International Journal of Applied Ceramic Technology
In the present study, the effect of fume silica content on preformed and in situ spinel containin... more In the present study, the effect of fume silica content on preformed and in situ spinel containing alumina spinel castable was studied by varying fume silica content at 1 and 4 wt.%. Spinel content for preformed alumina spinel castables varied from 10 to 30 wt.% and MgO content for in situ alumina spinel castables varied from 2.8 to 8.4 wt.%, respectively, and the distribution coefficient (q value) was maintained at .21 and .29 as per Dinger and Funk model. Different castable compositions were processed as per conventional processing technique and further evaluated for densification and strength studies after heat treatment at 110, 1000, and 1550°C. Fired samples at 1550°C were further evaluated for the hot modulus of rupture study at 1400°C and phase analysis study of the matrix phase. Also, the fired samples were studied for microstructural evaluation.
2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)
Aria The bot is a windows personal voice assistant software which performed various task which is... more Aria The bot is a windows personal voice assistant software which performed various task which is given by the user in the form of the voice command this concept is taken by the movie named “IRON MAN” character Jarvis which perform all the task assigned by Tony Stark. In this project we are going to make a windows voice assistant which is going perform all the task such as opening of file, perform searching on the web, getting result from the Wikipedia, reading pdf, opening of application and suggesting some jokes to you. By taking input by voice we are going to convert voice into text and according to dictionary which is present in the database we are going to perform task which is assign to the keyword, we are importing so much of library such as “os” for performing operating system tasks. “speech recognition” for to analyzing voice command, “pyttsx3” for converting voice command in to the text string, “Time” for fetching the current time and date, “web browser” for performing web based task on default web browser, we also add activation command to run the aria the bot which is used as state for this project.
A deterministic reaction-diffusion–drift model is used for the time kinetics of bulk gate insulat... more A deterministic reaction-diffusion–drift model is used for the time kinetics of bulk gate insulator trap generation in p-channel Field Effect Transistors (FETs) under inversion stress. The consistency of the deterministic and stochastic versions of the model is shown. The model is independently validated using stress-induced leakage current data from various reports. The model is incorporated into the already existing bias temperature instability (BTI) analysis tool framework and validated using negative BTI data. The measured data from FinFETs having different channel material, substrate type, gate insulator process, and fin length, as well as gate-all-around stacked nano sheet (GAA-SNS) FETs are modeled.
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022
Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic eman... more Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic emanation) from a device that performs some cryptographic operation and poses a serious threat in the present IoT era. In the last couple of decades, there have been a large body of research works dedicated to streamlining/improving the attacks or suggesting novel countermeasures to thwart those attacks. However, a closer inspection reveals that a vast majority of published works in the context of symmetric key cryptography is dedicated to block ciphers (or similar designs). This leaves the problem for the stream ciphers wide open. There are few works here and there, but a generic and systematic framework appears to be missing from the literature. Motivating by this observation, we explore the problem of SCA on stream ciphers with extensive details. Loosely speaking, our work picks up from the recent TCHES’21 paper by Sim, Bhasin and Jap. We present a framework by extending the efficiency of...
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018
Brain-Computer Interfaces (BCI) are systems that enable users to use neural signals, typically El... more Brain-Computer Interfaces (BCI) are systems that enable users to use neural signals, typically Electroencephalogram (EEG) to direct an application or an external device. Motor imagery (MI) based BCI detects subject motor intentions which could be further used as control signals. Due to spatial lateralization of different MI tasks, spatial filtering followed by band power extraction is the most commonly used algorithm for classification tasks in MI-based BCIs. Unfortunately, the spatial filtering approach significantly incorporates Amplitude characteristics when compared to Phase characteristics of the EEG signal. Single trial Phase locking value (sPLV) has been a popular statistics to extract phase based information for classification task in MI-based BCI. To utilize the phase characteristics for MI classification, this paper proposes a novel approach based on instantaneous phase difference (IPD) sequence to extract phase features that explicitly use the phase synchronization information between EEG sensors. We maximized the discriminability of IPD sequence using linear transformation calculated from common spatial pattern algorithm (CSP) on the IPD sequence. An evaluation of our method on BCI competition dataset led to around 15% increase in mean classification accuracies compared to sPLV approach and comparable accuracies to power feature based CSP algorithm. Furthermore, incorporating phase features from our method and power features from traditional algorithms using sparse feature selection technique increased the classification accuracy over both CSP and CSP on the IPD sequence.
