Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470729
Akhilendra Pratap Singh, Deeplata Sharma, Soumya K
Cell computing has seen a marked evolution over the past many years, beginning with text-primarily based interactions among carrier and device customers and progressing to visually pushed reports primarily based on large screens and touch inputs. Early mobile phones featured bodily keypads that enabled users to interact with the tool via written instructions, such as coming in with cellphone numbers and sending text messages. This approach changed into nicely appropriate for brief data retrieval and typing in brief bursts. As the generation matured, touchscreens became a more popular entry method. The creation of gestures, such as swiping and multi-contact, revolutionized the manner customers interacted with their gadgets, allowing them to freely discover and access content in a green and intuitive way. Moreover, larger bodily sizes coupled with excessive-decision presentations allowed customers to control content and better appreciate the visuals easily. The evolution of cell computing has additionally created a platform for bringing collectively disparate technologies. Cellular apps, for example, are able to combine text, photographs, sound, and video to offer a multi-modal reveal for users. It has made using cell devices more engaging and enabled customers to interaction with complex structures in an optimized form thing.
{"title":"Evolution of Mobile Computing: From Text-Based to Visual-Based Interactions","authors":"Akhilendra Pratap Singh, Deeplata Sharma, Soumya K","doi":"10.1109/ICOCWC60930.2024.10470729","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470729","url":null,"abstract":"Cell computing has seen a marked evolution over the past many years, beginning with text-primarily based interactions among carrier and device customers and progressing to visually pushed reports primarily based on large screens and touch inputs. Early mobile phones featured bodily keypads that enabled users to interact with the tool via written instructions, such as coming in with cellphone numbers and sending text messages. This approach changed into nicely appropriate for brief data retrieval and typing in brief bursts. As the generation matured, touchscreens became a more popular entry method. The creation of gestures, such as swiping and multi-contact, revolutionized the manner customers interacted with their gadgets, allowing them to freely discover and access content in a green and intuitive way. Moreover, larger bodily sizes coupled with excessive-decision presentations allowed customers to control content and better appreciate the visuals easily. The evolution of cell computing has additionally created a platform for bringing collectively disparate technologies. Cellular apps, for example, are able to combine text, photographs, sound, and video to offer a multi-modal reveal for users. It has made using cell devices more engaging and enabled customers to interaction with complex structures in an optimized form thing.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"66 36","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529742","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470777
Puneet Agarwal, Bhuvana J, Bhuvnesh Sharma
Designing software program answers for high-performance communications in community packages is complex. It calls for cautious attention to the underlying community hardware, protocols, and algorithms. The goal is to create a communique machine with minimal latency and go-platform compatibility while maintaining robust safety and imparting excessive throughput. Designing high-overall performance conversation software should remember the wishes of the software. For example, an internet server wishes to acquire and respond to many requests concurrently; thus, the community should efficiently present more than one simultaneous connection. Then again, a video streaming application will require the network to handle heavy visitors without experiencing unexpected delays or packet loss. The demanding situations of designing a communications software device are further exacerbated using the complexity of today's networks. Exclusive protocols ever require unique optimizations and adjustments to maximize overall performance. Community topology and the environment, including the nature of the relationship kind, must also be considered. Similarly, selecting protocols is a crucial issue, as each has its benefits and barriers. To navigate these complexities efficaciously, software program developers must have widespread enjoyment and profound know-how of each protocol and community environment. They must additionally apprehend the interaction among the additives of the network; the development of excessive-overall performance conversation software layout for network applications is a challenging mission for software program engineers and builders. Despite challenges with variable community situations, stop-consumer requirements, and a wide range of gadgets, reliable and excessive overall performance software must be designed for these networks. Designing for excessive-performance communications software entails the attention and assessment of numerous factors, expertise, and looking forward to personal requirements, bandwidth optimization, latency discount, protocol optimization, and more. Via incorporating strategies including using server-aspect answers, using caching, compression, and statistics streamlining, green usage of shipping-layer protocols and protocols for communications-software programs, and optimizing algorithm and statistics structures, developers can make sure that their software program designs are optimized for high-performance and reliability. Thru an aggregate of the right community and hardware layout, an effective combination of algorithms and information structures, and optimization to ensure reliability and excessive-overall performance, communications software programs for community applications may be designed and applied.
