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The modular multilevel converter (MMC) has become popular for medium-voltage applications. During MMC operation, unavoidable circulating currents flow through its arms and cause extra losses in the converter system. To generate a reference for circulating current, different current injection methods to control the circulating current have been reported. The conventional current injection techniques used so far relies on a mathematical model that excludes the effect of arm inductor voltages in its design. For high-voltage applications with many submodules (SMs), the above exclusion does not make much difference. But for medium-voltage applications with few SMs, the applied arm inductor voltages may be significant for a given dc-link voltage. In this article, inductor voltages are included in the proposed circulating current model, and a new reference for circulating current is obtained resulting in reduced circulating current through MMC arms. The proposed method is analyzed in detail on both seven-level and nine-level three-phase MMC, where advantages of the 2 N + 1 level phase disposition pulsewidth modulation (PD-PWM) scheme are retained. The simulation study is carried out in PLECS blockset to show the reduction in the rms magnitude of circulating current. The validation of the proposed technique in a lab-scale hardware prototype shows the effectiveness of the proposed method.

Today, the size of a multimedia file is increasing day by day from gigabytes to terabytes or even petabytes, mainly because of the evolution of a large amount of real-time data. As most of the multimedia files are transmitted through the internet, hackers and attackers try to access the users’ personal and confidential data without any authorization. Thus, maintaining a strong security technique has become a significant concerned to protect the personal information. Deoxyribonucleic Acid (DNA) computing is an advanced field for improving security, which is based on the biological concept of DNA. A novel DNA-based encryption scheme is proposed in this article for protecting multimedia files in the cloud computing environment. Here, a 1024-bit secret key is generated based on DNA computing and the user's attributes and password to encrypt any multimedia file. To generate the secret key, the decimal encoding rule, American Standard Code for Information Interchange value, DNA reference key, and complementary rule are used, which enable the system to protect the multimedia file against many security attacks. Experimental results, as well as theoretical analyses, show the efficiency of the proposed scheme over some well-known existing schemes.

This article presents an improved version of Fractional Order Darwinian PSO (IFODPSO) for segmenting 3D histogram-based color images at multiple levels of Berkley Segmentation Dataset (BSDS500). The success of convergence and accuracy rate of FODPSO algorithm depends on the value of fractional coefficient. This concept may provide drawback to the algorithm specially for multilevel problems of large dataset. So, to overcome the full dependency on fractional coefficient, delta potential model of quantum mechanics has been incorporated with FODPSO for updating the particle’s present as well as global position by destroying the worst particles (solutions), formulated using the introduction of the context parameter. Multi-level Massi Entropy (MME), of current interest, has been chosen here as the objective function for finding the threshold values in combination with IFODPSO. Further, the small segmented regions have been removed or merged into bigger regions for showing the better discrimination between different segmented objects. The effectiveness of the proposed MME-IFODPSO algorithm has been extensively investigated in terms of statistically and qualitatively in terms of the fidelity parameters with the state-of-art approaches and it has been found that the proposed method has improved at least 2–5% to the conventional methods in terms of accuracy.

With the rapid advancement of emerging technologies, a huge amount of data is created and processed in daily life. Nowadays, Cloud Computing (CC) technology is one of the frequently adopted technologies to access and store data over the internet. CC mainly provides on-demand and pay-per-use services to users. However, access control and security are two major issues that users face in a cloud environment. During data access from a cloud server, the searching time of a Data Owner (DO) and the data accessing time are high. Therefore, users utilize more cloud services and pay more. High system overhead is another issue of a cloud environment. In this paper, a novel access control model has been proposed to overcome all these concerns. Here, the Cloud Service Provider (CSP) maintains a temporary table based on the data type and popularity value of the DO for fast and efficient data accessing. The cloud service provider can easily search the data owner by using the table and the data accessing time is remarkably reduced. Experimental results and theoretical analysis of the proposed scheme prove its efficiency over the existing schemes.

This study proposes an improved cooperative quantum-behaved particle swarm optimisation (ICQPSO) algorithm to find multiple threshold levels for colour images with multilevel Renyi entropy (MRE). In the proposed method, the context vector of each particle is updated each time dynamically when a cooperation operation is completed with other particles. The improved search ability and optimisation performance of ICQPSO algorithm with MRE (hence called MRE-ICQPSO) extensively investigated with other well known nature-inspired algorithms such as Levi flight-guided firefly, cuckoo search, artificial bee colony, and beta differential evolution. The proposed method is applied to the Berkley segmentation dataset with 300 distinct colour images to show the effective performance of the algorithm in terms of fidelity parameters and computation time.