Journal of King Saud University - Computer and Information Sciences, 2020
Abstract In the medical field, Medical diagnosis using Computed tomography (CT) has become increa... more Abstract In the medical field, Medical diagnosis using Computed tomography (CT) has become increasingly popular due to their non-invasive approach and quick overall turnaround time. 3D visualization for CT Colonography involves the assessment and diagnosis of a patient to find the presence of cancerous polyps in the colon, by taking a Computed Tomography scan of the patient, and evaluating the reports. This technique reduces evaluation time by allowing the doctors themselves to analyze the CT scans without the need for a radiologist to generate an initial report. This technique also avoids an invasive procedure on the patient. This paper gives the insightsof developing computer aided system for the detection of Polyps in CT Colonography images using the principles of Image Processing and the Deep Learning, specifically an ensemble of Convolutional Neural Networks using GoogleNet architecture and 3D reconstruction of the same. The accuracy achieved by the proposed system for region classification, region 1 polyp detection and region 2 polyp detection are 98.75%, 93.75% and 94.03% respectively, and their F1 Scores are 0.88, 0.82 and 0.84 respectively.
International Journal of Advance Research and Innovative Ideas in Education, 2018
Smart cities is a major outbreak in implementation of different IT and computer technology. The f... more Smart cities is a major outbreak in implementation of different IT and computer technology. The focus is on providing environmental sustainability and efficiency, sustainable homes and buildings, efficient use of energy resources by implementing smart roads and buildings monitoring system, smart transportation, health care, global warming monitoring and security system. Various sensors are used to collect data like temperature, light, pressure and process it to form a meaningful interpretation. This technology is one of the major application of the smart city idea. The implementation of the technology ranges from homes and schools to large industries.The Proposed system enables to monitor different data sent from various sensors installed in a smart city.The data can be used to study the fluctuation and anticipate any possibilities of unnatural incident.
Brain–computer interface (BCI) is an emerging tool that has a variety of practical applications, ... more Brain–computer interface (BCI) is an emerging tool that has a variety of practical applications, including rehabilitation. BCIs are systems that extract and classify features in neural data, and then produce an output when a specific feature is detected. Motor imagery-based BCIs (MI BCIs), a more specific form of BCI, detect features that indicate the user is imagining a specific motor action, such as moving their arm or leg. There have been several studies released discussing the potential for BCIs to be used in a clinical setting for applications like rehabilitation. Spinal cord injuries (SCIs) are a form of injury that damages the spinal cord and causes either partial or total paralyzation. Those with SCI typically undergo rehabilitation for many years after the injury, and BCIs have begun to be tested for their benefits when included in SCI rehabilitation sessions. There are several ways for BCI systems to be used in SCI rehabilitation, which include virtual reality, exoskeletons, and neuroprosthesis. When using these methods as an output for a BCI system, SCI patients experience numerous benefits, most notably being an increase in mobility in the paralyzed region of their body. While there are several advantages to using BCIs for SCI rehabilitation, there are also several challenges that need to be addressed. In this chapter, we will discuss the current potential of BCIs for SCI rehabilitation, as well as what areas of this field need to be improved in the future.
Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG ... more Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this letter, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes. We determine the subspace for the proposed approach through optimization using gradient descent on an orthogonal manifold. The performance of the proposed stationarity enforcing algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on publicly available BCI competition IV dataset IIa. Results show that an improvement in average classification acc...
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the featur... more The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes. We determine the subspace for the proposed approach through optimization using gradient descent on an orthogonal manifold. The performance of the proposed stationarity enforcing algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on publicly available BCI competition IV dataset IIa. Results show that an improvement in average classification accuracies across the subjects over the baseline algorithms and thus essence of alleviating within session non-stationarities.
2021 IEEE International Reliability Physics Symposium (IRPS), 2021
A stochastic Reaction-Diffusion-Drift (RDD) model framework is proposed for trap time kinetics un... more A stochastic Reaction-Diffusion-Drift (RDD) model framework is proposed for trap time kinetics under Hot Carrier Degradation (HCD) stress and post-stress conditions. Consistency of the 3-D stochastic RDD, 3-D TCAD incorporated deterministic RDD and an “equivalent” 1-D deterministic RDD frameworks is shown. Measured HCD kinetics is decoupled into contributions by pure HCD and Bias Temperature Instability (BTI). The pure HCD time kinetics during and after stress is modeled using the above frameworks. Lack of recovery for the pure HCD component after stress is explained.
A stochastic reaction–diffusion drift model is used to simulate the time kinetics of interface an... more A stochastic reaction–diffusion drift model is used to simulate the time kinetics of interface and bulk oxide traps responsible for bias temperature instability (BTI), stress-induced leakage current (SILC), and time-dependent dielectric breakdown (TDDB) in MOSFETs. Trap generation and passivation are calculated using dissociation and repassivation of trap precursors and simultaneous diffusion and/or drift of atomic, molecular, and/or ionic species. The average of multiple stochastic simulations is used to qualitatively explain the measured BTI and SILC data. The difference in BTI and SILC time kinetics, variation in SILC time kinetics across reports, and oxide thickness dependence of TDDB Weibull slope variation are also qualitatively explained.
