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In recent years, the use of drones has drastically increased as the evolution of drone use in commercial sectors and reduced costs of the hardware. Earlier drone services were mostly used for military operations but nowadays the Unmanned Arial Vehicles (UAV) system is very advanced and its applications are not limited to military operations. The recent years have also witnessed a network evolution of UAVs from single ground to air network to multi-UAV network systems along with usage of wireless public networks like LTE which can act as UAV communication channel. In the proposed project, a communication system used in the UAS system is simulated to analyze the UAV behavior under different conditions with respect to mission planning and the communication networks used. A comprehensive study is done on communication networks used in controlling UAVs. For a safer approach, the proposed model is simulated using available software instead of hardware implementations. ArduPilot SITL, MAVProxy and Mission Planner are used to simulate the UAV system virtually. Whereas network simulations of Wi-Fi and LTE network are done with the help of NS-3 on a separate platform. Various network parameters like network delay, throughput, etc., are graphically represented.

Change in design of chassis, brings in (i) change in vibration pattern and (ii) stress pattern will also change for impact loading conditions. This paper aims to study the vibration characteristics and structural behaviour of three different Go-Kart chassis configurations. Chassis configurations are designed as per standards followed by major Go-Kart events organized in India and abroad. To study the vibration aspects, with change in design, modal analysis is done. Front, side and rear impact simulations are carried out to obtain displacement and stress distribution patterns. Three-dimensional CAD model is designed using SOLIDWORKS. Finite Element Analysis (FEA) software ANSYS Ver 16.0 is used to determine the Natural frequencies and impact testing. AISI 1018 material is taken for all the simulation cases. 4-G load is applied for front and rear impact while 2-G is applied for side impact test simulation. Results are extracted in terms of Natural Frequencies, von-Mises stress and deformation for all the simulation cases. Design 1 is found to have good vibration characteristics and is also possessing a higher degree of safety. The obtained results will be useful for students working to build their vehicle as well as industry people to fabricate safer and reliable Go-Kart vehicles.

Partially-connected MANETs are the Wi-Fi networks where most of the time a comprehensive path between source and destination does not exist due to narrow radio transmission range of the nodes, low density of nodes, wide deployment area, physical obstacles like high rise buildings, severe weather conditions, or other physical factors. The traditional routing protocols of the Mobile Ad-hoc Network presume that the network is connected. There are two possible solutions to solve the connectivity problem in sparse MANET. The first solution makes use of random movement of nodes to transfer messages whereas in the second, one node is given special privileges to roam in the deployment area to distribute messages in segmented areas, which is also called the Message Ferry (MF) approach for transmitting data. Message ferries keep moving in the deployment area and take the charge of storing, carrying and forwarding data of the regular nodes. In the present paper, the ferry-initiated approach has been adopted with 2 ferry nodes, which are allowed to move in such a way that they cover the maximum area of the grid. It is proposed that the ferry nodes will design their routes dynamically on the basis of current topology of MANET and in one round trip it will pass through the centers of the partitioned clusters formed by the regular nodes. The process of calculating cluster center is repeated in further trips. K-means clustering algorithm is applied to calculate the cluster centers of the partitioned clusters. The results indicate positive performances of the proposed model in various simulation experiments conducted.

Data warehouse is constructed with the purpose of supporting decision making. Decision making queries, being long and complex, consume a lot of time in processing against a continuously growing data warehouse. View materialization is one of the alternative ways of improving the response time of such analytical or decision making queries. This involves selection and materialization of views that minimize the analytical query response times while adhering to the resource constraints. This is referred to as the view selection problem, which is a NP-Hard problem. The view selection problem is concerned with simultaneously minimizing the cost of evaluating materialized and non-materialized views. This being a bi-objective optimization problem is addressed using NSGA-II in this paper. The proposed approach aims to achieve an acceptable trade-off between the afore-mentioned two objectives.

A projective synchronisation strategy is designed in this paper to synchronise a master manipulator having 12 states modelled using the assumed modes method and the slave manipulator with 8 states ...

This paper presents a novel simple 4-D dissipative autonomous hyperchaotic system with a saddle-point index-2 equilibrium point. The dynamics of the new system consists of nine terms with two nonlinear terms. Thus, the proposed system is one of the simplest 4-D hyperchaotic systems. The new system also shows multistability behavior for some range of its parameters. Bifurcation diagrams, Lyapunov spectrum, Kaplan-Yorke dimension, and phase plots are used to analyze the complex dynamical behavior of the system. The discrete-time equivalent of the presented hyperchaotic system is made by using the Euler algorithm for improving the embedded chaos-based engineering applications on FPGA. Then, the design of a novel TRNG having chaotic entropy seed has been performed utilizing FPGA-based 4-D hyperchaotic system structure. The Euler-based model in the creation phase on FPGA has been described using VHDL with 32-bit IEEE 754-1985 floating-point format. The designed hyperchaotic system and TRNG unit have been synthesized using Xilinx ISE Design Tools, and chip statistics obtained from place-and-route process have been presented by testing in the Virtex-6 FPGA chip of Xilinx family. The numbers generated in high data rates of 185.447 Mbit/s have been obtained with 370.894 MHz clock frequency for the implementation of the novel TRNG unit. In the last stage, randomness tests of the generated random numbers have been carried out and the generated random numbers have passed successfully all randomness tests.