Nowadays, the classification of medical images has become an essential part of identifying the disease. Among various existing critical diseases, identification of breast cancer has now come up with the topic of investigation. To identify the affected regions of the images, a deep learning-based approach has got wide attention for decades. Convolutional neural networks (CNN) among all deep learning techniques proved their best efficiency in this field. In this paper, one improved CNN-based approach has been proposed to classify the breast cancer images obtainable from the standard PatchCamelyon (PCam) benchmark dataset. It is available for free from the website link https://www.kaggle.com/c/histopathologic-cancer-detection/data. In the improved model, various existing layers like convolutional, ReLU, pooling, fully connected have been added as well as modified for better efficiency and efficacy of the algorithm. Further to be added that Adam optimizer has been used here with cross entropy as a loss function. This improved model has been compared with two recent CNN-based approaches applied to medical datasets. The comparative outcomes suggest strong improvements in terms of classification accuracy (probably 2–3%) and computational time of validation loss for both training and testing data over existing models.

Clustering-based image segmentation got wide attention for decades. Among various existing clustering techniques, K-means algorithm gained popularity for its better outcome. But the drawback of this algorithm can be found, when it is applied to noisy medical images. So, modification of the standard K-means algorithm is highly desired. This paper proposes an improved version of K-means algorithm called as (IKM) to get more effective and efficient outcomes. The efficiency of the algorithm depends on the speed of forming the clusters. So, in the proposed approach, new idea has been applied to find the minimum distance to generate the clusters. The proposed IKM algorithm has been applied to the set of noisy medical images, and the segmented outcomes have been evaluated by the standard quality measurement metrics, namely Peak-Signal-to-Noise-Ratio (PSNR) and structural similarity index measurement (SSIM). The outcomes have also been compared with the Watershed algorithm for showing the betterment of the proposed approach.

Image segmentation problem has been solved by entropy-based thresholding approaches since decades. Among different entropy-based techniques, fuzzy entropy (FE) got more attention for segmenting color images. Unlike grayscale images, color images contain 3-D histogram instead of 1-D histogram. As traditional fuzzy technique generates high time complexity to find multiple thresholds, so recursive approach is preferred. Further optimization algorithm can be embedded with it to reduce the complexity at a lower range. An updated robust nature-inspired evolutionary algorithm has been proposed here, named improved differential evolution (IDE) which is applied to generate the near-optimal thresholding parameters. Performance of IDE has been investigated through comparison with some popular global evolutionary algorithms like conventional DE, beta differential evolution (BDE), cuckoo search (CS), and particle swarm optimization (PSO). Proposed approach is applied on standard color image dataset known as Berkley Segmentation Dataset (BSDS300), and the outcomes suggest best near-optimal fuzzy thresholds with speedy convergence. The quantitative measurements of the technique have been evaluated by objective function’s values and standard deviation, whereas qualitative measures are carried out with popular three metrics, namely peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and feature similarity index measurement (FSIM), to show efficacy of the algorithm over existing approaches.

Modular Multilevel Converters (MMC) are known for their high modularity and good quality output power. The problems of circulating current and capacitor voltage balancing for the MMC have been widely studied in available literature but little work is done towards grid current control with fast dynamic response. Conventional current controllers use PI or PR controllers which are slow and thus degrade transient performance of the MMC. Recently reported Model Predictive Control (MPC) methods for MMC aim to control the grid current but MPC suffers from computational burden and difficulties in adjusting of weights. Space Vector based Hysteresis Current Controllers (SCHCC) have been well established for drive applications. In this work, an SVHCC has been developed for a grid connected MMC system. Simulation studies show fast current control for cases of sudden change of active power reference and current reference. The controller exhibits quick tracking of the current reference in all cases. The proposed method has been discussed for a MMC system with three submodules, but it may be extended to any number of submodules and can be easily implemented in a digital controller with less computational effort.

