Time-varying channel estimation in reconfigurable intelligent surface assisted communication system by Kai SHAO, Ben LU, Guangyu WANG

“…Aiming at the key problems need to be solved, such as cascade channel sparse representation, time-varying channel parameter tracking and signal reconstruction, for time-varying cascade channels estimation of reconfigurable intelligent surface (RIS) assisted communication system, a Khatri-Rao and hierarchical Bayesian Kalman filter (KR-HBKF) algorithm was proposed.Firstly, the Khatri-Rao product and Kronecker product transformations were used to obtain the sparse representation of RIS cascaded channels based on the sparse characteristics of channels, thus the RIS cascaded channel estimation problem was transformed into a low-dimensional sparse signal recovery problem.Then, according to the state evolution model of RIS cascaded channel, the time correlation parameter was introduced into the prediction model of HBKF algorithm, and the improved HBKF was applied to solve the problem of time-varying channel parameter tracking and signal reconstruction for completing the time-varying cascaded channels estimation.The sparsity and time correlation of the channel were comprehensively considered in the KR-HBKF algorithm, thus better estimation accuracy could be obtained with small pilot overhead.Compared with the traditional compressed sensing algorithm, the simulation results show that the proposed algorithm has about 5 dB estimated performance improvement, and better robustness performance under different time-varying channel conditions.…”
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Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement by Hongqiang Liu, Zhongliang Zhou, Haiyan Yang

“…Their approximate distribution functions are obtained by the use of the expectation maximization algorithm with Gaussian mixture model. Then the probability distribution and probability distribution of measurement prediction residual are combined into a new likelihood function to improve the efficiency of updating the model probability. …”
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A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction by Zhonghua Wu, Wenzhong Yang, Meng Zhang, Fuyuan Wei, Xinfang Liu

“…To bridge the gap between the optimization objective and task performance metrics, we employ the REINFORCE algorithm to optimize our generative model, designing a novel reward function to simultaneously capture the accuracy of temporal prediction and the quality of generation. …”
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A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…ObjectiveThis study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer. …”
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Image encryption using dna-sequences, chaotic dynamics and hash fuctions by A. V. Sidorenko, M. S. Shishko

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

“…Simulations are based on experimental results, and the number of calculations significantly decreases using the proposed algorithm. Simulations of the sample network and implementation of the proposed algorithm with MATLAB and EMTP‐RV prove the efficiency of the proposed method for optimum SA placement.…”
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IoT-based personal thermal comfort control for livable environment by Miao Zang, Zhiqiang Xing, Yingqi Tan

“…In the prediction stage, we introduce the cuckoo search algorithm, which converges rapidly, to solve the air temperature and air velocity with the learnt thermal comfort level. …”
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Antistray, Learning Smart: Creating Indoor Positioning Learning Environment for Augmenting Self-Regulated Learning by Tien-Chi Huang

“…The experiment is carried out to demonstrate the accuracy of the proposed indoor positioning algorithm. The results show that the proposed algorithm has a higher accuracy and lower error. …”
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Peningkatan Akurasi Klasifikasi Algoritma C 4.5 Menggunakan Teknik Bagging pada Diagnosis Penyakit Jantung by Erwin Prasetyo, Budi Prasetiyo

“…The purpose of the authors conducting this research is to determine the application of bagging techniques on the C4.5 algorithm, determine the accuracy of the results in the C4.5 algorithm, and compare the level of accuracy of the application of bagging techniques on the C4.5 algorithm. …”
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WEIGHTED Decoding of Cyclic codes using quantized SIGNAL DEMODULATOR on PM signals by N. A. Salas, A. E. Korolev, V. K. Konopelko

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Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture by Hemkant Nehete, Sandeep Soni, Tharun Kumar Reddy Bollu, Balasubramanian Raman, Brajesh Kumar Kaushik

“…This architecture includes a mapping algorithm that modulates inputs and map AFC to crossbar arrays directly, eliminating the need to predict approximated weights for evaluating output. …”
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Viscous Flow and Dynamic Stall Effects on Vertical-Axis Wind Turbines by A. Allet, I. Paraschivoiu

“…The numerical procedure used here, based on the control volume approach, is the widely known “SIMPLER” algorithm. The resulting algebraic equations are solved by the TriDiagonal Matrix Algorithm (TDMA) in the r- and z-directions and the Cyclic TDMA in the 0-direction. …”
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Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model Dengan Algoritma Evolusi Differensial by Alya Fitri Syalsabilla, Suci Astutik, Agus Fachrur Rozy

“…Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%). …”
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Optimal design strategy of traditional courtyard based on performance and data-driven method—A case study of Yanshen ancient town, China by Zhixin Xu, Xin Zheng, Xiangfeng Li

“…Based on the learning rates and various evaluation indicators, XGBoost is ultimately selected to classify and predict the overall building performance. Results indicate that the model achieves an average prediction accuracy of 83.6%. …”
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Penerapan Teknik Random Oversampling Untuk Memprediksi Ketepatan Waktu Lulus Menggunakan Algoritma Random Forest by Sri Diantika, Hiya Nalatissifa, Nurlaelatul Maulidah, Riki Supriyadi, Ahmad Fauzi

“…This research on the prediction of student graduation timeliness uses a random forest algorithm model. …”
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Forecasting temperature of the internal combustion engine using adaptive model by V. P. Denisov, A. P. Dombrovsky, S. S. Zhuravlev

“…The article presents an algorithm of obtaining prognostic parameters of a dynamic object on the example of forecasting temperature of the internal combustion engine. …”
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Indexer for document management system by M. V. Sterjanov

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Research on Management Model Based on Deep Learning by Yuting Zhao

“…In this study, the focus was on the development of management models and future prediction for the cost and risk by using an improved deep learning (DL) algorithm. …”
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Probabilistic coding of length of series with confirmation of repetition and preliminary sorting for compression of halftone images bits by H. K. Al-Bahadily, V. Yu. Tsviatkou, V. K. Kanapelka

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Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening by Keisuke Uchikawa, Kairi Furui, Masahito Ohue

“…Advances in protein structure prediction, notably AlphaFold2, have begun to address this gap. …”
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