Abstract

A method to prevent capturing of an AI module and an AI system thereof is disclosed. The AI system includes a submodule trained in accordance with method steps. Processing of the input data includes computing an instantaneous Frequency domain transformation signature of the received input by way of a computation module in the submodule. This is followed by comparing the instantaneous Frequency domain transformation signature with a set of pre-derived Frequency domain transformation signatures by way of a comparator module in the submodule. An attack vector is identified based on the comparison. Accordingly, a first output computed by the AI module or a modified output is sent out via the output interface.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

An AI system and a method to prevent capture of an AI module in an AI system are disclosed. The AI system includes at least: an input interface to receive input data from at least one user; a blocker module to detect validity to the received input data and to generate output data corresponding to invalid data being received as input data using a class swapping technique; a data set module to store valid input data sets; an AI module to process the input data and generate output data corresponding to the input data; a blocker notification module to transmit a notification to the owner of the AI system on detecting an invalid input data and an output interface to send the generated output data to the at least one user.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

A method of training a submodule and preventing capture of an AI module is disclosed. Input data received from an input interface is transmitted through a blocker module to an AI module, which computes a first output data by executing an AI model. A submodule in the AI system trained using methods steps processes the input data to identify an attack vector from the input data. The submodule distinguishes between a genuine input and an attack vector by identifying one or more non-robust features in the input. The identification information of the attack vector is sent to the information gain module.

IPC Classes  ?

• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements

Abstract

A method of validating a defense mechanism of an artificial intelligence (“AI”) system includes defining an optimizing function for a mean and a variance of parameters of input queries. The method further includes applying a Bayesian optimization algorithm on the input queries in dependence of the optimizing function followed by a Gaussian process at least twice to create a posterior distribution for the mean and the variance of parameters to reverse engineer the AI system. The method also includes feeding the AI system with a set of the input queries whose mean and variance of parameters have a high probability in the posterior distribution, and recording an output of the AI system to validate the defense mechanism of the AI system.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

A method for training a neural network adapted to generate random numbers and the system thereof is disclosed. The neural network is configured to generate random numbers identical to the distribution of a quantum random number generator based on the received random noise input using a generative adversarial network framework. The method includes feeding random noise to the generator to generate a noisy output. The noisy output is fed to the discriminator along with a real time output of a quantum random number generator to the discriminator. Further, the discriminator is trained to learn the entropy of the real-time output and distinguish it from the noisy output based on the learned entropy. Finally, the output of the discriminator is fed as feedback to the generator.

IPC Classes  ?

• G06F 7/58 - Random or pseudo-random number generators

Abstract

A gas sensor operates in an ultra-low power mode when a portable electronic device is inactive and in a normal power mode when the device is active. A baseline resistance value of the gas sensor is stored in the ultra-low power mode. The gas sensor transitions to a normal power mode from the ultra-low power mode, when the portable electronic device is active. A rate of stabilization of resistance value of the gas sensor is computed in the normal power mode. A stabilized resistance value of the gas sensor in the normal power mode is estimated using the rate of stabilization of resistance value of the gas sensor in the normal power mode, the baseline resistance value in the ultra-low power mode and a comparison chart of stabilized resistance values of the gas sensor in the ultra-low power mode and the normal power mode.

IPC Classes  ?

• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
• G01N 27/04 - Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
• G06F 1/3215 - Monitoring of peripheral devices

Abstract

A controller is connected to receive an input signal from at least one accelerometer. The controller comprises an interface facilitating input and output pins/ports, and a filter module to filter the input signal from at least one accelerometer. The controller includes a stroke segmentation module configured to determine at least two parameters comprising a first parameter and a second parameter from the filtered input signal, generate an envelope signal using the at least two parameters and the filtered input signal, and determine the swim stroke of the swimmer based on the filtered input signal and the envelope signal. Further, an activity detection module is used in combination with the stroke segmentation module.

IPC Classes  ?

• A63B 24/00 - Electric or electronic controls for exercising apparatus of groups

Abstract

A method and systems for generating interpretable and embeddings for a domain-specific small corpus of text-based documents are described. A processing module may obtain the plurality of text-based documents and perform a basic cleaning of each of the plurality of text-based documents. Further, the semantic infusion module may generate the semantically infused corpus using the semantic infusion technique. An embedding generation module is configured to compute the optimal dimensionality for the infused corpus and generate the infused optimal dimensional embeddings using word2vec technique. Further, the embedding generation module is configured to generate baseline optimal dimensional embeddings which can be used to evaluate in terms of interpretability and downstream classification task performance.--

IPC Classes  ?

• G06F 40/30 - Semantic analysis

Abstract

A method for detecting semantic trends within the categorical datasets from text-based documents includes using a processing module to obtain the plurality of text-based documents and perform a basic cleaning of each of the plurality of text-based documents. A semantic infusion module may generate an infused sentence in each of the plurality of text-based documents by inserting a word “A_ class (Ci) _time (Tj)” based on a computed infusion frequency value (Ifreq). A pattern generation module is configured to generate semantic trends by extracting the trending items from the word vector representation created by a word vector module, for each word of each infused sentence of each of the plurality of text-based documents.

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 16/35 - ClusteringClassification
• G06F 16/33 - Querying

Abstract

A controller (120) for vehicle is disclosed. The vehicle comprises position sensor (116) and a rotary electric machine (118) coupled to a primary shaft of a prime mover of the vehicle. Both of the position sensor (116) and the rotary electric machine (118) are connected/interfaced to/with the controller (120), characterized in that, the controller (120) configured to receive, upon rotation of the primary shaft, a position signal from the position sensor (116) positioned to monitor a modified trigger wheel (114). The controller (120) processes the position signal and determines a tooth pattern of the modified trigger wheel (114). The controller (120) determines a parameter in dependence of the determined tooth pattern comprising at least one of a direction of rotation of the primary shaft, a knocking of the engine and an electrical position of the rotary electric machine (118). A system (130), and method for the same is also disclosed.

IPC Classes  ?

