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Probabilistic Tile Visibility-Based Server-Side Rate Adaptation for Adaptive 360-Degree Video Streaming

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2020年第1期14卷 161-176页

作者： Zou, Junni Li, Chenglin Liu, Chengming Yang, Qin Xiong, Hongkai Steinbach, Eckehard Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Univ Dept Commun Engn Hangzhou 200072 Peoples R China Tech Univ Munich Chair Media Technol LMT D-80333 Munich Germany

In this article, we study the server-side rate adaptation problem for streaming tile-based adaptive 360-degree videos to multiple users who are competing for transmission resources at the network bottleneck. Specifically, we develop a convolutional neural network (CNN)-based viewpoint prediction model to capture the nonlinear relationship between the future and historical viewpoints. A Laplace distribution model is utilized to characterize the probability distribution of the prediction error. Given the predicted viewpoint, we then map the viewport in the spherical space into its corresponding planar projection in the 2-D plane, and further derive the visibility probability of each tile based on the planar projection and the prediction error probability. According to the visibility probability, tiles are classified as viewport, marginal and invisible tiles. The server-side tile rate allocation problem for multiple users is then formulated as a non-linear discrete optimization problem to minimize the overall received video distortion of all users and the quality difference between the viewport and marginal tiles of each user, subject to the transmission capacity constraints and users' specific viewport requirements. We develop a steepest descent algorithm to solve this non-linear discrete optimization problem, by initializing the feasible starting point in accordance with the optimal solution of its continuous relaxation. Extensive experimental results show that the proposed algorithm can achieve a near-optimal solution, and outperforms the existing rate adaptation schemes for tile-based adaptive 360-video streaming.

关键词： Streaming media Predictive models Servers Adaptation models signal processing algorithms Quality of experience Resource management 360-degree video tile-based adaptive streaming server-side rate adaptation viewpoint viewport prediction tile visibility probability

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Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors

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IEEE INTERNET OF THINGS JOURNAL 2024年第15期11卷 26203-26219页

作者： Macia-Lillo, Antonio Barrachina, Sergio Fabregat, German Dolz, Manuel F. Univ Alacant Dept Technol Informat & Computat San Vicente Del Raspeig 03690 Spain Univ Jaume 1 Dept Comp Sci & Engn Castellon de La Plana 12071 Spain

We perform a series of optimizations on the convolution operator within the ARM common microcontroller units software interface standard for neural network (CMSIS-NN) library to improve the performance of deep learning tasks on Arduino development boards equipped with ARM Cortex-M4 and M7 microcontrollers. To this end, we develop custom microkernels that efficiently handle the internal computations required by the convolution operator via the lowering approach and the direct method, and we design two techniques to avoid register spilling. We also take advantage of all the RAM on the Arduino boards by reusing it as a scratchpad for the convolution filters. The integration of these techniques into CMSIS-NN, when invoked by TensorFlow Lite for microcontrollers for quantised versions of VGG, SqueezeNet, ResNet, and MobileNet-like convolutional neural networks enhances the overall inference speed by a factor ranging from 1.13xto 1.50x .

关键词： Program processors Random access memory Registers Optimization Convolution Inference algorithms signal processing algorithms ARM Cortex-M common microcontroller units software interface standard for neural network (CMSIS-NN) convolution operator deep learning edge computing high performance microcontrollers

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Regularized Optimal Transport Layers for Generalized Global Pooling Operations

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 15426-15444页

作者： Xu, Hongteng Cheng, Minjie Renmin Univ Beijing Key Lab Big Data Management & Analy Meth Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Renmin Univ Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China

Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) representation. However, without solid mathematical fundamentals, its practical implementations often depend on empirical mechanisms and thus lead to sub-optimal, even unsatisfactory performance. In this work, we develop a novel and generalized global pooling framework through the lens of optimal transport. The proposed framework is interpretable from the perspective of expectation-maximization. Essentially, it aims at learning an optimal transport across sample indices and feature dimensions, making the corresponding pooling operation maximize the conditional expectation of input data. We demonstrate that most existing pooling methods are equivalent to solving a regularized optimal transport (ROT) problem with different specializations, and more sophisticated pooling operations can be implemented by hierarchically solving multiple ROT problems. Making the parameters of the ROT problem learnable, we develop a family of regularized optimal transport pooling (ROTP) layers. We implement the ROTP layers as a new kind of deep implicit layer. Their model architectures correspond to different optimization algorithms. We test our ROTP layers in several representative set-level machine learning scenarios, including multi-instance learning (MIL), graph classification, graph set representation, and image classification. Experimental results show that applying our ROTP layers can reduce the difficulty of the design and selection of global pooling - our ROTP layers may either imitate some existing global pooling methods or lead to some new pooling layers fitting data better.

