Abstract: This disclosure relates generally to surveillance, and more particularly to perform entry and exit monitoring of a user. In an embodiment, the system identifies an entry event (during which a user enters a room being surveilled) and/or an exit event (during which a user leaves a room being surveilled), based on a Bluetooth Low Energy (BLE) based system. This system uses two receivers one positioned outside and the other positioned inside the room being surveilled, to receive BLE beacon transmitted by a user who is in motion. Based on relative change in Received Signal Strength Indicator (RSSI) values of this beacon with respect to the first and second receivers, the system identifies direction of movement of the user, and in turn, determines one of the entry or exit events.

Abstract: Existing software defect text categorization approaches are based on use of supervised/semi-supervised machine learning techniques, which may require significant amount of labeled training data for each class in order to train the classifier model leading to significant amount of human effort, resulting in an expensive process. Embodiments of the present disclosure provide systems and methods for circumventing the problem of dependency on labeled training data and features derived from source code by performing concept based classification of software defect reports. In the present disclosure, semantic similarity between the defect category/type labels and the software defect report(s) is computed and represented in a concept space spanned by corpus of documents obtained from one or more knowledge bases, and distribution of similarity values are obtained.

Abstract: Development of sensor data based descriptive and prescriptive system involves machine learning tasks like classification and regression. Any such system development requires the involvement of different stake-holders for obtaining features. Such features typically obtained are not interpretable for 1-D sensor signals. Embodiments of the present disclosure provide systems and methods that perform signal analysis for features extraction and interpretation thereof wherein input is raw signal data where origin of a feature is traced to signal data, and mapped to domain/application knowledge. Feature(s) are extracted using deep learning network(s) and machine learning (ML) model(s) are implemented for sensor data analysis to perform causality analysis for prognostics. Layer(s) (say last layer) of Deep Network(s) contains the automatically derived features that can be used for ML tasks.

Abstract: A system and method for conducting a computer based candidate assessment in an examination center has been described. The system primarily involves a candidate assessment device in communication with the computer. The candidate assessment device further comprises an operating system and an assessment application. The candidate assessment device captures the proctoring data of the candidate during assessment. The system also includes plurality of sensors including camera and biometric sensor. The biometric data captured from biometric sensor is used to authenticate the identity of the candidate using the UIDAI database. The system also includes an external communication device to sniff a plurality of external communication within vicinity of the candidate. Further, the proctoring data is provided to an assessment server. And assessment server raise an alarm if one of a predefined suspicious activity is detected.

Abstract: This disclosure relates generally to a system and method for analyzing live activity on the conductive fabrics with intelligent touch. The system comprises a plurality of modules and these modules are in sync with the processor of the system to take action based on a plurality of instructions of the memory. Further, the system comprises fabrics, which are of conductive in nature, are used as a conductive touch pads for inputs to the system. The input data from touch pads are analyzed to determine pattern of actions. These patterns are associated with the IoT devices and the system uses them to trigger at least one action on the at least one IoT device based on a predefined training data set.

Abstract: Systems and methods for detecting and tracking a marker in real time is disclosed. Shape based segmentation of at least one object detected in a first frame from a sequence of frames is performed to define a region of interest (ROI) surrounding an object of interest corresponding to the marker. A marker detection model is dynamically trained based on sampling points from a plurality of pixels in and around the ROI. The marker is then tracked in real-time based on projected ROI in subsequent frames and the trained marker detection model. To optimize computation time required in classifying the pixels as marker pixels or non-marker pixels, the ROI is reduced to half its size, classification is performed on the reduced ROI and to improve accuracy, blob detection and classifying pixels along the boundary of the reduced ROI is performed by processing the ROI in original resolution.

Abstract: Systems and methods for quantification of postural balance of users in an augmented reality (AR) environment. Traditional systems and methods provide for quantifying the postural balance using the AR environment but none of them quantify or restrict the functional tasks performed by the users to a predefined level. Embodiments of the present disclosure provide for the quantification of the postural balance with a variable step height in the AR environment by acquiring first set of information comprising of data on skeletal joints, filtering the first set of information for obtaining a filtered set of data, computing the set of postural data based upon the filtered set of data and quantifying the postural balance based upon the set of postural data by computing threshold values for obtaining postural stability index scores and determining based upon the postural stability index scores, the postural balance of the users in the AR environment.

