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The goal of the current study was to calculate the genotypic correlation and genetic variability for 13 quantitative traits among 25 black-gram genotypes. The genotypes PLU-429 and PL-416 had a high seed yield. High PCV and GCV values were found for the traits biological yield per plant, harvest index, number of primary branches per plant, and number of pods per plant. The parameters of number of major branches per plant, number of pods per plant, biological yield per plant, and harvest index were found to have high heritability along with high GAM. According to association studies, there was a significant and positive genotypic and phenotypic correlation coefficient for the variables like principal branches per plant, clusters per plant, pods per plant, and harvest index. According to a path analysis, primary branches per plant, clusters per plant, pods per plant, and harvest index both at the genotypic and phenotypic levels all contributed significantly to the highest positive direct effect. In order to improve black-gram, it would therefore be more fruitful to select the aforementioned attributes simultaneously.

India is the largest producer and consumer of pulses in the world. Pulses play a crucial role in the nutrition of mankind and can be cultivated all over the world, making them important both economically as well as nutritionally. The present investigation was carried out with objective of assessing genetic variability, genetic parameters like GCV, PCV, heritability, genetic advance, correlation and path analysis on 26 Blackgram genotypes with one check using randomized block design with three replication during Kharif 2021 in experimental Farm of the Department Genetics and Plant Breeding, Naini Agricultural Institute, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh. Observations were recorded for 13 characters viz., days to 50% flowering, days to 50% pod setting, days to maturity, plant height (cm), number of primary branches, number of clusters per plant, number of pods per plant, number of seeds per pod, pod length (cm), biological yield per plant (g), harvest Index (%), seed index, seed yield per plant (g). based on mean performance MASH -338 found to be superior in seed yield per plant followed by IPU-99. Among the 13 quantitative characters, high estimates of GCV and PCV were recorded respectively for number of pods per plant followed by number of clusters per plant. Number of pods per plant showed high heritability and genetic advance. Positive and significant correlation with number of clusters per plant at genotypic and phenotypic level. At genotypic level, the highest positive direct effect on Seed yield per plant effect was found for number of clusters per plant and harvest index at genotypic level. It can be concluded that these traits can be used as selection criteria in blackgram for bringing improvement in seed yield.

The present study entitled “Estimation of genetic variability and character association for yield characters in Chickpea (Cicer arietinum L.)” was designed to assess the extent of variability, genetic advance, heritability and correlation of different traits of 22 chickpea genotypes using randomized block design with three replications. The observations were recorded on five randomly selected plants from each treatment and replication for 13 different quantitative traits. Based on the mean performance, high seed yield per plant were identified for the genotype ICC-13348, GNG-469 and IPC-9-50. High estimates of GCV and PCV were recorded for number of seeds per plant, biological yield per plant, seed yield per plant, Seed Index, number of pods per plant. Genetic parameters revealed that heritability and genetic advance as % mean values are high were recorded for biological yield per plant, Number of seeds per plant, seed index, Number of pods per plant. Correlation coefficient analysis revealed that seed yield per plant exhibited positive and significant association with biological yield, seed index and harvest index at genotypic and phenotypic levels. Path coefficient analysis revealed that characters seed index, biological yield and harvest index have positive direct effect on seed yield per plant at genotypic and phenotypic levels. Hence, these traits should be prelimin ary included in selection criteria for crop improvement programme in chickpea.

Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.

In this work, the evaluation of segmentation techniques towards the detection tumours or gliomas on BraTs 2018 data sets on T2 weighted 3D MRI images is carried out. Techniques experimented include pixel wise Linear Binary Pattern (LBP), region growing and Otsu thresholding to compare the efficiency of the same with proposed approach. In this work, 3D MRI images are initially subject to slicing from which about 10% from the top and bottom are normalized as those slices does not possess any significant details (empty slices). Further, the enhancement of tumour/gliomas regions is performed on normalized slices using high boost ‘convolution kernel processing in various trials with varying kernel sizes of high boost convolution kernel. The glioma regions enhanced using kernel are subject to local adaptive thresholding technique for extraction of gliomas from normalized slices. The detected gliomas are compared with the results of ground truth segmentation to determine the effectiveness of the proposed technique. It is observed in 80% of slices are successful in detection of gliomas both in case of Low Grade Glioma (LGG) and High Grade Glioma (HGG). By analyzing the results, it is observed that proposed method has outperformed with average Jaccard and Dice similarity recorded as 87.20% and 89.18% for HGG and where as in case of LGG it is found to be 83.77% and 87.74%.

