Minimum spanning tree can be obtained for connected weighted edges with no negative weight using classical algorithms such as Boruvka’s, Prim’s and Kruskal. This paper presents a survey on the classical and the more recent algorithms with different techniques. This survey paper also contains comparisons of MST algorithm and their advantages and disadvantages.
DESIGN OF DELAY COMPUTATION METHOD FOR CYCLOTOMIC FAST FOURIER TRANSFORMsipij
In this paper the Delay Computation method for Common Sub expression Elimination algorithm is being implemented on Cyclotomic Fast Fourier Transform. The Common Sub Expression Elimination algorithm is combined with the delay computing method and is known as Gate Level Delay Computation with Common Sub expression Elimination Algorithm. Common sub expression elimination is effective
optimization method used to reduce adders in cyclotomic Fourier transform. The delay computing method is based on delay matrix and suitable for implementation with computers. The Gate level delay computation method is used to find critical path delay and it is analyzed on various finite field elements. The presented algorithm is established through a case study in Cyclotomic Fast Fourier Transform over finite field. If Cyclotomic Fast Fourier Transform is implemented directly then the system will have high additive complexities. So by using GLDC-CSE algorithm on cyclotomic fast Fourier transform, the additive
complexities will be reduced and also the area and area delay product will be reduced.
A Performance Analysis of CLMS and Augmented CLMS Algorithms for Smart Antennas cscpconf
An adaptive beam former is a device, which is able to steer and modify an array's beam pattern
in order to enhance the reception of a desired signal, while simultaneously suppressing
interfering signals through complex weight selection. However, the weight selection is a critical
task to get the low Side Lobe Level (SLL) and Low Beam Width. It needs to have a low SLL and
low beam width to reduce the antenna's radiation/reception ability in unintended directions. The
weights can be chosen to minimize the SLL and to place nulls at certain angles. A vast number
of possible window functions that are available to provide the weights to be used in SmartAntennas. This paper presents various traditional windowing techniques such as Binomial, Kaiser-Bessel, Blackman, Gaussian, and so on for computing weights for adaptive beam forming and also neural based methods like, Least Mean Square (LMS), Complex LMS (CLMS) [5], and Augmented CLMS (ACLMS) [1] algorithms. This paper discusses about various observations on signal processing techniques of Smart Antennas, that compromise between SLL
and beam width (Directivity), to improve the base station capacity in Cellular and Mobile Communications and also the performance analysis of CLMS and ACLMS in terms of SLL and beam width, error convergence rate.
Microgrids are the solution to the growing demand for energy in the recent times. It has the potential to improve local reliability, reduce cost and increase penetration rates for distributed renewable energy generation. Inclusion of Renewable Energy Systems(RES) which have become the topic of discussion in the recent times due to acute energy crisis, causes the power flow in the microgrid to be bi-directional in nature. The presence of the RES in the microgrid system causes the grid to be reconfigurable. This reconfiguration might also occur due to load or utility grid connection and disconnection. Thus conventional protection strategies are not applicable to micro-grids and is hence challenging for engineers to protect the grid in a fault condition. In this paper various Minimum Spanning Tree(MST) algorithms are applied in microgrids to identify the active nodes of the current topology of the network in a heuristic approach and thereby generating a tree from the given network so that minimum number of nodes have to be disconnected from the network during fault clearance. In the paper we have chosen the IEEE-39 and IEEE-69 bus networks as our sample test systems.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM ijcisjournal
Color image segmentation algorithms in the literature segment an image on the basis of color, texture, and
also as a fusion of both color and texture. In this paper, a color image segmentation algorithm is proposed
by extracting both texture and color features and applying them to the One-Against-All Multi Class Support
Vector Machine classifier for segmentation. A novel Power Law Descriptor (PLD) is used for extracting
the textural features and homogeneity model is used for obtaining the color features. The Multi Class SVM
is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set
based membership functions capably handle the problem of overlapping clusters. The lower and upper
approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness in data.
