Data Processing Speed Research Articles

A Multi-Flow Production Line for Sorting of Eggs Using Image Processing

In egg production facilities, the classification of eggs is carried out either manually or by using sophisticated systems such as load cells. However, there is a need for the classification of eggs to be carried out with faster and cheaper methods. In the agri-food industry, the use of image processing technology is continuously increasing due to the data processing speed and cost-effective solutions. In this study, an image processing approach was used to classify chicken eggs on an industrial roller conveyor line in real-time. A color camera was used to acquire images in an illumination cabinet on a motorized roller conveyor while eggs are moving on the movement halls. The system successfully operated for the grading of eggs in the industrial multi-flow production line in real-time. There were significant correlations among measured weights of the eggs after image processing. The coefficient of linear correlation (R2) between measured and actual weights was 0.95.

Data Optimization for Industrial IoT-Based Recommendation Systems

The most common problems that arise when working with big data for intelligent production are analyzed in the article. The work of recommendation systems for finding the most relevant user information was considered. The features of the singular-value decomposition (SVD) and Funk SVD algorithms for reducing the dimensionality of data and providing quick recommendations were determined. An improvement of the Funk SVD algorithm using a smaller required amount of user data for analysis was proposed. According to the results of the experiments, the proposed modification improves the speed of data processing on average by 50–70% depending on the number of users and allows spending fewer computing resources. As follows, recommendations to users are provided in a shorter period and are more relevant. The faster calculation of modified Funk SVD to exchange the optimal parameters between nodes was proposed. It was determined that execution time can be reduced on average by 75% for using ten nodes exchanging the optimal decomposition parameter compared to using one. Using Spark technology for faster calculation on average by 20% compared to Hadoop was proposed. The architecture of the IIoT system was proposed, which uses a modified Funk SVD algorithm to optimize data on edge devices and monitors the effectiveness of providing recommendations using control centers and cloud resources.

ISort: SSD Internal Sorting Algorithm for Big Data

As a basic algorithm for big data processing, external sorting suffers from massive read and write operations in the external memory. Recent works separate part of the data processing work from the host side to the solid state drive (SSD) to reduce data transmission. However, the internal memory of the SSD is limited, and undesirable data retention could occur during the merge phase. Therefore, to improve the efficiency of memory, we propose an algorithm named ISort. Specifically, we build an index table between the memory and the address. The index table determines the order of pages being read in the merge phase according to their minimum values, which are read into memory sequentially to reduce the data residing in memory and improve memory efficiency. Since the merge phase is performed inside the SSD, ISort can take advantage of the high IO bandwidth within the SSD to speed up the execution of the merge phase. We search for the optimal ratio of read and write channels by comparing the “specialized channel” and the “hybrid channel” for data of read and write performance because the utilization of the channel will directly influence performance. Experimental results show that ISort can maintain better data processing speed when SSD memory is limited, outperforming other robust algorithms. In addition, the algorithm’s performance using the crossover strategy is better than that using the specialization strategy.

Brain Chips – A Novel Vision

Technology is helping us go back to our roots in order to address two big problems that humans are currently facing: the volume of data and the speed of data processing. Today’s rate of data production takes 10-20 minutes compared to the past few decades. A decade from now, it will be produced in 5-8 seconds, an undeniable fact no matter what technology we use. The importance of this relates to the fact that everything is becoming digital across the globe. This requires the de-velopment of brain chip interfaces which will improve the brain’s cognitive capacities. These interfaces can also be used for medical problems, such as for patients with neurological conditions including paralysis, stroke, or epilepsy. To assist those who lost control of their limbs or other physiological functions, a device was created. An implanted computer chip in the patient's brain tracks all brain activity and translates user intentions into computer commands.

Acceleration of spiral scanning of soil by means of continuous wavelet transform

The scientific article shows the application of the method of continuous wavelet data transform to estimate soil breaking up when tilling. Due to the high speed of experimental data processing, it became possible to reduce the total time of soil section scanning for determining its agrotechnical parameters. The reduction of time is achieved by both choosing the type of spiral along which scanning is performed and choosing the angular velocity. On the whole, scanning is performed twice as fast per one revolution depending on the studied area of the field. In some cases the number of revolutions can be reduced by 3-5 times.

