arXiv subjects

Zulin Wang

Publications and source records attributed to Zulin Wang.

At least 19 recordsLinked to original sources

arXiv2026

An Ambiguity-Function-Assisted Newtonized Channel Estimation Method for Pulse-Shaped AFDM Under Fractional Delay and Doppler

Accurate channel estimation for pulse-shaped AFDM systems over doubly selective channels with fractional normalized delay and Doppler remains challenging. This paper proposes a low-complexity ambiguity-function-assisted newtonized channel (AFNC) estimation method. Specifically, we first present a closed-form input-output relation for pulse-shaped affine frequency division multiplexing (AFDM) under fractional normalized delay and Doppler. As a further step, we demonstrate that the input-output relation admits a low-complexity representation by offline precomputing and storing the discretized ambiguity function of the shaping pulse, followed by tailored cyclic-shift and stacking operations. Building on this representation, AFNC performs fractional delay-Doppler channel estimation through Newtonized refinement, where the required Jacobian and Hessian updates are computed efficiently using the low-complexity input-output representation. Simulation results confirm the effectiveness of the proposed approach.

eess.SP↗

arXiv2025

An Anti-Interference AFDM System: Interference Impacts Analyses and Parameter Optimization

This paper proposes an anti-interference affine frequency division multiplexing (AFDM) system to ensure reliability and resource efficiency under malicious high-power interference originating from adversarial devices in high-mobility scenarios. Closed-form expressions of interferences in the discrete affine Fourier transform (DAFT) domain are derived by utilizing the stationary phase principle and the Affine Fourier transform convolution theorem, which indicates that interference impacts can be classified into stationary and non-stationary categories. On this basis, we reveal the analytical relationship between packet throughput and the paramerters of spread spectrum and error correction coding in our proposed anti-interference system, which enables the design of a parameter optimization algorithm that maximizes packet throughput. For reception, by jointly utilizing the autocorrelation function of spreading sequence and the cyclic-shift property of AFDM input-output relation, we design a linear-complexity correlation-based DAFT domain detector (CDD) capable of achieving full diversity gain, which performs correlation-based equalization to avoid matrix inversion. Numerical results validate the accuracy of the derived closed-form expressions and verify that the proposed anti-interference AFDM system could achieve high packet throughput under interference in high-mobility scenarios.

cs.IT↗

arXiv2025

Ambiguity Function Analysis of AFDM Under Pulse-Shaped Random ISAC Signaling

This paper investigates the ambiguity function (AF) of the emerging affine frequency division multiplexing (AFDM) waveform for Integrated Sensing and Communication (ISAC) signaling under a pulse shaping regime. Specifically, we first derive the closed-form expression of the average squared discrete period AF (DPAF) for AFDM waveform without pulse shaping, revealing that the AF depends on the parameter $c_1$ and the kurtosis of random communication data, while being independent of the parameter $c_2$. As a step further, we conduct a comprehensive analysis on the AFs of various waveforms, including AFDM, orthogonal frequency division multiplexing (OFDM) and orthogonal chirp-division multiplexing (OCDM). Our results indicate that all three waveforms exhibit the same number of regular depressions in the sidelobes of their AFs, which incurs performance loss for detecting and estimating weak targets. However, the AFDM waveform can flexibly control the positions of depressions by adjusting the parameter $c_1$, which motivates a novel design approach of the AFDM parameters to mitigate the adverse impact of depressions of the strong target on the weak target. Furthermore, a closed-form expression of the average squared DPAF for pulse-shaped random AFDM waveform is derived, which demonstrates that the pulse shaping filter generates the shaped mainlobe along the delay axis and the rapid roll-off sidelobes along the Doppler axis. Numerical results verify the effectiveness of our theoretical analysis and proposed design methodology for the AFDM modulation.

eess.SP↗

arXiv2025

A Secure Affine Frequency Division Multiplexing System for Next-Generation Wireless Communications

