Proposed Hybrid Scheme Research Articles

Likelihood ascent search‐aided low complexity improved performance massive MIMO detection in perfect and imperfect channel state information

SummaryMassive multiple‐input multiple‐output (MIMO) systems improve spectral efficiency and link reliability. Linear minimum mean‐squared error (MMSE) detectors can achieve optimal performance in massive MIMO detection but require large dimension matrix inversion, which is computationally intensive. Therefore, low complexity iterative detection schemes are proposed in the literature as an alternative to the exact MMSE method. However, the performance of these schemes is greatly influenced by the choice of the initial solution. Therefore, to improve the detection performance in this paper, we proposed three hybrid detection schemes, which are Newton–Schultz–Richardson (NS‐RI), Newton–Schultz–Chebyshev (NS‐Cheby), and Newton–Schultz–Gauss–Seidel (NS‐GS). The proposed hybrid schemes show significant performance improvement and a higher convergence rate compared to their original counterpart. The performance of the proposed detectors is further improved by the likelihood ascent search (LAS) stage, which corrects the detected symbols obtained from iterative MMSE methods through a neighborhood search. However, the complexity of the LAS algorithm primarily depends on the initialization step. In this work, we introduce an efficient Gram matrix computation in the real domain. Additionally, we have applied a band approximation of the Gram matrix for the LAS initialization, which reduces the order of computational complexity of the Gram matrix from to O(ωNTNR) where ω < <2NT.

Hybrid Convolutional Optoelectronic Reservoir Computing for Image Recognition

Photonic delay-based reservoir computers (RC) have emerged as an attractive high-speed, low-power alternative to traditional digital hardware for AI. We demonstrate experimentally a novel hybrid RC scheme in which input data is first preprocessed through several convolutional layers, either trained or untrained, digitally to generate novel feature maps. These random feature maps are then processed through an optoelectronic implementation of delay-based RC. Using the MNIST dataset of handwritten digits, experiments of our proposed hybrid scheme achieve classification error of 1.6% using untrained convolutions, and an error of 1.1% using trained convolutions, results comparable to that of state-of-the-art machine learning algorithms. Additionally, our experimental implementation can offer a potential 10x decrease in model training time, compared to that of common digital alternatives.

A Joint Time-Domain Channel Estimation with Hybrid PAPR Reduction Scheme in UFMC Systems

In this paper, pilot-based time-domain channel estimation (CE) along with peak-to-average power ratio (PAPR) reduction is proposed for universal filtered multicarrier (UFMC) system. The pilots that are inserted in time domain not only estimate the channel behavior but also can be used in the reduction of PAPR. For PAPR reduction, a linear companding scheme, which can treat amplitudes of the UFMC signal separately with a different scale, is proposed. The proposed companding scheme offers more design flexibility and better performance gains by using two inflexion points. However, the proposed companding scheme requires side information (SI) to perform de-companding at the receiver. The transmission of SI decreases the data efficiency, so a pilot-assisted UFMC system that can perform both data recovery and PAPR reduction without the requirement of SI transmission is proposed. In pilot-assisted UFMC system, the inserted time-domain pilots can enable SI cancellation inherently. Furthermore, a hybrid transform, which improves PAPR performance by employing clipping scheme to the linear companded signal, is proposed. Simulation results confirm that the proposed joint CE with linear companding scheme achieves an improved net gain of 6.5[Formula: see text]dB. Additionally, the proposed hybrid scheme with clipping threshold of 1.4 provides an improved PAPR reduction of 8.8[Formula: see text]dB and enhanced net gain of 7.3[Formula: see text]dB. Moreover, the proposed joint time-domain CE with hybrid PAPR reduction scheme of UFMC system is validated over real time by employing wireless open-access research platform (WARP) board.

Hybrid Finite Difference Schemes for Elliptic Interface Problems with Discontinuous and High-Contrast Variable Coefficients

For elliptic interface problems with discontinuous coefficients, the maximum accuracy order for compact 9-point finite difference scheme in irregular points is three [7]. The discontinuous coefficients usually have abrupt jumps across the interface curve in the porous medium of realistic problems, causing the pollution effect of numerical methods. So, to obtain a reasonable numerical solution of the above problem, the higher order scheme and its effective implementation are necessary. In this paper, we propose an efficient and flexible way to achieve the implementation of a hybrid (9-point scheme with sixth order accuracy for interior regular points and 13-point scheme with fifth order accuracy for interior irregular points) finite difference scheme in uniform meshes for the elliptic interface problems with discontinuous and high-contrast piecewise smooth coefficients in a rectangle $\Omega$. We also derive the $6$-point and $4$-point finite difference schemes in uniform meshes with sixth order accuracy for the side points and corner points of various mixed boundary conditions (Dirichlet, Neumann and Robin) of elliptic equations in a rectangle. Our numerical experiments confirm the flexibility and the sixth order accuracy in $l_2$ and $l_{\infty}$ norms of the proposed hybrid scheme.

Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN

Missing data is universal complexity for most part of the research fields which introduces the part of uncertainty into data analysis. We can take place due to many types of motives such as samples mishandling, unable to collect an observation, measurement errors, aberrant value deleted, or merely be short of study. The nourishment area is not an exemption to the difficulty of data missing. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposed hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examining their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.

Content caching for shared medium networks under heterogeneous users’ behaviors

Content caching is a widely studied technique aimed to reduce the network load imposed by data transmission during peak time while ensuring users’ quality of experience. It has been shown that when there is a common link between caches and the server, delivering contents via the coded caching scheme can significantly improve performance over conventional caching. However, finding the optimal content placement is a challenge in the case of heterogeneous users’ behaviors. In this paper we consider heterogeneous number of demands and non-uniform content popularity distribution in the case of homogeneous and heterogeneous user-preferences. We propose a hybrid coded–uncoded caching scheme to trade-off between popularity and diversity. We derive explicit closed-form expressions of the server load for the proposed hybrid scheme and formulate the corresponding optimization problem. Results show that the proposed hybrid caching scheme can reduce the server load significantly and outperforms the baseline pure coded and pure uncoded schemes and previous works in the literature for both homogeneous and heterogeneous user preferences.

Hybrid Active-and-Passive Relaying Model for 6G-IoT Greencom Networks with SWIPT.

In order to support a massive number of resource-constrained Internet-of-Things (IoT) devices and machine-type devices, it is crucial to design a future beyond 5G/6G wireless networks in an energy-efficient manner while incorporating suitable network coverage expansion methodologies. To this end, this paper proposes a novel two-hop hybrid active-and-passive relaying scheme to facilitate simultaneous wireless information and power transfer (SWIPT) considering both time-switching (TS) and power-splitting (PS) receiver architectures, while dynamically modelling the involved dual-hop time-period (TP) metric. An optimization problem is formulated to jointly optimize the throughput, harvested energy, and transmit power of a SWIPT-enabled system with the proposed hybrid scheme. In this regard, we provide two distinct ways to obtain suitable solutions based on the Lagrange dual technique and Dinkelbach method assisted convex programming, respectively, where both the approaches yield an appreciable solution within polynomial computational time. The experimental results are obtained by directly solving the primal problem using a non-linear optimizer. Our numerical results in terms of weighted utility function show the superior performance of the proposed hybrid scheme over passive repeater-only and active relay-only schemes, while also depicting their individual performance benefits over the corresponding benchmark SWIPT systems with the fixed-TP.

Design and performance analysis of spectral-efficient hybrid CPDM-CO-OFDM FSO communication system under diverse weather conditions

Abstract A comprehensive design is proposed for the free-space optical (FSO) communication system by hybridizing circular polarization division multiplexing (CPDM) with coherent optical orthogonal frequency division multiplexing (CO-OFDM) and its performance is investigated realistically under diverse turbulent weather conditions of Bangladesh. Here, we consider Gamma–Gamma distribution for the turbulent FSO channel model. Moreover, the proposed scheme presents an excellent performance since the CPDM technique not only maximizes the link capacity of the FSO system but also enhances the spectral efficiency of the system. Besides, multipath fading, which is appeared during the FSO transmission, is significantly mitigated by OFDM modulation. The outcomes from the simulation confirm the advantages of the proposed hybrid scheme and also it can serve as a reference for the FSO application even in turbulent weather conditions. Performance analysis of the proposed model is described in terms of the optical power spectrum, optical signal-to-noise ratio, bit error rate, Q factor, constellation diagrams, and eye diagrams.

Deep Learning for SWIPT: Optimization of Transmit-Harvest-Respond in Wireless-Powered Interference Channel

In this paper, we consider a wireless-powered two-way communication, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transmit-harvest-respond</i> , with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system

&lt;div&gt;This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.&lt;/div&gt;

Carbon/basalt hybrid composite bolted joint for improved bearing performance and cost efficiency

In this study, the bearing performance and cost efficiency of single-lap, single-bolt joint of carbon/basalt hybrid composite were assessed. Two non-hybrid laminate joints (pure basalt fiber and carbon fiber laminate joints), and four hybrid laminate joints (two with sandwich-like stacking sequences and two with non-sandwich-like stacking sequences) were fabricated using a vacuum-assisted resin transfer molding process. In comparison to pure carbon-based joints, the hybrid arrangement of basalt layers sandwiched between carbon layers demonstrated improved mechanical performance (23.4% increase in energy absorption and 4.7% increase in ultimate load) while saving 45% of the cost. The surface morphology of damaged specimens was studied by scanning electron microscopy (SEM) at various stages of the loading process. SEM imaging revealed that the proposed hybrid scheme exhibited shear failure, in contrast to all other hybrid designs, which showed interlaminar failure. This study exhibited a better hybrid laminate joint in terms of performance and cost efficiency; which encourages the use of composites in mid/low-end load-bearing applications.

