Abstract: This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNN model having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix.

Abstract: Systems and methods for generating control system solutions for robotics environments is provided. The traditional systems and methods provide robotics solutions but specialized to only a particular robotic application, domain, and selected structure.

Abstract: A data driven approach for fault detection in robotic actuation is disclosed. Here, a set of robotic tasks are received and analyzed by a Deep Learning (DL) analytics. The DL analytics includes a stateful (Long Short Term Memory) LSTM. Initially, the stateful LSTM is trained to match a set of activities associated with the robots based on a set of tasks gathered from the robots in a multi robot environment. Here, the stateful LSTM utilizes a master slave framework based load distribution technique and a probabilistic trellis approach to predict a next activity associated with the robot with minimum latency and increased accuracy. Further, the predicted next activity is compared with an actual activity of the robot to identify any faults associated robotic actuation.

Abstract: This disclosure relates generally to the use of distributed system for computation, and more particularly, relates to a method and system for optimizing computation and communication resource while preserving security in the distributed device for computation. In one embodiment, a system and method of utilizing plurality of constrained edge devices for distributed computation is disclosed. The system enables integration of the edge devices like residential gateways and smart phone into a grid of distributed computation. The edged devices with constrained bandwidth, energy, computation capabilities and combination thereof are optimized dynamically based on condition of communication network. The system further enables scheduling and segregation of data, to be analyzed, between the edge devices. The system may further be configured to preserve privacy associated with the data while sharing the data between the plurality of devices during computation.

Abstract: Path planning for a robot is a compute intensive task. For a dynamic environment this is more cumbersome where position and orientation of objects changes often. Embodiments of the present disclosure provide systems and methods for context based path planning for vector navigation in hexagonal spatial maps. A 2-D environment is represented into a hexagonal grid map that includes hexagonal grid cells, objects are identified based on a comparison of RGB value associated with contiguous cells. Candidate contexts are determined based on objects identified. The hexagonal grid map is rotated at various angles and compared with pre-defined map(s) to determine quantitative measure of similarity for contexts identification from the candidate contexts, based upon which a path is dynamically planned for easy and efficient vector navigation within the hexagonal grid map.

Abstract: Systems and methods of the present disclosure address the capacity constrained vehicle routing (CVRP) problem that may be applied to a warehouse scenario wherein multi-robot task allocation is required. Conventional methods can solve CVRP instances up to 100 nodes. In the present disclosure, a nearest-neighbor based Clustering And Routing (nCAR) approach is provided that makes the systems and methods of the present disclosure scalable wherein the number of nodes can be in the range of several hundreds to several thousands within an order wave.

Abstract: A system and method according to the principles of the invention identifies mobile phone aliases. The system processes mobile location data and call event data to generate mobility profiles. The profiles indicate a mobile's geographic zone history over a specified time. To produce a mobility profile, the system aggregates location data into zones and associates the zones with times of day, week or month. Particular zones for different mobiles can be compared according to weighting algorithms to provide data indicating whether the mobiles belong to the same user.

Abstract: Cloud robotics infrastructures generally support heterogeneous services that are offered by heterogeneous resources whose reliability or availability also varies widely with varying lifetime. For such systems, defining a static redundancy configuration for all services is difficult and often biased. Also, it is not feasible to define a redundancy configuration separately for each unique service. Therefore, in the present disclosure a trade-off between the two is ensured by providing At-most M-Modular Flexible Redundancy Model wherein an exact degree of redundancy is defined and is given to each service in a heterogeneous service environment and monitoring each task and subtask status to ensure that each subtask gets accomplished thereby enabling the tuning of the tradeoff between redundancy and cost and determining efficiency of the system by estimating number of resources utilized to complete specific subtask and comparing the resources utilization with the exact degree of redundancy defined.

Abstract: In certain embodiments, a transcritical refrigeration system provides refrigeration by circulating refrigerant through the system. The system includes a gas cooler, a heat exchanger, a high pressure expansion valve, a flash tank, refrigeration cases, and compressors. The gas cooler cools the refrigerant to a first temperature. The heat exchanger cools the refrigerant flowing from the gas cooler to the high pressure expansion valve to a second temperature. The high pressure expansion valve is coupled to the flash tank, which is coupled to the refrigeration cases. The refrigeration cases are coupled to the compressors, which are coupled to the gas cooler. An expansion valve between the gas cooler and the heat exchanger may cool the refrigerant flowing to the heat exchanger. A high pressure vapor compressor between the heat exchanger and the gas cooler may compress the refrigerant flowing from the heat exchanger to the gas cooler.