Quantum K-means Clustering and Classical k Means Clustering For Chest Pain Classification Using Qiskit

Abstract: Quantum computing is an upcoming field of technology which has a broad scope of increasing the current technology in a robust manner. The application area Quantum computing is very huge which ranges from battery research, protein structure research to advance computing and security areas like cryptography, quantum internet, quantum machine learning and quantum cyber security. The quantum machine learning area seems to be the most interesting because of the computing capability of a real time quantum computer. With the quantum machine learning algorithm, classical algorithm, processing speed of current quantum computers and available data in modern world we could gain insights and find patterns which were previously not possible at all.

Non-Destructive Measurement of Quality Parameters of Apple Fruit by Using Visible/Near-Infrared Spectroscopy and Multivariate Regression Analysis

The quality assessment and grading of agricultural products is one of the post-harvest activities that has received considerable attention due to the growing demand for healthy and better-quality products. Recently, various non-destructive methods have been used to evaluate the quality of agricultural products, which are very desirable and faster and more economical than destructive methods. Optical methods are one of the most important non-destructive methods that use the high speed of light detection and computer data processing and are able to evaluate the quality and classification of products with high accuracy. Among the optical methods, visible–near-infrared (Vis/NIR) spectroscopy is considered one of the most accurate methods. In this research, Vis/NIR spectroscopy technology was used in the spectral range of 350–1150 nm for non-destructive detection of some quality parameters including pH, TA, SSC, and TP of two varieties of Red Delicious and Golden Delicious apples. Various pre-processing models were developed to predict the parameters, which brought the desired results with high accuracy so that pH prediction results were for yellow apples (RMSEC = 0.009, rc = 0.991, SDR = 2.51) and for red apples (RMSEC = 0.005, rc = 0.998, SDR = 2.56). The results for TA were also (RMSEC = 0.003, rc = 0.996, SDR = 2.51) for red apples and (RMSEC = 0.001, rc = 0.998, SDR = 2.81) for yellow apples. The results regarding SSC were for red apples (RMSEC = 0.209, rc = 0.990 and SDR = 2.82) and for yellow apples (RMSEC = 0.054, SDR = 2.67 and rc = 0.999). In addition, regarding TP, the results were for red apples (RMSEC = 0.2, rc = 0.989, SDR = 2.05) and for yellow apples (RMSEC = 1.457, rc = 0.998, SDR = 1.61). The obtained results indicate the detection of the mentioned parameters with high accuracy by visible/infrared spectroscopic technology.

PROJECT CONCEPTION OF CAE-SYSTEM FOR WEAR ANALYSIS OF FRICTION BEARING UNDER CONDITIONS OF SHAFT AND LINER AXIS MISALIGNMENT

Modelling of wear processes is an important stage in the design of mechanisms. Such virtual studies allow predicting the service life of components and the machine as a whole. Modern algorithms of CAE-systems allow performing such analysis with high accuracy and taking into account not only material and operating conditions of the mechanism, but also errors that occurred during the manufacturing and assembly of a particular part. These parts require high requirements to the accuracy of manufacturing and assembly, but within the tolerances of the fields errors of mutual positioning are still allowed, which in turn can significantly affect the service life of the mechanism. Therefore, the task of modeling the wear process of the shaft and sleeve in conditions of mutual misalignment of the axes is relevant. Such sliding pairs exist in almost all machines, including car engines, power generation turbines, etc. In this article it is offered to use the module-integrated approach for modeling of wear processes in tribotechnics, providing use of CAE-systems for definition of functioning conditions of friction units as a system core, and construction of separate modules which in this or that degree are integrated into existing CAE systems. to define values of wear of elements of friction pairs that allows to consider mutual influence of wear and change of functioning conditions in the course of operation. The practical implementation of the module-integrated approach is shown by the example of solving the problem of radial plain bearings wear during the misalignment of the shaft and bush axes in the ANSYS environment. The module of representation of multidimensional matrixes of static or dynamic data, providing essential increase in speed of data processing and quality level of the further analysis of the visualized information from any subject area, is developed.

