arXiv subjects

Ping-Cheng Yeh

Publications and source records attributed to Ping-Cheng Yeh.

16 recordsLinked to original sources

arXiv2026

SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising

Electrocardiogram (ECG) artifact contamination frequently occurs in surface electromyography (sEMG) when muscles are recorded near the heart. Existing neural network (NN)-based approaches typically perform waveform-level end-to-end denoising with pointwise losses, but often fail to preserve the spectral structures of sEMG. In addition, these models do not exploit the prior knowledge that ECG mainly distorts the low-frequency band of sEMG. We propose SSEMG-Net, a spectrogram-domain model that explicitly captures sEMG-ECG interactions using a bidirectional Mamba backbone and a dual-head decoder for magnitude masking and wrapped-phase estimation. The magnitude masking module is physiologically aware, restricting denoising to the low-frequency sub-band where ECG activity predominates. Experimental results demonstrate that SSEMG-Net achieves superior signal quality and lower feature-extraction error compared to prior methods, underscoring its potential for clinical applications where preserving sEMG spectral features is essential.

eess.SP↗

arXiv2026

Field-Assisted Molecular Communication: Girsanov-Based Channel Modeling and Dynamic Waveform Optimization

Analytical modeling of field-assisted molecular communication under dynamic electric fields is fundamentally challenging due to the coupling between stochastic transport and complex boundary geometries, which renders conventional partial differential equation (PDE) approaches intractable. In this work, we introduce an effective stochastic modeling approach to address this challenge. By leveraging trajectory-reweighting techniques, we derive analytically tractable channel impulse response (CIR) expressions for both fully-absorbing and passive spherical receivers, where the latter serves as an exact theoretical baseline to validate our modeling accuracy. Building upon these models, we establish a dynamic waveform design framework for system optimization. Under a maximum \textit{a posteriori} decision-feedback equalizer (MAP-DFE) framework, we show that the first-slot received probability serves as the primary determinant of the bit error probability (BEP), while inter-symbol interference manifests as higher-order corrections. Exploiting the monotonic response of the fully-absorbing architecture and using the limitations of the passive model to justify this strategic focus, we reformulate BEP minimization into a distance-based optimization problem. We propose a unified, low-complexity Maximize Received Probability (MRP) algorithm, encompassing the Maximize Hitting Probability (MHP) and Maximize Sensing Probability (MSP) methods, to dynamically enhance desired signals and suppress inter-symbol interference. Numerical results validate the accuracy of the proposed modeling approach and demonstrate near-optimal detection performance.

cs.IT↗

arXiv2025

Exact 3-D Channel Impulse Response Under Uniform Drift for Absorbing Spherical Receivers

An exact channel impulse response (CIR) for the three-dimensional point-to-sphere absorbing channel under drift has remained unavailable due to symmetry breaking. This letter closes this gap by deriving an exact analytical CIR for a fully absorbing spherical receiver under uniform drift with arbitrary direction. By formulating the problem in terms of joint first-hitting time-location statistics and applying a Girsanov-based measure change, drift effects are isolated into an explicit multiplicative factor, yielding an exact series representation. The resulting CIR provides a rigorous reference model and enables efficient, noise-free evaluation of key channel metrics without relying on Monte Carlo simulations.

cs.IT↗

arXiv2024

MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution

Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a lower sampling rate. In this study, we propose MSECG, a compact neural network model designed for ECG SR. MSECG combines the strength of the recurrent Mamba model with convolutional layers to capture both local and global dependencies in ECG waveforms, allowing for the effective reconstruction of high-resolution signals. We also assess the model's performance in real-world noisy conditions by utilizing ECG data from the PTB-XL database and noise data from the MIT-BIH Noise Stress Test Database. Experimental results show that MSECG outperforms two contemporary ECG SR models under both clean and noisy conditions while using fewer parameters, offering a more powerful and robust solution for long-term ECG monitoring applications.

cs.LG↗

arXiv2024

MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters.

eess.SP↗

arXiv2024

TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement

Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remove sEMG contaminants, as non-invasive measurements render sEMG susceptible to various contaminants. However, these approaches often rely on heuristic-based optimization and are sensitive to the contaminant type. A more potent, robust, and generalized sEMG denoising approach should be developed for various healthcare and human-computer interaction applications. This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. It leverages the potent nonlinear mapping capability and data-driven nature of NNs. TrustEMG-Net adopts a denoising autoencoder structure by combining U-Net with a Transformer encoder using a representation-masking approach. The proposed approach is evaluated using the Ninapro sEMG database with five common contamination types and signal-to-noise ratio (SNR) conditions. Compared with existing sEMG denoising methods, TrustEMG-Net achieves exceptional performance across the five evaluation metrics, exhibiting a minimum improvement of 20%. Its superiority is consistent under various conditions, including SNRs ranging from -14 to 2 dB and five contaminant types. An ablation study further proves that the design of TrustEMG-Net contributes to its optimality, providing high-quality sEMG and serving as an effective, robust, and generalized denoising solution for sEMG applications.

