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Project selection has a critical role in the successful execution of the lean six sigma (LSS) program in any industry. The poor selection of LSS projects leads to limited results and diminishes the credibility of LSS initiatives. For this reason, in this article, we propose a method for the assessment and effective selection of LSS projects. Intuitionistic fuzzy sets based on the weighted average were adopted for aggregating individual suggestions of decision makers. The weights of selection criteria were computed using entropy measures and the available projects are prioritized using the multiattribute decision making approach, i.e., modified TOPSIS and VIKOR. The proposed methodology is validated through a case example of the LSS project selection in a manufacturing organization. The results of the case study reveal that out of eight LSS projects, the assembly section (A8) is the best LSS project. A8 is the ideal LSS project for swift gains and manufacturing sustainability. The robustness and reliability of the obtained results are checked through a sensitivity analysis. The proposed methodology will help manufacturing organizations in the selection of the best opportunities among complex situations, results in sustainable development. The engineering managers and LSS consultants can also adopt the proposed methodology for LSS project selections.

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.

Machines have to operate effectively and free from faults in order to enhance productivity, increase quality, and decrease costs. With automated and flexible action, this is far more likely. An accurate identification of the underlying cause Causes of failure that need repairs and the time for restoration include: A lot of Various monitoring methods are available to track the experimentally-recorded machine health. Maintaining instruments and sensors. The vibration monitoring methods in this list. It is the right fit, and it has shown itself over time. Measuring vibrations in this job three different locations resembling the stock of a gun: headstock, tailstock. When conducting turning operation with mild steel, the tool post of the lathe machine is made of metal. Data that is gathered through sensors is sent straight to the cloud in this case, such as Ubidots IoT platform. via way of nodeMCU. Whether a task is impacted or a tool is impacted, mission failure has occurred. Thus, in either instance, early warning is provided by a signature, thus the development does not progress as quickly machining. If the tail-stock seems to be broken, and this appears to be a consistent problem, then the whole batch must be inspected for errors to be inspected closely is anything that has been created under such circumstances. An optimal decision-making procedure light may be thrown on CM so processes may be machine-wise instituted in a shop there. Tools while talking about lathes that are large, medium, or tiny. Overall it is a method which is easy but yet capable of benefitting people at all levels, from the absolute beginner to experts Floor productivity is aided by training elements that are almost unnecessary.

Increasingly, individuals and companies adopt a cloud service provider as a primary data and IT infrastructure platform. The remote access of the data inevitably brings the issue of trust. Data encryption is necessary to keep sensitive information secure and private on the cloud. Yet adversaries can still learn valuable information regarding encrypted data by observing data access patterns. To solve such problem, Oblivious RAMs (ORAMs) are proposed to completely hide access patterns. However, most ORAM constructions are expensive and not suitable to deploy in a database for supporting query processing over large data. Furthermore, an ORAM processes queries synchronously, hence, does not provide high throughput for concurrent query processing. In this work, we design a practical oblivious query processing framework to enable efficient query processing over a cloud database. In particular, we focus on processing multiple range and kNN queries asynchronously and concurrently with high throughput. The key idea is to integrate indices into ORAM which leverages a suite of optimization techniques (e.g., oblivious batch processing and caching). The effectiveness and efficiency of our oblivious query processing framework is demonstrated through extensive evaluations over large datasets. Our construction shows an order of magnitude speedup in comparison with other baselines.

Cancer is known as one of the leading causes of morbidity and fatality, currently faced by our society. The prevalence of cancer related dieses is rapidly increasing around the world. To reduce the mortality rates, early diagnosis and subsequent treatment of cancer in timely manner is quite essential. Advancements have been made to achieve effective theranostics strategies to tackle cancerous dieses, yet very challenging to overcome this issue. Recently, advances made in the field of nanotechnology have shown tremendous potential for cancer theranostics. Different types of nanomaterials have been successfully employed to develop sophisticated diagnosis and therapy techniques. In this context, graphene and its derivatives e.g. graphene oxide (GO) and reduced graphene oxide (RGO) have been investigated as promising candidates to design graphene-based nanosystems for the diagnosis and therapeutic purpose. Further, to synthesize graphene and its derivatives different types of physicochemical methods are being adopted. However, each method has its own advantage and disadvantages. In this reference, among diverse biological methods, microbial technique can be one of the most promising and eco-friendly approach for the preparation of graphene and its derivatives, particularly GO and RGO. In this review, we summarize studies performed on the preparation of graphene and its derivatives following microbial routes meanwhile focus has been made on the preparation method and the possible mechanism involved therein. Thereafter, we have discussed applications of graphene and its derivatives to developed advanced nanosystem that can be imperative for the cancer theranostics. Results of recent studies exploring applications graphene based nanosystem for the preparation of different types of biosensors for early diagnosis; advanced therapeutic approaches by designing drug delivery nanosystems along with multifunctionality (e.g cancer imaging, drug delivery, photodynamic and photo thermal therapy) in cancer theranostics have been discussed. Particularly, emphasis has been given on the preparation techniques of graphene based nanosystems, being employed in designing of biosensing platforms, drug delivery and multifunctional nanosystems. Moreover, issues have been discussed on the preparation of graphene and its derivatives following microbial technique and the implementation of graphene based nanosystems in cancer theranostics.