Accurate and timely monitoring of crops can help in better agriculture management. Remote sensing-based crop monitoring helps to track the real-time crop condition. Small farm holding, intercropping, multi-cropping, inadequate irrigation infrastructure, and other challenges are quite common to Indian farms. Satellite-based crop monitoring uses pixel-based analysis poses many challenges as the majority of Indian farms are smallholdings. Hence, to overcome these challenges a novel method has been proposed based on image processing, and machine learning (ML) algorithms in this chapter. In the study, Morbi or Morvi district of Gujarat state, India has been selected, where majority of holdings are small and marginal with cultivation of cotton, cumin, wheat, bajra, sesamum, and groundnut as major crops. In the study, optical satellite data from Sentinel-2 was used to delineate farm boundaries using standard deviation and perform crop classification by using Random Forest (RF) algorithm with approximately 85% accuracy and an F1-score of 0.84. Open-source Geographic Information System (GIS) platform QGIS was used to perform post-processing operations to handle raster, and vector data analysis. The proposed method can be applied to other areas where different farm holding sizes and multiple crops are grown.

Application of BLDC motor is gradually growing day by day in every sector. But the primary problem with BLDC motors is ripples in the torque. The main reason for torque ripples in the BLDC motor is the presence of self-inductance per phase and different rate of rising and falling current in phases at the time of commutation. This paper represents a method for minimizing torque ripples by using a closed-loop DC-DC isolated buck–boost converter (flyback converter) before inverter. PI controller is employed in the feedback to make closed loop. Using flyback converter, there is isolation between the input and output sides so that inverter switches are protected in case of any fault condition. By using PI as a feedback, steady-state errors reduced and increase stability. Analysis using MATLAB or Simulink-centered simulations verifies the characteristics and reliability of the BLDC motor drive at constant speed with closed-loop flyback converter.

In a real-life scenario, extracting emotion from unstructured text is an active and challenging area of research. It has diverse applications in various aspects of our daily life To overcome various challenges involved in detecting emotion from text, researchers from diverse fields applied various machine learning algorithms. However, deep learning methods such as long short-term memory is effective to detect emotion by maintaining the sequence structure of the text. In this work, we use Bi-directional long short-term memory with attention layer for emotion detection for better accuracy for prediction. In addition, we employ a text preprocessing method to improve further results. We perform the experiments on three data sets and the models are evaluated based on the classification accuracy.

A data warehouse system uses materialized views extensively in order to speedily tackle analytical queries. Considering that all possible views cannot be materialized due to maintenance cost and storage constraints, the selection of an appropriate set of views to materialize that achieve an optimal trade-off among query response time, maintenance cost, and the storage constraint becomes an essential necessity. The selection of such an appropriate set of views for materialization is referred to as the materialized views selection problem, which is an NP-Complete problem. In the last two decades, several new selection approaches, based on heuristics, have been proposed. Most of these have used a single objective or weighted sum approach to address the various constraints. In this article, an attempt has been made to address the bi-objective materialized view selection problem, where the objective is to minimize the view evaluation cost of materialized views and the view evaluation cost of the non-materialized views, using the Improved Strength Pareto Evolutionary Algorithm. The experimental results show that the proposed multi-objective view selection algorithm is able to select the Top-K views that achieves a reasonable trade-off between the two objectives. Materializing these selected views would reduce the query response times for analytical queries and thereby facilitates the decision-making process.

In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.

In this paper, we propose a secret group-key generation scheme in physical layer, where an arbitrary number of multi-antenna LNs (LN) exist in mesh topology with a multi-antenna passive eavesdropper. In the first phase of the scheme, pilot signals are transmitted from selected antennas of all nodes and each node estimates channels linked to it. In the second phase, each node sequentially broadcasts a weighted combination of the estimated channel information using selected coefficients. The other LNs can obtain the channel information used for group-key generation while the eavesdropper cannot. Each node then can generate a group key by quantizing and encoding the estimated channels into keys. We apply well-known quantization schemes, such as scalar and vector quantizations, and compare their performance. To further enhance the key-generation performance, we also provide how to determine the antennas at each node used for group-key generation and the coefficients used in the broadcast phase. The simulation results verify the performance of the proposed secret group-key generation scheme using various key-related metrics. We also verify the practical robustness of our scheme by implementing a testbed using universal software radio peripheral. After generating secret common key among three nodes, we also test it using the National Institute of Standards and Technology test suit. The generated key passes the test and it is random enough for communication secrecy.

