Pub Date : 2024-01-01DOI: 10.2478/ijssis-2024-0002
B. L. Widjiantoro, K. Indriawati, T. S. N. Alexander Buyung, Kadek Dwi Wahyuadnyana
This study validates EKF-SLAM for indoor autonomous vehicles by experimentally integrating the MPU6050 sensor and encoder data using an extended Kalman filter. Real-world tests show significant improvements, achieving high accuracy with just 1% and 3% errors in the X and Y axes. RPLiDAR A1M8 is utilized for mapping, producing accurate maps visualized through RViz-ROS. The research demonstrates the novelty and practical utility of EKF-SLAM in real-world scenarios, showcasing unprecedented effectiveness and precision.
本研究通过使用扩展卡尔曼滤波器整合 MPU6050 传感器和编码器数据的实验，验证了 EKF-SLAM 在室内自动驾驶汽车中的应用。实际测试表明，EKF-SLAM 在 X 轴和 Y 轴上的误差仅为 1%和 3%，达到了很高的精度。RPLiDAR A1M8 用于测绘，通过 RViz-ROS 生成可视化的精确地图。这项研究证明了 EKF-SLAM 在实际应用场景中的新颖性和实用性，展示了前所未有的有效性和精确性。
{"title":"Experimental Validation: Perception and Localization Systems for Autonomous Vehicles using the Extended Kalman Filter Algorithm","authors":"B. L. Widjiantoro, K. Indriawati, T. S. N. Alexander Buyung, Kadek Dwi Wahyuadnyana","doi":"10.2478/ijssis-2024-0002","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0002","url":null,"abstract":"\u0000 This study validates EKF-SLAM for indoor autonomous vehicles by experimentally integrating the MPU6050 sensor and encoder data using an extended Kalman filter. Real-world tests show significant improvements, achieving high accuracy with just 1% and 3% errors in the X and Y axes. RPLiDAR A1M8 is utilized for mapping, producing accurate maps visualized through RViz-ROS. The research demonstrates the novelty and practical utility of EKF-SLAM in real-world scenarios, showcasing unprecedented effectiveness and precision.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140525435","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 : 2024-01-01DOI: 10.2478/ijssis-2024-0012
Tanmay Sinha Roy, J. K. Roy, N. Mandal, Subhas Chandra Mukhopadhyay
The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.
本文回顾了开发心音图（PCG）信号分析的里程碑和各种现代方法。它还解释了心音信号分析的不同阶段和方法。许多医生严重依赖心电图专家，导致医疗成本增加，并且不懂听诊器技能。因此，听诊并不是检测瓣膜性心脏病的简单方法；因此，医生更倾向于使用多普勒回波心电图和其他病理检查进行临床评估。然而，听诊和其他临床评估的优点可以与计算机辅助诊断方法联系起来，后者可以在测量和分析各种心音方面提供很大帮助。本综述涵盖了在预处理阶段对瓣膜心音进行分割的最新研究，如自适应模糊系统、香农能量、时频表示法和离散小波分布，用于分析和诊断各种心脏相关疾病。针对瓣膜心音分析，讨论了不同的基于卷积神经网络（CNN）的深度学习模型，如 LeNet-5、AlexNet、VGG16、VGG19、DenseNet121、Inception Net、Residual Net、Google Net、Mobile Net、Squeeze Net 和 Xception Net。在所有深度学习方法中，Xception Net 的准确率最高，为 99.43 + 0.03%，灵敏度最高，为 98.58 + 0.06%。综述还介绍了心音特征提取和分类技术的最新进展，这在很大程度上为研究人员和读者提供了帮助。
{"title":"Recent Advances in PCG Signal Analysis using AI: A Review","authors":"Tanmay Sinha Roy, J. K. Roy, N. Mandal, Subhas Chandra Mukhopadhyay","doi":"10.2478/ijssis-2024-0012","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0012","url":null,"abstract":"\u0000 The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518327","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 : 2024-01-01DOI: 10.2478/ijssis-2024-0003
T. Makarenya, A. S. Mannaa, Alexey I. Kalinichenko, Svetlana V. Petrenko
Under the current geopolitical conditions and the economic sanctions imposed on Russia, there is an objective need to formulate a strategic development plan for the economy as a whole and for specific sectors of the economy. Various methods and tools can be used for strategic planning. One of the methods of strategic planning is the program-targeted method of planning, which has proved to be an effective method of foresight. It is possible to speak about failures of planning activities, but these failures were related not only to shortcomings and application of science-based planning methods but also to the efficiency of the managerial apparatus, which took decisions. It should be noted that it was in the period when science-based planning methods were applied that our country managed to form and develop industrial production in various sectors, and the issue of import substitution did not arise then, as all the stages of the product life cycle were represented at all the enterprises. Currently, the country is facing the problem of strategic development in the context of the imposed economic sanctions. The volume of sanctions is increasing day by day and one can only speculate on the future restrictions imposed. Therefore, there is a need to forecast activities at the level of the whole country, individual industries, and enterprises. One such method is cognitive modeling based on fuzzy logic. This approach involves the use of cognitive principles and methods to understand the behavior of individuals in the system, as well as the interactions and feedback loops between the various components. The purpose of this paper is to retrospectively analyze the application of the cognitive method to modeling. Information systems that have been developed in our country to implement the tasks of cognitive modeling are reviewed, and an assessment of existing software products is made. Also, theoretical materials on cognitive approach in modeling are presented in order to understand the application of this toolkit for modeling socioeconomic systems using elements of fuzzy logic.
