Abstract: A method for operating a refrigeration system includes cooling a refrigerant using a gas cooler, decreasing the pressure of the refrigerant from the gas cooler using an expansion valve, and separating the refrigerant provided by the expansion valve into a vapor refrigerant and a liquid refrigerant using a flash tank. The method proceeds by determining whether to cool a medium temperature (MT) space or a low temperature (LT) space, and determining if heat exchange should occur for the refrigerant compressed by the MT compressor unit and the LT compressor unit, among other operations.

Abstract: A refrigeration system includes a low temperature (LT) evaporator unit, a medium temperature (MT) evaporator unit, or both. The system further includes a LT compressor unit, an MT compressor unit, or both. The system further includes a heat exchanger, a gas cooler, an expansion valve, and a flash tank.

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: A method for operating a refrigeration system includes compressing a refrigerant received from a medium temperature (MT) evaporator unit using a MT compressor unit, and compressing the refrigerant received from a low temperature (LT) evaporator unit using a LT compressor unit. The method includes transferring heat from a portion of the refrigerant provided by the MT compressor unit to a trapped portion of the refrigerant provided by the LT compressor unit using a heat exchanger to produce a pressurized heated refrigerant stream and a cooled refrigerant stream. Pressurizing the refrigerant using isochoric compression (constant volume process) and using waste heat energy increases the overall efficiency of a transcritical CO2 refrigeration system.

Abstract: A computing system obtains text that includes words and provides the text as input to an emotional classifier model that has been trained based upon emotional classification. The computing system obtains a textual embedding of the computer-readable text as output of the emotional classifier model. The computing system generates a phoneme sequence based upon the words of the text. The computing system, generates, by way of an encoder of a text to speech (TTS) model, a phoneme encoding based upon the phoneme sequence. The computing system provides the textual embedding and the phoneme encoding as input to a decoder of the TTS model. The computing system causes speech that includes the words to be played over a speaker based upon output of the decoder of the TTS model, where the speech reflects an emotion underlying the text due to the textual embedding provided to the encoder.

Abstract: Techniques are described with respect to managing a distributed device message in a computing infrastructure. Such techniques are enabled through a universal interface apparatus including a plurality of serial interface adapter boards and a system-on-a-chip microcontroller. The universal interface apparatus provides a universal gateway solution between one or more component interfaces associated with a certain premises or environment and a remote system. An associated method includes deriving core message content from a distributed device message originating from a source component in a computing infrastructure, converting the derived core message content to open standard file format message content, propagating the open standard file format message content to a virtualized management system, and receiving an open standard file format message response from the virtualized management system.

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 method for operating a refrigeration system includes compressing a refrigerant received from a medium temperature (MT) evaporator unit using a MT compressor unit, and compressing the refrigerant received from a low temperature (LT) evaporator unit using a LT compressor unit. The method includes transferring heat from a portion of the refrigerant provided by the MT compressor unit to a trapped portion of the refrigerant provided by the LT compressor unit using a heat exchanger to produce a pressurized heated refrigerant stream and a cooled refrigerant stream. Pressurizing the refrigerant using isochoric compression (constant volume process) and using waste heat energy increases the overall efficiency of a transcritical CO2 refrigeration system.

Abstract: Embodiments of the present disclosure provide a method and system for co-operative and cascaded inference on the edge device using an integrated Deep Learning (DL) model for object detection and localization, which comprises a strong classifier trained on largely available datasets and a weak localizer trained on scarcely available datasets, and work in coordination to first detect object (fire) in every input frame using the classifier, and then trigger a localizer only for the frames that are classified as fire frames. The classifier and the localizer of the integrated DL model are jointly trained using Multitask Learning approach. Works in literature hardly address the technical challenge of embedding such integrated DL model to be deployed on edge devices. The method provides an optimal hardware software partitioning approach for components or segments of the integrated DL model which achieves a tradeoff between latency and accuracy in object classification and localization.

Abstract: The present disclosure relates to methods and systems for time-series classification using a reservoir-based spiking neural network, that can be used at edge computing applications. Conventional reservoir based SNN techniques addressed either by using non-bio-plausible backpropagation-based mechanisms, or by optimizing the network weight parameters. The present disclosure solves the technical problems of TSC, using a reservoir-based spiking neural network. According to the present disclosure, the time-series data is encoded first using a spiking encoder. Then the spiking reservoir is used to extract the spatio-temporal features for the time-series data. Lastly, the extracted spatio-temporal features of the time-series data is used to train a classifier to obtain the time-series classification model that is used to classify the time-series data in real-time, received from edge devices present at the edge computing network.

Abstract: A system, computer program product, and method are presented for integrating centralized systems with disparate devices and non-standardized communications protocols and message specifications. The method includes identifying one or more interface systems for one or more facilities. Each facility includes a centralized computing system. The method also includes capturing one or more interface specifications for the respective interface systems. The method further includes creating one or more JavaScript Object Notation (JSON) files from the interface specifications. Each JSON file includes one or more logical properties associated with the respective interface systems. The method also includes creating one or more JSON file combinations through stitching at least a portion of the one or more JSON files. The method further includes establishing cloud-based communications between the interface systems and the respective centralized system of the facilities through the JSON file combinations.

Abstract: Techniques are described with respect to managing a distributed device message in a computing infrastructure. Such techniques are enabled through a universal interface apparatus including a plurality of serial interface adapter boards and a system-on-a-chip microcontroller. The universal interface apparatus provides a universal gateway solution between one or more component interfaces associated with a certain premises or environment and a remote system. An associated method includes deriving core message content from a distributed device message originating from a source component in a computing infrastructure, converting the derived core message content to open standard file format message content, propagating the open standard file format message content to a virtualized management system, and receiving an open standard file format message response from the virtualized management system.

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, computer program product, and method are presented for integrating centralized systems with disparate devices and non-standardized communications protocols and message specifications. The method includes identifying one or more interface systems for one or more facilities. Each facility includes a centralized computing system. The method also includes capturing one or more interface specifications for the respective interface systems. The method further includes creating one or more JavaScript Object Notation (JSON) files from the interface specifications. Each JSON file includes one or more logical properties associated with the respective interface systems. The method also includes creating one or more JSON file combinations through stitching at least a portion of the one or more JSON files. The method further includes establishing cloud-based communications between the interface systems and the respective centralized system of the facilities through the JSON file combinations.

Abstract: This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.

Abstract: A system and method for offloading scalable robotic tasks in a mobile robotics framework. The system comprises a cluster of mobile robots and they are connected with a back-end cluster infrastructure. It receives scalable robotic tasks at a mobile robot of the cluster. The scalable robotics tasks include building a map of an unknown environment by using the mobile robot, navigating the environment using the map and localizing the mobile robot on the map. Therefore, the system estimate the map of an unknown environment and at the same time it localizes the mobile robot on the map. Further, the system analyzes the scalable robotics tasks based on computation, communication load and energy usage of each scalable robotic task. And finally the system priorities the scalable robotic tasks to minimize the execution time of the tasks and partitioning the SLAM with computation offloading in edge network and mobile cloud server setup.

Abstract: This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.

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.