Volume-1, Issue-1, Jan-Jun-2024
Article-01
Design of Biogas Digester for Small Community of Salalah Countryside, Oman to Support the Lighting and Cooking Electrical Load
Author: Omar Al Gharibi1, Dharmasa Pawar
Pages: 1-8
DOI: https://doi.org/10.55306/CJDTES.2024.1101
Abstract
Bio-gas is a renewable energy source derived from the anaerobic digestion of organic materials such as agricultural, manure, and food waste. It emits methane and carbon dioxide, which reduce greenhouse gas emissions and promote circular economies by converting organic matter into valuable energy and nutrients. In this study, the biogas-based power generation possibilities are studied and reported based on the availability of biomass from animals like camels, sheep, goats, and cows. The data collection and analysis are carried out under different temperature conditions. The energy required is planned for eight people in each family. In the proposal work to design a digester, the location was chosen at the south-eastern part of the Arabian Peninsula (south of the Sultanate of Oman) for the place Madinat al Haqq. Afterwards, calculations are reported to estimate the size of the biogas digester and the output power generated from the digester. Here, the digester output is used for cooking and lighting for all members of the family. Further, the power output is viable based on the availability of biomass per day. The energy availability investigation is performed using the standard calorific value of the biomass per cubic meter. The proposed concept will allow the remote community to use a portal-sized biogas digester for their necessary electrical home load. Furthermore, the selection of the burner is reported to utilize the biogas efficiently.
Key Words:Supply chain optimization, Demand forecasting, Food industry, Machine learning, Time series models, Regression-based approaches, Long Short-Term Memory (LSTM), Gradient Boosting Regressors, Performance metrics, Accuracy, Consumer behavior, Temporal dependencies, Nonlinear relationships, Contextual variables, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
Citation: Omar Al Gharibi, Dharmasa Pawar “Design of Biogas Digester for Small Community of Salalah Countryside, Oman to Support the Lighting and Cooking Electrical Load", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 1-8, 2024.
Author: Omar Al Gharibi1, Dharmasa Pawar
Pages: 1-8
DOI: https://doi.org/10.55306/CJDTES.2024.1101
Abstract
Bio-gas is a renewable energy source derived from the anaerobic digestion of organic materials such as agricultural, manure, and food waste. It emits methane and carbon dioxide, which reduce greenhouse gas emissions and promote circular economies by converting organic matter into valuable energy and nutrients. In this study, the biogas-based power generation possibilities are studied and reported based on the availability of biomass from animals like camels, sheep, goats, and cows. The data collection and analysis are carried out under different temperature conditions. The energy required is planned for eight people in each family. In the proposal work to design a digester, the location was chosen at the south-eastern part of the Arabian Peninsula (south of the Sultanate of Oman) for the place Madinat al Haqq. Afterwards, calculations are reported to estimate the size of the biogas digester and the output power generated from the digester. Here, the digester output is used for cooking and lighting for all members of the family. Further, the power output is viable based on the availability of biomass per day. The energy availability investigation is performed using the standard calorific value of the biomass per cubic meter. The proposed concept will allow the remote community to use a portal-sized biogas digester for their necessary electrical home load. Furthermore, the selection of the burner is reported to utilize the biogas efficiently.
Key Words:Supply chain optimization, Demand forecasting, Food industry, Machine learning, Time series models, Regression-based approaches, Long Short-Term Memory (LSTM), Gradient Boosting Regressors, Performance metrics, Accuracy, Consumer behavior, Temporal dependencies, Nonlinear relationships, Contextual variables, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
Citation: Omar Al Gharibi, Dharmasa Pawar “Design of Biogas Digester for Small Community of Salalah Countryside, Oman to Support the Lighting and Cooking Electrical Load", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 1-8, 2024.
