arXiv subjects

Wang Hao

Publications and source records attributed to Wang Hao.

12 recordsLinked to original sources

arXiv2026

Angle Estimation via WFRFT Spatial-Domain Basis Decomposition: Breaking the Rayleigh Resolution Limit with Structured Waveform Diversity

We propose a MIMO radar angle estimation framework that uses the four-component weighted-type fractional Fourier transform (4-WFRFT) as a spatial-domain waveform diversity mechanism. Unlike conventional fractional Fourier (FrFT) MIMO radar where FrFT serves as a receiver-side time-frequency processing tool, our approach decomposes a data sequence into four WFRFT basis functions,original signal, its Fourier transform, time-reversal, and inverse Fourier transform, and transmits them simultaneously from a four-element uniform linear array. The spatial superposition of these basis functions at each far-field angle creates a unique angle-dependent waveform structure, enabling angle estimation through time-domain matched filtering with known waveforms. We demonstrate that this spatial-domain mixing achieves angular resolution surpassing the Rayleigh diffraction limit by a factor of 1.4$\times$ to 12.8$\times$, with the advantage most pronounced at low SNR where conventional beamforming fails completely. The Cram\'er-Rao bound is derived with a full 3-parameter Fisher information matrix, and the Fisher information is decomposed into geometry and waveform contributions, revealing that the WFRFT waveform structure contributes approximately 3$\times$ more information than array geometry alone. Extension to $M$-element arrays with $M$-component WFRFT demonstrates resolution gain scaling with array size. Simulations with linear chirp base sequences achieve 0\,dB PAPR and validate sub-Rayleigh resolution with a four-element array.

eess.SP↗

arXiv2026

Equation Asymmetry: An Algebraic Framework for Unifying Secrecy and Covertness in Information-Theoretic Security

This paper studies the algebraic structure underlying a broad class of information-theoretic security problems. We define the equation asymmetry degree (EAD) as $\Phi = (n - r)/n$, where $n$ is the signal embedding dimension and $r$ is the effective rank of the adversary's observation matrix. This single parameter is shown to simultaneously govern both secrecy (measured by equivocation $H(M|Y_E)$) and covertness (measured by detection error probability $P_e$). On finite fields $\mathbb{F}_q$, we establish the equivocation lower bound $H(M|Y_E) = \min(k, n - r_E) \log q$ with exact probabilistic conditions (Theorem~1), the secrecy capacity $C_s = (n - r_E) \log q$ with complete achievability and converse proofs (Theorem~2), and a strong converse (Theorem~8). In the continuous Gaussian regime, we derive a differential-entropy equivocation bound (Lemma~1), the high-SNR secrecy capacity asymptotics (Lemma~2), and a 2-Wasserstein distance covertness condition $W_2 \approx \sqrt{r_W} \cdot P / (2N\sigma) \to 0$ (Theorem~5'). The EAD-SDoF equivalence $d_s = n \cdot \Phi$ is established (Theorem~7). Both $\eta_s$ and $\eta_c$ are shown to be monotone functions of $\Phi$ (Theorem~6), with a Pearson correlation of $0.997$ in continuous-domain experiments. Seven existing security schemes -- matrix embedding, MIMO wiretap, secure network coding, FRFT multi-angle transmission, traffic steganography, group-key secure summation, and MDS secure summation -- are unified under the common form $C_s = (n - r) \log q$. Post-quantum security follows from the information-theoretic hardness of underdetermined linear systems (Theorem~9). All numerical experiments are reproducible with open-source code.

cs.IT↗

arXiv2026

WHTDM: Walsh-Hadamard Transform Division Multiplexing for Doubly-Selective Channels

We propose Walsh-Hadamard Transform Division Multiplexing (WHTDM), a multicarrier waveform that replaces the conventional IFFT/FFT pair in OFDM with a real-valued, unitary Walsh-Hadamard transform (WHT). WHTDM inherits the CP-OFDM transceiver structure while eliminating all complex multiplications from the transform stage, yielding a transmitter with zero real multipliers in the core modulation block. For detection under doubly-selective channels, we adopt a cross-domain memory approximate message passing (CD-MAMP) equalizer that operates on the banded structure of the equivalent WHT-domain channel matrix. Simulation results under the 3GPP TDL-C channel model at 28 GHz demonstrate that WHTDM with CD-MAMP significantly outperforms conventional OFDM 1-tap MMSE at high mobility, achieving over an order of magnitude lower BER at 120 km/h. Among the compared CD-MAMP-equalized new waveforms, WHTDM achieves the best BER performance while maintaining a transmitter complexity 2.5 $\times$ lower than OFDM and completely eliminating complex multipliers from the transform stage, making it well-suited for low-power IoT terminals.

eess.SP↗

arXiv2026

WH Statistics: Generalized Pauli Principle for Partially Distinguishable Particles