Abstract: The present disclosure relates to a process for the purification of water. The process includes leading water laden with microorganisms and arsenic through an arsenic adsorption media followed by treating the resultant arsenic deficient water with a disinfectant releasing system to obtain water deficient of arsenic and viable microorganisms. The present disclosure also provides an apparatus for the purification of water using the afore-stated process.

Abstract: Data cataloging has become a necessity for empowering organizations with analytical ability. Conventional cataloging systems may fail to provide proper visualization of data to the different stakeholders of an organization. The present disclosure provides a hierarchical dynamic cataloging system so that visualization of data at different levels would be possible for different stake holders. In the present disclosure, a hierarchical structure of algorithms and multiple stake holders along with relevant metadata is generated. Further, a catalog is generated by performing a mapping across components comprised in the hierarchical structure and identifying relationship across the components based on mapping. The catalog gets dynamically updated and provides a dynamic view of the algorithms and associated metadata to the multiple stakeholders of an organization.

Abstract: Systems and methods for modelling prediction errors in path-learning of an autonomous learning agent are provided. The traditional systems and methods provide for machine learning techniques, wherein estimation of errors in prediction is reduced with an increase in the number of path-iterations of the autonomous learning agent. Embodiments of the present disclosure provide for a two-stage modelling technique to model the prediction errors in the path-learning of the autonomous learning agent, wherein the two-stage modelling technique comprises extracting a plurality of fitted error values corresponding to a plurality of predicted actions and actual actions by implementing an Autoregressive moving average (ARMA) technique on a set of prediction error values; and estimating, by implementing a linear regression technique on the plurality of fitted error values, a probable deviation of the autonomous learning agent from each of an actual action amongst a plurality of predicted and actual actions.

Abstract: This disclosure relates generally to method and system for predicting effect of genomic variations on pre-mRNA splicing. The method include receiving genomic position information of at least one candidate variant, gene transcripts and genomic coordinates information of the gene transcripts; classifying the at least one candidate variant into one of a splice acceptor site region and a branch site region based on the coordinates information of the gene transcripts and the genomic position information of at least one candidate variant; evaluating effect of the at least one candidate variant on pre-mRNA splicing, based on a classified region from the classification of the at least one candidate variant and predicting pathogenicity of the at least one candidate variant based on the evaluated effect of the at least one candidate variant on the pre-mRNA splicing.

Abstract: This disclosure relates generally to image processing, and more particularly to generate a high resolution image from multiple low resolution images. In an embodiment, the system collects a plurality of low resolution images as input, processes the collected images, and generates a high resolution image as output. During this process, the system pre-processes the collected low resolution images, and during this process, at least one characteristic of each of the low resolution image is changed with respect to a reference image. After the pre-processing stage, the images are then enhanced based on a plurality of final enhancement factors that are dynamically determined. Further, the enhanced images are fused to generate the high resolution image.

Abstract: The embodiments herein disclose a method and a system for creating a singular platform to harness a plurality of technical capabilities in order to deliver multiple digital services such as end user device management, analytics, enterprise mobility, digital identity management, smart device management and so on by orchestrating certain service related support capabilities. The embodiments function as an interface between the user equipment and the applications that are running on several operating systems. Further, an enablement platform is created and modified for a digital ecosystem that sits on the network and user equipment to act as an interface. Essentially, a flexible and extensible API driven platform capable of seamlessly integrating multiple platforms spanning across network services and functions, analytics, device management and orchestration platforms in enabled.

