Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

PurposeSpeckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.Design/methodology/approachThe work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.FindingsThe proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.Originality/valueFuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

Tertiary wavelet model based automatic epilepsy classification system

PurposeThis work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.Design/methodology/approachIn this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.FindingsFor the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.Originality/valueEpilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

An optimized complex motion prediction approach based on a video synopsis

PurposeThe space-time variants algorithm will not give good results in practical scenarios; when no tubes increase, these techniques will not give the results. It is challenging to reduce the energy of the output synopsis videos. In this paper, a new optimized technique has been implemented that models and covers every frame in the output video.Design/methodology/approachIn the video synopsis, condensing a video to produce a low frame rate (FR) video using their spatial and temporal coefficients is vital in complex environments. Maintaining a database is also feasible and consumes space. In recent years, many algorithms were proposed.FindingsThe main advantage of this proposed technique is that the output frames are selected by the user definitions and stored in low-intensity communication systems and also it gives tremendous support to the user to select desired tubes and thereby stops the criterion in the output video, which can be further suitable for the user's knowledge and creates nonoverlapping tube-oriented synopsis that can provide excellent visual experience.Research limitations/implicationsIn this research paper, four test videos are utilized with complex environments (high-density objects) and show that the proposed technique gives better results when compared to other existing techniques.Originality/valueThe proposed method provides a unique technique in video synopsis for compressing the data without loss.

An experimental study on visual tracking based on deep learning

PurposeIn the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG & Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods.Design/methodology/approachThis paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking.FindingsBetter tracking algorithms are not mentioned in the existing method.Research limitations/implicationsVisual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success.Practical implicationsThe authors implement the multiple tracking methods, for better tracking purpose.Originality/valueThe main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.

A 3-Ф grid connected wind system with a technique of DC link control using fewer sensors with fuzzy controller

PurposeThis paper aims to provide a permanent magnet synchronous generator (PMSG) wind turbine, which feeds electric power (AC) to the power grid. The converter, located on the machine side, is used to produce the full amount of wind power. Research on wind energy conversion system (WECS) is carried out in this study using a direct wind turbine in MATLAB with constant and variable speeds.Design/methodology/approachThis paper is about WECS using PMSG and is connected to a grid of two serial converters with common DC connections.FindingsThis paper aims to provide the value of DC connection voltage at its base, regardless of the wind speed alterations, the inverter's output ac voltage can be kept constant.Originality/valueThis paper aims to provide a Hill Climb Search maximum power point tracking (MPPT) algorithm is an effective control system for extracting maximum energy, also called voltage control, pitch control, phase-locked loop (PLL) controls, from a wind turbine. Using the Fuzzy controller, the grid side converter is controlled.

A review on optimization of antenna array by evolutionary optimization techniques

PurposeOptimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.Design/methodology/approachDesign of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.FindingsIn the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.Originality/valueThe originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

PurposeThis paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.Design/methodology/approachThe proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.FindingsThe performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.Practical implicationsFor the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.Originality/valueThe proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

Enhanced GAF protocol based on graph theory to optimize energy efficiency and lifetime in WSN technology

PurposeThe purpose of this paper is to design an enhanced routing protocol to minimize energy consumed and extend network lifetime in sensor network (WSN).Design/methodology/approachWith the use of appropriate routing protocols, data collected by sensor nodes reache the BS. The entire network lifetime can be extended well beyond that of its single nodes by putting the nodes in sleep state when they are not in use, and make active just a single node at a time within a given area of interest. So that, the lowest-cost routing arises by minimizing the communication cost. This paper proposes an enhanced adaptive geographic fidelity (E-GAF) routing protocol based on theory of graphs approach to improve the discovery phase, select the optimal path, reduce the energy used by nodes and therefore extend the network lifetime. Following the simulations established by varying the number of grids and tests, a comparison is made between the E-GAF and basic GAF (B-GAF) based on the number of dead nodes and energy consumption.FindingsThe results obtained show that E-GAF is better than the existing basic GAF protocol in terms of energy efficiency and network lifetime.Originality/valueThis paper adopts the latest optimization algorithm know as E-GAF, which is used to solve the problem of energy and improve the network lifetime in a WSN. This is the first work that utilizes network lifetime in WSN.

Dual images in reversible data hiding with adaptive color space variation using wavelet transforms

PurposeSteganography is a data hiding technique used in the data security. while transmission of data through channel, no guarantee that the data is transmitted safely or not. Variety of data security techniques exists such as patch work, low bit rate data hiding, lossy compression etc. This paper aims to increase the security and robustness.Design/methodology/approachThis paper describes, an approach for multiple images steganography that is oriented on the combination of lifting wavelet transform (LWT) and discrete cosine transform (DCT). Here, we have one cover image and two secret images. The cover image is applied with one of the different noises like Gaussian, Salt & Pepper, Poisson, and speckle noises and converted into different color spaces of YCbCr, HSV, and Lab.FindingsDue to the vast development of Internet access and multimedia technology, it becomes very simple to hack and trace secret information. Using this steganography process in reversible data hiding (RDH) helps to prevent secret information.Originality/valueWe can divide the color space converted image into four sub-bands of images by using lifting wavelet transform. By selecting lower bands, the discrete cosine transform is computed for hiding two secret images into the cover image and again one of the transformed secret images is converted by using Arnold transform to get the encrypted/embedded/encoded image. To extract the Stego image, we can apply the revertible operation. For comparing the results, we can calculate PSNR, SSIM, and MSE values by applying the same process for all color spaces of YCbCr, HSV, and Lab. The experimental results give better performance when compared to all other spaces.