Model Predictive Control (MPC) is rapidly making inroads into power electronics converter control due to its simple and intuitive design and fast transient performance. However issues like heavy computational burden, tedious weights selection process and lack of fixed switching frequency seriously inhibit its application for large systems like the Modular Multilevel Converter (MMC). To tackle this problem, this article proposes a unique simplified two-stage MPC strategy for the Modular Multilevel Converter (MMC) using a simple search algorithm. The proposed technique falls under the category of Modulated MPC. One of the major advantages of this method includes fixed switching frequency for MMC with $2\mathrm{N}+1$ levels in the phase voltage, which is absent in many other MPC techniques in literature. Moreover the circulating current suppression and the output current control are decoupled. The capacitor voltage balancing is carried out using the simple reduced switching frequency type of sorting. It has lower computational complexity and ensures simplified digital implementation. The simulation results carried out using PLECS for 10 SM/0.7 MW MMC connected to a passive load validate the performance of this proposed algorithm.

The modular multilevel converter (MMC) used in medium and high voltage applications due to its several advantages related to scalability and modularity. An MMC consists of several sub-modules (SMs) which house several capacitors in each arm of each phase. For the working of MMC, the voltage across these capacitors needs to be measured. High voltage applications demand more number of SMs, which needs more capacitors and its voltage sensing devices for operation. In this paper, a discrete time adaptive observer (DTAO) is designed for the estimation of capacitor voltages of SMs by taking the discrete model of an MMC. It handles the uncertainties due to the modeling error and parasitic dynamics and makes the design robust. This design approach is very simple as it serves the engineer to choose the design parameter in an easy way. This method is also compared with discrete time sliding mode observer (DTSMO) approach.

The modular multilevel converter (MMC) has found a place in medium and high voltage applications because of its several advantages over the multilevel topologies. It comprises of several capacitors in each phase leg and their voltages need to be measured for its reliable operation. More the number of sub-modules (SMs); more is the number of required voltage measurement devices. The use of more number of physical voltage measurement devices makes the system costly and more complex in terms of hardware implementation. A unique capacitor voltage estimation technique is proposed in this paper, using a variable gain discrete time sliding mode observer (VGDTSMO) technique. These variable gains present in sliding mode observer make it simpler for implementation. This is a generalized method that can be scalable to any number of phases and SMs. The VGDTSMO technique is validated by a mathematical proof of error convergence and simulation results are provided to substantiate the theory. The results are compared with existing literature on discrete time sliding mode observer (DTSMO) based capacitor voltage estimation of an MMC.

Entropy-based thresholding techniques are quite popular and effective for image segmentation. Among different entropy-based techniques, minimum cross-entropy thresholding (MCET) has received wide attention in the field of image segmentation. Considering the high time complexity of MCET technique for multilevel thresholding, recursive approach to reducing its computational cost is highly desired. To reduce the complexity, further optimization techniques can be applied to find optimal multilevel threshold values. In this paper, a novel improved particle swarm optimization (IPSO)-based multilevel thresholding algorithm is proposed to search the near-optimal MCET thresholds. The general PSO algorithm often suffers from premature convergence problem which has been addressed in the IPSO by decomposing a high-dimensional swarm into several one-dimensional swarms, and then premature convergence is removed from each one-dimensional swarm. The proposed technique is applied to the set of grayscale images, and the experimental results infer that it produces better MCET optimal threshold values at a higher and faster convergence rate. The qualitative and quantitative results are compared with existing optimization techniques like modified artificial bee colony, Cuckoo search, Firefly, particle swarm optimization, and genetic algorithm. It has been observed that the proposed technique performs better in terms of producing better fitness value, less CPU time as quantitative measurements, and effective misclassification error, peak signal-to-noise ratio, feature similarity index measurement, complex wavelet structural similarity index measurement values as qualitative measurements compared to other considered state-of-the-art methods.

The Modular Multilevel Converter (MMC) that consists of several submodules (SMs) in its structure generates high quality output voltages. The controller of the MMC requiring all the SM capacitor voltages to be sensed makes it expensive and less reliable in terms of field implementation. In practical scenario, Sliding Mode Observer (SMO) can be used as a robust estimation technique in sensing the SM capacitor voltages. This paper proposes a Discrete Time Sliding Mode Observer (DTSMO) that estimates the capacitor voltages of all the SMs of an MMC based on discrete time model approach. This generalized technique can be suitably adopted for a MMC with any phases and any number of submodules. The proposed observer is simulated for medium voltage application in MATLAB/Simulink and results show its effectiveness in estimation. The problem of dissimilar initial charging among SM capacitors is also studied and the observer exhibits stable performance in such conditions. The MMC structure with this proposed observer can eliminate a large number of sensors particularly in high voltage applications.