Parameterization tools are not a prerequisite in defining Soft set theory. The goodness aspects of soft sets,
rough sets and fuzzy sets are incorporated in the proposed algorithm to achieve improved segmentation
performance. The Power Law Descriptor used for texture feature extraction has the advantage of being
dealt in the spatial domain thereby reducing computational complexity. The proposed algorithm is
comparable and achieved better performance compared with the state of the art algorithms found in the
literature.
DESIGN OF DELAY COMPUTATION METHOD FOR CYCLOTOMIC FAST FOURIER TRANSFORMsipij
In this paper the Delay Computation method for Common Sub expression Elimination algorithm is being implemented on Cyclotomic Fast Fourier Transform. The Common Sub Expression Elimination algorithm is combined with the delay computing method and is known as Gate Level Delay Computation with Common Sub expression Elimination Algorithm. Common sub expression elimination is effective
optimization method used to reduce adders in cyclotomic Fourier transform. The delay computing method is based on delay matrix and suitable for implementation with computers. The Gate level delay computation method is used to find critical path delay and it is analyzed on various finite field elements. The presented algorithm is established through a case study in Cyclotomic Fast Fourier Transform over finite field. If Cyclotomic Fast Fourier Transform is implemented directly then the system will have high additive complexities. So by using GLDC-CSE algorithm on cyclotomic fast Fourier transform, the additive
complexities will be reduced and also the area and area delay product will be reduced.
A Performance Analysis of CLMS and Augmented CLMS Algorithms for Smart Antennas cscpconf
An adaptive beam former is a device, which is able to steer and modify an array's beam pattern
in order to enhance the reception of a desired signal, while simultaneously suppressing
interfering signals through complex weight selection. However, the weight selection is a critical
task to get the low Side Lobe Level (SLL) and Low Beam Width. It needs to have a low SLL and
low beam width to reduce the antenna's radiation/reception ability in unintended directions. The
weights can be chosen to minimize the SLL and to place nulls at certain angles. A vast number
of possible window functions that are available to provide the weights to be used in SmartAntennas. This paper presents various traditional windowing techniques such as Binomial, Kaiser-Bessel, Blackman, Gaussian, and so on for computing weights for adaptive beam forming and also neural based methods like, Least Mean Square (LMS), Complex LMS (CLMS) [5], and Augmented CLMS (ACLMS) [1] algorithms. This paper discusses about various observations on signal processing techniques of Smart Antennas, that compromise between SLL
and beam width (Directivity), to improve the base station capacity in Cellular and Mobile Communications and also the performance analysis of CLMS and ACLMS in terms of SLL and beam width, error convergence rate.
Microgrids are the solution to the growing demand for energy in the recent times. It has the potential to improve local reliability, reduce cost and increase penetration rates for distributed renewable energy generation. Inclusion of Renewable Energy Systems(RES) which have become the topic of discussion in the recent times due to acute energy crisis, causes the power flow in the microgrid to be bi-directional in nature. The presence of the RES in the microgrid system causes the grid to be reconfigurable. This reconfiguration might also occur due to load or utility grid connection and disconnection. Thus conventional protection strategies are not applicable to micro-grids and is hence challenging for engineers to protect the grid in a fault condition. In this paper various Minimum Spanning Tree(MST) algorithms are applied in microgrids to identify the active nodes of the current topology of the network in a heuristic approach and thereby generating a tree from the given network so that minimum number of nodes have to be disconnected from the network during fault clearance. In the paper we have chosen the IEEE-39 and IEEE-69 bus networks as our sample test systems.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM ijcisjournal
Color image segmentation algorithms in the literature segment an image on the basis of color, texture, and
also as a fusion of both color and texture. In this paper, a color image segmentation algorithm is proposed
by extracting both texture and color features and applying them to the One-Against-All Multi Class Support
Vector Machine classifier for segmentation. A novel Power Law Descriptor (PLD) is used for extracting
the textural features and homogeneity model is used for obtaining the color features. The Multi Class SVM
is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set
based membership functions capably handle the problem of overlapping clusters. The lower and upper
approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness in data.