{"title":"Designing Solutions for High-Performance Communications Software Design in Network Applications","authors":"Puneet Agarwal, Bhuvana J, Bhuvnesh Sharma","doi":"10.1109/ICOCWC60930.2024.10470777","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470777","url":null,"abstract":"Designing software program answers for high-performance communications in community packages is complex. It calls for cautious attention to the underlying community hardware, protocols, and algorithms. The goal is to create a communique machine with minimal latency and go-platform compatibility while maintaining robust safety and imparting excessive throughput. Designing high-overall performance conversation software should remember the wishes of the software. For example, an internet server wishes to acquire and respond to many requests concurrently; thus, the community should efficiently present more than one simultaneous connection. Then again, a video streaming application will require the network to handle heavy visitors without experiencing unexpected delays or packet loss. The demanding situations of designing a communications software device are further exacerbated using the complexity of today's networks. Exclusive protocols ever require unique optimizations and adjustments to maximize overall performance. Community topology and the environment, including the nature of the relationship kind, must also be considered. Similarly, selecting protocols is a crucial issue, as each has its benefits and barriers. To navigate these complexities efficaciously, software program developers must have widespread enjoyment and profound know-how of each protocol and community environment. They must additionally apprehend the interaction among the additives of the network; the development of excessive-overall performance conversation software layout for network applications is a challenging mission for software program engineers and builders. Despite challenges with variable community situations, stop-consumer requirements, and a wide range of gadgets, reliable and excessive overall performance software must be designed for these networks. Designing for excessive-performance communications software entails the attention and assessment of numerous factors, expertise, and looking forward to personal requirements, bandwidth optimization, latency discount, protocol optimization, and more. Via incorporating strategies including using server-aspect answers, using caching, compression, and statistics streamlining, green usage of shipping-layer protocols and protocols for communications-software programs, and optimizing algorithm and statistics structures, developers can make sure that their software program designs are optimized for high-performance and reliability. Thru an aggregate of the right community and hardware layout, an effective combination of algorithms and information structures, and optimization to ensure reliability and excessive-overall performance, communications software programs for community applications may be designed and applied.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"54 23","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529897","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470930
Monika Abrol, Rajendra P. Pandey, Rahul Pawar
the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes; however, using a combination of those strategies has been visible to enhance the classification performance.
{"title":"Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images","authors":"Monika Abrol, Rajendra P. Pandey, Rahul Pawar","doi":"10.1109/ICOCWC60930.2024.10470930","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470930","url":null,"abstract":"the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes; however, using a combination of those strategies has been visible to enhance the classification performance.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"38 12","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529997","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}
This paper aims to evaluate the performance of numerous deep-gaining knowledge of fashions for detecting Uterine Sarcoma via Time series evaluation. Uterine Sarcoma is a malignant tumor that influences the uterus and different parts of the woman's reproductive machine. Time collection analysis techniques have been broadly used in scientific fact mining, specifically for clinical records, because of their capability to capture temporal traits of the data. In this look, quite several deeps getting to know fashions which include Convolutional Neural Networks (CNNs), long brief-time period reminiscence (LSTM), and Self-Organizing Maps (SOMs), were evaluated at the MIMIC-III database-the use of metrics such as accuracy, precision and bear in mind. The results showed that the CNN had the highest accuracy (zero.99%) and precision (zero.75%) and did not forget (0.90%) in predicting Uterine Sarcoma when compared with the opposite models. This examination serves as a starting point for a similar investigation into the potential capabilities of deep mastering for detecting Uterine Sarcoma and other illnesses in medical statistics. This paper evaluates deep learning processes for time series evaluation to hit upon uterine sarcoma. The strategies used in this examination are Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To assess the performance of the networks, the dataset from the yank university of Radiology (ACR) Uterine Sarcoma Imaging and Research Database changed used. The networks were evaluated for accuracy, sensitivity, and specificity. Moreover, the RNNs and CNNs were compared to evaluate their performance. The results show that the CNN performs better than the RNN with an accuracy of ninety-seven. 50%, a sensitivity of 95.05%, and specificity of ninety-nine. 25%. It is steady with previous studies implementing deep learning techniques for medical photograph evaluation. The outcomes of this observation reveal that both RNN and CNN are appropriate for diagnosing uterine sarcoma and that the CNN version is more excellent and correct for the assignment to hand.
