Pub Date : 2023-08-08DOI: 10.59544/tasa9927/ngcesi23p142
Sandhiyogha Lakshmi V, Nisha Evangelin L
The recent advancements in Internet of Things (IoT), cloud computing and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. The presented model encompasses different stages namely, data acquisition, pre-processing, classification, and parameter tuning. Heart disease is a major cause of morbidity and mortality globally and early detection is crucial for effective management. Machine learning models have been developed to aid in the prediction of heart disease with LightGBM being one such model. This study aims to analyse the performance of LightGBM in predicting heart disease. LightGBM was implemented using Python, and the model was trained using the training set. The performance of the model was evaluated using several metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. Further studies could be conducted to evaluate the model’s performance on larger datasets and to compare its performance with other machine learning mode. Diseases may have an impact on people both physically and emotionally, since getting and living with an illness can change a person’s outlook on life. An illness that affects several areas of an organism yet is not caused by an instant exterior damage. Diseases are frequently defined as medical disorders characterised by distinct symptoms and indicators. The most lethal illnesses in humans are arteria coronary disease, cerebrovascular disease and lower respiratory infections. Heart disease is the most unexpected and unpredictability. With machine learning, we can anticipate cardiac disease. To get high efficiency output, we employ CNN approaches.
{"title":"Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems","authors":"Sandhiyogha Lakshmi V, Nisha Evangelin L","doi":"10.59544/tasa9927/ngcesi23p142","DOIUrl":"https://doi.org/10.59544/tasa9927/ngcesi23p142","url":null,"abstract":"The recent advancements in Internet of Things (IoT), cloud computing and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. The presented model encompasses different stages namely, data acquisition, pre-processing, classification, and parameter tuning. Heart disease is a major cause of morbidity and mortality globally and early detection is crucial for effective management. Machine learning models have been developed to aid in the prediction of heart disease with LightGBM being one such model. This study aims to analyse the performance of LightGBM in predicting heart disease. LightGBM was implemented using Python, and the model was trained using the training set. The performance of the model was evaluated using several metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. Further studies could be conducted to evaluate the model’s performance on larger datasets and to compare its performance with other machine learning mode. Diseases may have an impact on people both physically and emotionally, since getting and living with an illness can change a person’s outlook on life. An illness that affects several areas of an organism yet is not caused by an instant exterior damage. Diseases are frequently defined as medical disorders characterised by distinct symptoms and indicators. The most lethal illnesses in humans are arteria coronary disease, cerebrovascular disease and lower respiratory infections. Heart disease is the most unexpected and unpredictability. With machine learning, we can anticipate cardiac disease. To get high efficiency output, we employ CNN approaches.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121276380","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 : 2023-08-08DOI: 10.59544/sqzt6762/ngcesi23p134
Athira C. A, Lekshmi Devi C. A
The coastal zone has become one of the most important activity zones in the world as it is thickly populated and has several industries and other establishments. Development activities in the coastal zone must be systematic within the framework of well-defined Coastal Zone Management Plans. Thorough understanding of coastal processes which are controlled by coastal hydrodynamics and the resulting sediment transport is essential for development of coastal zone management plans. The study of these processes can help in developing appropriate strategies for coastline management and conservation. This study has been conducted at Thiruvananthapuram coastal stretch (78km) for wave analysis and estimation of longshore sediment transport. Dedicated wave model has been developed using MIKE 21 SW model and the simulated wave climate has been used in LITPACK model for estimation of sediment transport which is calibrated and validated with the real data. Study has conducted from 2013 to 2022 during south-west monsoon season. Study shows that the Thiruvananthapuram district seashore experiences significant longshore sediment transport, with sediment moving predominantly towards the south during monsoon season with gross sediment transportation of 1.34×106m3