关键词： Task analysis Optimization Neural networks Machine learning Feature extraction Solids signal processing algorithms Global pooling regularized optimal transport Bregman ADMM Sinkhorn scaling set representation graph embedding

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The Diffusion Entropy Stochastic Gradient Descent Algorithm With Quasi-Optimal Combiners: Formulation and Analysis

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3948-3963页

作者： Xia, Wei Xia, Guoqing Chen, Junbin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Univ Leicester Sch Engn Leicester LE1 7RH Leics England TP LINK Int Chengdu Co Ltd Chengdu 610000 Sichuan Peoples R China

We consider herein the diffusion entropy stochastic gradient descent (DE-SGD) algorithm for non-circular complex-valued signals. We theoretically analyze the mean stability and the steady-state network mean-square-deviation (MSD) of the DE-SGD. We develop both the quasi-optimal static and adaptive combination strategies to enhance the DE-SGD algorithm so as to utilize the spatial variations of the noise circularity coefficients, noise variances, the regressor powers and the step sizes across the entire network. We validate the proposed adaptive combiners theoretically and experimentally in small step size scenarios. We also demonstrate the consistency of the convergence and steady-state performance of both the static combiners and the proposed adaptive combiners. Illustrative simulations and real-world data evaluation validate the superior transient and steady-state performance of the DE-SGD algorithm with the proposed quasi-optimal combination strategies.

关键词： signal processing algorithms Entropy Steady-state Adaptive systems Adaptation models Convergence Stochastic processes Non-circular signals adaptive network distributed estimation Gaussian entropy adaptive combiners

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ORTP: A Video SAR Imaging Algorithm Based on Low-Tubal-Rank Tensor Recovery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 1293-1308页

作者： Pu, Wei Wu, Junjie Huang, Yulin Yang, Jianyu UCL Dept Elect & Elect Engn London WC1E 6BT England Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

关键词： Tensors Synthetic aperture radar Radar polarimetry signal processing algorithms Matching pursuit algorithms Streaming media Sparse matrices Low-tubal-rank tensor recovery orthogonal rank-1 tensor pursuit (ORTP) video synthetic aperture radar (SAR) imaging

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ARMA DIGITAL LATTICE FILTER BASED ON NEW CRITERION

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1987年第6期34卷 617-628页

作者： MIYANAGA, Y NAGAI, N MIKI, N HOKKAIDO UNIV APPL ELECT RES INST SAPPORO HOKKAIDO 060 JAPAN

In this paper, a new ARMA digital lattice filter is proposed. First, a new criterion is defined for designing the ARMA digital lattice filter from given stochastic data. Based on this criterion, an ARMA parameter estimation algorithm is developed. This algorithm can estimate the ARMA parameters with little calculation cost. Then, from the fast algorithm of the ARMA parameter estimation, two elementary sections of the ARMA digital lattice filter are invented. Any ARMA model with an arbitrary AR order and an arbitrary MA order can be realized by using these elementary sections. In this paper, the proposed ARMA lattice filter is compared with other fast algorithms of ARMA parameter estimation and other ARMA lattice filters. Experimental results obtained from applying this filter to model reduction problems are shown. From these results, it is shown that this ARMA lattice filter is quite useful for ARMA digital signal processing.

关键词： Lattices Digital filters Parameter estimation signal processing algorithms Stochastic processes Costs Digital signal processing Matched filters Reduced order systems Speech recognition

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Robust 2-D DOA Estimation in a Polarization Sensitive Single User Environment

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IEEE ACCESS 2022年 10卷 115914-115929页

作者： Hasan, Md Imrul Saquib, Mohammad Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA

