Tata Consultancy Services Limited

Rishin Raj

Balamuralidhar Purushothaman

Jayavardhana Rama Gubbi Lakshminarasimha

Vishal Bajpai

Mahesh Rangarajan

Vishnu Hariharan Anand

Robin Tommy

Sreehari Kumar Bhogineni

Akshaya Ramaswamy

Karthik Seemakurthy

Mayank Mishra

Sunil D Joshi

Shripad Salsingikar

Gopi Kandaswamy

Dinesh Sasidharan Nair

Vishwa Chethan Dandenahalli Venkatappa

Vineet Lall

Kavita Sara Mathew

Shashank Lipate

Pradeep Kumar Mishra

Gangadhar Shankarappa

Meghala Mani

Gourav Singh Jat

Jegan Mohan Ponraj

Srinivas Kotamraju

Vaibhav Vyawahare

Rohan Chandrakant Vardekar

Sunil Dattatraya Joshi

Lakshminarasimha Jayavardhana Rama Gubbi

Mahesh Rangaraj

Rama Gubbi Lakshminarasimha Jayavardhana

Anand Vishnu Hariharan

Sasidharan Dinesh Nair

Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.

Identification of defect types in liquid pipelines for classification and computing severity thereof

Electronic sensing systems and methods are disclosed. The electronic sensing system (ESS) receive an olfactory product and one or more smell characteristics of the olfactory product are detected and extracted by identifying a headspace of the olfactory product. A comparison of the extracted smell characteristics with one or more smell characteristics associated with a historic training data stored in a database is performed and a match between the extracted smell characteristics and the one or more smell characteristics associated with the historic training data is determined using machine learning technique(s). Further, the ESS generates a report for the olfactory product comprising at least one of type of the consumable, name of the olfactory product, a status of the olfactory product, an age of the olfactory product, and a decaying index, and classifies the olfactory product into one or more categories based on the report and/or the historic training data.

Electronic sensing systems and methods thereof

This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.

Systems and methods for data acquisition and asset inspection in presence of magnetic interference

This disclosure relates generally to a method and a system for frame stitching based image construction for an indoor environment. The method enables constructing an image of a scene by stitching a plurality of key frames identified from a plurality of image frames captured by a mobile imaging device. The method overcomes multiple challenges posed by the indoor environment, effectively providing clean stitching of the key frames to construct the image of the scene. The method provides image stitching approach that combines visual data from the mobile imaging device and an inertial sensor from an Inertial Measurement Unit (IMU) mounted on the mobile imaging device, with a feedback for error correction to generate robust stitching outputs in indoor scenario.

Method and system for frame stitching based image construction in an indoor environment

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Bengaluru, India

Rishin Raj