Real Time Traffic Signal and Speed Violation Control System of Vehicles Using IOT techniques have been widely applied to control traffic and help the citizens to follow the rules precisely depending on various sensors. The IOT has gained popularity and enormous advance in real time applications used by people for their daily use. In this proposed system we detect the fast moving vehicles that detect the Red signal using RFID sensor. This module also be set up on Raspberry pi component which used for assisting in solving the actual difficulties such as traffic rules and accident. This system consists of an onsite module IOT module that monitors and sends signal to the database. This project primarily for the vehicle which cross the traffic signal during STOP sign (Red signal) and makes the vehicle to slow down in ALERT sign (yellow light) by giving the vehicle average velocity like a speed limit. So that the other lane vehicles can move or go according to the traffic rule. The system will be helpful for the people to follow traffic rules and for higher officials to take necessary actions. The RFID are set to the vehicles and RF-tags are set in the roads to detect the non-obeying vehicles. Categories can be obtained through reasonable analysis of the data. The government officials and the users can check their behaviour updated in an android app for further penalties.

This paper analyzes the global asymptotic stability of a class of neural networks with time delay in the leakage term and time-varying delays under impulsive perturbations. Here the time-varying delays are assumed to be piecewise. In this method, the interval of the variation is divided into two subintervals by its central point. By developing a new Lyapunov–Krasovskii functional and checking its variation in between the two subintervals, respectively, and then we present some sufficient conditions to guarantee the global asymptotic stability of the equilibrium point for the considered neural network. The proposed results which do not require the boundedness, differentiability and monotonicity of the activation functions, can be easily verified via the linear matrix inequality (LMI) control toolbox in MATLAB. Finally, a numerical example and its simulation are given to show the conditions obtained are new and less conservative than some existing ones in the literature.

This paper presents a computational investigation of the validity of eddy current testing (ECT) for defects embedded in steel using parametrically designed defects. Of particular focus is the depths at which defects can be detected through ECT. Building on this we characterize interior defects by parametrically describing them and then examining the response fields through measurement. Thereby we seek to establish the depth and direction of detectable cracks. As a second step, we match measurements from eddy current excitations to computed fields through finite element optimization. This develops further our previously presented methods of defect characterization. Here rough contours of synthesized shapes are avoided by a novel scheme of averaging neighbor heights rather than using complex Bezier curves, constraints and such like. This avoids the jagged shapes corresponding to mathematically correct but unrealistic synthesized shapes in design and nondestructive evaluation.

Most important journal papers in magnetics are selected from conference records with quick review and subject to stringent page limits. The literature as a result is unsatisfactory, inadequately attributing previous works and without sufficient details to replicate work presented. This paper therefore reviews mathematical optimization in synthesis and nondestructive evaluation (NDE) by the finite element method in magnetics. The review identifies the earliest papers. Thereafter this paper proposes and establishes the feasibility of coupled problem optimization using the genetic algorithm to avoid mesh induced minima which hurt gradient based methods. The genetic algorithm, while avoiding the need for derivatives, results in having to undertake even more numerous finite element solutions. Although the genetic algorithm has been applied in optimization, in coupled systems the number of object function evaluations doubles. We there examine the use of graphics processing units (GPUs) to handle the immense computational load. GPUs have recently been introduced in finite element analysis but their memory limits are often not recognized and are critically limiting when parallelizing the several solutions required in optimization. To overcome this limit, element-by-element finite element matrix processing is employed, making coupled problems practicable on GPUs. We overcome the memory limits faced by others.

Purpose – Inverting electroheat problems involves synthesizing the electromagnetic arrangement of coils and geometries to realize a desired heat distribution. To this end two finite element problems need to be solved, first for the magnetic fields and the joule heat that the associated eddy currents generate and then, based on these heat sources, the second problem for heat distribution. This two-part problem needs to be iterated on to obtain the desired thermal distribution by optimization. Being a time consuming process, the purpose of this paper is to parallelize the process using the graphics processing unit (GPU) and the real-coded genetic algorithm, each for both speed and accuracy. Design/methodology/approach – This coupled problem represents a heavy computational load with long wait-times for results. The GPU has recently been demonstrated to enhance the efficiency and accuracy of the finite element computations and cut down solution times. It has also been used to speedup the naturally parallel genetic algorithm. The authors use the GPU to perform coupled electroheat finite element optimization by the genetic algorithm to achieve computational efficiencies far better than those reported for a single finite element problem. In the genetic algorithm, coding objective functions in real numbers rather than binary arithmetic gives added speed and accuracy. Findings – The feasibility of the method proposed to reduce computational time and increase accuracy is established through the simple problem of shaping a current carrying conductor so as to yield a constant temperature along a line. The authors obtained a speedup (CPU time to GPU time ratio) saturating to about 28 at a population size of 500 because of increasing communications between threads. But this far better than what is possible on a workstation. Research limitations/implications – By using the intrinsically parallel genetic algorithm on a GPU, large complex coupled problems may be solved very quickly. The method demonstrated here without accounting for radiation and convection, may be trivially extended to more completely modeled electroheat systems. Since the primary purpose here is to establish methodology and feasibility, the thermal problem is simplified by neglecting convection and radiation. While that introduces some error, the computational procedure is still validated. Practical implications – The methodology established has direct applications in electrical machine design, metallurgical mixing processes, and hyperthermia treatment in oncology. In these three practical application areas, the authors need to compute the exciting coil (or antenna) arrangement (current magnitude and phase) and device geometry that would accomplish a desired heat distribution to achieve mixing, reduce machine heat or burn cancerous tissue. This process presented does it more accurately and speedily. Social implications – Particularly the above-mentioned application in oncology will alleviate human suffering through use in hyperthermia treatment planning in cancer treatment. The method presented provides scope for new commercial software development and employment. Originality/value – Previous finite element shape optimization of coupled electroheat problems by this group used gradient methods whose difficulties are explained. Others have used analytical and circuit models in place of finite elements. This paper applies the massive parallelization possible with GPUs to the inherently parallel genetic algorithm, and extends it from single field system problems to coupled problems, and thereby realizes practicable solution times for such a computationally complex problem. Further, by using GPU computations rather than CPU, accuracy is enhanced. And then by using real number rather than binary coding for object functions, further accuracy and speed gains are realized.