{"title":"Assessment of Longshore Sediment Transport Using LITPACK","authors":"Athira C. A, Lekshmi Devi C. A","doi":"10.59544/sqzt6762/ngcesi23p134","DOIUrl":"https://doi.org/10.59544/sqzt6762/ngcesi23p134","url":null,"abstract":"The coastal zone has become one of the most important activity zones in the world as it is thickly populated and has several industries and other establishments. Development activities in the coastal zone must be systematic within the framework of well-defined Coastal Zone Management Plans. Thorough understanding of coastal processes which are controlled by coastal hydrodynamics and the resulting sediment transport is essential for development of coastal zone management plans. The study of these processes can help in developing appropriate strategies for coastline management and conservation. This study has been conducted at Thiruvananthapuram coastal stretch (78km) for wave analysis and estimation of longshore sediment transport. Dedicated wave model has been developed using MIKE 21 SW model and the simulated wave climate has been used in LITPACK model for estimation of sediment transport which is calibrated and validated with the real data. Study has conducted from 2013 to 2022 during south-west monsoon season. Study shows that the Thiruvananthapuram district seashore experiences significant longshore sediment transport, with sediment moving predominantly towards the south during monsoon season with gross sediment transportation of 1.34×106m3","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115472509","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 : 2023-08-08DOI: 10.59544/edue7227/ngcesi23p124
Siva Ganaga Selvi G, Vino Rooban Singh M. E
This project proposes an automatic detection of breast cancer diagnosis and prognosis based on deep learning using Long Short-term Memory classifier. To reduce the noises in the image, the Adaptive filter is employed at the pre-processing stage. The pre-processed image is segmented by Fuzzy C-means (FCM) segmentation algorithm for active partition of image. The segmented features are extracted by Gray Level Co-occurrence Matrix Method, in which all the essential features are extracted for enhanced classification. An effective classifier, LSTM Classifier is used and final results are predicted. By using LSTM Classifier, the obtained results were accurate. This project is implemented with MATLAB simulation software and the output reveals the classification accuracy.
{"title":"An Automatic Detection of Breast Cancer Based On Deep Learning Using Long Short-Term Memory Classifier","authors":"Siva Ganaga Selvi G, Vino Rooban Singh M. E","doi":"10.59544/edue7227/ngcesi23p124","DOIUrl":"https://doi.org/10.59544/edue7227/ngcesi23p124","url":null,"abstract":"This project proposes an automatic detection of breast cancer diagnosis and prognosis based on deep learning using Long Short-term Memory classifier. To reduce the noises in the image, the Adaptive filter is employed at the pre-processing stage. The pre-processed image is segmented by Fuzzy C-means (FCM) segmentation algorithm for active partition of image. The segmented features are extracted by Gray Level Co-occurrence Matrix Method, in which all the essential features are extracted for enhanced classification. An effective classifier, LSTM Classifier is used and final results are predicted. By using LSTM Classifier, the obtained results were accurate. This project is implemented with MATLAB simulation software and the output reveals the classification accuracy.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360730","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 : 2023-08-08DOI: 10.59544/lgqz5151/ngcesi23p122
Riju S, Soni Meera G. V
This paper presents the built-in self-test (BIST) design of a C-testable high-speed carry-free divider which can be fully tested by 72 test patterns irrespective of the divider size. Using a graph labelling scheme, the test patterns, expected outputs, and control signals can be represented by sets of labels and generated by a simple circuitry. As a result, test patterns can be easily generated inside chips, responses to test patterns need not to be stored, and use of expensive test equipment is not necessary. Results show that the hardware cost for generating such labels is virtually constant irrespective of the circuit size. Dividing Circuits, Integrated Circuit Testing, Integrated Circuit Design, Design For Testability, Digital Arithmetic, Built In Self-Test, Graph Theory, Built In Self-Test Design, High Speed Carry Free Dividers, C Testable Circuits, Graph Labelling, Test Patterns, Control Signals, 64 Bit, Built In Self-Test, Circuit Testing, Automatic Testing, Test Pattern Generators, Hardware, Signal Generators, Test Equipment, Costs, Controllability, Observability, In Spartan3E FPGA device family, computation of 8-bit circular convolution using Modified Karatsuba Algorithm.