Abstract: Techniques for rule creation from natural language text (NLT) are disclosed. In an embodiment, a rule statement in the NLT is grammatically parsed to obtain a parse tree. Further, each of noun phrase (NP) core sub-tree and verb phrase (VP) core sub-tree, in the parse tree, is partitioned into at least one sub-tree. Furthermore, one or more operators are extracted using at least one sub-tree of NP or VP core sub-trees. The rule statement is substantially simultaneously splitted into groups of 3 adjacent words (3-grams). Each of the 3-grams is then compared with a predefined list to extract data properties and class concepts associated with the rule statement. Moreover, subscripts are assigned to the data properties, class concepts and operators. A rule head is then created based on the data properties and operators. Also, a rule is created using the data properties, operators, class concepts, associated subscripts and rule head.

Abstract: Feature based visual simultaneous localization and mapping (SLAM) do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing visual SLAMs produce partial reconstruction when the number of 3D-2D correspondences is insufficient for incremental camera estimation using bundle adjustment. Systems and methods of the present disclosure provide edge points based monocular visual SLAM that mitigates these problems. The SLAM is initialized through a validation process. A local optimization process is provided for stable pose estimation in situations where camera tracking becomes unreliable in a very low-textured challenging environment. An efficient and reliable loop closing process that uses structural properties of edges in the frames is also provided.

Abstract: Disclosed is a system and method for biometric authentication of a user using a personalised identification and associated biometric data. In one embodiment, a plurality of personalised identifiers and biometric data may be captured from a number of users and stored in a repository as stored records. The authentication process may be divided into two phases. In the first phase, either a speech recognition or character recognition process may be applied in order to determine the text spoken or written by the user. Subsequently a few records may be fetched from the repository on the basis of text mapping. In the second phase, biometric authentication may be performed by comparing the biometric sample with the stored biometric data corresponding to the fetched records to uniquely identify a single user. Further a machine learning technique may be applied in order to periodically refine a plurality of models stored in the repository.

Abstract: Systems and methods for detecting grasping poses for handling target objects is disclosed. The system solves problem of grasp pose detection and finding suitable graspable affordance for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse by creating multiple surface segments within bounding box obtained from a neural network based object recognition module. Surface patches are created using a region growing technique in depth space based on surface normals directions. A Gaussian Mixture Model based on color and depth curvature is used to segment surfaces belonging to target object from background, thereby overcoming inaccuracy of object recognition module trained on a smaller dataset resulting in larger bounding boxes for target objects. Target object shape is identified by using empirical rules on surface attributes thereby detecting graspable affordances and poses thus avoiding collision with neighboring objects and grasping objects more successfully.

Abstract: This disclosure relates to non-contact inspection of material for identifying and estimate composition of a material under inspection. Traditionally, material inspection is an invasive process involving contact based approaches. A radar-based approach requires placement of the radar at a specific location, which is a challenge since amplitude of the reflected signal, depends on the distance from the material under inspection. The present disclosure addresses this technical problem by providing a Continuous Wave radar-based approach that is based on absolute slope at extrema points on the reflected signal from the material under inspection.

Abstract: A growing need for inferencing to be run on fog devices exists, in order to reduce the upstream network traffic. However, being computationally constrained in nature, executing complex deep inferencing models on such devices has been proved difficult. A system and method for partitioning of deep convolution neural network for execution of computationally constraint devices at a network edge has been provided. The system is configured to use depth wise input partitioning of convolutional operations in deep convolutional neural network (DCNN). The convolution operation is performed based on an input filter depth and number of filters for determining the appropriate parameters for partitioning based on an inference speedup method. The system uses a master-slave network for partitioning the input. The system is configured to address these problems by depth wise partitioning of input which ensures speedup inference of convolution operations by reducing pixel overlaps.

Abstract: This disclosure relates generally to a method and system for multi-hop path selection for mobile robots based on cloud platform providing an optimal path for end-to-end communication in the multi-hop network. Multi-hop path selection for mobile robots, conventionally performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. Further, computation at mobile robot end is not an energy efficient solution. The disclosed cloud system communicates the optimal path to the source mobile robot to reach the destination mobile robot through the plurality of Access Points (APs). Multi-hop path selection for mobile robots, currently performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. The optimal path computed at cloud system increases the life-time of robotics network there by increasing the efficiency.