Volume 8, Issue 1

The increase of servers in data centers has become a significant problem in recent years that leads to a rise in energy consumption. The problem of high energy consumed by data centers is always related to the active hardware especially the servers that use virtualization to create a cloud workspace for the users. For this reason, workload placement such as virtual machines or containers in servers is an essential operation that requires the adoption of techniques that offer practical and best solutions for the workload placement that guarantees an optimization in the use of material resources and energy consumption in the cloud. In this article, we propose an approach that uses heuristics and meta-heuristics to predict cloud container placement and power consumption in data centers using a Genetic Algorithm (GA) and First Fit Decreasing (FFD). Our algorithms have been tested on CloudSim and the results showed that our methods gave better and more efficient solutions, especially the Genetic Algorithm after comparing them with Ant Colony Optimization (ACO) and Simulated Annealing (SA).

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Carbon nanotube field effect transistor (CNTFET) has a huge advantage over the Si- MOSFET. In MOSFET switching occurs by altering channel resistivity whereas in CNTFET switching occurs by modulation contact resistance. CNTFET generates three to four times of drive current than MOSFET. Transconductance of CNTFET is four times higher than the MOSFET. The average carrier velocity is also very high almost double in CNTFET than that is in MOSFET. Its power consumption is low. Electron mobility is high. Threshold voltage is also low. It has better control over channel formation. There is no direct tunneling and gate leakage current is also reduced. Herein, the main objective is to investigate the effect of gate-insulator thickness on CNTFET, and to optimize the thickness so that current carrying capacity may reach higher.A detailed simulations have been made and IV characterizationis done to investigate the effect of Gate-Insulator Thickness on Co-Axial Cylindrical Carbon Nanotube Field Effect Transistor.Report shows with the increasing gate-insulator thickness current is decreased significantly. Where as the variation of nano diameter shows that the increasing rate of current is increased when the carbon tube diameter is increased.

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Most anthropomorphic robotic hands use a lot of actuators to imitate the number of joints and the movement of the human hand. As a result, the forearm of the robot hand has a large size for the installation of all actuators. This robot hand is designed to reduce the number of actuators, but also retain the number of movable joints like a human hand by using the four-bar linkage mechanism and only flexion-extension movements. This stamen is added in the problem statement according to the reviewer’s comment. The special features of this robotic hand are the ability to adjust the link length and the range of rotation for each joint to suit various applications and can fabricate with 3D printing and standard parts with costing about $750. All hardware CAD files and equations are published on the GitHub website, which benefits for researchers to utilize as an open-source approach that their project might be further expanded in the future. The anthropomorphic robotic hand has five fingers, 16 joints, and 12 active Degrees of Freedom (DOFs) with 12 servo motors applied to finger motion and one for wrist motion. The structure of the hand is designed using the average of Asian human hands in combination with the golden ratio. All servo motors are installed in the forearm designed in a ventilated structure with 12V vent exhaust fan motor to stabilize the operating temperature of the robotic hand. Size and weight of the hand included with the forearm are 20×54×16.5 centimeters and 2.2 kilograms respectively. The hand has achieved human-like movement by using a four-bar linkage mechanism and tendon with PTFE tube to guide operation path of the tendon with the lowest friction force. This paper presents the design processes, the experimental set-up, and the evaluation of the finger movements. From the experiment of grasping objects, this hand was able to grasp 10 basic grasp types including 32 different objects, perform 9 common gestures, and lift the object to 450 grams. From this paper, the kinematic equation is proved that the designed finger structure can move exactly as the equation with maximum error of repeatability test around 1.6 degrees.

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Droughts are undesirable and highly unwanted form of disasters. It is essential to analyse the cause of such extreme events and act accordingly to pave the way for a sustainable future. The present research work conducts a seasonality and trend analysis of rainfall over the eight districts of Marathwada region. The study is carried out for the last 39 years ranging from 1980 to 2018. The rainfall data pertaining to pre-monsoon season, monsoon season (Kharif), and annual average have been analysed. The trend has been estimated using Sen’s slope estimation process along with Mann-Kendal test. It was determined that the all the eight districts of the region show a negative trend in the annual rainfall received. Nanded district showed the largest negative trend in the annual rainfall. Out of eight districts seven districts of the region show a decline in rainfall during the monsoon season. The district of Nanded showed largest decline in the rainfall received during monsoon season. The research work presents the discussion on possible causes of such trends estimated. The research creates a robust foundation of advanced computation techniques for prediction of droughts.

