Recent Submissions
Aggregation-Based Dynamic Channel Bonding to Maximise the Performance of Wireless Local Area Networks (WLAN)
(Hindawi Limited, 2022-06) Parashar, Vivek; Kashyap, Ramgopal; Rizwan, A.; Karras, Dimitrios Alexios; Cieza Altamirano, Gilder; Dixit, Ekta; Ahmadi, Fardin
Channel bonding is considered by the IEEE 802.11ac amendment to improve wireless local area network (WLAN) performance. In this article, the channel bonding and aggregation method were proposed to increase wireless local area network performance (WLANs). It combines many channels (or lanes) to boost the capacity of modem traffic. Channel bonding is the combination of two neighbouring channels within a certain frequency band to increase wireless device throughput. Wi-Fi employs channel bonding, also known as Ethernet bonding. Channel bandwidth is equal to the uplink/downlink ratio multiplied by the operational capacity. A single 20 MHz channel is divided into two, four, or eight power channels. At 80 MHz, there are more main and smaller channels. Performance of short-range WLANs is determined through graph-based approach. The twochannel access techniques including channel bonding proposed for the IEEE 802.11ac amendment are analysed and contrasted. The novel channel sizing algorithm based on starvation threshold is proposed to expand the channel size to improve WLAN performance. Second-cycle throughput is estimated at 20 Mbps, much beyond the starvation threshold. Our test reveals access points (AP) 1, 2, and 4 have enough throughput. A four-AP WLAN with a 5-Mbps starvation threshold is presented. C160 = 1 since there is only one 160 MHz channel. MIR (3, 160 (a, a, a)) =0, indicating that AP 3’s predicted throughput is 0. The algorithm rejects the 160 MHz channel width since ST is larger than 0. The channel width in MHz is given by B =0,1 MIR. The MIR was intended to maximise simultaneous broadcasts in WLANs. The authors claim that aggregation with channel bonding outperforms so all WLAN APs should have a single-channel width. It usually outperforms fairness-based measures by 15% to 20%. Wi-Fi standards advise “channel bonding,” or using higher frequency channels. Later standards allow channel bonding by increasing bands and channel lengths. Wider channels enhance average WLAN AP throughput, but narrower channels reduce appetite. Finally, it is concluded that APs are more useful than STAs.
Predicción de atributos de calidad de leche fresca no pasteurizada mediante espectroscopia dieléctrica acoplada a herramientas quimiométricas.
(Institute of Electrical and Electronics Engineers, 2022-06) Chuquizuta Trigoso, Tony Steven; Colunche, Y.; Rubio, M.; Oblitas, Jimy; Arteaga, Hubert; Castro, Wilson Manuel
El objetivo de esta investigación es predecir los atributos de calidad de la leche fresca no pasteurizada mediante espectroscopia dieléctrica acoplada a herramientas quimiométricas. Para ello, se trabajó con leche fresca no pasteurizada de la raza Pardo Suizo, obtenida del establo “La Lechera”. Se obtuvieron diluciones de agua y leche fresca del 70 al 100 %.25∘do, seguida de la caracterización fisicoquímica (densidad, sólidos totales, punto de congelación, sólidos grasos, proteínas y agua añadida) y las propiedades dieléctricas en el rango de 0,5 a 9 GHz mediante una sonda coaxial de extremo abierto (N1501A-001), conectada a un Analizador de Redes Vectoriales, modelo N9915A-Keysight Technologies. Asimismo, se empleó la regresión de mínimos cuadrados parciales para correlacionar las propiedades fisicoquímicas con las propiedades dieléctricas. Los resultados obtenidos en la predicción del punto de congelación, las proteínas, los sólidos grasos y el agua añadida de leche fresca no pasteurizada presentaron un coeficiente de determinación y un error cuadrático medio en el rango de [0,95-0,98] y [2]..57 ×10− 7− 7,46 ×10− 2]En consecuencia, se concluye que la técnica de espectroscopia dieléctrica y aprendizaje automático presenta potencial para la predicción de las características fisicoquímicas de la leche fresca no pasteurizada, pudiendo implementarse en las líneas de producción para evaluar de forma rápida y fiable la calidad de la leche de vaca.
A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services.
(Hindawi Limited, 2022-01) Jain, Paras; F. Alsanie, Walaa Fahad; Oseda Gago, Dulio; Cieza Altamirano, Gilder; Sandoval Núñez, Rafaél Artidoro; Rizwan, A.; Asakipaam, Simon Atuah
ECG (electrocardiogram) identi es and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman thlters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online lter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multiiterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multiiterative function takes less iteration, resulting in shorter execution times. De proposed multiiterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.
Solution to a Damped Duffing Equation Using He's Frequency Approach
(Hindawi Limited, 2022-07) Salas S., Alvaro H.; Cieza Altamirano, Gilder; Sánchez-Chero, Manuel Jesus
In this paper, we generalize He’s frequency approach for solving the damped Du ng equation by introducing a time varying amplitude. We also solve this equation by means of the homotopy method and the Lindstedt–Poincar´e method. High accurate formulas for approximating the Jacobi elliptic function cn are formally derived using Chebyshev and Pade approximation techniques.
Swarming Computational Techniques for the Influenza Disease System
(Tech Science Press, 2022-05) Noinang, Sakda; Sabir, Zulqurnain; Cieza Altamirano, Gilder; Zahoor Raja, Muhammad Asif; Sánchez-Chero, Manuel Jesus; Seminario-Morales, Maria Veronica; Weera, Wajaree; Botmart, Thongchai
Abstract: The current study relates to designing a swarming computational paradigm to solve the influenza disease system (IDS). The nonlinear system’s mathematical form depends upon four classes: susceptible individuals, infected people, recovered individuals and cross-immune people. The solutions of the IDS are provided by using the artificial neural networks (ANNs) together with the swarming computational paradigm-based particle swarm optimization (PSO) and interior-point scheme (IPA) that are the global and local search approaches. The ANNs-PSO-IPA has never been applied to solve the IDS. Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA. The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions (reference solutions). An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA. Furthermore, the reliability and competence of the proposed computing method are enhanced through the statistical performances
