Kehkashan Kanwal, Muhammad Asif, Syed Ghufran Khalid, Sarwar Wasi, Farhana Zafar, Iffat Kiran, Saad Abdullah
ABSTRACT
{"title":"Comparative Analysis of Photoplethysmography Signal Quality from Right and Left Index Fingers","authors":"Kehkashan Kanwal, Muhammad Asif, Syed Ghufran Khalid, Sarwar Wasi, Farhana Zafar, Iffat Kiran, Saad Abdullah","doi":"10.18280/ts.400537","DOIUrl":"https://doi.org/10.18280/ts.400537","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"26 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous advancement of medical imaging technology, a vast amount of multi-modal medical image data has been extensively utilized for disease diagnosis, treatment, and research. Effective management and utilization of these data becomes a pivotal challenge, particularly when undertaking image matching and retrieval. Although numerous methods for medical image matching and retrieval exist, they primarily rely on traditional image processing techniques, often limited to manual feature extraction and singular modality handling. To address these limitations, this study introduces an algorithm for medical image matching grounded in multi-task learning, further investigating a semantic-enhanced technique for cross-modal medical image retrieval. By deeply exploring complementary semantic information between different modality medical images, these methods offer novel perspectives and tools for the domain of medical image matching and retrieval.
{"title":"ulti-Modal Medical Image Matching Based on Multi-Task Learning and Semantic-Enhanced Cross-Modal Retrieval","authors":"Yilin Zhang","doi":"10.18280/ts.400522","DOIUrl":"https://doi.org/10.18280/ts.400522","url":null,"abstract":"With the continuous advancement of medical imaging technology, a vast amount of multi-modal medical image data has been extensively utilized for disease diagnosis, treatment, and research. Effective management and utilization of these data becomes a pivotal challenge, particularly when undertaking image matching and retrieval. Although numerous methods for medical image matching and retrieval exist, they primarily rely on traditional image processing techniques, often limited to manual feature extraction and singular modality handling. To address these limitations, this study introduces an algorithm for medical image matching grounded in multi-task learning, further investigating a semantic-enhanced technique for cross-modal medical image retrieval. By deeply exploring complementary semantic information between different modality medical images, these methods offer novel perspectives and tools for the domain of medical image matching and retrieval.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"85 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ranjith Subramanian, Jesu Jayarin, Arumugam Chandrasekar
ABSTRACT
{"title":"Hybrid Deep Learning Approach for 6G MIMO Channel Estimation and Interference Alignment HetNet Environments","authors":"Ranjith Subramanian, Jesu Jayarin, Arumugam Chandrasekar","doi":"10.18280/ts.400514","DOIUrl":"https://doi.org/10.18280/ts.400514","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"17 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Real-Time Recognition and Feature Extraction of Stratum Images Based on Deep Learning","authors":"Tong Wang, Yu Yan, Lizhi Yuan, Yanhong Dong","doi":"10.18280/ts.400542","DOIUrl":"https://doi.org/10.18280/ts.400542","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayan Alsaaidah, Zaid Mustafa, Moh’d Rasoul Al-Hadidi, Lubna A. Alharbi
ABSTRACT
{"title":"Automated Identification and Categorization of COVID-19 via X-Ray Imagery Leveraging ROI Segmentation and CART Model","authors":"Bayan Alsaaidah, Zaid Mustafa, Moh’d Rasoul Al-Hadidi, Lubna A. Alharbi","doi":"10.18280/ts.400543","DOIUrl":"https://doi.org/10.18280/ts.400543","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"19 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moving object detection from video sequences remains a focal point of research. To address the limitations evident in current methodologies, a synthesis of optical flow method and salient object fusion algorithm has been applied. Utilising the Graph-based Visual Saliency (GBVS) algorithm, significant target region signals from both static and dynamic images can be obtained. This technique captures valuable image target information, highlighting conspicuous targets within dynamic visuals. Concurrently, target signals can be isolated employing the Harmony Search (HS) algorithm, enhancing the accuracy in identifying moving objects. A weighted fusion of the extracted salient regions by the GBVS algorithm and the moving objects identified by the HS algorithm was executed in this study. This amalgamation demonstrates efficacy in extracting static objects in rudimentary environments and complex backgrounds alike. MATLAB simulation experiments have indicated that such a multi-modal fusion not only diminishes background noise but also proficiently isolates the entirety of the target. Building on traditional frame difference and background difference methods and considering the properties of the field programmable gate array (FPGA) alongside off-chip synchronous dynamic memory's access control prerequisites, adaptations for these algorithms were conceived using FPGA logic units.
