Abstract: Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).

Abstract: An interactive toy comprising a function device for performing user-perceptible, controllable functions. The interactive toy also includes a rechargeable power source and a charging circuit for contactless receipt of electrical energy and for charging the rechargeable power source when the interactive toy is positioned in a charging zone of a contactless charging device. The interactive toy is adapted to determine a charging rate at respective positions relative to the charging device and to generate a user-perceptible output indicative of the determined charging rate.

Abstract: A method for identifying a plurality of workloads in a heterogenous environment includes collecting a plurality of parameters from at least one layer of a system stack associated with the plurality of workloads, correlating the collected plurality of parameters from different layers of software stack, and creating a feature set based on the correlated plurality of parameters. The method further includes processing the feature set using a successively ordered classifier chain (SOCC) module to identify the presence of the plurality of workloads in the heterogenous environment in a data center.

Abstract: Users and providers register with a portal. Users are provided codes that link to the portal and contact information for the users. When a provider needs a user to physically sign a document, the user presents the code for scanning by a provider device, this redirects the document and the code to the portal. A ledger is maintained on the portal for the transaction, the portal forwards the document to the user using the contact information. The user uses an application to review the document and accept or reject the document. When the user accepts the document, a physical signature of the user is placed within the document at signature locations and forwarded back to the portal. The portal updates the ledger indicating the user physically signed and forwards the physically signed document to the provider. Neither the unsigned document nor the physically signed document are stored on the portal.

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: Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).

Abstract: Disadvantage of these existing approaches for spike encoding optimization are that they fail to process multivariate data and perform energy-efficient time series classification at edge. The disclosure herein generally relates to spike encoding optimization, and, more particularly, to a method and system for Mutual Information (MI) based spike encoding optimization. Before performing spike data optimization for a given input multivariate time series data, the system ensures by iteratively adding gaussian noise to the input data that the input data has achieved a maximum MI value. After the input data has achieved a maximum MI value, spike train optimization is done by the system, to generate optimized spike data and in turn a spike reservoir.

Abstract: A method for converting an electronic flash storage device having a byte addressable storage (ByAS) and a block addressable flash storage (BlAS) to a single byte addressable storage includes receiving, by a host, a request for memory allocation from the ByAS, the receiving being from a first application among of a plurality of applications running on a processor; deallocating, by the host, a least relevant page allocated to at least one second application among the plurality of applications; moving, by the host, a content to the BlAS at a first BlAS location, the content related to the least relevant page, the moving based on the deallocation; allocating, by the host, the least relevant page to the first application; and updating, by the host, a cache metadata and a page lookup table of the first application and the at least one second application based on the deallocation and allocation.

Abstract: This disclosure relates generally to time series forecasting, and, more particularly, to a system and method for online time series forecasting using spiking reservoir. Existing systems do not cater for efficient online time-series analysis and forecasting due to their memory and computation power requirements. System and method of the present disclosure convert a time series value F(t) at time ‘t’ to an encoded multivariate spike train and extracts temporal features from the encoded multivariate spike train by the excitatory neurons of a reservoir, predict a time series value Y(t + k) at time ‘t’ by performing a linear combination of extracted temporal features with read-out weights, compute an error for predicted time series value Y(t + k) with input time series value F(t + k), employs a FORCE learning on read-out weights using the error to reduce error in future forecasting. Feeding a feedback value back to the reservoir to optimize memory of the reservoir.

