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Method and system for monitoring a selected region of an airspace associated with local area networks of computing devices

A method for monitoring a selected region of an airspace associated with local area networks of computing devices is provided. The method includes providing one or more segments of a legacy local area network to be protected in a selected geographic region. The legacy local area network is characterized by an unsecured airspace within the selected geographic region. The method includes determining a security policy associated with the one or more segments of the legacy local area network. The security policy at least characterizes a type of wireless activity in the unsecured airspace to be permitted, denied, or ignored. Additionally, the method includes connecting one or more sniffer devices into the legacy local area network. The one or more sniffer devices are spatially disposed within the selected geographic region to cause at least a portion of the unsecured airspace to be secured according to the security policy. Moreover, the method includes coupling a security appliance to the legacy local area network. The method also includes determining if at least one of the sniffer devices is coupled to each of the one or more segments of the legacy local area network to be protected and determining if the one or more sniffer devices substantially covers the portion of the unsecured airspace to be secured. The method additionally includes monitoring wireless activity in the airspace using the one or more sniffer devices, and automatically classifying, using a classification process, a portion of information associated with the monitoring of the wireless activity to at least determine if the wireless activity communicates to at least one of the one or more segments to be protected. Further, the method includes detecting a violation of the security policy based upon at least the classifying of the portion of the information from the monitoring of the wireless activity, and automatically processing an action associated with the violation in accordance to the security policy for the one or more segments in the legacy local area network to be protected.
Owner:ARISTA NETWORKS

Machine learning of document templates for data extraction

The present system can perform machine learning of prototypical descriptions of data elements for extraction from machine-readable documents. Document templates are created from sets of training documents that can be used to extract data from form documents, such as: fill-in forms used for taxes; flex-form documents having many variants, such as bills of lading or insurance notifications; and some context-form documents having a description or graphic indicator in proximity to a data element. In response to training documents, the system performs an inductive reasoning process to generalize a document template so that the location of data elements can be predicted for the training examples. The automatically generated document template can then be used to extract data elements from a wide variety of form documents.
Owner:LEIDOS

Identification, storage and display of land data on a website

Disclosed is a land website that provides a personalized database on which data can be stored, retrieved, customized and communicated (e.g., by e-mail) relating to a particular piece of property. The database can be accessible via a password and a security code over the Internet and may be encrypted for transmission. Land websites can be established that contain image data, map libraries, virtual tours, legal descriptions, title information, e-documents, actual pictures of property and various other information. Unique 3-D imaging of composite images can be provided on the land website as well as fly-around composite 3-D images. The land website provides a unique way of packaging information relating to a piece of land in a single, accessible location. A boundary applet tool is provided on the land website portal that allows a user to simply and easily draw boundaries around the property of interest and then submit an order for more detailed information about the property of interest. Various map data and image data are provided to assist the user in drawing the boundaries. Acreage amounts are automatically calculated based upon the size and area drawn by the user. Properties of interest can be easily accessed by a global coordinate system or by searching on map data that is provided on a wide range of scales.
Owner:LANDNET CORP

Automated sniffer apparatus and method for monitoring computer systems for unauthorized access

An apparatus for wireless communication including an automated intrusion detection process is provided. The apparatus has a portable housing, which may have a length no greater than 1 meter, a width no greater than 1 meter, and a height of no greater than 1 meter. A processing unit (e.g., CPU) is within the housing. One or more wireless network interface devices are within the housing and are coupled to the processing unit. The apparatus has an Ethernet (or like) network interface device within the housing and coupled to the processing unit. A network connector is coupled to the Ethernet network device. One or more memories are coupled to the processing unit. A code is directed to perform a process for detection of a wireless activity within a selected local geographic region. According to a specific embodiment, the wireless activity is derived from at least one authorized device or at least an other device. A code is directed to receiving at least identity information associated with the wireless activity from the detection process in a classification process. A code is directed to labeling the identity information into at least one of a plurality of categories in the classification process. Depending upon the embodiment, other codes may exist to carry out the functionality described herein.
Owner:ARISTA NETWORKS

Automated sniffer apparatus and method for monitoring computer systems for unauthorized access

An apparatus for wireless communication including an automated intrusion detection process is provided. The apparatus has a portable housing, which may have a length no greater than 1 meter, a width no greater than 1 meter, and a height of no greater than 1 meter. A processing unit (e.g., CPU) is within the housing. One or more wireless network interface devices are within the housing and are coupled to the processing unit. The apparatus has an Ethernet (or like) network interface device within the housing and coupled to the processing unit. A network connector is coupled to the Ethernet network device. One or more memories are coupled to the processing unit. A code is directed to perform a process for detection of a wireless activity within a selected local geographic region. According to a specific embodiment, the wireless activity is derived from at least one authorized device or at least an other device. A code is directed to receiving at least identity information associated with the wireless activity from the detection process in a classification process. A code is directed to labeling the identity information into at least one of a plurality of categories in the classification process. Depending upon the embodiment, other codes may exist to carry out the functionality described herein.
Owner:ARISTA NETWORKS

Solar panel tilt mechanism

A tilt mechanism associated with a solar panel assembly whereby effort required to tilt panel assemblies comprising the solar panel assembly is reduced or minimized by appropriate placement of first and second tilt axes with respect to the center of mass and / or center of pressure of the panel assemblies due to wind. These arrays are suitable for use on mobile or static installations. The tilt mechanism is suited for harnessing wind energy if the panels are suitably shaped.
Owner:HABER MICHAEL B

Hierarchical component based object recognition

The present invention provides a method for the recognition of objects in an image, where the objects may consist of an arbitrary number of parts that are allowed to move with respect to each other. In the offline phase the invention automatically learns the relative movements of the single object parts from a sequence of example images and builds a hierarchical model that incorporates a description of the single object parts, the relations between the parts, and an efficient search strategy. This is done by analyzing the pose variations (e.g., variations in position, orientation, and scale) of the single object parts in the example images. The poses can be obtained by an arbitrary similarity measure for object recognition, e.g., normalized cross correlation, Hausdorff distance, generalized Hough transform, the modification of the generalized Hough transform, or the similarity measure. In the online phase the invention uses the hierarchical model to efficiently find the entire object in the search image. During the online phase only valid instances of the object are found, i.e., the object parts are not searched for in the entire image but only in a restricted portion of parameter space that is defined by the relations between the object parts within the hierarchical model, what facilitates an efficient search and makes a subsequent validation step unnecessary.
Owner:MVTEC SOFTWARE

System and methods for determining the location dynamics of a portable computing device

A location system for locating and determining the motion and velocity of a wireless device. The methods include direct inferences about whether a device is in motion versus static based on a statistical analysis of the variation of radio signal strengths over time. The system is trained according to a sparse set of identified locations from which signal strengths are measured. The system uses the signal properties of the identified locations to interpolate for a new location of the wireless device. The system uses a probabilistic graph where the identified locations of the floor plan, expected walking speeds of pedestrians, and independent inference of whether or not the device is in motion are used to determine the new location of the device.
Owner:MICROSOFT TECH LICENSING LLC
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