• F02P 7/067 - Electromagnetic pick-up devices
• F02N 11/08 - Circuits specially adapted for starting of engines
• G01P 13/04 - Indicating positive or negative direction of a linear movement or clockwise or anti-clockwise direction of a rotational movement
• F02D 35/02 - Non-electrical control of engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions
• G01D 5/244 - Mechanical means for transferring the output of a sensing memberMeans for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for convertingTransducers not specially adapted for a specific variable using electric or magnetic means influencing characteristics of pulses or pulse trainsMechanical means for transferring the output of a sensing memberMeans for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for convertingTransducers not specially adapted for a specific variable using electric or magnetic means generating pulses or pulse trains

Abstract

A method of training a submodule and preventing capture of an AI module is disclosed. Input data is received from at least one user through an input interface. It is transmitted through a blocker module to an AI module which computes a first output data by executing a first model based on the input data. Input data is pre-processed by a submodule to obtain at least one subset of the input data. This submodule is trained using methods steps. The input data and the at least one subset of the input data are processed by the submodule to identify an attack vector from the input data. The identification information of the attack vector is sent to the information gain module.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

A method of training a submodule and preventing capture of an AI module is disclosed. Input data is received from at least one user through an input interface. It is transmitted through a blocker module to an AI module, which computes a first output data by executing a first model based on the input data. Input data is pre-processed by a submodule to obtain at least one subset of the input data. This submodule is trained using methods steps. The input data and the at least one subset of the input data are processed by the submodule to identify an attack vector from the input data. The identification information of the attack vector is sent to the information gain module.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities

Abstract

The present disclosure proposes a method (300) to prevent capturing of an AI module (12) and an AI system thereof (10). The AI system (10) further comprises a submodule (14) trained in accordance with method steps (200). Processing of the input data comprises computing an instantaneous Frequency domain transformation signature of the received input by means of a computation module (141) in the submodule (14). This is followed by comparing the instantaneous Frequency domain transformation signature with a set of pre-derived Frequency domain transformation signatures by means of a comparator module (143) in the submodule (14). An attack vector based on said comparison. Accordingly, a first output computed by the AI module (12) or a modified output is sent out via the output interface (22).

IPC Classes  ?

• G06F 21/14 - Protecting executable software against software analysis or reverse engineering, e.g. by obfuscation
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 20/00 - Machine learning

Abstract

A method is for training a submodule and preventing capture of an AI module. Input data is received from at least one user through an input interface. The input data is transmitted through a blocker module to an AI module, which computes first output data by executing a first model based on the input data. A submodule in the AI system processes the input data to identify an attack vector from the input data. The submodule executes at least two models in which one model is the first model. Identification information of the attack vector is sent to an information gain module.--

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 20/00 - Machine learning
• G06F 21/14 - Protecting executable software against software analysis or reverse engineering, e.g. by obfuscation

Abstract

Method (200) and systems (100) for identifying mislabeled images from a set of labelled images, for a deep neural network, are described. A sequence of plurality of input labelled images (102) is provided as an input to a segmentation network (116) for generating predictions for each image from said set of labelled images (102). An scoring module (118) is configured to compute two or more scoring functions for each image form the set of images (102) using the predictions generated by the segmentation network (116). A quality check module (120) is configured to configured to identify mislabeled images from the set of labelled images (102) by visualizing said computed two or more scoring functions in multi-dimensional graphical representation.

IPC Classes  ?

• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/091 - Active learning
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/776 - ValidationPerformance evaluation
• G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features

Abstract

A safety circuit for a high-voltage (HV) electrical system and a HV electrical device are disclosed. The HV electrical system includes one or more one HV electrical devices. Each HV electrical device includes a low-voltage electrical line. A resistor is connected in parallel to the low-voltage electrical line. A controller is connected to the HV electrical device through the low-voltage electrical line.

IPC Classes  ?

• B60L 3/04 - Cutting-off the power supply under fault conditions
• B60L 3/00 - Electric devices on electrically-propelled vehicles for safety purposesMonitoring operating variables, e.g. speed, deceleration or energy consumption

Abstract

A system for collaborative execution of a task includes a plurality of edge units in communication with a principal processor. Each edge unit includes a collaborative intelligence module configured to establish a communication link amongst at least two of the edge units. The communication intelligence module is further configured to communicate task attributes of the task with at least one other edge unit, and to exchange sensor data from the set of sensors and a plan with the at least one other edge unit to create a joint workspace followed by execution of the task in collaboration with the at least one other edge unit.

IPC Classes  ?

• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

The present disclosure proposes a method of training a submodule (14) and preventing capture of an Al module (12). Input data received from an input interface (11) is transmitted through a blocker module (18) to an Al module (12), which computes a first output data by executing an Al model. A submodule (14) in the Al system (10) trained using methods steps (200) processes the input data to identify an attack vector from the input data. The submodule (14) comprises an xai classification model (142) and at least a preprocessing block (142). The xai classification model runs a pre-trained Al model on xai signatures. The submodule (14) distinguishes between a genuine input and an attack vector by identifying one or more xai signature features in the input.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 3/08 - Learning methods
• G06F 21/14 - Protecting executable software against software analysis or reverse engineering, e.g. by obfuscation

Abstract

An Al system and a method to prevent capturing of an Al module in an Al system is disclosed. The Al system 10 comprising at least: an input interface 12 to receive input data from at least one user; a blocker module 14 to detect validity to the received input data and to generate output data corresponding to invalid data being received as input data using a class swapping technique; a data set module 16 to store valid input data sets; an Al module 18 to process said input data and generate output data corresponding to the input data; a blocker notification module 20 to transmit a notification to the owner of the Al system 10 on detecting an invalid input data and an output interface 22 to send the generated output data to said at least one user.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

The present disclosure proposes a method for training a neural network (101) adapted to generate random numbers and the system thereof. The neural network (101) is configured to generate random numbers identical to the distribution of a quantum random number generator based on the received random noise input using a generative adversarial network (GAN) framework. Method steps comprise feeding random noise to the generator to generate a noisy output. This noisy output is fed to the discriminator along with a real time output of a quantum random number generator (QRNG) to the discriminator. Further the discriminator is trained to learn the entropy of the real-time output and distinguish it from the noisy output based on the learnt entropy. Finally, the output of the discriminator is fed as feedback to the generator.

IPC Classes  ?

• G06F 7/58 - Random or pseudo-random number generators

Abstract

An AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes an input interface, a signature verification module, an AI module configured to execute multiple AI models, a hash module, and a key generation module. A signature module and the key generation module provide an output to the user in response to a received input from the user. The AI module further includes an output interface configured to transmit an output response from the signature module to the user.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Abstract

The NILM device (110) configured to, measure electrical parameters comprising active power, reactive power, and apparent power for each phase of the supply line (112). The NILM device (110) extract a sample window from the measured electrical parameters, characterized in that, the NILM device (110) configured to apply RMS filter (122) to the sample window for removal of noise, process the filtered signal of the sample window through an event detection module (124) for event detection. The event detection module (124) is based on Mov-Mad. The NILM device (110) further processes the detected event through a feature extraction module (126). The feature extraction module (126) applies wavelet scattering to extract features and generates a signature. The NILM device (110) then classifies, using a KNN classifier (128), the extracted features of the sample window into one of a pre-trained clusters and consequently identifies the asset (130) based on the identified cluster.

IPC Classes  ?