Finite element mesh generators exist in the public domain, a few even based on a parametric device description. The typical mesh generator requires some man–machine interaction to define the points and boundary conditions, and does not work for non-stop optimization iterations for which we need a mesh dynamically evolving through the iterations with optimization variables as changing parameters. Such mesh generators as do exist are rare, commercial, and not easily available to researchers except at great cost and never with the code to modify them to suit individual needs. We take a regular open source mesh generator and write a script-based interface as open source to run non-stop for optimization. We then use it to create a non-destructive evaluation system for army ground vehicles’ defect characterization and use it equally for machine design. A simple scheme of averaging neighbour heights gives us a smooth geometry without having to use Bezier curves. The mesh runs on the central processing un...

Breast cancer became one of the deadliest cancer in women. It occurs when the growth of the cells in breast tissue become out of control. Cells are the building blocks for the organs and tissues in the body. When the growth of new cells are uncontrolled then they build-up mass of tissue called tumor. The tumors are categorized in to benign and malignant tumors. Early diagnosis needs an accurate diagnosis procedure that can be used by physicians to classify whether the tumor is benign or malignant. Classification algorithms are used to classify whether it is benign or malignant tumor. The main objective of this paper is to compare the results of supervised learning classification algorithms and combination of these algorithms using voting classifier technique. Voting is one of the ensemble approach where we can combine multiple models for the better classification. The dataset is taken from Wisconsin University database.

This paper is concerned with the passivity analysis problem for a class of discrete-time stochastic bidirectional associative memory neural networks with time-varying delays. Furthermore, the results are extended to the robust passivity analysis with mixed time delays that consist of both the discrete and distributed time delays, and the uncertainties are assumed to be time-varying norm bounded parameter uncertainties. By constructing a new Lyapunov---Krasovskii functional and introducing some appropriate free-weighting matrices, a delay-dependent passivity criterion is derived in terms of LMIs whose feasibility can be easily checked by some available software packages. Finally, two numerical examples with simulation results are given to demonstrate the effectiveness and usefulness of the proposed results.

This paper addresses the problem of global exponential dissipativity for a class of uncertain discrete-time BAM stochastic neural networks with time-varying delays, Markovian jumping and impulses. By constructing a proper Lyapunov–Krasovskii functional and combining with linear matrix inequality (LMI) technique, several sufficient conditions are derived for verifying the global exponential dissipativity in the mean square of such stochastic discrete-time BAM neural networks. The derived conditions are established in terms of linear matrix inequalities, which can be easily solved by some available software packages. One important feature presented in our paper is that without employing model transformation and free-weighting matrices our obtained result leads to less conservatism. Additionally, three numerical examples with simulation results are provided to show the effectiveness and usefulness of the obtained result.

This paper addresses the problem of stability analysis for a class of uncertain discrete-time stochastic BAM neural networks with time-varying delays and impulses. In this paper, we assume that stochastic disturbances are described by the Brownian motion and jumping parameters are generated from discrete-time discrete-state homogeneous Markov process. By employing the Lyapunov–Krasovskii functional and stochastic analysis theory, a set of novel sufficient conditions are derived to guarantee the robust global exponential stability of the equilibrium point in the mean square. The obtained results are shown to be less conservative than the existing one in the literature. Note that the obtained results are formulated in terms of linear matrix inequality (LMI) that can efficiently solved by the LMI toolbox in Matlab. Numerical examples are given to show that the proposed result significantly improve the allowable upper bounds of delays over some existing results in the literature.