This paper envisions cyber-farm systems along the lines of cyber-physical systems. It is imperative for corporates and nations to maintain health of the crops to ensure food security. In order to avoid any adversarial attack on agriculture sensors in farms, we propose a collaborative sensing based authentication protocol. It assures that the spoofing and tampering can be detected with high probability. The data collected from the experimental deployment of sensor nodes supports the solution proposed by the paper.

Second factor (2FA) or passwordless authentication based on notifications pushed to a user's personal device (e.g., a phone) that the user can simply approve (or deny) has become widely popular due to its convenience. In this paper, we show that the effortlessness of this approach gives rise to a fundamental design vulnerability. The vulnerability stems from the fact that the notification, as shown to the user, is not uniquely bound to the user's login session running through the browser, and thus if two notifications are sent around the same time (one for the user's session and one for an attacker's session), the user may not be able to distinguish between the two, likely ending up accepting the notification of the attacker's session. Exploiting this vulnerability, we present HIENA, a simple yet devastating attack against such "one-push" 2FA or passwordless schemes, which can allow a malicious actor to login soon after the victim user attempts to login triggering multiple near-concurrent notifications that seem indistinguishable to the user. To further deceive the user into accepting the attacker-triggered notification, HIENA can optionally spoof/mimic the victim's client machine information (e.g., the city from which the victim logs in, by being in the same city) and even issue other third-party notifications (e.g., email or social media) for obfuscation purposes. In case of 2FA schemes, we assume that the attacker knows the victim's password (e.g., obtained via breached password databases), a standard methodology to evaluate the security of any 2FA scheme. To evaluate the effectiveness of HIENA, we carefully designed and ran a human factors lab study where we tested benign and adversarial settings mimicking the user interface designs of well-known one-push 2FA and passwordless schemes. Our results show that users are prone to accepting attacker's notification in HIENA with high rates, about 83% overall and about 99% upon using spoofed information, which is almost similar to the rates of acceptance of benign login sessions. Even for the non-spoofed sessions (our primary attack), the attack success rates are about 68%, which go up to about 90-97% if the attack attempt is repeated 2-3 times. While we did not see a statistically significant effect of using third-party notifications on attack success rate, in real-life, the use of such obfuscation can be quite effective as users may only see one single 2FA notification (corresponding to attacker's session) on top of the notifications list which is most likely to be accepted. We have verified that many widely deployed one-push 2FA schemes (e.g., Duo Push, Authy OneTouch, LastPass, Facebook's and OpenOTP) seem directly vulnerable to our attack.

The proposed method of stock rate prediction is based on the general idea, “current news has much influence on stock price movements”. News of the selected stocks has been taken from two important sources - moneycontrol.com and daily newspaper, Economics Times. A fuzzy mathematical value between 1 to -1 has been calculated and used as input to the proposed system. Stocks & Other input values are normalized stock price and volume data for long-term, medium-term and short-term prospective. Data of Top 50 companies from National Stock Exchange (NSE) has been taken for the duration of 5 years. Converging Deep Learning Neural Network (CDL-NN) Architecture has been developed and trained with Error Back Propagation algorithm has been used. The results are compared with a two hidden layer neural network results on the same data. The proposed model produced 2 to 3 percent better results for NSE top 50 stocks.

Chaotic systems with cyclic symmetry are very rare and have been less discussed in the literature. Similarly, megastable oscillators, which can have a finite or infinite number of coexisting attractors, have also attracted researchers. We propose a class of cyclic symmetry oscillators with the megastable property with infinite coexisting attractors for the first time in the literature. Various dynamical properties of the proposed oscillators are discussed in detail. An application for the detection of a feeble signal by using the proposed circulant megastable oscillator is presented. Since chaotic oscillators are highly sensitive to a tiny change in the parameters or an external input to the oscillator, this property of the proposed oscillator is used for the detection of a feeble signal. Simulated results validate the effectiveness of the proposed application. After that, a new chaotic Sine-Cosine Algorithm (SCA) is developed using the randomness of megastable oscillators. Subsequently, this new chaotic sine-cosine algorithm is used to determine the PID controller parameters of time-delay systems concerning the objective function. As a result, the proposed chaotic sine-cosine algorithm presents better performance for time-delay systems when compared with the available algorithms in the literature. (c) 2021 Elsevier Ltd. All rights reserved. WOS:000670086700001 2-s2.0-85106914266

Route failures pursued by rerouting is common in WSNs due to the unpredictable network dynamics and autonomous node behaviors. Optimal selection of neighbor is henceforth necessary to ensure profitable packet delivery and application support. In this manuscript, we propose a Weighted Path Rediscovery (WPR) routing for improving the routing decisions of WSN nodes. This routing algorithm is intended to prevent backtracking and curtail looped routing in WSNs. WPR precis’s conventional routing based on link expiration time (let) and fun factor at the time of neighbor discovery and reinforced routing, respectively. The two distinct features are converged using link stability factor to ensure the improved routing satisfying node’s behavior constraints. This improves the quality of packet transmission and minimizes routing complexity. The proposed routing algorithm is assessed using the factors: Packet Delivery Ratio (PDR), End-to-End Delay, Overhead and Connection Ratio.