A hybrid FPM/BEM scalar potential formulation for field calculation in nonlinear magnetostatic analysis of superconducting accelerator magnets

A new hybrid numerical method for the solution of nonlinear magnetostatic problems in accelerator magnets is proposed. The methodology combines the Fragile Points Method (FPM) and the Boundary Element Method (BEM). The FPM is a Galerkin-type meshless method employing for field approximation simple, local, and discontinuous point – based interpolation functions. Because of the discontinuity of these functions, the FPM can be considered as a meshless discontinuous Galerkin formulation where numerical flux corrections are employed for the treatment of local discontinuity inconsistencies. The FPM possesses the advantages of a mesh-free method, evaluates with high accuracy field gradients, and treats nonlinear magnetostatic problems by utilizing, for the same problem, fewer nodal points than the Finite Element Method (FEM). In the present work, the nonlinear ferromagnetic material of an accelerator magnet is treated by the FPM, while the BEM is employed for the infinitely extended, surrounding air space. The proposed hybrid scheme is based on the scalar potential formulation of Mayergouz et.al (1987), also used in the BEM/BEM and FEM/BEM schemes of Rodopoulos et al. (2019, 2020). The applicability of the method is demonstrated with the solution of representative 2-D magnetostatic problems and the obtained numerical results are compared to those provided by the FEM/BEM scheme of Rodopoulos et al. (2020), as well as by the commercial FEM package ANSYS. Finally, the magnetic field utilized for stable bending of the particles’ trajectory in a 16 Tesla dipole magnet design for the Future Circular Collider (FCC) project of CERN is accurately evaluated with the aid of the proposed here FPM/BEM scheme.

A novel risk evaluation for vehicle failure modes using a hybrid method under fuzzy environment

This paper addresses a hybrid approach for evaluating the risk of vehicle failure modes based on Vlsekriterijumska optimizacija I KOmpromisno Resenje (VIKOR), Analytic Hierarchy Process (AHP) and maintenance data under fuzzy environment. Firstly, the failure modes and risk criteria are decided by expert team according to their experience and maintenance data. Secondly, the weights of risk criteria are determined by using AHP under fuzzy environment. Finally, the ranking of alternatives are presented by employing VIKOR method. In the end, the effectiveness of the proposed hybrid scheme is demonstrated by conducting a case study involving a vehicle type of Hyundai. Besides, a sensitivity analysis is performed to validate the obtained results. To sum up, our numerical analyses indicate that the proposed hybrid method can effectively assist enterprises and researchers in evaluating the risk of vehicle failure modes and improve vehicle safety.

Surface roughness prediction using a hybrid scheme of difference analysis and adaptive feedback weights

This research has presented an optimum model for surface roughness prediction in a shop floor machining operation. The proposed solution is premised on difference analysis enhanced with a feedback control model capable of generating transient adaptive weights until a converging set point is attained. The surface roughness results utilized herein were adopted from two prior experiments in the literature. The design of experiment herein is premised on three cutting parameters in both experimental scenarios viz: feed rate, cutting speed and depth of cut for experimental dataset one and cutting speed, feed rate and flow rate for experimental dataset two. Three experimental levels were considered in both scenarios resulting in twenty-seven outcomes each. The simulation trial anchored on Matlab software was divided into two sub-categories viz: prediction of surface roughness for cutting combinations with vector points off the edges of the mesh referred to as off-edge cutting combinations (Off-ECC) and recovery of cutting combinations with positions on the edges of the mesh referred to as on-edge cutting combinations (On-ECC). The proposed hybrid scheme of difference analysis with feedback control premised on the use of dynamic weights produced an accurate output in comparison with the abductive, regression analysis and artificial neural network techniques as earlier utilized in the literature. The novelty of the proposed hybrid model lies in its high degree of prediction and recovery of existing datasets with an error margin approximately zero. This predictive efficacy is premised on the use of set points and transient dynamic weights for feedback iterations. The proposed solution technique in this research is quite consistent with its outputs and capable of working with very small to complex datasets.

Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems

This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique —Case Based Reasoning (CBR)— and model-driven technique —Rule Based Reasoning (RBR)— for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations of environmental systems. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDS methodology. Furthermore, the IDS methodology is flexible and dynamic enough to be able to cope with the dynamic evolution of environmental systems, learning from its relevant experienced situations. The approach presented has been implemented in a real facility.

