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Statistical Multiplexing Gain Analysis of Centralized Open Radio Access Networks with Limited Cells and Heterogeneous Traffics

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Lu Zhou, Yiqing Liu, Ya-Feng Liu, Ling Shi, Ningzhe Tian, Lin Shi, Jinglin National Key Laboratory of Transient Impact Beijing102202 China 208 Research Institute of China Ordnance Industries Beijing102202 China Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Nanjing Institute of InforSuperBahn Nanjing211100 China

Centralized open radio access networks (O-RANs) aggregate computing resources of multiple cells to improve resource utilization and achieve statistical multiplexing gain (SMG), i.e., the ratio of the number of deployed resources (DepR) for distributed radio access network (D-RAN) to that for O-RAN. SMG of O-RAN has been investigated with infinite number of cells with homogeneous bursty-temporal traffic features. However, in practice, the number of cells is limited, and in the era of Internet of Things (IoT), cells with heterogeneous bursty-temporal traffics (het-traffics), should be considered. This paper analyzes the SMG of O-RAN with limited het-traffic cells. First of all, given traffic models of het-traffic cells following different bursty lognormal distribution and periodic time-varying features, required resources (ReqR) for D-RAN can be derived. Then, to obtain the distribution of ReqR for O-RAN with limited het-traffic cells, a Gaussian mixture model (GMM) based resource distribution approximation (GMM-RDA) is proposed. Thus SMG can be obtained for any given service rate. In simulations, the effectiveness of GMM-RDA is verified by Kolmogorov-Smirnov (K-S) test, and the accuracy of achieved SMG can be up to 99.7%, which is about 33% higher than that obtained by the law of large numbers (LLN) for infinite cells. Moreover, het-traffic cells resulting from service diversification can enhance SMG in O-RAN by 34.7%. © 1967-2012 IEEE.

关键词： Radio access networks

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ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data

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IEEE/ACM Transactions on computational Biology and Bioinformatics 2024年第3期21卷 492-500页

作者： Li, Weiling Malhotra, Raunaq Wu, Steven Jha, Manjari Rodrigo, Allen Poss, Mary Acharya, Raj Weill Cornell Medical College Institute for Computational Biomedicine New YorkNY10021 United States ScienceIO New YorkNY10100 United States National Taiwan University Department of Agronomy Taipei10617 Taiwan Pennsylvania State University School of Electrical Engineering and Computer Science University ParkPA16802 United States University of Auckland School of Biological Sciences Private Bag 92019 New Zealand University of Virginia Department of Hematology and Oncology School of Medicine CharlottesvilleVA22908 United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering BloomingtonIN47405 United States

We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH Deorowicz et al. 2015 based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations. We evaluated our pipeline on both simulated and real viral quasispecies data from HIV (and real data from SARS-COV-2). Experimental results show that ViPRA-Haplo, although still an overestimation in the number of true contigs, outperforms the existing tool, PEHaplo, providing up to 9% better genome coverage on HIV real data. In addition, ViPRA-Haplo also retains higher diversity of the viral population as demonstrated by the presence of a higher percentage of contigs less than 1000 base pairs (bps), which also contain k-mers with counts less than 100 (representing rarer sequences), which are absent in PEHaplo. For SARS-CoV-2 sequencing data, ViPRA-Haplo reconstructs contigs that cover more than 90% of the reference genome and were able to validate known SARS-CoV-2 strains in the sequencing data. © 2004-2012 IEEE.

关键词： Coronavirus

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Crow Search Algorithm for Energy-Efficient UAV Path Planning in Large-Scale Aerial Surveys 3

Crow Search Algorithm for Energy-Efficient UAV Path Planning...