Parameterization tools are not a prerequisite in defining Soft set theory. The goodness aspects of soft sets,
rough sets and fuzzy sets are incorporated in the proposed algorithm to achieve improved segmentation
performance. The Power Law Descriptor used for texture feature extraction has the advantage of being
dealt in the spatial domain thereby reducing computational complexity. The proposed algorithm is
comparable and achieved better performance compared with the state of the art algorithms found in the
literature.
While a standardised sales methodology has been proven effective, depending on your customer, it is best to nuance your process in order to suit each individual client.
The following tips are meant to serve only as general guidelines to approaching your clientele.
Saludinnova propone una propuesta de salud que quiere humanizar la tecnología , rediseñando y mejorando la relación medico paciente .
Queremos ser un apoyo a las personas , equipos de salud , instituciones y gobiernos .
www.saludinnova.cl
Intervento di Raffaele Colaizzo in occasione del LAB Governance e organizzazione di un Sistema Territoriale di Sviluppo (STS) del 07-05-13 organizzato da FormezPA all’interno della Linea A.2 - PROGETTARE dI Capacity SUD
هذا الكتاب يستهدف كل من يرغب في تنمية قدراته القيادية، واستغلال ذكائه الفطري، وإحساسه الطبيعي لتحقيق النجاح.
هذا الكتاب يساعد القارئ على فهم وتنفيذ المهام الأساسية، وتنمية المهارات التي تتيح له التحكم في منصبه الجديد بثقة وجدارة، والاستفادة من الحاسة السادسة التي وهبها الله للإنسان
Low Power Clock Distribution Schemes in VLSI DesignIJERA Editor
This paper reviewed the comparison between different clock distribution schemes which used for low power
VLSI design which are the most important aspect in the industry. The main clock distribution schemes are
single driver clock scheme and distributed buffers clock scheme. There are different tradeoffs in both the
techniques such as size of buffers, number of buffers etc.
Includes:
1).Cost Of Graph
2).Minimality
3).Loops And Parallel Algorithm
4).Minimum Spanning Tree
5).Greedy Algorithm
This will help to understand more of graph and tress, in addition talks about practical inventions which are used in modern technological advancements such as AI,ML etc.
A CPW-fed Rectangular Patch Antenna for WLAN/WiMAX ApplicationsIDES Editor
This paper presents a CPW fed Rectangular
shaped patch antenna for the frequency 3.42GHz which
falls in WiMAX and 5.25GHz for WLAN applications.
The measured -10dB impedance bandwidth is about
650MHz (2.98GHz-3.63GHz) for WiMAX and 833MHz
(4.95GHz-5.78GHz) for WLAN applications. The effect of
slot width, rectangular patch height, and substrate
dielectric constant have been evaluated. The results of
antenna are simulated by using Zeeland’s MOM based
IE3D tool. Two dimensional radiation patterns with
elevation and azimuth angles, VSWR<2, Return loss of
-24dB and -18dB for WiMAX and WLAN applications,
antenna efficiency about 90%, gain above 3.5dB are
obtained. The compact aperture area of the antenna is
46.2 X 41.66 mm2.
While a standardised sales methodology has been proven effective, depending on your customer, it is best to nuance your process in order to suit each individual client.
The following tips are meant to serve only as general guidelines to approaching your clientele.
Saludinnova propone una propuesta de salud que quiere humanizar la tecnología , rediseñando y mejorando la relación medico paciente .
Queremos ser un apoyo a las personas , equipos de salud , instituciones y gobiernos .
www.saludinnova.cl
Intervento di Raffaele Colaizzo in occasione del LAB Governance e organizzazione di un Sistema Territoriale di Sviluppo (STS) del 07-05-13 organizzato da FormezPA all’interno della Linea A.2 - PROGETTARE dI Capacity SUD
هذا الكتاب يستهدف كل من يرغب في تنمية قدراته القيادية، واستغلال ذكائه الفطري، وإحساسه الطبيعي لتحقيق النجاح.