Ad hoc network extension from MANET (Mobile Adhoc Network) and VANET (Vehicular Adhoc Network) to FANETs (Flying Adhoc Network) has eased coverage, expansion, and real-time features in the communication system. FANETs use ranges from the army and non-combat situations to the agriculture field as well. The deployment of traditional omnidirectional antenna lacks in addressing spatial reuse. Alternatively, a directional antenna’s implementation can remove this problem by increasing network performance on those above and other specific parameters. The empanelment of directional antennas while communication could anonymously extend the communication radius within UAVs and ground connection. The beginning of the article summarizes plan factors also communication necessities for FANETs described, examining the components concerning FANETs, including challenges induced over a directional antenna. Predicaments that demand further assessment are summed, expecting to illuminate the interested researchers to work in this area.

Disease prediction is an active area of research, which supports to make the best possible medical care decisions. Moreover, it helps to reduce the overhead work of a doctor and provides proper facility in the vicinity. In this work, we predict the diseases from a set of symptoms extracted using natural language processing and deep learning method. The proposed model takes input from the user as speech and output the most probable disease corresponding to the symptoms. In most of the existing works, structured data are used for disease prediction. However, in this work, we propose the method which takes unstructured data as input in the form of speech in layman terms. Here, unstructured data is converted into a suitable structured form to give input to deep learning model. In this work, neural network with an embedding layer, a long short-term memory (LSTM) layer, and a dense layer are employed. Here, the context dependencies are stored using LSTM layer, and the output is processed using a dense layer as the fully connected layer. The proposed method outperforms over competitive algorithms for the chosen dataset.

India is among the top producers of rice in the world. It is the staple food for the eastern and southern people of India. Therefore, the need to measure the quality of rice has become a necessity, but there are still a limited number of viable and safe options that can be used for the grading of the rice grains. This paper solves the prior problem of quality assessment using the reliable method of image processing. This technique allows us to get an idea of the dimensions of the rice grains and accordingly grade them. Previously, there have been numerous researches in the field of rice grain quality assessment taking chalky, the opaque white part of the grain into consideration. It is one of the most important characteristics while analyzing and grading the rice grain. This paper calculates the dimensions, classifies the grains into different quality grades and checks for chalkiness.

Text classification is the task of forming semantic groups of text documents by assigning predefined class labels. It has wide range of real-life applications in various domains such as engineering, medical science, life science, social sciences and humanities, marketing, governance. Currently, machine learning and deep learning algorithms became popular and effective methods to address text classification problems with labelled data. In this work, we analyse the performance of different machine learning and deep learning algorithms for text classification. For this purpose, we selected six machine learning algorithms using three different vectorization techniques and five deep learning algorithms for the performance evaluation which is evaluated based on classification accuracy. All experiments are conducted on the 20 newsgroups dataset. Results indicate that Logistic Regression outperforms over other ML algorithms and a Bi-channel Convolution Neural Network model gains exciting results compared to other deep learning models.

Power quality is now a major issue in homes, industry and agriculture as an inductive form such as single-phase and three-phase induction engines, pump station, computer fan engine, etc. The load is needed in this reactive current and the source provides the lagging current requested. Thus, that reactive power requirement increases and that active power also depends on it. So that, the power factor also decreases which causes the distribution system to increase the burden. Therefore, This paper improved power quality performance using Distribution Static Compensator (DSTATCOM) using the MATLAB Simulation tool which improves quality of power issues in the distribution network system. DSTATCOM is Facts device which is used in a distribution network system for quality of power conditioning. DSTATCOM is source and sink of reactive power demanded to a different type of non-linear load also Power quality is enhanced, Total Harmonic Distortion of source current (THD) of source current is reduced. DSTATCOM mitigates voltage flicker, swell, sag and spikes, which provides reactive power current to the distribution system. That reactive power control using the current injection from source to DSTATCOM. Working of DSTATCOM by taking the theory of IRP for nonlinear and unbalanced is evaluated by using the result of simulation done in the MATLAB.

This paper finds that actions of the teeth, namely tapping and sliding, produce vibrations in the jaw and skull. These vibrations are strong enough to propagate to the edge of the face and produce vibratory signals at an earphone. By re-tasking the earphone speaker as an input transducer - a software modification in the sound card - we are able to sense teeth-related gestures across various models of ear/headphones. In fact, by analyzing the signals at the two earphones, we show the feasibility of also localizing teeth gestures, resulting in a human-to-machine interface. Challenges range from coping with weak signals, distortions due to different teeth compositions, lack of timing resolution, spectral dispersion, etc. We address these problems with a sequence of sensing techniques, resulting in the ability to detect 6 distinct gestures in real-time. Results from 18 volunteers exhibit robustness, even though our system - EarSense - does not depend on per-user training. Importantly, EarSense also remains robust in the presence of concurrent user activities, like walking, nodding, cooking and cycling. Our ongoing work is focused on detecting teeth gestures even while music is being played in the earphone; once that problem is solved, we believe EarSense could be even more compelling.