Virtual CANBUS and Ethernet Switching in Future Smart Cars Using Hybrid Architecture

Smart cars have gained much attention in recent years due to the introduction of several safety and convenience features. In this paper, we propose a virtual CANBUS architecture that will improve the safety and data processing in future smart cars with the hybrid use of Ethernet technology deployed in conjunction with a CANBUS system to take advantage of the virtualization, speed, and quality of data processing. Data will be routed intelligently across the dual data paths of the traditional CANBUS and the Ethernet. The virtualized nature, with the help of a series of smart nodes and network traffic analyzers, will allocate the needed resources at the right time during the execution of different processes. This enables the possibility of routing data traffic over both Ethernet and CANBUS connections. The architecture is backward compatible with older vehicles and therefore takes advantage of the existing CANBUS system. The proposed architecture ensures that different segments are isolated from each other so that a breakdown in a segment does not bring down the entire system. The experimental results demonstrate the benefits of the proposed solution, which is to switch between two data pathways depending on the traffic loads. While the CANBUS is sufficient with low-bandwidth data, the Ethernet will create a better performance with high-bandwidth processes. The virtualized environment creates virtual topologies among communicating nodes, greatly simplifying the network management and enhancing the data traffic performance as the bandwidth requirement and the number of processors in future smart cars continue to scale.

METHOD OF IMPROVING THE ACCURACY OF NAVIGATION MEMS DATA PROCESSING OF UAV INERTIAL NAVIGATION SYSTEM

Context. Modern theory and practice of preparation and conduct of hostilities on land, at sea, in the air, and recently in cyberspace dictates the relentless modernization of military equipment. The development of fundamentally new weapons is carried out considering one of the main requirements – maximum automation of operational processes, which allows combatants to distance themselves from each other as much as possible.
 Among the newest models of armaments on the battlefield, due to the predominantly positional nature of the armed confrontation, unmanned aerial vehicles (UAVs) have become virtually indispensable due to their own multitasking. One of the ways to increase the efficiency of UAVs on the battlefield is to increase the level of technical perfection of flight control systems.
 Creating new approaches to the design of unmanned aerial vehicle navigation systems, in particular, based on a platformless inertial navigation system is an urgent task that will provide automatic control of the UAV flight route in the absence of corrective signals from the global satellite navigation system.
 Objective. The purpose of this work is to develop a method for improving the accuracy of MEMC navigation data processing of an inertial navigation system of an unmanned aerial vehicle based on an advanced Madgwik filter.
 This method will increase the speed of data processing of navigation parameters and the accuracy of determining the positioning parameters in the space of the UAV through the use of an advanced Madgwik filter.
 The paper shows the developed block diagram of MEMS PINS filtration on the basis of the improved Madgwik filter, the detailed mathematical description of filtration processes is carried out.
 This method was tested experimentally in the MATLAB software environment using a real set of data collected during the flight of the UAV.
 Method. To achieve this goal, the following methods were used: intelligent systems, theory of automatic control, pseudo-spectral method; methods based on genetic algorithm and fuzzy neural network apparatus.
 Results. A method for improving the accuracy of MEMC navigation data processing of an inertial navigation system of an unmanned aerial vehicle based on an advanced Madgwik filter has been developed. The possibility of practical application of the obtained results and in comparison, with traditional methods is investigated. An experiment was performed in the MatLab software environment, and a comparison was made with the method of processing navigation data based on the Madgwik filter and the Kalman filter.
 Conclusions. The developed method of increasing the accuracy of MEMC navigation data processing of an inertial navigation system of an unmanned aerial vehicle based on an advanced Madgwik filter shows an advantage over known methods in the absence of corrective signals from the global satellite navigation system for accuracy and speed of navigation data processing.

