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In this paper, the radial basis function neural network based on the orthogonal least squares (OLS-RBFNN) method is proposed for the first time for the calibration of the six-port receiver, which realises six-port accurate reception in the true sense. This algorithm overcomes the problems of an ageing circuit, narrow working frequency band and nonlinearity of the six-port receiver hardware. The RBFNN is a three-layer forward network, including an input layer, a hidden layer and an output layer. The transformation function of the hidden layer is the radial basis function. A six-port receiver test platform with an operating frequency band of 2.0–8.0 GHz is built to verify the performance of the six-port receiver based on OLS-RBFNN. The test signal is a 16-QAM signal with different bandwidths and different frequency bands. The test indicator uses error vector magnitude (EVM)， which represents the error between the received signal and the ideal signal sent. At last, the EVM of all tested data is less than 1.07%, and the best EVM is 0.57%. Therefore, the calibration accuracy of the OLS-RBFNN proposed in this paper is very high. [ABSTRACT FROM AUTHOR]

This paper presents a gradient-based optimisation method of antenna array for null controlling in specific regions and sidelobe suppression. The optimisation problem is formulated as optimising the electronic control parameters (excitation amplitude and phase) of the array to achieve that the maximum value of the peak sidelobe levels (PSLLs) is minimised and the depths of the nulls in specific regions are not greater than a threshold. The Kreisselmeier – Steinhauser (KS) function is used to describe the maximum value of the PSLLs, in this way, the optimisation problem becomes differentiable and can be solved efficiently by gradient-based algorithm. The sensitivities of the objective with respect to the design variables are analytically derived. Typical numerical examples and detailed evaluations are provided to validate the effectiveness of the proposed method. The results show that the null controlling in the predefined specific single, single-wide, dual, multiple, and annular regions is realised, and the PSLL suppression is performed comparing to the uniform planar arrays. In the 28 GHz base station transmitting antenna-array example, the proposed method effectively suppresses the PSLL and greatly saves the calculation time while maintaining the various specific null regions controlling. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid multilevel inverter (MLI) with reduced switch count, which can generate higher output voltage level with minimum number of DC input sources. The operation of this proposed MLI is carried out with unequal DC sources to achieve the desired output voltage level. The reduced MLI output voltage is set to minimal total harmonic distortion (THD) with the help of modified JAYA (MJAYA) algorithm. A JAYA algorithm with improved steps by adopting an accelerating parameter has been proposed in this research work to obtain a faster convergence of the objective function. The MJAYA algorithm has provided the suitable switching angles for the proposed three-phase 15-level MLI and reduced the output voltage THD to 2.23%, which satisfies the standard set by IEEE-519. To prove the efficiency of this proposed modified algorithm, the comparative analysis is carried out through MATLAB program and Simulink tool using common JAYA and modified particle swarm optimisation algorithms. The performance and productivity of the proposed MLI have been investigated through simulation and experimental setups. [ABSTRACT FROM AUTHOR]

In this paper, we consider a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) system, which is having I and Q (in-phase and quadrature) imbalance hardware impairment. The prime objective of this paper is to maximize the harvested energy for a SWIPT PS system, satisfying a minimum signal to noise ratio (SNR) requirement for information signal processing in the presence of IQ imbalance hardware impairment. The paper uses four types of bio-inspired algorithms like Particle Swarm Optimisation, Artificial Bee Colony, Firefly Algorithm and Invasive Weed Optimisation to attain the maximum harvested energy considering parameters of PS ratio, amplitude and phase imbalances in a Rayleigh fading environment. The simulation results show that under ideal power conversion efficiency and minimum hardware impairments, a maximum SNR of 16.7 dB and harvested energy of 11 dB is achieved for a transmit power of 20 dB in SWIPT systems for IWO bio-inspired algorithms in the 32 QAM modulation scheme. [ABSTRACT FROM AUTHOR]

