IEEE Signal Processing Society

Enabling Identity-Based Integrity Auditing and Data Sharing With Sensitive Information Hiding for Secure Cloud Storage

Enabling Identity-Based Integrity Auditing and Data Sharing With Sensitive Information Hiding for Secure Cloud Storage 845 469 ieeeeduweek

This webinar, which is based on our original article, addresses this problem and proposes a remote data integrity auditing scheme that realizes data sharing with sensitive information hiding.

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2003.html

FemtoPixel: Lensless Imaging with Compressive Ultrafast Sensing

FemtoPixel: Lensless Imaging with Compressive Ultrafast Sensing 840 469 ieeeeduweek

The FemtoPixel framework provides single-pixel imaging capabilities that are up to 50X faster compared to traditional single-pixel camera techniques. The webinar will start with an introduction to the building blocks of FemtoPixel: compressive sensing and ultrafast sensing. We’ll then introduce the FemtoPixel framework followed by a discussion of potential applications and future research directions.

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL:

Recent Advances of Deep Learning within X-ray Security Imaging

Recent Advances of Deep Learning within X-ray Security Imaging 842 469 ieeeeduweek

The goal of this webinar is to explore computerized X-ray security imaging methods by classifying them into traditional machine learning and modern deep learning applications. Th

IEEE-Affiliated Group Name: The IEEE Signal Processing Society

URL: https://rc.signalprocessingsociety.org/education/webinars/SPSWEB2202.html

Bayesian Multi-object Tracking: Probability Hypothesis Density Filter and Beyond

Bayesian Multi-object Tracking: Probability Hypothesis Density Filter and Beyond 842 469 ieeeeduweek

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

Infinite-Dimensional Expansion for Sound Field Estimation with Application to Spatial Audio

Infinite-Dimensional Expansion for Sound Field Estimation with Application to Spatial Audio 798 430 ieeeeduweek

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

Image Fusion with Convolutional Sparse Representation

Image Fusion with Convolutional Sparse Representation 793 426 ieeeeduweek

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

Empirical Wavelets

Empirical Wavelets 762 423 ieeeeduweek

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

URL:

Case Studies of Deep Learning for Channel Decoding and Power Control

Case Studies of Deep Learning for Channel Decoding and Power Control 762 430 ieeeeduweek

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

Learning a Convolutional Neural Network for Image Compact-Resolution

Learning a Convolutional Neural Network for Image Compact-Resolution 766 431 ieeeeduweek

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