This paper reports a modified 2D periodically forced oscillator. Numerical simulation results using the phase portrait and Lyapunov exponents’ spectrum say that the proposed oscillator depicts megastability. Offset boosting in the new system is shown using the variable boostable phenomenon. Very limited research is available on such type of oscillator. Further, in this paper, a controller based on contraction theory is designed for the synchronisation between the two identical modified chaotic oscillators. The performances of the designed controller are compared with two widely used and well-known controllers for the chaotic systems. These controllers are (i) nonlinear active controller (NAC) and (ii) backstepping controller (BSC). It is found that the contraction theory-based controller performs better in terms of the less synchronisation time, negligible steady-state error and low control energy. Further, synchronisation between two identical Rossler chaotic systems is also presented to validate the effectiveness of the contraction theory. The simulation results validate the objectives of this paper.

Community question answering (CQA) services have become a prominent place for seeking answers from experts and sharing knowledge in any domain. Retrieving semantically related historical questions for a new query is a critical task in CQA to eliminate duplicate questions and to avoid indefinite waiting time to get responses. One major challenge in question retrieval is the lexical gap between the new question and the question in the archive that is already answered. This problem can be eliminated to a great extent by giving proper embedding for questions. Traditional bag-of-words (BOW) representation for text is being replaced by semantic embedding models like word2vec, Glove, and FastText and the newly released Bidirectional Encoder Representations from Transformers (BERT) model. In this paper, we propose a modified BERT embedding using the topic information obtained by LDA on top of questions preprocessed using the RAKE keyword extraction algorithm. We perform question retrieval based on cosine similarity of question vectors and evaluated the accuracy on the Quora question pair dataset. Results show that the proposed question embedding improves the performance of question retrieval compared with competing models.

In an Industrial Control System (ICS), its complex network of sensors, actuators and controllers have raised security concerns. In this paper, we proposed a technique called Process Skew that uses the small deviations in the ICS process (herein called as a process fingerprint) for anomaly detection. The process fingerprint appears as noise in sensor measurements due to the process fluctuations. Such a fingerprint is unique to a process due to the intrinsic operational constraints of the physical process. We validated the proposed scheme using the data from a real-world water treatment testbed. Our results show that we can effectively identify a process based on its fingerprint, and detect process anomaly with a very low false-positive rate.

This work is motivated by the fact that the secret key generation and refreshment of the key at the physical layer, based on randomness from reciprocity in the wireless channel, is challenged by little variations in the channel, particularly in an indoor environment. We propose a new technique, AnaMPhy, which uses multi-fold anonymity to refresh key at a high rate at two participating transceivers. The key generation and agreement in AnaMPhy is functional in challenging environments with very low or no variations in wireless channels. The idea is to hide the identity of the transmitter and the receiver, using medium access control (MAC) and physical layer strategies (randomization in oscillator drift), and use pulse amplitude modulation (PAM) symbols to confuse and adversary. The secret key is the function of the message's source, chosen symbols and the channel's state. In doing so, confusion at the adversary increases manifold and Alice and Bob are able to refresh their secrets whenever needed. The proposed method is also implemented on software-defined radio (SDR). We argue that key refreshment using low complexity secret refreshment of AnaMPhy would be critical for decentralized systems in IoT and cyber physical systems (CPS) networks.

Mobile Ad hoc Network (MANET) is one of the emerging technologies to communicate between nodes and its decentralized structure, self-configuring nature are the few properties of this Ad hoc network. Due to its undefined structure, it has found its usage in the desired and temporary communication network. MANET has many routing protocols governing it and due to its changing topology, there can be many issues arise in recent times. Problems like no central node, limited energy, and the quality of service, performance, design issues, and security challenges have been bugging the researchers. The black hole attacks are the kind that cause ad hoc network to be at loss of information and make the source to believe that it has the actual least distance path to the destination, but in real scenario the packets do not get forwarded to neighbouring nodes. In this paper, we have discussed different solutions over the past years to deal with such attacks. A summary of the schemes with their results and drawbacks in terms of performance metrics is also given.

With the advancement in technology, the network has also adapted itself to be in recent trends. Mobile Ad hoc Network or MANET is the technology which is a collection of mobile nodes (MN). These nodes are connected with each other for communication without any central administering node. MANET has many applications in different areas of technology like communication, in battlefields, etc. with this the challenges of MANET like load balancing, energy efficiency, packet loss, connection failure. Typical features of MANET routing is so complex that routing is more important for defining the performance of the network and its connectivity capacity. Mobile nodes are battery dependent and not easily rechargeable which makes the energy consumption as an important issue in MANET. This paper presents various techniques to solve the problem of energy efficiency and load balancing in MANET. A comparative analysis is also provided for summarizing the survey.