eess.SP↗

arXiv2024

Bridging the Gap: Integrating Pre-trained Speech Enhancement and Recognition Models for Robust Speech Recognition

Noise robustness is critical when applying automatic speech recognition (ASR) in real-world scenarios. One solution involves the used of speech enhancement (SE) models as the front end of ASR. However, neural network-based (NN-based) SE often introduces artifacts into the enhanced signals and harms ASR performance, particularly when SE and ASR are independently trained. Therefore, this study introduces a simple yet effective SE post-processing technique to address the gap between various pre-trained SE and ASR models. A bridge module, which is a lightweight NN, is proposed to evaluate the signal-level information of the speech signal. Subsequently, using the signal-level information, the observation addition technique is applied to effectively reduce the shortcomings of SE. The experimental results demonstrate the success of our method in integrating diverse pre-trained SE and ASR models, considerably boosting the ASR robustness. Crucially, no prior knowledge of the ASR or speech contents is required during the training or inference stages. Moreover, the effectiveness of this approach extends to different datasets without necessitating the fine-tuning of the bridge module, ensuring efficiency and improved generalization.

cs.SD↗

arXiv2024

A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the quality of real-world sEMG data more effectively, this study proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG signals. QASE-net combines CNN-BLSTM with attention mechanisms and follows an end-to-end training strategy. Our experimental framework utilizes real-world sEMG and ECG data from two open-access databases, the Non-Invasive Adaptive Prosthetics Database and the MIT-BIH Normal Sinus Rhythm Database, respectively. The experimental results demonstrate the superiority of QASE-net over the previous assessment model, exhibiting significantly reduced prediction errors and notably higher linear correlations with the ground truth. These findings show the potential of QASE-net to substantially enhance the reliability and precision of sEMG quality assessment in practical applications.

eess.SP↗

arXiv2015

The Learnability of Unknown Quantum Measurements

Quantum machine learning has received significant attention in recent years, and promising progress has been made in the development of quantum algorithms to speed up traditional machine learning tasks. In this work, however, we focus on investigating the information-theoretic upper bounds of sample complexity - how many training samples are sufficient to predict the future behaviour of an unknown target function. This kind of problem is, arguably, one of the most fundamental problems in statistical learning theory and the bounds for practical settings can be completely characterised by a simple measure of complexity. Our main result in the paper is that, for learning an unknown quantum measurement, the upper bound, given by the fat-shattering dimension, is linearly proportional to the dimension of the underlying Hilbert space. Learning an unknown quantum state becomes a dual problem to ours, and as a byproduct, we can recover Aaronson's famous result [Proc. R. Soc. A 463:3089-3144 (2007)] solely using a classical machine learning technique. In addition, other famous complexity measures like covering numbers and Rademacher complexities are derived explicitly. We are able to connect measures of sample complexity with various areas in quantum information science, e.g. quantum state/measurement tomography, quantum state discrimination and quantum random access codes, which may be of independent interest. Lastly, with the assistance of general Bloch-sphere representation, we show that learning quantum measurements/states can be mathematically formulated as a neural network. Consequently, classical ML algorithms can be applied to efficiently accomplish the two quantum learning tasks.

quant-ph↗

arXiv2014

On Timing Synchronization for Quantity-based Modulation in Additive Inverse Gaussian Channel with Drift

In Diffusion-based Molecular Communications, the channel between Transmitter Nano-machine (TN) and Receiver Nano-machine (RN) can be modeled by Additive Inverse Gaussian Channel, that is the first hitting time of messenger molecule released from TN and captured by RN follows Inverse Gaussian distribution. In this channel, a quantity-based modulation embedding message on the different quantity levels of messenger molecules relies on a time-slotted system between TN and RN. Accordingly, their clocks need to synchronize with each other. In this paper, we discuss the approaches to make RN estimate its timing offset between TN efficiently by the arrival times of molecules. We propose many methods such as Maximum Likelihood Estimation (MLE), Unbiased Linear Estimation (ULE), Iterative ULE, and Decision Feedback (DF). The numerical results shows the comparison of them. We evaluate these methods by not only the Mean Square Error, but also the computational complexity.

cs.ET↗

arXiv2013

Mathematical Foundations for Information Theory in Diffusion-Based Molecular Communications

