Object (computer science)

About: Object (computer science) is a research topic. Over the lifetime, 106024 publications have been published within this topic receiving 1360115 citations. The topic is also known as: obj & Rq.

Relational database extenders for handling complex data types

Abstract: This invention is directed to relational extenders for a computer-based relational database. Each relational extender includes at least one column, in a first, business, table containing a user defined application database, dedicated to object handles for defining the complex data type of an object; a second, attribute, table containing at least one column defining a unique characteristic associated with the one object and one column dedicated to containing the object handle; and a third, metadata, table containing at least one column defining a common characteristic associated with all objects defined within the business table and one column dedicated to containing the object handle and at least one column dedicated to containing a reference to object data associated with the object. The relational extender further includes a fourth table containing a reference to each object handle column defined in the first table, and a fifth table containing the names of the second and third tables for each object defined in the first table.

A dynamic object tag and systems and methods relating thereto

Abstract: A dynamic object tag for a product and systems and methods relating thereto is disclosed. The dynamic object tag comprises a hardware security module, including an electronic storage module, and a communication module for communicating with an interrogation device. The hardware security module is adapted to establish a secure communication channel with the interrogation device, to exchange dynamic authentication parameters with the interrogation device, and to communicate product information stored on the electronic storage module to the interrogation device over the secure communication channel.

Automated web access for back-end enterprise systems

Abstract: A method and system for integrating back-end enterprise applications with Web clients is disclosed. One preferred embodiment comprises a method for invoking an object, comprising the steps of generating a description of the interface of the object; generating metadata representing the interface of the object from the description; storing the metadata; generating a representation of an invocation of the object in a markup language from the metadata; transmitting the representation of the invocation to a client program configured to invoke the object by interpreting the representation; receiving an invocation from the client program; based on the metadata, interpreting the received invocation. In one preferred embodiment, Web clients comprise Web applications generated automatically from CORBA IDL files describing interfaces to objects in the back-end enterprise applications.

Proximity location system

Abstract: A proximity location system (PLS) can allow for a user to interact with a user interface without touching or speaking into the interface. The system may include or be in communication with the user interface, which may be a touchscreen, for example. The system may also include or be in communication with one or more sensors that can sense an object's size, shape, speed, and/or location with respect to the user interface. From these sensed object characteristics, the system can determine the sensed object's type, feature, and/or state; and from that determination, the system can direct the interface and/or a device in communication with the interface to take an action.

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

Abstract: Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm's way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained on synthetic data operate correctly when exposed to real-world data. We explore the reality gap in the context of 6-DoF pose estimation of known objects from a single RGB image. We show that for this problem the reality gap can be successfully spanned by a simple combination of domain randomized and photorealistic data. Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data. To our knowledge, this is the first deep network trained only on synthetic data that is able to achieve state-of-the-art performance on 6-DoF object pose estimation. Our network also generalizes better to novel environments including extreme lighting conditions, for which we show qualitative results. Using this network we demonstrate a real-time system estimating object poses with sufficient accuracy for real-world semantic grasping of known household objects in clutter by a real robot.