Purdue Research Foundation

Infosys Limited

Olympus Corporation

Avinash C Kak

Girish Maskeri Rama

Hidekazu Iwaki

Santonu Sarkar

Nagaraja Nanjegowda Siddaramappa

Johnny Park

Henry Ponti Medeiros

Shubha Ramachandran

Bunyamin Sisman

Basavara Raju Muddu

Rohan Sarkar

A method, processing system, and tracking system for monitoring person-object interactions in an environment is disclosed. In particular, software architecture is provided for processing tracking and event information provided by independent trackers to identify basic interactions between the people in the environment and objects or storage entities in the environment. Based on the identified person-object interactions, the software architecture can associate individual persons with object and storage entities, detect and infer outcomes of their basic interactions, infer higher-level interactions, and detect any anomalous interactions. The software architecture is advantageously highly modular, scalable, and decentralized, and is designed to be substantially domain agnostic, such that it can be used to automate a wide variety of human-centric applications that involve humans interacting with objects.

Scalable event-driven software architecture for the automation of people-centric systems

A method of searching a corpus including a plurality of files includes automatically determining, using a processor, a historical ranking for each file using respective changeset information. Search term(s) of a query are received. A textual ranking is determined for each file using the search term(s) and contents of that file. The historical and textual rankings are combined to provide a result ranking. A data processing system includes a storage system storing the corpus including the plurality of files, a processor configured to determine the result rankings, and a user interface system configured to receive query results from the processor and present them to a user.

Bug localization using version history

A method and apparatus for tracking target objects includes a network of unidirectional sensors which correspond to nodes on the network. The sensors identify the presence of a target object, determine a first criteria for the relationship of the target object to the sensors and send a message to sensors neighboring the sensor. The message includes a unique identification of the target object and the first criteria. The sensors are ranked to determine which of the sensors should head a cluster of sensors for tracking the target object. Clusters are propagated and fragmented as the target object moves through a field of sensors.

Clustering protocol for directional sensor networks

The present disclosure relates to a sensor network including a plurality of nodes, each node having a directional sensor, a communication module, and a processor configured to receive local measurements of a calibration object from the directional sensor, receive additional measurements of the calibration object from neighboring nodes via the communication module, estimate an initial set of calibration parameters in response to the local and additional measurements, receive additional sets of calibration parameters from neighboring nodes via the communication module, and recursively estimate an updated set of calibration parameters in response to the additional sets of calibration parameters. Additional systems and methods for calibrating a large network of camera nodes are disclosed.

Calibration of large camera networks

Quality of modularization of source code is tested using different perspectives such as a structural modularity perspective, an architectural modularity perspective, a size perspective, and a similarity of purpose perspective. A history of changes in modularization may be kept such that the degree to which given source code is well-modularized can be determined over time. The changes made to the code by individual programmers may be kept, such that the degree to which their coding enhanced or harmed modularization may be tracked.

Evaluating programmer efficiency in maintaining software systems

A system, computer-implemented method, and computer-readable medium for determining the quality of an API includes receiving documentation of the API, determining one or more characteristics of the API based at least in part on the documentation of the API, determining or more measurement values based at least in part on the one or more characteristics of the API, and computing a quality score for the API based at least in part on the one or more measurement values.

Growing community of inventors

West Lafayette, IN, United States of America

Avinash C Kak