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News and Resources for Members of the IEEE Signal Processing Society

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This SPS webinar will introduce a novel data-driven cooperative localization and location data processing framework, called FedLoc, in line with the emerging machine learning and optimization techniques.

Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification, and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. 

Recently, deep convolutional neural network (CNNs) have been widely used in Single Image Super-Resolution (SISR) and have obtained great success. However, most of the existing methods are limited to local receptive field and equal treatment of different types of information; 

In the beginning of 2020, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, affecting billions of humans. Identifying and separating the infected people during the early stage is the most important step in controlling the pandemic.

The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.

The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.

The Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them. 

The Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them. 

In past decades, conventional communication primarily focused on how to accurately and effectively transmit symbols, which is categorized as the first level of communications by Shannon and Weaver. With the developments of cellular communication systems, the achieved transmission rate is gradually approaching to the Shannon limit. 

As the standardization of 5G gradually solidifies, researchers are speculating what 6G will be. One common theme is that radio sensing functionality would be integrated into 6G networks in a low-cost and fast manner. 

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