Search Results - (( data distribution channel algorithm ) OR ( data distribution function algorithm ))
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WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm
Published 2023“…The embedded GA optimization scheme was based on minimum downlink power consumption tailored for a WCDMA network that employs adaptive antennas in support of a heterogeneous user mix. The algorithm works by finding the minimum transmitter power with the help of Distributed Parallel Genetic Algorithm (DPGA) employed on an offload microcontroller system to form optimal beam coverage to reduce power usage of adaptive antenna at WCDMA base station. …”
Conference paper -
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Empowering Energy-Sustainable IoT Devices With Harvest Energy-Optimized Deep Neural Networks
Published 2024“…The algorithm jointly optimized the allocation of the channel, PS, and power control to solve the distributed non-convex and NP-hardness caused by co-channel interference. …”
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3
Distributed joint power control, beamforming and spectrum leasing for cognitive two-way relay networks
Published 2017“…In the first part of the thesis, distributed power control and beamforming algorithm is proposed in which users operating in the underlay mode can strategically adapt their power levels and maximize their own utilities. …”
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4
Maintenance dredging: allowable limit for dredge volume differences using SBES and MBES datasets / Raiz Razali
Published 2017“…As different hardware generates different data trends and distributions, different software uses various algorithms to calculate dredge volume and also due to different gridding sizes, it can be expected that the results with varying amounts of dredge volume will be generated. …”
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5
Maintenance derdging: allowable limit for dredge volume differences using SBES and MBES datasets / Raiz Razali
Published 2017“…As different hardware generates different data trends and distributions, different software uses various algorithms to calculate dredge volume and also due to different gridding sizes, it can be expected that the results with varying amounts of dredge volume will be generated. …”
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6
Effective pilot assignment schemes in massive MIMO systems
Published 2022“…The obtained results ensure that the proposed schemes have significantly improved the system performance in terms of achievable uplink rate and cumulative distribution function (CDF) for both SINR and uplink rate. …”
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7
Cognitive load assessment through EEG: a dataset from arithmetic and stroop tasks
Published 2025“…Each task was performed for a duration of 10–20 s, and three trials were conducted for comprehen- sive data collection. Employing an OpenBCI device with an 8- channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. …”
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Design And Analysis Of Modified-Proportional Fair Scheduler For LTELTE-Advanced
Published 2016“…Meanwhile, for Proportional Fair (PF) scheduler, the process of maximizing scheme of data rate could be very unfair and UE that experienced a bad channel quality conditions can be starved. …”
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Balanced femtocell model to enhance indoor coverage in LTE cellular network / Izwah Ismail
Published 2017“…Both functions of frequency channel allocation and transmission power adjustment are proposed. …”
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Balanced femtocell model to enhance indoor coverage in LTE cellular network / Izwah Ismail
Published 2018“…Both functions of frequency channel allocation and transmission power adjustment are proposed. …”
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Book Section -
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Enhancing quality of service in Multi-Radio Multi-Channel Wireless Mesh Networks
Published 2023“…The Priority-based Minimum Interference Channel Assignment (PRIMICA) algorithm is introduced to address this issue. …”
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12
An Improved Distributed Scheduling Algorithm for Wireless Sensor Networks
Published 2012“…To meet this challenge, we propose an Improved Distributed Scheduling Algorithm (IDSA), a novel scheduling technique that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, minimum message overhead and high channel utilization. …”
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Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm
Published 2018“…In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. …”
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15
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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16
A new Gompertz-three-parameter-lindley distribution for modeling survival time data
Published 2025“…The statistical properties of the proposed distribution including the shape properties, cumulative distribution, quantile functions, moment generating function, failure rate function, mean residual function, and stochastic orders are studied. …”
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Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. …”
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Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…The main contribution of this research is developing statistical approaches, and introducing new algorithms and resampling methods for analysing interval-censored data through AFT models.…”
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19
Slice sampler algorithm for generalized pareto distribution
Published 2018“…In this paper, we developed the slice sampler algorithm for the generalized Pareto distribution (GPD) model. …”
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…In order to evaluate the scalability at specific data size the appropriate regression models are fitted through the measured data as functions of number of workers. …”
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