Integrated optical asymmetric Mach-Zehnder interferometer-based dense wavelength division multiplexing (DWDM) channel interleaver structures (2 � 2) have been theoretically studied using various single-mode waveguide geometries in silicon-on-insulator platform. Subsequently, a dispersion-free interleaver design has been proposed, fabricated, and characterized. It was designed to separate alternate DWDM channels with 100 GHz spacings. Some of the fabricated prototype devices have been fiber pigtailed and packaged in a suitably designed metal housing. The transmitted spectra ( 1520 nm < ? < 1600 nm) at each of the output ports reveal that alternate DWDM channel wavelengths are separated with a uniform inter-channel extinction of ? 18 dB. Each of the channel passbands shows their 3-dB bandwidth of ? 40 GHz. � 2006 IEEE.

In nondestructive defect characterization, the genetic algorithm matches measurements to computations through finite element optimization of a parametrically described defect. This poses a huge computational load. A published element-by-element (EbE) conjugate gradients (CG) method for the graphics processing unit (GPU) parallelizes the matrix solution process. However, in finite element optimization for non-destructive evaluation (NDE), thousands of simultaneous finite element matrix solutions on parallel genetic algorithm threads on a GPU are required. This challenges the memory capabilities of a GPU and results in long waiting times making the NDE technique impracticable. To address these twin time and memory problems, we revive an old EbE processing by Gauss iterations. It yields speedups of 80+ which grow indefinitely with matrix size to values much bigger than those reported for EbE CG. For the latter the reported figures were less than 4 for the practicable matrix size of 91,333, and 96 for a size of over 1 million and falling thereafter. Our EbE implementation of CG gives a speedup of 147 for first order triangular meshes. No memory issues are faced.

In the nondestructive evaluation (NDE) of steel plates, besides the detection of cracks, what is also important is their characterization. Characterization is necessary for determining whether any discovered crack demands withdrawal of the part from service. In eddy current crack identification the response of a part to a coil is compared to the response without a crack. When different, the presence of the crack is flagged. But to characterize it, the computed response from eddy current analysis with a crack described by parameters is optimized to match measurements. This is heavy computation. Recently, Graphical Processing Unit (GPU) computing has had great success in many very large numerical computations. In this work we discuss the often undiscussed GPU memory limitation in Finite Element Optimization. In GPU computing the memory of the NVIDIA GPU is limited (4 GB on a PC today). This paper assesses the memory limits in terms of matrix size in light of the various ways to store a large matrix in order to overcome these limits. We revive old element-byelement finite element solvers from the early 1980s of working on a highly memory limited PC 282 to launch a GA kernel on thousands of CUDA threads exploiting the NVIDIA GPU architecture.

The computational algorithms for device synthesis and nondestructive evaluation (NDE) are often the same. In both we have a goal – a particular field configuration yielding the design performance in synthesis or to match exterior measurements in NDE. The geometry of the design or the postulated interior defect is then computed. Several optimization methods are available for this. The most efficient like conjugate gradients are very complex to program for the required derivative information. The least efficient zeroth order algorithms like the genetic algorithm take much computational time but little programming effort. This paper reports launching a Genetic Algorithm kernel on thousands of compute unified device architecture (CUDA) threads exploiting the NVIDIA graphics processing unit (GPU) architecture. The efficiency of parallelization, although below that on shared memory supercomputer architectures, is quite effective in cutting down solution time into the realm of the practicable. We carry this furt...

In this paper, the exponential stability for a class of stochastic neural networks with time-varying delays and impulsive effects is considered. By constructing suitable Lyapunov functionals and by using the linear matrix inequality optimization approach, we obtain sufficient delay-dependent criteria to ensure the exponential stability of stochastic neural networks with time-varying delays and impulses. Two numerical examples with simulation results are provided to illustrate the effectiveness of the obtained results over those already existing in the literature.

This paper is concerned with the problem of robust stabilization and H∞ control for a class of uncertain stochastic neural networks with time-varying delays and time-varying norm-bounded parameter uncertainties. The delay is of a time-varying nature, and the activation functions are assumed to be neither differentiable nor strictly monotonic. Moreover, the description of the activation functions is more general than the commonly used Lipschitz conditions. By using the Lyapunov function approach together with the linear matrix inequality (LMI) technique, for the robust stabilization we propose a state feedback controller to ensure that the closed loop system is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. For the robust H∞ control problem, a state feedback controller is designed such that in addition to the requirement of robust stability, a prescribed H∞ performance level is to be satisfied. The results obtained are formulated in terms of LMIs which can be easily checked by the MATLAB LMI control toolbox. Numerical examples are presented to illustrate the effectiveness of the obtained method and the improvement over some existing results.