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X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221006T203000
DTEND;TZID=Asia/Kolkata:20221006T213000
DTSTAMP:20260618T083416
CREATED:20220920T231630Z
LAST-MODIFIED:20220920T231758Z
UID:239899-1665088200-1665091800@ee.iisc.ac.in
SUMMARY:Thesis Defence of  Mr.  Paawan Kirankumar Dubal
DESCRIPTION:Thesis Title: Cyber Attack Resilient Breaker Failure Protection Scheme Using Wide Area measurements \nName of the Advisor: Prof. Sarasij Das \nDegree Registered: M.Tech-Research \nDate and Time: 6th October\, 2022\, 3:00 PM IST Online \nMeeting Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZWZkYzViOWQtNWIzYi00MWQ1LWE1ZTMtNzcwOTUxM2JkNGM0%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%2240bc61c0-cc62-49a2-80af-a06745a651ac%22%7d \nAbstract: Breaker Failure Protection (BFP) is a backup protection that comes into play when a circuit breaker fails to isolate the fault. If the circuit breaker fails to clear the fault\, the BFP scheme commands other required breakers to isolate the fault. BFP schemes are usually incorporated in microprocessor-based digital relays. Commonly employed BFP schemes use overcurrent element (50BF) and Breaker Failure Initiation (BFI) signals as inputs. The BFI signal is issued to the BFP relays from other digital relays. Line current sensed via current transformer is fed to the BFP relay for the overcurrent element (50BF). When both 50BF and BFI are high\, it waits for a specified time for the primary protection to operate. The 50BF element is usually set much lower than the rated load currents. So\, it will be high during the normal loading conditions. A cyber-attack can be launched by issuing false BFI or blocking a legitimate BFI signal to the BFP relay. Operation of BFP scheme usually leads to disconnection of a larger amount of loads. As a result\, mal-operation of BFP schemes can cause major disturbances in power systems. There is a need to make the BFP schemes resilient to cyber-attacks for reliable operation of power systems. Currently\, there is a lack of literature on the cyber-attack resilient BFP schemes. \nHence\, this thesis proposes a Wide-Area Measurement-Based Cyber-Resilient Breaker Failure Protection Scheme. The scope of the work is to develop an algorithm that will ascertain if the BFI received by the BFP relay is genuine. Blocking a legitimate BFI will cause the backup protection to operate and clear the fault. The proposition assumes that the BFP relay is not compromised in any manner. However\, a fake BFI can be issued by other digital relays\, which may cause unwanted BFP operations. In the proposed algorithm\, when the BFI is received. The BFP relay will communicate the receipt of BFI to the Phasor Data Concentrator (PDC). The proposed algorithm will run at the PDC\, which has access to the time-stamped measurements of the adjacent substations and the substation that triggered the algorithm. The decision of the proposed algorithm is communicated back to the BFP relay\, which will allow the tripping if the BFI is genuine. Hence\, we also propose modifications in the BF scheme in the BFP relay to incorporate the algorithm’s decision in issuing the final trip. The proposal running at PDC is a two-layer algorithm. The first layer randomly samples the bus voltages at the adjacent substations considering different groups of digital relays. The relay which has issued the BFI may be compromised. It makes relays of the same make and family more susceptible to a cyber-attack exploiting the same vulnerabilities. Hence we propose grouping of relays by their make and relay families. The first layer is meant to determine if there is a fault in the vicinity of the BFP relay that issued the trigger. The second layer provides discrimination between fault and cyber-attack by measuring the impedance observed at the two ends of the perceived-faulted line. Since the proposed solution is computationally lightweight\, it adheres to the timing requirement of the BFP. The proposition requires healthy communication between the PMUs and the PDC. Nevertheless\, the proposed method is fail-safe. It will resort to the conventional BFP scheme in case of loss of communication with the PDC. The proposed solution mitigates n number of cyber-attacks in a no-fault scenario. Additionally\, the proposed solution can detect one cyber-attack if the attacker times the cyber-attack during a fault condition. PSCAD simulations were performed to validate the proposition on IEEE 118 bus system. Furthermore\, the hardware was developed emulating the PMU-PDC communication as per IEEE C37.118-2 standard\, and the execution time of the proposal was verified to ensure adherence to the timing requirement of the BFP. \n  \nALL ARE CORDIALLY INVITED \n 