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3rd IEEE International Conference on Device Intelligence, computing and Communication Technologies, DICCT 2025

作者： Amutha, S. Ramanan, S.V. Ganesan, Selvendran Leena, B. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Ppg Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Mahendra Institute of Technology Department of Biomedical Engineering Tamil Nadu Namakkal India KGiSL Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331543358

UAVs have been extremely influential for applications such as remote/sensing, disaster response, surveillance, and infrastructure monitoring. They are valuable in modern-day operations because they can conduct high-precision aerial surveys autonomously. Most of the existing trajectory planning approaches are based on metaheuristic algorithms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are capable of providing satisfactory results. Still, they are computationally expensive with slow convergence and sensitive to local optima. In this paper, the challenges raised above are addressed by an Optimized Trajectory Planning Framework based on the Crow Search Algorithm (CSA). It is based on a nature-inspired optimization technique that mimics the intelligent foraging behavior of crows. The proposed approach dynamically adjusts the UAV flight path by balancing the exploration and exploitation, to compute the optimal trajectory quickly and meanwhile to save energy and avoid the obstacle. Unlike other metaheuristic methods, the Crow Search Algorithm does not confront the premature convergence problem and can guarantee adaptive path optimization even under uncertain environmental conditions. Results show that the energy efficiency performance of the Crow Search Algorithm is 15 percent better, the performance in terms of coverage is 20 percent improved, and the speed of convergence is 30 percent faster compared to various state-of-the-art methods. These experimental findings demonstrate that integrating CSA into UAV trajectory planning offers high computational efficiency, obstacle avoidance, and mission endurance, rendering it a superior UAV trajectory planning algorithm for real-time aerial navigation and autonomous surveillance applications. © 2025 IEEE.

关键词： Ant colony optimization

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： B Shoba T Raghunathan S.Sam Peter Karthick K Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Department of Artificial Intelligence and Data Science Dr.NG.p.Institute of Technology Coimbatore Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles.

关键词： Adaptation models Pedestrians Heuristic algorithms Feature extraction Real-time systems Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Crow Search Algorithm for Energy-Efficient UAV Path Planning in Large-Scale Aerial Surveys

Crow Search Algorithm for Energy-Efficient UAV Path Planning...

引用

Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： S. Amutha S.V. Ramanan Selvendran Ganesan B. Leena Akshya J Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering PPG Institute of Technology Coimbatore Tamil Nadu India Department of Biomedical Engineering Mahendra Institute of Technology Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

关键词： Surveys Trajectory planning Surveillance Heuristic algorithms Metaheuristics Autonomous aerial vehicles Energy efficiency Real-time systems Collision avoidance Convergence

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Projected gradient descent algorithm for ab initio crystal structure relaxation under a fixed unit cell volume

arXiv

引用

arXiv 2024年

作者： Hu, Yukuan Yin, Junlei Gao, Xingyu Liu, Xin Song, Haifeng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Solidification Processing Northwestern Polytechnical University Shaanxi Xi’an710072 China Innovation Center NPU Chongqing Chongqing401135 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China

This paper is concerned with ab initio crystal structure relaxation under a fixed unit cell volume, which is a step in calculating the static equations of state and forms the basis of thermodynamic property calculations for materials. The task can be formulated as an energy minimization with a determinant constraint. Widely used line minimization-based methods (e.g., conjugate gradient method) lack both efficiency and convergence guarantees due to the nonconvex nature of the feasible region as well as the significant differences in the curvatures of the potential energy surface with respect to atomic and lattice components. To this end, we propose a projected gradient descent algorithm named PANBB. It is equipped with (i) search direction projections onto the tangent spaces of the nonconvex feasible region for lattice vectors, (ii) distinct curvature-aware initial trial step sizes for atomic and lattice updates, and (iii) a nonrestrictive line minimization criterion as the stopping rule for the inner loop. It can be proved that PANBB favors theoretical convergence to equilibrium states. Across a benchmark set containing 223 structures from various categories, PANBB achieves average speedup factors of approximately 1.41 and 1.45 over the conjugate gradient method and direct inversion in the iterative subspace implemented in off-the-shelf simulation software, respectively. Moreover, it normally converges on all the systems, manifesting its unparalleled robustness. As an application, we calculate the static equations of state for the high-entropy alloy AlCoCrFeNi, which remains elusive owing to 160 atoms representing both chemical and magnetic disorder and the strong local lattice distortion. The results are consistent with the previous calculations and are further validated by experimental thermodynamic data. © 2024, CC BY.

关键词： Conjugate gradient method

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Improved Red Deer Algorithm for Scientific Workflow Scheduling in Cloud Environment 5

Improved Red Deer Algorithm for Scientific Workflow Scheduli...

