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This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers. [ABSTRACT FROM AUTHOR]

Digital filter design optimization counts as one of the most challenging problems in circuit development. Furthermore, the bit-width allocation has a crucial impact on the efficiency and accuracy of fixed-point digital filters. The conventional implementation procedure involves studying the bit-width allocation problem after determining the filter coefficients. This sequential procedure leads frequently to an excessive bit-width allocation, hence an extra implementation cost. The main contribution of this paper is to propose a new implementation procedure based on the simultaneous handling of both design issues and the bit-width optimization for the infinite impulse response filter. In this study, the problem is formalized by a multi-objective programming technique using the non-dominated sorting genetic algorithm II. The joint consideration of the two problems offers a better trade-off between design and bit-width allocation. The output of our approach is compared to the results of existing algorithms. Simulation results show that the multi-objective method significantly reduces the implementation cost while ensuring filter stability and an acceptable mean square error (MSE) compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive genetic algorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the high power-added efficiency (PAE), i.e., optimum. In the AGA, we aimed to avoid the local optima and slow convergence rate that exist in the conventional genetic algorithm (CGA). In the AGA, we proposed a parameter tuning method to fine-tune the set of PA circuit component values for faster optimization of the PA to have a high PAE. The proposed AGA provides a significant speed in optimization process as compared to the CGA, and the execution time of the AGA is faster than that of the CGA. The proposed AGA is also verified, and its performance is compared with that of the CGA through multiple multidimensional mathematical benchmark functions. The PA performance parameters are measured, and the results showed that the optimized PA achieves a high gain of 29.9 dB. The Psat of PA is measured as 14.21 dBm, and IIP3 is 13.8 dBm. The simulated result shows the optimized PA with the AGA has PAE of 49.7%, while that with the CGA has PAE of 47.5% at 24 GHz. The final measured PAE of AGA's optimized PA is 47.1%. The chip area of PA is 0.29 mm2. [ABSTRACT FROM AUTHOR]

A novel accuracy-aware modeling and solving approach of adopting a back-propagation neural network (BP-NN) targeting to a classical analog five-transistor operational transconductance amplifier (OTA) is proposed in this paper. Three complex BP-NN algorithms are described amply in performance model training between multiple design factors and performance metrics, where genetic algorithm (GA), particle swarm optimization (PSO) and mind evolutionary algorithm (MEA) are introduced to further speed up the searching process for global solutions. Effectiveness comparison is performed by running simulations using SMIC 180 nm/1.8 V CMOS technology, three complex algorithms with longer time-consuming of 10 h, 30 m and 28 m demonstrate significantly superior features in solving accuracy, which show lower mean square errors (MSEs) by improvements of 82.7%, 99.7% and 99.9%, respectively. The results show that the proposed BP-NN modeling approach can be effectively aided in accelerating the global design solution of a long-cycle, nonlinear and multivariable-solved analog circuit. [ABSTRACT FROM AUTHOR]

Recently, domain adversarial neural networks (DANN) have delivered promising results for out of domain data. This paper exploits DANN for speaker independent emotion recognition, where the domain corresponds to speakers, i.e. the training and testing datasets contain different speakers. The result is a speaker adversarial neural network (SANN). The proposed SANN is used for extracting speaker-invariant and emotion-specific discriminative features for the task of speech emotion recognition. To extract speaker-invariant features, multi-tasking adversarial training of a deep neural network (DNN) is employed. The DNN framework consists of two sub-networks: one for emotion classification (primary task) and the other for speaker classification (secondary task). The gradient reversal layer (GRL) was introduced between (a) the layer common to both the primary and auxiliary classifiers and (b) the auxiliary classifier. The objective of the GRL layer is to reduce the variance among speakers by maximizing the speaker classification loss. The proposed framework jointly optimizes the above two sub-networks to minimize the emotion classification loss and mini-maximize the speaker classification loss. The proposed network was evaluated on the IEMOCAP and EMODB datasets. A total of 1582 features were extracted from the standard library openSMILE. A subset of these features was eventually selected using a genetic algorithm approach. On the IEMOCAP dataset, the proposed SANN model achieved relative improvements of +6.025% (weighted accuracy) and +5.62% (unweighted accuracy) over the baseline system. Similar results were observed for the EMODB dataset. Further, in spite of differences with respect to models and features with state-of-the-art methods, significant improvement in accuracy values was also obtained over them. [ABSTRACT FROM AUTHOR]