The bond graph (BG) approach of modelling provides a unified approach for modelling the systems having components belonging to multi-energy domains. Moreover, as evident by its name, it is a graphical approach. The graphical nature provides a tool for conceptual visualization of the model. It also provides some algorithmic tools because of its formal structure and syntax, thereby enabling model consistency checks such as checking algebraic loops, etc. There are a large number of texts published in recent years that may be refereed for background material on the BG methodology. Though used in many applications, its use in modelling stress distribution in the system is limited. Finite element (FE) modelling has found wide applicability for the same. This chapter is aimed at providing background knowledge, a comparison of BG approach with the FE approach, and a review of research progress of past two decades in this direction.

In the literature of dynamical systems, many papers are seen on the synchronisation between chaotic/hyperchaotic systems. In these reported works, the synchronisation is done when the external disturbances are added to state of the slave system where controller is added, i.e. using matched disturbances. Occasional work is reported on the synchronisation between uncertain hyperchaotic systems in the presence of unmatched. In this paper, the synchronisation concept between two unidentically operated initial conditions highly complex hyperchaotic systems is shown by treating them as the master/transmitter and the slave/receiver systems. The synchronisation is achieved in the presence of added disturbance in the slave system, where controller is not inserted and also considering unidentified gains of the switching mode control law of the SMC. A nonlinear disturbance estimator-based adaptive sliding mode control (SMC) is proposed to realise the synchronisation. The MATLAB simulation results confirm that the synchronisation strategy is worked successfully, and the proposed works are validated.

In Asian countries, paddy is one of the major staple foods, and the agricultural sector is the largest employer in India’s economy. Hence, an effective mechanism should be adopted for food security and its production. Due to infections caused by pests like virus, fungus and bacteria, there is a huge loss in quality and quantity of the rice. This results in a huge loss to the farmers. This survey presents the different image processing techniques for paddy disease identification and further classification. The challenges involved in each step of diseases detection and classification are analyzed and discussed. Image preprocessing, segmentation, feature extraction and classification are the main stages involved in paddy disease identification and classification. All these four stages are well addressed here. This survey gives a complete overview along with the challenges of each stage and also focuses the research challenge and research gap. This survey concludes that automatic paddy leaf disease detection, and classification requires much more advancement. There is a need for more advanced techniques to automate the system effectively and efficiently.

Bioprinting deals with layer-by-layer generation of organs or parts of organs by additive manufacturing, and the materials used for the purpose contain living cells. Different bioprinting processes offer different capabilities in terms of cell viability, the structural integrity of the printed parts, physical properties of the materials that can be handled, etc. So, for a given type of bioprinting material, selection of the most suitable bioprinting process to optimize the biological as well as the mechanical properties requires trade-offs. In this paper, a fuzzy-TOPSIS MCDM process has been applied for this purpose. Characteristics of various bioprinting processes have been summarized and sorted before fuzzification, and for any set of required characteristics, the MCDM tool can identify the ideal solution, i.e., the solution having the least Euclidean distance from the positive ideal solution and the largest Euclidean distance from the negative ideal solution using TOPSIS method.

Underactuation and nonminimum phase properties of a robotic two-link flexible manipulator (TLFM) have attracted many researchers to use various types of control techniques for its desired outcome. The most focused problem of a two-link flexible manipulator is the trajectory tracking and vibration control. It is seen, in the literature, that some research works have tackled the tracking and vibration control of TLFM in the presence of disturbances added in the position where torque is added. However, in this paper, using a disturbance observer and sliding mode control technique is designed to accomplish the link vibration and joint angle trajectory tracking control of a TLFM in the presence of unmatched disturbances. The unmatched disturbance is estimated with the help of a disturbance observer. The simulation results obtained using MATLAB confirm that the joint angle trajectory tracking and link vibration control of the considered TLFM are achieved successfully and the proposed technique works effectively.

This paper presents the dynamic response of the Indian Railway track. Two track models are considered for the dynamic response in terms of vertical displacement and acceleration at different wheel speeds, keeping the moving point load at constant magnitude. The rail is treated as a beam either on viscoelastic foundation or on the discrete elastic support system. The governing equation is implemented in finite element analysis using ANSYS 14.0. For the validation of result from system equation are compared with those available in published literature and the maximum deviation for displacement at the midpoint of rail is found to be within 5 %. Different wheel speed generates variation in displacement and acceleration of the rail track. The study can be viewed as the foundation for the comparison of FEA based simulation of rail track to specify its dynamic response useful to provide better safety and comfort to commuters.

