Abstract: This disclosure relates generally to a method and system for sensing best-connected future path for a mobile telerobot based on radio signal strength (RSS) prediction algorithm through in-situ radio-sensing. State-of-the-art methods predict the future path from the plurality of possible paths based on a radio-source in the environment. However, prediction of the suitable future path in the absence of the radio-source or in no signal zone is not yet achieved. The proposed in-situ algorithm is based on Log-Normal Shadowing Model (LNSM) and found efficient for prediction error minimization. The method enables the mobile telerobot to predict the future path on a trajectory of the telerobot even without prior knowledge of a radio-source location. The mobile telerobot can predict the most suitable path from a plurality of possible paths for a move based on virtual location estimation.

Abstract: Nocturia has been defined as the need for an individual to wake up one or more times during the night to void. Further, Nocturia detection also requires analysis of sleeping pattern of the person. In such cases a lot of assumptions are made when the person is not in bedroom during nights. A method and system for detection and validation of Nocturia in the person has been provided. The system is utilizing a statistical based analysis, a rule based analysis, a machine learning based analysis and analysis of sleeping pattern of the person to detect and validate Nocturia. The system ensures that the person is not disturbed in his/her daily activities. Further, the processes deployed in the system are completely un-supervisory in nature meaning it does not have the dependency of needing to have trained machine learning dataset.

Abstract: The present disclosure provides a system for securing communication between Central Controller and signaling devices including actuator devices and signaling sensors used in railway and road traffic signaling networks. Conventional signaling systems use unsecured metal cables to communicate between the Central Controller and the signaling devices, making them vulnerable to unauthorized intrusion and mischief. In the present disclosure, two uniquely addressable communication modules, one (SCM1) securely housed with the Central Controller and another (SCM2) securely housed in an assembly also containing the signaling device and the related power switches are used to establish a transparent but standard secure digital communication protocol between them to authenticate and validate mutual communication, making them secure and safe from intrusion and undesirable manipulation.

Abstract: This disclosure relates generally to extraction of cause-effect relation from domain specific text. Cause-effect relation highlights causal relationship among various entities, concepts and processes in a domain specific text. Conventional state-of-the-art methods use named entity recognition for extraction of cause-effect (CE) relation which does not give precise results. Embodiments of the present disclosure provide a knowledge-based approach for automatic extraction of CE relations from domain specific text. The present disclosure method is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. The method extracts the CE relation in the form of a triplet comprising a causal trigger, a cause phrase and an effect phrase identified from the domain specific text. The disclosed method is used for extracting CE relations in biomedical text.

Abstract: This disclosure provides a system and method for flexibility based profit allocation for aggregator with distributed energy resources (DERs). The method of the present disclosure considers an aggregation model that allows an aggregator to orchestrate a set of heterogeneous DERs while enabling energy exchange among subscribed DERs and to participate in a day-ahead market. Further, a flexibility index is used to quantify the flexibility offered by a DER and a novel profit allocation model is proposed based on the flexibility index. Efficacy of the proposed models is demonstrated by evaluating their performance on a group of heterogeneous DERs using data traces from real-world electricity market.

Abstract: This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Abstract: A method and system that allows on-the-fly synthesis and evaluation of services a scale has been provided. The method and system provide a mechanism where the service offering and their price points are accessible in a machine format and allow themselves to be lent to synthesize new service offerings with budgetary constraints, within the parameters of the predefined time windows. The system allows both a service provider and a service consumer to use a shared screen environment for service synthesis and valuation with the service provider playing the role of navigator. The system comprises of re-routing and navigating a plurality of nodes in the service composition graphs based on specified optimization parameters as chosen by the service consumer and tuned by the service provider. The method comprises of generation of the graph and graph traversal algorithms for along with service composition nodes and their specifications.

Abstract: The method and system of the present disclosure facilitate automatic, and in an objective manner, selection of an optimal set of technical questions, from a question bank, personalized for a candidate. This ensures consistent, standardized, efficient and objective interviews that result in high quality recruitment, given the diversity of candidates, complexity of job requirements and interviews are inherently subjective. The state-of-the-art depends on responses so far, to generate on-the-fly questions causing a cognitive load on the interviewer. Also, there is no guarantee on the breadth and depth of concepts assessed in each interview. In the present disclosure, skill graphs are employed to create a semantically rich and detailed characterization of questions in terms of concepts. Optimization formulation uses the skill graph to generate constraints, content balancing and objective functions for selection of questions.

