We present and examine a new family of analytic functions that can be described by a q-Ruscheweyh differential operator. We discuss several novel results, including coefficient inequalities and other noteworthy properties such as partial sums and radii of starlikeness. Moreover, coefficient estimates for the class of Janowski starlike functions associated with symmetric conic domains are also discussed.

Heterogeneous Multi-Processor System-on-Chip architectures are prevalent in modern embedded system applications that target high-performance needs. This work comprises two parts to optimize the multiprocessor system structure concerning system speed, power, and area requirements. The first part includes the game model concept, which is remarkably advantageous in generating strategic decisions on incoming signals and minimizes traffic delays coming from the input portion of the system. Secondly, an improved moth search optimization is proposed to explore the design space and optimize the system space and power consumption. This research is implemented in the Field Programmable Gate Array (FPGA) Xilinx Zynq and Virtex platforms and evaluated by ExPRESS benchmarks. The Game Model-combined improved Moth Search Optimization (GMMSO) technique showed performance improvements of 69.34% in delay measurement, 46% in data rate and 36.34% in static power consumption compared with the traditional HMILP (Heuristic Mixed Integer Linear Programming) methods.

In the contemporary paper concerning double zeta functions, we demarcated two new-fangled subclasses of bi-starlike and bi-convex function of complex order in the open unit disc linked with shell-shaped region and acquired Taylor–Maclaurin coefficients $$|a_2|$$ and $$|a_3|$$ of functions in these classes. Furthermore, we determine the Fekete–Szegö inequalities and the significance of the results which are new and are also piercing out as corollaries.

In this paper, a digital hysteresis current limit controller is developed for Switched Inductor Quasi Z-Source Inverter (SLQZSI). Traditional methods like hysteresis current fixed limit and adjustable hysteresis current limit techniques changes the hysteresis bandwidth in accordance to modulating frequency and gradient of reference current. The operating shifting frequency of typical approaches oscillates and crosses the intended steady shifting frequency under noise. It leads to undesirable heavy interference between the phases and more power loss. In the planned digital hysteresis current limit technique, the hysteresis current limit is calculated by resolving the optimization problem. In the proposed approach the operating shifting frequency is kept same or inferior to the intended steady shifting frequency even under noise. Hence the planned digital hysteresis current limit algorithm maintains the output current steady and power loss is minimized which is not promised by the conservative techniques. To compare different control strategies in terms of nature of operating switching frequency and harmonic performance simulations are built on the MATLAB/SIMULINK

In this article we familiarize a new subclass of analytic functions comprising Erdély–Kober type integral operator linked with the Janowski functions. Further, we confer some significant geometric properties like necessary and sufficient condition, growth and distortion bounds convex combination, partial sums and Fekete–Szegő inequality for this newly demarcated class. Further we conferred Fekete–Szegő inequality related with neutrosophic Poisson distribution.

A microgrid is an powerful alternative promising solution, which fosters a reliable power supply to supplement conventional electrical framework. However, microgrid encounters various challenges subjected to energy flow, power quality, and net profit due to the high integration of distributed energy resources (DERs) in the network. Hence, the Mimosa pudica-based energy management scheme has been designed for optimal scheduling of microgrid to reduce the production overheads with underlying system constraints. Besides, the proposed approach is formulated to manage the power balancing among the distributed resources and utility through a standard communication protocol. Mainly, the optimization process is developed through the sensitive and intelligent behavior of mimosa plants that enable the adaptation of dynamic context by reusing past information through memory, and maintaining variation across the solution leads to good solution accuracy. Moreover, the proposed algorithm corroborates the potency of microgrid over isolated, grid-tied, and resynchronized conditions. Numerical results illustrate that the proposed technique has obtained more profit (8%) than the existing approaches with superior optimization ability and fast convergence.

Cloud computing is kind of presumably the main technology utilized lately, which permits clients (people and association) to get to registering assets (application tool, equipment, and program) as administrations distant over the web. Cloud computing is recognized since customary registering standards through its skillfulness, change expenses, availability, dependability, and on-request pay-more only as cost rise administrations. As cloud computing is serving a huge quantity of clients concurrently, it should meet all client demands requests with superior and assurance of the Quality of Service (QoS). In this manner, a proper job scheduling algorithmic program to meet these solicitations reasonably and productively needs to be executed. The job scheduling problem is an exceedingly the foremost basic issues in a cloud computing environment since cloud execution relies principally upon it. There are different sorts of scheduling algorithms; most of them are unit static scheduling algorithms that are applicable for immense scope for little or medium-scale cloud computing; and dynamic scheduling algorithms that are applicable for huge immense scope cloud computing environments. This paper presents a further developed scheduling algorithm, subsequently breaking down the customary algorithms which depend on client need and task length. After they arrive, people who are hyper-focused on errands are not given any special significance. These aspects are taken into account in the proposed approach.

