mobile device includes: receiving unique identifications for a mobile device; filtering out the unique identifications to obtain a true identifications for the mobile device; identifying cameras relevant to movement of the mobile device; receiving video streams; generating data structures for the video streams and tracking information of the mobile device, the data structure including time stamped videos, and viewpoints of the identified cameras; utilizing the data structures to retrieve, video and tracking information for the mobile device and the user, as the mobile moves in the site; and applying analytics to the retrieved video and tracking information to analyze behavior of the user and to predict what the user will do while on site.

Abstract: Petroleum Analytics Learning Machine (or PALM) system is a machine learning based, “brutally empirical” analysis system for use in all upstream and midstream oil and gas operations. PALM system optimizes exploration, production and gathering from at least one well of oil and natural gas fields to maximize production while minimizing costs. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual data are classified to correlate with optimal production to capture the dynamics of at least one or more wells of oil and natural gas fields and to provide categorization results from labeled data sets to identify patterns.

Abstract: Verification systems for testing food products or other samples include a mobile analytical device, a mobile accessory device such as a smart phone, and a remote, e.g., cloud-based, computing system. The mobile analytical device is adapted to generate a sensor output that is characteristic of the sample. The mobile accessory device may be adapted to receive the sensor output from the mobile analytical device. The remote computing system may be adapted to analyze analytical data using artificial intelligence (AI) and/or machine learning (M-L) to make an authentication determination of the sensor output relative to a. predefined product database. The mobile accessory device may be adapted to upload the sensor output to the remote computing system by a communication network, and the remote computing system may be adapted to download the authentication determination to the mobile accessory device by the communication network.

Abstract: System and method to produce an anonymized cohort, members of the cohort having less than a predetermined risk of re-identification. The method includes receiving a data query of requested traits to request in an anonymized cohort, querying a data source to find records that possess at least some of the traits, forming a dataset from at least some of the records, and calculating an anonymity histogram of the dataset. For each patient record within the dataset, the method anonymizes the dataset by calculating using a threshold selector whether a predetermined patient profile within the dataset should be perturbed, calculating using a value selector whether a value within the indicated patient profile should be perturbed, and suppressing an indicated value within the indicated patient profile. The anonymized dataset then is returned.

Abstract: A method includes receiving an initial dataset. Each record of the initial dataset comprises a set of quasi-identifier attributes and a set of non-quasi-identifier attributes. A processor assigns a link identifier to each record and replaces each set of quasi-identifier attributes with a range to form a generalized set. The processor removes duplicate records based on identical generalized sets to generate de-duplicated records. The processor generates a randomized record by replacing the generalized set of each de-duplicated record with a corresponding set of random values. The processor passes the set of random values of each randomized record through multiple hash functions to generate multiple outputs. The multiple outputs are mapped to a Bloom filter. The processor forms a dataset by combining each randomized record with one or more sets of non-quasi-identifier attributes. The set of random values is a fingerprint for a corresponding record of the dataset.

Abstract: Doppler correction of phase-encoded LIDAR includes a code indicating a sequence of phases for a phase-encoded signal, and determining a first Fourier transform of the signal. A laser optical signal is used as a reference and modulated based on the code to produce a transmitted phase-encoded optical signal. A returned optical signal is received in response. The returned optical signal is mixed with the reference. The mixed optical signals are detected to produce an electrical signal. A cross spectrum is determined between in-phase and quadrature components of the electrical signal. A Doppler shift is based on a peak in the cross spectrum. A device is operated based on the Doppler shift. Sometimes a second Fourier transform of the electrical signal and the Doppler frequency shift produce a corrected Fourier transform and then a cross correlation. A range is determined based on a peak in the cross correlation.

Abstract: A system and method for identifying mobile users in a site includes: receiving, in real time, data packets from wireless access points; identifying unique device identifications from the received data packets, and time stamping each identified unique device identification; determining repetitive or false unique device identifications in the identified unique device identification; and eliminating the repetitive or false unique device identifications in the identified unique device identification to obtain a plurality of accurate unique device identifications.

Abstract: A method or network that addresses cybersecurity vulnerabilities in an ADS-B network by validating and authenticating ADS-B messages. Nodes of the network maintain a validated ledger of ADS-B transactions and use validated information from this ledger to improve security against common cybersecurity attacks including, but not limited to, denial of service attacks, jamming attacks, spoofing attacks, flooding attacks and eavesdropping.