Apart from the conventional parameters (such as signal-to-noise ratio (SNR), array geometry and size, and the number of samples), several other factors (e.g., alignment of the antenna elements, polarization parameters) influence the performance of direction of arrival (DOA) estimation algorithms. When all the antenna elements are aligned in the same direction, the polarization parameters uniformly affect the steering vectors, which is the underlying assumption of almost all conventional DOA algorithms. Unfortunately, in this case, for a given set of DOA angles, there exists a range of polarization parameters resulting in a very low SNR across all the antenna elements in the array and vice versa. To avoid this type of unwanted event, different antenna elements must be aligned differently. However, this fact will make almost all commonly used DOA estimation algorithms inoperable since the steering vectors are contaminated unevenly by the polarization parameters. To the best of our knowledge, no work in the literature handles this issue using simple hardware and signal processing techniques even for a single user environment. In this paper, that line of inquiry is pursued. We consider a circular array with the minimum number (i.e., 4) of short dipole antenna elements and propose an antenna alignment scheme. This ensures that at any given point no more than one element will suffer significantly from low SNR due to the contribution of polarization. A thresholding technique to isolate the antenna element after being seriously contaminated by the polarization parameters is developed and analyzed. Two algorithms that are suited for operating reliably in all possible DOA and polarization environments are addressed. The first algorithm follows the working principle of the popular MUSIC algorithm after cleaning the polarization contributions from the non-signal subspace. The other one, which is found as a byproduct of the process of cleaning the non-signal subspace, can estima

关键词： Direction-of-arrival estimation signal-to-noise ratio Dipole antennas Antenna arrays signal processing algorithms Receiving antennas signal to noise ratio Dipole antennas Multiple signal classification Polarization DOA estimation polarization closed-form estimation MUSIC antenna alignment antenna mapping

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Meta-Material Sensor Based Internet of Things: Design, Optimization, and Implementation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第8期70卷 5645-5662页

作者： Hu, Jingzhi Zhang, Hongliang Di, Boya Han, Zhu Poor, H. Vincent Song, Lingyang Peking Univ Sch Elect Beijing 100871 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08540 USA Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

For many applications envisioned for the Internet of Things (IoT), it is expected that the sensors will have very low costs and zero power, which can be satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their constituent meta-materials can reflect wireless signals with environment-sensitive reflection coefficients, meta-IoT sensors can achieve simultaneous sensing and transmission without any active modulation. However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors. In this paper, we propose a joint sensing and transmission design method for meta-IoT systems with a large number of meta-IoT sensors, which can efficiently optimize the sensing accuracy of the system. Specifically, a computationally efficient received signal model is established to evaluate the joint influence of meta-material structure on sensing and transmission. Then, a sensing algorithm based on deep unsupervised learning is designed to obtain accurate sensing results in a robust manner. Experiments with a prototype verify that the system has a higher sensitivity and a longer transmission range compared to existing designs, and can sense environmental anomalies correctly within 2 meters.

关键词： Sensors Wireless sensor networks Wireless communication signal processing algorithms Computational modeling Antenna arrays 6G mobile communication Meta-materials Internet of Things passive sensors unsupervised learning

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A SYSTOLIC VLSI CHIP FOR IMPLEMENTING ORTHOGONAL-TRANSFORMS

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 1989年第2期24卷 466-469页

作者： BURLESON, WP SCHARF, LL GABRIEL, AR ENDSLEY, NH BALL AEROSP BOULDERCO 80306

The design of a systolic VLSI chip is described for the implementing of signal processing algorithms that may be decomposed into a product of simple real rotations. These include orthogonal transformations. Applications of this chip include projections, discrete Fourier and cosine transforms, and geometrical transformations. Large transforms may be computed by 'tilting' together many chips for increased throughput. A CMOS VLSI chip containing 138000 transistors in a 5*3 array of rotators has been designed, fabricated, and tested. The chip has a 32-MHz clock and performs real rotations at a rate of 30 MHz. The systolic nature of the chip makes use of a fully synchronous bit-serial interconnect and a very regular structure at the rotator and bit levels. A distributed arithmetic scheme is used to implement the matrix-vector multiplication of the rotation.< >

关键词： Discrete transforms Very large scale integration signal design Algorithm design and analysis signal processing algorithms Discrete Fourier transforms Fourier transforms Throughput Testing Clocks

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AN EFFICIENT TOOL FOR ANALYZING WEIGHTED MEDIAN FILTERS

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL signal processing 1994年第7期41卷 487-489页

作者： ASTOLA, J NEUVO, Y NOKIA ELECTR LTD HELSINKIFINLAND

We present an efficient method for analyzing the statistical properties of weighted median filters. It is based on a fast algorithm for computing the Hamming distribution of the zero-set of the defining positive Boolean function. We also show that the standard median filter is in a statistical sense optimal among the stack filters defined by self dual positive Boolean functions.

关键词： Nonlinear filters Finite impulse response filter Distributed computing Boolean functions signal processing algorithms Image processing Statistics Availability Filtering theory Passband

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