Molecular communication emerges as a promising communication paradigm for nanotechnology. However, solid mathematical foundations for information-theoretic analysis of molecular communication have not yet been built. In particular, no one has ever proven that the channel coding theorem applies for molecular communication, and no relationship between information rate capacity (maximum mutual information) and code rate capacity (supremum achievable code rate) has been established. In this paper, we focus on a major subclass of molecular communication - the diffusion-based molecular communication. We provide solid mathematical foundations for information theory in diffusion-based molecular communication by creating a general diffusion-based molecular channel model in measure-theoretic form and prove its channel coding theorems. Various equivalence relationships between statistical and operational definitions of channel capacity are also established, including the most classic information rate capacity and code rate capacity. As byproducts, we have shown that the diffusion-based molecular channel is with "asymptotically decreasing input memory and anticipation" and "d-continuous". Other properties of diffusion-based molecular channel such as stationarity or ergodicity are also proven.

cs.IT↗

arXiv2012

A Cramer-Rao Bound for Semi-Blind Channel Estimation in Redundant Block Transmission Systems

A Cramer-Rao bound (CRB) for semi-blind channel estimators in redundant block transmission systems is derived. The derived CRB is valid for any system adopting a full-rank linear redundant precoder, including the popular cyclic-prefixed orthogonal frequency-division multiplexing system. Simple forms of CRBs for multiple complex parameters, either unconstrained or constrained by a holomorphic function, are also derived, which facilitate the CRB derivation of the problem of interest. The derived CRB is a lower bound on the variance of any unbiased semi-blind channel estimator, and can serve as a tractable performance metric for system design.

cs.IT↗

arXiv2012

An Interpretation of the Moore-Penrose Generalized Inverse of a Singular Fisher Information Matrix

It is proved that in a non-Bayesian parametric estimation problem, if the Fisher information matrix (FIM) is singular, unbiased estimators for the unknown parameter will not exist. Cramer-Rao bound (CRB), a popular tool to lower bound the variances of unbiased estimators, seems inapplicable in such situations. In this paper, we show that the Moore-Penrose generalized inverse of a singular FIM can be interpreted as the CRB corresponding to the minimum variance among all choices of minimum constraint functions. This result ensures the logical validity of applying the Moore-Penrose generalized inverse of an FIM as the covariance lower bound when the FIM is singular. Furthermore, the result can be applied as a performance bound on the joint design of constraint functions and unbiased estimators.

cs.IT↗

arXiv2011

Cramer-Rao Bound for Blind Channel Estimators in Redundant Block Transmission Systems

In this paper, we derive the Cramer-Rao bound (CRB) for blind channel estimation in redundant block transmission systems, a lower bound for the mean squared error of any blind channel estimators. The derived CRB is valid for any full-rank linear redundant precoder, including both zero-padded (ZP) and cyclic-prefixed (CP) precoders. A simple form of CRBs for multiple complex parameters is also derived and presented which facilitates the CRB derivation of the problem of interest. A comparison is made between the derived CRBs and performances of existing subspace-based blind channel estimators for both CP and ZP systems. Numerical results show that there is still some room for performance improvement of blind channel estimators.

cs.IT↗

arXiv2011

PMI-based MIMO OFDM PHY Integrated Key Exchange (P-MOPI) Scheme

In the literature, J.-P. Cheng et al. have proposed the MIMO-OFDM PHY integrated (MOPI) scheme for achieving physical-layer security in practice without using any cryptographic ciphers. The MOPI scheme uses channel sounding and physical-layer network coding (PNC) to prevent eavesdroppers from learning the channel state information (CSI). Nevertheless, due to the use of multiple antennas for PNC at transmitter and beamforming at receiver, it is not possible to have spatial multiplexing nor use space-time codes in our previous MOPI scheme. In this paper, we propose a variant of the MOPI scheme, called P-MOPI, that works with a cryptographic cipher and utilizes precoding matrix index (PMI) as an efficient key-exchange mechanism. With channel sounding, the PMI is only known between the transmitter and the legal receiver. The shared key can then be used, e.g., as the seed to generate pseudo random bit sequences for securing subsequent transmissions using a stream cipher. By applying the same techniques at independent subcarriers of the OFDM system, the P-MOPI scheme easily allows two communicating parties to exchange over 100 secret bits. As a result, not only secure communication but also the MIMO gain can be guaranteed by using the P-MOPI scheme.

cs.IT↗

arXiv2010

Prediction-based Adaptation (PRADA) Algorithm for Modulation and Coding

In this paper, we propose a novel adaptive modulation and coding (AMC) algorithm dedicated to reduce the feedback frequency of the channel state information (CSI). There have been already plenty of works on AMC so as to exploit the bandwidth more efficiently with the CSI feedback to the transmitter. However, in some occasions, frequent CSI feedback is not favorable in these systems. This work considers finite-state Markov chain (FSMC) based channel prediction to alleviate the feedback while maximizing the overall throughput. We derive the close-form of the frame error rate (FER) based on channel prediction using limited CSI feedback. In addition, instead of switching settings according to the CSI, we also provide means to combine both CSI and FER as the switching parameter. Numerical results illustrate that the average throughput of the proposed algorithm has significant performance improvement over fixed modulation and coding while the CSI feedback being largely reduced.

cs.IT↗