{"title":"Assessing Deep Learning Approaches for Time Series Analysis to Detect Uterine Sarcoma","authors":"Gaurav Shukla, Meenakshi Dheer, Ramkumar Krishnamoorthy","doi":"10.1109/ICOCWC60930.2024.10470619","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470619","url":null,"abstract":"This paper aims to evaluate the performance of numerous deep-gaining knowledge of fashions for detecting Uterine Sarcoma via Time series evaluation. Uterine Sarcoma is a malignant tumor that influences the uterus and different parts of the woman's reproductive machine. Time collection analysis techniques have been broadly used in scientific fact mining, specifically for clinical records, because of their capability to capture temporal traits of the data. In this look, quite several deeps getting to know fashions which include Convolutional Neural Networks (CNNs), long brief-time period reminiscence (LSTM), and Self-Organizing Maps (SOMs), were evaluated at the MIMIC-III database-the use of metrics such as accuracy, precision and bear in mind. The results showed that the CNN had the highest accuracy (zero.99%) and precision (zero.75%) and did not forget (0.90%) in predicting Uterine Sarcoma when compared with the opposite models. This examination serves as a starting point for a similar investigation into the potential capabilities of deep mastering for detecting Uterine Sarcoma and other illnesses in medical statistics. This paper evaluates deep learning processes for time series evaluation to hit upon uterine sarcoma. The strategies used in this examination are Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To assess the performance of the networks, the dataset from the yank university of Radiology (ACR) Uterine Sarcoma Imaging and Research Database changed used. The networks were evaluated for accuracy, sensitivity, and specificity. Moreover, the RNNs and CNNs were compared to evaluate their performance. The results show that the CNN performs better than the RNN with an accuracy of ninety-seven. 50%, a sensitivity of 95.05%, and specificity of ninety-nine. 25%. It is steady with previous studies implementing deep learning techniques for medical photograph evaluation. The outcomes of this observation reveal that both RNN and CNN are appropriate for diagnosing uterine sarcoma and that the CNN version is more excellent and correct for the assignment to hand.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"50 12","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529909","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470502
Laxmi Goswami, Ashish Bishnoi, A. Kannagi
The combination of low-energy statistics is an excellent sized aspect of contemporary strength rules and policy. Powerful synthesis and aggregation of those sources can inform decisions and affect movements that have substantial effects. Bootstrap weighted technology (BWE) is a data aggregation method used in electricity studies and coverage. This evaluation examines the capacity of BWE for low-strength facts synthesis. Focusing on the deployed technology and their respective abilities, the benefits of BWE are apparent. BWE captures the nuanced complexities of low-energy data through its weighted vector method while imparting a well-known understanding of targeted areas. Furthermore, thru the aggregation of various resources of low-energy facts, BWE can offer a much extra comprehensive assessment than might otherwise be possible. As a result, this presents choice-makers with a more feel of self-assurance when making power-associated selections or guidelines. The improvement and successful application of BWE for low-power records collection continue to be an area of energetic studies, and ongoing refinements and optimizations are likely to result in more practical effects. Bootstrap weighted generation (BWERA) is a progressive, non-parametric statistical method for low-strength facts aggregation. The technique takes the benefit of energy resolution averaging (generation) and employs bootstrap strategies to improve the robustness of consequences within the presence of significant outliers. The approach is appropriate for scenarios wherein uncooked records are lacking or are unfastened by noise. BWERA affords a manner to use some facts points for inferring otherwise unknown houses, including the form of the electricity spectrum. This examination seeks to discuss the capability of BWERA for low-energy statistics aggregation and its implications for experimental design and statistics evaluation. To begin with, the authors speak about the motivations for the usage of BWERA. They explain that the method may be high quality because it could offer data inference and averaging in situations with restricted facts and noise-unfastened information. Moreover, it is a computationally efficient method, and its usage with non-parametric inference is attractive due to the difficulty of occasionally developing correct parametric fashions. Ultimately, the authors spotlight the benefits of using Bootstrap to create self-assurance bounds instead of error bar estimation..
