Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200012
Satvik Sabarad, Shubham Gupta
With a massive leap of increment in overall energy efficiency through the paradigm shift from internal combustion engine vehicles to electric vehicles (EVs), people have evolved their ways to make the transition towards sustainable transportation. Regenerative braking in the automobiles is considered as one of the efficient ways to recover the energy wasted during braking of a vehicle. It is also a practical approach for EVs to extend their driving range. The energy accumulated due to regenerative braking heavily depends on the driving style and traffic conditions. There is much research done in the past related to regenerative braking but inculcation of driving style and traffic conditions in the quantification of energy is limited. The originality of this study is to determine if the regenerative braking is effective for an electric vehicle over a particular duty cycle. This work presents a physical data acquisition setup, which consists of various sensors, data recorders, and micro-controllers that acquire the driving data like velocities at different instances, coasting distance and duration from the vehicle. This work also proposes a mathematical model that uses the acquired data to quantify the amount of energy generated over a duty cycle in a particular electric vehicle. Currently, a two-wheeler electric bike is considered for the quantification of regenerated braking energy over various duty cycles viz. urban, semi-urban, and highway driving. The physical setup and the process of quantification can be extended to various electric vehicles to quantify the regenerative braking energy over different duty cycles. A different approach of energy conservation method is demonstrated for the development of the mathematical model to accurately quantify the regenerated braking energy. This work also focuses on the comparison of energy generated due to regenerative braking between a two-wheeler electric and internal combustion vehicle.
{"title":"Quantification of regenerative braking energy in a two-wheeler incorporating various duty cycles","authors":"Satvik Sabarad, Shubham Gupta","doi":"10.1109/ComPE49325.2020.9200012","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200012","url":null,"abstract":"With a massive leap of increment in overall energy efficiency through the paradigm shift from internal combustion engine vehicles to electric vehicles (EVs), people have evolved their ways to make the transition towards sustainable transportation. Regenerative braking in the automobiles is considered as one of the efficient ways to recover the energy wasted during braking of a vehicle. It is also a practical approach for EVs to extend their driving range. The energy accumulated due to regenerative braking heavily depends on the driving style and traffic conditions. There is much research done in the past related to regenerative braking but inculcation of driving style and traffic conditions in the quantification of energy is limited. The originality of this study is to determine if the regenerative braking is effective for an electric vehicle over a particular duty cycle. This work presents a physical data acquisition setup, which consists of various sensors, data recorders, and micro-controllers that acquire the driving data like velocities at different instances, coasting distance and duration from the vehicle. This work also proposes a mathematical model that uses the acquired data to quantify the amount of energy generated over a duty cycle in a particular electric vehicle. Currently, a two-wheeler electric bike is considered for the quantification of regenerated braking energy over various duty cycles viz. urban, semi-urban, and highway driving. The physical setup and the process of quantification can be extended to various electric vehicles to quantify the regenerative braking energy over different duty cycles. A different approach of energy conservation method is demonstrated for the development of the mathematical model to accurately quantify the regenerated braking energy. This work also focuses on the comparison of energy generated due to regenerative braking between a two-wheeler electric and internal combustion vehicle.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"40 1","pages":"469-474"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90622544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200153
Ketanpreet Kaur, Vikrant Sharma, M. Sachdeva
In this ever updating digitalized world, everything is connected with just few touches away. Our phone is connected with things around us, even we can see live video of our home, shop, institute or company on the phone. But we can’t track suspicious activity 24*7 hence needed a smart system to track down any suspicious activity taking place, so it automatically notifies us before any robbery or dangerous activity takes place. We have proposed a framework to tackle down this security matter with the help of sensors enabled cameras(IoT) connected through a FOG layer hence called FOGIoT which consists of small servers configured with Human Activity Analysis Algorithm. Any suspicious activity analyzed will be reported to responsible personnel and the due action will be taken place.
{"title":"Framework for FOGIoT based Smart Video Surveillance System (SVSS)","authors":"Ketanpreet Kaur, Vikrant Sharma, M. Sachdeva","doi":"10.1109/ComPE49325.2020.9200153","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200153","url":null,"abstract":"In this ever updating digitalized world, everything is connected with just few touches away. Our phone is connected with things around us, even we can see live video of our home, shop, institute or company on the phone. But we can’t track suspicious activity 24*7 hence needed a smart system to track down any suspicious activity taking place, so it automatically notifies us before any robbery or dangerous activity takes place. We have proposed a framework to tackle down this security matter with the help of sensors enabled cameras(IoT) connected through a FOG layer hence called FOGIoT which consists of small servers configured with Human Activity Analysis Algorithm. Any suspicious activity analyzed will be reported to responsible personnel and the due action will be taken place.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"51 1","pages":"797-799"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90661855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200022
Jayashree Deka, S. Laskar
Detection of edge in image is a fundamental requirement involved in computer vision and image processing applications. In this paper, the performance of traditional edge detectors is compared with Grunwald-Letnikov(G-L) based Fractional Order Derivative (FOD) based edge detector. The performance is measured for both types of detectors under noise free and noisy conditions on fish images. Image quality assessment (IQA) parameters Mean Square Error (MSE), Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) are used for quantitative comparison of the edge detection. From the experimental results, it is observed that FOD based edge detector shows better results than the traditional edge detectors under noisy conditions either in terms of quality or quantity.
