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Cascade AOA estimation technology based on combined array antenna with computational complexity analysis

ICT EXPRESS 2022年第4期8卷 539-543页

作者： Kim, Tae-yun Lee, Hua Hwang, Suk-seung Chosun Univ Dept Elect Engn Gwangju South Korea Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Chosun Univ Sch Elect Engn Interdisciplinary Program IT Bio Convergence Syst Gwangju South Korea

Since most studies for estimating an angle-of-arrival (AOA) based on the antenna array have considered the antenna array with a single configuration, they are not proper to simultaneously estimate AOAs of multiple signals with various frequencies. In this paper, we introduce a cascade AOA estimation technique consisting of CAPON and Beamspace Multiple Signal Classification (MUSIC), based on a Combined Array Antenna (CAA) with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA), for enhancing the above problem. In addition, we provide the computational complexity analysis for showing the low computational complexity of this technique comparing to the conventional technique. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences.

关键词： computational complexity analysis Combined array antenna AOA estimation Cascade estimation

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On the Logical computational complexity analysis of Turbo Decoding Algorithms for the LTE Standards

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期118卷 1591-1619页

作者： Beeharry, Y. Fowdur, T. P. Soyjaudah, K. M. S. Univ Mauritius Fac Engn Dept Elect & Elect Engn Reduit Mauritius Multicarrier Mauritius Ltd Port Louis Mauritius

Evaluating the computational complexity of decoders is a very important aspect in the area of Error Control Coding. However, most evaluations have been performed based on hardware implementations. In this paper, different decoding algorithms for binary Turbo codes which are used in LTE standards are investigated. Based on the different mathematical operations in the diverse equations, the computational complexity is derived in terms of the number of binary logical operations. This work is important since it demonstrates the computational complexity breakdown at the binary logic level as it is not always evident to have access to hardware implementations for research purposes. Also, in contrast to comparing different Mathematical operations, comparing binary logic operations provides a standard pedestal in view to achieve a fair comparative analysis for computational complexity. The usage of the decoding method with fewer number of binary logical operations significantly reduces the computational complexity which in turn leads to a more energy efficient/power saving implementation. Results demonstrate the variation in computational complexities when using different algorithms for Turbo decoding as well as with the incorporation of Sign Difference Ratio (SDR) and Regression-based extrinsic information scaling and stopping mechanisms. When considering the conventional decoding mechanisms and streams of 16 bits in length, Method 3 uses 0.0065% more operations in total as compared to Method 1. Furthermore, Method 2 uses only 0.0035% of the total logical complexity required with Method 1. These computational complexity analysis at the binary logical level can be further used with other error correcting codes adopted in different communication standards.

关键词： computational complexity analysis Turbo codes LTE DVB-RCS Binary logical operations

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Short-term forecasting of emergency medical services demand exploring machine learning☆

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Shahidian, Nika Abreu, Paulo Santos, Daniel Barbosa-Povoa, Ana Univ Lisbon CEGIST Inst Super Tecn Lisbon Portugal

This study addresses the challenge of forecasting short-term demand in Emergency Medical Services (EMS) using machine learning techniques, which is essential for improving resource allocation, optimizing response times, and enhancing overall system efficiency. In emergency situations, the swift allocation of medical personnel and vehicles is crucial for ensuring faster response times and increasing survival rates. To tackle this, the study explores nine machine learning algorithms, including Gaussian Mixture Models, Naive Bayes, KNearest-Neighbors, Support Vector Machine, Random Forest, Extremely Randomized Trees, Gradient Boosting, Adaptive Boosting, and Bagging, to provide accurate forecasts. Model performance is analyzed using two variable selection methods: filter and wrapper methods. The best-performing algorithms, Gradient Boosting and AdaBoost, undergo further analysis after hyperparameter fine-tuning. Principal results show that Gradient Boosting and AdaBoost outperformed other algorithms, with Random Search and Genetic Algorithm methods enhancing model performance. The study also demonstrates how the combination of different methods influences both performance and computational complexity. In this context, the developed models not only achieve high levels of accuracy in predicting short-term EMS demand but also serve as versatile tools. They distinguish priority calls and vehicle types when predicting call volume per hour from different districts and the expected number of dispatches at each base per shift, respectively. These models can be used as standalone tools or integrated with other optimization approaches, providing valuable inputs for optimizing staff scheduling, location, and relocation of emergency vehicles, thus contributing to the efficiency and performance improvement of EMS systems.

