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1. 发明专利

AU2020102681A4 EFFECTIVE COMMUNICATION SYSTEM FOR DISABLED PERSONS USING NEURO SIGNALS 未知
公开(公告)号：AU2020102681A4
公开(公告)日：2021-01-14
申请号：AU2020102681
申请日：2020-10-12
申请人： BALAMURUGAN M DR , GUPTA SHANKAR LAL DR , GUPTA VARUN DR , MARIMUTHU K DR , PAVANI T DR , PONNUSAMY SIVAKUMAR DR , RAJASEKARAN P PROF , SHARMA MANOJ DR , USHA KUMARI CH DR , VALLI R DR
发明人： RAJASEKARAN P , GUPTA SHANKAR LAL , BALAMURUGAN M , GUPTA VARUN , SHARMA MANOJ , USHA KUMARI CH , PAVANI T , MARIMUTHU K , VALLI R , PONNUSAMY SIVAKUMAR
IPC分类号： G06F3/01 , A61F4/00 , G06F3/00
摘要： EFFECTIVE COMMUNICATION SYSTEM FOR DISABLED PERSONS USING NEURO SIGNALS The dynamic needs of communication between physically disabled people who cannot communicate verbally with normal people has to be addressed. Twenty five percent of people in the world face this major problem at time of paralysis and even from birth itself. Hence it is an utmost need to bring out a solution to this problem and bridge the gap between paralyzed and normal people. Not only physically handicapped people but people suffering from various disabilities also suffer from depressions. Mental depressions lead to lot of health problems. The need of communication system to identify their feelings to do their day to day tasks is to be addressed. This can be achieved by having an effective communication system with simple text conversation by using neural signals. Simple feelings and thoughts running in brain can be brought out with the help of automatic writing machine using brain sensors and Neural signals to text and speech conversion algorithms. Hence, we look forward to helping the physically handicapped to get good involvement in the society among the normal people. 11 P a g e EFFECTIVE COMMUNICATION SYSTEM FOR DISABLED PERSONS USING NEURO SIGNALS Diagram Bran enor -- EoGDSP . etT Voice Writing processor Spehsensor module software 1|Pagewbmmw mmmmm mmmmmwbmmmm ommmm

2. 发明专利

AU2020102678A4 Realistic electric load profiling system of textile mills by considering dynamic and stochastic behaviour using GAN 未知
公开(公告)号：AU2020102678A4
公开(公告)日：2021-01-28
申请号：AU2020102678
申请日：2020-10-12
申请人： ARUNMOZHI S DR , BABY MARUTHI P DR , GIRI PRASAD R DR , GUPTA VARUN DR , KALVALA RAJENDAR MR , PAVATE ARUNA ANIMISH MRS , PUNEETH KUMAR B S DR , RAMANATHAN L DR , SUBBARAYUDU CHILUKURI BALA VENKATA PROF , VIJAY SANKAR U DR
发明人： BABY MARUTHI P , GUPTA VARUN , GIRI PRASAD R , VIJAY SANKAR U , RAMANATHAN L , PAVATE ARUNA ANIMISH , ARUNMOZHI S , PUNEETH KUMAR B S , SUBBARAYUDU CHILUKURI BALA VENKATA , KALVALA RAJENDAR
IPC分类号： G06Q50/06 , G06N3/02 , H02J3/12
摘要： REALISTIC ELECTRIC LOAD PROFILING SYSTEM OF TEXTILE MILLS BY CONSIDERING DYNAMIC AND STOCHASTIC BEHAVIOURS USING GENERATIVE ADVERSARIAL NETWORKS (GAN) As electric load profiling is emerging in the textile industries, it is important to essential to check the dynamic and stochastic behaviors through implementing various algorithms. The manual finding of electric loading is time consuming and inaccurate. To remedy this problem the textile industry should be implemented using smart grid. So the measurement in many sub areas of the textile industry production was carried out. This was subjected to the k-means of clustering in which the cluster centers and the each cluster distance was measured there by finding the clusters. This is further subjected to Generative Adversarial Networks (GAN) which will consider this generated input and the real value both are fed into the discriminator there by producing the output. This led to a high performance in the energy conservation. 1|Page REALISTIC ELECTRIC LOAD PROFILING SYSTEM OF TEXTILE MILLS BY CONSIDERING DYNAMIC AND STOCHASTIC BEHAVIOURS USING GENERATIVE ADVERSARIAL NETWORKS (GAN) Drawings Smart Grid K-Means clustering Generator input Random Noise Generator Neural Network Real Data Discriminator Generator Output Image/ Text/Data Neural Network Image/Text/Data Discriminator Output How real the data looks _________ (scaledfr Orealto generated) Updated Discriminatorbased on loss on real and generated data Update Generator based on loss on generated data Figure 1: Overall architecture of the load profile finding system 11 P a g e

