We present the development of a portable embedded system for real-time electrocardiogram (ECG) beat classification for ventricular dysfunction related arrhythmias and conditions. These conditions are clinically relevant for monitoring heart failure patients and possible Left Ventricular Assist Device (LVAD) need, as well as potentially enabling the monitoring of remodeling of the native heart after LVAD implantation. We create a dataset combining single-lead standard datasets from Physionet repository. The system processes single-lead ECG signals using a lightweight, quantized, hardware-accelerated model based on residual connections. We run the proof of concept in an ARM Cortex-M33-based SoC, measuring an inference time of 119 milliseconds with 0.95 mJ of energy consumption at 78 MHz, Flash usage of 709.6KB, and runtime RAM usage of 24.7 KB, with an average AUC of 0.9946 in the multiclass classification task. We validated the system end-to-end using a patient simulator and the MAX30003 sensor. The proposed system enables continuous monitoring without external computation, paving the way towards integration with LVAD systems to allow for patient monitoring.
Segmentation of magnetic resonance images (MRI) facilitates analysis of human brain development by delineating anatomical structures. However, in infants and young children, accurate segmentation is challenging due to development and imaging constraints. Pediatric brain MRI is notoriously difficult to acquire, with inconsistent availability of imaging modalities, substantial non-head anatomy in the field of view, and frequent motion artifacts. This has led to specialized segmentation models that are often limited to specific image types or narrow age groups, or that are fragile for more variable images such as those acquired clinically. We address this method fragmentation with BabySeg, a deep learning brain segmentation framework for infants and young children that supports diverse MRI protocols, including repeat scans and image types unavailable during training. Our approach builds on recent domain randomization techniques, which synthesize training images far beyond realistic bounds to promote dataset shift invariance. We also describe a mechanism that enables models to flexibly pool and interact features from any number of input scans. We demonstrate state-of-the-art performance that matches or exceeds the accuracy of several existing methods for various age cohorts and input configurations using a single model, in a fraction of the runtime required by many existing tools.
This paper proposes a data-driven generative framework for learning ray-tracing behavior directly from base station and user equipment locations. The channel is represented in the angular-delay domain, and a conditional denoising diffusion probabilistic model (cDDPM) is trained to learn its distribution conditioned on spatial coordinates. To enhance the use of location information, the model employs a cross-attention mechanism. The proposed approach captures geometry-dependent channel behavior without requiring explicit 3D scene information. Experiments in a realistic urban environment show that it outperforms both a convolutional neural network baseline and a naive cDDPM in predicting dominant path characteristics. These results highlight the promise of cross-attention-enhanced diffusion models for location-conditioned and data-driven synthesis of ray-traced wireless channels.
Despite significant advancements in deep learning-based CSI compression, some key limitations remain unaddressed. Current approaches predominantly treat CSI compression as a source coding problem, neglecting transmission errors. In finite block length regimes, separate source and channel coding proves suboptimal, with reconstruction performance deteriorating significantly under challenging channel conditions. While autoencoder-based compression schemes can be adapted for joint source-channel coding, they struggle to accurately model increasingly complex channel distributions.We propose Residual-Diffusion Joint Source-Channel Coding (RD-JSCC), a novel framework that leverages diffusion models to learn robust CSI representations. Our architecture integrates a lightweight autoencoder with a residual diffusion module that iteratively refines CSI reconstruction, enabling: (i) graceful performance degradation across variable SNR conditions, and (ii) robust CSI estimation under realistic multipath fading scenarios. Our flexible decoding strategy balances computational efficiency and performance by dynamically selecting between low-complexity autoencoder decoding and sophisticated diffusion-based refinement based on channel conditions. Comprehensive simulations demonstrate that RD-JSCC significantly outperforms existing autoencoder-based approaches, particularly in challenging wireless environments. Furthermore, we demonstrate substantial decoding latency reduction through a single-step inference variant of our diffusion model, offering an efficient and practical solution for next-generation wireless communication systems.
Hybrid parasitic arrays are one approach to scale array aperture sizes with limited number of radio frequency (RF) chains and low power consumption. In this architecture, several active elements are connected to RF chains while parasitic elements are connected to digitally controlled reactances. Achieving good communication performance requires sufficient mutual coupling between active and parasitic elements. In this paper, we optimize the assignment of parasitic and active elements at the transmitter based on the signal-to-noise ratio (SNR) at the receiver. For a fixed fraction of parasitic antenna elements, this problem becomes a constrained assignment task, which is NP-hard due to its combinatorial nature. We employ a simulated annealing approach to find near-optimal antenna configurations that work well in a variety of channel conditions. The numerical results show how the optimized assignment patterns become beneficial for channels with stronger line-of-sight (LOS) component.
