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The development of a new signal processing program at the Queensland University of technology

IEEE TRANSACTIONS ON EDUCATION 1996年第2期39卷 186-191页

作者： Boashash, B Sridharan, S Chandran, V Signal Processing Research Center School of Electronic Systems Engineering Queensland University of Technology Brisbane Australia

The School of Electrical and Electronic Systems Engineering at Queensland University of technology, Brisbane, Australia (QUT), offers three bachelor degree courses in electrical and computer engineering. In all its courses there is a strong emphasis on signal processing. A newly established signal processing Research Center (SPRC) has played an important role in the development of the signal processing units in these courses. This paper describes the unique design of the undergraduate program in signal professing at QUT, the laboratories developed to support it, and the criteria that influenced the design.

关键词： Biomedical signal processing signal processing algorithms signal processing Australia Digital signal processing signal design Biomedical engineering Geophysical signal processing Radar signal processing Systems engineering and theory

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Nonlinear Schur-Type Audio signal Parameterization for Convolutional Networks

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IEEE signal processing Letters 2025年 32卷 1665-1669页

作者： Biernacki, Pawel Libal, Urszula Wroclaw University of Science and Technology Department of Acoustics Multimedia and Signal Processing Wroclaw50-370 Poland

This article introduces a novel signal parameterization approach, termed nonlinear Schur-type signal parameterization, designed to enhance machine learning tasks such as signal classification and recognition. Traditional linear parameterization methods often struggle with the complex, nonlinear nature of real-world data. The mathematical foundation of the proposed parameterization method is extraction of Schur coefficients. The presented method is scalable and can be adjusted to the signal nature. The nonlinear Schur parameterization produces a matrix of Schur coefficients in time, dedicated to be a 2D input of convolutional neural networks (CNN). The performed experiments for the audio signals from open access datasets show that the signal representation in the form of the Schur coefficients is very efficient for recognition performance. The results obtained by CNN show an improvement in the classification accuracy in comparison with solutions based on preprocessing in frequency domain as FFT or MFCC. © 1994-2012 IEEE.

关键词： Convolutional neural networks

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Uncertainty prediction for prominence classification with chroma features

Uncertainty prediction for prominence classification with ch...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Linke, Julian Steger, Sophie Steinwender, Philipp Kubin, Gernot Pernkopf, Franz Schuppler, Barbara Signal Processing and Speech Communication Laboratory Graz University of Technology Austria

ISBN: (纸本)9798350368741

This paper presents methods for prominence classification in conversational speech. Most existing tools rely on prosodic features extracted at syllable- or phone-level, performing well on read speech. This is not the case for conversational speech, where the quality of automatic segmentation is significantly worse. We introduce entropy-based chroma features, requiring only word-level segmentations. They perform equally well as a random forest classifier with prosodic features (requiring phone-level segmentation), with accuracies in the range of the human inter-rater agreement. We further use Bayesian deep learning to quantify the epistemic and aleatoric uncertainty of the prediction for prosodic and chroma features. Whereas the aleatoric uncertainty is, as expected, consistent with inter-rater agreement and similarly high for both feature sets, the epistemic uncertainty is lower for the classifier based on chroma features, indicating higher classification consistency across the corpus. © 2025 IEEE.

关键词： Austrian German chromagram conversational speech prosodic prominence uncertainty prediction

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Noise-Resilient Unlimited Sampling and Recovery of Sparse signals

Noise-Resilient Unlimited Sampling and Recovery of Sparse Si...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Joseph, Geethu Signal Processing Systems Group Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350368741

In this paper, we investigate the use of modulo-ADCs in compressed sensing to handle the issue of the limited dynamic range of standard ADCs. The current state-of-the-art algorithm for modulo-compressed sensing uses an 1-norm-based approximation of the sparsity constraint, resulting in a computationally demanding mixed-integer linear optimization. Handling noisy measurements further complicates the problem, requiring mixed-integer quadratic programming, a problem known to be NP-hard. We present an alternative iterative hard-thresholding approach to address this issue. Our solution is computationally simpler and capable of handling noisy measurements. Additionally, we provide theoretical guarantees that the algorithm can successfully recover sparse vectors if the sampling operator satisfies the integer augmented-restricted isometry property, which holds when the number of measurements is sufficiently large. © 2025 IEEE.

关键词： iterative hard thresholding Modulo-compressed sensing restricted isometry property self-reset ADC

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Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2333-2344页

作者： Zhao, Jia-Le Zhang, Xian-Jie Ding, Xiao Zhang, Xingyi Zhang, Hai-Feng Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China

Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on pairwise graphs often limits their capacity to capture latent higher-order topological semantic information. Thus, it is crucial to find a way to extract the latent higher-order information of graphs without missing the lower-order information of the original graph. To address this issue, we here develop a method to construct hypergraph based on motifs, and then a novel neural network framework, named MD-HGNN, is proposed for enhanced graph learning. Specifically, we first utilize motifs of the original graph to construct the hypergraph and eliminate nested structures within the hypergraph to prevent information redundancy. Subsequently, GNNs and hypergraph neural networks (HGNNs) are employed separately to extract the lower-order and higher-order topological semantic information of the graph. Finally, the lower-order and higher-order information are integrated to obtain an embedded representation of graph. Extensive experimental results demonstrate that MD-HGNN preserves the original lower-order graph structure information while effectively extracting higher-order features. Moreover, its performance and robustness are validated across different downstream tasks. © 2013 IEEE.

