arXiv subjects

Khanh Tran

Publications and source records attributed to Khanh Tran.

4 recordsLinked to original sources

arXiv2026

ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification

Continuous cardiac monitoring in wearable devices demands classifiers that are simultaneously accurate, energy-efficient, and deployable on resource-constrained hardware. While deep neural network approaches have demonstrated high classification accuracy for electrocardiogram (ECG) arrhythmia detection, their substantial parameter counts and reliance on multiply-accumulate-intensive operations make them impractical for low-cost edge platforms. In this work, we propose ECG-LDC, a hardware-software co-design framework that adapts Low-Dimensional Computing (LDC) for real-time ECG arrhythmia classification. ECG-LDC employs a dual-encoder architecture with dedicated value and feature codebooks to independently encode morphological waveform features and RR-interval temporal features, enabling effective capture of both intra-beat and inter-beat cardiac dynamics. The framework encompasses data preprocessing, model training, and a hardware accelerator architecture prototyped on the Pynq-Z2 platform. Implemented using binary representations and XOR/XNOR-based operations, ECG-LDC achieves $97.18\%$ accuracy with a memory footprint of only $3.86\ \text{kB}$. ECG-LDC sacrifices approximately $1.8\%$ accuracy versus SOTA TinyML classifiers but achieves $11$~$ 570\times$ reduction in memory usage; among FPGA-based five-class arrhythmia classifiers, it delivers the highest accuracy with up to $2.4\times$ fewer LUTs and zero DSP block utilization, affirming its suitability for real-time arrhythmia detection on resource-constrained wearable platforms.

eess.SP↗

arXiv2024

A manufacturable platform for photonic quantum computing

Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable platform for quantum computing with photons. We benchmark a set of monolithically-integrated silicon photonics-based modules to generate, manipulate, network, and detect photonic qubits, demonstrating dual-rail photonic qubits with $99.98\% \pm 0.01\%$ state preparation and measurement fidelity, Hong-Ou-Mandel quantum interference between independent photon sources with $99.50\%\pm0.25\%$ visibility, two-qubit fusion with $99.22\%\pm0.12\%$ fidelity, and a chip-to-chip qubit interconnect with $99.72\%\pm0.04\%$ fidelity, not accounting for loss. In addition, we preview a selection of next generation technologies, demonstrating low-loss silicon nitride waveguides and components, fabrication-tolerant photon sources, high-efficiency photon-number-resolving detectors, low-loss chip-to-fiber coupling, and barium titanate electro-optic phase shifters.

quant-ph↗

arXiv2023

HUVECs-encapsulation via Millimeter-sized Alginate Droplets

Droplet microfluidics are a powerful approach for hydrogel cell encapsulations. Much of the field has focused on single-cell encapsulations with pico-nanoliter droplet volumes necessary for single-cell sequencing or high-throughput screening. These small volumes, however, limit the use of hydrogel droplets for tissue engineering or cell therapies. We describe simple droplet microfluidics to generate millimeter-sized alginate droplets and demonstrate their use for cell encapsulations. This effort builds on our recent efforts, specifically by replacing the glass slide forming the bottom layer of the chamber with a more hydrophobic acrylic (PMMA) layer to improve the alginate-in-oil droplet formation. Using glass layer and PMMA layer devices, we characterized the tunable production of water-in-oil droplets (average droplet lengths ranged from 0.8 to 3.7 mm). Next, PMMA layer devices were used to demonstrate the tunable generation of alginate-in-oil droplets (average droplet lengths ranged from 3-6 mm). Increasing the flow ratio (Q.ratio = Q.oil/Q.alginate) led to more uniform droplets as measured by the coefficient of variance, which was approximately 5%. Finally, a proof-of-use experiment used HUVEC-encapsulated alginate droplets as part of a scratch-healing assay. Specifically, HUVEC-encapsulated droplets (AH droplets) led to the recovery of 3T3 fibroblast monolayers compared to no droplets or cell-free droplets (A droplets). Our results extended the use of simple microfluidics to generate and retrieve millimeter-sized alginate droplets for effective cell encapsulations.

physics.flu-dyn↗

arXiv2022

A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency

Deep learning has achieved great success in many applications. However, its deployment in practice has been hurdled by two issues: the privacy of data that has to be aggregated centrally for model training and high communication overhead due to transmission of a large amount of data usually geographically distributed. Addressing both issues is challenging and most existing works could not provide an efficient solution. In this paper, we develop FedPC, a Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency. The framework allows a model to be learned on multiple private datasets while not revealing any information of training data, even with intermediate data. The framework also minimizes the amount of data exchanged to update the model. We formally prove the convergence of the learning model when training with FedPC and its privacy-preserving property. We perform extensive experiments to evaluate the performance of FedPC in terms of the approximation to the upper-bound performance (when training centrally) and communication overhead. The results show that FedPC maintains the performance approximation of the models within $8.5\%$ of the centrally-trained models when data is distributed to 10 computing nodes. FedPC also reduces the communication overhead by up to $42.20\%$ compared to existing works.

cs.DC↗