{"title":"Multi-Modal Fusion for Moving Object Detection in Static and Complex Backgrounds","authors":"Huali Jiang, Xin Li","doi":"10.18280/ts.400513","DOIUrl":"https://doi.org/10.18280/ts.400513","url":null,"abstract":"Moving object detection from video sequences remains a focal point of research. To address the limitations evident in current methodologies, a synthesis of optical flow method and salient object fusion algorithm has been applied. Utilising the Graph-based Visual Saliency (GBVS) algorithm, significant target region signals from both static and dynamic images can be obtained. This technique captures valuable image target information, highlighting conspicuous targets within dynamic visuals. Concurrently, target signals can be isolated employing the Harmony Search (HS) algorithm, enhancing the accuracy in identifying moving objects. A weighted fusion of the extracted salient regions by the GBVS algorithm and the moving objects identified by the HS algorithm was executed in this study. This amalgamation demonstrates efficacy in extracting static objects in rudimentary environments and complex backgrounds alike. MATLAB simulation experiments have indicated that such a multi-modal fusion not only diminishes background noise but also proficiently isolates the entirety of the target. Building on traditional frame difference and background difference methods and considering the properties of the field programmable gate array (FPGA) alongside off-chip synchronous dynamic memory's access control prerequisites, adaptations for these algorithms were conceived using FPGA logic units.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136102642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Road safety has been prioritized by governments globally, resulting in the implementation of numerous initiatives aimed at curtailing traffic accidents. Despite these efforts, the complete eradication of accidents remains unattainable. Therefore, swift and accurate responses to accident sites, accompanied by appropriate medical aid, are paramount in saving lives. Existing systems, primarily designed to alert medical personnel in the aftermath of an accident, rely solely on Vehicle Damage (V d ) to assess accident severity, neglecting Human Injury (H i ) considerations. This study proposes a hybrid model equipped with an attention mechanism, designed to classify accident severity based on both V d and H i . The proposed model accepts video or image inputs and classifies accident severity levels accordingly. Moreover, an extension of the model has been developed to obfuscate sensitive areas in accident imagery based on severity, particularly when such images are disseminated on public platforms without obtaining necessary consent. The proposed hybrid model, therefore, not only facilitates a more comprehensive severity assessment of traffic accidents but also ensures the protection of privacy and promotes ethical image sharing practices.
{"title":"Utilizing a Hybrid Model for Human Injury Severity Analysis in Traffic Accidents","authors":"Uddagiri Sirisha, Bolem Sai Chandana","doi":"10.18280/ts.400540","DOIUrl":"https://doi.org/10.18280/ts.400540","url":null,"abstract":"Road safety has been prioritized by governments globally, resulting in the implementation of numerous initiatives aimed at curtailing traffic accidents. Despite these efforts, the complete eradication of accidents remains unattainable. Therefore, swift and accurate responses to accident sites, accompanied by appropriate medical aid, are paramount in saving lives. Existing systems, primarily designed to alert medical personnel in the aftermath of an accident, rely solely on Vehicle Damage (V d ) to assess accident severity, neglecting Human Injury (H i ) considerations. This study proposes a hybrid model equipped with an attention mechanism, designed to classify accident severity based on both V d and H i . The proposed model accepts video or image inputs and classifies accident severity levels accordingly. Moreover, an extension of the model has been developed to obfuscate sensitive areas in accident imagery based on severity, particularly when such images are disseminated on public platforms without obtaining necessary consent. The proposed hybrid model, therefore, not only facilitates a more comprehensive severity assessment of traffic accidents but also ensures the protection of privacy and promotes ethical image sharing practices.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advancement of modern industrial technology, complex mechanical systems have found extensive applications across various industries. Gears, integral components of these systems, play a crucial role in determining the stability and safety of the entire system. Wear and aging of system components during prolonged operations might lead to performance degradation or system failures. Historically, numerous methods for vibration signal analysis and gear wear detection have been proposed. However, these methods often exhibit limitations when applied to intricate systems, such as reliance on empirical rules and sub-optimal handling of nonlinear vibration signals. In light of these challenges, the vibration genesis mechanism in complex mechanical systems has been deeply investigated. A “Gear Health Factor” has been introduced, and a wear prediction model for gears, incorporating Bidirectional Long Short-Term Memory (Bi-LSTM) networks and attention mechanisms, has been developed. This research offers fresh perspectives and methods for the health management of complex mechanical systems and holds significant practical implications.