Abstract: A method or network that addresses cybersecurity vulnerabilities in an ADS-B network by validating and authenticating ADS-B messages. Nodes of the network maintain a validated ledger of ADS-B transactions and use validated information from this ledger to improve security against common cybersecurity attacks including, but not limited to, denial of service attacks, jamming attacks, spoofing attacks, flooding attacks and eavesdropping.

Abstract: A system is provided comprising a risk engine coupled to a financial system that includes an account. The risk engine generates an account model corresponding to a user and events of the account. Generation of the account model uses event parameters of a previous event performed by the user in the account. The risk engine uses the account model to generate a first probability of observing event parameters assuming the user is conducting the next event. The risk engine uses a fraud model to generate a second probability of observing event parameters assuming a fraudster is conducting the next event. The risk engine generates a risk score, using the first and second probabilities, which indicates the relative likelihood the next event is performed by the user. The system includes a risk application comprising an analytical user interface that displays for any event the risk score and/or event parameters.

Abstract: Doppler correction of broadband LIDAR includes mixing, during a first time interval, a returned optical signal with an in-phase version of the transmitted signal to produce a first mixed optical signal that is detected during the first time interval to produce a first electrical signal. During a non-overlapping second time interval the returned optical signal is mixed with a quadrature version of the transmitted signal to produce a second mixed optical signal that is detected during the second time interval to produce a second electrical signal. A complex digital signal uses one of the digitized electrical signals as a real part and a different one as the imaginary part. A signed Doppler frequency shift of the returned optical signal is determined based, at least in part, on a Fourier transform of the complex digital signal. A device is operated based on the Doppler frequency shift.

Abstract: The present system and method for analyzing sentiment is based on fuzzy set theory and clustering to classify text as positive, negative, or objective. The method for training and testing a document collection to analyze sentiment comprises computing a frequency matrix comprising at least one row and at least column vectors, executing term reduction of the terms, enumerating categories, computing centroids of the enumerated categories and computing a fuzzy polarity map. The row vectors may correspond to terms and the column vectors may correspond to documents. The frequencies of terms in a document indicate the relevance of the document to a query.

Abstract: A computer-implemented system and method to reduce re-identification risk of a data set. The method includes the steps of retrieving, via a database-facing communication channel, a data set from a database communicatively coupled to the processor, the data set selected to include patient medical records that meet a predetermined criteria; identifying, by a processor coupled to a memory, direct identifiers in the data set; identifying, by the processor, quasi-identifiers in the data set; calculating, by the processor, a first probability of re-identification from the direct identifiers; calculating, by the processor, a second probability of re-identification from the quasi-direct identifiers; perturbing, by the processor, the data set if one of the first probability or second probability exceeds a respective predetermined threshold, to produce a perturbed data set; and providing, via a user-facing communication channel, the perturbed data set to the requestor.

Abstract: The disclosed embodiments include systems, methods, and computer readable media for applying DR tags using a computing device, comprising: loading a plurality of documents for review into a memory of the portable computing device; displaying, via a display of the portable computing device, a first document of the plurality of documents; receiving, via the display, a first touchscreen user drag-and-drop input related to the first document, wherein the first touchscreen user drag-and-drop input corresponds to a first tag and a first tag value associated with the first tag; upon initiation of the first touchscreen user drag-and-drop input related to the first document, displaying as an overlay to the first document a first visual indicator corresponding to the touchscreen user drag-and-drop input; in response to completion of the first touchscreen user drag-and-drop input, applying to the first document the first tag value corresponding to the first tag; storing, in the memory, the first tag value associated wit

Abstract: A document relationship analysis system. Aspects of the system include ingesting, discovering, recommending, analyzing, and exporting documents of interest. The system dynamically searches large or streaming datasets using a tiered, multi-step approach that includes discovery techniques and recommender components to filter and refine these larger datasets to smaller datasets of documents of interest. The system dynamically selects and renders an appropriate visualization for result datasets based on predetermined measures that allow for facilitate analysis of the documents of interest.

Abstract: System and method to predict risk of re-identification of a cohort if the cohort is anonymized using a de-identification strategy. An input anonymity histogram and de-identification strategy is used to predict the anonymity histogram that would result from applying the de-identification strategy to the dataset. System embodiments compute a risk of re-identification from the predicted anonymity histogram.

Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.