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Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network

IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第1期16卷 97-112页

作者： Yang, Peng Guo, Kun Xi, Xing Quek, Tony Q. S. Cao, Xianbin Liu, Chenxi Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China Beihang Univ Minist Ind & Informat Technol China Key Lab Adv Technol Near Space Informat Syst Beijing 100083 Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore East China Normal Univ Sch Commun & Elect Engn Shanghai 200062 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this paper, we investigate a private and cache-enabled unmanned aerial vehicle (UAV) network for content provision. Aiming at delivering fresh, fair, and energy-efficient content files to terrestrial users, we formulate a joint UAV caching, UAV trajectory, and UAV transmit power optimization problem. This problem is confirmed to be a sequential decision problem with mixed-integer non-convex constraints, which is intractable directly. To this end, we propose a novel algorithm based on the techniques of subproblem decomposition and convex approximation. Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique. Next, an iterative optimization scheme incorporating a successive convex approximation (SCA) technique is explored to tackle the challenging mixed-integer non-convex subproblems. Besides, we analyze the convergence of the proposed algorithm and derive the theoretical value of the expected peak age of information (PAoI) to estimate the content freshness. Simulation results demonstrate that the proposed algorithm can achieve the expected PAoI close to the theoretical value and is more 22.11% and 70.51% energy-efficient and fairer than benchmark algorithms.

关键词： Unmanned aerial vehicles Trajectory signal processing algorithms Wireless networks Servers Authentication 5G mobile communication Fresh content private UAV network UAV caching trajectory design power control

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A New Approach for Secure Cloud-Based Electronic Health Record and its Experimental Testbed

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IEEE ACCESS 2022年 10卷 1082-1095页

作者： Jusak, Jusak Mahmoud, Seedahmed S. Laurens, Roy Alsulami, Musleh Fang, Qiang Dinamika Univ Dept Comp Engn Surabaya 60298 Indonesia James Cook Univ Sch Sci & Technol Singapore 387380 Singapore Shantou Univ Dept Biomed Engn Shantou 515063 Peoples R China Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Umm Al Qura Univ Dept Informat Syst Mecca 24382 Saudi Arabia

The tremendous development of the Internet of Things (IoT) technology in the last decades has fostered advancement in automatic medical assistive devices to support the existing Electronic Health Record (EHR) system. As an integral part of the EHR electronic model, public cloud servers store medical data. Unfortunately, public cloud servers are prone to security and privacy breach. This paper introduces a novel non-cryptographic approach to preserve electrocardiograph (ECG) data confidentiality and integrity in the EHR environment. The main objective of the proposed anonymization algorithm is to obscure the patient's cardiac information during transmission and to protect information stored in the cloud database. Although we focus on ECG data, generalization to other types of clinical data can be derived using the proposed method. Performance evaluation of the proposed scheme showed that the algorithm conceals both fiducial and non-fiducial features of the data. Therefore, confidentiality feature is preserved. This paper examined confidentiality of the anonymized data using the Percentage Residual Difference (PRD) and investigated the integrity of the reconstructed data in terms of cross-correlation. Security analysis carried out using the PRD, brute force attack, and performance comparison between the proposed algorithm and existing methods. Evaluation showed that the proposed scheme offers a secure non-cryptographic model for transmission and storing clinical data in the cloud. Moreover, in terms of processing time, the proposed algorithm is ten times faster than the existing wavelet packet method when processing long ECG data, 65,536 sample points. In a real-time experimental testbed, the implemented proposed system was successful.

关键词： Electrocardiography Cloud computing Security signal processing algorithms Wavelet packets Encryption Data privacy Eelectrocardiography electronic health record fast Fourier transform information security

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Advanced Digital signal processing and Variable-Rate Coding for Unrepeatered Optical Transmission

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2022年第4期14卷 1页

作者： Helio, Jose C. Junior, Jr. Sutili, Tiago Figueiredo, Rafael C. Mello, Darli A. A. Univ Estadual Campinas Sch Elect & Comp Engn BR-13083970 Campinas Brazil CPQD Opt Commun Solut BR-13086902 Campinas Brazil

In this paper, we evaluate the selective combination of algorithms for fiber nonlinearity compensation, transceiver impairments mitigation and variable-rate coding in unrepeatered optical transmission. A post-emphasis filter and a two-stage 4x4 multiple-input and multiple-output equalizer compensate the impact of bandwidth limitations and in-phase and quadrature skew. Nonlinear compensation is accomplished by a fast-converging adaptive digital back-propagation algorithm and maximum likelihood sequence estimation. Forward error correction is provided by a variable-rate spatially-coupled low-density parity-check code. The performance and complexity of the proposed digital subsystems are experimentally evaluated by the unrepeatered wavelength division multiplexing transmission of 17x200-Gb/s (DP-16QAM 32-GBd) channels over 350-km of large effective area and low-loss single-mode fibers. The experimental results for different combinations of algorithms elucidate the trade-off of complexity and performance in unrepeatered optical transmission.