Abstract: State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in a container. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting food quality in a non-invasive manner. A Color Changing Indicator (CCI) in a biosensor strip forming a component of the enclosed package in which the liquid food item is packed, changes color when came in contact with the liquid food item. For different quality of the liquid food item the CCI has different color. Based on the color of the CCI, and ambient temperature and relative humidity at the time the color of the CCI is determined, a machine learning model determines rate of deterioration of the liquid food item, and then predicts remaining shelf life, which in turn provided as output to a user.

Abstract: This disclosure relates to extraction of tasks from documents based on a weakly supervised classification technique, wherein extraction of tasks is identification of mentions of tasks in a document. There are several prior arts addressing the problem of extraction of events, however due to crucial distinctions between events-tasks, task extraction stands as a separate problem. The disclosure explicitly defines specific characteristics of tasks, creates labelled data at a word-level based on a plurality of linguistic rules to train a word-level weakly supervised model for task extraction. The labelled data is created based on the plurality of linguistic rules for a non-negation aspect, a volitionality aspect, an expertise aspect and a plurality of generic aspects. Further the disclosure also includes a phrase expansion technique to capture the complete meaning expressed by the task instead of merely mentioning the task that may not capture the entire meaning of the sentence.

Abstract: Unaddressed technical problem is how to make the window manager select and update the appropriate visual design given the Just-In-Time constraint to optimize Profit/Economic Goal for a Basic Emergent User and not have a locking/permanent binding on a particular Socio Economic Cohort (SEC) or Contract Management System Companies (CMSCs). A method and system is disclosed that provides interface layout to discover and optimize economics of information exchange of digitally virtualized physical space for a Basic Emergent User (BEU). A correlation score is determined based on correlation between i) the expected profit range provided by the BEU indicating the tolerance around the SP(selling price) and ii) the buying price(BP) and the tolerance specified by the SEC or the CMSC to identify a best match for the BEU based on goal provided by the BEU in terms of expected profit range and corresponding time range within which it is to be achieved.

Abstract: Conventionally, mechanical methods of measuring linear position over a scale required physical coupling with system(s), such as potentiometer-based feedback etc. Such kinds of mechanical feedback mechanisms need geared coupling. And along with a mechanical system comes problems of wear and tear of parts which can lead to increased inaccuracy of the setup over time. With increased capabilities of computer vision-based techniques, a non-contact image-based measurement of linear position has become of prime importance. Embodiments of the present disclosure provide system and method that implement direct visual measurement of liquid level inside a linear measurement setup (syringe here) by employing various techniques such as computer vision, machine learning techniques, and the like, wherein various features are extracted that in turn allow to have a measurement of a liquid level identified or indicator (plug/meniscus) position in a syringe/container.

Abstract: Privacy enabled inter-bank settlements and auditing of fund transfer has been addressed using private and shared databases in the blockchain. These are resource inefficient and incur additional overhead on banks to maintain the extra databases. These also pose some security concerns. A method and system disclosed leverages blockchain and Conditional Homomorphic Proxy Re-encryption (CH-PRE) to preserve the privacy of the customer information (balances and transfer amounts) and enables faster auditable settlement between the banks in the presence of governing body. Perform computations on encrypted data (customer balances and transfer amounts) to audit and validate the transaction data without revealing customer information such as balances. As customer balances and transfer amounts are stored in encrypted format, an attacker who is able to gain access to this encrypted data, will not be able to read the customer balances and transfer amounts, as the attacker does not possess bank's private key.