Multilevel inverters are currently the subject of extensive investigation and are used primarily for medium and high voltage distribution systems. The work presented in this paper provides a cost-effective and realistic solution to mitigate power quality (PQ) problems. A Cascaded H-Bridge multilevel inverter (CHB-MLI) has been configured as a shunt active power filter (SAPF). It is well reported that large-scale use of power electronics-based devices leads to several PQ problems such as poor power factor, unregulated dc voltage, harmonics, voltage regulation etc. In this paper, an improved immune feedback control algorithm is proposed and developed to mitigate PQ issues by predicting the weighted active component of load current and to generate reference current for the grid. Extensive MATLAB simulation and experimental work have been reported for validation on a single-phase system. A low prototype model is developed in the laboratory to study and compare the steady-state and dynamic results of the proposed controller with conventional Least Mean Square (LMS) and Immune algorithm. [ABSTRACT FROM AUTHOR]

One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively. [ABSTRACT FROM AUTHOR]

In this paper, an anti-jamming scheme based on transmitted multiple pulses through random initial phases and an adaptive iterative filtering algorithm is considered for counteracting range-velocity deceptive jamming in Pulse-Doppler (PD) radar system. This anti-jamming scheme relies on the fact that the digital radio frequency memory (DRFM) repeated jammer needs more than one pulse repetition interval PRI (at least one PRI) to identify the radar transmitted pulses, and therefore uses the captured pulses of the lag PRIs to reproduce multiple false targets. As a consequence, an adaptive-iterative algorithm to estimate the range-Doppler plane of the real targets in range dimension processing and Doppler dimension processing can appropriately suppress the deceptive false targets, which employed the lagged PRIs for the radar deception. Firstly, the proposed method achieves the optimal estimation of the true targets and false targets in the range dimension. Then, with the estimation information in the range dimension, the presented method estimates the range-Doppler plane of the true targets and false targets. Finally, we evaluate the performance of the proposed method as well as achieve a good trade-off between anti-jamming performance and computational complexity via numerical simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel modulation algorithm based on vector decomposition and model predictive control for 5-level active neutral-point-clamped (5 L-ANPC) inverter. Firstly, the control of 5-level inverter is transformed into that of 3-level inverter by vector decomposition, which reduces the control complexity. Secondly, based on the framework of 3-level inverter, the optimal zero sequence voltage and switch state sequence are selected by multi-objective optimisation of model predictive control to balance the neutral point voltage and floating capacitors voltages simultaneously. Finally, according to the optimal choice, single-carrier comparison is used to generate the drive signal, which does not require traditional dual-carrier or vector synthesis, and is relatively easy to implement. Experimental results verify the validity of the method. [ABSTRACT FROM AUTHOR]

Advancement in CMOS technology demands to revamp the design of basic functional units in digital systems. The binary addition is the basic operation in digital units like microprocessors, digital signal processors and data processing units in ASICs. Scaling down the technology no longer supports power density and energy-efficient digital architectures. The energy-efficient adder design demands the exploration of recurrence structures, existing algorithms, circuit design techniques, system constraints, energy and delay trade-offs. This paper presents the systematic design approach towards the design of an energy-efficient 64-bit parallel-prefix adder. A novel two-bit sum block reduces the power dissipation and the proposed recurrence algorithm further optimises the overall energy consumption. The 64-bit adder is implemented in static CMOS design and simulated in 45, 32, 22 and 16 nm technology nodes to validate the energy efficiency. In 45 nm technology, the proposed 64-bit parallel-prefix adder consumes 64.61% and 15.3% less energy compared to Brent–Kung and Ling adder respectively whereas in 16 nm technology, the proposed adder consumes 57.54% lesser energy than Brent–Kung adder and 14.28% lesser energy compared to the Ling adder. [ABSTRACT FROM AUTHOR]