关键词： Machine learning Short-term forecasting Genetic algorithms computational complexity analysis Boosting algorithms

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Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第6期42卷 1941-1954页

作者： Al-Nahhal, Mohamed Al-Nahhal, Ibrahim Dobre, Octavia A. Soman, Sunish Kumar Orappanpara Mem Univ Fac Engn & Appl Sci St John NF A1C 5S7 Canada Ulster Univ Sch Engn Belfast BT15 1ED North Ireland

This paper proposes two novel neural network (NN) structures to estimate long-term steady linear and nonlinear signal-to-noise ratio (SNR) components in optical fiber communication systems. The first proposed structure is a parallel NN-based (ParNN) estimator, which estimates each SNR component using a different NN structure and input feature set. A combination of gated recurrent unit and dense layers is used to estimate the linear SNR component. On the other hand, the nonlinear SNR component is estimated using a combination of convolutional layer with dense layer. The proposed input features of the ParNN estimator are generated solely from the received signal without knowledge of the transmitted signal. These features are formed of the lower quartile, upper quartile, and entropy, which can accurately characterize the behavior of the SNR components by measuring the received signal spread and uncertainty. For further improvement of the ParNN estimator, an additional stage is added to form the proposed enhanced ParNN (EParNN) estimator. This additional stage consists of two feedforward NNs (FFNNs), each with a single dense layer, where the first FFNN is used to estimate the linear SNR component and the second one estimates the nonlinear SNR component. The input of this additional stage is a combination of the input features and output of the ParNN estimator. The computational complexity is derived for the proposed estimators. The training and testing dataset is built from 16-ary quadrature amplitude modulation of a dual-polarization on a wide range of standard single-mode fiber system configurations, e.g., number of wavelength division multiplexing channels, optical launch power, and number of spans. Numerical results demonstrate that the proposed ParNN estimator achieves better SNR estimation accuracy with comparable computational complexity compared to the most efficient work in the literature. The proposed ParNN estimator can independently estimate each SNR compone

关键词： computational complexity analysis convolutional layer dense layer gated recurrent unit optical fiber communications signal-to-noise ratio (SNR) estimation

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Hierarchical planning and reasoning about partially ordered plans-From theory to practice

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AI MAGAZINE 2022年第4期43卷 353-364页

作者： Bercher, Pascal Australian Natl Univ Coll Engn Comp & Cybernet Sch Comp Canberra Australia

This invited paper (part of the New Faculty Highlights Invited Speaker Program of AAAI'21*) surveys my work done until today. The reviewed work focuses on hierarchical task network (HTN) planning as well as on partial order causal link (POCL) planning. Lines of research include theoretical investigations (mostly computational complexity analyses), heuristic search, as well as the practical application of the technology for planning-based assistants, which support a human user in carrying out various tasks.

关键词： Planning Human-computer interaction computational complexity analysis

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Cooperative and Parallel Fog Discovery and Pareto Optimal Fog Commerce Bargaining

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IEEE TRANSACTIONS ON computational SOCIAL SYSTEMS 2022年第4期9卷 1112-1121页

作者： Sim, Kwang Mong University of Ottawa ON Canada

While fog resource discovery involves propagating resource requests to a huge number of fog nodes, fog commerce refers to the activity of buying and selling fog resources. This work devises: 1) the KM-gossip algorithm for bolstering fog discovery and 2) a bargaining mechanism for pricing fog resources. The KM-gossip algorithm is a generalization of the gossip algorithm. It uses K broker agents (BAs) to cooperatively ``gossip'' requests among themselves, and in parallel, each BA relays the requests to M fog nodes. While computational complexity analysis validates that the KM-gossip algorithm has logarithmic time complexity (i.e., it is computationally efficient), empirical results show that it significantly outperforms the existing gossip and flooding algorithms. Supplementing and complementing previous empirical results, game-theoretic analysis in this work validates that the bargaining mechanism generates Pareto optimal (economically efficient) solutions. The solutions also satisfy the famous Nash's axioms that prescribe highly desirable properties of bargaining solutions.

关键词： Barium Edge computing Relays Pareto optimization Cloud computing Business Time complexity Bargaining theory computational complexity analysis distributed intelligent systems gossip algorithm

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computational complexity analysis and Algorithm Design for Combinatorial Optimization Problems

Computational Complexity Analysis and Algorithm Design for C...