{"title":"Cognitive Modeling as a Forecasting Tool","authors":"T. Makarenya, A. S. Mannaa, Alexey I. Kalinichenko, Svetlana V. Petrenko","doi":"10.2478/ijssis-2024-0003","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0003","url":null,"abstract":"\u0000 Under the current geopolitical conditions and the economic sanctions imposed on Russia, there is an objective need to formulate a strategic development plan for the economy as a whole and for specific sectors of the economy. Various methods and tools can be used for strategic planning. One of the methods of strategic planning is the program-targeted method of planning, which has proved to be an effective method of foresight. It is possible to speak about failures of planning activities, but these failures were related not only to shortcomings and application of science-based planning methods but also to the efficiency of the managerial apparatus, which took decisions. It should be noted that it was in the period when science-based planning methods were applied that our country managed to form and develop industrial production in various sectors, and the issue of import substitution did not arise then, as all the stages of the product life cycle were represented at all the enterprises. Currently, the country is facing the problem of strategic development in the context of the imposed economic sanctions. The volume of sanctions is increasing day by day and one can only speculate on the future restrictions imposed. Therefore, there is a need to forecast activities at the level of the whole country, individual industries, and enterprises. One such method is cognitive modeling based on fuzzy logic. This approach involves the use of cognitive principles and methods to understand the behavior of individuals in the system, as well as the interactions and feedback loops between the various components. The purpose of this paper is to retrospectively analyze the application of the cognitive method to modeling. Information systems that have been developed in our country to implement the tasks of cognitive modeling are reviewed, and an assessment of existing software products is made. Also, theoretical materials on cognitive approach in modeling are presented in order to understand the application of this toolkit for modeling socioeconomic systems using elements of fuzzy logic.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523258","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}
In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.
{"title":"Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model","authors":"Nilkanth Mukund Deshpande, Shilpa Gite, Biswajeet Pradhan","doi":"10.2478/ijssis-2024-0013","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0013","url":null,"abstract":"\u0000 In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140525266","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 : 2024-01-01DOI: 10.2478/ijssis-2024-0008
S. Zadoo, Yashwant Singh, Pradeep Kumar Singh
Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It is acquired by the progressive demise of the dopaminergic neurons existing in the substantia nigra pars compacta region of the human brain. In the absence of a single accurate test, and due to the dependency on the doctors, intensive research is being carried out to automate the early disease detection and predict disease severity also. In this study, a detailed review of various artificial intelligence (AI) models applied to different datasets across different modalities has been presented. The emotional intelligence (EI) modality, which can be used for the early detection and can help in maintaining a comfortable lifestyle, has been identified. EI is a predominant, emerging technology that can be used to detect PsD at the initial stages and to enhance the socialization of the PsD patients and their attendants. Challenges and possibilities that can assist in bridging the differences between the fast-growing technologies meant to detect PsD and the actual implementation of the automated PsD detection model are presented in this research. This review highlights the prominence of using the support vector machine (SVM) classifier in achieving an accuracy of about 99% in many modalities such as magnetic resonance imaging (MRI), speech, and electroencephalogram (EEG). A 100% accuracy is achieved in the EEG and handwriting modality using convolutional neural network (CNN) and optimized crow search algorithm (OCSA), respectively. Also, an accuracy of 95% is achieved in PsD progression detection using Bagged Tree, artificial neural network (ANN), and SVM. The maximum accuracy of 99% is attained using K-nearest Neighbors (KNN) and Naïve Bayes classifiers on EEG signals using EI. The most widely used dataset is identified as the Parkinson's Progression Markers Initiative (PPMI) database.