URL:https://ee.iisc.ac.in/event/thesis-defence-of-mr-paawan-kirankumar-dubal/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221007T203000
DTEND;TZID=Asia/Kolkata:20221007T213000
DTSTAMP:20260618T083416
CREATED:20221006T043709Z
LAST-MODIFIED:20221006T045715Z
UID:240021-1665174600-1665178200@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Mr. Shreyas Ramoji @3pm
DESCRIPTION:Date: October 7th\, Friday\, 3-4pm \nDegree Registered: PhD \nVenue: EE\, MMCR [1st Floor\, C241] and in Microsoft Teams at https://tinyurl.com/2rfbs7ke \nThesis Title: Supervised Learning Approaches for Language and Speaker Recognition \nAbstract: In the age of artificial intelligence\, one of the important goals of the research community is to get machines to automatically figure out who is speaking and in what language – a task that humans are naturally capable of. Developing algorithms that automatically infer the speaker\, language\, or accent from a given segment of speech are challenging tasks for machines and has been a topic of research for at least three decades. While most of the prior successes have been through the development of unsupervised embedding extractors\, the main aim of this doctoral research is to propose novel supervised approaches for robust speaker and language recognition. \nIn the first part of this talk\, we propose a supervised version of a popular embedding extraction approach called the i-vector. The i-vector is a popular technique for front-end embedding extraction in speaker and language recognition. In this approach\, a database of speech recordings (in the form of a sequence of short-term feature vectors) is modeled with a Gaussian Mixture Model\, called the Universal Background Model (GMM-UBM). The deviation in the mean components is captured in a lower dimensional latent space called the i-vector space using a factor analysis framework. In our work\, we proposed a fully supervised version of the i-vector model\, where each label class is associated with a Gaussian prior with a class-specific mean parameter. The joint prior (marginalized over the sample space of classes) on the latent variable becomes a GMM. The choice of prior is motivated by the Gaussian back-end\, where the conventional i-vectors for each language are modeled with a single Gaussian distribution. With detailed data analysis and visualization\, we showed that the supervised i-vector (s-vector) features yield representations succinctly capture the language (accent) label information and do a significantly better job distinguishing the various accents of the same language. We performed language recognition experiments on the NIST Language Recognition Evaluation (LRE) 2017 challenge dataset\, which has test segments ranging from 3 to 30 seconds. With the s-vector framework\, we observe relative improvements between 8% to 20% in terms of the Bayesian detection cost function\, 4% to 24% in terms of EER\, and 9% to 18% in terms of classification accuracy over the conventional i-vector framework. We also perform language recognition experiments showing similar improvements on the RATS dataset and Mozilla Common Voice dataset\, and speaker classification experiments using LibriSpeech. \nIn the second part of the talk\, we explore the problem of speaker verification\, where a binary decision has to be made on a test speech segment as to whether it is spoken by a target speaker or not\, based on a limited duration of enrollment speech. The state-of-the-art approach to speaker verification was to extract fixed-dimensional embeddings from speech of arbitrary duration and train a back-end generative model called the Probabilistic Linear Discriminant Analysis (PLDA) which was used to make decisions using a Bayesian decision framework. We proposed a neural network approach for back-end modeling\, where the likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function\, and the learnable parameters of the score function are optimized using a verification cost. The proposed model\, termed as neural PLDA (NPLDA)\, is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF) used as one of the evaluation