• G01R 19/25 - Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
• G01R 21/133 - Arrangements for measuring electric power or power factor by using digital technique

Abstract

The present disclosure proposes a method (200) to extend lifespan of an Electronic Control Unit (ECU) and the ECU (100) thereof. The ECU (100) can reside within any anything ranging from vehicles to consumer electronics and performs a plurality of functions (F1, F2, ……Fn) and comprises at least plurality of flash cells (101) (D1, D2, ….Dn). Method step 201 comprises calculating a remaining service life of each of the plurality of flash cells (101). Method step 202 comprises aggregating the remaining service life of each of the plurality of flash cells (101) to predict lifespan of the ECU (100). Method step 203 comprises mapping the plurality of functions (F1, F2, ……Fn) to the plurality of flash cells (101) (D1, D2, ….Dn). Method step 204 comprises restricting one or more functions of the ECU (100) based on calculated remaining service and said mapping to extend lifespan of the ECU (100).

IPC Classes  ?

• G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
• G06F 11/00 - Error detectionError correctionMonitoring

Abstract

A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes at least an AI module executing a model, a dataset, and the module adapted to be trained. The method includes receiving input data in the module adapted to be trained, labelling data as good data and bad data in the module adapted to be trained, classifying binarily the labelled good data and the labelled bad data in the module adapted to be trained, inputting the binarily classified data into the AI module, and recording internal behavior of the AI module in response to the binarily classified data on the module adapted to be trained.

IPC Classes  ?

• G06N 5/02 - Knowledge representationSymbolic representation

Abstract

The present disclosure proposes a method and a control system (101) for re-baselining a plurality of AI models (103) residing in a plurality of independent edge devices (102). The AI models self-learn in the edge devices and are extracted from the edge devices to a version-controlled database. This is followed by diagnoses of the learnings of the self-learnt AI models on a digital twin environment of the edge device. A group of self-learnt AI models with good learnings are selected based on said diagnosis. Next these group of selected AI models are subjected to federated learning to get a re-baselined model. The re-baselined model is validated using the digital twin and pushed into the plurality of edge devices (102) using firmware over the air.

IPC Classes  ?

• G06N 3/ -

Abstract

The system (100) comprises a sensing unit (104) clamped to power cables of a work machine (102) in a non-intrusive manner. The sensing unit (104) measures and collects electrical power consumption data (current, voltage, power, etc.) of the machine (102) during a working cycle. The working cycle corresponds to a time shift or time zone during which the machine (102) is operated (or is to be operated) to process a workpiece as part of manufacturing a product. A cloud server (120) is in communication with the sensing unit (104), characterized in that, an edge device (110) connected/interfaced between the sensing unit (104) and the cloud server (120). In alternative, the edge device (110) itself contains the sensing unit (104). The edge device (110) comprises a controller configured to determine operation parameters of the machine (102) based on a configuration received from the cloud server (120).

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring

Abstract

The apparatus 120 is shown as a part of a system 100 which comprises a sensing unit 104 clamped to power cables of a work machine 102 in a non-intrusive manner. The sensing unit 104 measures and collects electrical power consumption data of the machine 102 during a working cycle. The apparatus 120 comprises the controller, which is configured to, receive power consumption signals, as measured by the sensing unit 104, and generates an apparent power signal. The apparatus 120 is characterized by, the controller configured to filter the apparent power signal and identify prominent repetitive patterns. The controller further extracts active peaks from the apparent power signal having values higher than a production threshold. The controller then transforms the extracted peaks using wavelet scattering and determines the cycle time based on events identified in the transformed signal using the wavelet scattering.

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring

Abstract

The present disclosure proposes a method for checking integrity of an Al model using distributed ledger technology (DLT) and a system thereof. The proposed method leverages the state-of-the-art watermarking mechanism and ties it up with DLT to generate proof of origin (provenance) in a tamper-proof way. The Al model is registered on the distributed ledger (DL) by uploading full checksum, selective checksums, watermark data and at least the predefined output of the watermark data, a unique model ID is received upon registration. The ownership and integrity of Al model is then determined by matching the model and the output of the watermark data followed by verification of the full and selective checksum of the Al model.

IPC Classes  ?

• G06F 21/16 - Program or content traceability, e.g. by watermarking
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures

Abstract

A system is for development and deployment of a dynamically editable graphical user interface on a connected real-time device. The system includes an input module configured to receive and process a plurality of graphical user interface inputs, and a GUI specification generator configured to parse the processed graphical user interface inputs and generate machine understandable graphical user interface specification from the plurality of graphical user interface inputs. The system also includes a GUI configurator interactively connected to the GUI specification generator and configured to inject performance load balancing parameters and configuration data along with the graphical user interface configuration data. The system further includes a real-time module configured to automatically deploy the machine executable graphical user interface specification on the connected real time system and to edit the graphical user interface inputs in real time while dynamically optimizing the GUI performance using the load balancing parameters.

IPC Classes  ?

• G06F 9/451 - Execution arrangements for user interfaces
• G06F 8/38 - Creation or generation of source code for implementing user interfaces

Abstract

The present disclosure proposes a method (200) of validating defense mechanism of an AI system (10). In step 201, an optimizing function for a mean and a variance of parameters of the said input queries is defined. In step 202, a Bayesian optimization algorithm is applied on said input queries in dependence of the said optimizing function followed by a Gaussian process at least twice to create a posterior distribution for the mean and variance of parameters to reverse engineer the AI system (10). In step 203, the AI system (10) is fed with a set of input queries whose mean and variance of parameters have a high probability in the posterior distribution. In step 204, the output of the AI system (10) is recorded to validate defense mechanism of the AI system (10).

IPC Classes  ?

• G06N 7/00 - Computing arrangements based on specific mathematical models
• G06F 21/14 - Protecting executable software against software analysis or reverse engineering, e.g. by obfuscation
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

The device comprises at least one accelerometer, and a controller receiving input signals from the at least one accelerometer. The controller configured to filter stroke characteristics from the input signal using a filter module. The controller then applies a first statistical module on the filtered signal and obtains a first output signal. Due to the first statistical module, the first output signal is obtained, which is agnostic to type of swim stroke employed by the swimmer. The controller then determines the swim metric based on the first output signal and an adaptive threshold value. The swim metric is lap completion or lap count or turn event, during swimming by a swimmer. The device consumes less power and also agnostic to swim styles and turn styles employed by swimmers.

IPC Classes  ?

• G01P 15/18 - Measuring accelerationMeasuring decelerationMeasuring shock, i.e. sudden change of acceleration in two or more dimensions
• A63B 71/06 - Indicating or scoring devices for games or players
• G07C 1/22 - Registering, indicating, or recording the time of events or elapsed time, e.g. time-recorders for work people in connection with sports or games

Abstract

A method is for route identification that is conducive for successful diagnosis of an exhaust gas treatment system of a vehicle. In the method, an ECU in the vehicle receives a value of GPS coordinates from a GPS module, and the ECU retrieves a value of day, date, and time from an ECU clock. The ECU monitors a value of engine operating conditions with reference to the received value of GPS coordinates and the retrieved value of day, date, and time. The ECU identifies a segment of GPS coordinates for successful diagnosis of an exhaust gas treatment system based on the monitored value. Upon the identification, the segment of GPS coordinates is stored in ECU memory.