{"title":"A Discussion of the Potential for Bootstrap Weighted-ERA for Low-Energy Data Aggregation","authors":"Laxmi Goswami, Ashish Bishnoi, A. Kannagi","doi":"10.1109/ICOCWC60930.2024.10470502","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470502","url":null,"abstract":"The combination of low-energy statistics is an excellent sized aspect of contemporary strength rules and policy. Powerful synthesis and aggregation of those sources can inform decisions and affect movements that have substantial effects. Bootstrap weighted technology (BWE) is a data aggregation method used in electricity studies and coverage. This evaluation examines the capacity of BWE for low-strength facts synthesis. Focusing on the deployed technology and their respective abilities, the benefits of BWE are apparent. BWE captures the nuanced complexities of low-energy data through its weighted vector method while imparting a well-known understanding of targeted areas. Furthermore, thru the aggregation of various resources of low-energy facts, BWE can offer a much extra comprehensive assessment than might otherwise be possible. As a result, this presents choice-makers with a more feel of self-assurance when making power-associated selections or guidelines. The improvement and successful application of BWE for low-power records collection continue to be an area of energetic studies, and ongoing refinements and optimizations are likely to result in more practical effects. Bootstrap weighted generation (BWERA) is a progressive, non-parametric statistical method for low-strength facts aggregation. The technique takes the benefit of energy resolution averaging (generation) and employs bootstrap strategies to improve the robustness of consequences within the presence of significant outliers. The approach is appropriate for scenarios wherein uncooked records are lacking or are unfastened by noise. BWERA affords a manner to use some facts points for inferring otherwise unknown houses, including the form of the electricity spectrum. This examination seeks to discuss the capability of BWERA for low-energy statistics aggregation and its implications for experimental design and statistics evaluation. To begin with, the authors speak about the motivations for the usage of BWERA. They explain that the method may be high quality because it could offer data inference and averaging in situations with restricted facts and noise-unfastened information. Moreover, it is a computationally efficient method, and its usage with non-parametric inference is attractive due to the difficulty of occasionally developing correct parametric fashions. Ultimately, the authors spotlight the benefits of using Bootstrap to create self-assurance bounds instead of error bar estimation..","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"56 43","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529885","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470629
Davendra Kumar Doda, M.S. Nidhya, Kalyan Acharjya
Automated synthesizable internal power-gating (ASIPG) offers a promising technology for enhancing the power efficiency of application precise included Circuits (ASICs). This research evaluates the possible impact of using ASIPG for the electricity efficiency of an ASIC. Multiple methods of ASIC strength consumption are tested, which include fixed voltage and frequency, dynamic frequency scaling, and strength-gating. Chip-degree information from two ASICs processing the CNN and GEMM kernels are provided to demonstrate the efficiency of ASIPG compared to traditional power-gating. The evaluation process compares the strength performance and price performance of designs that rent and do not hire ASIPG. Results suggest that designs based on ASIPG display stepped forward power performance by means of over 26% for the CNN kernel as compared to a traditional electricity-gating design and 19% for the GEMM kernel. These outcomes guide the potential of ASIPG to enhance power efficiency for ASIC designs.
