Pub Date : 2024-08-20DOI: 10.1109/MSP.2024.3420491
Hanseok Ko;Monson Hayes;John Hansen
When we set out to host ICASSP 2024, in Seoul, South Korea, we had three goals in mind: organize an outstanding technical program, provide an excellent and engaging venue to foster meetings to exchange ideas, and deliver the most welcoming experience to our attendees. With the hard work and commitment from the outstanding organizing committee (OC), we were able to achieve these goals. The culturally rich and vibrant city of Seoul welcomed ICASSP attendees and the IEEE Signal Processing Society (SPS) community with open arms and “jeong,” which embodies the warm and friendly spirit of the Korean people. Seoul is a city where tradition and technology coexist and support each other, showing a true sense of rich Korean culture.
{"title":"Meeting the Challenges of a Growing ICASSP: Highlights from ICASSP 2024 [Conference Highlights]","authors":"Hanseok Ko;Monson Hayes;John Hansen","doi":"10.1109/MSP.2024.3420491","DOIUrl":"https://doi.org/10.1109/MSP.2024.3420491","url":null,"abstract":"When we set out to host ICASSP 2024, in Seoul, South Korea, we had three goals in mind: organize an outstanding technical program, provide an excellent and engaging venue to foster meetings to exchange ideas, and deliver the most welcoming experience to our attendees. With the hard work and commitment from the outstanding organizing committee (OC), we were able to achieve these goals. The culturally rich and vibrant city of Seoul welcomed ICASSP attendees and the IEEE Signal Processing Society (SPS) community with open arms and “jeong,” which embodies the warm and friendly spirit of the Korean people. Seoul is a city where tradition and technology coexist and support each other, showing a true sense of rich Korean culture.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/MSP.2024.3415248
Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes
Hypercomplex signal and image processing extends upon conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. The special issue is divided into two parts and is focused on current advances and applications in computational signal and image processing in the hypercomplex domain. The first part offered well-rounded coverage of the field, with seven articles that focused on overviews of current research, color image processing, signal filtering, and machine learning.
{"title":"Hypercomplex Signal and Image Processing: Part 2 [From the Guest Editors]","authors":"Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes","doi":"10.1109/MSP.2024.3415248","DOIUrl":"https://doi.org/10.1109/MSP.2024.3415248","url":null,"abstract":"Hypercomplex signal and image processing extends upon conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. The special issue is divided into two parts and is focused on current advances and applications in computational signal and image processing in the hypercomplex domain. The first part offered well-rounded coverage of the field, with seven articles that focused on overviews of current research, color image processing, signal filtering, and machine learning.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/MSP.2024.3381808
Alabi Bojesomo;Panos Liatsis;Hasan Al Marzouqi
Hypercomplex numbers, such as quaternions and octonions, have recently gained attention because of their advantageous properties over real numbers, e.g., in the development of parameter-efficient neural networks. For instance, the 16-component sedenion has the capacity to reduce the number of network parameters by a factor of 16. Moreover, hypercomplex neural networks offer advantages in the processing of spatiotemporal data as they are able to represent variable temporal data divisions through the hypercomplex components. Similarly, they support multimodal learning, with each component representing an individual modality. In this article, the key components of deep learning in the hypercomplex domain are introduced, encompassing concatenation, activation functions, convolution, and batch normalization. The use of the backpropagation algorithm for training hypercomplex networks is discussed in the context of hypercomplex algebra. These concepts are brought together in the design of a ResNet backbone using hypercomplex convolution, which is integrated within a U-Net configuration and applied in weather and traffic forecasting problems. The results demonstrate the superior performance of hypercomplex networks compared to their real-valued counterparts, given a fixed parameter budget, highlighting their potential in spatiotemporal data processing.
{"title":"Deep Hypercomplex Networks for Spatiotemporal Data Processing: Parameter efficiency and superior performance [Hypercomplex Signal and Image Processing]","authors":"Alabi Bojesomo;Panos Liatsis;Hasan Al Marzouqi","doi":"10.1109/MSP.2024.3381808","DOIUrl":"https://doi.org/10.1109/MSP.2024.3381808","url":null,"abstract":"Hypercomplex numbers, such as quaternions and octonions, have recently gained attention because of their advantageous properties over real numbers, e.g., in the development of parameter-efficient neural networks. For instance, the 16-component sedenion has the capacity to reduce the number of network parameters by a factor of 16. Moreover, hypercomplex neural networks offer advantages in the processing of spatiotemporal data as they are able to represent variable temporal data divisions through the hypercomplex components. Similarly, they support multimodal learning, with each component representing an individual modality. In this article, the key components of deep learning in the hypercomplex domain are introduced, encompassing concatenation, activation functions, convolution, and batch normalization. The use of the backpropagation algorithm for training hypercomplex networks is discussed in the context of hypercomplex algebra. These concepts are brought together in the design of a ResNet backbone using hypercomplex convolution, which is integrated within a U-Net configuration and applied in weather and traffic forecasting problems. The results demonstrate the superior performance of hypercomplex networks compared to their real-valued counterparts, given a fixed parameter budget, highlighting their potential in spatiotemporal data processing.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/MSP.2024.3437488
{"title":"SPS Social Media","authors":"","doi":"10.1109/MSP.2024.3437488","DOIUrl":"https://doi.org/10.1109/MSP.2024.3437488","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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