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Section: Publications & Resources
The IEEE Signal Processing Society (SPS) invites nominations for the position of Editor-in-Chief for the following journals: IEEE Signal Processing Letters for a 3-year term starting 1 January 2023.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
The papers in this special section focuses on the exploitation of the radar spectrum for use of commercial wireless communication, as well as emerging applications requiring joint communication and sensing designs. The integration of radar and communication systems has recently attracted a lot of research and commercial interest. The emergence of spectrum-hungry applications have necessitated the exploitation of the permanently allocated, but potentially under-utilized spectral resources, and sharing the frequency bands between radar and communication systems has attracted substantial attention.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, by sharing the majority of hardware, signal processing modules and, in a typical case, the transmitted signal. The close cooperation of the communication and sensing functions can enable significant improvement of spectrum efficiency, reduction of device size, cost and power consumption, and improvement of performance of both functions.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multi-output (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multi-user communications.
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.