Abstract: The disclosure relates generally to symbiotic warning methods and systems to warn nearby target actors while driving for collision avoidance. Current ADAS systems are limited in assisting only the driver of the host vehicle in the potential dangers. The present disclosure identifies various driving scenarios and gives necessary warning to the target vehicles, pedestrians, and animals around the host vehicle. A symbiotic warning method involves receiving one or more road contextual parameters to create a 360-degree scene perception of road surroundings of a host vehicle, with one or more actors. Then, estimating one or more 3-dimensional (3-D) scene semantics of each of the one or more actors and detecting one or more priority actors those lead to probable collisions. Further, deciding to generate a symbiotic warning signal, to one or more priority actors, and generating the symbiotic warning signal to one or more priority actors those lead to probable collisions.

Abstract: Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.

Abstract: A method and system for dynamic value determination and performance based royalty calculation of non-fungible tokens (NFTs) created for agricultural assets is disclosed. The method enables reliable and authentic ownership tracking and the removal of frauds/biases affecting the valuation of the said asset(s), as all the transactions are recorded and visible to everybody on the platform. Various biases, such as location (or spatial), temporal or human judgment-based biases in the existing valuation systems are removed by having dynamic and performance-based valuation approach proposed in this invention. NFT valuations of farm diary and farm art have hardly been proposed in earlier works. Royalty or reward computations from agricultural NFTs has not been attempted. Thus, the method provides performance or creation-based (creation of IP, Knowledge base, Geographical Indication IP) royalties/rewards for the NFT owner or entities involved in the process.

Abstract: This disclosure relates generally to material quality inspection. Conventional approaches available for material quality inspection are unable to address concerns of complexity and cost involved. The technical problem of occluded object detection and material quality inspection for intrinsic defects identification is addressed in the present disclosure. The present disclosure provides a system and method for non-intrusive material quality inspection using three-dimensional monostatic radar based imaging, where the object under inspection undergoes a circular translation motion on a rotating platform. A modified delay-and-sum (m-DAS) algorithm is built by incorporating virtual antenna array to obtain a 3D image reconstruction of the object. From 3D reconstructed images, radial displacement as well as the angular locations of the object is identified which are further used for quality inspection of the material comprised in the object.

Abstract: Non-communicable diseases (NCDs) are the pandemics of modern era and are generating huge impact in the modern society. Conventional methods are inaccurate due to a challenge in handling data from heterogenous sensors. The present disclosure is capable of tracking fitness parameters of a user even with heterogenous sensors. Initially, the system receives a raw data from a plurality of heterogenous sensors associated with the user. The raw data is further transformed into a metadata format associated with the corresponding sensor. The transformed data is temporally aligned based on a time based slotting. An algorithm pipeline corresponding to a disorder to be analyzed is selected from a Directed Acyclic Graph (DAG) based on a sensor metadata and a plurality of algorithm metadata corresponding to a plurality of algorithms stored in an algorithm database and an algorithm pipeline. The corresponding disorder is analyzed using the algorithm pipeline.

Abstract: As discussed earlier, labelling techniques that are available for labelling of unlabelled tabular data use some semi supervised models for identification purposes. However, they require sample labeled data for training purposes. Further, the same labelling model/technique cannot be used for all data types. Present disclosure provides method and system for identifying labels of unlabeled column data. The system uses a hybrid approach i.e., it uses language models, regular expressions and known dictionaries for labelling of unlabelled tabular data. For performing labelling, system first classifies received unlabelled tabular data into one or more data buckets. The system then uses appropriate techniques, based on data types, for identification of labels of unlabeled data present in data buckets. Thereafter, system uses feedback mechanism which will impart maturity to system over time. Finally, once system is matured, system can identify labels for all types of data.

Abstract: This disclosure provides a system and method for transform based subspace interpolation for unsupervised domain adaptation for machine inspection. Embodiments of the present disclosure present a deep transform-based subspace interpolation method to cater to challenging unsupervised adaptation scenario for machine inspection of different but related machines. In the present disclosure, source and target domain data are modeled as low-dimensional subspace using deep transforms. The intermediate domains connecting the two domains are then learned to generate domain invariant features for cross-domain classification. The requisite formulation employing deep transform learning and the closed-form updates for the transforms and their corresponding coefficients are presented. The method of the present disclosure demonstrates potential in learning reliable data representations, particularly in limited data scenario and real-life industrial applications requiring adaptation between different machines.