{"title":"Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges","authors":"S. Zadoo, Yashwant Singh, Pradeep Kumar Singh","doi":"10.2478/ijssis-2024-0008","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0008","url":null,"abstract":"\u0000 Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It is acquired by the progressive demise of the dopaminergic neurons existing in the substantia nigra pars compacta region of the human brain. In the absence of a single accurate test, and due to the dependency on the doctors, intensive research is being carried out to automate the early disease detection and predict disease severity also. In this study, a detailed review of various artificial intelligence (AI) models applied to different datasets across different modalities has been presented. The emotional intelligence (EI) modality, which can be used for the early detection and can help in maintaining a comfortable lifestyle, has been identified. EI is a predominant, emerging technology that can be used to detect PsD at the initial stages and to enhance the socialization of the PsD patients and their attendants. Challenges and possibilities that can assist in bridging the differences between the fast-growing technologies meant to detect PsD and the actual implementation of the automated PsD detection model are presented in this research. This review highlights the prominence of using the support vector machine (SVM) classifier in achieving an accuracy of about 99% in many modalities such as magnetic resonance imaging (MRI), speech, and electroencephalogram (EEG). A 100% accuracy is achieved in the EEG and handwriting modality using convolutional neural network (CNN) and optimized crow search algorithm (OCSA), respectively. Also, an accuracy of 95% is achieved in PsD progression detection using Bagged Tree, artificial neural network (ANN), and SVM. The maximum accuracy of 99% is attained using K-nearest Neighbors (KNN) and Naïve Bayes classifiers on EEG signals using EI. The most widely used dataset is identified as the Parkinson's Progression Markers Initiative (PPMI) database.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140524027","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 : 2024-01-01DOI: 10.2478/ijssis-2024-0001
R. K. Singh, Khairunnisa Amreen, S. Dubey, S. Goel
Herein, a novel miniaturized sensor for sulfide detection is presented. The sensor was fabricated over a flexible polyimide substrate via CO2 laser ablation followed by surface modification with methylene blue acting as a redox mediator. The sensor showed an acceptable linear detection range (0.5 μM–1 mM), and excellent limit of detection (0.435 μM) and limit of quantification (2.45 μM). Further, remarkable sensitivity of 0.295 μA/(μM mm2) for 0.5–50 μM and 0.0047 μA/(μM mm2) for 100–1 mM was obtained. The signal-to-noise ratio was found to be 2.76 and the performance was validated by real lake water samples.