关键词： Optical fiber amplifiers Optical fiber communication signal processing algorithms MIMO communication Equalizers Adaptive optics Stimulated emission Coherent optical communications transceiver impairments mitigation fiber nonlinearity compensation forward error correction unrepeatered transmission

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Algorithm Analysis and Application of Digital signal processing Technology

Algorithm Analysis and Application of Digital Signal Process...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Wang Yuanmin Zhang Li Leshan Vocational and Technical College Leshan Sichuan China

Digital signal processing is a rapidly developing field of science and technology. In the 1960s and 1970s, it experienced the development stages of digital filtering theory, signal Fourier transform, convolution and fast correlation calculation. Subsequently, the rapid development of computer technology and microelectronics technology promoted the development of digital signal processing technology and pushed it to a climax. Algorithm analysis is the study of algorithms. An algorithm is a set of rules that explain how to solve computer programming problems. The most common algorithm is the search and sort algorithm, which tells you how to find what you need in a large group of items. It is also used to search files or databases, group data, or find patterns in a dataset. The method adopted in this paper is to use FPGA to complete the signal preprocessing and STFT algorithm implementation. The necessary parameters and data results obtained from FPGA processing are transferred to DSP through the interface in a handshake manner, and the DSP completes the subsequent modulation analysis and parameter estimation. This method is much more efficient and reliable than using DSP alone to complete all processes in the past, and greatly shortens the cycle of intra pulse analysis.

关键词： Technological innovation Digital signal processors signal processing algorithms Digital signal processing Control systems Real-time systems Filtering theory

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Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

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IEEE ACCESS 2022年 10卷 17430-17440页

作者： Favato, Andrea Carlet, Paolo Gherardo Toso, Francesco Torchio, Riccardo Ortombina, Ludovico Bruschetta, Mattia Carli, Ruggero Alotto, Piergiorgio Bolognani, Silverio Rodriguez, Jose Univ Padua Dept Ind Engn I-35151 Padua Italy Univ Padua Dept Informat Engn I-35151 Padua Italy Univ Andres Bello Fac Engn Santiago 8370146 Chile

This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications. A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.

关键词： Mathematical models Linear systems Reluctance motors signal processing algorithms Prediction algorithms Licenses Computational modeling Electric drives model predictive control (MPC) permanent magnet synchronous motor (PMSM) synchronous reluctance motor (SyRM) quadratic programming (QP)

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Achieving Online Regression Performance of LSTMs With Simple RNNs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7632-7643页

作者： Vural, N. Mert Ilhan, Fatih Yilmaz, Selim F. Ergut, Salih Kozat, Suleyman Serdar Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Univ Toronto Dept Comp Sci Toronto ON M5S Canada DataBoss AS TR-06800 Ankara Turkey Rumeli Univ Dept Comp Engn TR-34570 Istanbul Turkey

Recurrent neural networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, long short-term memory networks (LSTMs) are commonly preferred in practice, as these networks are capable of learning long-term dependencies while avoiding the vanishing gradient problem. However, due to their large number of parameters, training LSTMs requires considerably longer training time compared to simple RNNs (SRNNs). In this article, we achieve the online regression performance of LSTMs with SRNNs efficiently. To this end, we introduce a first-order training algorithm with a linear time complexity in the number of parameters. We show that when SRNNs are trained with our algorithm, they provide very similar regression performance with the LSTMs in two to three times shorter training time. We provide strong theoretical analysis to support our experimental results by providing regret bounds on the convergence rate of our algorithm. Through an extensive set of experiments, we verify our theoretical work and demonstrate significant performance improvements of our algorithm with respect to LSTMs and the other state-of-the-art learning models.

