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A high-performance model with a gravitational search algorithm (GSA)-based generalized parallel two-box (GPTB) structure is suggested for digital predistortion in modern transmitters exhibiting memory effects, where the GSA is applied to identify the minimum dimension for the GPTB model. An indirect learning structure in conjunction with the GSA method is employed to identify the coefficients of the GSA-based GPTB model. The GPTB–GSA method is verified using simulations of a transmitter excited by quadrature amplitude modulation (QAM) signals in ADS software and simulations of the GSA in MATLAB software. The MATLAB results demonstrate the ability of the GSA to determine the dimension of the GPTB model efficiently. Also, the adjacent channel power ratio (ACPR) measure is decreased by about 16 dB according to the simulation. The proposed model and algorithm can reduce the number of coefficients by approximately 25% in comparison with the memory polynomial model. [ABSTRACT FROM AUTHOR]

Multi-camera surveillance systems generally are used by intelligent vision applications to analysis moving objects in the urban environments. Object tracking is an essential component of these applications. Real-time tracking of moving objects in the crowded urban scenes is a challenging problem because the number of moving objects in the scene varies and is usually unpredictable. In order to meet the real-time requirements of intelligent vision applications, it is necessary to control the surveillance workload over processing system and enforce the utilization bound on the system. The utilization control is challenging especially when the workload in the system is unpredictable. The workload for a particular camera depending on the number of targets in its view and execution rate of services that are used to detect and track the moving objects. In this paper, an adaptive real-time system based on the feedback-control framework is proposed to control the system utilization by adjusting input video frame rate that guarantees real-time performance and quality of intelligent vision applications. This approach can handle the workload uncertainties by identifying the parameters of system model online by solving an optimization problem and can simultaneously adjust the frame rate in each sampling period. In each sampling instance, the system utilization is monitored and the error is obtained by comparing it with the set point. The real-time operation of the computing system is achieved by adjusting the input video frame rate to keep the utilization at a given set point slightly below their schedulable bound. Evaluation results demonstrate proposed scheme outperforms the existing schemes especially under workload uncertainties. [ABSTRACT FROM AUTHOR]

A large number of connected devices made Internet of Things (IoT). IoT devices may provide the same service but with different quality parameters such as high availability, cost and delay. Nowadays cloud infrastructures provide an entry point for discovery, selection, fusion and consuming such distributed IoT services. Hence, a new kind of middleware service should be devised in cloud to select and compose the required services based on the end user quality of service requirements. This new kind of cloud service for IoT is named as virtual sensor. In this paper, we propose an architecture for such a virtual sensor service in cloud and propose a multi-objective metaheuristic algorithm for sensor-service selection and composition in cloud middleware. In particular, a quantum-inspired genetic algorithm-based approach is used to address the problem. Simulations with sample IoT workflows were conducted to evaluate efficiency and performance of the proposed method. Our proposed approach for selection and composition of IoT services yields about 60% improvement in overall quality of service of the virtual sensor compared to rival algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a generalized parallel two-box structure that is proposed for modeling and digital predistortion of power amplifiers and wireless transmitters exhibiting memory effects. The proposed predistortion scheme consists of two separable boxes; the first is utilized to model the static behavior of the power amplifier, while the second is proposed to consider the memory effect and nonlinear distortion of the power amplifier. The coefficients of the proposed model are identified by applying an indirect learning structure and a least square method. The validation of the proposed model is carried out using the simulation of the power amplifier and the digital predistortion excited by a 64QAM signal in the advanced design system software. According to the simulation results, the criterion of adjacent channel power ratio reduced by about 16 dB. The simulation results reveal an adjacent channel power ratio of almost − 48 dB. Indeed, the proposed model leads to a better performance in terms of spectral regrowth in comparison with the memory polynomial model, and it also reduces the number of coefficients by approximately 22%. This proposed model enables a more accurate modeling of nonlinear distortion and memory effects compared to previous linearization methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel digital predistorter design based on the Hammerstein structure is proposed in order to linearize radio frequency power amplifiers. A genetic algorithm optimization method has been proposed to accurately identify the coefficients of a Wiener model for the power amplifier. Digital predistorter design based on the proposed Hammerstein model has been carried out according to the accurate Wiener model. The validation of the suggested model is carried out using the simulation of the power amplifier and the digital predistortion excited by 64QAM signals in the advanced design system software. According to the simulation results, the criterion of an adjacent channel power ratio decreased by about 16 dB. The simulation results show the adjacent channel power ratio of almost − 46 dBc. In order to assess the feasibility of the proposed predistorter, it is completely implemented in the Kintex FPGA using Vivado HLS. This proposed model enables a more accurate modeling of nonlinear distortion and memory effects compared to the previous linearization methods. This paper presents the new linearization method using the genetic algorithm based Hammerstein structure. [ABSTRACT FROM AUTHOR]