arXiv subjects

Tiancheng Cao

Publications and source records attributed to Tiancheng Cao.

11 recordsLinked to original sources

arXiv2026

Reevaluating Bluetooth Low Energy for Ingestible Electronics

Bluetooth Low Energy (BLE) is widely used in wearable devices but has rarely been considered for ingestible electronics because of severe tissue attenuation at 2.4 GHz. In this work, we systematically reevaluate BLE for ingestible applications by benchmarking it against representative Sub-GHz schemes in terms of tissue attenuation, power consumption, throughput, reliability, bidirectional communication, latency, and system integration. Water-phantom and in vivo measurements confirm stronger attenuation at 2.4 GHz, by approximately 40 dB; however, front-end-module (FEM)-assisted BLE compensates for this loss while maintaining practical received signal levels and energy efficiency. For ingestible sensing applications requiring below 100 kbps, BLE with a 20 dBm transmission power achieves lower power consumption than the evaluated Sub-GHz alternatives. BLE also provides protocol-level reliability through retransmission and in-order packet delivery, whereas Sub-GHz links can suffer packet loss under high attenuation. In addition, BLE supports bidirectional communication with limited throughput degradation, while the evaluated Sub-GHz implementation shows a substantial reduction. End-to-end measurements further demonstrate lower BLE latency because of its native compatibility with modern computing infrastructure. Finally, BLE offers advantages in antenna form factor, ecosystem support, and system integration. These results demonstrate that properly configured BLE is a practical communication solution for next-generation ingestible electronics.

eess.SY↗

arXiv2026

Dual IMU System for Accurate Gastrointestinal Motility Tracking

Gastrointestinal (GI) motility disorders impair the coordinated movement of food through the digestive tract. The Wireless Motility Capsule was developed to indirectly assess GI motility by recording pressure, pH, and temperature along the GI tract. Although an inertial measurement unit (IMU) enables precise and direct motion tracking, a single-IMU system is highly susceptible to artifacts caused by body movement and respiration. To address this limitation, we propose a dual-IMU system designed to distinguish intrinsic GI motility from extrinsic body motion. One IMU is integrated into an ingestible capsule to monitor internal movement, while a second IMU is worn externally to capture whole-body dynamics. Experimental validation was conducted using a turntable to simulate random body movements and a Stewart platform oscillating at 0.1 Hz to emulate GI motility. Three sensor fusion algorithms, including the Extended Kalman Filter, Madgwick Filter, and Mahony Filter, were benchmarked to calculate the real-time relative orientation between the two IMUs using quaternion analysis. The results show that the dual-IMU system effectively differentiates platform motion from turntable motion, demonstrating its potential to isolate GI-specific motility. Pearson correlation coefficients greater than 0.8 between the baseline and noise-cancelled motility signals indicate strong agreement and effective noise suppression.

eess.SP↗

arXiv2026

Enhanced-BLE: A Hybrid BLE-ESB Framework for Dynamically Reconfigurable and Energy-Efficient 2.4 GHz IoT Communication

Bluetooth Low Energy (BLE) is widely used in IoT systems because of its low power consumption, interoperability, and reliable bidirectional communication. However, its connection-oriented architecture introduces trade-offs among wake-up latency, throughput, and energy efficiency, limiting its suitability for burst-mode and on-demand sensing applications. Enhanced ShockBurst (ESB), a lightweight connectionless protocol supported by the same 2.4 GHz Nordic Semiconductor hardware, enables fast wake-up and efficient data transmission, but does not provide BLE-level robustness for sustained bidirectional communication. This work systematically benchmarks BLE and ESB on a unified Nordic nRF54L15 platform and proposes Enhanced-BLE, a hybrid framework that integrates the two protocols to extend conventional BLE operation. Experimental results show that ESB nearly halves packet transmission time and energy compared with BLE, doubles the achievable forward throughput, and reduces wake-up latency and energy by nearly twentyfold during intermittent operation. However, ESB reverse transmission may suffer packet loss, whereas BLE maintains reliable bidirectional communication. Enhanced-BLE addresses this trade-off through adaptive radio scheduling and coexistence-aware connection management, combining ESB-based high-throughput forward transmission with BLE-based reliable reverse communication. The framework enables BLE-to-ESB handover within approximately 18 ms and restores BLE operation within 49 ms from standby mode. Enhanced-BLE also achieves approximately twofold higher forward throughput than BLE while reducing wake-up latency. These results demonstrate a practical and hardware-compatible strategy for low-latency, high-throughput, energy-efficient, and reliable 2.4 GHz IoT communication.