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In this paper, a new syndrome block for Reed Solomon RS and BCH codes used respectively in digital Video broadcasting DVB-S and DVB-S2 has been presented in order to reduce the number of iterations compared to the existed block, which can be found in the literature, the new method is based on a factorization of the equation corresponds to the syndrome block, which allows us to conceive another circuit. However, this reduction can approximately attain 40%. First, we developed and concepted the design of the proposed algorithm. Second, we transformed the circuits on hardware description language VHDL and finally we generated and simulated the basic and proposed algorithms using Quartus software tools.

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The paper presents two robust and efficient control algorithms based on (i) Optimal Control Allocation (OCA) and (ii) Nonlinear Model Predictive Control (NMPC). The robotics system consists of two rovers with mecanum wheels and mounted two 7-DOF arms carrying a common load. The overall system is an underdetermined one with non-holonomic constraints. The developed control algorithms focus on providing an optimal solution to the wheel and joint torque saturation problem, which is typically encountered while manipulating a large and heavy payload. The first control algorithm based on OCA minimizes a quadratic cost function consisting of robot joint and rover wheel torques, contact forces, and moments using only the current state values and the system dynamics. It is computationally very efficient. The NMPC algorithm minimizes a quadratic cost function which not only includes the current states but also the future state estimates, and the control inputs over a specified prediction horizon. The system consisting of multi-rover with a dual arm is highly non-linear. The linear MPC technique on which most of the previous studies relied is not adequate. On the other hand, the computational difficulties of a generic NMPC algorithm is remarkably high. In this paper, an elegant, discretized technique with exact realization is implemented to take into account the full non-linear model and yet provide a simple real-time solution satisfying a minimum performance index subject to constraints. The results show that the developed control algorithms OCA and NMPC work efficiently, and the minimum the contact moments and forces, and the joint torques are realized while two arms carry a common load and successfully track a reference end-effector trajectory. The results also indicate that although NMPC algorithm is computationally more involved, it provides superior results in reducing joint and wheel torques as well as contact moments and forces.

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Edge detection-based image steganography schemes usually embed data in edge pixels only. However, some schemes embed data in non-edge pixels as well. In that case, the schemes embed more bits in the edges than in the smoothed areas. In all cases, the schemes perform large changes in a tiny area of the image during small data embedding. Detecting such local modifications is comparatively easier for a steganalyzer. As a result, it is preferable to distribute bits evenly across the image. Again, the schemes struggle to hide large messages in a cover image due to the algorithmic approach of hiding a fixed number of bits per pixel. In this research, we have overcome that problem by employing multiple edge detectors in generating a resultant edge image. Depending on the embedding needs, we have checked whether a single edge detector is sufficient to help in conceiving all bits or not. If it is not possible for a single-edge detector, we have hybridized them. Hybridization of edge images is performed either by logical AND, OR or OR with dilation. When the message size is very small, we have generated the resultant edge image by doing a logical AND operation among the edge images. That strategy have reduced the number of edge pixels as well as helped in distributing the to-be-embedded bits over the image in a more evenly manner. Similarly, to meet a larger embedding demand, we have performed a logical OR operation among the same edge images to increase the number of edge pixels. Even, to meet more embedding demand, we have dilated the OR-resultant image. These processes were carried out dynamically in the research according to an embedding demand. The experimental results deduce that this scheme embeds 92.37%, 73.92%, 74.78%, and 9.60% more bits than four competing methods. Similarly, for small embedding demand, the proposed scheme demonstrates 37.45%, 46.87%, 44.21%, and 55.56% higher PSNR values than the others. Moreover, statistical analyses state that this scheme demonstrates stronger security against attacks.