هذا الكتاب يساعد القارئ على فهم وتنفيذ المهام الأساسية، وتنمية المهارات التي تتيح له التحكم في منصبه الجديد بثقة وجدارة، والاستفادة من الحاسة السادسة التي وهبها الله للإنسان
Low Power Clock Distribution Schemes in VLSI DesignIJERA Editor
This paper reviewed the comparison between different clock distribution schemes which used for low power
VLSI design which are the most important aspect in the industry. The main clock distribution schemes are
single driver clock scheme and distributed buffers clock scheme. There are different tradeoffs in both the
techniques such as size of buffers, number of buffers etc.
Includes:
1).Cost Of Graph
2).Minimality
3).Loops And Parallel Algorithm
4).Minimum Spanning Tree
5).Greedy Algorithm
This will help to understand more of graph and tress, in addition talks about practical inventions which are used in modern technological advancements such as AI,ML etc.
A CPW-fed Rectangular Patch Antenna for WLAN/WiMAX ApplicationsIDES Editor
This paper presents a CPW fed Rectangular
shaped patch antenna for the frequency 3.42GHz which
falls in WiMAX and 5.25GHz for WLAN applications.
The measured -10dB impedance bandwidth is about
650MHz (2.98GHz-3.63GHz) for WiMAX and 833MHz
(4.95GHz-5.78GHz) for WLAN applications. The effect of
slot width, rectangular patch height, and substrate
dielectric constant have been evaluated. The results of
antenna are simulated by using Zeeland’s MOM based
IE3D tool. Two dimensional radiation patterns with
elevation and azimuth angles, VSWR<2, Return loss of
-24dB and -18dB for WiMAX and WLAN applications,
antenna efficiency about 90%, gain above 3.5dB are
obtained. The compact aperture area of the antenna is
46.2 X 41.66 mm2.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...ijasuc
In wireless sensor network, devices or nodes are generally battery powered devices. These nodes have
limited amount of initial energy that are consumed at different rates, depending on the power level. The
lifetime of the network is defined as the time until the first node fails (or runs out of battery). In this paper
different type of energy efficient routing algorithms are discussed and approach of these algorithms is to
maximize the minimum lifetime of wireless sensor network. Special attention has been devoted for
algorithms formulate the routing problem as a linear programming problem, which uses the optimal flow
path for data transmission and gives the optimum results. Advantages, limitations as well as comparative
study of these algorithms are also discussed in this paper.
Design Analysis of Delay Register with PTL Logic using 90 nm TechnologyIJEEE
This paper presents low area and power efficient delay register using CMOS transistors. The proposed register has reduced area than the conventional register. This resistor design consists of 6 NMOS and 6 PMOS. The proposed delay register has been designed in logic editor and simulated using 90nm technology. Also the layout simulation and parametric analysis has been done to find out the results. In this paper register has been designed using full automatic layout design and semicustom layout design. Then the performance of these different designs has been analyzed and compared in terms of power, delay and area. The simulation result shows that circuit design of delay register using PTL techniques improved by power 0.05% and 61.8% area.
Designing of Low Power CNTFET Based D Flip-Flop Using Forced Stack TechniqueIJERA Editor
Low Power devices in small packages is the need of present and future electronic devices. Electronics Industry is making devices which can be planted in human bodies. CMOS Technology won‟t be able to deliver such devices because it shows short channel effects in Nano scale. So, to overcome the problems of CMOS technology we use CNTs (Carbon Nano Tubes). In electronic devices, power is consumed by various elements like flip-flop, latches, clock sources. So in order to reduce power of a system we used to reduce power consumed by flip-flops. In this paper we design an existing flip-flop “Low power clocked pass transistor flip-flop (LCPTFF)” on CNTFET using Stanford CNTFET model for reference. We propose a design of CNTFET based Forced Stack Low Power Clocked Pass Transistor Flip-Flop (CN-FS-LCPTFF) and observe 12% to 25% power reduction in various conditions like temperature change, CNTFET diameter change, and different voltage supply.
