arXiv subjects

Jinyang Sun

Publications and source records attributed to Jinyang Sun.

6 recordsLinked to original sources

arXiv2026

Enabling Agile Ambient IoT Networking via a Parameterized Hybrid Radio

The emergence of Ambient IoT signals a paradigm shift toward massive batteryless networking. However, the absence of an agile physical layer substrate remains a fundamental barrier to research and standardization. Current testbeds are hindered by decoupled radio paths, high static power, and cumbersome control methods, which stifle rapid protocol prototyping. In this paper, we present Janus, the first hybrid active-passive configurable radio architected for agile Ambient IoT networking. Janus introduces a parameterized architecture that unifies passive and active transmission into a single RF front end, abstracting complex physical layer behaviors into concise parameters. This design enables a system-level control plane for dynamic mode transitions and an energy management plane for fine-grained harvesting across multiple sources. We implement a compact PCB prototype and evaluate its performance across diverse protocol landscapes, including 3GPP A-IoT, IEEE 802.11 AMP, and Bluetooth SIG. Our experimental results demonstrate that Janus achieves communication performance on par with dedicated radios while significantly reducing configuration overhead. Ultimately, Janus serves as a versatile enabler for validating emerging protocols and accelerating the standardization of next-generation low-power networks.

cs.NI↗

arXiv2025

A Composable Multimodal Framework for cine CMR-Text-Driven Prediction of Heart Failure Outcomes

Objective. Heart failure is one of the leading causes of death worldwide, with millions of deaths each year, according to data from the World Health Organization (WHO) and other public health agencies. While significant progress has been made in the field of heart failure, leading to improved survival rates and improvement of ejection fraction, there remains substantial unmet needs, due to the complexity and multifactorial characteristics. This study aims to propose and evaluate a composable strategy framework for assessment and treatment optimization in heart failure, designed to provide more holistic patient evaluation and management. Approach. The framework leverages multi-modal algorithms to analyze a comprehensive range of patient data, explicitly integrating cine cardiac magnetic resonance (cine CMR) sequences, structured clinical metrics (e.g., lab results, demographics), and unstructured textual records (e.g., medical history, prescriptions). By integrating these various data sources, our framework offers a more holistic evaluation and optimized treatment plan for patients. Main results. The multi-modal framework demonstrates superior accuracy in HF prognosis prediction compared to single-modal AI algorithms. Additionally, it enables a detailed evaluation of the impact of various pathological indicators on HF outcomes. Significance. By integrating heterogeneous clinical data in a systematic manner, this approach supports more comprehensive prognosis assessment and facilitates optimized, personalized treatment planning for heart failure patients.

cs.LG↗

arXiv2024

Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design

Non-Abelian braiding has attracted substantial attention because of its pivotal role in describing the exchange behaviour of anyons, in which the input and outcome of non-Abelian braiding are connected by a unitary matrix. Implementing braiding in a classical system can assist the experimental investigation of non-Abelian physics. However, the design of non-Abelian gauge fields faces numerous challenges stemmed from the intricate interplay of group structures, Lie algebra properties, representation theory, topology, and symmetry breaking. The extreme diversity makes it a powerful tool for the study of condensed matter physics. Whereas the widely used artificial intelligence with data-driven approaches has greatly promoted the development of physics, most works are limited on the data-to-data design. Here we propose a self-reasoning assistant learning framework capable of directly generating non-Abelian gauge fields. This framework utilizes the forward diffusion process to capture and reproduce the complex patterns and details inherent in the target distribution through continuous transformation. Then the reverse diffusion process is used to make the generated data closer to the distribution of the original situation. Thus, it owns strong self-reasoning capabilities, allowing to automatically discover the feature representation and capture more subtle relationships from the dataset. Moreover, the self-reasoning eliminates the need for manual feature engineering and simplifies the process of model building. Our framework offers a disruptive paradigm shift to parse complex physical processes, automatically uncovering patterns from massive datasets.

cs.LG↗

arXiv2024

Composable Generation Strategy Framework Enabled Bidirectional Design on Topological Circuits

Topological insulators show important properties, such as topological phase transitions and topological edge states. Although these properties and phenomena can be simulated by well-designed circuits, it is undoubtedly difficult to design such topological circuits due to the complex physical principles and calculations involved. Therefore, achieving a framework that can automatically to complete bidirectional design of topology circuits is very significant. Here, we propose an effective bidirectional collaborative design framework with strong task adaptability, which can automatically generate specific results according to our requirements. In the framework, a composable generation strategy is employed, which involves building a shared multimodal space by bridging alignment in the diffusion process. For simplicity, a series of two-dimensional (2D) Su-Schrieffer-Heeger (SSH) circuits are constructed with different structural parameters. The framework at first is applied to find the relationship between the structural information and topological features. Then, the correctness of the results through experimental measurements can be verified by the automatically generated circuit diagram following the manufacture of Printed Circuit Board (PCB). The framework is demonstrated by achieving good results in the reverse design of circuit structures and forward prediction of topological edge states, reaching an accuracy of 94%. Overall, our research demonstrates the enormous potential of the proposed bidirectional deep learning framework in complex tasks and provides insights for collaborative design tasks.

physics.app-ph↗

arXiv2024

Deep learning for the design of non-Hermitian topolectrical circuits

Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multi-layer perceptron (MLP), and convolutional neural network (CNN) in the field of deep learning, to predict the winding of eigenvalues non-Hermitian Hamiltonians. Subsequently, we use the smallest module of the periodic circuit as one unit to construct high-dimensional circuit data features. Further, we use the Dense Convolutional Network (DenseNet), a type of convolutional neural network that utilizes dense connections between layers to design a non-Hermitian topolectrical Chern circuit, as the DenseNet algorithm is more suitable for processing high-dimensional data. Our results demonstrate the effectiveness of the deep learning network in capturing the global topological characteristics of a non-Hermitian system based on training data.

physics.app-ph↗

arXiv2024

Photonic Modes Prediction via Multi-Modal Diffusion Model

The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell's equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the Multi-Modal Diffusion model to predict the photonic modes in one certain structure. The Contrastive Language-Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces Multi-Modal deep learning to construct complex mapping between structural information and light field as high-dimensional vectors, and generates light field images based on this mapping.

physics.optics↗