IPC Classes  ?

• F01N 11/00 - Monitoring or diagnostic devices for exhaust-gas treatment apparatus
• G01S 19/42 - Determining position

Abstract

A device and method determines available regenerative braking energy in a route for a target vehicle. The device is in communication with a navigation unit and is configured to split the at least one route between a source and a destination into multiple segments followed by determining vehicular data of the target vehicle. The device determines available/recoverable regenerative braking energy in the at least one route for the target vehicle based on selective vehicular data of other vehicles similar to the target vehicle, which have travelled through the at least one route. The device is configured to dynamically predict the available regenerative braking energy originating from random longitudinal deceleration maneuvers for the chosen route from source location to the chosen destination location for any hybrid or electric vehicle. The device improves fuel efficiency and reduces emissions.

IPC Classes  ?

• B60L 7/10 - Dynamic electric regenerative braking
• B60L 7/00 - Electrodynamic brake systems for vehicles in general
• G07C 5/00 - Registering or indicating the working of vehicles
• G07C 5/02 - Registering or indicating driving, working, idle, or waiting time only

Abstract

The present disclosure proposes a method of training a submodule (14) and preventing capture of an AI module (12). Input data received from an input interface (11) is transmitted through a blocker module (18) to an AI module (12), which computes a first output data by executing a first model (M). A submodule (14) in the AI system (10) trained using methods steps (200) processes the input data to identify an attack vector from the input data. The submodule (14) executes the first model (M) and at least a second model. The first model (M) and the second model have a first and second set of network parameters and hyper-parameters respectively. The identification information of the attack vector is sent to the information gain module (16).

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

An AI system includes an input interface configured to receive input data from at least one user, and a blocker module configured (i) to detect the received input data is an attack input data, (ii) to manipulate output data of the blocker module, and (iii) to send the output data from the blocker module to the at least one user. The AI system further includes an AI module configured to process the input data received by the blocker module and to generate output data corresponding to the input data. The AI system also includes a blocker notification module configured to transmit a notification to the owner of the AI system on detecting an attack input data, and an output interface configured to send the generated output data to the at least one user.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

The invention discloses a method and a system (100) of operating a gas sensor 10 in a portable electronic device (50). The gas sensor (10) operates in an ultra-low power mode when the portable electronic device (50) is inactive and the gas sensor (10) operates in a normal power mode when the portable electronic device (50) is active. The method involves storing a baseline resistance value of the gas sensor (10) in the ultra-low power mode. The gas sensor (10) transitions to a normal power mode from the ultra-power mode, when the portable electronic device (50) is active. A rate of stabilization of resistance value of the gas sensor (10) is computed in the normal power mode. A stabilized resistance value of the gas sensor (10) in the normal power mode is estimated based on the rate of stabilization of resistance value of the gas sensor (10) in the normal power mode, the baseline resistance value in the ultra-low power mode and a comparison chart of stabilized resistance values of the gas sensor (10) in the ultra-low power mode and the normal power mode.

IPC Classes  ?

• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups

Abstract

The present disclosure proposes a method of training a submodule (14) and preventing capture of an AI module (12). Input data received from an input interface (11) is transmitted through a blocker module (18) to an AI module (12), which computes a first output data by executing a first model (M). A submodule (14) in the AI system (10) trained using methods steps (200) processes the input data to identify an attack vector from the input data. The submodule (14) executes the first model (M) and at least a second model. The first model (M) and the second model have a first and second set of network parameters and hyper-parameters respectively. The identification information of the attack vector is sent to the information gain module (16).

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06N 3/08 - Learning methods
• H04L 29/06 - Communication control; Communication processing characterised by a protocol

Abstract

The wheel assembly (10) comprises a hub motor unit (12) having at least two magnets (14). The wheel assembly (10) further comprises a shaft (16) passing through the hub motor unit (12). The rim (11) is mounted above the hub motor unit (12). The rim (11) is made of a first component (18) and a second component (20), wherein the first component (18) has integrated supporting elements (22). With the above-disclosed rim (11), the entry of the foreign materials like water/dirt particles is prevented as the rim (11) seals the hub motor unit (12). The new design of the rim (11) does not require a separate cover to close the hub motor unit (12), thereby the screws used to fasten the cover is not needed during assembly of motor unit (12). The rotor/rotating part being precast to rim (11) eliminates a welding process required between the parts.

IPC Classes  ?

• B60K 7/00 - Disposition of motor in, or adjacent to, traction wheel
• B60B 3/08 - Disc wheels, i.e. wheels with load-supporting disc body with disc body formed by two or more axially- spaced discs
• H02K 1/27 - Rotor cores with permanent magnets
• H02K 7/00 - Arrangements for handling mechanical energy structurally associated with dynamo-electric machines, e.g. structural association with mechanical driving motors or auxiliary dynamo-electric machines
• B60K 11/06 - Arrangement in connection with cooling of propulsion units with air cooling

Abstract

The present disclosure proposes a system for optimizing power consumption of an industrial facility (101) and a method thereof. The industrial facility (101) comprising a plurality of machines executing a plurality of processes. The system is characterized by a plurality of energy metering devices (102) associated with each of the plurality of machines, a processing module (103) and at least an output interface (104). The processing module (103) comprises a data processing means (1031), Al module (1033) and at least a database (1032). The processing module (103) configured to optimize power supplied to each machine by means of a processing module (103); optimize machine operating parameters for the plurality of machines by means of the processing module (103); predict cost savings in a process.

IPC Classes  ?

• G06Q 10/00 - AdministrationManagement
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 50/04 - Manufacturing
• G06Q 50/06 - Energy or water supply

Abstract

The controller (110) connected to receive input signals (120) from at least one accelerometer (102). The controller (110) comprises an interface (104) facilitating input and output pins/ports, a filter module (106) to filter the input signal (120) from at least one accelerometer (102). The controller (110), characterized by, a stroke segmentation module (108) adapted to determine at least two parameters comprising a first parameter (208) and a second parameter (210) from said filtered signal (122), generate an envelope signal (206) using the at least two parameters and the filtered signal (122), and determine the swim stroke of the swimmer based on the filtered signal (122) and the envelope signal (206). Further, an activity detection module (112) is used in combination with the stroke segmentation module (108). The present invention obtains the stroke segments based only on the inputs signals from at least one accelerometer (102), providing reduced cost and complexity.

IPC Classes  ?

