Tata Consultancy Services Patente

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Conventional ESPRIT (Estimation of Signal Parameters via Rational Invariance Techniques) cannot be directly applied to SFCW MIMO radar for localization of targets as the performance would be restricted by geometry of spatial MIMO. Thus, the present disclosure provides a method and system for localization of targets using SFCW MIMO radar. In this method, the channel response of the virtual uniform rectangular array (vURA) obtained by scanning at uniformly spaced frequency points is combined to form a larger array referred as Space-Frequency (SF) array. The 3D localization of targets is done by estimating azimuth angle, elevation angle and range using this SF array. The localization capability of the disclosed method largely depends upon the number of frequency scanning points and enables localizing far more targets than the dimension of the vURA. In addition, the inter-element spacing requirement of vURA is also greatly relaxed.

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Training of Artificial Intelligence (AI) data models requires huge amount of data to be processed, which requires a huge amount of resources to be allocated for the data processing. For the same reason, time involved in training of the AI data model also substantially increases, which is a challenge existing AI data model training approaches fail to address. Embodiments disclosed herein provide a method and system for quantum computing based pre-training of AI data models. In this quantum computing based approach, the system, by means of achieving synchronization across various quantum states and further by optimizing activation functions being used, pre-trains the AI data model in iterations, till convergence with a determined global minima is achieved.

This disclosure relates generally to a method and system for implementing density functional theory (DFT) on quantum processors through generalized gradient approximation (GGA). Conventional methods implement DFT simulations on classical processors and state of the art methods implement DFT on a combination of classical and quantum processors. The embodiments of the present disclosure perform complex calculations of the DFT through GGA on quantum processors such as iteratively updating a density matrix by computing a direct matrix, a correlation exchange matrix, a gradient of the collocation matrix, an electronic density, an electronic density gradient, a derivative of the electronic density gradient and finally a Fock matrix at each iteration. A final density matrix is determined based on a convergence criteria for the iterative updating. These computations are performed using several quantum circuit components.

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State of the art code matching approaches have the disadvantage that they rely on the line numbers provided by source code and binary parsers to establish mapping and do not work since changes introduced by compiler may be much more complex. Some approaches do not work as the source code of application and its binary version may not share the vocabulary. Machine learning based techniques have the disadvantage that they require significant amount of training data, which may not be available in abundance. Method and system disclosed herein provide a mechanism matching score for each of a plurality of code fragments in the intermediate representation of the source code file for each of a plurality of binary fragments in the intermediate representation of the binary file.

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This disclosure relates generally to systems and methods for object detection using a geometric semantic map based robot navigation using an architecture to empower a robot to navigate an indoor environment with logical decision making at each intermediate stage. The decision making is further enhanced by knowledge on actuation capability of the robots and that of scenes, objects and their relations maintained in an ontological form. The robot navigates based on a Geometric Semantic map which is a relational combination of geometric and semantic map. In comparison to traditional approaches, the robot's primary task here is not to map the environment, but to reach a target object. Thus, a goal given to the robot is to find an object in an unknown environment with no navigational map and only egocentric RGB camera perception.

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This disclosure relates generally to methods and systems for determining the power load disaggregation profile of a building. Most of the conventional techniques are algorithmic centric, specific to certain scenarios and does not employ the low-sampling rate data due to the complexity involved. Present disclosure determines the power load disaggregation profile of the building using the low- sampling rate power consumption data accurately. According to the present disclosure, firstly, the background power loads are detected and removed from the low- sampled data samples. Next, a robust event detection mechanism is employed to detect the events when the change in the power consumption occurred, and such events are paired using the iterative pairing technique. Further, a set of event clusters are formed using the density-based clustering technique and lastly, each of the set of event clusters are classified with each appliance type using a rule-based classification technique.

This disclosure relates generally to recommending tool configurations in machining. The machining tool configuration selection involves the selection of several tool specification parameters concerning the material, geometry and composition of the machining tool. The state-of-the-art methods uses a rule and knowledge-based system to select tool configuration, however these methods do not recommend tool configurations which satisfy customer requirement. Embodiments of the present disclosure uses a hierarchical model which is trained to predict acceptable tool specification parameters for a given requirement by learning the patterns from past tool selection data. Further a probabilistic approach is used to predict the top set of recommendations of tool configurations with a probability score for each prediction. The disclosed method is used for recommending tool configurations in a cylindrical grinding wheel process.

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