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3rd International Conference on Networking and Computing (ICNC)

作者： Watanabe, Toshimasa Hiroshima Univ Grad Sch Engn Higashihiroshima 724 Japan

The paper shows summary of the author's research subjects from 1973 through 2012. Additional explanation on these subjects and related references are omitted because of space limitation. They will be given at pres... 详细信息

ISBN: (纸本)9780769548937

关键词： Combinatorial Problems computational complexity analysis Algorithm Design Graph Connectivity Petri Nets

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analysis of VVC Intra Prediction Block Partitioning Structure

Analysis of VVC Intra Prediction Block Partitioning Structur...

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IEEE International Conference on Visual Communications and Image Processing (VCIP) - Visual Communications in the Era of AI and Limited Resources

作者： Saldanha, Mario Sanchez, Gustavo Marcon, Cesar Agostini, Luciano Univ Fed Pelotas Grad Program Comp PPGC Pelotas RS Brazil IF Farroupilha Alegrete Brazil Pontificial Catholic Univ Rio Grande do Sul Porto Alegre RS Brazil

ISBN: (纸本)9781728185514

This paper presents an encoding time and encoding efficiency analysis of the Quadtree with nested Multi-type Tree (QTMT) structure in the Versatile Video Coding (VVC) intra-frame prediction. The QTMT structure enables VVC to improve the compression performance compared to its predecessor standard at the cost of a higher encoding complexity. The intra-frame prediction time raised about 26 times compared to the HEVC reference software, and most of this time is related to the new block partitioning structure. Thus, this paper provides a detailed description of the VVC block partitioning structure and an in-depth analysis of the QTMT structure regarding coding time and coding efficiency. Based on the presented analyses, this paper can guide outcoming works focusing on the block partitioning of the VVC intra-frame prediction.

关键词： computational complexity analysis Compression efficiency analysis Block partitioning Versatile Video Coding

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analysis of Algorithm Efficiency for Heat Diffusion at Nanoscale Based on a MEMS Structure Investigation

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ENERGIES 2020年第10期13卷 2520-2520页

作者： Raszkowski, Tomasz Zubert, Mariusz Lodz Univ Technol Dept Microelect & Comp Sci PL-90924 Lodz Poland

This paper presents an analysis of the time complexity of algorithms prepared for solving heat transfer problems at nanoscale. The first algorithm uses the classic Dual-Phase-Lag model, whereas the second algorithm employs a reduced version of the model obtained using a Krylov subspace method. This manuscript includes a description of the finite difference method approximation prepared for analysis of the real microelectromechanical system (MEMS) structure manufactured by the Polish Institute of Electron Technology. In addition, an approximation scheme of the model, as well as the Krylov subspace-based model order reduction technique are also described. The paper considers simulation results obtained using both investigated versions of the Dual-Phase-Lag model. Moreover, the relative error generated by the reduced model, as well as the computational complexity of both algorithms, and a convergence of the proposed approach are analyzed. Finally, all analyses are discussed in detail.

关键词： Dual-Phase-Lag heat transfer model Krylov subspace-based model order reduction algorithm efficiency analysis relative error analysis algorithm convergence analysis computational complexity analysis thermal simulation algorithm finite difference method scheme Grunwald-Letnikov fractional derivative

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A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第1期11卷 337-349页

作者： Wang, Jianzhou Niu, Tong Lu, Haiyan Yang, Wendong Du, Pei Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Dept Software Engn Sydney NSW 2007 Australia

The path towards wind power forecasting has yielded huge socio-economic benefits at a global scale. However, most of the previous studies tend to emphasize the improvement of deterministic forecasting, usually losing sight of the significance of probabilistic forecasting. In this paper, a novel forecasting system that can perform deterministic and probabilistic forecasting of wind power simultaneously, composed by the modules of feature selection, forecasting, system optimization, and system evaluation is presented to further supplement the existing studies in this field. Concretely, a hybrid feature selection strategy is proposed in the feature selection module to determine optimal system input;superior to traditional gradient descent algorithm, a dynamic reservoir theory-based recurrent neural network is developed in the forecasting module;an enhanced multi-objective optimization algorithm with the objectives of accuracy and stability is proposed in the system optimization module to provide an optimal scenario for system parameters;the effectiveness and feasibility of the proposed system is then validated in the evaluation module. Moreover, the comprehensive performance analysis of the proposed system is investigated in depth. Finally, the experimental results demonstrate that the proposed system has a significant advantage over the benchmarks considered, further verifying its tremendous potential to be used in a practical wind power system.

关键词： Forecasting Wind power generation computational modeling Reservoirs Predictive models Feature extraction Probabilistic logic Feature selection multi-objective optimization reservoir computing model wind power forecasting computational complexity analysis

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