Pub Date : 2020-07-01DOI: 10.1109/ComPE49325.2020.9200129
Md Sadik Tasrif Anubhove, N. Ashrafi, A. M. Saleque, Morsheda Akter, Shadman Uddin Saif
This paper is highlighting an outline of disease detection in tomato using computer vision and machine learning algorithms. Readily available hardware is used to build a system where a camera mounted system can detect and identify spot disease in tomatoes in real-time. As an initial prototype only spot disease can be detected. The complete development can be divided into two parts. The first part is the software and algorithm which aimed to detect and identify disease in crops and generate a report for the user. It is successful in building the algorithm and GUI (graphical user interface) for the user which can detect spot disease in tomatoes. Using the Viola-Jones algorithm and Haar like feature extraction method for the machine learning process in MATLAB, an XML (an image trained file) file for spot disease in tomatoes is designed using 377 images of infected tomatoes. The second part is the hardware implementation which consists of a simple robot rig that carries the camera and the system scans the tomatoes for the disease. For the vast majority of the time, spot detection is accurate. Many other diseases which exist for the animal, human and crops can easily be added to the system. In terms of reliability, the system is a success with acceptable false positives.
{"title":"Machine Learning Algorithm based Disease Detection in Tomato with Automated Image Telemetry for Vertical Farming","authors":"Md Sadik Tasrif Anubhove, N. Ashrafi, A. M. Saleque, Morsheda Akter, Shadman Uddin Saif","doi":"10.1109/ComPE49325.2020.9200129","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200129","url":null,"abstract":"This paper is highlighting an outline of disease detection in tomato using computer vision and machine learning algorithms. Readily available hardware is used to build a system where a camera mounted system can detect and identify spot disease in tomatoes in real-time. As an initial prototype only spot disease can be detected. The complete development can be divided into two parts. The first part is the software and algorithm which aimed to detect and identify disease in crops and generate a report for the user. It is successful in building the algorithm and GUI (graphical user interface) for the user which can detect spot disease in tomatoes. Using the Viola-Jones algorithm and Haar like feature extraction method for the machine learning process in MATLAB, an XML (an image trained file) file for spot disease in tomatoes is designed using 377 images of infected tomatoes. The second part is the hardware implementation which consists of a simple robot rig that carries the camera and the system scans the tomatoes for the disease. For the vast majority of the time, spot detection is accurate. Many other diseases which exist for the animal, human and crops can easily be added to the system. In terms of reliability, the system is a success with acceptable false positives.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"1 1","pages":"250-254"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79560302","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/ComPE49325.2020.9200002
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/ComPE49325.2020.9200022
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/ComPE49325.2020.9200162
Anunay Kumar, Yashwardhan Sahi, Swet Chandan, P. Suresh
Helical gear is one of the parts of a machine which undergoes mechanical operation with some loading condition acting upon it. Helical gear deals with high contact and friction which reduces the slippage compared to spur gears. The load may cause damage to the gears generally to the tooth surface and breakage of gear tooth and other damages to the gear that may include deterioration of plastic material and the rim or web breakages. When the gears are used in assemblies are named as gear train. The leading factor of gear failures are the stress and surface strength of a gear tooth. So, it has been interesting in the research area to minimise the stress acted on the gear and optimal design of gear. In this paper the design of gear train at specific parameter is done in Solidworks and the aluminium alloy material is considered. The further analysis is done in ansys 18.0 where some loading condition is applied for the analysis. Two types of analysis is done to the simulation which is vibration and stress analysis. It is 500 N-m of moment is applied at gear train and the stress in the tooth and shafts are determined that whether the result analysis obtained is within the yield tensile limit of the used material.
{"title":"Modal Analysis of Helical Gear Train using Ansys","authors":"Anunay Kumar, Yashwardhan Sahi, Swet Chandan, P. Suresh","doi":"10.1109/ComPE49325.2020.9200162","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200162","url":null,"abstract":"Helical gear is one of the parts of a machine which undergoes mechanical operation with some loading condition acting upon it. Helical gear deals with high contact and friction which reduces the slippage compared to spur gears. The load may cause damage to the gears generally to the tooth surface and breakage of gear tooth and other damages to the gear that may include deterioration of plastic material and the rim or web breakages. When the gears are used in assemblies are named as gear train. The leading factor of gear failures are the stress and surface strength of a gear tooth. So, it has been interesting in the research area to minimise the stress acted on the gear and optimal design of gear. In this paper the design of gear train at specific parameter is done in Solidworks and the aluminium alloy material is considered. The further analysis is done in ansys 18.0 where some loading condition is applied for the analysis. Two types of analysis is done to the simulation which is vibration and stress analysis. It is 500 N-m of moment is applied at gear train and the stress in the tooth and shafts are determined that whether the result analysis obtained is within the yield tensile limit of the used material.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"28 1","pages":"604-607"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76802863","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}
Ground penetrating radar (GPR) uses electromagnetic (EM) wave to detect the subsurface objects. Interpretation and analysis of GPR signals are still challenging tasks as it requires skilled user (geologists in most cases). Particularly difficult is the prediction of the object sizes. This paper proposes a new method for predicting size of buried objects. First, standard scaling pre-processing techniques are used to optimise the B-Scan data. The features are then supplied to Random Forest (RF) and Support Vector Machine (SVM) classifiers to automatically predict the size of the buried object. The proposed feature based RF classifier shows similar performance in the accuracy of classification compared to SVM (Radial Basis Function kernel) system.