Similar to OTP, Phishing, QR code, Shares, Visual Cryptography. (20)
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
OTP, Phishing, QR code, Shares, Visual Cryptography.
1. Nimesh Patel Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 1( Part 3), January 2015, pp.06-10
www.ijera.com 6 | P a g e
A Survey on: Enhancement of Minimum Spanning Tree
Nimesh Patel*, Dr. K. M. Patel**
*(Department of Computer Engineering, R K University at Rajkot, INDIA)
** (Department of Computer Engineering, R K University at Rajkot, INDIA)
ABSTRACT
Minimum spanning tree can be obtained for connected weighted edges with no negative weight using classical
algorithms such as Boruvka’s, Prim’s and Kruskal. This paper presents a survey on the classical and the more
recent algorithms with different techniques. This survey paper also contains comparisons of MST algorithm and
their advantages and disadvantages.
Keywords – Cord, DWCM, FCM, Graph, LC-MST, MST, Tree
I. INTRODUCTION
A Minimum Spanning Tree of a weighted graph
is a spanning tree in which the sum of the weight of
all its edges is a minimum of all such possible
spanning tree of the graph. Minimum spanning Tree
must be finding from the Graph. A collection of
vertices and edges makes a graph, and each edge
connects a pair of vertices [1, 2, 3, 4,].
Fig.1 graph and its MST
There are two types of graph, Directed graph and
undirected graph. A directed graph is graph in which
a set of vertices are connected together, where all the
edges are directed from one vertex to another. A
directed graph is also known as digraph or a directed
network. In contrast, a graph where the edges are
bidirectional is called an undirected graph. In the
directed graph edges have a direction associated with
them. An undirected graph is one in which edges have
no orientation. The edge (a, b) is identical to the edge
(b, a).The maximum number of edges in an
undirected graph without a self-loop is n (n - 1)/2
[10].
1.1 Application of MST
1.1. Applications of MST are used in the design
of computer and communication networks, telephone
networks, links road network, islands connection,
pipeline network, electrical circuits, utility circuit
printing, obtaining an independent set of circuit
equations for an electrical network, etc.
1.1.2 It offers a method of solution to other problems
to which it applies less directly, such as network
reliability, clustering and classification problems.
1.1.3 Used to find the approximation solution for the
NP hard problems.
1.2 Objective of MST
1.2.1 To minimize cost of the tree spanning tree
for both directed and undirected.
1.2.2 To minimize load on the network.
1.2.3 To eliminate the cycle from the graph from
the MST.
1.2.4 To improve the complexity of the MST.
II. MST CLASSICAL ALGORITHM
There are various classical algorithms available
which describe below.
Kruskal’s , Prim's and Boruvka’s algorithm is a
greedy algorithm which used to find a minimum
spanning tree for a connected weighted undirected
graph. This means when the total weight of all the
edges is minimized in the tree, at that time it finds a
subset of the edges which forms a tree which includes
every vertex
2.1 Kruskal algorithm:
This algorithm first appeared in Proceedings of
the American Mathematical Society during 1956, and
was written by Joseph Kruskal. In Kruskal’s
algorithm all edges are shorted in non decreasing
order and selected the lowest edges first for becoming
a minimum spanning tree. If there is a cycle generated
during the implantation then selected edges will be
removed from the graph and next lowest edges are
selected. This will repeat till (n-1) edges will be added
in to the graph. Using simple data structure Kruskal's
algorithm complexity is O (E log E) time, or
equivalently, O (E log V) time. Where E is the
number of edges in the graph and V is the number of
vertices [1, 2, 21,].
Advantages are:
RESEARCH ARTICLE OPEN ACCESS
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1) Easy to understand
2) Give good result for large number of vertices
and edges.
Disadvantages are:
1) Difficulty of checking whether arcs form
cycles makes it slow and hard to program
2) Same weight may increase the complexity.
2.2 Prim’s algorithm:
The algorithm was developed in 1930 by Jarnik
and later rediscovered by computer scientist Robert C.
