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We will present our paper 'Differentiable Uncalibrated Imaging' where we propose a differentiable imaging framework to address uncertainty in measurement coordinates, such as sensor locations and projection angles. Our approach leverages implicit neural networks and spline-based neural fields to perform joint calibration and image reconstruction. This makes it possible, for example, to use pre-trained neural networks to configurations different from those for which they were originally trained. We will begin by presenting the general optimization framework underlying our method.

Lensless cameras possess significant advantages when compared to lens-based ones. By replacing the lens with a coded aperture, the camera device can be made lighter, smaller, amenable towards compressive sensing techniques, and can be designed with less physical constraints. We leverage this freedom of design to propose a novel optimized coded mask that effectively extends the depth of field of a lensless camera. In this webinar, we will start by introducing the concept of lensless imaging along with the reason as to why general lensless cameras have a limited depth of field.

Fractional programming (FP) refers to optimization problems involving functions of ratios. FP plays an important role in signal processing and machine learning, because many problems in these application areas are fractionally structured. This talk focuses on a state-of-the-art FP method called the quadratic transform and illustrates the use of quadratic transform for discrete optimization problems through two application examples.

Augmented/Mixed/Extended Reality (AR/MR/XR) is emerging as one of the key technologies in the future of computing. Audio cues are critical for maintaining a high degree of realism, social connection, and spatial awareness for various AR/MR applications such as training, gaming, remote work, education, and virtual social gatherings.

Speech can easily be manipulated through techniques, such as text-to-speech synthesis, voice conversion, replay, tampering, adversarial attacks, and more. However, when the manipulation is applied only to a minor portion of an audio, the remaining real segments can have a dominant influence upon human listeners and make machine detection extremely challenging. Therefore, there is an urgent need to explore such a scenario, where synthetic speech is embedded within otherwise real audio.

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The IEEE Signal Processing Society is now accepting nominations for the newly formed Best Paper Award Review Committees, which will report to the SPS Awards Board. Nominations open until 17 March 2026.

Nominations for several Society leadership roles are now open.