Neuromorphic devices based on fluorite‐structured ferroelectrics

AbstractA continuous exponential rise has been observed in the storage and processing of the data that may not curtail in the foreseeable future. The required data processing speed and power consumption are restricted by the buses between the logic and memory devices that are characteristic of the von Neumann computing architecture. Bio‐mimicking neuromorphic computing has garnered considerable academic and industrial interest to resolve these challenges. Additionally, devices based on emerging nonvolatile memories capable of mimicking the behaviors of synapses and neurons, which are the main elements in biological computing systems (brains), are attracting significant interest from the device community. With the discovery of ferroelectricity in fluorite‐structured oxides, such as HfO2 and ZrO2, which are compatible with the state‐of‐the‐art complementary‐metal‐oxide‐semiconductor processes, ferroelectric devices have rapidly evolved as the main direction of these research and development activities. Fundamental science related to fluorite‐structured ferroelectrics has been intensively studied over the last decade. At present, the focus is gradually moving to practical applications, including neuromorphic computing and advanced classical processing or memory units in the conventional von Neumann architecture. However, despite its rapid development, the wealth of recent progress in neuromorphic computing devices based on fluorite‐structured ferroelectrics has not been reviewed and systemized. This progress report comprehensively reviews and systemizes the recent progress in artificial synaptic and spiking neuron devices for neuromorphic computing based on fluorite‐structured ferroelectrics.image

From halfmetallicity to dielectric tensor: Atomistic study of phase stability and optical spectrum of Sr2FeMoO6 double perovskite and its potential applications

Structures and the physical properties of the current physical materials and other hypothetical models can be visualized and estimated with considerable success using density functional theory. As promising materials Double Perovskites satisfy the need of fulfillment of energy requirements in current scenario. Probing and investigating such structures theoretically, saves time and curtails the expenditure. This Perspective highlights the optical response functions viz Dielectric Tensor, Polarizability, Reflectivity, Optical conductivity, Absorption Coefficient and Extinction Coefficient of the material in the presence of electromagnetic field of varying energy (0–5eV) and offers an outlook of the potential use of such properties in spintronics and sensors. In our study the effective PBE (Perdew, Burke, Ernzerhof) functional was used to investigate the electronic and optical properties of Sr2FeMoO6 and the thermodynamic stability of the material was estimated using the convex hull approach. The formation energy calculated from total energies of the bulk material (SFMO) and the individual total energies of the constituent elements in their corresponding pure bulk phase indicate the simulated structure to be thermodynamically stable with an energy value of −2.533eV. The study confirms SFMO as an anisotropic dispersive medium, with non-linear optical properties wherein apart from differential rates of absorption along different axis of polarization, nonlinearity and dispersion with respect to optical conductivity, dielectric constant can also be observed. The absorptive bandwidth of the material estimated by density functional approach lies between 2 and 4.5 eV and the absorption function resembles the Gaussian curve. The optical conductivity curve indicates that Sr2FeMoO6 is a direct band gap material with a bandgap of 2.1 eV. The absorption coefficient “μ” peaks at an energy values of 3.2 eV and the material is absorptive for most part of the ultra violet region. The pourbiax analysis of the material has been presented and the stability in presence of varying chemical potentials and pH scales suggest non toxicity and stability associated with the material. The extinction coefficient “K” for the two directions of polarizing field behave analogous to their respective imaginary dielectric functions and the peak value of the extinction coefficient shoots to 0.8. The reflectivity plots indicate the maximum reflectance reaches to about 0.2 (20% reflectance) and then decreases with the increase in the energy of the perturbing field. Spin Polarization and the observed TDOS (total density of sates) APDOS (atom projected density of states) graphics suggest that Sr2FeMoO6 exhibits near-half metallic behaviour and can dramatically enhance device performance by controlling speed and various other properties wherein spin can be manipulated. Consequently, SFMO can be tailored for spintronic applications especially in case of multilayered spin-operated junctions/devices and nonvolatile memory elements, quantum computing and faster data processing speeds.

Assessment of Innovative Architectures, Challenges and Solutions of Edge Intelligence

Data collecting, caching, analysis, and processing in close proximity to where the data is collected is referred to as "edge intelligence," a group of linked devices and systems. Edge Intelligence aims to improve data processing quality and speed while also safeguarding the data's privacy and security. This area of study, which dates just from 2011, has shown tremendous development in the last five years, despite its relative youth. This paper provides a survey of the architectures of edge intelligence (Data Placement-Based Architectures to Reduce Latency; 2) Orchestration-Based ECAs- IoT. 3) Big Data Analysis-Based Architectures; and 4) Security-Based Architectures) as well as the challenges and solutions for innovative architectures in edge intelligence.