引用

5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Venkata Srinivas, Chandra Mouli Rena, Hajiyeva Arunarani, A.R. Naitik, S.T. Al Ansari, Mohammed Saleh Younas, Ammar Bonam Venkata Chalamayya Engineering College Andhra Pradesh Odalarevu India Azerbaijan Western Caspian University Department of Information Technologies Azerbaijan School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dayananda Sagar University Cse Department Karnataka Bangalore India College of Engineering University of Bahrain Department of Chemical Engineering Bahrain Tashkent State University of Law Department of Business Law Uzbekistan

ISBN: (纸本)9798350321425

One of the most difficult problems in the technological sector is the versatility of cloud computing, which can offer a flexible and responsive infrastructure in the realm of information technology. A significant problem in cloud computing that is addressed in recent research is workflow scheduling. With the quick advancement of cloud computing, scheduling the intricate scientific process on the cloud has grown to be an incredibly difficult task. It has been identified that one of the key challenges to optimizing the performance of cloud computing is scheduling workflow tasks so that it is processed by the most effective cloud network resources. Meta-heuristic optimization algorithms are frequently utilized to find a solution to effective task scheduling because of the intricacy of the problem and the extent of the search space. This research suggests a unique Improved Red Deer Algorithm (IRDA) to shorten execution time subject to a set budget. The performance of the proposed algorithm is evaluated against conventional optimization algorithms by applying it to scientific workflows including Montage and Epigenomics. The experimental results reveal that the proposed algorithm works better than other compared methods in reducing workflow task execution time and cost. © 2023 IEEE.

关键词： Cloud computing

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An extended framework for the HMC in lattice gauge theory

arXiv

引用

arXiv 2024年

作者： Christ, Norman H. Jin, Lu-Chang Lehner, Christoph Lundstrum, Erik Matsumoto, Nobuyuki Physics Department Columbia University New YorkNY10027 United States Physics Department University of Connecticut StorrsCT06269-3046 United States Fakultät für Physik Universität Regensburg Universitätsstraße 31 Regensburg93040 Germany Hariri Institute for Computing and Computational Science and Engineering Boston University BostonMA02215 United States

We develop an extended framework for the hybrid Monte Carlo (HMC) algorithm in lattice gauge theory by embedding the SU(N) group into the space of general complex matrices, MN(C). Auxiliary directions will be completely factorized in the path integral, and the embedding does not alter the expectation values of the original theory. We perform the molecular dynamics updates by using the matrix elements of W ∈ MN(C) as the dynamical variables without group theoretic constraints. The framework enables us to introduce non-separable Hamiltonians for the HMC in lattice gauge theory exactly, whose immediate application includes the Riemannian manifold HMC. © 2024, CC BY.

关键词： Hamiltonians

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Generalization Error Estimates of Machine Learning Methods for Solving High Dimensional Schrödinger Eigenvalue Problems

arXiv

引用

arXiv 2024年

作者： Yu, Hao Guo, Yixiao Ming, Pingbing LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

We propose a machine learning method for computing eigenvalues and eigenfunctions of the Schrödinger operator on a d-dimensional hypercube with Dirichlet boundary conditions. The cut-off function technique is employed to construct trial functions that precisely satisfy the homogeneous boundary conditions. This approach eliminates the error caused by the standard boundary penalty method, improves the overall accuracy of the method, as demonstrated by the typical numerical examples. Under the assumption that the eigenfunctions belong to a spectral Barron space, we derive an explicit convergence rate of the generalization error of the proposed method, which does not suffer from the curse of dimensionality. We verify the assumption by proving a new regularity shift result for the eigenfunctions when the potential function belongs to an appropriate spectral Barron space. Moreover, we extend the generalization error bound to the normalized penalty method, which is widely used in practice. Copyright © 2024, The Authors. All rights reserved.

关键词： Boundary conditions

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A Novel Approach for Classifying DNA Barcodes Using Ensemble NLP Models

A Novel Approach for Classifying DNA Barcodes Using Ensemble...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Abhivyakti Yadav Prassana Kumar R Balu Bhasuran I R Oviya Department of Computer Science and Engineering (AI) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Bakar Computational Health Sciences Institute (BCHSI) University of California San Francisco

DNA Barcodes, which are distinct fragments derived from brief sections of DNA (such as mitochondria, nucleus, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose an Ensemble NLP + Multinomial Classifier workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 96 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.

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