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In the present contribution, a robust output H∞ control ensuring the stability, reliability and security for nonlinear systems when actuator attacks (data deception attacks) occur. A new design method based on the polytopic rewriting of the attacked system as an uncertain one subject to external disturbances will be detailed. Robust polytopic state feedback observer sta- bilizing controller based on the PDC (Parallel Distributed Compensation) polytopic framework with disturbance attenuation for the obtained uncertain system will also be considered. The obtained methodology is used to ensure the stability and security of a quadrotor/UAV subject to stealthy actuator attacks. State and attacks estimations signals are given in order to highlight the efficiency of the developed approach.

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Sleep stage performs a vital role in people’s daily lives in the detection of sleep-related diseases. Conventional automated sleep stage classifier models are not efficient due to the complexity linked to the design of mathematical models and extraction of hand-engineering features. Further, quick oscillations amongst sleep stages frequently lead to indistinct feature extraction, which might result in the imprecise classification of sleep stages. To resolve these issues, deep learning (DL) models are applied, which make use of many layers of linear and nonlinear processing components for learning the hierarchical representation or feature from input data and have been used for sleep stage classification (SSC). Therefore, this paper proposes an ensemble of voting-based DL models, namely the recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU), with activation Regularization (AR) functions for SSC. The penalty addition of L1, L1_L2, and L2 on the layers of the model fine-tunes it in proportion to the magnitude of the activation function in the model by reducing overfitting. Subsequently, the presented model integrates the results of every classification model to the max voting combination rule. Finally, experimental results of the proposed approach using the benchmark Sleep Stage dataset are evaluated using various metrics. The experimental results illustrates that the Ensemble RNN, Ensemble GRU, and Ensemble LSTM models have achieved an accuracy of 85.57%, 87.41%, and 89.01%, respectively.

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Subsurface Utility Engineering (SUE) is an international model for mapping and classifying underground surfaces according to their accuracy (acquisition method). Utilizing Geographic Information System (GIS) to map and present the SUE levels paved the way for producing a new Decision Support System (DSS) for the utility mapping process. The proposed system represents an efficient tool for managing, operating and maintaining utilities. This Article aims to design a prototype in Unified Modeling Language (UML) of a new DSS system to operate SUE maps using digital spatial maps (GIS-compatible). Although SUE and GIS are not new technologies, integrating them is. The result is a prototype that makes utility management and maintenance cost estimation more efficient. This prototype facilitates and automates the cost estimation of exposing, maintaining, or locating subsurface objects, such as utilities. In addition, it may apply to Municipal Solid Waste (MSW) and void mapping.

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The “Electronic Nose” approach, which is exclusive to gas measurement systems, uses gas sensors as odor detectors. Design faults exist in the existing electronic nose (e-nose) chamber, such as its large volume, difficult construction, etc. In order to obtain measurements in a satisfactory state, we want to create a gas chamber that can provide favorable conditions for the sensor array, taking into account the ideal gas flow morphology and detector placement. To describe and identify the design capable of offering the best performance for a genuine idea, the e-nose chamber was created using ParaVIEW simulation and FreeCAD conception. According to the results, the spherical sensing container with connections from both pipes in a tangential arc style gives the highest performance in terms of turbulence reduction, in that case, we are printing this chamber and put it in a gas flow prototype to see the performance of the quality measurement of the sensors inside it, and the result shows that these sensors have good acquisition responses by testing the homogeneity distribution inside the chamber.

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Public organizations are subjected to a complex security situation, which can be addressed by permanently strengthening and evaluating their cybersecurity capabilities. The objective of this research is to develop a model to identify the cybersecurity management capacity of public organizations. The deductive method was applied for the review and analysis of criteria, factors and variables related to cybersecurity capacity in public organizations. It resulted in a model to identify the Cybersecurity Management Capacity of public organizations, with its process to assess and categorize organizations according to their level of cybersecurity capacity. It was concluded that public organizations from developed countries in cybersecurity such as Spain have better capacities (greater than 60% CMC) than less developed countries such as Ecuador (less than 60% CMC), due to the cybersecurity context where these organizations operate. To obtain a high level of cybersecurity, public organizations must have the support of the governments of the different political divisions of a country, as well as permanent international collaboration in the field of cybersecurity.