Affine frequency division multiplexing (AFDM) has garnered significant attention due to its superior performance in high-mobility scenarios, coupled with multiple waveform parameters that provide greater degrees of freedom for system design. This paper introduces a novel secure affine frequency division multiplexing (SE-AFDM) system, which advances prior designs by dynamically varying an AFDM pre-chirp parameter to enhance physical-layer security. In the SE-AFDM system, the pre-chirp parameter is dynamically generated from a codebook controlled by a long-period pseudo-noise (LPPN) sequence. Instead of applying spreading in the data domain, our parameter-domain spreading approach provides additional security while maintaining reliability and high spectrum efficiency. We also propose a synchronization framework to solve the problem of reliably and rapidly synchronizing the time-varying parameter in fast time-varying channels. The theoretical derivations prove that unsynchronized eavesdroppers cannot eliminate the nonlinear impact of the time-varying parameter and further provide useful guidance for codebook design. Simulation results demonstrate the security advantages of the proposed SE-AFDM system in high-mobility scenarios, while our hardware prototype validates the effectiveness of the proposed synchronization framework.

eess.SP↗

arXiv2025

A Secure Affine Frequency Division Multiplexing for Wireless Communication Systems

This paper introduces a secure affine frequency division multiplexing (SE-AFDM) for wireless communication systems to enhance communication security. Besides configuring the parameter c1 to obtain communication reliability under doubly selective channels, we also utilize the time-varying parameter c2 to improve the security of the communications system. The derived input-output relation shows that the legitimate receiver can eliminate the nonlinear impact introduced by the time-varying c2 without losing the bit error rate (BER) performance. Moreover, it is theoretically proved that the eavesdropper cannot separate the time-varying c2 and random information symbols, such that the BER performance of the eavesdropper is severely deteriorated. Meanwhile, the analysis of the effective signal-to-interference-plus-noise ratio (SINR) of the eavesdropper illustrates that the SINR decreases as the value range of c2 expands. Numerical results verify that the proposed SE-AFDM waveform has significant security while maintaining good BER performance in high-mobility scenarios.

eess.SP↗

arXiv2025

Affine-Doppler Division Multiplexing for High-Mobility Wireless Communications Systems

Affine Frequency Division Multiplexing (AFDM) has been regarded as a candidate integrated sensing and communications (ISAC) waveform owing to its superior communication performance, outperforming the Orthogonal Time-Frequency Space (OTFS) that has been researched for a longer time. However, since the above two waveforms are incompatible with each other, the state-of-the-art methods well-designed for OTFS may not be directly applicable to AFDM. This paper introduces a new orthogonal multicarrier waveform, namely Affine-Doppler Division Multiplexing (ADDM), which can provide a generic framework and subsume the existing OTFS and AFDM as a particular case. ADDM modulating information symbols in the Affine-Doppler (A-D) domain based on a two-dimensional (2D) transform can enjoy both excellent unambiguous Doppler and Doppler resolution, which is the same as AFDM but outperforms OTFS. Moreover, benefiting from the 2D transform, the symbols block of ADDM in the A-D domain undergoes a 2D cyclic shift produced by the delay and the Doppler of the channel, similar to the 2D cyclic shift in the delay-Doppler domain of cyclic prefix (CP)-OTFS. This offers a potential to directly apply the state-of-the-art methods well-designed for OTFS and AFDM to ADDM. Numerical results show that ADDM achieves comparable BER performance with AFDM but outperforms OTFS in high-mobility scenarios.

eess.SP↗

arXiv2025

Ambiguity Function Analysis of AFDM Signals for Integrated Sensing and Communications

Affine frequency division multiplexing (AFDM) is a promising chirp-based waveform with high flexibility and resilience, making it well-suited for next-generation wireless networks, particularly in high-mobility scenarios. In this paper, we investigate the ambiguity functions (AFs) of AFDM signals, which fundamentally characterize their range and velocity estimation capabilities in both monostatic and bistatic settings. Specifically, we first derive the auto-ambiguity function (AAF) of an AFDM chirp subcarrier, revealing its "spike-like" local property and "periodic-like" global property along the rotated delay and Doppler dimensions. This structure naturally forms a parallelogram for each localized pulse of the AAF of the AFDM chirp subcarrier, enabling unambiguous target sensing. Then, we study the cross-ambiguity function (CAF) between two different AFDM chirp subcarriers, which exhibits the same local and global properties as the AAF but with an additional shift along the Doppler dimension. We then extend our analysis to the AF of various typical AFDM frames, considering both deterministic pilot and random data symbols. In particular, we demonstrate that inserting guard symbols in AFDM facilitates interference-free sensing. Simulation results validate our theoretical findings, highlighting AFDM's strong potential for ISAC applications.

