The document describes the development of an FPGA-based system to monitor neutron flux in fast breeder reactors over 10 decades. It aims to calculate frequency based on the area under the neutron detector signal curve. Testing established that area under the curve has the best linear relationship with frequency, even with random amplitude and noise. The system was tested across different frequency ranges and overlap levels. Code was then developed to calculate frequency from the area under an input signal based on the identified patterns.
Delaunay based two-phase algorithm for connected cover in WSNsMaynooth University
Monitoring applications are one of the main usages of wireless sensor networks, where the sensor nodes are responsible to report any event of interest in the monitoring area. Due to their limited energy storage, the nodes are prone to fail, which may lead to network partitioning problem. To cope with this problem, the number of deployed sensor nodes in an area is more than the required quantity. The challenge is to turn on a minimal number of nodes to preserve network connectivity and area coverage. In this paper, we apply computational geometry techniques to introduce a new 2-phase algorithm, called Delaunay Based Connected Cover (DBCC), to find a connected cover in an omnidirectional wireless sensor network. In the first phase, the Delaunay triangulation of all sensors is computed and a minimal number of sensors is selected to ensure the coverage of the region. In the second phase, connectivity of the nodes is ensured. The devised method is simulated by NS2 and is compared with two well-known algorithms, CCP and OGDC. For the case, where the communication and the coverage radii are equal, our method requires 23% and 45% fewer nodes compared to the aforementioned methods, respectively. In the second simulation case, the communication radius is set to 1.5 times of the coverage radius. The results demonstrate that DBCC chooses 14% and 34% fewer nodes, respectively.
P-Wave Onset Point Detection for Seismic Signal Using Bhattacharyya DistanceCSCJournals
In seismology Primary p-wave arrival identification is a fundamental problem for the geologist worldwide. Several numbers of algorithms that deal with p-wave onset detection and identification have already been proposed. Accurate p- wave picking is required for earthquake early warning system and determination of epicenter location etc. In this paper we have proposed a novel algorithm for p-wave detection using Bhattacharyya distance for seismic signals. In our study we have taken 50 numbers of real seismic signals (generated by earthquake) recorded by K-NET (Kyoshin network), Japan. Our results show maximum standard deviation of 1.76 sample from true picks which gives better accuracy with respect to ratio test method.
Greedy – based Heuristic for OSC problems in Wireless Sensor NetworksIJMER
This paper contains optimize set coverage problem in wireless sensor networks with adaptable sensing range. Communication and sensing consume energy, so efficient power management can extended the network lifetime. In this paper we consider a enormous number of sensors with adaptable sensing range that are randomly positioned to monitor a number of targets. Every single target may be redundantly covered by various sensors. For preserving energy resources we organize sensors in sets stimulated successively. In this paper we introduce the Optimize Set Coverage (OSC) problem that has in unbiased finding with an extreme number of set covers in which every sensor node to be activated is connected to the base station. A sensor can be participated in various sensor sets, but the overall energy consumed in all groups is forced by the primary energy reserves. We show that the OSC problem is NP-complete and we propose the solutions: an integer programming for OSC problem, a linear programming for OSC problem with greedy approach, and a distributed and localized heuristic. Simulation results are presented and validated to our approaches.
Delaunay based two-phase algorithm for connected cover in WSNsMaynooth University
Monitoring applications are one of the main usages of wireless sensor networks, where the sensor nodes are responsible to report any event of interest in the monitoring area. Due to their limited energy storage, the nodes are prone to fail, which may lead to network partitioning problem. To cope with this problem, the number of deployed sensor nodes in an area is more than the required quantity. The challenge is to turn on a minimal number of nodes to preserve network connectivity and area coverage. In this paper, we apply computational geometry techniques to introduce a new 2-phase algorithm, called Delaunay Based Connected Cover (DBCC), to find a connected cover in an omnidirectional wireless sensor network. In the first phase, the Delaunay triangulation of all sensors is computed and a minimal number of sensors is selected to ensure the coverage of the region. In the second phase, connectivity of the nodes is ensured. The devised method is simulated by NS2 and is compared with two well-known algorithms, CCP and OGDC. For the case, where the communication and the coverage radii are equal, our method requires 23% and 45% fewer nodes compared to the aforementioned methods, respectively. In the second simulation case, the communication radius is set to 1.5 times of the coverage radius. The results demonstrate that DBCC chooses 14% and 34% fewer nodes, respectively.
P-Wave Onset Point Detection for Seismic Signal Using Bhattacharyya DistanceCSCJournals
In seismology Primary p-wave arrival identification is a fundamental problem for the geologist worldwide. Several numbers of algorithms that deal with p-wave onset detection and identification have already been proposed. Accurate p- wave picking is required for earthquake early warning system and determination of epicenter location etc. In this paper we have proposed a novel algorithm for p-wave detection using Bhattacharyya distance for seismic signals. In our study we have taken 50 numbers of real seismic signals (generated by earthquake) recorded by K-NET (Kyoshin network), Japan. Our results show maximum standard deviation of 1.76 sample from true picks which gives better accuracy with respect to ratio test method.