探地雷达(GPR)是一种利用电磁波探测地下物体的雷达。GPR信号的解释和分析仍然是一项具有挑战性的任务，因为它需要熟练的用户(大多数情况下是地质学家)。特别困难的是物体大小的预测。本文提出了一种预测埋藏物尺寸的新方法。首先，采用标准缩放预处理技术对b扫描数据进行优化。然后将这些特征提供给随机森林(RF)和支持向量机(SVM)分类器，以自动预测被埋物体的大小。所提出的基于特征的射频分类器在分类精度上与SVM (Radial Basis Function kernel，径向基函数核)系统相当。
{"title":"Prediction of Size of Buried Objects using Ground Penetrating Radar and Machine Learning Techniques","authors":"Nairit Barkataki, Sharmistha Mazumdar, Rajdeep Talukdar, Priyanka Chakraborty, B. Tiru, Utpal Sarma","doi":"10.1109/ComPE49325.2020.9200094","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200094","url":null,"abstract":"Ground penetrating radar (GPR) uses electromagnetic (EM) wave to detect the subsurface objects. Interpretation and analysis of GPR signals are still challenging tasks as it requires skilled user (geologists in most cases). Particularly difficult is the prediction of the object sizes. This paper proposes a new method for predicting size of buried objects. First, standard scaling pre-processing techniques are used to optimise the B-Scan data. The features are then supplied to Random Forest (RF) and Support Vector Machine (SVM) classifiers to automatically predict the size of the buried object. The proposed feature based RF classifier shows similar performance in the accuracy of classification compared to SVM (Radial Basis Function kernel) system.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"25 1","pages":"781-785"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84876085","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/ComPE49325.2020.9200006
Siddhanta Borah, R. Kumar, Writtick Pakhira, Subhradip Mukherjee
With the advancement of new technologies now a days sensors systems have become more intelligent and compact in size as compared to other traditional sensor systems. In this research work, an IoT (Internet of Things) based low cost and power efficient hardware system is presented to monitor soil moisture. A capacitive soil moisture sensor is designed and calibrated in this work. The output data are compared with a standard SEN0193 capacitive soil moisture sensor to check the reliability of the sensor. To make the sensor system a 32 bit ESP32 controller has been used. The controller was programmed according to an algorithm named "Low Threshold Power Optimization (LTPO)" to consume low power. The ESP32 controller has an inbuilt IoT chip that provides more flexibility to the system. A small OLED display is also interfaced with the controller to monitor sensor value directly in the field. In this paper an open source server ThingSpeak.com is used to visualize and store moisture data.
{"title":"Design and Analysis of Power Efficient IoT Based Capacitive Sensor System to Measure Soil Moisture","authors":"Siddhanta Borah, R. Kumar, Writtick Pakhira, Subhradip Mukherjee","doi":"10.1109/ComPE49325.2020.9200006","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200006","url":null,"abstract":"With the advancement of new technologies now a days sensors systems have become more intelligent and compact in size as compared to other traditional sensor systems. In this research work, an IoT (Internet of Things) based low cost and power efficient hardware system is presented to monitor soil moisture. A capacitive soil moisture sensor is designed and calibrated in this work. The output data are compared with a standard SEN0193 capacitive soil moisture sensor to check the reliability of the sensor. To make the sensor system a 32 bit ESP32 controller has been used. The controller was programmed according to an algorithm named \"Low Threshold Power Optimization (LTPO)\" to consume low power. The ESP32 controller has an inbuilt IoT chip that provides more flexibility to the system. A small OLED display is also interfaced with the controller to monitor sensor value directly in the field. In this paper an open source server ThingSpeak.com is used to visualize and store moisture data.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"57 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84879790","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/ComPE49325.2020.9200049
A. Charan, J. K. Verma
This article is an attempt to identify the role of social media in regulating hate crimes and cyber racism in India. The freedom of speech is given to the citizens in many countries but a few disturbing elements are misusing this freedom and suppressing the voice of marginalized and Dalits. It is observed that the Fake news contents, hate crimes and cyber racism are increasing in India. The vigilance of internet contents and digital media is in nascent phase and need to be governed in the light of human rights and freedom of speech [1].One side network of haters and fake news bibliophiles are expanding their network in digital media at a faster rate. On the other side cohort of advocates dealing in International agreements, Human Rights and International Laws are establishing a sound framework for raising voice of marginalized on all possible decisive platforms. User generated contents are future of social media therefore, sketching the characters and identifying cyber crimes in advance must be an integral feature of the programming on social networking sites. This article is intended to identify overall impact of hate crimes on the society in general and on the Dalit and marginalized sections more specifically. The article will suggest some proactive measures for characterizing social media contents in order to regulate these contents by designated authorities or popular social networks.
{"title":"Characterizing Social Media Contents for Regulating Hate Crimes and Cyber Racism against Marginalized and Dalits In India","authors":"A. Charan, J. K. Verma","doi":"10.1109/ComPE49325.2020.9200049","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200049","url":null,"abstract":"This article is an attempt to identify the role of social media in regulating hate crimes and cyber racism in India. The freedom of speech is given to the citizens in many countries but a few disturbing elements are misusing this freedom and suppressing the voice of marginalized and Dalits. It is observed that the Fake news contents, hate crimes and cyber racism are increasing in India. The vigilance of internet contents and digital media is in nascent phase and need to be governed in the light of human rights and freedom of speech [1].One side network of haters and fake news bibliophiles are expanding their network in digital media at a faster rate. On the other side cohort of advocates dealing in International agreements, Human Rights and International Laws are establishing a sound framework for raising voice of marginalized on all possible decisive platforms. User generated contents are future of social media therefore, sketching the characters and identifying cyber crimes in advance must be an integral feature of the programming on social networking sites. This article is intended to identify overall impact of hate crimes on the society in general and on the Dalit and marginalized sections more specifically. The article will suggest some proactive measures for characterizing social media contents in order to regulate these contents by designated authorities or popular social networks.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"30 1","pages":"864-871"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85386319","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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