Abstract: A method for dynamically replicating data in a disaggregated distributed storage system includes receiving, by a processor, a request from a controller to replicate data; dynamically selecting, by the processor, a storage node for replicating the data from among a plurality of storage nodes, based on one or more replication parameters associated with each storage node of the plurality of storage nodes, wherein the one or more replication parameters include at least one of a flash factor corresponding to the each storage node, and a latency between the controller and the each storage node; and providing, by the processor, information about the selected storage node to the controller for replicating the data

Abstract: A method for distributing and managing an Input/Output (IO) request in a disaggregated storage architecture includes receiving the IO request including IO data to be distributed in the disaggregated storage architecture, generating IO metadata corresponding to the IO data included in the received IO request, determining one or more controller parameters for each of a plurality of controllers of the disaggregated storage architecture, determining a first priority weight of each controller parameter of the one or more controller parameters based on a network type of the disaggregated storage architecture, determining a first IO management weight for each of the plurality of controllers based on the one or more controller parameters and corresponding first priority weights, and statically mapping each of the IO metadata and the IO data to at least one controller of the plurality of controllers based on the first IO management weights of the plurality of controller.

Abstract: A method of performing replication recovery operation in a disaggregated distributed storage system (DDSS) includes identifying an affected storage node included in the storage pool based on an occurrence of one or more predetermined conditions; selecting at least one replacement storage node from among one or more storage nodes included in the storage pool to recover replication data stored in the affected storage node; selecting at least one controller for performing a replication recovery operation from among one or more controllers included in the controller pool; and assigning one or more replication recovery jobs to the at least one controller; and receiving, by the replication recovery device, a notification from the at least one controller based on the one or more replication recovery jobs being completed.

Abstract: Gesture recognition is a key requirement for Human Computer Interaction (HCI) and multiple modalities are explored in literature. Conventionally, channel taps are estimated using least square based estimation and tap corresponding to finger motion is tracked. These assume that noise component is negligible and can reduce the tracking accuracy for low SNR. Thus, to mitigate the above-mentioned limitation, the system and method of the present disclosure explore the feasibility of using speaker and microphone setup available in most of smart devices and transmit inaudible frequencies (acoustic) for detecting the human finger level gestures accurately. More specifically, System implements the method for millimeter level finger tracking and low power gesture detection on this tracked gesture. The system uses a subspace based high resolution technique for delay estimation and use microphone pairs to jointly estimate the multi-coordinates of finger movement.

Abstract: This disclosure generally relates optimized spike encoding for spiking neural networks (SNNs). The SNN processes data in spike train format, whereas the real world measurements/input signals are in analog (continuous or discrete) signal format; therefore, it is necessary to convert the input signal to a spike train format before feeding the input signal to the SNNs. One of the challenges during conversion of the input signal to the spike train format is to ensure retention of maximum information between the input signal to the spike train format. The disclosure reveals an optimized encoding method to convert the input signal to optimized spike train for spiking neural networks. The disclosed optimized encoding approach enables maximizing mutual information between the input signal and optimized spike train by introducing an optimal Gaussian noise that augments the entire input signal data.

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 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: A method for converting an electronic flash storage device having a byte addressable storage (ByAS) and a block addressable flash storage (BlAS) to a single byte addressable storage includes receiving, by a host, a request for memory allocation from the ByAS, the receiving being from a first application among of a plurality of applications running on a processor; deallocating, by the host, a least relevant page allocated to at least one second application among the plurality of applications; moving, by the host, a content to the BlAS at a first BlAS location, the content related to the least relevant page, the moving based on the deallocation; allocating, by the host, the least relevant page to the first application; and updating, by the host, a cache metadata and a page lookup table of the first application and the at least one second application based on the deallocation and allocation.

Abstract: A document image is obtained and fine-grain item-specific information within the document image is encrypted with varying levels of encryption, each encryption level associated is with a specific security access role. When the document image is presented to an end-user, the end-user's security access role determines which portions of the fine-grain item-specific information is decrypted and which portions remain encrypted, such that some portions of the document image are obfuscated from the end-user based on the end-user's security access role.

Abstract: Identifying data associated with a deposit is linked to contact information associated with an issuer of the deposit. During deposit processing, the issuer is identified, and an out-of-band message is sent to a device operated by the issuer requesting that the deposit be validated or be declined. Responsive to an instruction received from the issuer, the deposit processing continues, or the deposit is declined and not further processed.