In this paper, we devise a new hybrid method for pattern synthesis of large linear arrays. To simplify the beamforming network, the uniform linear array is divided into multiple non-overlapped subarrays with contiguous elements and is only weighted at subarrays. Unlike previous approaches, the proposed method introduces a special vector to specify the subarray partition and converts the subarray pattern synthesis to the problem of finding the optimal subarray partition and subarray weighting vectors simultaneously. To solve this problem efficiently, the genetic algorithm is utilized to find the optimal partition scheme from all possible subarray partitions. For the fixed subarray partition, the optimal subarray weighting vector is obtained by solving the convex sub-problem. Numerical results demonstrate the superiority of our method over the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the precision analysis of filters in the parallel form framework. The precision analysis consists of determining suitable fractional bit-widths to set a tradeoff between resource consumption and computational accuracy. Although the stability of the parallel form is relatively better controlled than the related direct form, its bit-width optimization did not receive much consideration in the literature, despite its significant contribution to the optimization of the filter's physical implementation. To carry out the needed bit-width optimization, we present two heuristics based on the Estimation of Distribution Algorithm, which falls within the category of probabilistic model-building genetic algorithm. The performance of the proposed approach is discussed, and compared to chosen benchmarks, the results show that our hardware implementations reduce the cost of the resulted circuits up to 37 % . [ABSTRACT FROM AUTHOR]

Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new four-dimensional (4D) chaotic system which contains an active flux-controlled memristor characterized with a smooth continuous cubic nonlinearity is introduced. The objective is to obtain a 4D chaotic system which contains quadratic terms by using the memristor model which is designed as a new cubic emulator. A new chaotic circuit equation is obtained by using this emulator. The chaotic behavior of the system is observed both theoretically and graphically. The fundamental dynamics of the chaotic system are examined by using equilibrium points, phase portraits, Lyapunov exponents and bifurcation diagrams. The proportional–integral–derivative (PID) control design obtained using optimization algorithms is presented for memristor-based chaotic system. Firefly algorithm and genetic algorithm are used in PID control design for obtaining optimal PID controller gains that provides optimization between master and slave circuits. To verify system performance, this proposed PID-based chaotic circuit is used to communicate communication systems. Experimental results of this system are obtained and presented in real time. [ABSTRACT FROM AUTHOR]

In this paper, a novel method for designing an optimum infinite impulse response digital differentiator of the first and second orders is presented. The proposed method interpolates bilinear transform and rectangular transform fractionally, and then, unknown variables of the generalized equation are optimized using the genetic algorithm. The results obtained by the proposed designs are superior to all state-of-the-art designs in terms of magnitude responses. The first-order and second-order differentiator attains mean relative magnitude error as low as - 27.702 (dB) and - 35.04 (dB), respectively, in the complete Nyquist range. Besides, suggested low-order, differentiator design equations can also be optimized of any desired Nyquist frequency range, which makes it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a full-parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposed in this paper is able to work with more variable from some adjustments on hardware architecture. The results showed that the GA full-parallel implementation achieved throughput about 16 millions of generations per second and speedups between 17 and 170,000 associated with several works proposed in the literature. [ABSTRACT FROM AUTHOR]

In grey-level image restoration, a prior knowledge of degraded areas allows, thanks to the selective filtering, to achieve a good protection of the image features. In this paper, we propose a quadratic programming-based technique that deals with the issue of details preservation during the restoration process. Based on the classical model of image restoration, we build a modified model by introducing a set of binary variables that indicate the pixel categories. We combine each pixel with the median of its neighbours in a decision rule so that one of them generates the optimal solution. The obtained model is a nonlinear mixed-integer problem where resolution by exact methods is not feasible. In this regard, we use both of the continuous Hopfield neural network and the genetic algorithm to solve the suggested model. Performance of our method is demonstrated numerically and visually by several computational tests. [ABSTRACT FROM AUTHOR]