An Approach for a Fog-centric Secure Cloud Storage Scheme

Currently, cloud computing is being considered as a possible replacement for providing storage services. Security concerns with cloud storage might prevent its mainstream adoption. New security hazards to cloud storage include data breaches, malicious alterations, and data loss. For safe storage employing different clouds, a three-tier architecture built on Fog servers has recently been described. Hash Solomon's code and a specially created hash algorithm are the fundamental methods employed to accomplish the task. This does not, however, provide improved change detection and data recovery and results in the loss of a tiny amount of data to cloud servers. This research suggests a unique secure cloud storage system focused on fog to guard against illegal access, alteration, and destruction of data. The suggested strategy employs a novel method of data obscuration to prevent unwanted access. Additionally, the outcomes of Block - Management to stop unauthorised access and provide greater recovery in case of data loss. In addition, we provide a method based on a hash algorithm to make it simpler to find high-probability changes. Through security analysis, we show that the suggested approach is reliable. Experimental findings support the suggested scheme's performance advantage over modern alternatives in terms of data processing speed.

Behavior Data Analysis of English Learners Based on Discrete Dynamic System Modeling Method.

With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.

BEST-Blockchain-Enabled Secure and Trusted Public Emergency Services for Smart Cities Environment.

In the last few years, the Internet of things (IoT) has recently gained attention in developing various smart city applications such as smart healthcare, smart supply chain, smart home, smart grid, etc. The existing literature focuses on the smart healthcare system as a public emergency service (PES) to provide timely treatment to the patient. However, little attention is given to a distributed smart fire brigade system as a PES to protect human life and properties from severe fire damage. The traditional PES are developed on a centralised system, which requires high computation and does not ensure timely service fulfilment. Furthermore, these traditional PESs suffer from a lack of trust, transparency, data integrity, and a single point of failure issue. In this context, this paper proposes a Blockchain-Enabled Secure and Trusted (BEST) framework for PES in the smart city environment. The BEST framework focuses on providing a fire brigade service as a PES to the smart home based on IoT device information to protect it from serious fire damage. Further, we used two edge computing servers, an IoT controller and a service controller. The IoT and service controller are used for local storage and to enhance the data processing speed of PES requests and PES fulfilments, respectively. The IoT controller manages an access control list to keep track of registered IoT gateways and their IoT devices, avoiding misguiding the PES department. The service controller utilised the queue model to handle the PES requests based on the minimum service queue length. Further, various smart contracts are designed on the Hyperledger Fabric platform to automatically call a PES either in the presence or absence of the smart-home owner under uncertain environmental conditions. The performance evaluation of the proposed BEST framework indicates the benefits of utilising the distributed environment and the smart contract logic. The various simulation results are evaluated in terms of service queue length, utilisation, actual arrival time, expected arrival time, number of PES departments, number of PES providers, and end-to-end delay. These simulation results show the effectiveness and feasibility of the BEST framework.

Anti-monopoly supervision model of platform economy based on big data and sentiment.

With the advent of the cloud computing era, big data technology has also developed rapidly. Due to the huge volume, variety, fast processing speed and low value density of big data, traditional data storage, extraction, transformation and analysis technologies are not suitable, so new solutions for big data application technologies are needed. However, with the development of economic theory and the practice of market economy, some links in the industrial chain of natural monopoly industries already have a certain degree of competitiveness. In this context, the article conducts a research on the anti-monopoly supervision mode of platform economy based on big data and sentiment analysis. This paper introduces the main idea of MapReduce, the current software implementation specifies a Map function that maps a set of key-value pairs into a new set of key-value pairs. It specifies a concurrent Reduce function that guarantees that each of all mapped key-value pairs share the same set of keys. establishes a vector space model, and basically realizes the extraction of text emotional elements. It introduces the theoretical controversy of antitrust regulation of predatory pricing behavior of third-party payment platforms, and conducted model experiments. The experimental results show that the throughput of 40 test users in 1 h of test is determined by two factors, QPS and the number of concurrent, where QPS = 40/(60*60) transactions/second. The time for each test user to log in to the system is 10 min, and the average response time is 10*60 s, then the number of concurrency = QPS*average response time = 40/(60*60)*10*60 = 6.66. This paper has successfully completed the research on the anti-monopoly supervision model of platform economy based on big data and sentiment analysis.