3. 发明专利

AU2020103050A4 Textile antennas for human body wearable applications using bent conditions 未知
公开(公告)号：AU2020103050A4
公开(公告)日：2021-01-28
申请号：AU2020103050
申请日：2020-10-28
申请人： SHILPI MRS , GUPTA VARUN DR , KANUNGO ABHAS MR , KUMAR PARVIN DR , KUMAR PANKAJ DR
发明人： KUMAR PANKAJ , KANUNGO ABHAS , GUPTA VARUN , KUMAR PARVIN , SHILPI
IPC分类号： H01Q1/27 , A41D13/00
摘要： TEXTILE ANTENNAS FOR HUMAN BODY WEARABLE APPLICATIONS USING BEND CONDITIONS Wearable gadgets have become the current technology deploying smart devices with internet of things. The electronic gadgets that have been used frequently were deployed with wireless textile antenna to communicate the information to the cloud for computation. The wearable gadget that has been designed has to be checked for bend conditions so that it fits with the shape of the human body with ease comfort. The smart antenna is designed and fabricated with fiber or fabric with singer future embroidery machine. The adaptability and the effects of the bending must be tested practically with some experiments and simulation test must be done. The simulation test is done by electromagnetic simulation software program with finite element. The effect of strain can be observed by performing a three-point bend test experimentally. By using antenna prototype, the resonance characteristics are observed. The text fixture is performed to estimate the impedance matching. The experimental results and the simulation results are compared, and the results are displayed to know the textile antenna adaptability, durability, efficiency and accuracy. 1 P a g e TEXTILE ANTENNAS FOR HUMAN BODY WEARABLE APPLICATIONS USING BEND CONDITIONS 1TEXTIL ANTENNA FABRICATION E-XPERIMNTAL SIMULATION EXERIMENTAL RESUL T & SMIUATION RE SUL T R ESULT S - SMEA RT D)EVTCE~r Fig. 1. PROCESS FLOW DIAGRAM 1 P a g e

4. 发明专利

AU2021104513A4 A MEMORY BASED BRAIN COMPUTER INTERFACE (BCI) SYSTEM FOR MONITORING PHYSIOLOGICAL SIGNALS OF BRAIN 未知
公开(公告)号：AU2021104513A4
公开(公告)日：2022-02-03
申请号：AU2021104513
申请日：2021-07-23
申请人： CHOUBEY CHANDAN MR , DIWANIA SOURAV DR , GUPTA VARUN DR , KANUNGO ABHAS DR , KUMAR RAJEEV DR , MITTAL MONIKA DR , MITTAL VIKAS DR , SHARMA HIMANSHU DR , SINGH SUDHIR KUMAR MR
发明人： GUPTA VARUN , MITTAL VIKAS , SHARMA HIMANSHU , MITTAL MONIKA , KANUNGO ABHAS , CHOUBEY CHANDAN , DIWANIA SOURAV , KUMAR RAJEEV , SINGH SUDHIR KUMAR
IPC分类号： G06F3/01 , A61B5/00 , A61B5/291 , A61B5/369
摘要： A MEMORY BASED BRAIN COMPUTER INTERFACE (BCI) SYSTEM FOR MONITORING PHYSIOLOGICAL SIGNALS OF BRAIN The present disclosure relates to a memory based brain computer interface (BCI) system 5 (100) for monitoring physiological signals of brain. The system comprises of an agal (102) which has electroencephalogram for sensing the brain signals, a transmitter unit (104) which transmit the brain signals from the agal to the central processing unit (106), a display unit (108) for monitoring the processed brain signals obtained from the central processing unit (106) and the power supply unit (110) for supplying power to the central processing unit 10 (106), display unit (108) as well as the transmitter unit (104). The signal sensed by the agal (102) is sent to the central processing unit (106) using the transmitter unit (104) which are then processed and displayed on the display unit (108). The display unit (108) is an AMOLED display wherein each pixel in the display unit (108) acts as LED light technique which supports in power conservation and the projection of physiological brain signals. (FIG. 1 will be the reference figure) - 13 - Application no.: Total no. of sheets: 1 Page 1 of 1 Agal Tramitter Umt (102) (104) enftd g Display Unit (106) Poner Supply Unit (110) Fig.1: Block Diagram of the system

5. 发明专利

AU2021107048A4 RESTORATION OF ARTWORK USING GENERATIVE ADVERSARIAL NETWORKS 未知
公开(公告)号：AU2021107048A4
公开(公告)日：2021-12-02
申请号：AU2021107048
申请日：2021-08-24
申请人： GUPTA VARUN DR , KUMAR PRAVEEN MR
发明人： GUPTA VARUN , KUMAR PRAVEEN
IPC分类号： G06N3/02 , G06K9/00 , G06K9/36 , G06N5/04 , G06T5/00
摘要： A method for restoration of artworks using generative adversarial networks comprises the following steps: damaged artwork images along with their original versions are loaded; generator as modified U-Net having encoder as ResNet34 architecture and decoder as U-Net layers, is built and pretrained for generating restored versions of the damaged artwork images; the decoder consisting of an initial block of convolutional, dropout and residual layers followed by four blocks of a dropout and two convolutional layers is created and pretrained for assessing the quality of images generated by the generator; the loss function is defined as the Mean Squared Error (MSE) for the generator and Binary Cross-Entropy (BCE) for the discriminator; generative adversarial network having pretrained generator and pretrained discriminator is trained; inference using damaged artwork test images is performed and restored versions of the test images are generated.

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