Design of hybridly-connected hybrid precoding in millimeter-wave massive MIMO system by Hongyu ZHAO, Hongyan YAO

“…In order to improve the spectral efficiency of hybridly-connected hybrid precoding,the optimal hybrid precoding matrix under the ideal conditions was firstly obtained by using the principle of successive interference cancellation (SIC).Secondly,the optimal hybrid precoding matrix was decomposed into the digital precoding matrix and the analog precoding matrix by using the gradient descent theory.Finally,considering the constant modulus constraint condition of the analog precoding matrix,the digital and analog precoding matrices were optimized by using the alternating minimization method aim to maximize the spectral efficiency.Due to the hybridly-connected structure,the proposed hybrid precoding design algorithm is significantly superior to the partially-connected and fully-connected hybrid precoding in terms of the system energy efficiency.Meanwhile,the algorithm does not increase any hardware complexity and only increases a small amount of computation of the hybridly-connected hybrid precoding.Computer simulation results exhibit that the proposed algorithm can improve the system spectral efficiency of the hybridly-connected hybrid precoding,and the upgrade of spectral efficiency is more significant especially in the conditions that the number of radio frequency (RF) links is greater than the number of data streams.Since the sub-blocks are not necessary to satisfy orthogonality conditions,the proposed algorithm is more suitable for practical application than the existing hybridly-connected hybrid precoding.…”
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Two-stage spline-approximation in linear structure routing by D. A. Karpov, V. I. Struchenkov

“…At the second stage, the parameters of the spline element are optimized. The algorithms of nonlinear programming are used. …”
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Semi-supervised anchorless single-engine grip detection by Yun Shi, Gang Zhang, Min Kong, Jie Fang

“…Finally, the boundary value of the minimum boundary rectangle is obtained by judging the optimal target and the optimal grasping point of the inference module, and the final result is obtained by rotating back to the coordinate output on the original image area. …”
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A quantum random access memory (QRAM) using a polynomial encoding of binary strings by Priyanka Mukhopadhyay

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Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling by Pimolkan Piankitrungreang, Kantawatchr Chaiprabha, Worathris Chungsangsatiporn, Chanat Ratanasumawong, Peemdej Chancharoen, Ratchatin Chancharoen

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An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values by Kumarmangal Roy, Muneer Ahmad, Kinza Waqar, Kirthanaah Priyaah, Jamel Nebhen, Sultan S Alshamrani, Muhammad Ahsan Raza, Ihsan Ali

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Wireless Sensor Networks Energy Effectively Distributed Target Detection by Xiaogang Qi, Peng Wei, Lifang Liu, Mande Xie, Guoyong Cai

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Exact Overlap Rate Analysis and the Combination with 4D BIM of Time-Cost Tradeoff Problem in Project Scheduling by Guofeng Ma, Lingzhi Zhang

“…The method makes use of overlapping strategy matrix (OSM) to illustrate the dependency relationships between activities. This method then optimizes the genetic algorithm (GA) to compute an overlapping strategy with exact overlap rates by means of overlapping and crashing. …”
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Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots by Weimin Zhang, Guoyong Wang

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A new method for surge arrester placement in high‐voltage substations considering environmental effects by Faridoddin Safaei, Mohsen Niasati

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City-scale industrial tank detection using multi-source spatial data fusion by Zhibao Wang, Mingyuan Zhu, Lu Bai, Jinhua Tao, Mei Wang, Xiaoqing He, Anna Jurek-Loughrey, Liangfu Chen

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A Method of Sample Models of Program Construction in Terms of Petri Nets by D. I. Kharitonov, E. A. Golenkov, G. V. Tarasov, D. V. Leontyev

“…Petri net samples with certain characteristics are necessary in programming new algorithms for program analysis; in particular, they can be used for developing or optimizing algorithms of Petri nets compositions and decompositions, building the reachability tree, checking invariants and so on. …”
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Dynamic energy consumption monitoring and scheduling for green buildings: A comprehensive approach by Hua Zheng, Pengming Wang

“…Meanwhile, the particle swarm optimization (PSO) algorithm is used to solve the multi-objective scheduling problem to achieve the global objectives of energy conservation, cost reduction, and comfort optimization. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Although advancements in variogram estimation and kriging techniques have optimized sampling strategies, and improved prediction accuracy, challenges persist in computational efficiency and uncertainty quantification. …”
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Prototypical Few-Shot Learning for Histopathology Classification: Leveraging Foundation Models With Adapter Architectures by Kazi Rakib Hasan, Sijin Kim, Junghwan Cho, Hyung Soo Han

“…These findings underscore the effectiveness of the proposed approach in addressing challenges posed by low-data regimes in the computer-aided histopathology domain and the potential for optimizing foundation models with minimal labeled data using prototypical few-shot algorithms.…”
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Q-learning global path planning for UAV navigation with pondered priorities by Kevin B. de Carvalho, Hiago de O.B. Batista, Leonardo A. Fagundes-Junior, Iure Rosa L. de Oliveira, Alexandre S. Brandão

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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Block-Based Adaptive Compressed Sensing by Using Edge Information for Real-Time Reconstruction by V. Pavitra, V. B. S. Srilatha Indira Dutt

“…Adaptive Block-Based Compressed Sensing (ABCS) enables optimization of image and video sensing platforms with limited resources, using novel algorithms for efficient reconstruction and real-time operations. …”
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Flexible Configurable Modular Neural Network-Based OFDM Receiver by A. B. Sergienko, P. V. Apalina, A. D. Lebedinskaya

“…Computer simulation in the MATLAB environment.Results. …”
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