In this paper an integrated hybrid converter topology, unique DC input as well as at same time of outputs DC and AC that use a hybrid control method. At established integrated converter, accomplish high step up voltage and implement outputs of DC as well as AC to greater reliability, switching topology is utilised. The established hybrid strategy is joint execution of spotted hyena optimiser (SHO) and random forest algorithm (RFA), therefore, it called as SHORFA method. In proposed strategy, SHO is utilised to optimise combination of the controller parameters based on the voltage variation. In light of the optimal datasets, the RFA gives the optimal switching pulses of the driver. The RFA learning process is improved to utilise SHO algorithm at aspect of objective function as minimal error. By the established converter design, voltage stress over diodes as well as power switch is lessened. Additionally, the proposed converter directs the output voltage, power factor correction, reduces the voltage spikes over the switches and restrains the switching losses. The proposed strategy performed at worksite of MATLAB/Simulink platform compared to various existing methods. [ABSTRACT FROM AUTHOR]

The main challenge of Cognitive Radio (CR) is to identify the occurrence of primary users (PU) effectively to mitigate interference with licenced communications. The occurrence of PU is identified by spectrum sensing (SS). Due to shadowing and deep fading, SS may produce false sensing results. Cooperative spectrum sensing (CSS) can mitigate these problems but at the cost of extra energy consumption especially when the CR users are battery functioned. In CSS, the fusion rule directly affects the energy efficiency (EE) of CR users, it can be optimised to increase EE. Moreover, the energy consumption of the CR users can be minimised if they are grouped into clusters. In this paper intra and inter fusion schemes have been proposed in a cluster-based CSS (CB-CSS). The overall EE is maximised by optimising the inter and intra fusion rule threshold of CB-CSS. An iterative algorithm has been presented that determines the optimal value of inter and intra fusion rule. Results show that for 6X6cluster size, optimum inter and intra fusion rule threshold is found to be 4 and 5, respectively. [ABSTRACT FROM AUTHOR]

Most of the research on identifying and classifying signal models is limited to single signals such as AM, QAM or PSK, and there is a lack of research on the parameters of the identified signals. To make up for this deficiency, an OA (Optimality Confidence and Amplitude Ratio factor) algorithm model is proposed to identify and classify 13 kinds of modulation signals, including 2 kinds of analogue modulation signals (AM and FM), 11 kinds of signal digital modulation (M-SK and M-QAM). Based on the characteristics of the constellation diagram, Confidence and Amplitude Ratio factors are constructed to identify and classify signals with different parameters such as 'Format", "Centre Frequency" and "Sample Rate' and different modulation types. In addition, because of the mis-judgement and confusion problems in identifying classification signals, a three-layer optimisation algorithm is proposed to optimise the OA algorithm model. Numerical simulation results show that the comprehensive identification efficiency of the proposed optimisation algorithm is increased by 18.84%. [ABSTRACT FROM AUTHOR]

The position of MPP of a solar panel changes with changes in weather conditions such as radiation, temperature and partial Shadow. In such a situation, several local MPPs and one GMPP are created in the P-V curve, and the common methods of MPPT(P&O, IC,...) have converged due to the failure to recognise the local MPP. They cause a lot of losses to the photovoltaic system. Therefore, in this article, it has been tried to provide a new method of particle swarm optimisation with variable coefficients, which in combination with the perturb and observe method can reach the GMPP in the fastest possible time. In this proposed method, we first reach the convergence of the GMPP using the VCPSO method, and after converging to it, we go to the P&O method and use P&O until significant changes are made in the system, and if observed Variations To re-examine the GMPP, we return to the VCPSO algorithm. The results of the simulation of the efficiency of the proposed method show that this method was able to reach the GMPP in the shortest time, and the efficiency of the photovoltaic system increases and the effects of partial shading are reduced. [ABSTRACT FROM AUTHOR]

