Abstract: The present disclosure provides an intermittent tape (100) disposed of around a pair of conductors. The intermittent tape (100) has a top dielectric layer (102), a bottom dielectric layer (106) and a conductive layer (104). The conductive layer (104) is sandwiched between the top dielectric layer (102) and the bottom dielectric layer (106). The conductive layer (104) includes conductive segments (108) and non-conductive segments (110). The non-conductive segments (110) are defined by an absence of the conductive segments (108). The conductive segments (108) and the non-conductive segments (110) are arranged alternatingly. A width of the non-conductive segments (110) between the conductive segments (108) is constant.

Abstract: The present application relates to managing memory of a KLL sketch for a streaming set. A computer system allocates an initial amount of memory from a computer memory to a buffer of a KLL sketch data structure based on a sparse sketch size independent of a data type of the streaming set. The system allocates an incremental amount of memory greater than or equal to the initial amount of memory from the computer memory to the buffer in response to a size of the streaming set exceeding a supported stream size of the KLL sketch data structure. The system determines a number of levels of the KLL sketch data structure based on a total allocated amount of memory and a size of the data type of the streaming set. The system adds new elements from the streaming set to the KLL sketch data structure based on the number of levels.

Abstract: The present application relates to managing memory of a KLL sketch for a streaming set. A computer system allocates an initial amount of memory from a computer memory to a buffer of a KLL sketch data structure based on a sparse sketch size independent of a data type of the streaming set. The system allocates an incremental amount of memory greater than or equal to the initial amount of memory from the computer memory to the buffer in response to a size of the streaming set exceeding a supported stream size of the KLL sketch data structure. The system determines a number of levels of the KLL sketch data structure based on a total allocated amount of memory and a size of the data type of the streaming set. The system adds new elements from the streaming set to the KLL sketch data structure based on the number of levels.

Abstract: The present disclosure provides an intermittent tape (100) disposed of around a pair of conductors. The intermittent tape (100) has a top dielectric layer (102), a bottom dielectric layer (106) and a conductive layer (104). The conductive layer (104) is sandwiched between the top dielectric layer (102) and the bottom dielectric layer (106). The conductive layer (104) includes conductive segments (108) and non-conductive segments (110). The non-conductive segments (110) are defined by an absence of the conductive segments (108). The conductive segments (108) and the non-conductive segments (110) are arranged alternatingly. A width of the non-conductive segments (110) between the conductive segments (108) is constant.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: The disclosure herein generally relates to the field of autonomous navigation, and, more particularly, to a diverse trajectory proposal for autonomous navigation. The embodiment discloses a hierarchical network based diverse trajectory proposal for autonomous navigation. The hierarchical 2-stage neural network architecture maps the perceived surroundings to diverse trajectories in the form of trajectory waypoints, that an autonomous navigation system can choose to navigate/traverse. The first stage of the disclosed hierarchical 2-stage Neural Network architecture is a Trajectory Proposal Network which generates a set of diverse traversable regions in an environment which can be occupied by the autonomous navigation system in the future. The second stage is a Trajectory Sampling network which predicts a fine-grained trajectory/trajectory waypoint over the diverse traversable regions proposed by Trajectory Proposal Network.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: The disclosure herein generally relates to the field of autonomous navigation, and, more particularly, to a diverse trajectory proposal for autonomous navigation. The embodiment discloses a hierarchical network based diverse trajectory proposal for autonomous navigation. The hierarchical 2-stage neural network architecture maps the perceived surroundings to diverse trajectories in the form of trajectory waypoints, that an autonomous navigation system can choose to navigate/traverse. The first stage of the disclosed hierarchical 2-stage Neural Network architecture is a Trajectory Proposal Network which generates a set of diverse traversable regions in an environment which can be occupied by the autonomous navigation system in the future. The second stage is a Trajectory Sampling network which predicts a fine-grained trajectory/trajectory waypoint over the diverse traversable regions proposed by Trajectory Proposal Network.

Abstract: The present disclosure relates to a method and system for optimizing a test suite comprising plurality of test cases. In an embodiment, the pluralities of test cases are received from data sources. The method identifies one or more redundant and non-redundant test cases among the plurality of test cases in the test suite and computes similarity percentage between each of the non-redundant test cases by comparing test description and steps of each non-redundant test case with test description and steps of other non-redundant test cases. The non-redundant test cases are grouped into clusters based on the similarity percentage. Further, the method identifies test functions in each of the clusters for determining reusable test functions. Upon identifying the reusable test functions, test cases in each of the clusters are reconstructed by replacing each of the test cases with the reusable test function references thereby optimizing the test suite.

Abstract: The present disclosure relates to a method and system for optimizing a test suite comprising plurality of test cases. In an embodiment, the pluralities of test cases are received from data sources. The method identifies one or more redundant and non-redundant test cases among the plurality of test cases in the test suite and computes similarity percentage between each of the non-redundant test cases by comparing test description and steps of each non-redundant test case with test description and steps of other non-redundant test cases. The non-redundant test cases are grouped into clusters based on the similarity percentage. Further, the method identifies test functions in each of the clusters for determining reusable test functions. Upon identifying the reusable test functions, test cases in each of the clusters are reconstructed by replacing each of the test cases with the reusable test function references thereby optimizing the test suite.