The Modular Multilevel Converter (MMC) has found considerable use in medium voltage applications like MVDC in recent times. Circulating currents that do not appear at the output and flow between the phase legs, pose serious concern causing unnecessary converter losses. Several methods of circulating current control have been proposed and they are based on simplified average models of the MMC. Such models are well suited for high voltage systems where there are large number of Sub-Modules (SMs). But, for medium voltage MMC using fewer SMs, circulating current control can be better managed by considering more accurate model. This paper proposes an improved model that includes the inductor voltage in its design. In this approach, the circulating current reference is first obtained and then it is used to choose a correct switching redundancy to control the circulating current. The proposed work uses a phase leg based PD 2N+1 modulation scheme that has inherently better harmonic performance compared to conventional arm-based modulation. Through simulation studies, it is proved here that the rms circulating current gets reduced resulting in reduced conduction loss. The proposed work is applicable for MVDC applications which involve fewer number of SMs.

Presently, the prediction of share is a challenging issue for the research community as share market is a chaotic place. The reason behind it, there are several factors such as government policies, international market, weather, performance of company. In this article, a model has been developed using long short term memory (LSTM) to predict the share price of DLF group. Moreover, for the experimental purpose the data of DLF group has been taken from yahoo financial services in the time duration of 2008 to 2018 and the recurrent neural network (RNN) model has been trained using data ranging from 2008 to 2017. This RNN based model has been tested on the data of year 2018. For the performance comparison purpose, other linear regression algorithms i.e. k-nn regression, lasso regression, XGboost etc has been executed and the proposed algorithm outperforms with 2.6% root mean square error.

In this paper we study the performance of several text vectorization algorithms on a diverse collection of 73 publicly available datasets. Traditional sparse vectorizers like Tf-Idf and Feature Hashing have been systematically compared with the latest state of the art neural word embeddings like Word2Vec, GloVe, FastText and character embeddings like ELMo, Flair. We have carried out an extensive analysis of the performance of these vectorizers across different dimensions like classification metrics (.i.e. precision, recall, accuracy), dataset-size, and imbalanced data (in terms of the distribution of the number of class labels). Our experiments reveal that the sparse vectorizers beat the neural word and character embedding models on 61 of the 73 datasets by an average margin of 3-5% (in terms of macro f1 score) and this performance is consistent across the different dimensions of comparison.

Bipedal locomotion has been an active area of research for many decades, it has wide ranging applications in the field of humanoid locomotion, as well as in the understanding of the biomechanics of normal human gait. Inherently human gait is a complex non-linear dynamic system, which is usually modeled by a set of differential equations satisfying a given set of constraints. In this paper an attempt has been made to view gait from the perspective of software engineering. In doing so, the entire gait cycle has been discretized into phases and sub-phases and modeled using a hybrid automaton, subsequently the automaton has been integrated with the BIP(Behavior, Interaction, Priority) framework, thereby creating a component based computational framework for modeling biped locomotion. The correctness of the developed model has been validated and verified through simulation runs in OpenSim. Modeling of human gait using Hybrid Automaton.Construction of atomic components to model human gait.Integration of the atomic components and the hybrid automata in the BIP (Behavior, Interaction, Priority) Framework.Modeling the interactions between the atomic components.Verification of the proposed model in OpenSim for both normal and crouch gait.

Space Vector Modulation (SVM) has already shown its effectiveness in multi-level converter applications. An innovative sample based SVM technique has been adopted here for the first time for the Modular Multi-level Converter (MMC). This proposed technique uses mapping of the original reference vector in the multilevel space vector structure to a two level hexagon in order to find out a generalized PWM technique for any n-level MMC. The switching vectors found in the two level SVM structure are escalated to actual switching vectors to generate gating pulses using simple logic. Computational complexity is reduced by adopting a generalized algorithm which is even effective for high voltage applications where number of submodule can increase substantially. The gating pulses are generated for the submodules in a manner that the voltage across the inductor is actively controlled and hence, better control on the output voltage is achieved. A desktop simulation study based on 9-level MMC is carried out for validation purpose. The main focus of this paper is on the novel SVM technique and hence, ideal dc sources are used for submodules for proving the above mentioned PWM technique.