Analog and mixed-signal (AMS) blocks are often a crucial and time-consuming part of System-on-Chip (SoC) design, primarily due to manual circuit and layout iterations. Existing automated solutions for selecting circuit parameters for a given target specification are often not efficient, accurate, or reliable. In order for an automated sizing tool to be practical, we posit that it must: 1) return valid results for a large range of target specifications, 2) understand where and why it is unable to meet certain specifications, 3) consider true layout parasitic simulations for complete end-to-end design, and 4) be automated, allowing most of the design effort to fall on the tool. In this paper, we address these critical points by establishing an automated reinforcement learning framework, AutoCkt, by 1) successfully deploying it on a complex two-stage transimpedance amplifier and two-stage folded-cascode with biasing in 16nm FinFet technology, 2) implementing a new combined distribution deployment algorithm to improve efficiency, 3) analyzing in-depth the efficacy of the trained agent and 4) demonstrating the functionality of this tool when considering a topology that is highly sensitive to layout parasitics. Our algorithm not only successfully reaches unique, valid, and practical performances, but also does so in state-of-the-art run time, up to 38X more efficient than prior work. In addition, our tool averages just four parasitic simulations obtained by using the Berkeley Analog Generator, to achieve a target specification post-layout for the folded-cascode. AutoCkt successfully generates LVS-passed designs with validation in process corner variation results.

This paper proposes an optimized Genetic Algorithm (GA) based sustainable and secure green data collection/transmission method for IoT enabled WSN in healthcare by optimizing intra cluster distance, systematic utilization of node’s energy and reducing hop count. For secure transmission of data, the communication data is encrypted us ing stream cipher and a pseudo randomly generated security key. Additionally, the proposed movable sink and data collection/transmission strategies shorten communication distance between sink and cluster head (CH) which diminishes the hotspot problem. The direct data collection helps in transmitting data directly to the sink, when the sinks are nearer to the sensor nodes with re spect to CH. Further, the incorporated dynamic sensing range minimizes overlapping of sensing range with a significant decrement in the transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics such as remaining energy, lifetime, stability period, throughput and number of clusters per rounds.

The paper demonstrates the potential of a hybrid Wind–PV farm as STATCOM (Static Synchronous Compensator) for damping and control of overall chaotic oscillations in a two-area power system. Kundur’s modified system associated with a hybrid Wind–PV farm is simulated in MATLAB to demonstrate the detailed performance assessment of the hybrid farm. A unique controller is deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. A swarm-based hybrid metaheuristic optimizer PSO-BFOA optimally tunes and controls the PI controller parameters. The system is compensated to an optimum level of 85% and exposed to a 3-phase fault. Zero dampings are accompanied by additional disturbances of a 20% change in electric torque and the reference voltage. The disturbances are made to model the worst conditions to examine the rigorous performance of the hybrid Wind–PV​ farm in alleviating the overall chaotic oscillations. The simulation results for the performance assessment in different cases, i.e., without control, with Wind-STATCOM, with PV-STATCOM, and with the hybrid Wind–PV-STATCOM, reveal the potential of the hybrid Wind–PV Farm as STATCOM in alleviating the overall chaotic oscillations.

Indubitably, security is an integral aspect of the development of quality software. More importantly, usability is also an elemental and pivotal factor for developing quality software. In fact, it has been noticed that most of the practitioners are trying to develop a highly secure design while maintaining high usability. Unfortunately, the highly secure design of software becomes worthless because the usability of software is very low. Further, usable security is in more demand due to the increasing usage of computers with enhanced usability and need of security in it too. When improving the usability with security of software, underlying security and usability attributes play an important role. For this reason, usable security assessment employs security and usability attributes to achieve the desired security solutions with usability. Different consecutive versions of two software have been taken in this work to assess usable security. Authors are using Fuzzy-AHP methodology to assess the priorities and overall usable-security. In addition, the impact of the security on usability and impact of the usability on security have been evaluated quantitatively. The results obtained and conclusions are useful for practitioners to improve usable-security of software.