This article studies the performance of two metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biological populations. Some researchers advocate the superiority of PSO over GA and highlight its capacity to solve complex problems thanks to its ease of implementation. In this paper, different versions of PSOs and GAs including our specific GA scheme are compared for FIR filter design. PSO generally outperforms standard GAs in some performance criteria, but our adaptive genetic algorithm is shown to be better on all criteria except CPU runtime. The study also underlines the importance of introducing intelligence in metaheuristics to make them more efficient by embedding self-tuning strategies. Furthermore, it establishes the potential complementarity of the approaches when solving this optimization problem. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a methodology for multi-objective radio frequency (RF) low-noise amplifier (LNA) optimization using an analytical model of the MOS transistor in combination with genetic computation. The optimum performance is defined by a figure of merit (FoM) that considers both the power efficiency and the RF performance of the system. Using a short-channel EKV model, the analysis of this FoM suggests that the optimum MOS inversion level lies in the moderate inversion. This knowledge can be used as a strong starting point for the design and optimization procedures. Initially, the LNA component values are extracted using the analytical model. The model does not fully take into consideration the parasitic behaviour of the components in a real design; thus, it produces an approximation of the optimum design. The final circuit fine tuning is achieved with the use of a genetic algorithm that takes advantage of the aforementioned approximation as an initialization aiming at faster convergence. To demonstrate the effectiveness and the operation of this methodology, a 5 GHz common source LNA with inductive degeneration has been designed using the proposed design and optimization methodology. The same design has been statistically investigated using Monte Carlo simulations to address process variability as well as temperature and supply voltage variations. Finally, the optimization procedure is demonstrated also on different topologies including cascode or common gate structures, as well as multi-stage distributed and resistive shunt feedback amplifiers. [ABSTRACT FROM AUTHOR]

Accurate capacitance matching is one of the main design issues in switched-capacitor (SC) filters. Using identical unit capacitors in parallel to implement each filter capacitor, combined with a careful layout design, can provide an accuracy of 0.1% in the filter coefficients. The disadvantage of this technique is the fact that it can be directly applied only if the coefficients can be expressed as ratios of integer numbers. As a result, coefficient approximations are required, leading to frequency response errors. In this paper, a genetic algorithm (GA) is used to find the optimum capacitance ratio approximations by rational numbers that minimize the total number of unit capacitors for a given error tolerance in the frequency response. Design examples in 0.35 μm CMOS are presented and simulated to illustrate the proposed approach and verify its effectiveness. [ABSTRACT FROM AUTHOR]

Power consumption in VLSI (Very Large Scale Integration) design is becoming a mainstream issue that cannot be neglected. Low power solution for SoC (system-on-chip) flow gives designers a powerful methodology to analyze, estimate, and optimize today's increasing power concerns. In this paper, a new power macro-modeling technique at architectural level for the digital electronic systems is presented. This technique allows estimating the power dissipation of intellectual property (IP) components to their statistical knowledge of the primary inputs/outputs. During power estimation procedure, the sequence of an input stream is generated by a genetic algorithm (GA) using input metrics and the macro-model function is used to construct a set of functions that map the input metrics of a macro-block to its output metrics. Then, a Monte Carlo zero-delay simulation is performed for register transfer level (RTL) and the power dissipation is predicted by a macro-model function. The most important contribution of the method is that it allows fast power estimation of IP-based design by a simple addition of individual power consumption. This makes the power modeling of SoCs an easy task that permits evaluation of power features at the architectural level. In order to evaluate our model, we have constructed IP-based digital systems using different IP macro-blocks. In experiments with an individual IP macro-block, the average error is 1-2 %, and for an entire IP-based system with interconnects, the error is measured in the range of 9-15 %. [ABSTRACT FROM AUTHOR]