Mars Reconnaissance Orbiter's Mars Color Imager (MARCI): A New Workflow for Processing Its Image Data

AbstractThe Mars Reconnaissance Orbiter's (MRO's) Mars Color Imager (MARCI) has returned approximately daily, approximately global image data of Mars since late 2006, in up to seven different colors, from ultraviolet through near‐infrared. To‐date, that is over 5,300 Mars days of data, nearly eight full Mars Years, or more than 15 Earth years. The data are taken at up to nearly 500 m per pixel, and the nearly circular orbit of MRO and its consistent early afternoon imaging provide an unprecedented baseline of data with which to study Mars' atmosphere and surface processes. Unfortunately, processing MARCI data is difficult, fraught with exploding file sizes, issues that require work‐arounds in free software, and other problems that make this a severely under‐utilized data set. This article discusses a workflow to process MARCI data to their fullest, including suggestions on how to work around issues unique to MARCI and how the data work with the current version of the free software ISIS (Integrated Software for Imagers and Spectrometers). Discussion of some trades that can be made to dramatically speed data processing are also described. Examples of processed MARCI images, mosaics, and color composites are shown, demonstrating the abilities of this workflow on global, regional, and local areas at the full, 96 pixels per degree scale afforded by MARCI.

BUILDING A LOGICAL NETWORK FOR SOLVING THE PROBLEM OF CAR RENTAL BY MEANS ALGEBRA OF FINITE PREDICATES

The article is devoted to the research of the tools of algebra of finite predicates for the system analysis and formalization of the task of automating car rental according to the selected parameters. In particular, the process of optimal car selection depends on the following parameters: car class, brand, availability of driver, type of trip and its duration, fuel type, tariff type, as well as season and weather conditions. Each of these criteria has its own area of definition, where you need to take into account all the relationships and influences between the values of the entered variables (criteria). The aim of the work is to increase the speed of data processing in the problem of car rental by dividing the input multi-place ratio into a binary composition. The technique is based on the means and methods of algebra of finite predicates. Introduction of the predicate of object recognition in the specified subject area allowed to formally describe data of any type, and the applied method of construction of logical networks provides increase in speed of information processing due to parallelization of processing processes. Thus, a complex multi-place relation was divided into a composition of binary relations described in the language of predicate algebra, taking into account the detailed system analysis of the subject area. A scientific novelty is the constructed mathematical model of the car rental problem, which is represented by a predicate that depends on thirteen variables. This predicate is characterized by a system of twelve binary relations, which are represented in the article by dual graphs and formulas of the corresponding predicates. The model predicate is a composition of all constructed binary predicates. The practical significance is due to the logical network built on the basis of a mathematical model, which allows from the relationship "many to many" to move to the relationship "to each other" and parallelize the process of information processing. The result is a logical network of car rental problems, which works iteratively until it receives stable results in two consecutive steps and allows you to solve problems of analysis and synthesis for car rental according to selected parameters.

A Stable Low Power Dissipating 9 T SRAM for Implementation of 4 × 4 Memory Array with High Frequency Analysis

Today’s high speed data processing and memory storage operations demand immediate data write and retrieval to meet up to benchmark. To act as a volatile or nonvolatile data storage for electronic devices such as mobile phones, laptops, the Static Random-Access Memory (SRAM) has been perfect choice for industrialists. So, memory usage is significant and more than 65% of electronic devices uses memory as its heart. Nevertheless, memory turns out to be a leading factor affecting speed, power and data retention in a handheld system. The urge for optimization in power is all time relevant. The proposed system is designed to optimize a single bit memory cell of conventional static random-access memory and hence developed a stable system with low power consumption and obtained significantly low Power-Delay-Product (PDP) by varying operating frequencies in MHz (Mega Hertz) range. Also, a comparative analysis of a 4 × 4 SRAM array is carried out between 6 T SRAM cell and 9 T SRAM cell. Here 62.83% power reduction is obtained in the proposed system as compared with the existing system at an operating frequency of 2 GHz. In this paper, a power reduction of 62.273% is obtained for the array structure. The power dissipation and Power Delay Product [PDP] of the single bit 9 T SRAM cell is also lower than the conventional 6 T SRAM. Thus, the paper implements the proposed scheme of SRAM into an array along with all connecting peripherals.