Value stream mapping (VSM) is a vital lean manufacturing tool accustomed to identify and reduce the process lead time and the work in process inventory for improvement of the overall efficiency of an organization. This paper aims to investigate and implement value stream mapping in water distillation plant. The data is collected from the various production processes carried out in plant after analyzing the all collected data. We had prepared the current state map and value-added activities and non-value-added activities had found. After conducting an impressive investigation on non-value-added activities, suggestions are made to minimize the process lead time and work in process inventory. The lean techniques like Kaizen, 5S are used to make necessary changes to improve the overall efficiency and after considering the proposed changes future state value stream map was drawn. After successful implementation of VSM Technique in water distillation plant the improvements are, the processing time is reduced from 171 s to 143 s and lead time is decreased from 8057 s to 6344 s.

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the World Health Organization (WHO) on March 11, 2020. COVID-19 has already affected more than 211 nations. In such a bleak scenario, it becomes imperative to analyze and identify those regions in Saudi Arabia that are at high risk. A preemptive study done in the context of predicting the possible COVID-19 hotspots would facilitate in the implementation of prompt and targeted countermeasures against SARS-CoV-2, thus saving many lives. Working towards this intent, the present study adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, for assessing the risk of COVID-19 in different regions of Saudi Arabia. AHP gives the ability to measure the risks numerically. Moreover, numerical assessments are always effective and easy to understand. Hence, this research endeavour employs Fuzzy based computational method of decision making for its empirical analysis. Findings in the proposed paper suggest that Riyadh and Makkah are the most susceptible regions, implying that if sustained and focused preventive measures are not introduced at the right juncture, the two cities could be the worst afflicted with the infection. The results obtained through Fuzzy based computational method of decision making are highly corroborative and would be very useful for categorizing and assessing the current COVID-19 situation in the Kingdom of Saudi Arabia. More specifically, identifying the cities that are likely to be COVID-19 hotspots would help the country's health and medical fraternity to reinforce intensive containment strategies to counter the ills of the pandemic in such regions. © 2022 CRL Publishing. All rights reserved.

Analogical reasoning, for example, inferring that teacher is to chalk as mechanic is to wrench, plays a fundamental role in human cognition. However, whether brain activity patterns of individual words are encoded in a way that could facilitate analogical reasoning is unclear. Recent advances in computational linguistics have shown that information about analogical problems can be accessed by simple addition and subtraction of word embeddings (e.g., wrench = mechanic + chalk − teacher). Critically, this property emerges in artificial neural networks that were not trained to produce analogies but instead were trained to produce general-purpose semantic representations. Here, we test whether such emergent property can be observed in representations in human brains, as well as in artificial neural networks. fMRI activation patterns were recorded while participants viewed isolated words but did not perform analogical reasoning tasks. Analogy relations were constructed from word pairs that were categorically or thematically related, and we tested whether the predicted fMRI pattern calculated with simple arithmetic was more correlated with the pattern of the target word than other words. We observed that the predicted fMRI patterns contain information about not only the identity of the target word but also its category and theme (e.g., teaching-related). In summary, this study demonstrated that information about analogy questions can be reliably accessed with the addition and subtraction of fMRI patterns, and that, similar to word embeddings, this property holds for task-general patterns elicited when participants were not explicitly told to perform analogical reasoning.

There is a dearth of compressed-sensing localization in the domain of underwater acoustic sensor networks (UASNs). To address this limitation, this paper proposes SAP2: a Sparse-sensing Acoustic Positioning scheme for Stratified and Perturbed UASNs. Three conditions of underwater perturbation are defined, namely, Spin perturbation, Rectilinear dispersion condition, and Radially perturbed condition to model the sudden disturbances in the underwater network topology. Then, “Footprint Variation” is proposed to express the behavior of UASN node topology. Theorem 2.2.b is proposed for the exact retrieval of sparsely deployed UASN location under perturbation conditions. The derivation of a unique solution to the insertion vector in Lemma 3.1.a holds the estimated location of target, necessitating the incoherence of the sensing matrix with the basis vector. Block matrix representation of the coefficients is derived in Lemma 3.1.b. Invertibility of the block matrix is proven in Lemma 3.1.c to establish sparsity of underwater sensor networks. An example is illustrated with a realistic scenario to observe parameters such as correlation of sparse coefficients, distinctness of discretized samples and footprint variation based on perturbation. Footprint variation is found to be below 100m2 for uncorrelated discrete measurements using SAP2 technique. Through rigorous mathematical justification and realistic underwater channel scenario, it is established that compressed sensing based underwater localization is feasible for a sparse sensor network.