关键词： Graph neural networks

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Learning Structured Compressed Sensing with Automatic Resource Allocation

Learning Structured Compressed Sensing with Automatic Resour...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Han Pérez, Eduardo Huijben, Iris A.M. van Gorp, Hans van Sloun, Ruud Römer, Florian Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing IZFP Germany Department of Electrical Engineering Eindhoven University of Technology Netherlands Electronic Measurements and Signal Processing Ilmenau University of Technology Germany

ISBN: (纸本)9798350368741

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimizable parameters. Recent advances in machine learning (ML) have enabled task-based supervised learning of subsampling matrices, albeit at the expense of complex downstream models. Additionally, the sampling resource allocation across dimensions is often determined in advance through heuristics. To address these challenges, we introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy. SCOSARA adaptively distributes samples across sampling dimensions while maximizing Fisher information content. Using ultrasound localization as a case study, we compare SCOSARA to state-of-the-art ML-based and greedy search algorithms. Simulation results demonstrate that SCOSARA can produce high-quality subsampling matrices that achieve lower Cramér Rao Bound values than the baselines. In addition, SCOSARA outperforms other ML-based algorithms in terms of the number of trainable parameters, computational complexity, and memory requirements while automatically choosing the number of samples per axis. © 2025 IEEE.

关键词： Compressed Sensing Information Theory Subsampling Unsupervised Learning

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signal processing education at Queensland University of technology Australia

Signal processing education at Queensland University of Tech...

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Asilomar Conference on signals, Systems & Computers

作者： B. Boashash S. Sridharan Signal Processing Research Centre School of Electrical and Electronic Systems Engineering Queensland University of Technology Brisbane QLD Australia

The School of Electrical and Electronic Systems Engineering at Queensland University of technology (QUT) has 3 bachelor's degree programs in Electrical and Computer Engineering. In all these programs there is a strong emphasis on the area of signal processing. The school also conducts a MEngSe coursework program in signal processing. In addition, challenging research programs in the area of signal processing leading to Masters and PhD research degrees are also offered. The teaching of signal processing at QUT is strongly influenced by the presence of a University centre within the School called the signal processing Research Centre. The centre has 10 academics with a PhD in the area of signal processing. This paper describes the unique nature of the undergraduate and postgraduate courses in signal processing, the laboratories that have been developed to support these courses and the future directions of signal processing education at QUT.< >

关键词： signal processing Educational technology Laboratories Speech processing Speech analysis Systems engineering and theory Educational institutions Computer science education Educational programs Biomedical signal processing

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TBIA-DBNet: A Two-Branch Image-Adaptive DBNet for Scene Text Detection in Real-World Foggy Scenes 27th

TBIA-DBNet: A Two-Branch Image-Adaptive DBNet for Scene Tex...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Zhaoxi Zhou, Gang He, Runlin Zhang, Mengnan Jia, Zhenhong Ma, Jing Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781186

Though deep learning-based scene text detection methods have achieved promising results on conventional datasets, these methods are unable to maintain optimal performance in adverse weather conditions, such as foggy weather. To alleviate this problem, we propose a Two-Branch Image-Adaptive DBNet (TBIA-DBNet) framework. Specifically, to avoid missing discriminable features from the original image in one branch, we design an Image Enhancement Network (IENet) in another branch. Additionally, we design a Fusion Module based on Coordinate Attention (FMCA) to fully integrate original and enhanced features. Experimental results demonstrate that TBIA-DBNet significantly enhances scene text detection performance in foggy weather. Notably, it improves detection accuracy by nearly 10% in real-world foggy weather conditions compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Identifying important nodes of hypergraph:An improved PageRank algorithm

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Chinese Physics B 2025年第4期34卷 162-171页

作者： Yu-Hao Piao Jun-Yi Wang Ke-Zan Li School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin 541004China

Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important *** this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed *** algorithm introduces the Jaccard similarity of directed *** comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between *** improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation *** addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed ***,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and *** of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.

关键词： hypergraph important node PageRank susceptible-infected(SI)model centrality index

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E-RNS: Enhancing Negative Sample Quality from Gradient Perspective for Graph Recommendation

E-RNS: Enhancing Negative Sample Quality from Gradient Persp...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Feng, Qiangsheng Qin, Jiwei Ma, Jie School of Computer Science and Technology Xinjiang University China Key Laboratory of Signal Detection and Processing China

ISBN: (纸本)9798350368741

Bayesian Personalized Ranking (BPR) is a widely used optimization function in GNN-based recommender systems, and negative samples are usually obtained through the Random Negative Sampling (RNS) method during BPR training. However, from the gradient perspective, RNS tends to select low-quality samples with minimal information, resulting in small gradients. These small gradients contribute little to BPR optimization, limiting the model's ability to effectively distinguish between positive and negative samples. To alleviate this issue, we propose a general negative sample information enhancement method: Enhancing Random Negative Sampling (E-RNS), which constructs hard negative samples by enhancing the information in randomly selected negative samples. Specifically, in the Noise Injection step, it generates initial noise and injects a certain amount of noise in the same direction into the vector dimensions of positive samples to create enriched information. Then, in the Information Fusion step, this enriched information is mixed with the negative samples to synthesize new hard negative samples. Extensive experiments demonstrate that applying E-RNS to GNN-based recommender models significantly improves performance. © 2025 IEEE.

关键词： Collaborative Filtering Graph Neural Networks Negative Sampling Recommender Systems

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