International Journal of Applied Ceramic Technology
In the present study, the effect of fume silica content on preformed and in situ spinel containin... more In the present study, the effect of fume silica content on preformed and in situ spinel containing alumina spinel castable was studied by varying fume silica content at 1 and 4 wt.%. Spinel content for preformed alumina spinel castables varied from 10 to 30 wt.% and MgO content for in situ alumina spinel castables varied from 2.8 to 8.4 wt.%, respectively, and the distribution coefficient (q value) was maintained at .21 and .29 as per Dinger and Funk model. Different castable compositions were processed as per conventional processing technique and further evaluated for densification and strength studies after heat treatment at 110, 1000, and 1550°C. Fired samples at 1550°C were further evaluated for the hot modulus of rupture study at 1400°C and phase analysis study of the matrix phase. Also, the fired samples were studied for microstructural evaluation.
2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)
Aria The bot is a windows personal voice assistant software which performed various task which is... more Aria The bot is a windows personal voice assistant software which performed various task which is given by the user in the form of the voice command this concept is taken by the movie named “IRON MAN” character Jarvis which perform all the task assigned by Tony Stark. In this project we are going to make a windows voice assistant which is going perform all the task such as opening of file, perform searching on the web, getting result from the Wikipedia, reading pdf, opening of application and suggesting some jokes to you. By taking input by voice we are going to convert voice into text and according to dictionary which is present in the database we are going to perform task which is assign to the keyword, we are importing so much of library such as “os” for performing operating system tasks. “speech recognition” for to analyzing voice command, “pyttsx3” for converting voice command in to the text string, “Time” for fetching the current time and date, “web browser” for performing web based task on default web browser, we also add activation command to run the aria the bot which is used as state for this project.
A deterministic reaction-diffusion–drift model is used for the time kinetics of bulk gate insulat... more A deterministic reaction-diffusion–drift model is used for the time kinetics of bulk gate insulator trap generation in p-channel Field Effect Transistors (FETs) under inversion stress. The consistency of the deterministic and stochastic versions of the model is shown. The model is independently validated using stress-induced leakage current data from various reports. The model is incorporated into the already existing bias temperature instability (BTI) analysis tool framework and validated using negative BTI data. The measured data from FinFETs having different channel material, substrate type, gate insulator process, and fin length, as well as gate-all-around stacked nano sheet (GAA-SNS) FETs are modeled.
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022
Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic eman... more Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic emanation) from a device that performs some cryptographic operation and poses a serious threat in the present IoT era. In the last couple of decades, there have been a large body of research works dedicated to streamlining/improving the attacks or suggesting novel countermeasures to thwart those attacks. However, a closer inspection reveals that a vast majority of published works in the context of symmetric key cryptography is dedicated to block ciphers (or similar designs). This leaves the problem for the stream ciphers wide open. There are few works here and there, but a generic and systematic framework appears to be missing from the literature. Motivating by this observation, we explore the problem of SCA on stream ciphers with extensive details. Loosely speaking, our work picks up from the recent TCHES’21 paper by Sim, Bhasin and Jap. We present a framework by extending the efficiency of...
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018
Brain-Computer Interfaces (BCI) are systems that enable users to use neural signals, typically El... more Brain-Computer Interfaces (BCI) are systems that enable users to use neural signals, typically Electroencephalogram (EEG) to direct an application or an external device. Motor imagery (MI) based BCI detects subject motor intentions which could be further used as control signals. Due to spatial lateralization of different MI tasks, spatial filtering followed by band power extraction is the most commonly used algorithm for classification tasks in MI-based BCIs. Unfortunately, the spatial filtering approach significantly incorporates Amplitude characteristics when compared to Phase characteristics of the EEG signal. Single trial Phase locking value (sPLV) has been a popular statistics to extract phase based information for classification task in MI-based BCI. To utilize the phase characteristics for MI classification, this paper proposes a novel approach based on instantaneous phase difference (IPD) sequence to extract phase features that explicitly use the phase synchronization information between EEG sensors. We maximized the discriminability of IPD sequence using linear transformation calculated from common spatial pattern algorithm (CSP) on the IPD sequence. An evaluation of our method on BCI competition dataset led to around 15% increase in mean classification accuracies compared to sPLV approach and comparable accuracies to power feature based CSP algorithm. Furthermore, incorporating phase features from our method and power features from traditional algorithms using sparse feature selection technique increased the classification accuracy over both CSP and CSP on the IPD sequence.