metrics in various speaker verification challenges. Further\, we explore a fully neural approach where the neural model outputs the verification score directly\, given the acoustic feature inputs. This Siamese neural network (SiamNN) model combines embedding extraction and back-end modeling into a single processing pipeline. The development of the single neural Siamese model allows the joint optimization of all the modules using a verification cost. We provide a detailed analysis of the influence of hyper-parameters\, choice of loss functions\, and data sampling strategies for training these models. Several speaker recognition experiments were performed using Speakers in the Wild (SITW)\, VOiCES\, and NIST SRE datasets where the proposed NPLDA and SiamNN models are shown to improve over the state-of-art significantly. \nWe conclude the talk by highlighting some of the noteworthy approaches that were published during the course of this research work and identifying some important future directions that can be explored. \nBio: Shreyas Ramoji is a Ph.D. scholar at the Learning and Extraction of Acoustic Patterns (LEAP) Laboratory\, Department of Electrical Engineering\, Indian Institute of Science\, Bengaluru. He obtained his Bachelor of Engineering degree from the Department of Electronics and Communication Engineering\, PES Institute of Technology\, Bangalore South Campus in 2016. He is a student member of the IEEE Signal Processing Society and ISCA. His research interests include Speaker Verification\, Language and Accent Identification\, Neuroscience\, Machine learning\, and Artificial Intelligence. \n—————– \n​All are welcome. \n  \n 
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-mr-shreyas-ramoji-3pm/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221011T173000
DTEND;TZID=Asia/Kolkata:20221011T183000
DTSTAMP:20260618T083416
CREATED:20221006T052743Z
LAST-MODIFIED:20221006T052956Z
UID:240024-1665509400-1665513000@ee.iisc.ac.in
SUMMARY:Thesis Defence of Mr. Jerrin Thomas Panachakel
DESCRIPTION:Degree Registered:      Ph.D.\n\nDate and Time:            Oct. 11\, 2022 (Tuesday)  12 Noon\n\nVenue:                           MMCR\, Hall No. C 241\, II Floor\, Dept. of Electrical Engineering.\nMS Teams Link:            https://teams.microsoft.com/l/channel/19%3a7nfRrQdIH7fPf5u003TVCBAp18cRM7dyFwv1eqJRGjY1%40thread.tacv2/General?groupId=53beb192-2a2f-4f20-8a0c-dbb67a65f532&tenantId=6f15cd97-f6a7-41e3-b2c5-ad4193976476\n \nTitle: Machine Learning for Decoding Imagined words and Altered State of Consciousness from EEG\n\nAbstract: The thesis explores several architectures for accurately decoding the cognitive activity from EEG recorded during speech imagery and Rajayoga meditation. The major contributions of the thesis are listed below:\n\n\n\n\n\n\n\n\n\n\n\n\n\nNeural Correlates of Phonological Category in Speech Imagery EEG\n\n\nWe have shown that neural correlates of phonological categories exist in the EEG recorded during speech imagery. These correlates lead to significant differences in the mean phase coherence (MPC) values of the EEG across several cortical regions.\nWe have also shown that MPC values can be used for accurately classifying the EEG recorded during speech imagery based on the phonological category of the prompts. The proposed architecture for this task has an accuracy of 84.9%.\n\n\n\nDecoding Imagined Words from EEG\n\n\nOne of the challenges in designing systems for decoding imagined words from EEG is the limited availability of data. We have presented three architectures for decoding imagined words from EEG. All three architectures alleviate this problem of limited availability of data.\nThe transfer learning-based architecture employs MPC and magnitude-squared coherence values along with a ResNet50-based classifier. This architecture achieves an accuracy of 92.8% on a publicly available EEG dataset in classifying speech imagery.\n\nClassification of Altered State of Consciousness from Resting State\n\n\nWe have presented three architectures for classifying the altered state of consciousness during Rajayoga meditation from the resting state.\nBoth CSP-LDA-LSTM (common spatial pattern-linear discriminant analysis-long short-term memory) and SVD-DNN (singular value decomposition-deep neural network) architectures are able to capture subject-invariant features.\nThe best intra-subject accuracy obtained is 98.2% and the best inter-subject accuracy is 96.4%.