{"title":"A Highly Selective Real-Time Electroanalytical Detection of Sulfide Via Laser-Induced Graphene Sensor","authors":"R. K. Singh, Khairunnisa Amreen, S. Dubey, S. Goel","doi":"10.2478/ijssis-2024-0001","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0001","url":null,"abstract":"\u0000 Herein, a novel miniaturized sensor for sulfide detection is presented. The sensor was fabricated over a flexible polyimide substrate via CO2 laser ablation followed by surface modification with methylene blue acting as a redox mediator. The sensor showed an acceptable linear detection range (0.5 μM–1 mM), and excellent limit of detection (0.435 μM) and limit of quantification (2.45 μM). Further, remarkable sensitivity of 0.295 μA/(μM mm2) for 0.5–50 μM and 0.0047 μA/(μM mm2) for 100–1 mM was obtained. The signal-to-noise ratio was found to be 2.76 and the performance was validated by real lake water samples.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516899","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 : 2024-01-01DOI: 10.2478/ijssis-2024-0005
Garima Garg, Raman Kumar
The rationale behind this research stems from practical implementations in real-world scenarios, recognizing the critical importance of efficient image compression in fields such as medical imaging, remote sensing, and multimedia communication. This study introduces a hybrid image compression technique that employs adaptive block size selection and a synergistic combination of the discrete cosine transform (DCT) and singular value decomposition (SVD) to enhance compression efficiency while maintaining picture quality. Motivated by the potential to achieve significant compression ratios imperceptible to human observers, the hybrid approach addresses the escalating need for real-time image processing. The study pushes the boundaries of image compression by developing an algorithm that effectively combines conventional approaches with the intricacies of modern images, aiming for high compression ratios, adaptive picture content, and real-time efficiency. This article presents a novel hybrid algorithm that dynamically combines the DCT, SVD, and adaptive block size selection to enhance compression performance while keeping image quality constant. The proposed technique exhibits noteworthy accomplishments, achieving compression ratios of up to 60% and a peak signal-to-noise ratio (PSNR) exceeding 35 dB. Comparative evaluations demonstrate the algorithm’s superiority over existing approaches in terms of compression efficiency and quality measures. The adaptability of this hybrid approach makes significant contributions across various disciplines. In multimedia, it enhances data utilization while preserving image integrity; in medical imaging, it guarantees accurate diagnosis with compression-induced distortion (CID) below 1%; and in remote sensing, it efficiently manages large datasets, reducing expenses. The flexibility of this algorithm positions it as a valuable tool for future advancements in the rapidly evolving landscape of technology.
{"title":"Adaptive block size selection in a hybrid image compression algorithm employing the DCT and SVD","authors":"Garima Garg, Raman Kumar","doi":"10.2478/ijssis-2024-0005","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0005","url":null,"abstract":"\u0000 The rationale behind this research stems from practical implementations in real-world scenarios, recognizing the critical importance of efficient image compression in fields such as medical imaging, remote sensing, and multimedia communication. This study introduces a hybrid image compression technique that employs adaptive block size selection and a synergistic combination of the discrete cosine transform (DCT) and singular value decomposition (SVD) to enhance compression efficiency while maintaining picture quality. Motivated by the potential to achieve significant compression ratios imperceptible to human observers, the hybrid approach addresses the escalating need for real-time image processing. The study pushes the boundaries of image compression by developing an algorithm that effectively combines conventional approaches with the intricacies of modern images, aiming for high compression ratios, adaptive picture content, and real-time efficiency. This article presents a novel hybrid algorithm that dynamically combines the DCT, SVD, and adaptive block size selection to enhance compression performance while keeping image quality constant. The proposed technique exhibits noteworthy accomplishments, achieving compression ratios of up to 60% and a peak signal-to-noise ratio (PSNR) exceeding 35 dB. Comparative evaluations demonstrate the algorithm’s superiority over existing approaches in terms of compression efficiency and quality measures. The adaptability of this hybrid approach makes significant contributions across various disciplines. In multimedia, it enhances data utilization while preserving image integrity; in medical imaging, it guarantees accurate diagnosis with compression-induced distortion (CID) below 1%; and in remote sensing, it efficiently manages large datasets, reducing expenses. The flexibility of this algorithm positions it as a valuable tool for future advancements in the rapidly evolving landscape of technology.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140517413","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}
Temperature measurement and transmission of a signal safely to the control room for further processing is important for the process industry. In this paper, a modified head-mounted temperature measurement system using a thermocouple with opto-isolation has been developed. Here, the thermocouple is connected to the terminals, mounted on the ceramic base in the head of the thermo-well. It consists of two signal conditioners for thermocouple and AD590, both signal conditioning outputs applied to a summer circuit. The output of the summer circuit which is in the range of 1.73–3.43V, adjusted by a signal conditioning circuit, is applied to the middle electrode of Mach-Zehnder interferometer (MZI). MZI produces normalized optical signals according to the variations in temperature. These optical signals are then transmitted to the control room safely in the inflammable process industry. The transmitted signals are demodulated in the control room and then sent to the PC through an Opto-isolator circuit and DAS card. The necessary theory as well as mathematical equation has been derived. The experimental and simulation results are reported here.