cs.NI↗

arXiv2026

BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring

Electrocardiogram (ECG) monitoring in Internet of Medical Things (IoMT) networks is constrained by strict data-sharing regulations and privacy concerns. Federated learning (FL) enables collaborative learning by keeping raw ECG data on devices, but frequent transmissions of high-dimensional model updates incur heavy per-round traffic over bandwidth-limited links. To alleviate this bottleneck, federated distillation (FD) replaces parameter exchange with logit-based knowledge transfer. However, the performance of FD often degrades under the non-independent and identically distributed (non-IID) and long-tailed label distributions in ECG deployments. To address these challenges, we propose a bidirectional federated knowledge distillation (BiFedKD) framework that employs an aggregation-by-distillation pipeline with temperature scaling to produce a stable global distillation signal for cross-client alignment. Experiments on the MIT-BIH Arrhythmia dataset show that BiFedKD improves accuracy and Macro-F1 over the baseline by $3.52\%$ and $9.93\%$, respectively. Moreover, to reach the same Macro-F1, BiFedKD reduces communication overhead by $40\%$ and computation cost by $71.7\%$ compared with the baseline.

cs.AI↗

arXiv2026

Multiprotocol Wireless Timer Synchronization for IoT Systems

Accurate time synchronization is essential for Internet of Things (IoT) systems, where multiple distributed nodes must share a common time base for coordinated sensing and data fusion. However, conventional synchronization approaches suffer from nondeterministic transmission latency, limited precision, or restricted bidirectional functionality. This paper presents a protocol-independent wireless timer synchronization method that exploits radio timeslots to transmit precisely timestamped beacons in a proprietary radio mode. By decoupling synchronization from upper-layer packet retransmissions and leveraging hardware-timed radio events, the proposed approach significantly reduces scheduling uncertainty and achieves nanosecond-level synchronization accuracy. Comprehensive experiments evaluate the impacts of synchronization frequency, RSSI, BLE connection interval, and throughput on synchronization performance. The results demonstrate that an optimal synchronization frequency of 1000 Hz yields an approximately 20 ns delay in the absence of communication stack activity while maintaining sub-500 ns accuracy under most realistic BLE traffic conditions. Furthermore, larger connection intervals, lower application throughput, and higher RSSI consistently improve synchronization quality by reducing radio resource contention and packet loss. The proposed scheme provides a general and high-precision synchronization solution suitable for resource-constrained IoT systems.

cs.NI↗

arXiv2026

A Capsule-Sized Multi-Wavelength Wireless Optical System for Edge-AI-Based Classification of Gastrointestinal Bleeding Flow Rate

Post-endoscopic gastrointestinal (GI) rebleeding frequently occurs within the first 72 hours after therapeutic hemostasis and remains a major cause of early morbidity and mortality. Existing non-invasive monitoring approaches primarily provide binary blood detection and lack quantitative assessment of bleeding severity or flow dynamic, limiting their ability to support timely clinical decision-making during this high-risk period. In this work, we developed a capsule-sized, multi-wavelength optical sensing wireless platform for order-of-magnitude-level classification of GI bleeding flow rate, leveraging transmission spectroscopy and low-power edge artificial intelligence. The system performs time-resolved, multi-spectral measurements and employs a lightweight two-dimensional convolutional neural network for on-device flow-rate classification, with physics-based validation confirming consistency with wavelength-dependent hemoglobin absorption behavior. In controlled in vitro experiments under simulated gastric conditions, the proposed approach achieved an overall classification accuracy of 98.75% across multiple bleeding flow-rate levels while robustly distinguishing diverse non-blood gastrointestinal interference. By performing embedded inference directly on the capsule electronics, the system reduced overall energy consumption by approximately 88% compared with continuous wireless transmission of raw data, making prolonged, battery-powered operation feasible. Extending capsule-based diagnostics beyond binary blood detection toward continuous, site-specific assessment of bleeding severity, this platform has the potential to support earlier identification of clinically significant rebleeding and inform timely re-intervention during post-endoscopic surveillance.