In this paper, we propose a maximum likelihood (ML) estimator in the frequency domain for estimating multichannel time delay and parameters with missing observations. The missing observations are described by a random Bernoulli pattern. In this context, the ML estimator for missing observations is highly sensitive to the initial conditions and complexity of a given problem. In conventional calculations, the complexity of problems will often make it difficult to obtain the optimal results. Thus, we adopted an iterative method using a genetic algorithm (GA) to develop an ML estimator for a model signal, the time delay, and the missing probability in the frequency domain. Several simulation examples were analyzed to evaluate the performance of the proposed method. The simulation results show that the performance is significantly improved if the effect of missing observations on the ML estimator is considered. [ABSTRACT FROM AUTHOR]

In this paper, we present an optimization method based on a multi-objective Genetic Algorithm (GA) for the design of linear phase filter banks for an image coding scheme. To be effective, the filter banks should satisfy a number of desirable criteria related to such a scheme. Instead of imposing the entire PR condition as in conventional designs, we introduce flexibility in the design by relaxing the Perfect Reconstruction (PR) condition and defining a PR violation measure as an objective criterion to maintain near perfect reconstruction (N-PR) filter banks. Particularly in this work, the designed filter banks are near-orthogonal. This has been made possible by minimizing the deviation from the orthogonality in the optimization process. The optimization problem is formulated as a constrained multi-objective, and a modified Nondominated Sorting Genetic Algorithm NSGAII is proposed in this work to find the Pareto optimal solutions that achieve the best compromise between the different objective criteria. The experimental results show that the filter banks designed with the proposed method outperform significantly the 9/7 filter bank of JPEG2000 in most cases. Furthermore, the filter banks are near orthogonal. This is very helpful, especially where embedded coding is required. [ABSTRACT FROM AUTHOR]

An optimized color image watermarking algorithm with stationary wavelet transform and singular value decomposition has been proposed in this work. The algorithm uses two watermarks to address the issues of copyright protection and tamper localization. A gray-scale image watermark is embedded in the transform domain of the green plane of the cover image to address the issue of copyright protection. A binary logo watermark is inserted in the spatial domain of the blue plane of the cover image by the least significant bit approach. It helps to locate the visible and invisible tampers in the cover image. This work is optimized through the genetic algorithm (GA) to achieve better perceptual quality and robustness. A novel fitness function has been proposed to use with the GA to figure out an optimal scaling factor for the embedding process. The strength of the algorithm has been examined by enforcing several attacks on the watermarked image. The embedding capacity of the algorithm and the time complexity of the proposed fitness function are compared with other existing works. [ABSTRACT FROM AUTHOR]

In this study, we use an event-based mechanism for the quantitative control of nonlinear systems to obtain an optimized stable controller. We define a nonlinear system model based on an event trigger mechanism and a quantization mechanism. The layout of the model includes a sampler, which is continuously monitored, and the sampled signal. The latter is detected by the event trigger mechanism when a given threshold is fulfilled. The quantizer is used to discretize the incoming control signal, the controller output, the feedback, and the nonlinear system. As the second step, we define a neural network controller that is optimized using a suitable genetic algorithm. To reduce the conservativeness of the system, we use a piecewise Lyapunov–Krasovskii functional method. By analyzing the inherent transmission time delay, the synchronous controller is converted into the equivalent stability problem for the corresponding time-delayed system. The effectiveness and advantages of the proposed method are shown by numerical simulations based on an inverted pendulum. [ABSTRACT FROM AUTHOR]

Gate diffusion input (GDI) method using a simple cell makes it possible to design low-power logic gates with reduced chip area and less complexity. In this work, a novel design of single-bit optimized reversible logic-based magnitude arithmetic unit (RMAU) circuit, using appropriate standard reversible gates with carbon nanotube (CNT) field-effect transistors (CNTFETs), based on modified-GDI (m-GDI) method for nanoscales is presented. In order to optimize the performance of the proposed circuit, and to achieve minimum power consumption and propagation delay, transistor sizes are adjusted using the non-dominated sorting genetic algorithm II (NSGA-II) in MATLAB tool. The simulation results show improvement in evaluating the figure of merits in worst-case delay and power consumption of the proposed optimized arithmetic unit, in comparison with a non-optimized RMAU circuit using a similar design method but counterpart structures. The effects of different process parameters (such as the diameter of CNTs) and voltage and temperature (PVT) variations are extensively evaluated by the Monte Carlo procedure in standard 32 nm technology utilizing the Synopsys HSPICE simulator. According to the outcomes obtained, the proposed optimized RMAU circuit is robust against PVT variations and noise-tolerable criterions, compared to those competitors with similar design in non-optimized conditions. The proposed optimized and non-optimized circuits were used in image processing as real environment assessments, and results depicted their excellent ability in being implemented in various large reversible-based applications, such as future generations of FPGA chips and CNTFET-based computers. [ABSTRACT FROM AUTHOR]

