Tata Consultancy Services Patente

A system and method for real time estimation of heart rate (HR) from one or more face videos acquired in non-invasive manner. The system receives face videos and obtains several blocks as ROI (Region of Interest) consisting of facial skin areas. Subsequently, the temporal fragments are extracted from the blocks and filtered to minimize the noise. In the next stage, several temporal fragments are extracted from the video. The several temporal fragments, corrupted by noise are determined using an image processing range filter and pruned for further processing. The HR of each temporal fragment, referred as local HR is estimated along with its quality. Eventually, a quality based fusion is applied to estimate a global HR corresponding to the received face videos. In addition, the disclosure herein is also applicable for frontal, profile and multiple faces and performs in real-time.

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A method and system of a low-latency FPGA framework based on reliable UDP and TCP re-assembly middleware is disclosed. The need for low-latency communication in digital systems has increased drastically. The disclosed FPGA framework enables low-latency communication as a hybrid framework that supports both UDP & TCP communication. As known in art, TCP provides error checking support hence making TCP more reliable as compared to UDP, while UDP is faster but not reliable. Hence the disclosed low-latency FPGA framework latency utilizes the advantage of both UDP and TCP by utilizing UDP for its speed, while switching to TCP in case of a missing sequence in UDP. Further, the disclosed system proposes a TCP re-assembly middleware architecture for processing TCP with a lower-latency, wherein the TCP re-assembly middleware is an independent middleware that is a modular and a plug-play independent middleware.

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This disclosure relates generally to visio-linguistic understanding. Conventional methods use contextual visio-linguistic reasoner for visio-linguistic understanding which requires more compute power and large amount of pre-training data. Embodiments of the present disclosure provide a method for visio-linguistic understanding using contextual language model reasoner. The method converts the visual information of an input image into a format that the contextual language model reasoner understands and accepts for a downstream task. The method utilizes the image captions and confidence score associated with the image captions along with a knowledge graph to obtain a combined input in a format compatible with the contextual language model reasoner. Contextual embeddings corresponding to the downstream task is obtained using the combined input. The disclosed method is used to solve several downstream tasks such as scene understanding, visual question answering, visual common-sense reasoning and so on.

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Product recommendation is a very important aspect of e-commerce applications. Traditional product recommendation systems recommend products similar to a query image provided by a user and allows minimum or no personalization. It is challenging to incorporate personalization due to the presence of overlapping fine-grained attributes, variations in attribute style and visual appearance, small inter-class variation and class imbalance in the images of products. Embodiments of present disclosure address these challenges by a method of personalized substitute product recommendation using Personalized Attribute Search Networks (PAtSNets) comprising neural network layers interleaved with Attentive Style Embedding (ASE) modules to generate attribute-aware feature representation vector of a query image provided by the user and conforming to the personalization instructions specified by the user. This feature representation vector is then used to recommend substitute products to the user. Thus, embodiments of present disclosure enable accurate substitute product recommendation suiting user requirements.

Conventional fork type autonomous mobile robots (AMRs) are suitable to handle pallets and are typically designed with two forks. Such AMRs are very bulky and designed for a cart handling application, and usually have large openings and less suitable for lifting roller carts. Present disclosure provides chassis with integrated single fork assembly for AMRs/Autonomous Guided Vehicles (AGVs) for transporting roller cages/carts within warehouses. Chassis with integrated single fork assembly enables performing tasks given by end users. Chassis carries steer and drive wheel and swivel wheels to increase stability of the AMR wherein a fork mechanism is provided which includes fork wheels and lifting mechanism. Such design and mechanism of the chassis with integrated single fork assembly overcomes the limitations of smaller widths between wheels of roller cage/carts for placement/movement of payload within warehouses and logistics environments and counter imbalance and deflection of payload and fork assembly caused therebetween.

Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

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Object manipulation space optimization is a challenging task and used in applications such as heat treatment operation and transport packaging. Traditionally manual intervention is involved to grasp and place the objects for ensuring multi object stacking with zero gap between adjacent objects. This leads to higher cost infrastructure and low productivity. Further, conventional gripper devices fail to optimize object manipulation space and are unable to handle objects of different cross sections with larger lengths. The present disclosure provides a gripper apparatus addressing a single gripper design comprising an adaptor holding unit with a provision for different modular object holding units which are varied in accordance with size, shape and length of the object to be grasped and placed by the gripper apparatus. The modular object holding unit comprises a plurality of fingers which are actuated for grasping and stacking one or more objects such that object manipulation space is optimized.

Existing systems for behavioural tracking and identification have the disadvantage that they do not analyse data in behavioural aspects. As a result, they lack ability to preempt scenarios involving actions that adversely affect user health. The disclosure herein generally relates to behavior prediction, and, more particularly, to a method and system for identifying unhealthy behavior trigger and providing nudges. The system generates a casual inference model, which is a reverse causality model facilitating mapping of context with one or more behaviour of the user. The system further collects and processes real-time data using the casual inference model, to perform behavioral analysis of the user.

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