{"title":"An Investigation into the Impact of Using Automated Synthesisable Internal Power-Gating on Improved Power Efficiency for ASICs","authors":"Davendra Kumar Doda, M.S. Nidhya, Kalyan Acharjya","doi":"10.1109/ICOCWC60930.2024.10470629","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470629","url":null,"abstract":"Automated synthesizable internal power-gating (ASIPG) offers a promising technology for enhancing the power efficiency of application precise included Circuits (ASICs). This research evaluates the possible impact of using ASIPG for the electricity efficiency of an ASIC. Multiple methods of ASIC strength consumption are tested, which include fixed voltage and frequency, dynamic frequency scaling, and strength-gating. Chip-degree information from two ASICs processing the CNN and GEMM kernels are provided to demonstrate the efficiency of ASIPG compared to traditional power-gating. The evaluation process compares the strength performance and price performance of designs that rent and do not hire ASIPG. Results suggest that designs based on ASIPG display stepped forward power performance by means of over 26% for the CNN kernel as compared to a traditional electricity-gating design and 19% for the GEMM kernel. These outcomes guide the potential of ASIPG to enhance power efficiency for ASIC designs.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"55 26","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529892","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470679
D. Yadav, Pramod Kumar Faujdar, Sanjeev Kumar Mandal
This technical summary discusses the layout and fabrication of high-sensitivity MEMS strain sensors for aerospace applications. There may be a need for fairly particular and dependable pressure sensors that can screen the stress in the plane cabin, gas tanks, and different systems. MEMS pressure sensors are appropriate for such programs because they provide improved accuracy, flexibility, and strength consumption. The design of high-sensitivity MEMS strain sensors for aerospace programs needs to remember some of the necessities that are unique to such programs. As an example, the sensor needs to be capable of resisting the excessive temperatures and pressures associated with operations at high altitudes, as well as the potentially corrosive and extraordinarily electrically conductive environment of the cabin. The sensors need to additionally provide excessive sensitivity and speedy reaction times at the same time as keeping excessive accuracy and stability. A number of fabrication and design techniques may be applied. As an example, using lasers, photolithography, thin movie deposition, etching, and different microfabrication techniques can permit the fabrication of excessive decision MEMS systems with extraordinarily small characteristic sizes.
{"title":"Design and Fabrication of High Sensitivity MEMS Pressure Sensors for Aerospace Applications","authors":"D. Yadav, Pramod Kumar Faujdar, Sanjeev Kumar Mandal","doi":"10.1109/ICOCWC60930.2024.10470679","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470679","url":null,"abstract":"This technical summary discusses the layout and fabrication of high-sensitivity MEMS strain sensors for aerospace applications. There may be a need for fairly particular and dependable pressure sensors that can screen the stress in the plane cabin, gas tanks, and different systems. MEMS pressure sensors are appropriate for such programs because they provide improved accuracy, flexibility, and strength consumption. The design of high-sensitivity MEMS strain sensors for aerospace programs needs to remember some of the necessities that are unique to such programs. As an example, the sensor needs to be capable of resisting the excessive temperatures and pressures associated with operations at high altitudes, as well as the potentially corrosive and extraordinarily electrically conductive environment of the cabin. The sensors need to additionally provide excessive sensitivity and speedy reaction times at the same time as keeping excessive accuracy and stability. A number of fabrication and design techniques may be applied. As an example, using lasers, photolithography, thin movie deposition, etching, and different microfabrication techniques can permit the fabrication of excessive decision MEMS systems with extraordinarily small characteristic sizes.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"59 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529611","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470487
K. Hussain, S. Kaliappan, Arul Joseph Amalraj. M, Parvesh Saini, S. K. Nandha Kumar, J. Dhanraj
The ability of PMUs to provide precise, synchronized readings of voltage, current and frequency has made them valuable for the observation of microgrids. In some microgrids, PMU s are utilized without a current transformer and only measure voltage phasor values. This research outlines a procedure to use μPMU (or micro-PMU) voltage readings to ascertain electric loads or photovoltaic (PV) production through gauging power flow (PF). The results of a study conducted at the Federal University of Paraná's Polytechnic School (UFPR) in Brazil demonstrated that utilizing the power flow calculated by a “virtual CT” approach, as measured by a standard power meter and with a higher time resolution from a microPMU, is a reliable and efficient method for recognizing events, monitoring PV generation, and non-intrusively monitoring load (NILM).