{"title":"Performance Comparison of FOD based Edge Detector and Traditional Edge Detectors on Fish Image Edge Detection","authors":"Jayashree Deka, S. Laskar","doi":"10.1109/ComPE49325.2020.9200022","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200022","url":null,"abstract":"Detection of edge in image is a fundamental requirement involved in computer vision and image processing applications. In this paper, the performance of traditional edge detectors is compared with Grunwald-Letnikov(G-L) based Fractional Order Derivative (FOD) based edge detector. The performance is measured for both types of detectors under noise free and noisy conditions on fish images. Image quality assessment (IQA) parameters Mean Square Error (MSE), Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) are used for quantitative comparison of the edge detection. From the experimental results, it is observed that FOD based edge detector shows better results than the traditional edge detectors under noisy conditions either in terms of quality or quantity.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"468 1","pages":"485-490"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74801409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200165
Chayanika Sharma, Utpal Sarma
Microheaters have been extensively investigated for its wide application in designing a Metal Oxide Semiconductor based gas sensor. Indium Tin Oxide (ITO) deposited on a thin glass film can be made use to detect various Volatile Organic Compounds (VOCs) at different elevated temperatures. To achieve this higher temperature requirement, power management is also a very crucial part of gas sensor design. In this paper, four different structures of microheater are discussed. The simulation was carried out using Finite Element Method. The length and structure of the microheater were varied for optimization. From the simulated designs of microheater, the optimized one was calculated by considering two important aspects, power management and uniform temperature distribution over the gas sensitive layer of the gas sensor. Hence this kind of gas sensor design with an inbuilt temperature modulating part shows potential application towards VOC profiling in future work.
{"title":"Design and Optimization of a Microheater for the Application of Indium Tin Oxide (ITO) based Gas Sensor in VOC Detection","authors":"Chayanika Sharma, Utpal Sarma","doi":"10.1109/ComPE49325.2020.9200165","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200165","url":null,"abstract":"Microheaters have been extensively investigated for its wide application in designing a Metal Oxide Semiconductor based gas sensor. Indium Tin Oxide (ITO) deposited on a thin glass film can be made use to detect various Volatile Organic Compounds (VOCs) at different elevated temperatures. To achieve this higher temperature requirement, power management is also a very crucial part of gas sensor design. In this paper, four different structures of microheater are discussed. The simulation was carried out using Finite Element Method. The length and structure of the microheater were varied for optimization. From the simulated designs of microheater, the optimized one was calculated by considering two important aspects, power management and uniform temperature distribution over the gas sensitive layer of the gas sensor. Hence this kind of gas sensor design with an inbuilt temperature modulating part shows potential application towards VOC profiling in future work.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"62 1","pages":"694-698"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75269640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200002
S. Mishra, Rajesh Birok
Automated neurocognitive performance assessment (NCP) of a subject is a pertinent theme in neurological and medical studies. NCP signifies the human mental/cognitive ability to perform any allocated job. It is hard to establish any certain methodology for research since the NCP switches the subject in an unknown manner. Sleep is a neurocognitive performance that varies in time and can be used to learn new NCP techniques. A detailed electroencephalographic signals (EEG) study and understanding of human sleep are important for a proper NCP assessment. However, sleep deprivation can cause prominent cognitive risks while carrying out activities like driving, and can even lead to lack of concentration in individuals. Controlling a generic unit in non-rapid eye movement (NREM), which is the first phase of sleep or stage N1is highly important in NCP study.Our method is built on RNN-LSTM which classifies different sleep stages using raw EEG single-channel which is obtained from the openly available sleep-EDF dataset. The single raw channel helps classify the REM stage particularly, because a single raw channel, human motion, and movement are not considered. The features selected constituted as the RNNs network inputs. The goal of this work is to efficiently classify the performance in sleep stage N1, as well as improvement in the subsequent stages of sleep.