In this research, an improved PTS algorithm is projected for diminishing peak to average power ratio (PAPR) of well accepted and effective orthogonal frequency division multiplexing (OFDM) system. OFDM is also considered as trustworthy method for several wireless communication applications and obtained a lot of research interests but unfortunately, one of the most important concern is very high PAPR in OFDM signals, which deteriorates system performance thus this is the most important drawback. To address this problem, a novel partial transmit sequence (PTS) algorithm is projected based upon centering phase sequences matrix (PTS-CPSM-Algo). These phase sequences are used for optimization of data so that minimum PAPR can be obtained. This algorithm obtains significant reduction in PAPR which leads to the enhancement in performance of OFDM system. From the developed analysis and simulation results, supremacy can be clearly seen for proposed PTS-CPSM-Algo over already existing OFDM system, selected mapping (SLM) and partial transmit sequence (PTS). [ABSTRACT FROM AUTHOR]

In a grid connected photovoltaic (PV) system, the maximum power will be tracked by the conventional Perturb and Observe (P&O) algorithm. It generally produces high oscillations around maximum power point, and it fails to attain extreme power under prompt environmental conditions. To overcome these issues, this work proposes a deep neural network (DNN) based Maximum Power Point tracking (MPPT) algorithm and PR controller-based SMO for PV grid-connected system. The DNN consists of many algorithms in that here we use RNN in MPPT algorithm for tracking the maximum power. The Recurrent Neural Network (RNN) hidden layer has more number of layers. The deep network weight function is optimised by meta-heuristic-based Bald Eagle Search (BES) Optimisation. The proposed tracked RNN based Maximum Power Point (MPP) is passed through the DC/DC buck-boost converter. The inverter is tuned by Proportional Resonant (PR) controller, and it has two gain parameters like a proportional and integral gain. The PR controller gain parameters are tuned by the global optimisation algorithm of Spider Monkey Optimisation (SMO) to achieve better performance. [ABSTRACT FROM AUTHOR]

In heterogeneous networks, Vertical Handover (VH) plays a vital consequence of customer mobility as they have a high impact on networking performance like delay, throughput and call block probability. Even in the presence of various existing works, VH management exhibit certain drawbacks like method complexity, difficult network modelling and inaccurate handover. Thus, a hybrid methodology is proposed in this work which can provide accurate VH and considerable reduction in working complexity. Here, a combination of Deep Residual Neural (DRN) and Wind-Driven Water Wave Optimisation (WDWWO) is introduced to perform VH. Accurate prediction of Received Signal Strength (RSS) is a difficult task for mobile users while moving from one network to another. In order to tackle this situation, DRN is used with WDWWO based weight optimisation, hence the name Optimised DRN (ODRN). Almost every networking parameters like bandwidth, delay, throughput, velocity, BER, SNR, energy consumption, monetary cost and data traffic are included in ODRN modelling. The proposed work is implemented in NS2 platform and resultant performances like energy consumption, RSS, throughput, packet delivery ratio, packet loss, handover failure rate, algorithm convergence and latency are compared with conventional methods of D-TOPSIS, FIS-ENN and F-AHP. [ABSTRACT FROM AUTHOR]

The coordinate rotation digital computer (CORDIC) is a class of shift-add algorithm for the rotation of vectors on a plane. The major problem in this CORDIC algorithm is the linear rate of convergence with the speed of the iteration. The main aim of the improved CORDIC algorithm is to utilise an integrated adder subtractor in place of binary adder subtractor to decrease the count of iterations and hardware reduction technique. The improved CORDIC splits the rotation angle into several series of micro-rotation angles to calculate the rotation and the new set of angle provides a fast convergence. The canonical signed-digit (CSD) approach together with Hcub algorithm employs for the number of adder subtractor reduction and shifters in CORDIC architecture design. The performances of the proposed CORDIC design have been verified by employing it in FFT implementation. The simulation result indicates the higher frequency of 77.20%, 82.78%, 78.30% and 76.57% when compared with conventional methods. The evaluation of FFT is also done by comparing with the conventional methods. The power consumption, number of iterations and the hardware complexity reduced by using the improved CORDIC and the working of this proposed algorithm is evaluated through the FPGA implementation. [ABSTRACT FROM AUTHOR]