{"title":"PV Generation Monitoring Using Calculated Power Flow from μPMUS","authors":"K. Hussain, S. Kaliappan, Arul Joseph Amalraj. M, Parvesh Saini, S. K. Nandha Kumar, J. Dhanraj","doi":"10.1109/ICOCWC60930.2024.10470487","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470487","url":null,"abstract":"The ability of PMUs to provide precise, synchronized readings of voltage, current and frequency has made them valuable for the observation of microgrids. In some microgrids, PMU s are utilized without a current transformer and only measure voltage phasor values. This research outlines a procedure to use μPMU (or micro-PMU) voltage readings to ascertain electric loads or photovoltaic (PV) production through gauging power flow (PF). The results of a study conducted at the Federal University of Paraná's Polytechnic School (UFPR) in Brazil demonstrated that utilizing the power flow calculated by a “virtual CT” approach, as measured by a standard power meter and with a higher time resolution from a microPMU, is a reliable and efficient method for recognizing events, monitoring PV generation, and non-intrusively monitoring load (NILM).","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"16 7","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529796","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 recent years, deep gaining knowledge has emerged as an effective device for medical photo segmentation. This paper proposes a unique model that mixes convolutional neural networks and recurrent neural networks with an attention mechanism to improve the accuracy of segments for medical pictures, including magnetic resonance images. The eye mechanism is used to weigh each pixel, focusing the model's interest on regions of a photo that might be more applicable to classifying the item being segmented. The version is examined on medical imaging datasets - the clinical Segmentation Decathlon and the medical Segmentation Benchmark. The effects demonstrate that using the attention-based recurrent neural networks model considerably outperforms convolutional neural networks and recurrent neural networks on my own, with a median increase in dice score of up to ten%. Those effects suggest that the proposed technique can improve the accuracy of medical photo segmentation and help further facilitate the improvement of deep gaining knowledge of-based medical photograph analysis applications
{"title":"Enhancing Medical Image Segmentation with Attention-Based Recurrent Neural Networks","authors":"Rakesh Kumar Dwivedi, Ananya Saha, Meenakshi Sharma","doi":"10.1109/ICOCWC60930.2024.10470617","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470617","url":null,"abstract":"In recent years, deep gaining knowledge has emerged as an effective device for medical photo segmentation. This paper proposes a unique model that mixes convolutional neural networks and recurrent neural networks with an attention mechanism to improve the accuracy of segments for medical pictures, including magnetic resonance images. The eye mechanism is used to weigh each pixel, focusing the model's interest on regions of a photo that might be more applicable to classifying the item being segmented. The version is examined on medical imaging datasets - the clinical Segmentation Decathlon and the medical Segmentation Benchmark. The effects demonstrate that using the attention-based recurrent neural networks model considerably outperforms convolutional neural networks and recurrent neural networks on my own, with a median increase in dice score of up to ten%. Those effects suggest that the proposed technique can improve the accuracy of medical photo segmentation and help further facilitate the improvement of deep gaining knowledge of-based medical photograph analysis applications","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"17 2","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529800","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 : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470719
Ankita Agarwal, Rekha Devrani, A. Kannagi