{"title":"High Speed Built in Self-Test via Pattern Generation","authors":"Riju S, Soni Meera G. V","doi":"10.59544/lgqz5151/ngcesi23p122","DOIUrl":"https://doi.org/10.59544/lgqz5151/ngcesi23p122","url":null,"abstract":"This paper presents the built-in self-test (BIST) design of a C-testable high-speed carry-free divider which can be fully tested by 72 test patterns irrespective of the divider size. Using a graph labelling scheme, the test patterns, expected outputs, and control signals can be represented by sets of labels and generated by a simple circuitry. As a result, test patterns can be easily generated inside chips, responses to test patterns need not to be stored, and use of expensive test equipment is not necessary. Results show that the hardware cost for generating such labels is virtually constant irrespective of the circuit size. Dividing Circuits, Integrated Circuit Testing, Integrated Circuit Design, Design For Testability, Digital Arithmetic, Built In Self-Test, Graph Theory, Built In Self-Test Design, High Speed Carry Free Dividers, C Testable Circuits, Graph Labelling, Test Patterns, Control Signals, 64 Bit, Built In Self-Test, Circuit Testing, Automatic Testing, Test Pattern Generators, Hardware, Signal Generators, Test Equipment, Costs, Controllability, Observability, In Spartan3E FPGA device family, computation of 8-bit circular convolution using Modified Karatsuba Algorithm.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124366843","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 : 2023-08-08DOI: 10.59544/boae2576/ngcesi23p119
Abisha L, Sindhu K.
Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two Tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL).By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are foused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method out performs the existing tongue characterization methods. The process of tongue diagnosis by extracting meaningful features from tongue images and segmenting the relevant regions for analysis. The deep auto encoder neural network is employed to learn a compact representation of tongue images by encoding and decoding the input data.
{"title":"Automated Tongue Diagnosis: A Deep Autoencoder Neural Network and Clustering-Based Image Segmentation Approach","authors":"Abisha L, Sindhu K.","doi":"10.59544/boae2576/ngcesi23p119","DOIUrl":"https://doi.org/10.59544/boae2576/ngcesi23p119","url":null,"abstract":"Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two Tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL).By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are foused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method out performs the existing tongue characterization methods. The process of tongue diagnosis by extracting meaningful features from tongue images and segmenting the relevant regions for analysis. The deep auto encoder neural network is employed to learn a compact representation of tongue images by encoding and decoding the input data.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114269650","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 : 2023-08-08DOI: 10.59544/poda4062/ngcesi23p130
Brain tumor is the third-most common cause of cancer related deaths in the world. Fortunately, it can be detected using MRI. Computer-aided diagnosis (CADx) systems can help clinicians identify cancer from brain diseases more accurately. In this project, propose a CAD system that distinguishes and classifies brain tumor from pre-cancerous conditions. The system uses a deplearning model. Deep CNN which involves depth wise separable convolutions, to classify cancer and non-cancers. The proposed method consist of two steps: Google’s Auto Augment for augmentation and the CV2 based feature selection for image segmentation during pre- processing. These approaches produce a feasible methods of distinguishing and classifying cancers from other brain diseases. Our methods are fully automated without the manual specification of region-of-interests for the test and with a random selection of images for model training. This methodology may play a crucial role in selecting effective treatment options without the need for a surgical biopsy.
脑肿瘤是世界上第三大癌症相关死亡原因。幸运的是，它可以通过MRI检测到。计算机辅助诊断(CADx)系统可以帮助临床医生更准确地从脑部疾病中识别癌症。在这个项目中，提出一个CAD系统来区分和分类脑肿瘤和癌前病变。该系统采用耗尽模型。深度CNN涉及深度可分离卷积，用于分类癌症和非癌症。该方法包括两个步骤:用于增强的Google Auto Augment和用于预处理过程中基于CV2的特征选择的图像分割。这些方法产生了一种将癌症与其他脑部疾病区分和分类的可行方法。我们的方法是完全自动化的，无需手动指定测试的兴趣区域，并且随机选择图像进行模型训练。这种方法可能在选择有效的治疗方案中发挥关键作用，而不需要手术活检。
{"title":"Brain Tumor Detection Using Deep Convolutional Neural Network","authors":"","doi":"10.59544/poda4062/ngcesi23p130","DOIUrl":"https://doi.org/10.59544/poda4062/ngcesi23p130","url":null,"abstract":"Brain tumor is the third-most common cause of cancer related deaths in the world. Fortunately, it can be detected using MRI. Computer-aided diagnosis (CADx) systems can help clinicians identify cancer from brain diseases more accurately. In this project, propose a CAD system that distinguishes and classifies brain tumor from pre-cancerous conditions. The system uses a deplearning model. Deep CNN which involves depth wise separable convolutions, to classify cancer and non-cancers. The proposed method consist of two steps: Google’s Auto Augment for augmentation and the CV2 based feature selection for image segmentation during pre- processing. These approaches produce a feasible methods of distinguishing and classifying cancers from other brain diseases. Our methods are fully automated without the manual specification of region-of-interests for the test and with a random selection of images for model training. This methodology may play a crucial role in selecting effective treatment options without the need for a surgical biopsy.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906423","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 : 2023-08-08DOI: 10.59544/zbua6077/ngcesi23p138
Soumya T
Medical imaging is an essential data source that has been leveraged worldwide in health- care systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption
{"title":"Detection and Differentiation of blood cancer cells using Edge Detection method","authors":"Soumya T","doi":"10.59544/zbua6077/ngcesi23p138","DOIUrl":"https://doi.org/10.59544/zbua6077/ngcesi23p138","url":null,"abstract":"Medical imaging is an essential data source that has been leveraged worldwide in health- care systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121463382","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 : 2023-08-08DOI: 10.59544/rejc9137/ngcesi23p126
Abinaya K.