eess.SP↗

arXiv2025

An Integrated Sensing and Communications System Based on Affine Frequency Division Multiplexing

This paper proposes an integrated sensing and communications (ISAC) system based on affine frequency division multiplexing (AFDM) waveform. To this end, a metric set is designed according to not only the maximum tolerable delay/Doppler, but also the weighted spectral efficiency as well as the outage/error probability of sensing and communications. This enables the analytical investigation of the performance trade-offs of AFDM-ISAC system using the derived analytical relation among metrics and AFDM waveform parameters. Moreover, by revealing that delay and the integral/fractional parts of normalized Doppler can be decoupled in the affine Fourier transform-Doppler domain, an efficient estimation method is proposed for our AFDM-ISAC system, whose unambiguous Doppler can break through the limitation of subcarrier spacing. Theoretical analyses and numerical results verify that our proposed AFDM-ISAC system may significantly enlarge unambiguous delay/Doppler while possessing good spectral efficiency and peak-to-sidelobe level ratio in high-mobility scenarios.

eess.SP↗

arXiv2022

An AFDM-Based Integrated Sensing and Communications

This paper considers an affine frequency division multiplexing (AFDM)-based integrated sensing and communications (ISAC) system, where the AFDM waveform is used to simultaneously carry communications information and sense targets. To realize AFDM-based sensing functionality, two parameter estimation methods are designed to process echoes in the time domain and the discrete affine Fourier transform (DAFT) domain, respectively. It allows us to decouple delay and Doppler shift in the fast time axis and can maintain good sensing performance even in large Doppler shift scenarios. Numerical results verify the effectiveness of our proposed AFDM-based system in high mobility scenarios.

eess.SP↗

arXiv2021

Understanding and Predicting the Memorability of Outdoor Natural Scenes

Memorability measures how easily an image is to be memorized after glancing, which may contribute to designing magazine covers, tourism publicity materials, and so forth. Recent works have shed light on the visual features that make generic images, object images or face photographs memorable. However, these methods are not able to effectively predict the memorability of outdoor natural scene images. To overcome this shortcoming of previous works, in this paper, we provide an attempt to answer: "what exactly makes outdoor natural scenes memorable". To this end, we first establish a large-scale outdoor natural scene image memorability (LNSIM) database, containing 2,632 outdoor natural scene images with their ground truth memorability scores and the multi-label scene category annotations. Then, similar to previous works, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of outdoor natural scenes. In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores. Moreover, combining the deep features with the category feature can further boost the performance of memorability prediction. Therefore, we propose an end-to-end DNN based outdoor natural scene memorability (DeepNSM) predictor, which takes advantage of the learned category-related features. Then, the experimental results validate the effectiveness of our DeepNSM model, exceeding the state-of-the-art methods. Finally, we try to understand the reason of the good performance for our DeepNSM model, and also study the cases that our DeepNSM model succeeds or fails to accurately predict the memorability of outdoor natural scenes. Code: github.com/JiaxinLu-home/Natural-Scene-Memorability-Dataset.

cs.CV↗

arXiv2021

Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video

Blind visual quality assessment (BVQA) on 360{\textdegree} video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360{\textdegree} video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360{\textdegree} video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360{\textdegree} video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360{\textdegree} video over two datasets, the code of which has been public in \url{https://github.com/yanglixiaoshen/ProVQA.}

cs.CV↗

arXiv2019

Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach

Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects' HM in panoramic video sequences. From this database, we find that the HM data are highly consistent across subjects. Furthermore, we find that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Based on our findings, we propose a DRL-based HM prediction (DHP) approach with offline and online versions, called offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to determine potential HM positions at each panoramic frame. Then, a heat map of the potential HM positions, named the HM map, is generated as the output of offline-DHP. In online-DHP, the next HM position of one subject is estimated given the currently observed HM position, which is achieved by developing a DRL algorithm upon the learned offline-DHP model. Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.

