Related papers: Advanced Machine Learning Techniques for Self-Inte…

We propose a nonlinear acoustic echo cancellation system, which aims to model the echo path from the far-end signal to the near-end microphone in two parts. Inspired by the physical behavior of modern hands-free devices, we first introduce…

Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for their deployment in resource-constrained applications, e.g.,…

Neural networks have seen an explosion of usage and research in the past decade, particularly within the domains of computer vision and natural language processing. However, only recently have advancements in neural networks yielded…

The rapid proliferation of Deep Learning is increasingly constrained by its heavy reliance on high-performance hardware, particularly Graphics Processing Units (GPUs). These specialized accelerators are not only prohibitively expensive and…

A photonics-based digital and analog self-interference cancellation approach for in-band full-duplex communication systems and frequency-modulated continuous-wave radar systems is reported. One dual-drive Mach-Zehnder modulator is used to…

Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic…

Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices. In this paper, we investigate reducing the computational and memory requirements of neural networks…

Deep learning has become an area of interest in most scientific areas, including physical sciences. Modern networks apply real-valued transformations on the data. Particularly, convolutions in convolutional neural networks discard phase…

Passive intermodulation (PIM) has emerged as a critical source of self-interference in modern MIMO-OFDM systems, especially under the stringent requirements of 5G and beyond. Conventional cancellation methods often rely on complex nonlinear…

Speaker-independent speech separation has achieved remarkable performance in recent years with the development of deep neural network (DNN). Various network architectures, from traditional convolutional neural network (CNN) and recurrent…

Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks. Starting from a pre-trained network, the process is as follows: remove redundant parameters, retrain, and…

One of the main bottlenecks in practical full-duplex systems is the oscillator phase noise, which bounds the possible cancellable self-interference power. In this paper, a digitaldomain self-interference cancellation scheme for full-duplex…

Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the…

Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do not support the use of complex-valued data. MRI data is…

In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train…

Full-duplex (FD) systems have the capability to transmit and receive at the same time in the same frequency band. FD systems can reduce congestion and latency and improve coverage and spectral efficiency. As a relay, they can increase range…

In this paper, we focus on reduced complexity full duplex Multiple-Input Multiple-Output (MIMO) systems and present a joint design of digital transmit and receive beamforming with Analog and Digital (A/D) self-interference cancellation. We…

In a cognitive radio system the failure of secondary user (SU) transceivers to promptly vacate the channel can introduce significant access-latency for primary or high-priority users (PU). In conventional cognitive radio systems, the…

In this paper we propose and analyze a novel self-interference cancellation structure for in-band MIMO full-duplex transceivers. The proposed structure utilizes reference receiver chains to obtain reference signals for digital…

Deep neural networks have reshaped modern machine learning by learning powerful latent representations that often align with the manifold hypothesis: high-dimensional data lie on lower-dimensional manifolds. In this paper, we establish a…