Traditional statistical mechanics is constrained by the binary paradigms of identical/distinguishable and bosonic/fermionic particle statistics, leading to a fundamental logical gap in describing systems with partial distinguishability. We propose WH Statistics, a unified theoretical framework governed by three key parameters: continuous distinguishability {\lambda}, exclusion weight \k{appa}, and intrinsic exclusivity {\gamma}. By deriving the microstate count and entropy, we show that this framework naturally recovers the Bose-Einstein, Fermi-Dirac, and Maxwell-Boltzmann statistics, while also incorporating anyons and the classical hard-core (Langmuir) limit. We introduce a class of generalized quasiparticles, termed WHons, which exhibit exotic physical phenomena including non-monotonic degeneracy pressure peaks, Schottky-like specific heat anomalies, and tunable interference effects, driven by the interplay between fractional distinguishability and exclusion. This framework bridges the century-old discontinuity between quantum and classical exclusion principles, providing a powerful tool for investigating strongly correlated systems and programmable quantum matter.

cond-mat.stat-mech↗

arXiv2025

CRMD: Complex Robust Modal Decomposition

Compared to real-valued signals, complex-valued signals provide a unique and intuitive representation of the phase of real physical systems and processes, which holds fundamental significance and is widely applied across many fields of science and engineering. In this paper, we propose a robust modal decomposition (RMD) in the complex domain as a natural and general extension of the original real-valued RMD. We revisit and derive the mathematical principles of RMD in the complex domain, and develop an algorithmic version tailored for this domain. Extensive experiments are conducted on synthetic simulation datasets and real-world datasets from diverse fields, including a millimeter-wave radar physiological signal detection dataset, a faulty bearing dataset, a radio-frequency unmanned aerial vehicle identification dataset, and a WiFi CSI-based respiration detection dataset. The results demonstrate that the proposed complex-domain robust modal decomposition significantly improves performance across these various applications.

eess.SP↗

arXiv2025

HIT-ROCKET: Hadamard-vector Inner-product Transformer for ROCKET

Time series classification holds broad application value in communications, information countermeasures, finance, and medicine. However, state-of-the-art (SOTA) methods-including HIVE-COTE, Proximity Forest, and TS-CHIEF-exhibit high computational complexity, coupled with lengthy parameter tuning and training cycles. In contrast, lightweight solutions like ROCKET (Random Convolutional Kernel Transform) offer greater efficiency but leave substantial room for improvement in kernel selection and computational overhead. To address these challenges, we propose a feature extraction approach based on Hadamard convolutional transform, utilizing column or row vectors of Hadamard matrices as convolution kernels with extended lengths of varying sizes. This enhancement maintains full compatibility with existing methods (e.g., ROCKET) while leveraging kernel orthogonality to boost computational efficiency, robustness, and adaptability. Comprehensive experiments on multi-domain datasets-focusing on the UCR time series dataset-demonstrate SOTA performance: F1-score improved by at least 5% vs. ROCKET, with 50% shorter training time than miniROCKET (fastest ROCKET variant) under identical hyperparameters, enabling deployment on ultra-low-power embedded devices. All code is available on GitHub.

cs.LG↗

arXiv2025

Encoding orbital angular momentum of light in space with optical catastrophes

Light beams carrying orbital angular momentum (OAM) possess an unbounded set of orthogonal modes, offering significant potential for optical communication and security. However, exploiting OAM beams in space has been hindered by the lack of a versatile design toolkit. Here, we demonstrate a strategy to tailor OAM across multiple transverse planes by shaping optical caustics leveraging on catastrophe theory. With complex-amplitude metasurfaces fabricated using two-photon polymerization lithography, we construct these caustics to steer Poynting vectors and achieve arbitrary shapes of OAM beams. Interestingly, we use such an approach to realize hidden OAM along the propagation trajectory, where the intensity of the beam is spread out thus avoiding detection. The OAM of these beams can be intrinsic, which avoids OAM distortions arising from the mixing of intrinsic and extrinsic components. By exploiting this intrinsic nature of OAM, we demonstrate the detection of encoded information in optical encryption. Our approach provides a unique framework for dynamic control of OAM in space, with promising applications in optical trapping and sensing, high-capacity data storage, and optical information security.

physics.optics↗

arXiv2025

Electrochemical properties of solid oxide fuel cells under the coupling effect of airflow pattern and airflow velocity

