Tata Consultancy Services Patente

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Computer-aided diagnosis algorithms require a large volume of training data. The existing methods for simulating artificial biomedical signals are mostly based on physics driven mathematical models that require too many assumptions, making them challenging to simulate on a large scale. Alternatively, conventional deep learningbased approaches are pure data driven and hence, do not have physiological interpretation. The present disclosure provides a method that effectively combines both physiological domain knowledge and deep learning to enable simulation of realistic cardiovascular disease specific biomedical signals. An ensemble Generative Adversarial Network (GAN) including a Long Short-Term Memory GAN (LSTM-GAN) configured to generate a Heart Rate Variability (HRV) pattern associated with the cardiovascular disease condition and a Deep Convolutional GAN (DCGAN) configured to create a morphology of a representative cardiac cycle is provided. A complete waveform is simulated by combining an output from each GAN.

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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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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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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.

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.

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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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