• G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
• A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A63B 71/06 - Indicating or scoring devices for games or players
• A63B 69/00 - Training appliances or apparatus for special sports

Abstract

The present disclosure proposes a method of training a submodule (14) and preventing capture of an AI module (12). Input data is received from at least one user through an input interface (11). It is transmitted through a blocker module (18) to an AI module (12), which computes a first output data by executing a first model (M) based on the input data. Input data is pre-processed by a submodule (14) to obtain at least one subset of the input data. This submodule (14) is trained using methods steps (200). The input data and said at least one subset of the input data are processed by the submodule (14) to identify an attack vector from the input data. The identification information of the attack vector is sent to the information gain module (16).

IPC Classes  ?

• G06F 21/12 - Protecting executable software
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 3/00 - Computing arrangements based on biological models

Abstract

The present disclosure proposes a method of training a submodule (14) and preventing capture of an Al module (12). Input data is received from at least one user through an input interface (11). It is transmitted through a blocker module (18) to an Al module (12), which computes a first output data by executing a first model (M) based on the input data. A submodule (14) in the Al system (10) trained using methods steps (200) processes the input data to identify an attack vector from the input data. The submodule (14) executes at least two models, where one model is the first model. The identification information of the attack vector is sent to the information gain module (16).

IPC Classes  ?

• G06F 21/14 - Protecting executable software against software analysis or reverse engineering, e.g. by obfuscation
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 20/00 - Machine learning
• G06N 3/00 - Computing arrangements based on biological models

Abstract

The present disclosure proposes a method of training a submodule (14) and preventing capture of an Al module (12). Input data is received from at least one user through an input interface (11). It is transmitted through a blocker module (18) to an Al module (12), which computes a first output data by executing a first model (M) based on the input data. Input data is pre-processed by a submodule (14) to obtain at least one subset of the input data. This submodule (14) is trained using methods steps (200). The input data and said at least one subset of the input data are processed by the submodule (14) to identify an attack vector from the input data. The identification information of the attack vector is sent to the information gain module (16).

IPC Classes  ?

• G06F 21/14 - Protecting executable software against software analysis or reverse engineering, e.g. by obfuscation
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 20/00 - Machine learning

Abstract

The bio analyte device (10) comprises a photo detector (14) and a filter element (16) positioned between the light source (12) and the photo detector (14). The light source (12) comprises at least one light element (13). The control unit (11) receives a current value and an amplifier gain corresponding to each of the light element (13) of the light source (12) from a communication device (18). The control unit (10) calculates a light intensity (15) corresponding to each of the light element (13) of the light source (12), from corresponding received current value and received amplifier gain. The control unit (11) computes an actual current multiplication factor and an actual gain multiplication factor from the calculated light intensities. The control unit (11) further determines actual incident intensity of light source from the actual current multiplication factor and the actual gain (20) multiplication factor.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
• A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
• A61B 5/024 - Measuring pulse rate or heart rate

Abstract

The controller (110) of the wearable device (100) is used to determine swim characteristics of a swimmer. The controller (110) connected to at least one motion sensor (120) selected from a group comprising a multi-axis gyroscope (112) and a multi-axis accelerometer (114). The controller (110) adapted to, detect input signals (202) from the at least one motion sensor (120), characterized by, the controller (110) further adapted to perform dynamic stroke segmentations based on at least one of the input signals (202) using a stroke segmentation module (102), extract feature vectors, using feature extraction module (104), from the at least one input signal (202) based on the stroke segmentations, and determine the swim characteristics by using the feature vectors through a classifier module (106). The present invention provides the controller (110) and method which dynamically adapts to the style of the swimmer to detect the swim characteristics.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Abstract

A holder (11) comprises an opening (16) having multiple cut-sections (18), to accommodate a filter element (20) in at least one cut-section (18). The holder (11) is adapted to hold the filter element (20) in a bio-analyte device (10) between a light source (12) and a photo detector (14). With the above-disclosed holder (11), the error in the intensity of the light received by the photo detector (14) is reduced, as the filter element (20) is designed to allow only a predefined wavelength range of light.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G01N 21/62 - Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
• B01L 9/00 - Supporting devicesHolding devices

Abstract

A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system. A method of training a module in an AI system, the AI system comprises at least an AI module executing a model, a dataset and the module adapted to be trained. The method comprises the following steps: receiving input data in the AI module, and recording internal behavior of the AI module in response to the input data on the module. The internal behavior of the AI module is recorded in the module.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 21/31 - User authentication

Abstract

A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system is disclosed. A method of training a module (16) in an AI system (10), the AI system (10) comprises at least an AI module (14) executing a model, a dataset (12) and the module (16) adapted to be trained. The method comprises the following steps: receiving input data in the module (16), labelling data as good data and bad data in the module (16), classifying binarily the labelled good data and the labelled bad data in the module (16), inputting the binarily classified data into the AI module (14) and recording internal behavior of the AI module (14) in response to the binarily classified data on the module (16).

IPC Classes  ?

• G06F 21/60 - Protecting data
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06F 21/88 - Detecting or preventing theft or loss

Abstract

The control unit (10) adapted to identify multiple zones (14) to focus in the sample (12) and to control a movement of an objective lens (16) over at least one zone (14)(a) of the sample (12) in a predefined distance range from the sample (12). The control unit (10) further adapted to detect a focused point within the predefined distance range corresponding to the at least one focused zone (14) (a) of the sample (12). The control unit (10) determines a new distance range to focus the objective lens (16) over at least one next zone (14) (b) of the sample (12), from the detected focused point The control unit (10) detects a new focused point within the new distance range corresponding to the at least one next zone (14) (b) of the sample (12). The focus on the sample (12) in the perpendicular (z-axis) direction is more precise and obtained with ease.

IPC Classes  ?

• G02B 21/24 - Base structure
• G02B 7/28 - Systems for automatic generation of focusing signals
• G02B 21/00 - Microscopes

Abstract

The device (10) comprises a sample holder (12) having the sample (14), and a movable objective holder (18) having the objective lens (12). The objective holder (18) is positioned above the sample holder (16). The device (10) further comprises an actuator (20) connected to the objective holder (18) having the objective lens (12), and a control unit (22) that is electronically connected to the actuator (20). The control unit (22) adapted to provide at least one signal to adjust the objective holder (14) via the actuator (20). The device (10) further comprises a movable pin (24) connected to the objective holder (18) and a sensing element (26) positioned in proximity to the movable pin (24). The sensing element (26) adapted to detected a position of the movable pin (24), and the control unit (22) adapted to control the movement of the objective lens (12) on/over the sample (14), based on the detected position of the movable pin (24).

IPC Classes  ?

