IEEE Signal Processing Society

This talk provides an overview of the PHD filter and how the same RFS framework can be used to address multi-object trajectory estimation. By using labels to distinguish individual trajectories, this approach admits MOT filters that alleviate integration over multiple scans and enables modeling/estimation of ancestry for spawning objects. Labeled RFS posterior/filtering densities are closed under truncation and admit analytic truncation errors critical for numerical approximations.

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2201.html

Sound field estimation using a microphone array is a fundamental problem in acoustic signal processing, which has a wide variety of applications, such as visualization/auralization of an acoustic field, spatial audio reproduction using a loudspeaker array or headphones, and active noise cancellation in a spatial region. We recently proposed a sound field estimation method based on infinite-dimensional basis expansion of sound fields. This method can also be regarded as the kernel method for inte

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2111.html

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion during the last decade. However, due to the patch-based manner adopted in standard SR models, most existing SR-based image fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to mis-registration, while these two issues are of great concern in image fusion. We introduce a recently emerged signal decomposition model known as convolu

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2110.html

Adaptive (i.e., data-driven) methods have become very popular these last decades. Among the existing techniques, the empirical mode decomposition has proven to be very efficient in extracting accurate time-frequency information from non-stationary signals. However, it is a purely algorithmic method and lacks of solid theoretical foundations. To overcome this issue, we propose the construction of adaptive wavelets, called empirical wavelets. Their aim is to decompose a signal into its harmonic

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

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This webinar will demonstrate how deep learning can solve difficult communication problems that prior approaches often fail with two case studies. The first half will discuss a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2108.html

We study the dual problem of image super-resolution (SR), which we term image compact-resolution (CR). Opposite to image SR that hallucinates a visually plausible high-resolution image given a low-resolution input, image CR provides a low-resolution version of a high-resolution image, such that the low-resolution version is both visually pleasing and as informative as possible compared to the high-resolution image. We propose a convolutional neural network (CNN) for image CR, namely, CNN-CR, ins

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2107.html

This webinar will discuss the MMSE channel estimator for a simple SIMO system model, without knowledge of the required channel statistics. Although the derived MMSE estimator is computationally intractable in the general form, its structure can be used to motivate a neural network architecture with lower complexity. The complexity reduction is based on a set of assumptions on the system model that simplify the MMSE estimator. The performance of the simplified MMSE estimator degrades significantl

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2105.html

Graphs are generic models of signal structure that can help to learn in several practical problems. To learn from graph data, we need scalable architectures that can be trained on moderate dataset sizes and that can be implemented in a distributed manner. Drawing from graph signal processing, the webinar will define graph convolutions and use them to introduce graph neural networks (GNNs). It will prove that GNNs are permutation equivariant and stable to perturbations of the graph, properties th

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2104.html

With the current rollout of 5G, the focus of the research community is shifting towards the design of the next generation of mobile systems, e.g., 6G mobile networks. Non-orthogonal multiple access (NOMA) has been recognized as an essential enabling technology for the forthcoming 6G networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The principle of NOMA is to encourage users for spectrum sharing, where multi

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2102.html