Nowadays, the utilization of IoT technology has been rapidly increased in various applications such as smart city, smart banking, smart transport, etc. The internet of things allows the user to collect the data easily using the different sensors installed at various locations in the open environment. The data collection process by the IoT sensors is giving access to the various services. However, due to the open communication medium, it is difficult to provide secure access to these services. In this paper, a data transmission technique has been proposed, which will provide secure communication in IoT infrastructure for smart city applications. In this method, each IoT sensor have to prove their legitimacy to the reader and the base station before the transmission of data. Hence, the IoT sensors can transmit the required data in a secure and efficient way. In the proposed technique, the proof of correction shows that the required information is not supposed to send through an online medium, it is obtained at the receiver using the Euclidean parameters shared by the IoT sensors. The proposed technique is compatible to provide the security against most of the attacks performed by the attackers. Two random variables and complex mathematical calculation are making the proposed technique more reliable than others. This technique will significantly improve the security of different data transmission services which will be helpful to improve the smart city infrastructure.

Being as an integral part of data exchange in disaster management, Internet of Thing (IoT) is an essential component to update the disaster management information to all the connected nodes. In such scenarios, the nodes are deployed with a limited battery, batteries of these nodes are non-rechargeable and such networks consume huge energy in data exchange. Therefore, the practical implementation of such type of networks is a difficult task as routing avoids the paths and consume a tremendous amount of energy during collection/transmission of data. This paper proposes an optimized Genetic Algorithm (GA) based green data collection/transmission method for IoT based WSN in disaster management by satisfying multiple constraints i.e., optimizing intra-cluster distance, systematic utilization of node’s energy in the cluster and reducing hop count. The proposed direct data collection/transmission and movable sink strategies shorten the communication distance between the sink and cluster head (CH) which diminishes the hotspot problem. The direct data collection helps in transmitting data directly to the sink when the sinks are nearer to the sensor nodes with respect to CH. Further, the incorporated dynamic sensing range minimizes overlapping of sensing range of CH along with a significant decrement in the transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics namely network’s remaining energy, lifetime, stability period, and throughput per rounds.

PurposeProductivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.Design/methodology/approachAn integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.FindingsAnalysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.Originality/valueProposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

The paper demonstrates a comprehensive performance assessment of the two metaheuristic swarm-based optimization algorithms namely PSO (Particle swarm optimization), BFOA (Bacterial foraging optimization algorithm), and the hybrid PSO-BFOA optimizer for the alleviation and control of the power oscillations in a two-area four generator system integrated with a large-scale PV-farm. After sunset, the PV-plant operates as VSC (Voltage Source Converter)-STATCOM (Static synchronous compensator) using its overall inverting capabilities for the power system stability improvement. While in the daytime during the faults, the PV-farm immediately stops the active power production and behaves as PV-STATCOM until the normal operating conditions are resumed. The modified version of Kundur’s two-area system comprising of a large-scale PV-farm is simulated with MATLAB software. An innovative control strategy employing the two PI controllers distinctly controls the DC-AC currents of the PV-STATCOM. The series compensation is set to an optimal value of 85% and subjected to a 3-φ fault. Zero mechanical dampings, along with extra disturbances of 20% variation in reference voltage and electromagnetic torque are introduced to flaunt the worst damping scenarios. The simulation outcomes and time-domain analysis for various test conditions: without a controller, with PSO-based PV-STATCOM, with BFOA-based PV-STATCOM, and with the Hybrid PSO-BFOA-based PV-STATCOM, reveal that all the system modes are stabilized with PSO application. The stability of modes is progressively improved with BFO control, eventually, the modes are optimally stabilized by deploying the hybrid PSO-BFO algorithm.