Prim in 1957 and then again rediscovered by Edsger
Dijkstra in 1959. Therefore it is also known as the
DJP (Dijkstra-Jarnik Problem) algorithm, the Jarnik
algorithm, or the Prim–Jarnik algorithm. Using a
simple binary heap data structure complexity is O(|E|
log |V|) where |E| is the number of edges and |V| is the
number of vertices. Using Fibonacci heap in dense
graph complexity is O(|E| + |V| log |V|), which is
asymptotically faster [2, 9, 12, 16].prim’s algorithm
steps are given below:
1) Create a set MST Set that keeps track of
vertices already included in MST.
2) Assign a key value to all vertices in the input
graph. Initialize all key values as INFINITE. Assign
key value as 0 for the first vertex so that it is picked
first.
3) While MST set doesn’t include all vertices
a) Pick a vertex u which is not there in MST
Set and has minimum key value.
b) Include u to MST Set.
c) Update key value of all adjacent vertices of
u. To update the key values, iterate through all
adjacent vertices. For every adjacent vertex v, if
weight of edge u-v is less than the previous key value
of v, update the key value as weight of u-v.
Advantages are:
1) Easy to understand.
2) Root node is selected so clear about the
starting node.
Disadvantages are:
1) Time taken to check for smallest weight arc
makes it slow for large numbers of nodes.
2) Difficult to program, though it can be
programmed in matrix form
3) Same weight may increase the complexity
when one of the weights is eliminated in a cycle
2.3 Boruvka’s algorithm:
It was first published by Otakar Boruvka in 1926.
It is a method of constructing an efficient electricity
network. This algorithm was again discovered by
Choquet in 1938, then it rediscovered by Florek,
Lukasiewicz, Perkal, Steinhaus, and Zubrzycki in
1951, then again discovered by Sollin in 1965. Sollin
was the only computer scientist in this list living in an
English speaking country. So, this algorithm is
frequently called Sollin's algorithm. The algorithm
starts visiting each vertex and adding the cheapest
edge from that vertex to another vertex in the graph, if
edges already added in the graph then in will
neglected. It will continue joining these edges until all
vertices is visited in spanning tree. Boruvka’s
algorithm taken O (log V) iterations of the outer loop
until it terminates. Therefore it take O (E log V) time
to run. Where E is the number of edges, and V is the
number of vertices in graph [12, 18].
Advantages are:
1) If the edge costs are distinct, or are made
distinct by using a tie-breaking rule then Boruvka's
algorithm can be serialized into a specialization of the
generic algorithm.
Disadvantages are:
1) It is complicated to implement without
serialization.
2.4 Karger, Klein Tarjan:
Use random sampling in combination with linear
time algorithm for verifying spanning tree. This
computational model is unit cost random access with
restriction. This operation allowed on edges weights
for binary comparison. It runs in O(m) time with high
probability in restricted random access model. This
algorithm proposed by Karger whose time complexity
is O(n log n + m)[12, 17]. The O (m) time complexity
is due to Klein and Tarjan follow two properties. 1)
Cycle property: For any cycle in the graph, if weight
of an edge of cycle is larger than the weights of all
other edges of cycle, then this edge cannot belong to
an MST. 2) Cut property: For any cut cycle in the
graph, if the weight of an edge of cycle is strictly
smaller than the weights of all other edges of cycle,
then this edge belongs to all MST of the graph.
Advantages are:
1) It’s allowed on edges weights for binary
comparison.
Disadvantages are:
1) Random sampling in combination with linear
time algorithm gives restriction for unit cost while
access randomly.
2.4.1 Cycle property:
For any cycle in the graph, if weight of an edge
of cycle is larger than the weights of all other edges of
cycle, then this edge cannot belong to an MST.
2.4.2 Cut property:
For any cut cycle in the graph, if the weight of an
edge of cycle is strictly smaller than the weights of all
other edges of cycle, then this edge belongs to all
MST of the graph.