This paper proposes a Recursive Neural framework for the clever mindset evaluation of network interference customers. Our technique builds on previous work achieved in sentiment analysis using extracting a person's man or woman mindset from complicated and incomplete statistics streams. The framework, to begin with, gets the sentiment layers based on consumer interactions from the datasets, after which it integrates this fact with various Recursive Neural networks to seize the sentiment of a single user. The community extracts capabilities associated with the user and learns to distinguish between the behaviors of two users inside the community. Once the community is educated on the datasets, it may classify the sentiment of users based on various contextual cues. We evaluated our framework through crowd-sourced sentiment annotation datasets from a web forum, and it confirmed superior overall performance than different present approaches. We proposed a Recursive Neural framework that utilizes contextual schemas and sentiment to analyze user attitudes and behaviors for community interference scenarios. It can open up promising new opportunities for observing consumer mindset and behavior in online networks. This paper offers a recursive neural framework for competent mindset evaluation of network interference customers. Recursive Neural Networks, broadly carried out in natural language processing responsibilities with sentiment analysis, combine word embeddings with a recursive architecture to gain a perception of the syntactic shape of sentences. On this, look at the Recursive Neural Network (RNN) architecture tailored to research the sentiment mindset of community interference users. The information amassed from Twitter, Weibo, and different open-supply platforms had been pre-processed using the frequency inverted report frequency technique before constructing an RNN for its modeling. Checks at the built community proved that the proposed model furnished pleasant consequences, reaching a median accuracy of 88.36%. In an evaluation with a conventional non-recursive network, the RNN version resulted in a 7.3% relative growth in classification accuracy, demonstrating its efficacy in sentiment evaluation. The outcomes produced by using this examination are promising and may be tremendous for protection practitioners in helping to higher recognize consumer sentiment for network interference.
{"title":"The Smart Attitude Analysis of Network Interference User using Recursive Neural Framework","authors":"Ankita Agarwal, Rekha Devrani, A. Kannagi","doi":"10.1109/ICOCWC60930.2024.10470719","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470719","url":null,"abstract":"This paper proposes a Recursive Neural framework for the clever mindset evaluation of network interference customers. Our technique builds on previous work achieved in sentiment analysis using extracting a person's man or woman mindset from complicated and incomplete statistics streams. The framework, to begin with, gets the sentiment layers based on consumer interactions from the datasets, after which it integrates this fact with various Recursive Neural networks to seize the sentiment of a single user. The community extracts capabilities associated with the user and learns to distinguish between the behaviors of two users inside the community. Once the community is educated on the datasets, it may classify the sentiment of users based on various contextual cues. We evaluated our framework through crowd-sourced sentiment annotation datasets from a web forum, and it confirmed superior overall performance than different present approaches. We proposed a Recursive Neural framework that utilizes contextual schemas and sentiment to analyze user attitudes and behaviors for community interference scenarios. It can open up promising new opportunities for observing consumer mindset and behavior in online networks. This paper offers a recursive neural framework for competent mindset evaluation of network interference customers. Recursive Neural Networks, broadly carried out in natural language processing responsibilities with sentiment analysis, combine word embeddings with a recursive architecture to gain a perception of the syntactic shape of sentences. On this, look at the Recursive Neural Network (RNN) architecture tailored to research the sentiment mindset of community interference users. The information amassed from Twitter, Weibo, and different open-supply platforms had been pre-processed using the frequency inverted report frequency technique before constructing an RNN for its modeling. Checks at the built community proved that the proposed model furnished pleasant consequences, reaching a median accuracy of 88.36%. In an evaluation with a conventional non-recursive network, the RNN version resulted in a 7.3% relative growth in classification accuracy, demonstrating its efficacy in sentiment evaluation. The outcomes produced by using this examination are promising and may be tremendous for protection practitioners in helping to higher recognize consumer sentiment for network interference.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"47 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529772","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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