Cloud computing leverages the processing force and resources as an administration to the clients present across the world. This plan was acquainted as a route with an end for the customers around the world by giving elite at a more affordable expense in contrast to the devoted high-performance computing machines. For running HPC, business and web applications, cloud computing remains an exceptionally versatile and financially savvy stage. Enormous amount of electrical energy are utilized by server ranches for achieving a high help cost and carbon-di-oxide transmission. The power productivity issues with server ranches are basically a cost of force and cooling structure that has a significant bit of their operative expenses. Energy adequacy is considered as a significant challenge and it is demanding to win in a green answer to deal with the entire patterns that influence the cloud energy utilization. There are four strategies for creating energy effectiveness: equipment level force improvement, energy cognizant planning in lattice structures, server consolidation with the useful resources of virtualization and power minimization. Thus, there is an increasing need for green cloud computing solutions that remains most effective to store power and, however, also reduce operational prices. The imaginative and prescient-based energy efficient control on cloud computing environments is supplied right here. EALB algorithm has been introduced to foresee the heap and incorporate energy productivity in overloaded and under stacked framework.

The evolution of clean energy technology brings notable favor to the microgrid to tackle the ever-increasing energy demand. However, the inconsistent and dynamic behavior of renewable sources necessitates the incorporation of an energy management scheme (EMS) to coordinate the distributed components in microgrid efficiently along with ensuring high efficiency and stability over the network. Thus, the proposed energy management approach is designed to regulate the energy flow optimally and for balancing the generation-demand ratio in a distributed network. With this scenario, a new muddy soil fish optimization algorithm (MSFOA) is developed with the foraging pattern of fishes aiming at minimizing the production overheads and curtail the expenses of energy import from the grid together with considering system constraints. The proposed EMS is implemented in a microgrid framework that includes solar PV, wind, diesel generator, and energy storage units. Moreover, several aspects of case studies under isolated and grid-tied scenarios substantiate the potentiality of the presented MSFOA approach. The simulation output illustrates the outstanding performance of the proposed MSFOA technique over convergence speed and solution precision with reasonably low execution time resulted in obtaining more profit than the existing method. Besides, the numerical output exhibits that the proposed MSFOA technique minimizes the overall production cost compared to the heuristic-based model predictive technique, predictor-corrector proximal multiplier, and improved differential search algorithm by 3.76%, 8.32%, and 19.3%, respectively. Finally, the proposed optimization technique has been deployed in a small testbed system to demonstrate the feasibility over real-time conditions.

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming-based tabu search (EPTS) method. The objective of this paper is to determine the optimal or a near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie- lines. The evolutionary programming-based tabu search method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie- lines depends upon the operating cost of generation at each hour and tie- line transfer limits. The tie -line transfer limits were considered as a set of constraints during optimization process to ensure the system security and reliability. The overall algorithm can be implemented on an IBM PC, which can process a fairly large system in a reasonable period of time. Case study of four areas with different load pattern each containing 26 units connected via tie- lines has been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based tabu search method with conventional dynamic programming (DP), evolutionary programming (EP), Partical Swarm Optimization (PSO), Simulated Annealing (SA), Evolutionary Programming based Partical Swarm Optimization (EPPSO), Evolu

The reduction of multiple reverse conversions in an individual AC or DC microgrid will be analysed through Solid State Transformer (SST). It also facilitates connections from wind and solar power generation to microgrid. The SST interfaced with renewable energy system in microgrid system and its centralized power management strategy is proposed. The proposed AC microgrid system can access the distribution system without bulky transformers and can manage both the central grid and renewable systems. Wind and solar power are uncontrollable resource and also makes a challenging integration for the micro grid, particularly in terms of stability and power quality. The SST interfaced Renewable energy systems such as wind and solar power are proposed with the integrated functions of active power transfer, reactive power compensation, and voltage conversion. The SST acts as an energy router and assure for the benefit of the future residential systems. This system is modeled and simulated using Matlab /Simulink software such that, it can be suitable for modeling some kind of wind and solar power configurations. To analyze more deeply about the performance of the wind and solar system, both the normal and fault conditions will be applied. Keywords-AC Microgrid-three phase solid state transformer (SST)-Wind generation –solar generationGrid connection