III. LITERATURE SURVEY
In this section, lot of research works has been
recorded from past few years. They are presented
here:
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3.1 Combinatorial algorithm [5]:
This algorithm is based on Difference Weighted
Circuit Matrix. To find a minimum spanning tree for a
connected weighted graph with no negative weight
can be obtained using classical algorithms such as
Prim’s and Kruskal. Both of two give the single
minimum spanning tree. It sometimes needs to
generate the second minimum spanning tree, third,
fourth and so on. This algorithm performs two major
tasks. 1) Cord: The edges that are not in the spanning
tree of a graph are called the chord. That is the sub
graph S is the collection of Chord of the graph G with
respect to S the Spanning tree of the graph. 2)
DWCM: The abbreviation is Difference Weighted
Circuit Matrix. It is the little bit of modification of the
FCM. A sub matrix in which all rows correspond to a
set of fundamental circuits is called a Fundamental
circuit matrix. If n is the number of vertices and e is
the number of edges in a connected graph, then the
matrix is an (e-n-1) *(n-1) matrix. Here the branch
weights are present on the column head as branch
mark. The chords (e-n-1) are for the row
representation. Here each cell of the matrix is
assigned difference weight of the chord and the
branches participating for generating circuit. When
the column head presented, this chord is joined to the
spanning tree.
Advantages are:
Sometimes in real life that minimal path can’t be
reached due to some circumstances, in that case the
next minimal spanning tree is useful.
Disadvantages are:
1) Complexity is more because of generating
more than one spanning tree more.
3.2 Euclidean based MST algorithm [6]:
It is based on the well separated pair
decomposition. This is introduced by Callahan and
Kosaraju.
There are two standard techniques for
implementing adding edge process: one is the Union-
find data structure, another one is the labeling
method. Here structure wrapped with the tree map
method. In which each index of the labeling maps
with integer key type data for the sequence of vertices
in a cycle. At the time of execution loop adding a new
edge into the minimum spanning tree, it checks that if
both vertices have not been added to any of the cycle
yet. If it wasn’t added then it create a new key. Then it
maps this integer value with the new cycle. In that
case one vertex has been added to one of the cycles
with other vertex, it just add this new vertex into the
cycle containing the other vertex. Another situation is
that both vertices have been added to the same cycle.
In this case edge is redundant for the new
minimum spanning tree. Through this algorithm
storage space and running time efficiency is improve.
To compute the EMST of n points in the space, one
can link each pair of edges.
Advantages are:
1) It greatly improves the storage space and
running time efficiency over traditional approaches.
Disadvantages are:
1) Hard to implement
3.3 LC-MST algorithm [7]:
Here, least-cost minimum spanning tree (LC-
MST) problem is defined as a method to construct a
minimum cost spanning tree that has the least-cost
edges in the network by using the distance or cost
matrix. The method presents a new algorithm based
on the distance matrix to solve the LC-MST
problem.LC-MST algorithm steps are given below.
1. Input the distance matrix D = [dij] nxn for the
weighted graph G (V, E), where V is the set of
vertices and E is the set of edges.
2. For all i, j find the least-cost element in each
column j and set the other elements to zero.
3. Construct the preferred link matrix (PLM) by
using step 2.
4. Construct the nodes-set matrix (NSM) by using
PLM matrix constructed in step 3.
5. Combine the node-pairs in step 4 to construct
the candidate spanning tree.
6. If there are any duplicating node-pairs, keep
one of them. If there is a set of node-pairs, construct a
cycle, remove the one that has the largest cost.
7. Output the least-cost minimum spanning tree.
Time complexity is less than the DC-MST and
CMST algorithms. Still complexity is O(n^2).
Advantages are:
1) Simple and efficient method to solve the LC-
MST problem in less time.
LC-MST time complexity is less than the time
complexity of both the DC-MST and CMST
algorithms.
Disadvantages are:
1) Complexity is higher O(n^2).