URL:https://ee.iisc.ac.in/event/thesis-defence-of-mr-jerrin-thomas-panachakel/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221013T203000
DTEND;TZID=Asia/Kolkata:20221013T220000
DTSTAMP:20260618T083416
CREATED:20221019T033655Z
LAST-MODIFIED:20221019T033655Z
UID:240065-1665693000-1665698400@ee.iisc.ac.in
SUMMARY:Thesis Defense of Kiran Kumar Challa
DESCRIPTION:Degree registered: PhD \nThesis Title: Algorithms and Testbed for Synchronous Generator Parameter Estimation \nGuide: Prof. Gurunath Gurrala \nExaminer: Prof. K. Shanti Swarup\, IIT Madras \nDate & TIme: 13th October 2022\, Thursday\, 3pm – 4.30pm \nMode:   Hybrid mode\, both Teams and Physical \nVenue: MMCR\, 1st floor C-wing\, EE Department\, IISc \n\n\nMicrosoft Teams meeting \n\n\n\nJoin on your computer\, mobile app or room device \n\nClick here to join the meeting \n\n\n\nMeeting ID: 438 079 328 744Passcode: q4pugu \n\n\n\n\n\nAbstract: The development of dynamic power system components models became increasingly important in the modern grids dominated by high penetration of renewables because of the increased dependency of planning and operational decisions on dynamic simulation studies. The parameters of synchronous machines and associated control models play significant role in the overall model of the grid\, which need to be updated regularly by the utilities. So\, the parameters of the power plants are calibrated/estimated either using off-line testing or online measurements from phasor measurement units (PMU) or digital fault recorders (DFR). Development of individual generator models is feasible only if the PMU/DFR data is available for each generator in a power plant. Otherwise\, they can provide only aggregate model of a generating plant as PMU/DFRs are usually placed in substations. Digital protective relay (DPR) records are available for individual generators in any generating plant. This thesis explores the possibilities of utilizing DPR records of individual generators for parameter estimation. About 200 relay records have been collected from a hydro plant and a thermal plant in Karnataka. It is found that most of the records contain at the most 3 seconds data. Existing methods of parameter estimation using PMU/DFR data failed to work with the short duration records. There is no prior work reported in the literature which uses short relay records for parameter estimation of the synchronous generators. Constrained iterated unscented Kalman filter (CIUKF) and enhanced scattered search (eSS) algorithms are proposed for the parameter estimation using DPR records in this thesis. The parameters of a turbo alternator and its excitation system (210 MW) are estimated from the relay records collected using the proposed algorithms and the results are found be accurate. This is a first of its kind effort in the literature to the best of our knowledge. It is also found that the relay records should contain pre-fault data\, during fault data and some post-fault data for accurate estimation. However\, from the collected records only a small percentage of the records are found to be useful. To generate realistic data in the laboratory an experimental test bed development\, replicating the field implementation aspects of the digital relays\, is proposed in this thesis. A realistic scaled-down generalized substation model for translational research in smart grids is developed\, which can be configured to operate in 7 widely used substation bus bar schemes with prevalent current transformer (CT) configurations. All the potential transformers (PT) and CT measurements\, circuit breaker (CB)\, isolator and earth switch status signals are made available to configure any protection strategy like bus-bar protection\, unit protection schemes\, etc. precisely the same way they get implemented in the field. A systematic procedure for the development of an experimental scaled-down frequency-dependent transmission line model of a 230 kV transmission line is proposed. A lumped parameter frequency dependent transmission line model using modal transformation is derived for a 230 kV transmission line and scaled-down to 220 V. Clarke and inverse Clarke transformations are implemented using specially designed 1-phase transformers. The inductances of the scaled-down model are realized using amorphous cores. A new algorithm is proposed to fit a reduced-order R-L equivalent circuit to the frequency response of the modal impedances of the transmission lines. A close enough fitting is achieved with lesser number of passive elements using the proposed method compared to the widely used vector fitting algorithm. This kind of physical realization of a frequency dependent power transmission line model in the laboratory is first of its kind effort in the literature to the best of our knowledge. \n\nNote: Know how generated from the implementation of the generalized substation panels and transmission line models has been licensed to MCore Technologies Pvt Ltd\, Bangalore for commercialization. \nAcknowledgements: This work is supported by Fund for Improvement of Science and Technology (FIST) program\, DST\, India\, No.SR/FST/ETII-063/2015 (C) and (G) under the project “Smart Energy Systems Infrastructure – Hybrid Test Bed”. Acknowledge partial funding support from Robert Bosch Centre for Cyber Physical Systems (RBCCPS)\, IISc. Also acknowledge the Tata Trust Travel Grant.