Article-02
Enhancing the Performance of Optical Hardware through Software Applications: A Study
Author: Rajani. D, Mahesh. B
Pages: 9-26
DOI: https://doi.org/10.55306/CJDTES.2024.1102
Abstract
The process of image demosaicing, essential for reconstructing full-colour images from incomplete colour samples obtained from a colour-filter array (CFA), has garnered renewed interest, particularly with the prevalent use of the Bayer pattern. This resurgence can be attributed to the growing availability of source codes and executables, facilitating reproducible research in the field. In this article, a thorough survey encompassing over fifty published works on demosaicing since 1999. The objective is to complement prior reviews and provide insights into the evolution and current landscape of demosaicing techniques. By addressing key issues and delineating fundamental differences among various approaches, a thorough overview of the field is made. A notable finding from the survey is the popularity of spatial demosaicing methods, where the initial step involves interpolating the luminance channel, followed by reconstructing the chrominance channels based on the recovered luminance information. The article highlights three crucial areas requiring additional research. Firstly, addressing the difficulty of demosaicking images with low spectral correlation is paramount, along with enhancing our comprehension of the trade-off between spatial correlation and spectral correlation. It is also found that there is a necessity for more comprehensive investigation into evaluating the performance of demosaicking algorithms, particularly concerning the utilization of reference images, and understanding supplementary components within the imaging pipeline.
Key Words:Demosaicking, Bayer, Colour Filter Array (CFA), spatial demosaicing methods, luminance channel, chrominance channel, spatial correlation, spectral correlation.
Citation: Rajani. D, Mahesh. B. “Enhancing the Performance of Optical Hardware through Software Applications: A Study ", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 9-26, 2024.
Author: Rajani. D, Mahesh. B
Pages: 9-26
DOI: https://doi.org/10.55306/CJDTES.2024.1102
Abstract
The process of image demosaicing, essential for reconstructing full-colour images from incomplete colour samples obtained from a colour-filter array (CFA), has garnered renewed interest, particularly with the prevalent use of the Bayer pattern. This resurgence can be attributed to the growing availability of source codes and executables, facilitating reproducible research in the field. In this article, a thorough survey encompassing over fifty published works on demosaicing since 1999. The objective is to complement prior reviews and provide insights into the evolution and current landscape of demosaicing techniques. By addressing key issues and delineating fundamental differences among various approaches, a thorough overview of the field is made. A notable finding from the survey is the popularity of spatial demosaicing methods, where the initial step involves interpolating the luminance channel, followed by reconstructing the chrominance channels based on the recovered luminance information. The article highlights three crucial areas requiring additional research. Firstly, addressing the difficulty of demosaicking images with low spectral correlation is paramount, along with enhancing our comprehension of the trade-off between spatial correlation and spectral correlation. It is also found that there is a necessity for more comprehensive investigation into evaluating the performance of demosaicking algorithms, particularly concerning the utilization of reference images, and understanding supplementary components within the imaging pipeline.
Key Words:Demosaicking, Bayer, Colour Filter Array (CFA), spatial demosaicing methods, luminance channel, chrominance channel, spatial correlation, spectral correlation.
Citation: Rajani. D, Mahesh. B. “Enhancing the Performance of Optical Hardware through Software Applications: A Study ", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 9-26, 2024.
Article-03
Shaping Radiation with Line Source Antenna for Ramp Pattern Generation
Author: Ravindranath. J
Pages: 27-33
DOI: https://doi.org/10.55306/CJDTES.2024.1103
Abstract
Array antennas are essential in communication technology because they can produce focused beams of electromagnetic radiation that can be precisely shaped and directed for various applications. These antennas typically generate a radiation pattern with a primary lobe, the main beam, accompanied by smaller minor lobes. The size and distribution of these lobes depend on the array's design and configuration. For large array antennas, the main beam becomes extremely narrow, enabling precise point-to-point communication over long distances. This precision is especially beneficial for applications requiring accurate targeting or transmission to specific locations. However, achieving such optimized performance requires careful design and engineering. This study, builds on Woodward's amplitude control method, initially developed for generating sector beams, to create ramp patterns with unique applications in antenna engineering. By adjusting the amplitudes of the signals fed into each element of the array, the researchers can shape the resulting radiation pattern to meet specific requirements. The paper provides detailed analysis and computational data demonstrating the effectiveness of this approach in achieving the desired beam patterns. These results offer valuable insights into antenna design and optimization, potentially advancements in wireless communication, radar systems, and satellite communication.
