arXiv subjects

Dayuan Tan

Publications and source records attributed to Dayuan Tan.

6 recordsLinked to original sources

arXiv2026

Question Answering for Diagram-Rich Technical Meeting Videos

Software engineering increasingly relies on asynchronous communication artifacts, including recorded meetings where stakeholders discuss concerns, rationale, and decisions. These meetings often include diagram-based representations of requirements, system behavior, component interactions, and trace dependencies. Accessing knowledge from these meetings is challenging because recordings are long and relevant evidence is distributed across speech, slides, and technical diagrams. This paper reports our industrial experience developing and evaluating LMVQA, an LLM-based multimodal question-answering system for technical meeting videos. Developed in collaboration with engineers at Ciena, LMVQA supports the understanding of requirements and design intent by grounding answers in audio and visual evidence, with explicit handling of diagram-rich content such as requirements and UML diagrams. It processes each video once to build a reusable time-stamped evidence corpus for grounded question answering. Across a Ciena dataset and a public dataset, we show that LMVQA significantly improves answer accuracy compared to a state-of-the-art baseline, from 31% to 94% on the Ciena dataset and from 21% to 88% on the public dataset, with larger gains on diagram-rich videos. We further show that, after one-time indexing, LMVQA reduces average response time from 81.3s to 3.3s on Ciena and from 98.4s to 9.2s on the public dataset, while lowering average token-based LLM API cost by about 75%. Finally, our interviews with three domain experts show that engineers particularly value LMVQA for locating software-engineering-relevant information, revisiting rationale, and tracing answers to specific video segments.

cs.SE↗

arXiv2026

RFSS: A Multi-Standard RF Signal Source Separation Dataset with 3GPP-Standardized Channel and Hardware Impairments

The coexistence of heterogeneous cellular standards (2G-5G) in shared spectrum demands sophisticated RF source separation techniques, yet no public dataset exists for data-driven research on this problem. We present RFSS (RF Signal Source Separation), an open-source dataset of 100,000 multi-source RF signal samples generated with full 3GPP standards compliance. The dataset covers GSM (TS 45.004), UMTS (TS 25.211), LTE (TS 36.211), and 5G NR (TS 38.211), with 2-4 simultaneous sources per sample plus 4,000 single-source reference samples, at 30.72 MHz sample rate. Each sample passes through independent 3GPP TDL multipath fading channels and realistic hardware impairments: carrier frequency offset, I/Q imbalance, phase noise, DC offset, and PA nonlinearity (Rapp model). Two mixing modes are provided: co-channel (all sources at baseband) and adjacent-channel (each source frequency-shifted to its standard-specific carrier). The dataset totals 103 GB in HDF5 format with a 70/15/15 train/validation/test split. We benchmark five methods: FastICA, Frobenius-norm NMF, Conv-TasNet, DPRNN, and a CNN-LSTM baseline, evaluated using permutation-invariant SI-SINR (PI-SI-SINR). Conv-TasNet achieves -21.18 dB PI-SI-SINR on 2-source mixtures versus -34.91 dB for ICA, a 13.7 dB improvement. On co-channel mixtures, Conv-TasNet reaches -12.34 dB versus -28.04 dB for ICA and -16.19 dB for NMF. The dataset and evaluation code are publicly released at submission time.

eess.SP↗

arXiv2025

RFSS: A Comprehensive Multi-Standard RF Signal Source Separation Dataset with Advanced Channel Modeling

The rapid evolution of wireless communication systems has created complex electromagnetic environments where multiple cellular standards (2G/3G/4G/5G) coexist, necessitating advanced signal source separation techniques. We present RFSS (RF Signal Source Separation), a comprehensive open-source dataset containing 52,847 realistic multi-standard RF signal samples with complete 3GPP standards compliance. Our framework generates authentic baseband signals for GSM, UMTS, LTE, and 5G NR with advanced channel modeling including multipath fading, MIMO processing up to 8 by 8 antennas, and realistic interference scenarios. Experimental validation demonstrates superior performance of CNN-LSTM architectures achieving 26.7 dB SINR improvement in source separation tasks, significantly outperforming traditional ICA (15.2 dB) and NMF (18.3 dB) approaches. The RFSS dataset enables reproducible research in RF source separation, cognitive radio, and machine learning applications while maintaining complete open-source accessibility

eess.SP↗

arXiv2025

Weakly Supervised Convolutional Dictionary Learning for Multi-Label Classification

Convolutional Dictionary Learning (CDL) has emerged as a powerful approach for signal representation by learning translation-invariant features through convolution operations. While existing CDL methods are predominantly designed and used for fully supervised settings, many real-world classification tasks often rely on weakly labeled data, where only bag-level annotations are available. In this paper, we propose a novel weakly supervised convolutional dictionary learning framework that jointly learns shared and class-specific components, for multi-instance multi-label (MIML) classification where each example consists of multiple instances and may be associated with multiple labels. Our approach decomposes signals into background patterns captured by a shared dictionary and discriminative features encoded in class-specific dictionaries, with nuclear norm constraints preventing feature dilution. A Block Proximal Gradient method with Majorization (BPG-M) is developed to alternately update dictionary atoms and sparse coefficients, ensuring convergence to local minima. Furthermore, we incorporate a projection mechanism that aggregates instance-level predictions to bag-level labels through learnable pooling operators.Experimental results on both synthetic and real-world datasets demonstrate that our framework outperforms existing MIML methods in terms of classification performance, particularly in low-label regimes. The learned dictionaries provide interpretable representations while effectively handling background noise and variable-length instances, making the method suitable for applications such as environmental sound classification and RF signal analysis.

eess.SP↗

arXiv2024

A novel pedestrian road crossing simulator for dynamic traffic light scheduling systems

The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians' quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37 percent can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians' waiting time without sacrificing vehicular throughput.

eess.SY↗

arXiv2024

Dynamic Road Management in the Era of CAV

Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the vehicular traffic flow. Leveraging recent advances in wireless communication, researchers have proposed solutions based on vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communication in order to detect traffic jams and better disseminate data from on-road and on-vehicle sensors. Moreover, the development of connected autonomous vehicles (CAV) have motivated a paradigm shift in how traffic will be managed. Overall, these major technological advances have motivated the notion of dynamic traffic management (DTM), where smart road reconfiguration capabilities, e.g., dynamic lane reversal, adaptive traffic light timing, etc. will be exploited in real-time to improve traffic flow and adapt to unexpected incidents. This chapter discusses what the challenges in realizing DTM are and covers how CAV has revolutionized traffic management. Moreover, we highlight the issues for handling human-driven vehicles while roads are transitioning to CAV only traffic. Particularly, we articulate a new vision for inter-vehicle communication and assessment of road conditions, and promote a novel system for traffic management. Vehicle to on-road sensors as well as inter-vehicle connectivity will be enabled through the use of handheld devices such as smartphones. This not only enables real-time data sharing but also expedites the adoption of DTM without awaiting the dominant presence of autonomous vehicle on the road. ...

cs.NI↗