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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hannah Och Shabhrish Reddy Uddehal Tilo Strutz André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts Coburg Germany

Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only employed if the previous stage can not be applied since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant color palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1.07% on the evaluated data. Compared to VVC and HEVC, the proposed method needs roughly 0.44 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.

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Improved Screen Content Coding in VVC Using Soft Context Formation

Improved Screen Content Coding in VVC Using Soft Context For...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

Screen content images typically contain a mix of natural and synthetic image parts. Synthetic sections usually are comprised of uniformly colored areas and repeating colors and patterns. In the VVC standard, these properties are exploited using Intra Block Copy and Palette Mode. In this paper, we show that pixel-wise lossless coding can outperform lossy VVC coding in such areas. We propose an enhanced VVC coding approach for screen content images using the principle of soft context formation. First, the image is separated into two layers in a block-wise manner using a learning-based method with four block features. Synthetic image parts are coded losslessly using soft context formation, the rest with VVC. We modify the available soft context formation coder to incorporate information gained by the decoded VVC layer for improved coding efficiency. Using this approach, we achieve Bjontegaard-Delta-rate gains of 4.98% on the evaluated data sets compared to VVC.

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Independent Vector Extraction Constrained on Manifold of Half-Length Filters 31

Independent Vector Extraction Constrained on Manifold of Hal...

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31st European signal processing Conference, EUSIPCO 2023

作者： Koldovský, Zbyněk Čmejla, Jaroslav Adali, Tülay O'Regan, Stephen Acoustic Signal Analysis and Processing Group Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Czech Republic Department of Electrical and Computer Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Naval Surface Warfare Center Carderock Division West BethesdaMD United States

ISBN: (纸本)9789464593600

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction. © 2023 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： Extraction

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Reflectarray-based Intelligent Reflecting Surface to Improve mm-Wave 5G coverage in outdoor scenarios

Reflectarray-based Intelligent Reflecting Surface to Improve...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Vaquero, Alvaro F. Martinez-De-Rioja, Eduardo Arrebola, Manuel Carrasco, Eduardo Encinar, José A. Salman, Safa Achour, Maha Universidad de Oviedo Group of Signal Theory and Communications Department of Electrical Engineering Gijón33203 Spain Universidad Rey Juan Carlos Department of Signal Theory and Communications and Telematic Systems and Computing Fuenlabrada28942 Spain Universidad Politécnica de Madrid Information Processing and Telecommunications Center Madrid28040 Spain Metawave Corporation Palo AltoCA94303 United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This work presents the use of a reflectarray panel as an efficient and low-cost solution to improve millimeter-wave coverage in 5G scenarios. A dual-polarized shaped-beam is designed to radiate a flat-top beam to cover a blind zone without coverage. The design is carried out using a phase-only synthesis considering the beam specifications for the outdoor scenario. Then, a single-layer reflectarray element is used to perform a design at 27.7 GHz. This element provides an angularly-stable phase response at the operational frequency with an independent phase control in each linear polarization. The reflectarray panel shows a stable gain and a low both side-lobe level and cross-polarization within 1 GHz bandwidth. © 2022 IEEE.

关键词： Millimeter waves

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Adaptive Sparse Gaussian Process

arXiv

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arXiv 2023年

作者： Gómez-Verdejo, Vanessa Parrado-Hernández, Emilio Martínez-Ramón, Manel Signal Processing and Communications Department University Carlos III de Madrid Leganés Madrid28911 Spain Electrical and Computer Engineering Department The University of New Mexico AlbuquerqueNM87106 United States

Adaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating. Existing solutions only partially cover these needs. Here, we propose the first adaptive sparse Gaussian Process (GP) able to address all these issues. We first reformulate a variational sparse GP algorithm to make it adaptive through a forgetting factor. Next, to make the model inference as simple as possible, we propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives. As a result, the algorithm presents a fast convergence of the inference process, which allows an efficient model update (with a single inference iteration) even in highly non-stationary environments. Experimental results demonstrate the capabilities of the proposed algorithm and its good performance in modeling the predictive posterior in mean and confidence interval estimation compared to state-of-the-art approaches. © 2023, CC BY.