{"title":"Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears","authors":"Suzhen Wu","doi":"10.18280/ts.400527","DOIUrl":"https://doi.org/10.18280/ts.400527","url":null,"abstract":"With the advancement of modern industrial technology, complex mechanical systems have found extensive applications across various industries. Gears, integral components of these systems, play a crucial role in determining the stability and safety of the entire system. Wear and aging of system components during prolonged operations might lead to performance degradation or system failures. Historically, numerous methods for vibration signal analysis and gear wear detection have been proposed. However, these methods often exhibit limitations when applied to intricate systems, such as reliance on empirical rules and sub-optimal handling of nonlinear vibration signals. In light of these challenges, the vibration genesis mechanism in complex mechanical systems has been deeply investigated. A “Gear Health Factor” has been introduced, and a wear prediction model for gears, incorporating Bidirectional Long Short-Term Memory (Bi-LSTM) networks and attention mechanisms, has been developed. This research offers fresh perspectives and methods for the health management of complex mechanical systems and holds significant practical implications.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"56 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optical aberrations degrade the detecting performance in electron spectrometers. It is very difficult to calculate optical aberration parameters for complex electrostatic lens systems. In order to overcome this difficulty, the genetic algorithm method as a solution is introduced in this study. GAs are an intuitive research method based on the principle of generating new sequences of chromosomes in order to solve complex ordered problems. These algorithms target the global optimization of mathematical functions. This study uses a genetic algorithm to demonstrate the results of optimum aberration coefficients as a function of magnification for three-element electrostatic cylinder lenses. This algorithm is used to search for high-performance values. Different mutation and crossover probability values and also different selection and crossover types are tested. The optimum solution is obtained with a mutation rate of 0.01 and uniform crossover with a rate of 0.7. The proposed approach ensures the optimal solution for the aberration problems of the electrostatic lenses.
{"title":"Calculation of the Spherical and Chromatic Aberrations for Electrostatic Lenses Using Genetic Algorithm","authors":"Nimet Isik","doi":"10.18280/ts.400541","DOIUrl":"https://doi.org/10.18280/ts.400541","url":null,"abstract":"Optical aberrations degrade the detecting performance in electron spectrometers. It is very difficult to calculate optical aberration parameters for complex electrostatic lens systems. In order to overcome this difficulty, the genetic algorithm method as a solution is introduced in this study. GAs are an intuitive research method based on the principle of generating new sequences of chromosomes in order to solve complex ordered problems. These algorithms target the global optimization of mathematical functions. This study uses a genetic algorithm to demonstrate the results of optimum aberration coefficients as a function of magnification for three-element electrostatic cylinder lenses. This algorithm is used to search for high-performance values. Different mutation and crossover probability values and also different selection and crossover types are tested. The optimum solution is obtained with a mutation rate of 0.01 and uniform crossover with a rate of 0.7. The proposed approach ensures the optimal solution for the aberration problems of the electrostatic lenses.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"629 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136023525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous advancement of modern educational technologies, there has been a growing interest in multimedia visual expression methods within the domain of music pedagogy. These methods aim to provide students with an intuitive and vivid learning environment, facilitating a more profound understanding of music's structure and emotions. However, despite the availability of various image enhancement techniques, many tend to focus only on certain image attributes, often neglecting a comprehensive perspective on image aesthetics and authenticity. Addressing this issue, innovative image enhancement techniques are introduced in this study: an adaptive Gamma correction method for luminance adjustment, a saturation correction method based on luminance components, and a multimedia image enhancement method founded on an improved CLAHE algorithm. These methods not only significantly elevate the visual effects of multimedia in music teaching but also offer substantial technical support for the modernization of music education.
{"title":"New Enhancement Techniques for Optimizing Multimedia Visual Representations in Music Pedagogy","authors":"Mengmeng Chen, Chuixiang Xiong","doi":"10.18280/ts.400530","DOIUrl":"https://doi.org/10.18280/ts.400530","url":null,"abstract":"With the continuous advancement of modern educational technologies, there has been a growing interest in multimedia visual expression methods within the domain of music pedagogy. These methods aim to provide students with an intuitive and vivid learning environment, facilitating a more profound understanding of music's structure and emotions. However, despite the availability of various image enhancement techniques, many tend to focus only on certain image attributes, often neglecting a comprehensive perspective on image aesthetics and authenticity. Addressing this issue, innovative image enhancement techniques are introduced in this study: an adaptive Gamma correction method for luminance adjustment, a saturation correction method based on luminance components, and a multimedia image enhancement method founded on an improved CLAHE algorithm. These methods not only significantly elevate the visual effects of multimedia in music teaching but also offer substantial technical support for the modernization of music education.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"118 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：