• G02B 7/08 - Mountings, adjusting means, or light-tight connections, for optical elements for lenses with mechanism for focusing or varying magnification adapted to co-operate with a remote control mechanism
• G02B 21/24 - Base structure
• G02B 21/36 - Microscopes arranged for photographic purposes or projection purposes
• G01B 11/00 - Measuring arrangements characterised by the use of optical techniques

Abstract

The system (100) comprises a server (102) and an ECU (110) of a host vehicle (106). The server (102) configured to detect a current location of the host vehicle (106) and identify a first cell B2 in a grid map (120) through the signal received from the ECU (110). The server (102) then transmits value of at least one parameter corresponding to the first cell B2 to the ECU (110). The server (102) further adapted to transmit value of the at least one parameter for at least one second cell C3 adjacent to the first cell B2, where the second cell neighbors the first cell B2. The ECU (110) performs the at least one vehicle function in the second cell C3 by using a corresponding obtained value when a loss of connection is detected between the host vehicle (106) and the server (102) in the second cell C3.

IPC Classes  ?

• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

The device (100) comprises at least one accelerometer (120), and a controller (110) receiving input signals from the at least one accelerometer (120). The controller (110) configured to filter stroke characteristics from the input signal using a filter module (102). The controller (110) then applies a first statistical module (104) on the filtered signal and obtains a first output signal. Due to the first statistical module (104), the first output signal is obtained, which is agnostic to type of swim stroke employed by the swimmer. The controller (110) then determines the swim metric based on the first output signal and an adaptive threshold value. The swim metric is lap completion or lap count or turn event, during swimming by a swimmer. The device (100) consumes less power and also agnostic to swim styles and turn styles employed by swimmers.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
• G07C 1/22 - Registering, indicating, or recording the time of events or elapsed time, e.g. time-recorders for work people in connection with sports or games

Abstract

The voltage clamp circuit (10) as seen in figure (2), is adapted to clamp, voltage of an energy limited signal. The voltage clamp circuit (10) comprising a semiconductor component (12) electrically connected to a source (14), a capacitive component (16) electrically connected between one end of the semiconductor component (12) and a ground terminal and a precharging circuit electrically connected between one of said semiconductor component and a voltage supply line.

IPC Classes  ?

• H02M 1/34 - Snubber circuits
• H02M 1/36 - Means for starting or stopping converters

Abstract

A system (100) for development and deployment of dynamically editable graphical user interface on a connected real-time device. The system 100 comprises an input module (102) configured to receive and process a plurality of graphical user interface inputs. Further, the system (100) comprises a GUI specification generator (104) configured to parse the processed graphical user interface inputs and generate machine understandable graphical user interface specification from the plurality of graphical user interface inputs. A GUI configurator (106) is interactively connected to the GUI specification generator (104) and configured to inject performance load balancing parameters and configuration data along with the graphical user interface configuration data. The system (100) further comprises a real-time module (108) configured to automatically deploy the machine executable graphical user interface specification on the connected real time system and edit the graphical user interface inputs in real time while allowing dynamically optimizing the GUI performance using the load balancing component (112).

IPC Classes  ?

• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

A system (100) for development and deployment of dynamically editable graphical user interface on a connected real-time device. The system 100 comprises an input module (102) configured to receive and process a plurality of graphical user interface inputs. Further, the system (100) comprises a GUI specification generator (104) configured to parse the processed graphical user interface inputs and generate machine understandable graphical user interface specification from the plurality of graphical user interface inputs. A GUI configurator (106) is interactively connected to the GUI specification generator (104) and configured to inject performance load balancing parameters and configuration data along with the graphical user interface configuration data. The system (100) further comprises a real-time module (108) configured to automatically deploy the machine executable graphical user interface specification on the connected real time system and edit the graphical user interface inputs in real time while allowing dynamically optimizing the GUI performance using the load balancing component (112).

IPC Classes  ?

• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

The bioanalyte monitoring device (10) comprises a movable block (12) slidably positioned on a fixed block (14). The device (10) comprises a profile (15) having a seat (16) and a cover (18), wherein the seat (16) is fitted on the fixed block (14) and the cover (18) fitted to a bottom portion of the movable block (12). The seat (16) and the cover (18) forms a slot (20) in the device (10) to accommodate a body appendage (22) of a living being. The seat (16) comprises multiple cut-outs (24) of different dimensions to accommodate different sizes of the body appendage (22). The user can analyze and detect at least one parameter of the human body without much hassle. Since there is no probe is used for connecting the detecting portion (where body appendage (22) is placed) and the analyzing portion (the seat (16)), the efficiency of the analysis is increased.

IPC Classes  ?

• A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value

Abstract

The invention discloses a system and a method to prevent unauthorized capturing of models in an Artificial Intelligence based system (AI system) (100). The method comprises the steps: receiving an input (103) from a user; checking whether the input (103) is right input or wrong input; computing information gain extracted by said user when the input (103) is wrong input; locking out said system (100) when said information gain exceeds a pre-defined threshold.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 7/00 - Computing arrangements based on specific mathematical models
• G06N 20/00 - Machine learning
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 5/00 - Computing arrangements using knowledge-based models

Abstract

The device (10) comprises a housing (12) having a push button (14) fixed on the housing (12). The device (10) comprises a slot (16) created in the device (10) to accommodate a body appendage (18) of a living being. The device (10) comprises a lever (20) positioned inside the device (10). The lever (20) comprises a first end (22) and a second end (24). The first end (22) is connected to the push button (14) and the second end (24) is connected to a movable block (26). The lever (20) is adapted to move the movable block (26) upon operating the push button (14) to create the slot (16) in the device (10). With the above-disclosed device (10), the user can analyze and detect at least one parameter of the human body without much hassle.

IPC Classes  ?

• A61B 5/024 - Measuring pulse rate or heart rate
• A61B 5/026 - Measuring blood flow
• A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons

Abstract

The invention discloses a system and a method to prevent unauthorized capturing of models in an Artificial Intelligence based system (Al system) (100). The method is comprising the steps: receiving an input (103) from an user; checking whether the input (103) corresponds to at least one of classes (107) in a data set; returning a class (107) which corresponds to the input (103), from the data set, if the input (103) corresponds to at least one of the classes (107) in the data set; returning a p re-defined class (116) (Don't Know Class - DKC) from the data set if the input does not correspond to any of the said classes in the data set.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 5/04 - Inference or reasoning models

Abstract

The digital focusing device (10) comprises a sample holder (12) adapted to hold a sample (13). The device (10), characterized in that, further comprises a first plate (18) comprising a first guide rail assembly (14) and a second guide rail assembly (16). The device comprises a second plate (20) slidably fitted on top of the first and second guide rail assemblies (14,16). The device (10) comprises a third guide rail assembly (22) mounted on the second plate (20). The device (10) further comprises a first connecting block (26) movably fitted on the third guide rail assembly (22) and is connected to the sample holder (12).