eess.IV↗

arXiv2025

Step-Audio 2 Technical Report

This paper presents Step-Audio 2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent audio encoder and reasoning-centric reinforcement learning (RL), Step-Audio 2 achieves promising performance in automatic speech recognition (ASR) and audio understanding. To facilitate genuine end-to-end speech conversation, Step-Audio 2 incorporates the generation of discrete audio tokens into language modeling, significantly enhancing its responsiveness to paralinguistic information such as speaking styles and emotions. To effectively leverage the rich textual and acoustic knowledge in real-world data, Step-Audio 2 integrates retrieval-augmented generation (RAG) and is able to call external tools such as web search to mitigate hallucination and audio search to switch timbres. Trained on millions of hours of speech and audio data, Step-Audio 2 delivers intelligence and expressiveness across diverse conversational scenarios. Evaluation results demonstrate that Step-Audio 2 achieves state-of-the-art performance on various audio understanding and conversational benchmarks compared to other open-source and commercial solutions. Please visit https://github.com/stepfun-ai/Step-Audio2 for more information.

cs.CL↗

arXiv2025

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model

Large Audio-Language Models (LALMs) have significantly advanced intelligent human-computer interaction, yet their reliance on text-based outputs limits their ability to generate natural speech responses directly, hindering seamless audio interactions. To address this, we introduce Step-Audio-AQAA, a fully end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks. The model integrates a dual-codebook audio tokenizer for linguistic and semantic feature extraction, a 130-billion-parameter backbone LLM and a neural vocoder for high-fidelity speech synthesis. Our post-training approach employs interleaved token-output of text and audio to enhance semantic coherence and combines Direct Preference Optimization (DPO) with model merge to improve performance. Evaluations on the StepEval-Audio-360 benchmark demonstrate that Step-Audio-AQAA excels especially in speech control, outperforming the state-of-art LALMs in key areas. This work contributes a promising solution for end-to-end LALMs and highlights the critical role of token-based vocoder in enhancing overall performance for AQAA tasks.

cs.SD↗

arXiv2025

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression Variational Autoencoder, Video-VAE, is designed for video generation tasks, achieving 16x16 spatial and 8x temporal compression ratios, while maintaining exceptional video reconstruction quality. User prompts are encoded using two bilingual text encoders to handle both English and Chinese. A DiT with 3D full attention is trained using Flow Matching and is employed to denoise input noise into latent frames. A video-based DPO approach, Video-DPO, is applied to reduce artifacts and improve the visual quality of the generated videos. We also detail our training strategies and share key observations and insights. Step-Video-T2V's performance is evaluated on a novel video generation benchmark, Step-Video-T2V-Eval, demonstrating its state-of-the-art text-to-video quality when compared with both open-source and commercial engines. Additionally, we discuss the limitations of current diffusion-based model paradigm and outline future directions for video foundation models. We make both Step-Video-T2V and Step-Video-T2V-Eval available at https://github.com/stepfun-ai/Step-Video-T2V. The online version can be accessed from https://yuewen.cn/videos as well. Our goal is to accelerate the innovation of video foundation models and empower video content creators.

cs.CV↗

arXiv2025

Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction

Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in voice data collection, weakness in dynamic control, and limited intelligence. To address these challenges, this paper introduces Step-Audio, the first production-ready open-source solution. Key contributions include: 1) a 130B-parameter unified speech-text multi-modal model that achieves unified understanding and generation, with the Step-Audio-Chat version open-sourced; 2) a generative speech data engine that establishes an affordable voice cloning framework and produces the open-sourced lightweight Step-Audio-TTS-3B model through distillation; 3) an instruction-driven fine control system enabling dynamic adjustments across dialects, emotions, singing, and RAP; 4) an enhanced cognitive architecture augmented with tool calling and role-playing abilities to manage complex tasks effectively. Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following. On open-source benchmarks like LLaMA Question, shows 9.3% average performance improvement, demonstrating our commitment to advancing the development of open-source multi-modal language technologies. Our code and models are available at https://github.com/stepfun-ai/Step-Audio.

cs.CL↗

arXiv2016

Bilinear identities for an extended B-type Kadomtsev-Petviashvili hierarchy

In this paper, we construct the bilinear identities for the wave functions of an extended B-type Kadomtsev-Petviashvili (BKP) hierarchy, which contains two types of (2+1)-dimensional Sawada-Kotera equation with a self-consistent source (2d-SKwS-I and 2d-SKwS-II). By introducing an auxiliary variable corresponding to the extended flow for the BKP hierarchy, we find the tau-function and the bilinear identities for this extended BKP hierarchy. The bilinear identities can generate all the Hirota's bilinear equations for the zero-curvature forms of this extended BKP hierarchy. As examples, the Hirota's bilinear equations for the two types of 2d-SKwS (both 2d-SKwS-I and 2d-SKwS-II) will be given explicitly.

nlin.SI↗