Large-scale deployment of Electric Vehicles (EVs) is deleterious for distribution system operations. Unplanned installation of EV Charging Station (EVCS) deteriorates the voltage stability, reliability, and power quality of the distribution system leading to customer discontentment. This paper proposes a novel approach for the proper placement of EVCS in a distribution network maintaining the system performance. The design of a test station with Vehicle-to-Grid (V2G) capability is outlined in this work. The test station is developed with five charging columns taking into consideration the standards and guidelines outlined in the Government of India EV policy. The charging station is connected to the power grid by a DC bus to which numerous EVs are attached. EVCS placement in the IEEE-33 bus radial distribution system is performed by developing six test scenarios. The test case results substantiate the potency of the reliability-index based approach for allocating EVCS both slow and fast chargers in a distribution network. Further, the maximal threshold value for placing the EVCS in various buses in a distribution system is determined using a novel Electric Vehicle Placement Index (EVPI). The novelty of the approach lies in the fact that it can include all operational parameters influenced owing to EV deployment thereby maintaining the overall system performance without any physical restructuring of the network. The efficacy of the approach and the severity level of EVCS placement is validated for a real-time distribution system in Kerala, India.

Electrification of power take-off (ePTO) system is an effective way to minimize the fossil fuel consumption from heavy duty electric vehicles, where a high gain dc/dc converter is needed to integrate PV/fuel-cell. Popular high-gain dc/dc converters such as PWM type, clamped and unclamped quasi-resonant converters cant serve such applications as they fail to achieve high-efficiency, wide load and input voltage range for soft-switching, constant switching frequency and high transformer utilization (TUF) simultaneously. In this paper, a LCL series resonance based high-gain load resonant dc/dc converter is proposed to eliminate the above-mentioned drawbacks. A series resonant tank in the converter is able to adapt tank current in proportion to load demand leading to minimum losses and high TUF. Further, energy is stored in the series and parallel resonant inductors, in proportion to load demand and constant load voltage respectively, ensures soft-switching of the converter through full load to light load conditions. On the other hand, proposed converter operates with constant switching frequency to adopt simple PWM modulation to regulate load voltage against input and load disturbances. This paper presents operation, analysis, modeling and design of the proposed converter. Hardware setup has been mounted and tested to verify he proposed theory.

The Human Reference Atlas (HRA) aims to map all of the cells of the human body to advance biomedical research and clinical practice. This Perspective presents collaborative work by members of 16 international consortia on two essential and interlinked parts of the HRA: (1) three-dimensional representations of anatomy that are linked to (2) tables that name and interlink major anatomical structures, cell types, plus biomarkers (ASCT+B). We discuss four examples that demonstrate the practical utility of the HRA.

Longtime readers of this column will recall that the proliferation of space debris in near-Earth orbits is an issue that I have previously expressed concern about. Last May, the inevitable happened when a piece of space debris too small to be tracked has hit and damaged part of the International Space Station, namely, the Canadarm2 robotic arm [1] . The object punctured the thermal blanket and damaged the boom underneath, but fortunately, the instrument is still operational [1] .

In recent years video-based crowd counting and density estimation (CCDE) have become essential for crowd analysis. Current approaches rarely exploit spatial–temporal features for CCDE, and they also usually do not consider measures to minimize the frame's background influence for obtaining crowd density maps, which has resulted in lower performance in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Again, attention to individual feature set's response toward crowd counting is also neglected. To this end, we are motivated to design an end-to-end trainable attentive multi-stream convolutional neural network (AMS-CNN) for crowd counting. At first, a multi-stream CNN (MS-CNN) is designed to obtain crowd density maps. The MS-CNN comprises three streams to fuse deep spatial, temporal, and spatial foreground features from different cues of the crowd video dataset, like frames, the volume of frames, and foregrounds of frames. To improve the accuracy, we designed three stream-wise attention modules to generate attentive crowd density maps, and their relative average is obtained using a relative averaged attentive density-map (RAAD) layer. The relative averaged density map is concatenated with the MS-CNN output, followed by two-stage CNN blocks to get the final density map. The experiments are demonstrated on three publicly available crowd density video datasets: Mall, UCSD, and Venice. We obtained promising and better results in terms of MAE and RMSE as compared with state-of-the-art approaches.

The Lempel-Ziv 78 ( LZ78 ) and Lempel-Ziv-Welch ( LZW ) text factorizations are popular, not only for bare compression but also for building compressed data structures on top of them. Their regular factor structure makes them computable within space bounded by the compressed output size. In this article, we carry out the first thorough study of low-memory LZ78 and LZW text factorization algorithms, introducing more efficient alternatives to the classical methods, as well as new techniques that can run within less memory space than the necessary to hold the compressed file. Our results build on hash-based representations of tries that may have independent interest.