IPC Classes  ?

• G02B 21/26 - StagesAdjusting means therefor

Abstract

The antenna system (10) comprises a first printed circuit board (PCB) (12) and a second printed circuit board (PCB) (14) positioned perpendicular to the first PCB (12), at one end of the first PCB (12). The second PCB (14) has different dimensions than the first PCB 12.The first and the second PCB (12, 14) has a common feeding pin (18).

IPC Classes  ?

• H01Q 1/38 - Structural form of radiating elements, e.g. cone, spiral, umbrella formed by a conductive layer on an insulating support
• H01Q 5/335 - Individual or coupled radiating elements, each element being fed in an unspecified way using frequency dependent circuits or components, e.g. trap circuits or capacitors at the feed, e.g. for impedance matching
• H01Q 5/364 - Creating multiple current paths
• H01Q 9/42 - Resonant antennas with feed to end of elongated active element, e.g. unipole with folded element, the folded parts being spaced apart a small fraction of the operating wavelength
• H01Q 21/30 - Combinations of separate antenna units operating in different wavebands and connected to a common feeder system
• H01Q 1/24 - SupportsMounting means by structural association with other equipment or articles with receiving set
• H01Q 13/10 - Resonant slot antennas

Abstract

A control unit is configured to determine a first parameter from a back EMF signal, and to determine at least one second parameter from the back EMF signal corresponding to the first parameter. The control unit verifies, based on the first parameter, at least one condition containing the at least one second parameter, and detects stall or step-loss in the stepper motor based on the verification. The control unit uses a pattern detection by identifying characteristic features of peaks of the back EMF signal, which are order, magnitude and polarity. These characteristic features are independent of RPM, backlash, load and do not require complex calculations and are very robust even at lower RPMs.

IPC Classes  ?

• H02P 8/38 - Protection against faults, e.g. against overheating or step-outIndicating faults the fault being step-out

Abstract

The helmet (100), comprises at least one microphone (104) located on a shell (102) of the helmet (100) to detect a sound signal generated in a surrounding of the (helmet 100). At least one speaker (106) located on interior surface of the helmet (100), and a controller (110) in electronic communication with the at least one microphone (104) and the at least one speaker (106), characterized in that, the controller (110) adapted to select and play a desired sound from the detected sound signal, through at least one speaker (106). The helmet (100) enables selective noise isolation or cancellation, in comparison to the active noise cancellation in the existing art. The selective noise or sound play in the helmet (100) provides the wearer to avoid distractions. Further, the controller (110) combines the desired sound with the audio signals based on psychoacoustic analysis model to avoid masking.

IPC Classes  ?

• A42B 3/04 - Parts, details or accessories of helmets
• A42B 3/30 - Mounting radio sets or communication systems

Abstract

The antenna system (10) comprises an antenna (12) having a first portion (14), a second portion (16) and a third portion (18). The first, the second and the third portions (14, 16, 18) are made of same or different dimensions to each other. With the disclosed antenna design, the height of the product can be reduced and still achieves the required antenna efficiency with the acceptable impedance matching in the lower and upper frequency bands.

IPC Classes  ?

• H01Q 1/24 - SupportsMounting means by structural association with other equipment or articles with receiving set
• H01Q 5/35 - Individual or coupled radiating elements, each element being fed in an unspecified way for different propagation modes using two or more simultaneously fed points
• H01Q 7/00 - Loop antennas with a substantially uniform current distribution around the loop and having a directional radiation pattern in a plane perpendicular to the plane of the loop
• H01Q 9/04 - Resonant antennas

Abstract

The various embodiment herein provides a mounting arrangement (110) for the Electronic Control Module (ECM) (100). The ECM (100) comprises the PCB (202) positioned on a connector (104) through at least two attachment elements (102). The guide plate (108) is positioned between the connector (104) and the PCB (202). The mounting arrangement (110) is characterized by, the guide plate (108) positioned on the at least two attachment elements (102) on which the PCB (202) is mounted. The ECM (100) with the mounting arrangement (110) is also disclosed. The present invention simplifies the connection of the connector (104), the guide plate (108) and the PCB (202) in the housing (114) of the ECM (100). The present invention results in improved assembly process of the ECMs (100) with reduction in faulty ECMs (100).

IPC Classes  ?

• H05K 5/00 - Casings, cabinets or drawers for electric apparatus
• H01R 12/70 - Coupling devices

Abstract

A system (10), for swapping at least one battery in an electric vehicle is disclosed. The system (10) comprises a communication means (13) adapted to establish a communication between at least one battery ( 14) and the electric vehicle (12) via an authentication technique. The system (10) comprises an alerting means (22) adapted to indicate removal of the battery (12) from the vehicle (12), when the authentication is successful. The system (10) comprises a communication device (38) adapted to transfer information regarding the removed battery (14) to a cloud repository (40), upon establishing a communication with the removed battery (14), via the authentication technique. The system (10) further comprises a new battery (32) plugged into the vehicle (12), when the authentication between the new (battery 32) and the vehicle (12) is successful.

IPC Classes  ?

• B60L 50/60 - Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
• B60L 53/124 - Detection or removal of foreign bodies
• B60L 53/126 - Methods for pairing a vehicle and a charging station, e.g. establishing a one-to-one relation between a wireless power transmitter and a wireless power receiver
• B60L 53/68 - Off-site monitoring or control, e.g. remote control
• B60L 53/65 - Monitoring or controlling charging stations involving identification of vehicles or their battery types
• B60L 53/80 - Exchanging energy storage elements, e.g. removable batteries

Abstract

The present disclosure discloses a method of configuring a plurality of position co- ordinates for indoor positioning. The method includes retrieving a reference map of an indoor location, establishing a first level relationship between a plurality of first- level sub-locations with respect to the at least one labelled location, establishing a second level relationship between a plurality of second level sub-locations within each of the plurality of first level sub-locations by grouping the plurality of second level sub-locations into at least one cluster, determining at least one position co-ordinate based on the grouping and storing the at least one position co-ordinate in the reference map for enabling the indoor positioning. Therefore the position co-ordinates are configured automatically in real-time without the need for tedious process of getting the training data.

IPC Classes  ?

• G01C 21/20 - Instruments for performing navigational calculations
• G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves

Abstract

A non-intrusive elevator monitoring device (10) in electrical connection with an electrical power system (11) and an elevator is disclosed. The elevator monitoring device (10) comprising: a receiver (12) for receiving an electrical signal, the electrical signal being power supplied to the elevator by the electrical power system (11), a sampling circuit (14) for sampling the electrical signal with a pre-defined sampling rate for obtaining a plurality of signature waveforms, the plurality of signature waveforms corresponding to varying amplitudes and time intervals and a processor (16) for processing a plurality of segments in each of the plurality of signature waveforms, the plurality of segments corresponding to a plurality of electrical parameters of the elevator. Therefore enabling monitoring and predicting maintenance of the elevators.