cs.CV↗

arXiv2019

Assessing Visual Quality of Omnidirectional Videos

In contrast with traditional video, omnidirectional video enables spherical viewing direction with support for head-mounted displays, providing an interactive and immersive experience. Unfortunately, to the best of our knowledge, there are few visual quality assessment (VQA) methods, either subjective or objective, for omnidirectional video coding. This paper proposes both subjective and objective methods for assessing quality loss in encoding omnidirectional video. Specifically, we first present a new database, which includes the viewing direction data from several subjects watching omnidirectional video sequences. Then, from our database, we find a high consistency in viewing directions across different subjects. The viewing directions are normally distributed in the center of the front regions, but they sometimes fall into other regions, related to video content. Given this finding, we present a subjective VQA method for measuring difference mean opinion score (DMOS) of the whole and regional omnidirectional video, in terms of overall DMOS (O-DMOS) and vectorized DMOS (V-DMOS), respectively. Moreover, we propose two objective VQA methods for encoded omnidirectional video, in light of human perception characteristics of omnidirectional video. One method weighs the distortion of pixels with regard to their distances to the center of front regions, which considers human preference in a panorama. The other method predicts viewing directions according to video content, and then the predicted viewing directions are leveraged to allocate weights to the distortion of each pixel in our objective VQA method. Finally, our experimental results verify that both the subjective and objective methods proposed in this paper advance state-of-the-art VQA for omnidirectional video.

eess.IV↗

arXiv2019

Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach

Rain removal has recently attracted increasing research attention, as it is able to enhance the visibility of rain videos. However, the existing learning based rain removal approaches for videos suffer from insufficient training data, especially when applying deep learning to remove rain. In this paper, we establish a large-scale video database for rain removal (LasVR), which consists of 316 rain videos. Then, we observe from our database that there exist the temporal correlation of clean content and similar patterns of rain across video frames. According to these two observations, we propose a two-stream convolutional long- and short- term memory (ConvLSTM) approach for rain removal in videos. The first stream is composed of the subnet for rain detection, while the second stream is the subnet of rain removal that leverages the features from the rain detection subnet. Finally, the experimental results on both synthetic and real rain videos show the proposed approach performs better than other state-of-the-art approaches.

cs.CV↗

arXiv2019

Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning

When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects' head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this paper proposes a novel approach to predict saliency of head fixations on ODIs, named SalGAIL. First, we establish a dataset for attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset is large-scale, which contains the head fixations of 30 subjects viewing 600 ODIs. Next, we mine our AOI dataset and determine three findings: (1) The consistency of head fixations are consistent among subjects, and it grows alongside the increased subject number; (2) The head fixations exist with a front center bias (FCB); and (3) The magnitude of head movement is similar across subjects. According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward. Then, multi-stream DRL is developed to yield the head fixations of different subjects, and the saliency map of an ODI is generated via convoluting predicted head fixations. Finally, experiments validate the effectiveness of our approach in predicting saliency maps of ODIs, significantly better than 10 state-of-the-art approaches.

cs.CV↗

arXiv2019

MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we firstly develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code is available at https://github.com/RyanXingQL/MFQEv2.0.git.

cs.CV↗

arXiv2019

Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Over the past few years, deep neural networks (DNNs) have exhibited great success in predicting the saliency of images. However, there are few works that apply DNNs to predict the saliency of generic videos. In this paper, we propose a novel DNN-based video saliency prediction method. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which provides sufficient data to train the DNN models for predicting video saliency. Through the statistical analysis of our LEDOV database, we find that human attention is normally attracted by objects, particularly moving objects or the moving parts of objects. Accordingly, we propose an object-to-motion convolutional neural network (OM-CNN) to learn spatio-temporal features for predicting the intra-frame saliency via exploring the information of both objectness and object motion. We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames. Therefore, we develop a two-layer convolutional long short-term memory (2C-LSTM) network in our DNN-based method, using the extracted features of OM-CNN as the input. Consequently, the inter-frame saliency maps of videos can be generated, which consider the transition of attention across video frames. Finally, the experimental results show that our method advances the state-of-the-art in video saliency prediction.

cs.CV↗

arXiv2018

What Makes Natural Scene Memorable?

Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable. However, a clear understanding and reliable estimation of natural scene memorability remain elusive. In this paper, we provide an attempt to answer: "what exactly makes natural scene memorable". Specifically, we first build LNSIM, a large-scale natural scene image memorability database (containing 2,632 images and memorability annotations). Then, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of natural scene. In particular, we find that high-level feature of scene category is rather correlated with natural scene memorability. Thus, we propose a deep neural network based natural scene memorability (DeepNSM) predictor, which takes advantage of scene category. Finally, the experimental results validate the effectiveness of DeepNSM.

cs.CV↗