温度测量和将信号安全地传输到控制室进行进一步处理对加工工业非常重要。本文开发了一种改进型头戴式温度测量系统，使用带有光电隔离功能的热电偶。在这里，热电偶被连接到安装在恒温槽头部陶瓷基座上的端子上。它由两个用于热电偶和 AD590 的信号调节器组成，两个信号调节器的输出均应用于夏季电路。经信号调节电路调节后，1.73-3.43V 范围内的夏季电路输出被应用于马赫-泽恩德干涉仪（MZI）的中间电极。MZI 根据温度变化产生归一化的光信号。然后，这些光信号被安全地传输到易燃加工行业的控制室。传输的信号在控制室经过解调后，通过光隔离器电路和 DAS 卡发送到个人电脑。必要的理论和数学公式已经得出。在此报告实验和模拟结果。
{"title":"A temperature measurement technique using optical channel as a signal transmitting media","authors":"Anindya Ghosh, Brajesh Kumar, Sushila Sharma, Vinay Kumar Chaudhary, R. Sarkar","doi":"10.2478/ijssis-2024-0009","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0009","url":null,"abstract":"\u0000 Temperature measurement and transmission of a signal safely to the control room for further processing is important for the process industry. In this paper, a modified head-mounted temperature measurement system using a thermocouple with opto-isolation has been developed. Here, the thermocouple is connected to the terminals, mounted on the ceramic base in the head of the thermo-well. It consists of two signal conditioners for thermocouple and AD590, both signal conditioning outputs applied to a summer circuit. The output of the summer circuit which is in the range of 1.73–3.43V, adjusted by a signal conditioning circuit, is applied to the middle electrode of Mach-Zehnder interferometer (MZI). MZI produces normalized optical signals according to the variations in temperature. These optical signals are then transmitted to the control room safely in the inflammable process industry. The transmitted signals are demodulated in the control room and then sent to the PC through an Opto-isolator circuit and DAS card. The necessary theory as well as mathematical equation has been derived. The experimental and simulation results are reported here.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516830","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 : 2024-01-01DOI: 10.2478/ijssis-2024-0007
Shaheen Parveen, Obaidur Rahman, M. Ajmal Khan, Javid Ali, Shabana Mahfuz, Tarikul Islam, S. A. Khan
Condition monitoring of oil-immersed in-service transformers to facilitate preventive maintenance is still a challenge. Monitoring of 2-Furfuryldehyde (2-FAL), released in the transformer oil as a result of paper insulation degradation, and moisture ingress can provide insight into the health of the insulation of transformers. Since 2-FAL and moisture are high dielectric constant contamination, capacitive sensor-based detection is a potential solution. A novel Inter digital Capacitive (IDC) sensor is reported in this paper to measure the concentration of 2-FAL and moisture uses Zinc Oxide (ZnO) as a sensing film. The sensor shows good sensitivity, approximately linear characteristics, and low characteristic drift.
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Pub Date : 2024-01-01DOI: 10.2478/ijssis-2024-0010
Mario Ligwa, V. Balyan
The non-orthogonal multiple access (NOMA) multiple access technique, due to its non-orthogonality and providing access to users together, which have the same frequency and time resource, made it a front runner to meet the need of high traffic requirements networks. In this paper, a downlink, NOMA, and cooperative NOMA (CNOMA) are compared with varying different parameters: source transmit power, user transmit power, and power allocation for achievable sum rates. Simulation results show that the CNOMA achieves a higher sum rate as compared to NOMA for all the parameters.
{"title":"Cooperative Power Domain Noma Transmission Using Relays","authors":"Mario Ligwa, V. Balyan","doi":"10.2478/ijssis-2024-0010","DOIUrl":"https://doi.org/10.2478/ijssis-2024-0010","url":null,"abstract":"\u0000 The non-orthogonal multiple access (NOMA) multiple access technique, due to its non-orthogonality and providing access to users together, which have the same frequency and time resource, made it a front runner to meet the need of high traffic requirements networks. In this paper, a downlink, NOMA, and cooperative NOMA (CNOMA) are compared with varying different parameters: source transmit power, user transmit power, and power allocation for achievable sum rates. Simulation results show that the CNOMA achieves a higher sum rate as compared to NOMA for all the parameters.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522885","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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