{"title":"Sleep Classification using CNN and RNN on Raw EEG Single-Channel","authors":"S. Mishra, Rajesh Birok","doi":"10.1109/ComPE49325.2020.9200002","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200002","url":null,"abstract":"Automated neurocognitive performance assessment (NCP) of a subject is a pertinent theme in neurological and medical studies. NCP signifies the human mental/cognitive ability to perform any allocated job. It is hard to establish any certain methodology for research since the NCP switches the subject in an unknown manner. Sleep is a neurocognitive performance that varies in time and can be used to learn new NCP techniques. A detailed electroencephalographic signals (EEG) study and understanding of human sleep are important for a proper NCP assessment. However, sleep deprivation can cause prominent cognitive risks while carrying out activities like driving, and can even lead to lack of concentration in individuals. Controlling a generic unit in non-rapid eye movement (NREM), which is the first phase of sleep or stage N1is highly important in NCP study.Our method is built on RNN-LSTM which classifies different sleep stages using raw EEG single-channel which is obtained from the openly available sleep-EDF dataset. The single raw channel helps classify the REM stage particularly, because a single raw channel, human motion, and movement are not considered. The features selected constituted as the RNNs network inputs. The goal of this work is to efficiently classify the performance in sleep stage N1, as well as improvement in the subsequent stages of sleep.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"1 1","pages":"232-237"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77524547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200087
D. Devi, S. Biswas, B. Purkayastha
The classification task carries a significant role in the field of effective data mining and numerous classification models are proposed over the years to carry out the job. However, standard classification models are sensitive to the underlying characteristics of the datasets. When employed to a dataset with skewed class distribution, standard classification models tend to misclassify the rare instances as it gets biased towards the majority patterns. This is where the issue of class imbalance makes it mark and causes to significantly degrade the performance of the standard classifiers. Among the several reported solutions for class imbalance issue, undersampling approaches are quite prevalent which offers to balance the class distribution by discarding insignificant majority instances. In this paper, an insight of class imbalance issue is presented in regard of its impact on classification models, the reported solutions and the effectiveness of the undersampling approaches in solving the issue.
{"title":"A Review on Solution to Class Imbalance Problem: Undersampling Approaches","authors":"D. Devi, S. Biswas, B. Purkayastha","doi":"10.1109/ComPE49325.2020.9200087","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200087","url":null,"abstract":"The classification task carries a significant role in the field of effective data mining and numerous classification models are proposed over the years to carry out the job. However, standard classification models are sensitive to the underlying characteristics of the datasets. When employed to a dataset with skewed class distribution, standard classification models tend to misclassify the rare instances as it gets biased towards the majority patterns. This is where the issue of class imbalance makes it mark and causes to significantly degrade the performance of the standard classifiers. Among the several reported solutions for class imbalance issue, undersampling approaches are quite prevalent which offers to balance the class distribution by discarding insignificant majority instances. In this paper, an insight of class imbalance issue is presented in regard of its impact on classification models, the reported solutions and the effectiveness of the undersampling approaches in solving the issue.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"45 1","pages":"626-631"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77099410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9199999
Tripurari Sharan, Akho John Richa
This paper presents a positive and negative both of second-generation current conveyor (DO-CCII) cell as a single circuit. The input core of this cell has utilized an adaptively biased bulk-driven pMOS input pair and folded cascode load based OTA. This OTA section has ensured GBW, PM and CMRR of 13.7 kHz, 86.5 degree and 113 dB, respectively with a 15 pF load capacitor and a ± 0.25 V bias supply. The OTA section provided a wide input common mode range, wide output signal swing with good linearity. The output section of DO-CCII cell uses two CMOS inverter to yield its X and Z+ terminals whereas its Z− terminal is generated by using cross coupled low-voltage current mirrors. The DO-CCII cell has provided wide voltage and current DC sweep range with very good linearity. When measured between the frequency ranges of 1 Hz to 100 kHz, the voltage gain and current gains are found to be close to unity. The designed DO-CCII cells have been utilized in design of current mode SIMO filter, oscillator and variable gain current mode instrumentation amplifier (CMIA) which confirms its usability in small frequency bio-signal processing applications. These circuits have been simulated in 180 nm CMOS bulk process technology using Tanner EDA tool of version 16.1.
{"title":"Low-voltage BD-FC-OTA based-DO-CCII and its Applications for Low-Frequency Signal Processing","authors":"Tripurari Sharan, Akho John Richa","doi":"10.1109/ComPE49325.2020.9199999","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9199999","url":null,"abstract":"This paper presents a positive and negative both of second-generation current conveyor (DO-CCII) cell as a single circuit. The input core of this cell has utilized an adaptively biased bulk-driven pMOS input pair and folded cascode load based OTA. This OTA section has ensured GBW, PM and CMRR of 13.7 kHz, 86.5 degree and 113 dB, respectively with a 15 pF load capacitor and a ± 0.25 V bias supply. The OTA section provided a wide input common mode range, wide output signal swing with good linearity. The output section of DO-CCII cell uses two CMOS inverter to yield its X and Z+ terminals whereas its Z− terminal is generated by using cross coupled low-voltage current mirrors. The DO-CCII cell has provided wide voltage and current DC sweep range with very good linearity. When measured between the frequency ranges of 1 Hz to 100 kHz, the voltage gain and current gains are found to be close to unity. The designed DO-CCII cells have been utilized in design of current mode SIMO filter, oscillator and variable gain current mode instrumentation amplifier (CMIA) which confirms its usability in small frequency bio-signal processing applications. These circuits have been simulated in 180 nm CMOS bulk process technology using Tanner EDA tool of version 16.1.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"71 1","pages":"584-591"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78185600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200004
N. Das, Kaushik Chandra Deva Sarma
A theoretical process of obtaining depletion width for surrounded channel Junction less field effect transistor is presented. Solution of 1-D Poisson’s equation under partial depletion leads to development of depletion width model. An analysis on how depletion width values varies with applied gate field, gate dielectric thickness, gate dielectric materials and channel position is also performed.