Journal of King Saud University - Computer and Information Sciences, 2020
Abstract In the medical field, Medical diagnosis using Computed tomography (CT) has become increa... more Abstract In the medical field, Medical diagnosis using Computed tomography (CT) has become increasingly popular due to their non-invasive approach and quick overall turnaround time. 3D visualization for CT Colonography involves the assessment and diagnosis of a patient to find the presence of cancerous polyps in the colon, by taking a Computed Tomography scan of the patient, and evaluating the reports. This technique reduces evaluation time by allowing the doctors themselves to analyze the CT scans without the need for a radiologist to generate an initial report. This technique also avoids an invasive procedure on the patient. This paper gives the insightsof developing computer aided system for the detection of Polyps in CT Colonography images using the principles of Image Processing and the Deep Learning, specifically an ensemble of Convolutional Neural Networks using GoogleNet architecture and 3D reconstruction of the same. The accuracy achieved by the proposed system for region classification, region 1 polyp detection and region 2 polyp detection are 98.75%, 93.75% and 94.03% respectively, and their F1 Scores are 0.88, 0.82 and 0.84 respectively.
International Journal of Advance Research and Innovative Ideas in Education, 2018
Smart cities is a major outbreak in implementation of different IT and computer technology. The f... more Smart cities is a major outbreak in implementation of different IT and computer technology. The focus is on providing environmental sustainability and efficiency, sustainable homes and buildings, efficient use of energy resources by implementing smart roads and buildings monitoring system, smart transportation, health care, global warming monitoring and security system. Various sensors are used to collect data like temperature, light, pressure and process it to form a meaningful interpretation. This technology is one of the major application of the smart city idea. The implementation of the technology ranges from homes and schools to large industries.The Proposed system enables to monitor different data sent from various sensors installed in a smart city.The data can be used to study the fluctuation and anticipate any possibilities of unnatural incident.
Brain–computer interface (BCI) is an emerging tool that has a variety of practical applications, ... more Brain–computer interface (BCI) is an emerging tool that has a variety of practical applications, including rehabilitation. BCIs are systems that extract and classify features in neural data, and then produce an output when a specific feature is detected. Motor imagery-based BCIs (MI BCIs), a more specific form of BCI, detect features that indicate the user is imagining a specific motor action, such as moving their arm or leg. There have been several studies released discussing the potential for BCIs to be used in a clinical setting for applications like rehabilitation. Spinal cord injuries (SCIs) are a form of injury that damages the spinal cord and causes either partial or total paralyzation. Those with SCI typically undergo rehabilitation for many years after the injury, and BCIs have begun to be tested for their benefits when included in SCI rehabilitation sessions. There are several ways for BCI systems to be used in SCI rehabilitation, which include virtual reality, exoskeletons, and neuroprosthesis. When using these methods as an output for a BCI system, SCI patients experience numerous benefits, most notably being an increase in mobility in the paralyzed region of their body. While there are several advantages to using BCIs for SCI rehabilitation, there are also several challenges that need to be addressed. In this chapter, we will discuss the current potential of BCIs for SCI rehabilitation, as well as what areas of this field need to be improved in the future.
Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG ... more Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this letter, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes. We determine the subspace for the proposed approach through optimization using gradient descent on an orthogonal manifold. The performance of the proposed stationarity enforcing algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on publicly available BCI competition IV dataset IIa. Results show that an improvement in average classification acc...
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the featur... more The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes. We determine the subspace for the proposed approach through optimization using gradient descent on an orthogonal manifold. The performance of the proposed stationarity enforcing algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on publicly available BCI competition IV dataset IIa. Results show that an improvement in average classification accuracies across the subjects over the baseline algorithms and thus essence of alleviating within session non-stationarities.
2021 IEEE International Reliability Physics Symposium (IRPS), 2021
A stochastic Reaction-Diffusion-Drift (RDD) model framework is proposed for trap time kinetics un... more A stochastic Reaction-Diffusion-Drift (RDD) model framework is proposed for trap time kinetics under Hot Carrier Degradation (HCD) stress and post-stress conditions. Consistency of the 3-D stochastic RDD, 3-D TCAD incorporated deterministic RDD and an “equivalent” 1-D deterministic RDD frameworks is shown. Measured HCD kinetics is decoupled into contributions by pure HCD and Bias Temperature Instability (BTI). The pure HCD time kinetics during and after stress is modeled using the above frameworks. Lack of recovery for the pure HCD component after stress is explained.
A stochastic reaction–diffusion drift model is used to simulate the time kinetics of interface an... more A stochastic reaction–diffusion drift model is used to simulate the time kinetics of interface and bulk oxide traps responsible for bias temperature instability (BTI), stress-induced leakage current (SILC), and time-dependent dielectric breakdown (TDDB) in MOSFETs. Trap generation and passivation are calculated using dissociation and repassivation of trap precursors and simultaneous diffusion and/or drift of atomic, molecular, and/or ionic species. The average of multiple stochastic simulations is used to qualitatively explain the measured BTI and SILC data. The difference in BTI and SILC time kinetics, variation in SILC time kinetics across reports, and oxide thickness dependence of TDDB Weibull slope variation are also qualitatively explained.
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