URL:https://ee.iisc.ac.in/event/thesis-defense-of-kiran-kumar-challa/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221019T213000
DTEND;TZID=Asia/Kolkata:20221019T223000
DTSTAMP:20260618T083416
CREATED:20221019T032748Z
LAST-MODIFIED:20221019T032809Z
UID:240063-1666215000-1666218600@ee.iisc.ac.in
SUMMARY:Thesis Defence of Anwesha Roy
DESCRIPTION:Degree Registered:  M. Tech. (Research) \nGuide: Prof. Prasanta Kumar Ghosh \nDate  & Time: 19th October\, 2022\, Wednesday\, 4:00 PM \nVenue:     Online link : https://tinyurl.com/3rystxed \nTitle: Improved air-tissue boundary segmentation in real-time magnetic resonance imaging videos using speech articulator specific error criterion \nAbstract: Real-time Magnetic Resonance Imaging (rtMRI) is a tool used exhaustively in speech science and linguistics to understand the dynamics of the speech production process across languages and health conditions. rtMRI has two advantages over other methods which capture articulatory movement\, like X-ray\, Ultrasound and Electromagnetic articulography – it is non-invasive\, and it captures a complete view of the vocal tract including pharyngeal structures. The rtMRI video provides spatio-temporal information of speech articulatory movements\, which helps in modeling speech production. For this purpose\, a common step is to obtain the air-tissue boundary (ATB) segmentation in all frames of the rtMRI video. The accurate estimation of ATBs of the upper airway of the vocal tract is essential for many speech processing applications like speaker verification\, text-to-speech synthesis\, visual augmentation for synthesized articulatory videos\, and analysis of vocal tract movement. Thus\, it is necessary to have an accurate air-tissue boundary segmentation in every frame of the rtMRI videos. \nThe best performance in ATB segmentation of rtMRI videos in speech production\, in unseen subject conditions\, is known to be achieved by a 3-dimensional convolutional neural network (3D-CNN) model. In seen subject conditions\, both 3D-CNN and 2-dimensional deep convolutional encoder-decoder network (SegNet) show similar performance. However\, the evaluation of these models\, as well as other ATB segmentation techniques reported in literature\, has been done using Dynamic Time Warping (DTW) distance between the entire original and predicted boundaries or contours. Such an evaluation measure may not capture local errors in the predicted contour. Careful analysis of predicted contours reveals errors in regions like the velum part and tongue base section\, which are not captured in a global evaluation metric like DTW distance. In this thesis\, we automatically detect such errors and propose a novel correction scheme for them. We also propose two new evaluation metrics for ATB segmentation\, separately for each contour\, to explicitly capture errors in these contours. \nMoreover\, the state-of-the-art models use overall binary cross entropy as the loss function during model training. However\, such a global loss function does not give enough emphasis on regions which are more prone to errors. In this thesis\, together with global loss\, we explore the use of regional loss functions which focus on areas of the contours which have been analyzed as error prone in our analysis. Two different losses are considered in the regions around velum and tongue base – binary cross entropy (BCE) loss and dice loss. It is observed that dice-loss based models perform better than their BCE loss based counterparts.
URL:https://ee.iisc.ac.in/event/thesis-defence-of-anwesha-roy/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221021T194500
DTEND;TZID=Asia/Kolkata:20221021T210000
DTSTAMP:20260618T083416
CREATED:20221024T234607Z
LAST-MODIFIED:20221024T234711Z
UID:240071-1666381500-1666386000@ee.iisc.ac.in
SUMMARY:EE and CPS Seminar by Prof. Ketan Savla
DESCRIPTION:Title: Microscopic Traffic Flow Control\nDate: 21 October\nTime: 2:15pm\nVenue: EE MMCR \nAbstract: Design and performance evaluation of traffic control techniques such as ramp metering are typically based on macroscopic traffic flow models. These models\, obtained by spatio-temporal averaging of microscopic vehicle-to-vehicle/infrastructure interactions\, do not have sufficient resolution to model safety\, or to study the impact of emerging paradigms  of autonomy and connectivity. We present coordinated ramp metering algorithms that regulate entry into the freeway network at the vehicle level\, based on information about state of vehicles in the network\, but do not require information about travel demand. Under these algorithms\, each on-ramp operates under cycles during which it does not release more vehicles than its queue size at the beginning of the cycle. \nAdditionally\, the algorithms\, dynamically\, either introduce pause at the end of the cycle\, or modulate the release rate during the cycle\, or modulate safety distance for release during the cycle. Under standard safe vehicle-following and merging protocols\, these algorithms are shown to keep the network undersaturated for maximal travel demand and result in lower travel time than known ramp metering algorithms.Biography of the speaker: Ketan Savla is an associate professor and the John and Dorothy Shea Early Career Chair in Civil Engineering at the University of Southern California. His current research interest is in distributed optimal and robust control\, dynamical networks\, state-dependent queuing systems\, and mechanism design\, with applications in civil infrastructure systems. His recognitions include NSF CAREER\, George S. Axelby Outstanding Paper Award\, and the Donald P. Eckman Award. He serve(d) as an associate editor of the IEEE Transactions on Control of Network Systems\, IEEE Control Systems Letters\, and IEEE Transactions on Intelligent Transportation Systems. He is also a co-founder and the chief science officer of Xtelligent\, Inc.