Clustering is an unsupervised classification task in data mining, which partitions data set into certain number of clusters. Though many metaheuristics have been suggested to optimize clustering results, most of them are marred by the three key issues. First, clustering performance is devised on a single validity measure; hence, it produces a single best solution biased toward the criterion; it is unacceptable as clustering has multiple conflicting criteria. Second, they find difficulty in avoiding local optima owing to lack of balancing in exploration and exploitation in the search space. Third, they require number of clusters a priori which is difficult to decide in the real-world problems. To deal with the first issue, we follow Pareto-based approach to obtain diverse trade-off solutions by optimizing conceptually contradicting validity measures sum of squared error and connectedness. The well-known swarm intelligence-based metaheuristic particle swarm optimization (PSO) has a severe drawback that it converges prematurely owing to single directional information sharing mechanism among particles in the swarm. Therefore, for the second issue, we introduce fitness predator optimizer (FPO) to enhance diversity in the PSO in multi-objective optimization scenario; it is named as FPO-MOPSO. To address the third issue, we use dynamic clustering method to decide an optimum number of clusters (k) in the range of 2 to $$\sqrt{n}$$ , where n is number of objects in the data set. We use eight real-world and two artificial data sets for comparison of the FPO-MOPSO results with three well-known competitive methods MOPSO, MABC, and NSGA-II using four quality measures convergence, diversity, coverage, and ONVG. The obtained results show superiority of the FPO-MOPSO over its competitors. Two external validity indices of clustering results F-measure and Rand index also establish the above finding. As the decision maker picks only a single solution from the set of trade-off solutions, we employ an additional measure silhouette index to select a final single solution from the archive and compare it with known number of clusters in the data sets.

In this paper, a synchronisation strategy between a controlled master and three-slave two-link flexible manipulators is proposed. Two out of the three slaves are identical with the master, whereas the third one is non-identical. The master and the slave manipulators are modelled by assumed modes and lumped parameter methods, respectively. The 12 states of the master manipulator are synchronised to the 8 states of each slave manipulator. Such projective synchronisation is also not available in the literature. A global sliding mode controller is designed first for the master manipulator to track the desired trajectory. Next, the synchronisation between the master and the slaves is achieved by designing an adaptive time-varying super-twisting global sliding mode controller. The simulation results reveal that the performances of the proposed controller in terms of (i) steady-state error of synchronisation, (ii) synchronisation time and (iii) links deflection are much better than the existing controller proposed in 2016.

This paper attempts to construct a new 3-D chaotic system which is easily hardware realisable and fulfil the requirement of a real-life application. The proposed system is relatively more chaotic (based on the first Lyapunov exponent) and has larger bandwidth than 50 available chaotic systems. Lyapunov spectrum and bifurcation diagram of the system reveal that it has chaotic behaviour for a wider range of its parameters. Such characteristic is helpful for an easy hardware realisation of the system. It is to be noted that the reported systems with hidden attractors are not considered here for the comparison. The proposed system has more complexity and disorder due to several unique properties like asymmetry to principle coordinates, dissimilar and asymmetrical equilibria, and non-uniform contraction and expansion of volume in phase space. The proposed system also exhibits asymmetric pairs of coexisting attractors during its operation in two modes. The new system has different routes to chaos including crisis, an inverse crisis, period-doubling and reverse period-doubling routes to chaos with the variation of parameters. MATLAB simulation results confirm the claims, and the results of hardware circuit realisation validate the simulation results. An application of the new system is shown by masking and retrieving an information signal. It is also shown that the proposed system is better than a well-known Lorenz chaotic system for this application. A system with the above unique properties is rare in the literature.

Objectives of the paper are (1) to design two new real and complex no equilibrium point hyperchaotic systems, (2) to design synchronisation technique for the new systems using the contraction theory and (3) to validate the results by using circuit realisation. First a new no equilibrium point hyperchaotic system is developed using a 3-D generalised Lorenz system; then using the new system a new complex no equilibrium point hyperchaotic system is reported. Both the new systems have hidden chaotic attractors. Various dynamical behaviours are observed in the new systems like chaotic, periodic, quasi-periodic and hyperchaotic. Both the systems have inverse crisis route to chaos with the variation of parameter a and crisis route to chaos with the variation of parameters $$b,\ c$$ and d. These phenomena along with hidden attractors in a complex hyperchaotic system are not seen in the literature. Synchronisation between the identical new hyperchaotic systems is achieved using the contraction theory. Further the synchronisation between the identical new complex hyperchaotic systems is achieved using adaptive contraction theory. The proposed synchronisation strategies are validated using the MATLAB simulation and circuit implementation results. Further, an application of the proposed system is shown by transmitting and receiving an audio signal.

In recent years, chaotic systems with no equilibrium point are the hottest topic for research as these systems can have neither homoclinic nor heteroclinic orbits and show hidden attractors. This paper presents a novel fractional-order memristor-based chaotic jerk system with no equilibrium point. The proposed system is new in the sense that it is a fractional-order memristor-based chaotic jerk system and has no equilibrium point and also has very few terms even after the incorporation of memristor in the system. The rich chaotic behaviour of the proposed system is demonstrated by various numerical tools which include bifurcation diagram, attractors plot, and instantaneous phase plot. A fractional-order backstepping controller is designed to stabilise the chaos in the system.