{"title":"Depletion Width Modelling of Surrounded Channel Junctionless Field Effect Transistor","authors":"N. Das, Kaushik Chandra Deva Sarma","doi":"10.1109/ComPE49325.2020.9200004","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200004","url":null,"abstract":"A theoretical process of obtaining depletion width for surrounded channel Junction less field effect transistor is presented. Solution of 1-D Poisson’s equation under partial depletion leads to development of depletion width model. An analysis on how depletion width values varies with applied gate field, gate dielectric thickness, gate dielectric materials and channel position is also performed.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"11 1","pages":"611-614"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81779481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200000
A. K. Raibaruah, Angshumala Talukdar, Kaushik Chandra Deva Sarma
We present here characteristics study of an undoped Double gate Junctionless field effect transistor (UnDGJLFET). The body of the device is intrinsic in nature. A comparative simulation study on electrical performance of the undoped DGJLFET and a DGJLFET with a doping concentration of 1019/cm3 has been done in TCAD. The study shows that the UnDGJLFET exhibits much lower subthreshold swing and higher threshold voltage than the doped one. However due to less number of charge carriers the on current is much lower for the UnDGJLFET compared to that of doped JLFET.
{"title":"Undoped Junctionless Field Effect Transistor","authors":"A. K. Raibaruah, Angshumala Talukdar, Kaushik Chandra Deva Sarma","doi":"10.1109/ComPE49325.2020.9200000","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200000","url":null,"abstract":"We present here characteristics study of an undoped Double gate Junctionless field effect transistor (UnDGJLFET). The body of the device is intrinsic in nature. A comparative simulation study on electrical performance of the undoped DGJLFET and a DGJLFET with a doping concentration of 1019/cm3 has been done in TCAD. The study shows that the UnDGJLFET exhibits much lower subthreshold swing and higher threshold voltage than the doped one. However due to less number of charge carriers the on current is much lower for the UnDGJLFET compared to that of doped JLFET.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"111 1","pages":"725-727"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82693771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200144
Bolivia Konthoujam, Satyabrat Malla Bujar Baruah, Soumik Roy
Synapse is the point of impulse conduction between neurons. After receiving action potential at the axonal terminal, the presynaptic neuron releases neurotransmitter chemicals to the postsynaptic neuron. The agonist molecules bind to the receptors of the postsynaptic neuron and change its functions. The proposed work is centered on interference in two synaptic system due to mobility of NMDA neurotransmitter with probability of leaking to the nearby synapse. Such leaky event of neurotransmitters from one synapse to nearby synapses could contribute either positively or negatively, depending on the type of ion channels it opens. The proposed model suggests change in amplitude and strength of the post-synaptic received signal significantly depends upon this leaky parameter. The model suggests increase in leakage of NMDA neurotransmitter results in lowered depolarization of post-synaptic potential and increased depolarization in nearby post-synapse and vice versa.
{"title":"Interference Model for NMDA Receptor","authors":"Bolivia Konthoujam, Satyabrat Malla Bujar Baruah, Soumik Roy","doi":"10.1109/ComPE49325.2020.9200144","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200144","url":null,"abstract":"Synapse is the point of impulse conduction between neurons. After receiving action potential at the axonal terminal, the presynaptic neuron releases neurotransmitter chemicals to the postsynaptic neuron. The agonist molecules bind to the receptors of the postsynaptic neuron and change its functions. The proposed work is centered on interference in two synaptic system due to mobility of NMDA neurotransmitter with probability of leaking to the nearby synapse. Such leaky event of neurotransmitters from one synapse to nearby synapses could contribute either positively or negatively, depending on the type of ion channels it opens. The proposed model suggests change in amplitude and strength of the post-synaptic received signal significantly depends upon this leaky parameter. The model suggests increase in leakage of NMDA neurotransmitter results in lowered depolarization of post-synaptic potential and increased depolarization in nearby post-synapse and vice versa.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"191 1","pages":"715-718"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82707275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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