Pipeline surveillance along the international border using hybrid optimization algorithm

Pipelines are often used for transmission of oil, gas, water and other resources to neighboring countries. These pipelines often pose risk and sometimes intervene with national security. Hence, to safeguard the pipeline, various technological usage is often encouraged. Unlike traditional non-linear structure, pipeline systems demand linear structure. Pipeline systems are often setup for greater transmission distance, for maximum utilization. Linear communication in wireless sensor network is always a time-consuming aspect due to variability in the coordinator node selection. To cater the surveillance needs of such transmission pipeline systems, we propose a cost-efficient optimization scheme which enables the network for greater communication efficiency through which maximum coverage can be achieved. The proposed scheme uses a hybrid optimization approach which encompasses ACO-PSO-GA-LOA to address the routing challenges. The proposed hybrid scheme is evaluated against traditional optimization algorithm using metrics such as communication end-to end delay, network throughput and normalized lifetime. The simulation results establish the effectiveness of the proposed scheme.

Spectral Efficient Asymmetrically Clipped Hybrid FBMC for Visible Light Communication

Filter bank multicarrier (FBMC) modulation has shown sufficient potential for wireless communication. A hybrid optical FBMC technique is proposed to improve the spectral efficiency of a visible light communication (VLC) system. In this technique, a hybrid asymmetrically clipped optical offset quadrature amplitude modulation FBMC (HACO-OQAM-FBMC) modulation technique is used. Asymmetrically clipped optical FBMC (ACO-FBMC) is used for odd subcarriers, and pulse amplitude modulation-discrete multitone (PAM-DMT) is used for the even subcarriers. The proposed hybrid scheme uses an intensity modulation/direct detection (IM/DD) channel. It is shown that there is no interference on odd subcarriers using the proposed method and receiver demodulation is similar to that of ACO-FBMC receiver. However, clipping noise of ACO-FBMC falls on PAM-DMT subcarriers, which can be cancelled at receiver processing after estimation. The analytical performance of the proposed technique is compared using parameters, namely, bit error rate (BER), spectral efficiency, computational complexity, and peak to average power ratio (PAPR). It is found that HACO-OQAM-FBMC is more spectral efficient than ACO-FBMC and other OFDM-based techniques.

Investigating Variable Speed Wind Turbine Transient Performance Considering Different Inverter Schemes and SDBR

In wind energy applications, voltage source converters are employed to achieve energy conservation. Recently, multilevel converters have been showing promising advantages compared to the traditional 2-level converter scheme, due to the fact that they can overcome certain limitations during transient conditions. This paper investigates the transient performance of variable DFIG-based speed wind turbines taking into account different scheme configurations of the power converter system. The schemes investigated are a 2-level six step IGBT inverter, a parallel interleaved 2-level six step IGBT inverter, and a 3-level IGBT inverter. All schemes were compared during severe three-phase to ground fault at the terminal of the DFIG wind turbine using the conventional Phase Lock Loop (PLL) and a DC-chopper protection. A coordinated approach of improving the performance of all the converter schemes with series dynamic braking resistor (SDBR) was analyzed. Investigation of the best location for the SDBR in the DFIG architecture considering the best switching signal was also carried out. Furthermore, a new control strategy of PLL for the DFIG system was proposed in conjunction with the SDBR scheme for the converter systems. Simulations were carried out in Power System Computer Aided Design and Electromagnetic Transient Including DC (PSCAD/EMTDC). The results show that the proposed PLL and SDBR hybrid scheme in the various inverter topologies considered in the study can enhance the performance of the wind generator variables during severe three-phase to ground fault. This is because the proposed hybrid scheme could help to boost the capability of the current and recovery of the wind generator after post-fault scenarios. Also, the voltage source converter leg switched output voltage would be enhanced to maximum change in common mode voltage by the inverter schemes’ modulation of the space vector using the proposed strategy.

A Novel Hybrid Approach for Risk Evaluation of Vehicle Failure Modes.

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes.

Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Optimization

The constraints and the vast solution space of operational research optimization problems make them hard to cope with. However, Computational Intelligence, and especially Nature-Inspired Algorithms, has been a useful tool to tackle hard and large space optimization problems. In this paper, a very consistent and effective hybrid optimization scheme to tackle cardinality constrained portfolio optimization problems is presented. This scheme consists of two nature-inspired algorithms, i.e. Sonar Inspired Optimization algorithm and Genetic Algorithm. Also, the incorporation of heuristic information, i.e. an expert’s knowledge, etc., to the overall performance of the hybrid scheme is tested and compared to previous studies. More specifically, under the framework of a financial portfolio optimization problem, the heuristic information-enhanced hybrid scheme manages to reach a new optimal solution. Additionally, a comparison of the proposed hybrid scheme with other hybrid schemes applied to the same problem with the same data is performed.