The objective of this Letter is to report the development of the simplest hyperchaotic system in comparison with all existing simplest hyperchaotic systems. The proposed system does not contain any non-linear (product, quadratic or cubic) term and has a total of eight terms including the only one piecewise linear term. The circuit of the proposed simplest hyperchaotic system is implemented in hardware with fewer components and without using a multiplier. To the best of the authors’ knowledge, such simplest hyperchaotic system is not reported in the literature.

Clustering is an unsupervised classification method used to group the objects of an unlabeled data set. The high dimensional data sets generally comprise of irrelevant and redundant features also along with the relevant features which deteriorate the clustering result. Therefore, feature selection is necessary to select a subset of relevant features as it improves discrimination ability of the original set of features which helps in improving the clustering result. Though many metaheuristics have been suggested to select subset of the relevant features in wrapper framework based on some criteria, most of them are marred by the three key issues. First, they require objects class information a priori which is unknown in unsupervised feature selection. Second, feature subset selection is devised on a single validity measure; hence, it produces a single best solution biased toward the cardinality of the feature subset. Third, they find difficulty in avoiding local optima owing to lack of balancing in exploration and exploitation in the feature search space. To deal with the first issue, we use unsupervised feature selection method where no class information is required. To address the second issue, we follow pareto-based approach to obtain diverse trade-off solutions by optimizing conceptually contradicting validity measures silhouette index (Sil) and feature cardinality (d). For the third issue, we introduce genetic crossover operator to improve diversity in a recent Newtonian law of gravity-based metaheuristic binary gravitational search algorithm (BGSA) in multi-objective optimization scenario; it is named as improved multi-objective BGSA for feature selection (IMBGSAFS). We use ten real-world data sets for comparison of the IMBGSAFS results with three multi-objective methods MBGSA, MOPSO, and NSGA-II in wrapper framework and the Pearson’s linear correlation coefficient (FM-CC) as a multi-objective filter method. We employ four multi-objective quality measures convergence, diversity, coverage and ONVG. The obtained results show superiority of the IMBGSAFS over its competitors. An external clustering validity index F-measure also establish the above finding. As the decision maker picks only a single solution from the set of trade-off solutions, we employee the F-measure to select a final single solution from the external archive. The quality of final solution achieved by IMBGSAFS is superior over competitors in terms of clustering accuracy and/or smaller subset size.

Clustering is an unsupervised classification method in the field of data mining and plays an essential role in applications in diverse fields. The K-means and K-Medoids are popular examples of conventional partitional clustering methods, which have been prominently applied in various applications. However, they possess several disadvantages, e.g., final solution is dependent on the initial solution, they easily struck into a local optimum solution. The nature-inspired swarm intelligence (SI) methods are global search optimization methods, which offer to be effective to overcome deficiencies of the conventional methods as they possess several desired key features like upgrading the candidate solutions iteratively, decentralization, parallel nature, and a self organizing behavior. The Artificial Bee Colony (ABC) algorithm is one of the recent and well-known SI method, which has been shown effective in various real-world problems. However, it exhibits lack of balance in the exploration and exploitation and shows a poor convergence speed when the number of features (dimensions) increases. Therefore, we make two modifications in it to enhance its exploration and exploitation capabilities to improve quality of the clustering. First, we introduce a gbest-guided search procedure for the fast convergence, which works effectively in large number of features also as it considers all the dimensions simultaneously. Second, in order to avoid being trapped in a local optima and to enhance the information exchange (social learning) between bees for improved search, we incorporate a crossover operator of the genetic algorithm (GA) into it. The proposed strategy is named as Hybrid Gbest-guided Artificial Bee Colony (HGABC) algorithm. We compare clustering results of the HGABC with ABC, variants of the ABC and other recent competitive methods in the swarm and evolutionary intelligence domain on ten real and two synthetic data sets using external quality measures F-measure and Rand-index. The obtained results demonstrate superiority of the proposed method over its competitors in terms of efficiency and effectiveness.

The objectives of the paper are (i) to develop a new 4-D conservative chaotic system with hidden chaotic orbits, (ii) to design a second order adaptive time varying sliding mode control for the synchronization between two identical proposed chaotic systems in the presence of matched disturbances and (iii) to compare the performances of the proposed controller with two available controllers which have been published recently. The chaotic nature of the proposed system is validated using theoretical and numerical tools like divergence property, Lyapunov exponents, Lyapunov spectrum, bifurcation diagram, phase portrait, Poincaré map and a frequency spectrum. The new conservative chaotic system exhibits the coexistence of hidden chaotic orbits with no equilibrium point. The new system is synchronized with itself using the proposed second-order adaptive time varying sliding mode control technique in the presence of matched disturbances and by considering different initial conditions. During synchronization, the parameters of both the systems, gains of the first order and second order sliding surfaces and the gains of the switching laws are considered as unknown and estimated adaptively. Only two control inputs are used to synchronize all the four states of the system. The effectiveness of the proposed controller is compared with two available controllers for the synchronization of chaotic systems and it is found that the proposed controller performs much better than the two available controllers.