关键词： Training signal processing algorithms Recurrent neural networks Logic gates Convergence Approximation algorithms Time complexity Neural network training online gradient descent online learning recurrent neural networks (RNNs) regression

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Projection-Free Stochastic Bi-Level Optimization

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 6332-6347页

作者： Akhtar, Zeeshan Bedi, Amrit Singh Thomdapu, Srujan Teja Rajawat, Ketan Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 India Univ Maryland Inst Syst Res College Pk MD 20742 USA

Bi-level optimization, where the objective function depends on the solution of an inner optimization problem, provides a flexible framework for solving a rich class of problems such as hyper-parameter optimization and model-agnostic meta-learning. This work puts forth the first Stochastic Bi-level Frank-Wolfe (SBFW) algorithm to solve the stochastic bi-level optimization problems in a projection-free manner. We utilize a momentum based gradient tracker that results in a sample complexity of O(epsilon(-3)) for convex outer objectives and O(epsilon(-4)) for non-convex outer objectives with strongly convex inner objectives. The stochastic compositional optimization problems (a special case of bi-level optimization problems entailing the minimization of a composition of two expected-value functions) are also considered within the same rubric. The proposed Stochastic Compositional Frank-Wolfe (SCFW) algorithm is shown to achieve a sample complexity of O (epsilon(-2)) for convex objectives and O(epsilon(-3)) for non-convex objectives, at par with the state-of-the-art rates of projection-free algorithms for single-level problems. The usefulness and flexibility of SBFW and SCFW algorithms are demonstrated via extensive numerical tests. We show that SBFW outperforms the state-of-the-art methods for the problem of matrix completion with denoising, and achieve improvements of up to 82% in terms of the wall-clock time required to achieve a particular level of accuracy. Furthermore, we demonstrate the improved performance of SCFW over the competing projection-free variants on the policy evaluation problem in reinforcement learning.

关键词： Optimization signal processing algorithms Stochastic processes Noise reduction Complexity theory Noise measurement Data models Bi-level stochastic optimization frank-wolfe algorithm projection free algorithm

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Identification of Power System Oscillation Modes Using Empirical Wavelet Transform and Yoshida-Bertecco Algorithm

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IEEE ACCESS 2022年 10卷 48927-48935页

作者： Philip, Joice G. Yang, Yejin Jung, Jaesung Ajou Univ Dept Energy Syst Res Suwon 16499 South Korea

Oscillations occurring in the power system are one of the biggest threats to its secure operation. Although they occur rarely, these oscillations can cause severe damage to the power system if they are not detected at the earliest. Hence, this work focuses on identifying the parameters of oscillations in the power system using a combination of Empirical Wavelet Transform and Yoshida-Bertecco algorithm. As these oscillations occur rarely, a preprocessing method based on Teager Kaiser Energy Operator is used to check whether the signal under consideration contains any oscillation modes. The effectiveness of the proposed method is tested using a test signal, simulated power system signal, and PMU data from an actual power system under various levels of noise contamination. Further, the performance of the proposed method is compared with a VMD-Hilbert transform-based, Prony-based, and SSI-based methods in the literature. Results reveal the superiority of the proposed method irrespective of the parameters of the signal under consideration.

关键词： Oscillators Power systems Wavelet transforms Phasor measurement units Wavelet analysis signal to noise ratio signal processing algorithms Empirical wavelet transform power system oscillations Prony's method stochastic subspace identification Teager Kaiser energy operator variational mode decomposition

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Non-Linear Gaussian Smoothing With Taylor Moment Expansion

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IEEE signal processing LETTERS 2022年 29卷 80-84页

作者： Zhao, Zheng Sarkka, Simo Aalto Univ Dept Elect Engn & Automat Helsinki 02150 Finland

This letter is concerned with solvingcontinuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments show that the proposed smoother outperforms a number of baseline smoothers.

关键词： Smoothing methods Mathematical models signal processing algorithms Numerical models Approximation algorithms Stochastic processes Frequency modulation State-space models continuous-discrete filtering and smoothing non-linear filtering and smoothing stochastic differential equations Taylor moment expansion

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Boosting Independent Component Analysis

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IEEE signal processing LETTERS 2022年 29卷 1367-1371页

作者： Li, YunPeng Ye, ZhaoHui Swiss Fed Inst Technol D ITET CH-8092 Zurich Switzerland Tsinghua Univ Dept Automat Beijing 100190 Peoples R China

Independent component analysis is intended to recover the mutually independent components from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. To alleviate the dependency on prior knowledge concerning unknown sources, many nonparametric methods have been proposed. In this paper, we present a novel boosting-based algorithm for independent component analysis. Our algorithm consists of maximizing likelihood estimation via boosting and seeking unmixing matrix by the fixed-point method. A variety of experiments validate its performance compared with many of the presently known algorithms.

关键词： Boosting signal processing algorithms Mathematical models Maximum likelihood estimation Splines (mathematics) Complexity theory Source separation Independent component analysis boosting non- parametric maximum likelihood estimation fixed-point method

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