Space vector modulation (SVM) is well established for conventional multilevel topologies and it can be very useful for MMC owing to its superior harmonic performance. This paper proposes a dual SVM strategy which is a generalized approach and has same number of steps for any level converter. This technique is based on mapping of the actual reference vector to a two level mapped reference vector and finding of dwell times of corresponding equivalent two level switching vectors. Once these are obtained, equivalent switching vectors are transformed to the actual switching vectors in the multilevel space vector structure using simple predetermined transformation relations. Here, two SVMs are used to modulate the upper and lower arms. The problem of circulating current is also included in the analysis and a circulating current controller is used for compensating the dc and second harmonic arm currents. This circulating current controller uses both PI and resonant controller to control the dc and second harmonic currents. This work focuses on the simple generalized digital implementation of SVM for MMC and hence ideal submodules (SMs) are chosen with fixed DC voltages.

The earlier developed two stage hybrid automata is not a perfect representation of human walk as it is a combination of discrete and continuous phases and the whole human GAIT has 8 stages. Our major contribution is eight stage hybrid automata for large push recovery and various dynamic parameter studies for stable walk model. We have developed a controller to verify different stage of human locomotion by using OpenSim data for model 3DGaitModel2354 and lower extremity data. We verified the hybrid automata model using the real human GAIT data for normal person. We identify the?importance?of?the human lower extremity?for locomotion and push recovery from large perturbation. The novelty of research work is to model the bipedal locomotion as a re-usable component based framework. Our original contribution lies in the fact that we have tried to view it from a software engineering perspective. Hybrid automata for eight stage of GAIT cycle of human is implemented.Design the controller for stable walk.Defined all the dynamic and static parameter.Described the domain break down of humanoid locomotion as hybrid system.Proposed the canonical equation for universal for moment of different join angle to produce exact human locomotion.

This Paper represents a multi-level image thresholding approach based on the normalized index value of image and probability of pixel intensities. One new objective function proposed, which is the multiplication of normalized index value and probability, to obtain the scenario. This multiplication measure is then optimized to obtain the thresholds of the image. In order to solve an optimization problem, Differential Evolution (DE) as a meta-heuristic approach is used, which results a fast and accurate convergence towards the optimal solution. The performance of DE is compared to other well-known optimized algorithms like Particle swarm optimization (PSO), Genetic Algorithms (GA). The outcomes of images are compared with Kapur entropy, Tsalli entropy and Otsu method, both visually and statistically for establishing the perceptible difference in image.

Carrier based SPWM techniques are the simplest and widely reported methods in the literature when it comes to controlling Modular Multilevel Converter (MMC) for high or medium voltage applications. In this paper, various multilevel SPWM techniques for MMC are simulated, analyzed and compared with each other. The conventional half bridge submodule (SM) is considered here for the study. The effect of the SM capacitance on the output voltage waveform is also analyzed and the optimal value of the capacitance is found out. Differences in the quality of output voltage between traditional phase disposition (PD) PWM and the proposed unipolar PD PWM have been observed and their comparative performances are shown based on 8-SM per phase MMC structure in the simulation results. Closed loop control of grid connected MMC is also validated by simulation study and then superiority of the unipolar PD PWM in comparison to any other carrier based SPWM technique is established.

In this paper, a new approach has been introduced for speed controller design of Indirect Field Oriented Controlled (IFOC) induction machines. The usual practice in preceding literature has been to neglect the electrical dynamics of the system while developing the mathematical model. This paper attempts to retain the electrical dynamics of the system and uses Model Order Reduction. FOC method has a major drawback in the form of degradation in performance as a result of parametric variations, arising out of various factors. Parametric variation pertaining to operating conditions has been taken up in this work and the optimal speed controller designed using LQR method, is extended to be a robust controller, to take into account the effect of parametric variations. In order to realize the system with parametric variation, INTLAB has been used which realizes the plant as an interval system. System response with the new model so obtained, is then realized and validated through credible Real Time simulator Opal RT Op 4500 using Software in the Loop (SIL) techniques.

This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.

The internet is a big giant in the today’s world playing the backbone for information and communication technologies. Most of the network application needs a very reliable and one-way authentication process to ensure system resources are securely accessed by authorised users over the Internet. Password-based authentication is the most common mechanism and is easy to implement for authentication purpose. However, such general scheme will be vulnerable to attackers to steal the static credential combination with ease using eavesdropping, brute forcing, password replay, etc. One-time password (OTP) is the promising solutions to overcome from such attacks. It generates the different unique passwords each time for the user login into the system. This paper proposes a novel method of OTP authentication using image features. Here the system calculates different features from randomly selected areas of an image and applies symmetric key cryptography to generate the random OTP for the user. The proposed approach is convenient and computationally less expensive offering high-level security.