Nowadays there are vast enlargements in the grid applications; the control strategy of power electronics systems has also spread their locality extensively. Single processor of complex hardware is used in the conventional method, to generate the control strategy for microgrid. To avoid this issue, we cultivate a low-power microgrid with the help of NoC. Reconfigurable Double Tailed Sense Amplifier (RDTSA) based on the NoC is selected for the reduction of power. History based Dynamic Frequency Scaling (HDFS) is implemented in RDTSA for further power reduction. The combination of this is named as a Mixture of Algorithm with NoC (MAN) architecture, which has been refined from the ALPIN architecture. Cultivation of the entire design results in delay and power reduction compared to the conventional method. The performance of delay, data rate static power and energy are evaluated compared to conventional method and the RDTSA. The overall performance of Heuristic Asynchronous NoC for Universal power electronics application (HANU) is superior to that of the conventional approaches.

In this paper, we examine the connections between the new subclasses of γ− parabolic starlike and γ− uniformly convex functions of order δ by using a convolution operator involving the Pascal distribution series. Further we point out significance of our main results .

In recent years, Field Programmable Gate Array (FPGA) has different, and novel combinations of soft and hard cores embedded with accelerators in the same chip. At present, "Heterogeneous Multiprocessor System-on-Chip" technology meets the needs of the FPGA architecture as it not only consumes less space but also its design is implemented to enhance the performance resulting in decreased power consumption. However, traditional approaches can work within a limited range of values incurring high-power consumption, and they need substantial hardware resources involving complex procedures. In our proposed work, the combination of Graph Theory Estimator and Divergence State Estimation with Biogeography-Based Optimization (DSEBBO) is introduced. The principle notion of this proposed scheme is to optimize the required area resources and power consumption of the overall architecture by exploring the design space in a minimal time. The architecture uses a less number of hardware resources with a better outcome. The performances such as the latency, power consumption, delay, area and data rate are achieved better than the traditional works, and it is also application-aware with a multitask system. While comparing the outcome with the conventional asymmetric heterogeneous MPSoC method, the new DSEBBO design exhibits the performance improvement of 26.87% in data rate, and 37.18% reduction in average power consumption and 63.61% reduction in delay analysis under various traffic scenarios.

The sequence impedance of the network describes the behaviour of the system under asymmetrical fault conditions. The performance of the system determines by calculating the impedance offered by the different element of the power system to the flow of the different phase sequence component of the current. Every power system component (static or rotating) has three values of impedance one for each symmetrical value of current. The sequence impedance of power system is of three types namely positive sequence impedance, negative sequence impedance and zero sequence impedance.

Hybrid main memory comprising of DRAM and PRAM becomes quite popular because of the less standby power benefit of PRAM and high performance of DRAM.In this work, the runtime-adaptive control and DRAM bypassing methods are introduced in order to minimize DRAM refresh energy that occupies a considerable portion of total system power.The work is carried out by using Xilinx 12.1 simulation tool and the experimental result proves that in the proposed work power consumption is greatly reduced i.e., only requires 3 %, with less area overhead while maintaining the speed parameter by comparing with the conventional method .

Renewable energy sources are proven to be reliable and accepted as the best alternative for fulfilling our increasing energy needs. Solar photovoltaic energy is the emerging and enticing clean technologies with zero carbon emission in today's world. To harness the solar power generation, it is indeed necessary to pay serious attention to its maintenance as well as application. The IoT based solar energy monitoring system is proposed to collect and analyzes the solar energy parameters to predict the performance for ensuring stable power generation. The main advantage of the system is to determine optimal performance for better maintenance of solar PV (photovoltaic). The prime target of PV monitoring system is to offer a cost-effective solution, which incessantly displays remote energy yields and its performance either on the computer or through smart phones. The proposed system is tested with a solar module of 125watts to monitor string voltage, string current, temperature, and irradiance. This PV monitoring system is developed by a smart Wi-Fi enabled CC3200 microcontroller with latest embedded ARM processor that communicates and uploads the data in cloud platform with the Blynk application. Also the Wireless monitoring system maximizes the operational reliability of a PV system with minimum system cost.