One or both of the lungs are affected by pneumonia, which is the enlargement of the lung tissue. Infection with organisms including bacteria, viruses, and fungi leads to its occurrence. Although its severity varies, its typical symptoms include coughing, breathing problems, fever, and chest pain. The respiratory illness COVID-19 is spreadable and is brought on by the SARS- CoV-2 virus. COVID-19 has similar symptoms to viral pneumonia and the patients of COVID-19 may also be subject to secondary bacterial infections. This study separates COVID-19 from other illnesses like mycoplasma, bacterial pneumonia, viral pneumonia, and other infections using a variety of deep learning techniques and computed tomography (CT) images. The results show that for all three cases, FCNN is one of the best performing architectures.
{"title":"A Supervised Framework for COVID-19 Classification Using FCNN","authors":"Abinaya K.","doi":"10.59544/rejc9137/ngcesi23p126","DOIUrl":"https://doi.org/10.59544/rejc9137/ngcesi23p126","url":null,"abstract":"One or both of the lungs are affected by pneumonia, which is the enlargement of the lung tissue. Infection with organisms including bacteria, viruses, and fungi leads to its occurrence. Although its severity varies, its typical symptoms include coughing, breathing problems, fever, and chest pain. The respiratory illness COVID-19 is spreadable and is brought on by the SARS- CoV-2 virus. COVID-19 has similar symptoms to viral pneumonia and the patients of COVID-19 may also be subject to secondary bacterial infections. This study separates COVID-19 from other illnesses like mycoplasma, bacterial pneumonia, viral pneumonia, and other infections using a variety of deep learning techniques and computed tomography (CT) images. The results show that for all three cases, FCNN is one of the best performing architectures.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116425589","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 : 2023-08-08DOI: 10.59544/mbbm1955/ngcesi23p125
Dr Naeema Nazar, Perumal Sankar
This research aims to enhance the accuracy of Direction of Arrival (DOA) estimation using hydrophone arrays through the application of beamforming methods, particularly focusing on the Delay and Sum Beamforming technique. The study follows a systematic approach, utilizing Matlab for signal generation, analysis, and testing, accompanied by power plots. It also includes a series of steps for the analysis and testing of Delay and Sum Beamforming. The investigation compares traditional DOA estimation methods with Delay and Sum Beamforming and proposes future enhancements, such as exploring alternative beamforming techniques and potential applications of advanced methods like Deep Neural Networks.
