Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing by Yiyang HU, Lina QI

“…Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.…”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Research on the Multi-Objective Optimal Design of Adjusting Mechanisms Considering Force Transmission Performance by Qi Yang, Mingxin Shan, Yangli Tian, Boyang Guan, Jingyu Zhai, Wei Sun

“…Through case studies, significant reductions in motion precision errors and the peak stagnation force and maximum differences in stagnation force were achieved, validating the feasibility of this optimization design approach.…”
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Chronological age estimation from human microbiomes with transformer-based Robust Principal Component Analysis by Tyler Myers, Se Jin Song, Yang Chen, Britta De Pessemier, Lora Khatib, Daniel McDonald, Shi Huang, Richard Gallo, Chris Callewaert, Aki S. Havulinna, Leo Lahti, Guus Roeselers, Manolo Laiola, Sudarshan A. Shetty, Scott T. Kelley, Rob Knight, Andrew Bartko

“…TRPCA improves age prediction accuracy from human microbiome samples, achieving the largest reduction in Mean Absolute Error for WGS skin (MAE: 8.03, 28% reduction) and 16S skin (MAE: 5.09, 14% reduction) samples, compared to conventional approaches. …”
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Diffusion models enable zero-shot pose estimation for lower-limb prosthetic users. by Tianxun Zhou, Muhammad Nur Shahril Iskandar, Keng-Hwee Chiam

“…The zero-shot approach achieved substantial reductions in keypoint coordinate errors of 37% for transtibial and 76% for transfemoral prosthetic limbs compared to OpenPose on the original videos. …”
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Towards enhanced photovoltaic Modeling: New single diode Model variants with nonlinear ideality factor dependence by Martin Ćalasan, Snežana Vujošević, Gojko Krunić

“…Experimental validation was conducted on a standard solar cell (RTC France) and three commercial modules (MSX60, PWP201, KC200GT), demonstrating significant reductions in modeling errors (RMSE) compared to classical SDM, DDM, and TDM approaches. …”
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Advancements in fully homomorphic encryption over the integers: a comprehensive survey and analysis by Rohitkumar R. Upadhyay, Sahadeo Padhye

“…These improvements include reductions in public key sizes, batched processing capabilities, scale-invariant properties, faster bootstrapping, and parameter selection for enhanced security. …”
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Optimizing Cold Chain Logistics with Artificial Intelligence of Things (AIoT): A Model for Reducing Operational and Transportation Costs by Hamed Nozari, Maryam Rahmaty, Parvaneh Zeraati Foukolaei, Hossien Movahed, Mahmonir Bayanati

“…So, EPO has achieved the optimal value of the objective function compared to a 70% reduction in the solution time. Further analyses show the effectiveness of EPO in the indicators of average objective function, average RPD error, and solution time. …”
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Application of quasi-oppositional driving training-based optimization for a feasible optimal power flow solution of renewable power systems with a unified power flow controller by Tushnik Sarkar, Chandan Paul, Susanta Dutta, Provas Kumar Roy, Ghanshyam G. Tejani, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

“…Obtaining minimum total cost comes under the single-objective function. Simultaneous reduction in the overall cost and emission, concurrent reduction in overall cost and voltage deviation (VD), and simultaneous reduction in overall cost and voltage stability index come under multi-objective cases. …”
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Framework for Integrating Requirements Engineering and DevOps Practices in Robotic Process Automation with a Focus on Optimizing Human–Computer Interaction by Leonel Patrício, Leonilde Varela, Zilda Silveira

“…Key results include an 83% reduction in processing time, an 81.25% decrease in error rates, and an 80% reduction in manual tasks, alongside improved compliance and scalability. …”
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Forgery Detection in Dynamic Signature Verification by Entailing Principal Component Analysis by Shohel Sayeed, S. Andrews, Rosli Besar, Loo Chu Kiong

“…The raw signals that are captured using 14- and 5-electrode data gloves for this purpose have a noisy and voluminous nature. Reduction of electrodes may reduce the volume but it may also reduce the efficiency of the system. …”
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The Treatment of Hospital Wasterwater Electrocoagulation Using Iron Electrodes: Analysis by Response Surface Methodology by Darmadi M. Yusuf, Mirna Rahmah Lubis, Adisalamun Adisalamun

“…The reduction percentage is TSS 72.45%.…”
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Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing by Yiyang HU, Lina QI

“…Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.…”
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Determination of the Shortest Hamiltonian Paths in an Arbitrary Graph of Distributed Databases by E. G. Andrianova, V. K. Raev, D. I. Filgus

“…A method has been developed for finding the shortest Hamiltonian path in an arbitrary graph based on the rank approach, which provides high efficiency and a significant reduction in the error in solving the problem of organizing the process of managing multiple transactions and queries when they are implemented in network databases. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation.…”
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Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set by Majid Akbari, S. Alireza Davari, Reza Ghandehari, Freddy Flores-Bahamonde, Jose Rodriguez

“…This error can significantly raise the total harmonic distortion (THD) output current of the inverter. …”
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Fusion-Based Localization System Integrating UWB, IMU, and Vision by Zhongliang Deng, Haiming Luo, Xiangchuan Gao, Peijia Liu

“…Furthermore, compared with the UWB/IMU fusion model, the proposed method achieves a 50.0% reduction in RMSE and a 59.1% reduction in maximum error.…”
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Advanced Machine Learning Approaches for Predicting Machining Performance in Orthogonal Cutting Process by Sabrina Al Bukhari, Salman Pervaiz

“…The analysis supports that the XGBoost model is the most accurate, with a 34.1% reduction in the mean squared error and a 17.1% reduction in the mean absolute error over these values for the Decision Tree. …”
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INVARIAN AUTOMATIC CONTROL SYSTEM, USING THE INTERMEDIATE-FREQUENCY SIGNALS OF HEAT POWER PARAMETERS by G. T. Kulakov, A. N. Kukhorenko, I. M. Golinko

“…And that is why it leads to the further reduction of maximal dynamic regulation error in processing of external disturbance by consumption of steam, and this allows to improve the quality of control.…”
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Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels by Qiwei Ji, Huabing Wu

“…Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. …”
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