Real time Underwater sensor networks (UWSNs) suffer from localisation issues due to a dearth of incorporation of different geometric scenarios in UWSN scenarios. To address these issues, this paper visualises three specific scenarios of perturbation. First, small sized and large numbered particles of perturbance moving in a tangential motion to the sensor nodes; second, a single numbered and large-sized particle moving in a rectilinear motion by displacing the sensor nodes into sideward and forward direction, and third, a radially outward propagating perturbance to observe the influenced sensor nodes as the perturbance moves outwards. A novel target localisation and tracking is facilitated by including marine vehicle navigation as a source of perturbation. Using semidefinite programming, the proposed perturbation models minimise localisation errors, thereby enhancing physical security of underwater sensor nodes. By leveraging the spin, cleaving motion and radial cast-away behaviour of underwater sensor nodes, the results confirm that the proposed propagation models can be conveniently applied to real time target detection and estimation of underwater target nodes.

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

Internet of Things (IoT) has emerged as a novel paradigm that focuses on connecting a large number of devices with the Internet infrastructure. To address the performance requirements of IoT devices, the Content-Centric Networking (CCN) becomes an encouraging future Internet architecture that emphasizes name-based content access, instead of searching for the host-location in the network. The in-network content caching is an essential characteristic for rapid information dissemination and efficient content delivery in the CCN. To this end, a novel content caching scheme has been proposed for comprehensive utilization of the available caching resources. The proposed scheme partitions the CCN-enabled IoT networks hierarchically to reduce content redundancy and excessive cache replacement operations. For content caching decisions, the proposed caching strategy considers normalized distance-based metrics along with dynamic threshold heuristics to improve content retrieval delay by placing the contents near the IoT devices. Extensive simulation analysis on realistic network configurations demonstrates that the proposed caching scheme outperforms the existing competing content placement strategies on performance parameters such as network cache hit-ratio, hop-count, delay, and average network traffic. Thus, the proposed caching scheme becomes more promising for CCN based IoT applications.

Aim: The accuracy of the non-cooperative iris recognition is highly dependent on the proper segmentation of the iris region from the input eye image. The traditional non-cooperative iris segmentation algorithms decrease significantly because of several noise factors such as specular reflections, occlusions, eyelashes, and eyelids. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. Background: Recently, segmentation of iris image is the most important process in a robust iris recognition system due to the images captured from non-cooperative environments which introduce occlusions, blur, specular reflections, and off-axis. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. In this research, an effective two-stage of iris segmentation technique is proposed in a non-cooperative environment. Objective: To proposed an effective two-stage of iris segmentation technique in a non-cooperative environment. Methods: Modified Geodesic Active Contour-based level set segmentation with Particle Swarm Optimization (PSO) is employed for iris segmentation. In this, the PSO algorithm is used to minimize the energy of the gradient descent equation in a region-based level set segmentation algorithm. The global threshold-based segmentation (enhanced Otsu’s method) is employed for pupil region segmentation. Results: The experiment considered two well-known databases such as UBIRIS.V1 and UBIRIS.V2. The simulation outcomes demonstrate that the proposed novel approach attained more accurate and robust iris segmentation under non-cooperative conditions. Also, the results of the modified Geodesic Active Contour-based level set segmentation with the PSO algorithm attained better results than the conventional segmentation techniques. Conclusion: An effective two-stage of iris segmentation is proposed in a non-cooperative environment using Geodesic active contour-based level set segmentation with particle swarm optimization and an enhanced Otsu method is employed for pupil segmentation. The proposed two-stage of iris segmentation technique segments the iris in its genuine shape which will provide a more robust and precise iris recognition system.

In Event-driven wireless systems, mostly data transmission depends on events occurring in the sensor field. Most of the time, sensor nodes are silent or in sleep mode. When events occur in the sensor field, a single event can trigger many nodes for data transmission. In such a scenario, the nodes collect the correlated information due to the overlapped coverage area. Existing epidemiological designs do not consider the nodes’ behavior to investigate infection dynamics for this scenario. In this paper, a susceptible-exposed-infectious-recovered- susceptible (SEIRS) is designed by considering the spatial correlation for analyzing the dynamics of the virus spreading in event-driven wireless systems. Firstly, we show how strongly correlated nodes and less correlated nodes are formed in a WSN based on sensor coverage. The differential equations of SEIRS are then derived. An analysis on system stability is performed for finding the basic reproduction number $$R_0$$ . The value of $$R_0$$ gives important significance in terms of spatial correlation for analyzing virus spreading. Experiments are performed to validate the model using various parameters such as correlation, node density, the basic reproduction number. Comparisons with existing models show the effectiveness of the SEIRS model. Based on the analysis, it is observed that the virus spread control can be possible by reducing $$R_0$$ . It is also found that the threshold of virus propagation is strongly dependent on the spatial correlation between nodes in the network. The virus is the network persists at virus-free equilibrium when $$R_0 > 1$$ with higher spatial correlation, whereas it becomes globally stable for $$R_0 < 1$$ .