Abstract: Signal analysis is applied in various industries and medical field. In signal analysis, wavelet analysis plays an important role. The wavelet analysis needs to identify a mother wavelet associated with an input signal. However, identifying the mother wavelet associated with the input signal in an automatic way is challenging. Systems and methods of the present disclosure provides signal analysis with automatic selection of wavelets associated with the input signal. The method provided in the present disclosure receives the input signal and a set of parameters associated with the signal. Further, the input signal is analyzed converted into waveform. The waveforms are analyzed to provide image units. Further, the image units are processed by a plurality of deep architectures. The deep architectures provides a set of comparison scores and a matching wavelet family is determined by utilizing the set of comparison scores.

Abstract: This disclosure relates generally to robotics, and more particularly to collaborative task execution by a robotic group using a distributed semantic knowledge base. In one embodiment, a method and a system of collaborative task execution by a robotic group using a distributed semantic knowledge base are provided. The distributed semantic knowledge base is distributed between multiple robots that form a robotic group. Each robot triggers a self query as well as one or more external queries to gather required data pertaining to a plurality of task specific parameters, and uses the gathered data to execute one or more tasks assigned to the robotic group.

Abstract: A system and method according to the principles of the invention identifies mobile phone aliases. The system processes mobile location data and call event data to generate mobility profiles. The profiles indicate a mobile's geographic zone history over a specified time. To produce a mobility profile, the system aggregates location data into zones and associates the zones with times of day, week or month. Particular zones for different mobiles can be compared according to weighting algorithms to provide data indicating whether the mobiles belong to the same user.

Abstract: In current distributed simultaneous localization and mapping (SLAM) implementations on multiple robots in a robotic cluster, failure of a leader robot terminates a map building process between multiple robots. Therefore, a technique for fault-tolerant SLAM in robotic clusters is disclosed. In this technique, robotic localization and mapping SLAM is executed in a resource constrained robotic cluster such that the distributed SLAM is executed in a reliable fashion and self-healed in case of failure of the leader robot. To ensure fault tolerance, the robots are enabled, by time series analysis, to find their individual failure probabilities and use that to enhance cluster reliability in a distributed manner.

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: 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: A system and method according to the principles of the invention identifies mobile phone aliases. The system processes mobile location data and call event data to generate mobility profiles. The profiles indicate a mobile's geographic zone history over a specified time. To produce a mobility profile, the system aggregates location data into zones and associates the zones with times of day, week or month. Particular zones for different mobiles can be compared according to weighting algorithms to provide data indicating whether the mobiles belong to the same user.

Abstract: A system and method for determining a configuration of a plurality of tasks to meet the specified deadline of a linear workflow of a real-time heterogeneous network. Often times, while meeting expected application performance in the heterogeneous network, it may possible to have graceful degradation of quality for ensuring timing constraints at the same time. In a multi-layered architecture, where each layer is equipped with multiple computational resources, the time optimization for each of the plurality of tasks can be achieved through approximate computing and analyzing all possible configurations of each task in a workflow within a particular layer.

Abstract: Cloud robotics infrastructures generally support heterogeneous services that are offered by heterogeneous resources whose reliability or availability also varies widely with varying lifetime. For such systems, defining a static redundancy configuration for all services is difficult and often biased. Also, it is not feasible to define a redundancy configuration separately for each unique service. Therefore, in the present disclosure a trade-off between the two is ensured by providing At-most M-Modular Flexible Redundancy Model wherein an exact degree of redundancy is defined and is given to each service in a heterogeneous service environment and monitoring each task and subtask status to ensure that each subtask gets accomplished thereby enabling the tuning of the tradeoff between redundancy and cost and determining efficiency of the system by estimating number of resources utilized to complete specific subtask and comparing the resources utilization with the exact degree of redundancy defined.

Abstract: Systems and methods for generating control system solutions for robotics environments is provided. The traditional systems and methods provide robotics solutions but specialized to only a particular robotic application, domain, and selected structure.

Abstract: A method for data partitioning in an internet-of-things (IoT) network is described. The method includes determining number of computing nodes in the IoT network capable of contributing in processing of a data set. At least one capacity parameter associated with each computing node in the IoT network and each communication link between a computing node and a data analytics system can be ascertained. The capacity parameter can indicate a computational capacity for each computing node and communication capacity for each communication link. An availability status, indicating temporal availability, of each of computing nodes and each communication link is determined. The data set is partitioned into subsets, based on the number of computing nodes, the capacity parameter and the availability status, for parallel processing of the subsets.