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The relevance of static synchronous compensator (STATCOM) controllers in controlling power network parameters is causing them to be included in contemporary networks. But for the intended objectives to be attained, the best device positioning and parameter settings are essential. This work compares the performance of the particle swarm optimization (PSO) and firefly algorithm (FA) in sizing and placing a STATCOM device for the dual objectives of loss reduction and voltage deviation abasement. The effective mitigation of network loss and voltage fluctuations in the network will be achieved by the deployment of the efficient method during device allocation. While PSO and FA were taken into consideration due to their computational efficiency among other metaheuristic algorithms, STATCOM was chosen from among the Flexible Alternating Current Transmission System (FACTS) controllers as a consequence of its reactive power compensation capability. The MATLAB software was used to implement the simulations on an IEEE 14-bus system. When STATCOM was optimized with PSO and FA, it resulted in active power loss reductions of 432 and 733 kW, respectively, and reactive power loss reductions of 1622 and 2100 kVAr, respectively. As a result, the reductions in voltage variation and power losses in this instance show some benefits of FA over PSO. Additionally, this work has shown that metaheuristic algorithms are beneficial for allocating FACTS devices.

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Corn is one of the most important agricultural products in the world. However, climate change greatly threatens corn yield, further increasing already prevalent diseases. Northern corn leaf blight (NLB) and Gray Leaf Spot are two major corn diseases with lesion symptoms that look very similar to each other, and can lead to devastating loss if not treated early. While early detection can mitigate the amount of fungicides used, manually inspecting maize leaves one by one is time consuming and may result in missing infected areas or misdiagnosis. To address these issues, a novel deep learning method is introduced based on the low latency YOLOv3 object detection algorithm, Dense blocks, and Convolutional Block Attention Modules, i.e., CBAM, which can provide valuable insight into the location of each disease symptom and help farmers differentiate the two diseases. Datasets for each disease were hand labeled, and when combined, the base YOLOv3, Dense, and Dense-attention had AP_0.5 NLB lesions/AP_0.5 Gray leaf spot lesions value pairs of 0.769/0.459, 0.763/0.448, and 0.785/0.483 respectively.

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This communication proposes a grounded immittance function simulator that, depending on the proper choice of the passive components, can simulate parallel-type impedances of the R-L, R-C, and L-C forms. Only two grounded passive components and two voltage differencing buffered amplifiers (VDBAs) are used to implement the suggested circuit. All three simulated equivalent elements, namely Req, Leq, and Ceq, can be electronically adjusted through the VDBA’s transconductance gain. The impact of the non-ideality of the VDBA device on the developed simulator is examined in detail. The voltage-mode bandpass filter has been implemented using the suggested active LC parallel impedance simulator to show that it performs as predicted. To prove the theory, the proposed circuit is simulated using the PSPICE tool. The findings of the experimental measures are also presented to demonstrate the circuit’s feasibility.

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Currently one of the urgent goals of mathematical education is the organization of effective work with gifted students. Based on the study of various approaches to teaching mathematically gifted students, many years of experience of teachers, students’ work, and an analysis of curricula and materials for schools with in-depth study of mathematics, an author’s model for the training of gifted students was developed. The novelty of this model is that it ensures a rational combination of various forms of education for gifted children on the basis of differentiation, individualization of the process of teaching mathematics, advanced learning, openness, democracy, reflection, and adequate control. The pedagogical experiment was carried out for two years in the Abulfazl Balami gymnasium for gifted children in the city of Vahdat, Republic of Tajikistan. 41 students and 18 teachers took part in the experiment. The data obtained from the experimental and control groups were subjected to qualitative and quantitative analysis. Over the same time interval there were significant changes in the performance of students in the experimental groups, with 40% of the students moving to a higher level. In the control groups, the change was not significant.

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