3.4 Heap base bucket sorting algorithm [8]:
In this algorithm, edges are in group and place in
to the bucket. Then select the minimal edges from the
bucket and put in to the heap. If there is a cycle
generated then removes those edges from the bucket
and then next minimal edges will select. This will
repeat till n-1 edges will find out from the heap. A
clear cut method to form the buckets is to link the
elements which describing the edges. In the same
bucket to form a linear list and to use an array of list
heads which point to the front of the lists. Note that
this is not the only possible storage organization. The
method of math sort through also possible to arrange
the edges by making a chain of exchange operations.
Complexity for sorting edge is improving in O(m)
instead of O(m log m) [5]. It will work effectively in
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case of uniformly distributed random number for wide
class.
Advantages are:
1) This algorithm is very fast if the edge costs are
from a distribution which is close to uniform.
Disadvantages are:
1) Complexity is O(m log m) in worst case
occurs when there is a strong peak in the distribution
of the edge costs.
3.5 Modified prim’s algorithm [9]:
In this algorithm, Instead of choosing randomly,
root node is chosen with minimum edge weight.
Remaining procedure is same as used by prims. Due
to this only minimum weight edges are included.
Although complexity remains same as prim’s
algorithm.
Advantages are:
It gives slightly better performance in case where
minimum weight edge is required from the starting
phase of minimum spanning tree formation.
Disadvantages are:
Complexity is remaining same.
3.6 Visit, Mark and Construct MST
algorithm [10]:
In this algorithm, adjacency matrix is used which
help to reduce the step at the time of constructing
MST. This method is based on the kruskal algorithm
with modification which used improve the complexity
of the MST algorithm for the undirected graph. This
method is purely for the undirected graph. So the
weight of the 1 to 2 vertices is same for the 2 to 1
vertices. See the below Fig.2. In which edges 1 to 2
contain 52 weights and the weight for the edges 2 to 1
is also 52.So, it is same for undirected graph.
Fig.2 n*n weighted matrix
Now for the vertices 1 to 1, 2 to 2....n to n. there
is no weight and if there is a weight then it
automatically removed because of generating a cycle.
So, it places 0 as infinite. Now if we have n vertices
then we have n*n adjacency matrix. So, need to
perform n^2 steps, because it will check all the
elements from the graph. So, author first removes
unused row column from the adjacency matrix. Here
first row and last column are never used during the
implementation because edges 1 to 2 has the same 2
to 1 and edges 1 to 1 is always 0 or automatically
eliminated because of generating cycle. So, adjacency
matrix has (n-1) rows and (n-1) columns. See the
below adjacency Fig.3 and Fig.4.
Fig.3 Reducing the n*n order matrix to order
m*m where m = (n-1)
Fig.4 m*m operational weight Matrix
So, n^2− 2n +1 steps will be performed. So,
complexity is O (m^2) where m is (n-1) [7]. This
algorithm works in the following two passes.
1) Mark Phase: In which algorithm marks the
candidate edge from the graph for the minimum
spanning tree. 2) MST Construction Phase: In the
second phase, the algorithm constructs the desired
minimum spanning tree T including only the marked
edges from the upper triangular weight matrix M,
which were marked during Marking Pass.
In this algorithm, minimum weight are marked
and visited first. Once weight is visited and it doesn’t
create a cycle then it will be added to the list of
minimum spanning tree edges. Otherwise it will be
removed and next minimum weight should be taken
for the further procedure. This will happen till n-1
edges get for the minimum spanning tree edges. Once
the n-1 edges are get than it will stop algorithm and
calculate total cost.
Advantages are:
Complexity is O(n) for the best case.
Disadvantages are:
Complexity is O(n^2) for the best case.
IV. CONCLUSION
This survey paper presents classical algorithms
and advance MST algorithm & it is observed that
complexity is very high because of cycle in the graph
and the edges with the same weight. It also observed
that complexity can be improved using following
steps. 1) Marked and visit the maximum weight of the
edges. 2) If it creates a cycle then eliminate those
edges from the current graph. 3) Above two steps will
be repeated till edges = vertices-1.
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