URL:https://ee.iisc.ac.in/event/ee-and-cps-seminar-by-prof-ketan-savla/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221021T213000
DTEND;TZID=Asia/Kolkata:20221021T223000
DTSTAMP:20260618T083416
CREATED:20221020T042250Z
LAST-MODIFIED:20221020T042943Z
UID:240068-1666387800-1666391400@ee.iisc.ac.in
SUMMARY:EE Faculty Colloquium by Prof. A G Ramakrishnan
DESCRIPTION:Title: Analyzing patterns in EEG: from biometrics to altered states of consciousness \nSpeaker: Prof. Ramakrishnan A. G.\, MILE Lab\, Dept of Electrical Engineering\, Indian Institute of Science \nVenue: EE MMCR \nTime: 4pm\, 21 October 2022 \nAbstract: The electrical activity of brain\, the key control organ of our body\, carries a lot of information about our current activity\, health\, mental status and identity. We\, in MILE Lab\, have been focussing for the past few years on analysing the various patterns present in the electroencephalogram (EEG). When someone is in deep sleep\, coma or under anesthesia\, the level of consciousness is lower than that of waking state. When someone has locked-in syndrome\, the level of consciousness is the same as that of waking state. On the other hand\, during meditation\, the consciousness level is higher than that of waking. Hypnosis is another completely different altered state of consciousness. The interrelationship between the different EEG channels is also distinctly different during inhalation\, breath-hold and exhalation. We are studying the patterns in EEG under all the above conditions. While working on the above topics\, we unexpectedly made a discovery that some measures based on the functional connectivity between the channels are distinct for each individual and can very well be used to identify people. Using other measures\, we are also able to predict the word imagined by a person out of a set of words.Speaker bio: Ramakrishnan A. G. is a professor of Electrical Engineering and an associate faculty member at the Centre for Neuroscience. He obtained his Masters in Electrical Engineering and Ph D in Biomedical Engineering from the Indian Institute of Technology\, Madras. He has graduated 19 Ph.D.s\, 16 M.Tech.s by research\, and guided over 100 M. Tech. projects at IISc. He is a Fellow of the Indian National Academy of Engineering\, As the leader of a research consortium\, he was instrumental in creating handwriting recognition technologies for eight Indian languages. He received Manthan award (South East Asia and Asia Pacific) twice for creating audio books for blind students through his OCR and TTS in Tamil and Kannada. His current areas of research include speech recognition in Indic languages\, decoding of imagined words from EEG\, brain functional connectivity analysis in modified states of consciousness and the study of the neural control and physiological mechanisms behind the health and therapeutic effects of deep breathing. For his earlier work on evoked potentials from leprosy patients\, he had received Sir Watt Kay Young Researcher’s Prize from the Royal College of Physicians and Surgeons\, Glasgow. He was a Senior Research Scientist at Hewlett Packard Research Labs\, Bangalore India from May 2002 to August 2003. He is an invited member of the Senate of IIIT-Allahabad\, Prayagraj and the Federation of Indian Chambers of Commerce and Industry – Indian Language Internet Alliance. He was a member of the Knowledge Commission\, Government of Karnataka during 2017-2020. He is also one of the founder directors of RaGaVeRa Indic Technologies private limited recognized by Karnataka Government as one of the Elevate 2019 Startup winners. The Kannada TTS developed by RaGaVeRa has been evaluated to be better in quality than Google’s Wavenet TTS and Nuance’s Kannada TTS. He is also the Advisor-Neuroscience of Feedfront Technologies Pvt Ltd\, Bengaluru.
URL:https://ee.iisc.ac.in/event/ee-faculty-colloquium-by-prof-a-g-ramakrishnan/
LOCATION:EE\, MMCR
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