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Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Dual-Functional Radar-Communication (DFRC) is a promising paradigm to achieve Integrated Sensing and Communication (ISAC) in beyond 5G. In parallel, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna Rate-Splitting (RS) by splitting messages into common and private streams at the transmitter and Successive Interference Cancellation (SIC) at the receivers, has emerged as a new strategy for multi-user multi-antenna communications systems. I
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
In this letter, we consider Bayesian parameterestimation using mixed-resolution data consisting of both analog and 1-bit quantized measurements. We investigate the use of the partially linear minimum mean-squared-error (PL-MMSE) estimator for this mixed-resolution scheme. The use of the PL-MMSE estimator, proposed for general models with “straightforward” and “complicated” parts, has not been demonstrated for quantized data.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first designing a novel deep learning architecture to generate the static semantic segmentation and recover the static image directly from the corresponding dynamic image.
Section: Publications & Resources
Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to produce new high-quality math problems and introduce new knowledge points that can benefit learning the mathematical reasoning logic.
Section: Publications & Resources
We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine. The proposed NWC also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process.
Section: Publications & Resources
Transcribing structural data into readable text (data-to-text) is a fundamental language generation task. One of its challenges is to plan the input records for text realization. Recent works tackle this problem with a static planner, which performs record planning in advance for text realization. However, they cannot revise plans to cope with unexpected realized text and require golden plans for supervised training. To address these issues, we first propose a model that contains a dynamic planner.
Section: Publications & Resources
The perception of one’s own voice influences the acceptance of hearing devices, such as headphones, headsets or hearing aids. When these devices fully or partially occlude the ear canal, the wearer’s own voice sounds boomy or like talking in a barrel. This is called occlusion effect . Occluding the ear canal results in an amplification of body-conducted sounds, mainly at low frequencies, and an attenuation of air-conducted sounds, predominantly at high frequencies, compared to the open ear.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for continuous representation of measurements. Unlike traditional DL methods that learn a mapping from the measurements to the desired image, CoIL trains a multilayer perceptron (MLP) to encode the complete measurement field by mapping the coordinates of the measurements to their responses. CoIL is a self-supervised method that requires no training examples besides the measurements of the test object itself.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
In this paper, we propose a new design for single sensor compressive HDR light field cameras, combining multi-ISO photography with coded mask acquisition, placed in a compressive sensing framework. The proposed camera model is based on a main lens, a multi-ISO sensor and a coded mask located in the optical path between the main lens and the sensor that projects the coded spatio-angular information of the light field onto the 2D sensor. The model encompasses different acquisition scenarios with different ISO patterns and gains.
Section: Publications & Resources
Privacy-preserving techniques for processing sets of information have attracted the research community’s attention in recent years due to society’s increasing dependency on the availability of data at any time. One of the fundamental problems in set operations is known as Private Set Intersection (PSI). The problem requires two parties to compute the intersection between their sets while preserving correctness and privacy. Although several efficient two-party PSI protocols already exist, protocols for PSI in the multi-party setting (MPSI) currently scale poorly with a growing number of parties, even though this applies to many real-life scenarios.