Under the dual background of deep adjustment of global energy pattern and severe challenges of environmental problems, solid oxide fuel cell (SOFC) has become the focus of research on efficient and clean energy conversion technology due to its many excellent characteristics. The electrochemical performance of SOFC is affected by various factors such as gas flow pattern (co-flow, counter-flow, cross-flow), flow rate (cathode and anode channel gases), and operating voltage. Accurately analysing the variation of electrochemical indexes with each factor is the basis for proposing the design scheme of high efficiency reaction of the cell. Therefore, a three-dimensional multi-field coupling model of SOFC is established in this study, and the model parameters and boundary conditions covering electrochemistry, gas flow, substance diffusion, etc. are set to study the influence of the coupling between factors on the electrochemical performance of the cell. These results show that with the decrease of operating voltage, the electrochemical reaction rate of the cell increases significantly, the gas mole fraction gradient increases, and the inhomogeneity of the electrolyte current density distribution is enhanced. Under low-voltage operating conditions, the cross-flow flow pattern shows better electrochemical performance advantages, and its power density profile takes the lead in different current density intervals. With the increase of the flow rate of the flow channel gas, the output power density curve of the cell shows an overall upward trend, and then the driving effect of the flow rate increase on the power density increase is gradually weakened due to the saturated cathodic reaction. This study reveals the influence of the coupling of flow pattern, flow rate and voltage on the electrochemical performance of SOFC, and provides guidance for the commercial application of SOFC.

physics.app-ph↗

arXiv2025

RMD: Robust Modal Decomposition with Constrained Bandwidth

Modal decomposition techniques, such as Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Singular Spectrum Analysis (SSA), have advanced time-frequency signal analysis since the early 21st century. These methods are generally classified into two categories: numerical optimization-based methods (EMD, VMD) and spectral decomposition methods (SSA) that consider the physical meaning of signals. The former can produce spurious modes due to the lack of physical constraints, while the latter is more sensitive to noise and struggles with nonlinear signals. Despite continuous improvements in these methods, a modal decomposition approach that effectively combines the strengths of both categories remains elusive. Thus, this paper proposes a Robust Modal Decomposition (RMD) method with constrained bandwidth, which preserves the intrinsic structure of the signal by mapping the time series into its trajectory-GRAM matrix in phase space. Moreover, the method incorporates bandwidth constraints during the decomposition process, enhancing noise resistance. Extensive experiments were conducted to validate its performance: on the synthetic dataset front, we focused on low-SNR sine wave separation tasks and nonlinear signal processing experiments to verify the ability of RMD to extract weak signals and handle nonlinear distortions; on real-world data, we tested it on a real-collected millimeter-wave micro-motion energy dataset to demonstrate its practical applicability in radar-related micro-motion signature analysis. All code and dataset samples are publicly available on GitHub for reproducibility: https://github.com/Einstein-sworder/RMD.

eess.SP↗

arXiv2023

Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization

Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence (target text) aloud. However, most existing alignment algorithms assume that the content involved in the two modalities is perfectly matched, thus leading to difficulty in locating such mismatch between speech and text. In this work, we develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal sequential data, especially for speech-text sequences. More specifically, we propose a hierarchical Bayesian deep learning model, dubbed mismatch localization variational autoencoder (ML-VAE), which decomposes the generative process of the speech into hierarchically structured latent variables, indicating the relationship between the two modalities. Training such a model is very challenging due to the discrete latent variables with complex dependencies involved. To address this challenge, we propose a novel and effective training procedure that alternates between estimating the hard assignments of the discrete latent variables over a specifically designed mismatch localization finite-state acceptor (ML-FSA) and updating the parameters of neural networks. In this work, we focus on the mismatch localization problem for speech and text, and our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations for model training.

cs.LG↗

arXiv2022

Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation

LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single frame 3D point cloud data, and the temporal information is ignored in those methods. We argue that the temporal information across the frames provides crucial knowledge for 3D scene perceptions, especially in the driving scenario. In this paper, we focus on spatial and temporal variations to better explore the temporal information across the 3D frames. We design a temporal variation-aware interpolation module and a temporal voxel-point refiner to capture the temporal variation in the 4D point cloud. The temporal variation-aware interpolation generates local features from the previous and current frames by capturing spatial coherence and temporal variation information. The temporal voxel-point refiner builds a temporal graph on the 3D point cloud sequences and captures the temporal variation with a graph convolution module. The temporal voxel-point refiner also transforms the coarse voxel-level predictions into fine point-level predictions. With our proposed modules, the new network TVSN achieves state-of-the-art performance on SemanticKITTI and SemantiPOSS. Specifically, our method achieves 52.5\% in mIoU (+5.5% against previous best approaches) on the multiple scan segmentation task on SemanticKITTI, and 63.0% on SemanticPOSS (+2.8% against previous best approaches).

cs.CV↗

arXiv2019

FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones and propose a smart-food logging system: FoodAI, which offers state-of-the-art deep-learning based image recognition capabilities. FoodAI has been developed in Singapore and is particularly focused on food items commonly consumed in Singapore. FoodAI models were trained on a corpus of 400,000 food images from 756 different classes. In this paper we present extensive analysis and insights into the development of this system. FoodAI has been deployed as an API service and is one of the components powering Healthy 365, a mobile app developed by Singapore's Heath Promotion Board. We have over 100 registered organizations (universities, companies, start-ups) subscribing to this service and actively receive several API requests a day. FoodAI has made food logging convenient, aiding smart consumption and a healthy lifestyle.

cs.CV↗