Key Words:Array Antennas, Communication, radiation pattern, lobes, Beam, performance, Ramp patterns, computational data, Wireless Communication.
Citation: Ravindranath. J. “Shaping Radiation with Line Source Antenna for Ramp Pattern Generation", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 27-33, 2024.
Author: Ravindranath. J
Pages: 27-33
DOI: https://doi.org/10.55306/CJDTES.2024.1103
Abstract
Array antennas are essential in communication technology because they can produce focused beams of electromagnetic radiation that can be precisely shaped and directed for various applications. These antennas typically generate a radiation pattern with a primary lobe, the main beam, accompanied by smaller minor lobes. The size and distribution of these lobes depend on the array's design and configuration. For large array antennas, the main beam becomes extremely narrow, enabling precise point-to-point communication over long distances. This precision is especially beneficial for applications requiring accurate targeting or transmission to specific locations. However, achieving such optimized performance requires careful design and engineering. This study, builds on Woodward's amplitude control method, initially developed for generating sector beams, to create ramp patterns with unique applications in antenna engineering. By adjusting the amplitudes of the signals fed into each element of the array, the researchers can shape the resulting radiation pattern to meet specific requirements. The paper provides detailed analysis and computational data demonstrating the effectiveness of this approach in achieving the desired beam patterns. These results offer valuable insights into antenna design and optimization, potentially advancements in wireless communication, radar systems, and satellite communication.
Key Words:Array Antennas, Communication, radiation pattern, lobes, Beam, performance, Ramp patterns, computational data, Wireless Communication.
Citation: Ravindranath. J. “Shaping Radiation with Line Source Antenna for Ramp Pattern Generation", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 27-33, 2024.
Article-04
Redefining Cataract Diagnosis: Leveraging MobileNetV2-Based CNN for Automated Detection from Fundus Images
Author: A. Sri Nagesh, Popuri Keerthika, Raguthu Dheeraj, Vetcha Sai Manikanta Saketh
Pages: 34-45
DOI: https://doi.org/10.55306/CJDTES.2024.1104
Abstract
Cataracts remain a prevalent cause of global blindness, emphasizing the urgent need for timely and accurate diagnostic solutions. This study introduces an innovative automated cataract detection method using fundus images, employing three distinct convolutional neural network architectures: MobileNetV2, EfficientNetB0, and ResNet50. Through transfer learning, these types of models were developed using a dataset containing both cataract and non-cataract fundus images. The outcomes show how successful the suggested models were in precisely classifying fundus images, with notable performance indicators including F1-score, recall, accuracy, and precision. The MobileNetV2-based model achieved 98% accuracy, EfficientNetB0 attained 68% accuracy, while the ResNet50 model, enhanced with additional dense layers, attained 69% accuracy. Furthermore, comprehensive evaluation including classification reports and confusion matrices validates the robustness and generalization capabilities of the models, confirmed through cross-validation on independent test sets. This convolutional neural network-based approach holds promise for scalable, cost-effective automated cataract detection in clinical settings, with the potential for further advancements in model interpretability and dataset diversification to enhance its applicability in diverse populations.
Key Words:Cataracts, blindness, diagnostic solutions, fundus images, convolutional neural network, confusion matrices, detection.
Citation: A. Sri Nagesh, Popuri Keerthika, Raguthu Dheeraj, Vetcha Sai Manikanta Saketh, “Redefining Cataract Diagnosis: Leveraging MobileNetV2-Based CNN for Automated Detection from Fundus Images", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 34-45, 2024.
Author: A. Sri Nagesh, Popuri Keerthika, Raguthu Dheeraj, Vetcha Sai Manikanta Saketh
Pages: 34-45
DOI: https://doi.org/10.55306/CJDTES.2024.1104
Abstract
Cataracts remain a prevalent cause of global blindness, emphasizing the urgent need for timely and accurate diagnostic solutions. This study introduces an innovative automated cataract detection method using fundus images, employing three distinct convolutional neural network architectures: MobileNetV2, EfficientNetB0, and ResNet50. Through transfer learning, these types of models were developed using a dataset containing both cataract and non-cataract fundus images. The outcomes show how successful the suggested models were in precisely classifying fundus images, with notable performance indicators including F1-score, recall, accuracy, and precision. The MobileNetV2-based model achieved 98% accuracy, EfficientNetB0 attained 68% accuracy, while the ResNet50 model, enhanced with additional dense layers, attained 69% accuracy. Furthermore, comprehensive evaluation including classification reports and confusion matrices validates the robustness and generalization capabilities of the models, confirmed through cross-validation on independent test sets. This convolutional neural network-based approach holds promise for scalable, cost-effective automated cataract detection in clinical settings, with the potential for further advancements in model interpretability and dataset diversification to enhance its applicability in diverse populations.