关键词： Gaussian distribution

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A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas

arXiv

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arXiv 2023年

作者： Fister, Iztok Fister, Iztok Fister, Dušan Podgorelec, Vili Salcedo-Sanz, Sancho Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method’s main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression

arXiv

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arXiv 2023年

作者： Burakhan Koyuncu, A. Jia, Panqi Boev, Atanas Alshina, Elena Steinbach, Eckehard The Technical University of Munich School of Computation Information and Technology Department of Computer Engineering Media Technology Munich80333 Germany The Friedrich-Alexander University Department of Electrical-Electronic-Communication Engineering Multimedia Communications and Signal Processing Erlangen91058 Germany The Huawei Munich Research Center Munich80992 Germany

Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achieving a high compression ratio, however, at the expense of a significant computational effort. In this work, we introduce the Efficient Contextformer (eContextformer) - a computationally efficient transformer-based autoregressive context model for learned image compression. The eContextformer efficiently fuses the patch-wise, checkered, and channel-wise grouping techniques for parallel context modeling, and introduces a shifted window spatio-channel attention mechanism. We explore better training strategies and architectural designs and introduce additional complexity optimizations. During decoding, the proposed optimization techniques dynamically scale the attention span and cache the previous attention computations, drastically reducing the model and runtime complexity. Compared to the non-parallel approach, our proposal has ∼145x lower model complexity and ∼210x faster decoding speed, and achieves higher average bit savings on Kodak, CLIC2020, and Tecnick datasets. Additionally, the low complexity of our context model enables online rate-distortion algorithms, which further improve the compression performance. We achieve up to 17% bitrate savings over the intra coding of Versatile Video Coding (VVC) Test Model (VTM) 16.2 and surpass various learning-based compression models. © 2023, CC BY-NC-ND.

关键词： Image compression

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MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a Multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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Experimental Assessment of Misalignment Effects in Terahertz communications

Experimental Assessment of Misalignment Effects in Terahertz...

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IEEE International Conference on communications (ICC)

作者： Hasan Nayir Erhan Karakoca Güneş Karabulut Kurt Ali Görçin Department of Electronics and Communication Engineering İstanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Laboratory TÜBİTAK BİLGEM Kocaeli Turkey Department of Electrical Engineering Poly-Grames Research Center Poly technique Montréal Montréal QC Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Terahertz (THz) frequencies play a crucial role in the advancement of next-generation wireless systems, primarily owing to their substantial available bandwidths. The inherent limitation of limited range, attributed to high attenuation in these frequencies, can be effectively addressed by implementing densely deployed heterogeneous networks, complemented by Unmanned Aerial Vehicles (UAVs) within a three-dimensional hyperspace. Yet, the success of THz communications relies on the precise alignment of beams. Inadequate handling of beam alignment can lead to diminished signal strength at the receiver, significantly affecting THz signals more than their conventional counter-parts. This research underscores the paramount importance of meticulous alignment in THz communication systems. The profound impact of proper alignment is substantiated through comprehensive measurements conducted using a state-of-the-art measurement setup, facilitating accurate data collection across the 240 GHz to 300 GHz spectrum. These measurements encompass varying angles and distances within an anechoic chamber to eliminate reflections. Through a meticulous analysis of the channel frequency and impulse responses derived from these extensive measurements, this study pioneers quantifiable results, providing an assessment of the effects of beam misalignment in THz frequencies.

关键词： Wireless communication Receivers Bandwidth Data collection Autonomous aerial vehicles Reflection Heterogeneous networks

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Berman Codes: A Generalization of Reed-Muller Codes that Achieve BEC Capacity

Berman Codes: A Generalization of Reed-Muller Codes that Ach...

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IEEE International Symposium on Information Theory

作者： Lakshmi Prasad Natarajan Prasad Krishnan Department of Electrical Engineering Indian Institute of Technology Hyderabad Sangareddy India Signal Processing and Communications Re-Search Center International Institute of Information Technology Hyderabad India

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

We identify a family of binary codes whose structure is similar to Reed-Muller (RM) codes and which include RM codes as a strict subclass. The codes in this family are denoted as ${\mathcal{C}_n}(r,m)$, and their duals are denoted as ${{\mathcal{B}}_n}(r,m)$. The length of these codes is n m , where n ≥ 2, and r is their ‘order’. When n = 2, ${\mathcal{C}_n}(r,m)$ is the RM code of order r and length 2 m . The special case of these codes corresponding to n being an odd prime was studied by Berman (1967) and Blackmore and Norton (2001). Following the terminology introduced by Blackmore and Norton, we refer to ${{\mathcal{B}}_n}(r,m)$ as the Berman code and ${\mathcal{C}_n}(r,m)$ as the dual Berman code. We identify these codes using a recursive Plotkin-like construction, and we show that these codes have a rich automorphism group. Applying a result of Kumar et al. (2016) to this set of automorphisms, we show that these codes achieve the capacity of the binary erasure channel (BEC) under bit-MAP decoding.

关键词： Codes Terminology Binary codes Reed-Muller codes Decoding

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