The automatic detection of cardiac arrhythmias is a challenging task since the small variations in electrocardiogram (ECG) signals cannot be distinguished by the human eye. We propose a fast recognition method to diagnose heart diseases that is less time consuming and achieves better performance by combining wavelet de-noising with a genetic algorithm (GA)-based least squares twin support vector machine (LSTSVM). First, adaptive wavelet de-noising is employed for noise reduction. Second, power spectral density in combination with timing interval features is extracted to evaluate the classifier. Finally, a GA, particle swarm optimization (PSO), and chaotic PSO are compared for parameter optimization of the proposed directed acyclic graph LSTSVM multiclass classifiers. ECG heartbeats taken from the MIT-BIH arrhythmia database are used to examine the proposed method and other traditional classifiers such as multilayer perception, probabilistic neural network, learning vector quantization, extreme learning machine, SVM, and current TWSVMs. Number of our training samples is <3.2 % of all samples. Our proposed method demonstrates a high classification accuracy of 99.1403 % with low ratio of training and testing sample sizes; furthermore, it achieves a more rapid training and testing time of 0.2044 and 55.7383 s, respectively. [ABSTRACT FROM AUTHOR]

Floorplanning is an important physical design step in the ASIC design flow. It is the process of estimating the area to be occupied by various blocks in a layout together with a precise interconnection pattern. In this work, a smart decision-making hybrid particle swarm optimization-genetic algorithm that aims at reducing the area, wirelength, and hotspot by distributing the temperature evenly across the chip is presented. B*-tree is used to generate the initial floorplan and later a PSO-GA based hybrid algorithm is used to obtain an optimal placement solution. Temperature-driven floorplanning is considered at the perturbation stage to separate the hotspots, thereby reducing the average and maximum temperature. The experimental results of the proposed algorithm are compared with other stochastic algorithms using MCNC and Alpha processor floorplan benchmark circuits. The result shows that the proposed algorithm performs efficient floorplanning, with reduced average and peak temperature. [ABSTRACT FROM AUTHOR]

The aim of this work is to present a noninvasive method to classify normal voice signals and those corresponding to voice disorders. Use of wavelet decomposition is prevalent for feature extraction in this field and provides good frequency resolution in lower-frequency subbands. In this work, to provide better frequency resolution in higher-frequency subbands as well, we use M-band wavelet decomposition for feature extraction, employing a genetic algorithm to determine the parameters of the optimal wavelet. Moreover, a support vector machine is used as the final classifier. By employing a well-known pathological voice database, normal and pathological cases are classified using a five-band wavelet system for feature extraction, showing good performance. [ABSTRACT FROM AUTHOR]

An automatic synthesis method is introduced to design voltage followers (VFs) and voltage mirrors (VMs) by performing evolutionary operations. It is shown that the nullor element is useful to introduce a new genetic representation to codify the behavior of the VF by a chromosome divided by four genes: small-signal (genSS), synthesis of the nullor by MOSFET (genSMos), bias (genBias), and synthesis of current mirrors (genCM). Further, it is shown that the behavior of the VM can be codified by evolving the chromosome of the VF. The proposed synthesis method uses SPICE to evaluate the fitness of the VF and VM. Finally, we show the synthesis of several VFs and VMs which are designed using standard CMOS technology of 0.35 μm. The applications and evolution of the VF and VM to synthesize more complex devices such as current conveyors (CCs) and inverting CCs are briefly discussed. [ABSTRACT FROM AUTHOR]