Key Words:Cataracts, blindness, diagnostic solutions, fundus images, convolutional neural network, confusion matrices, detection.
Citation: A. Sri Nagesh, Popuri Keerthika, Raguthu Dheeraj, Vetcha Sai Manikanta Saketh, “Redefining Cataract Diagnosis: Leveraging MobileNetV2-Based CNN for Automated Detection from Fundus Images", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 34-45, 2024.
Article-05
An Efficient VLSI Architecture for Bi-Cubic Interpolation using Carry Skip Adder
Author: Rao. C. R, Mandal S. K
Pages: 46-57
DOI: https://doi.org/10.55306/CJDTES.2024.1105
Abstract
Cataracts remain a prevalent cause of global blindness, emphasizing the urgent need for timely and accurate diagnostic solutions. This study introduces an innovative automated cataract detection method using fundus images, employing three distinct convolutional neural network architectures: MobileNetV2, EfficientNetB0, and ResNet50. Through transfer learning, these types of models were developed using a dataset containing both cataract and non-cataract fundus images. The outcomes show how successful the suggested models were in precisely classifying fundus images, with notable performance indicators including F1-score, recall, accuracy, and precision. The MobileNetV2-based model achieved 98% accuracy, EfficientNetB0 attained 68% accuracy, while the ResNet50 model, enhanced with additional dense layers, attained 69% accuracy. Furthermore, comprehensive evaluation including classification reports and confusion matrices validates the robustness and generalization capabilities of the models, confirmed through cross-validation on independent test sets. This convolutional neural network-based approach holds promise for scalable, cost-effective automated cataract detection in clinical settings, with the potential for further advancements in model interpretability and dataset diversification to enhance its applicability in diverse populations.
Key Words:Cataracts, blindness, diagnostic solutions, fundus images, convolutional neural network, confusion matrices, detection.
Citation: Rao. C. R, Mandal. S.K. “An Efficient VLSI Architecture for Bi-Cubic Interpolation using Carry Skip Adder", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 46-57, 2024.
Author: Rao. C. R, Mandal S. K
Pages: 46-57
DOI: https://doi.org/10.55306/CJDTES.2024.1105
Abstract
Cataracts remain a prevalent cause of global blindness, emphasizing the urgent need for timely and accurate diagnostic solutions. This study introduces an innovative automated cataract detection method using fundus images, employing three distinct convolutional neural network architectures: MobileNetV2, EfficientNetB0, and ResNet50. Through transfer learning, these types of models were developed using a dataset containing both cataract and non-cataract fundus images. The outcomes show how successful the suggested models were in precisely classifying fundus images, with notable performance indicators including F1-score, recall, accuracy, and precision. The MobileNetV2-based model achieved 98% accuracy, EfficientNetB0 attained 68% accuracy, while the ResNet50 model, enhanced with additional dense layers, attained 69% accuracy. Furthermore, comprehensive evaluation including classification reports and confusion matrices validates the robustness and generalization capabilities of the models, confirmed through cross-validation on independent test sets. This convolutional neural network-based approach holds promise for scalable, cost-effective automated cataract detection in clinical settings, with the potential for further advancements in model interpretability and dataset diversification to enhance its applicability in diverse populations.
Key Words:Cataracts, blindness, diagnostic solutions, fundus images, convolutional neural network, confusion matrices, detection.
Citation: Rao. C. R, Mandal. S.K. “An Efficient VLSI Architecture for Bi-Cubic Interpolation using Carry Skip Adder", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(1), pp. 46-57, 2024.