{"title":"Delay and Sum Beam forming Technique to Detect the Arrival Estimation of Sound Waves","authors":"Dr Naeema Nazar, Perumal Sankar","doi":"10.59544/mbbm1955/ngcesi23p125","DOIUrl":"https://doi.org/10.59544/mbbm1955/ngcesi23p125","url":null,"abstract":"This research aims to enhance the accuracy of Direction of Arrival (DOA) estimation using hydrophone arrays through the application of beamforming methods, particularly focusing on the Delay and Sum Beamforming technique. The study follows a systematic approach, utilizing Matlab for signal generation, analysis, and testing, accompanied by power plots. It also includes a series of steps for the analysis and testing of Delay and Sum Beamforming. The investigation compares traditional DOA estimation methods with Delay and Sum Beamforming and proposes future enhancements, such as exploring alternative beamforming techniques and potential applications of advanced methods like Deep Neural Networks.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129297994","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 : 2023-08-07DOI: 10.59544/qvzv4524/ngcesi23p75
The construction industry of the world is rapidly developing with the abrupt increase of the urban population. To meet the needs of the evolving industry and the surging population, the need of raw materials for the construction industry is rising day by day. Energy consumption in the building sector is very high. Carbondioxide emission are connected with offsite manufacturing of building materials and components ( cradle to site) .the materials such as cement ,hollow concrete block, bricks, reinforcement bars etc. emit un considerable amount of carbondioxide during the manufacturing process. Carbondioxide emission from 1 meter cube of coarse aggregate, fine aggregate and cement are 25.47 kg, 63 kg and 417.6kg respectively. Embodied energy can be consumed directly in construction of building and other relative processes or indirectly for extracting raw materials manufacturing the building materials and relative products and transporting. In the present study we are entirely replacing the traditional material with sustainable material. Construction industry consumes more than 50 percentage of the raw materials obtained from the earth’s crust. In the nearby future these resources will get emptied. So it’s time to find the suitable sustainable alternative for the building components. Geopolymer concrete is the new development in the field of building construction in which cement is totally replaced by pozzolanic material like fly ash and activated by alkaline solution. This gives the effect of concentration of sodium hydroxide, temperature and duration of overheating on compressive strength of fly ash based geopolymer concrete. The Wool Glass Shell Brick (WGSB) is filled with waste materials from plants and other industries. Bamboo reinforced concrete construction follows the same design, mix proposition and construction techniques as used for steel reinforced. Steel reinforcement is replaced with bamboo reinforcement. Natural materials, bamboo has been widely used for many purposes. Mainly as a strength bearing material. Then wool glass shell brick, geopolymer concrete slab reinforced with bamboo, and geopolymer concrete block are manufactured. The manufactured materials are subjected to their respective tests and prototype is also constructed. From the study of materials, it is observed that percentage economy can be achieved using this sustainable material .The test results showed that the compressive strength, tensile strength and of the manufactured materials are much better than the conventional construction materials.
{"title":"Sustainable Building Replacing Normal Construction Materials with Sustainable Materials","authors":"","doi":"10.59544/qvzv4524/ngcesi23p75","DOIUrl":"https://doi.org/10.59544/qvzv4524/ngcesi23p75","url":null,"abstract":"The construction industry of the world is rapidly developing with the abrupt increase of the urban population. To meet the needs of the evolving industry and the surging population, the need of raw materials for the construction industry is rising day by day. Energy consumption in the building sector is very high. Carbondioxide emission are connected with offsite manufacturing of building materials and components ( cradle to site) .the materials such as cement ,hollow concrete block, bricks, reinforcement bars etc. emit un considerable amount of carbondioxide during the manufacturing process. Carbondioxide emission from 1 meter cube of coarse aggregate, fine aggregate and cement are 25.47 kg, 63 kg and 417.6kg respectively. Embodied energy can be consumed directly in construction of building and other relative processes or indirectly for extracting raw materials manufacturing the building materials and relative products and transporting. In the present study we are entirely replacing the traditional material with sustainable material. Construction industry consumes more than 50 percentage of the raw materials obtained from the earth’s crust. In the nearby future these resources will get emptied. So it’s time to find the suitable sustainable alternative for the building components. Geopolymer concrete is the new development in the field of building construction in which cement is totally replaced by pozzolanic material like fly ash and activated by alkaline solution. This gives the effect of concentration of sodium hydroxide, temperature and duration of overheating on compressive strength of fly ash based geopolymer concrete. The Wool Glass Shell Brick (WGSB) is filled with waste materials from plants and other industries. Bamboo reinforced concrete construction follows the same design, mix proposition and construction techniques as used for steel reinforced. Steel reinforcement is replaced with bamboo reinforcement. Natural materials, bamboo has been widely used for many purposes. Mainly as a strength bearing material. Then wool glass shell brick, geopolymer concrete slab reinforced with bamboo, and geopolymer concrete block are manufactured. The manufactured materials are subjected to their respective tests and prototype is also constructed. From the study of materials, it is observed that percentage economy can be achieved using this sustainable material .The test results showed that the compressive strength, tensile strength and of the manufactured materials are much better than the conventional construction materials.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114936138","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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