IPC Classes  ?

• B66B 5/00 - Applications of checking, fault-correcting or safety devices in elevators
• B66B 1/34 - Control systems of elevators in general Details

Abstract

The system is provided to calibrate the ECU of the vehicle. The system comprises a remote computer, a central server, a local computer and setup comprising at least a dynamo meter, and at least one actuator. The dynamo meter and the actuator are interfaced and operated with the local computer. The central server is connected to the local computer by a second networking means, and a remote computer is connected to the central server by a first networking means. The remote computer, uploads instructions to the central server, executes the instructions through the local computer to operate the dynamo meter and the actuator, and calibrates the ECU of the vehicle. The instructions are downloaded to the local computer by the second networking means.

IPC Classes  ?

• F02D 41/24 - Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
• F02D 41/26 - Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using computer, e.g. microprocessor
• G01M 17/007 - Wheeled or endless-tracked vehicles
• G01M 15/04 - Testing internal-combustion engines

Abstract

The present invention discloses a controller (110) and method to determine incident intensity of a light source (108) and total hemoglobin concentration using the Hemoglobin monitoring device (102). The controller (110) is adapted to supply a reduced rated power to the at least one light source (108), when the device (102) is switched ON. The rated power is reduced by a predetermined factor. The controller (110) measures a value of the incident intensity of emitted light from the at least one light source (108) through a photodetector (106). The controller (110) then determines the incident intensity by multiplying the measured value with the predetermined factor. The controller (110) measures attenuated intensities in the presence of the body appendage (114) when the light sources (108) are supplied with rated power. Further, the controller (110) is adapted to determine the total hemoglobin by processing the incident intensity and the attenuated intensity.

IPC Classes  ?

• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
• A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons

Abstract

Techniques for electromagnetic modelling of EM structures are described. Krylov subspace of a second EM structure is augmented with Eigen vectors of a first EM structure to form an augmented space. The second EM structure is a design variant of the first EM structure and the first EM structure is already EM modelled and simulated. Thereafter, Maxwell's equations for the second EM structure are solved using the augmented space.

IPC Classes  ?

• G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
• G06F 17/16 - Matrix or vector computation
• G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
• G06F 111/10 - Numerical modelling

Abstract

The direct plug connector (108) is provided for an Electronic Control Unit (ECU) (102). The ECU (102) comprises a Printed Circuit Board (PCB) (104) with contact pads (120). The direct plug connector (108) comprises an end cover (112). The end cover (112) comprises a flexible member (118) which functions as a seal. A plurality of contact terminals (110) are arranged through the end cover (112). The plurality of contact terminals (110) makes contact with the contact pads (120) of the ECU (102). A plurality of cables (116) are connected to the plurality of contact terminals (110) with respective stripped ends. The plurality of cables (116) comprises multi-strand wires. The direct plug connector 108 is characterized by, an insulating material sealing a connection between the plurality of contact terminals (110) and the stripped ends of the plurality of cables (116).

IPC Classes  ?

• H01R 4/72 - Insulation of connections using a heat shrinking insulating sleeve
• H01R 12/71 - Coupling devices for rigid printing circuits or like structures
• H01R 13/52 - Dustproof, splashproof, drip-proof, waterproof, or flameproof cases
• H01R 12/72 - Coupling devices for rigid printing circuits or like structures coupling with the edge of the rigid printed circuits or like structures

Abstract

Techniques for controlling state of an electrical device (102) are described. A system (100) for controlling the state of the electrical device (102) includes a load switch (104) connected to the electrical device (102). The load switch (104) can be actuated to change the state of the electrical device (102). A controller (106) may actuate the load switch (104) based on at least one of an actuation signal from a remote source (108) and a toggling signal from a local switch (110). Further, a transfer circuit (112) may be activated in response to a failure of the controller (106). The transfer circuit (112) may be activated or deactivated by a latch (114) connected to the transfer circuit (112) and to the controller (106).

IPC Classes  ?

• H01H 47/00 - Circuit arrangements not adapted to a particular application of the relay and designed to obtain desired operating characteristics or to provide energising current
• H01H 9/00 - Details of switching devices, not covered by groups

Abstract

A device (40) for monitoring wear of a brake pad (12) in a braking system (10) in a vehicle (50) is disclosed. The brake pad (12) is connected to a brake cylinder (14) for applying braking force to a wheel (24). The braking system (10) comprises a brake line (26) connected between the brake cylinder (14) and a master cylinder (16). The braking system (10) comprises a motor (18) connected to the master cylinder (16), and a control unit (20) controlling the motor (18). The control unit (20) detects the wear of the brake pad (12) based on vibrations sensed at the motor (18) due to the movement of the brake pad (12) and the brake cylinder (14) when the vehicle (50) is in motion.

IPC Classes  ?

• F16D 66/02 - Apparatus for indicating wear
• B60T 17/22 - Devices for monitoring or checking brake systemsSignal devices

Abstract

Techniques for electromagnetic modelling of EM structures are described. Krylov subspace of a second EM structure (104, 504) is augmented with Eigen vectors of a first EM structure (102, 502) to form an augmented space. The second EM structure (104, 504) is a design variant of the first EM structure (102, 502) and the first EM structure (102, 502) is already EM modelled and simulated. Thereafter, Maxwell's equations for the second EM structure (104, 504) are solved using the augmented space.

IPC Classes  ?

• G06F 17/50 - Computer-aided design

Abstract

A controller is provided to drive an inverter circuit for a PMSM. The inverter circuit is connected to a battery through a DC link capacitor, and is driven in one safe state during a fault condition. The controller monitors at least one parameter with respective threshold value to drive the inverter circuit in one safe state comprising an active Short Circuit (SC) and a Freewheel (FW). While in FW state, the controller switches from the FW state to the SC state if the at least one parameter is above the respective threshold. While in SC state, the controller controls engine speed to bring the PMSM to a predetermined speed when the stator temperature is more than a threshold temperature value. The controller switches from the SC state to the FW state.

IPC Classes  ?

• H02P 27/06 - Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using DC to AC converters or inverters
• B60L 3/00 - Electric devices on electrically-propelled vehicles for safety purposesMonitoring operating variables, e.g. speed, deceleration or energy consumption
• B60L 11/18 - using power supplied from primary cells, secondary cells, or fuel cells
• H02H 7/08 - Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for dynamo-electric motors
• H02M 7/53846 - Control circuits
• H02P 25/022 - Synchronous motors
• H02M 1/32 - Means for protecting converters other than by automatic disconnection
• H02M 7/797 - Conversion of AC power input into DC power outputConversion of DC power input into AC power output with possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only