Nowadays, Reversible Data Hiding (RDH) is used extensively in information sensitive communication domains to protect the integrity of hidden data and the cover medium. However, most of the recently proposed RDH methods lack robustness. Robust RDH methods are required to protect the hidden data from security attacks at the time of communication between the sender and receiver. In this paper, we propose a Robust RDH scheme using IPVO based pairwise embedding. The proposed scheme is designed to prevent unintentional modifications caused to the secret data by JPEG compression. The cover image is decomposed into two planes namely HSB plane and LSB plane. As JPEG compression most likely modifies the LSBs of the cover image during compression, it is best not to hide the secret data into LSB planes. So, the proposed method utilizes a pairwise embedding to embed secret data into HSB plane of the cover image. High fidelity improved pixel value ordering (IPVO) based pairwise embedding ensures that the embedding performance of the proposed method is improved.

Background The ability of 3D printing using plastics and resins that are magnetic resonance imaging (MRI) compatible provides opportunities to tailor design features to specific imaging needs. In this study an MRI compatible cradle was designed to fit the need for repeatable serial images of mice within a mouse specific low field MRI. Methods Several designs were reviewed which resulted in an open style stereotaxic cradle to fit within specific bore tolerances and allow maximum flexibility with interchangeable radiofrequency (RF) coils. CAD drawings were generated, cradle was printed and tested with phantom material and animals. Images were analyzed for quality and optimized using the new cradle. Testing with multiple phantoms was done to affirm that material choice did not create unwanted image artifact and to optimize imaging parameters. Once phantom testing was satisfied, mouse imaging began. Results The 3D printed cradle fit instrument tolerances, accommodated multiple coil configurations and physiological monitoring equipment, and allowed for improved image quality and reproducibility while also reducing overall imaging time and animal safety. Conclusions The generation of a 3D printed stereotaxic cradle was a low-cost option which functioned well for our laboratory.

Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.

The massive volume of data stored in computer files and databases is rapidly increasing. Users of these data, on the other hand, demand more complex information from databases. The video data have exponential growth towards accessing and storing. The vital problem associated to video data is efficient, qualitative and fast accessing. We talk about how video pictures are clustered. We presume video clips have been divided into shots, each of which is denoted by a collection of key frames. As a result, video clustering is limited to still key frame pictures. In amble database finding the qualified data set (clusters) is quite time-taking job. The video data mining relate to multi–lingual text, numeric, image, video, audio, graphical, temporal, relational and categorical data. It may be any kind of information medium that can be represented, processed, stored, fast accessing or summarization of clusters are required due to which significant frame-set is formed. Due to sampling error and test reliability in video, substantial changes of more than one frame are predicted. The goal of this article is to show how to employ a familiar and easy nonparametric statistical approach (chi-square) to select eligible data/framesets for analysis. The chi-square model illustrated here is a straightforward, sensible, fast, reduce saddle, and easiest method. Skimming/ Summarization and clipping technique are further enhanced by this technique along with video database maintenance technique from simple descriptors to a complex description schemes like spatial and temporal or high dimensional indexing.&nbsp

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

Dynamic frequency stability concerns are engendered in the modern electric power system due to the large penetration of low inertia inverter interfaced distributed generation (IIDG) into the utility grid. Therefore, inertial support is needed similar to the conventional synchronous generator. From recent studies, it was observed that frequency stability is considered as one of the major challenges for the system operators and is required to be alleviated. In this methodology, the virtual inertia (VI) concept is suggested as a promising solution to boost the inertial response of IIDG. Full state feedback through a robust linear quadratic regulator (LQR) is employed to find optimal inertia and damping coefficients to emulate VI in IIDG. These coefficients are used to evaluate the additional power required to provide inertia emulation by a virtual synchronous generator (VSG). To upgrade the performance index in LQR, a bacterial foraging optimization algorithm (BFOA) is suggested for optimal parameters (weights) selection in the problem search space. The proposed optimal controller ameliorates VI dynamics and brings system frequency faster to the nominal value and allows more penetration of IIDGs. Time-domain simulated results and observed test system eigenvalues corroborate the effectiveness of a proposed controller. Time-domain simulations have been obtained using MATLAB/Sim Power Systems toolbox, and the LINMOD function in MATLAB is used to linearize the suggested system.

Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a separate controller for each mode, and verify correctness by proving that (i) each controller is correct within its mode, and (ii) transitions between modes are correct. This compositional strategy not only improves scalability of both learning and verification, but also enables our approach to verify correctness for arbitrary compositions of the subtasks. To handle partial observability (e.g., LiDAR), we additionally learn and verify a mode predictor that predicts which controller to use. Finally, our framework also incorporates an algorithm that, given a set of controllers, automatically synthesizes the pre- and postconditions required by our verification procedure. We validate our approach in a case study on a simulation model of the F1/10 autonomous car, a system that poses challenges for existing verification tools due to both its reliance on LiDAR observations, as well as the need to prove safety for complex track geometries. We leverage our framework to learn and verify a controller that safely completes any track consisting of an arbitrary sequence of five kinds of track segments.

An instructional technology application of QR (quick response) codes for introducing commonly used chemistry laboratory apparatus for visually impaired students is presented. Audio commentaries describing commonly used apparatus were recorded. The commentaries consisted of concise introductions and applications of various apparatus used in high school and undergraduate chemistry laboratories. The QR code for each apparatus was produced and linked to the corresponding audio commentary via a unique URL. The QR code labels were attached to the apparatus. The QR codes were scanned on a mobile device and an audio commentary was played. Sixteen visually impaired students participated in testing the activity. The results of the testing activity underscored the efficacy of the technology offering an assisted physical learning experience. Technology presented in this article does not give visually impaired students an independent learning experience, rather it gives them an assisted learning experience which enhances their sense of inclusion in a laboratory environment.

Content-centric networking (CCN) has emerged as a promising future Internet architecture that brings effective content retrieval mechanism. In CCN, the contents are accessed using their names instead of searching for the host location in the network. To improve Quality-of-Service for the users, CCN offers the in-network caching capability that places the incoming contents in the intermediate on-path nodes. During content retrieval, the in-network caching reduces network delay and bandwidth requirements as the requester access the content from the nearest node having a copy of the required content. Therefore, it is crucial to control the content caching decisions in the CCN as the efficiency of the caching scheme largely affects the performance of the network. In this context, a novel content placement scheme is proposed that considers node degree centrality and content provider distance based on the network bandwidth parameters for effective content placement decisions. A heuristic approach has been proposed that investigates the compound effect of these parameters to minimize average delay and network traffic during retrieval of the requested content. The proposed caching scheme has been implemented on the Abilene network topology and evaluated for different content access patterns and caching capacities. Extensive simulation results demonstrate the superiority of the proposed scheme on the existing peer schemes for various performance parameters such as average network hop-count, delay, and network traffic load.

This paper presents an electrocardiogram amplifier with operational transconductance amplifier at 0.18 µm. Comb active filters are used for designing of amplifier circuits and the selected frequencies are removed from various signals. The designing of filter circuit is based on only transconductance amplifiers and capacitors are very suitable for implementation of ICs. Different ECG signals 60, 180, 300 and 420 Hz is applied to the analog circuits’ know the working capability of analog circuits. Different ECG parameters are measured with the help of cadence Virtuoso tool, this tool is analog design environment, the performance parameters are bandwidth and gain increases for an increase in bias current.

With the advancements and increase in the transportation system in the current scenario, making transports intelligent is an important aspect for enhancing the safety, security and related commercial applications. Vehicular ad-hoc network has been evolved for the implementation of intelligent transportation systems. Vehicular scenario comprising of multipath fading, interference, dispersion and mobility distort the communication among the vehicles and between vehicles and surroundings. This work implements adaptive modulation and coding technique in the existing vehicular communication transmission process. Simulations were carried out for different transmission schemes with different code rate over several wireless channels for varying signal-to-noise ratio for performance evaluation. The results of this simulation testify that the proposed technique serves better than fixed transmission scheme in terms of bit error rate and spectral efficiency. Adaptive modulation together with turbo coding shows an approximate gain of 10 dB signal-to-noise ratio relative to fixed schemes.