Greedy – based Heuristic for OSC problems in Wireless Sensor NetworksIJMER
This paper contains optimize set coverage problem in wireless sensor networks with adaptable sensing range. Communication and sensing consume energy, so efficient power management can extended the network lifetime. In this paper we consider a enormous number of sensors with adaptable sensing range that are randomly positioned to monitor a number of targets. Every single target may be redundantly covered by various sensors. For preserving energy resources we organize sensors in sets stimulated successively. In this paper we introduce the Optimize Set Coverage (OSC) problem that has in unbiased finding with an extreme number of set covers in which every sensor node to be activated is connected to the base station. A sensor can be participated in various sensor sets, but the overall energy consumed in all groups is forced by the primary energy reserves. We show that the OSC problem is NP-complete and we propose the solutions: an integer programming for OSC problem, a linear programming for OSC problem with greedy approach, and a distributed and localized heuristic. Simulation results are presented and validated to our approaches.
Development of Nonlinear Optical Microscope and ScannerRohan Sharma
For my internship at the Femtosecond Spectroscopy & Nonlinear Optics Lab at IIT Delhi, I worked on the development of nonlinear optical microscope and scanner for 10 weeks.During the internship, I developed a multimodal nonlinear optical microscope employing white light and femtosecond laser to record the optical imaging, transmission imaging as well as the second harmonic generation (SHG) imaging of a sample.
Security based Clock Synchronization technique in Wireless Sensor Network for...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Compact Coding Using Multi-Photon Tolerant Quantum Protocols For Quantum Comm...ijcisjournal
This paper presents a new encryption scheme called Compact Coding that encodes information in time, phase, and intensity domains, simultaneously. While these approaches have previously been used one at a time, the proposed scheme brings to bear for the first time their strengths simultaneously leading to an increase in the secure information transfer rate. The proposed scheme is applicable to both optical fibers and free space optics, and can be considered as an alternative to polarization coding. This paper applies the proposed compact coding scheme to multi-photon tolerant quantum protocols in order to produce quantum-level security during information transfer. We present the structure of the proposed coding scheme in a multi-photon environment and address its operation.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
This presentation covers types of noise in communication system, noise modelling, thermal noise, shot noise, experimental determination of noise figure, noise figure, friss formula with numerical.
Development of Nonlinear Optical Microscope and ScannerRohan Sharma
For my internship at the Femtosecond Spectroscopy & Nonlinear Optics Lab at IIT Delhi, I worked on the development of nonlinear optical microscope and scanner for 10 weeks.During the internship, I developed a multimodal nonlinear optical microscope employing white light and femtosecond laser to record the optical imaging, transmission imaging as well as the second harmonic generation (SHG) imaging of a sample.
Security based Clock Synchronization technique in Wireless Sensor Network for...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Optimum Sensor Node Localization in Wireless Sensor Networkspaperpublications3
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Compact Coding Using Multi-Photon Tolerant Quantum Protocols For Quantum Comm...ijcisjournal
This paper presents a new encryption scheme called Compact Coding that encodes information in time, phase, and intensity domains, simultaneously. While these approaches have previously been used one at a time, the proposed scheme brings to bear for the first time their strengths simultaneously leading to an increase in the secure information transfer rate. The proposed scheme is applicable to both optical fibers and free space optics, and can be considered as an alternative to polarization coding. This paper applies the proposed compact coding scheme to multi-photon tolerant quantum protocols in order to produce quantum-level security during information transfer. We present the structure of the proposed coding scheme in a multi-photon environment and address its operation.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
This presentation covers types of noise in communication system, noise modelling, thermal noise, shot noise, experimental determination of noise figure, noise figure, friss formula with numerical.
An Overview of Array Signal Processing and Beam Forming TechniquesAn Overview...Editor IJCATR
For use as hydrophones, projectors and underwater microphones, there is always a need for calibrated sensors. Overview of
multi path and effect of reflection on acoustic sound signals due to various objects is required prior to finding applications for different
materials as sonar domes, etc. There is also a need to overview multi sensor array processing for many applications like finding
direction of arrival and beam forming. Real time data acquisition is also a must for such applications.
Performance analysis on the basis of a comparative study between multipath ra...ijmnct
Interference is the most important issue for present wireless communication. There are various kinds of
channel used in wireless communication. Here I want to show a performance analysis on the basis of two
different channels – AWGN and Multipath Rayleigh fading channel. This is the comparative analysis with
different kinds of modulation techniques. Here I have also measured the Bit Error Rate with respect to
different modulation techniques and compare the rate in different channels. My objective is to compare the
different characteristics of the transmitter and receiver for different types of channels and modulators.
Energy Efficient Power Failure Diagonisis For Wireless Network Using Random G...idescitation
This paper deals with the discussion of an innovative and a design for the
efficient power management and power failure diagnosis in the area of wireless sensors
networks. A Wireless Network consists of a web of networks where hundreds of pairs are
connected to each other wirelessly. A critical issue in the wireless sensor networks in the
present scenario is the limited availability of energy within network nodes. Therefore,
making good use of energy is necessary in modeling a sensor network. In this paper we have
tried to propose a new model of wireless sensors networks on a three-dimensional plane
using the percolation model, a kind of random graph in which edges are formed between the
neighboring nodes. An algorithm has been described in which the power failure diagnosis is
made and solved. This paper also involves proper selection of the ideal networks by the
concepts of Random Graph theory. The paper involves the mathematics of complete fault
diagnosis including solving the problem and continuing the process flow.
Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference SystemIOSR Journals
Abstract: The rapidly increasing need for information communication requires higher speed data transmission over the existing channels. The data rate over these channels is limited mainly by Inter Symbol Interference (ISI). Channel equalizers are used to reduce the effect of ISI. In this paper, a new equalizer based on Adaptive Neuro-Fuzzy Inference System is presented. The performance of the proposed equalizer is evaluated for both linear as well as non-linear channels in terms of bit-error rate for different noise powers. Simulation results show that the proposed equalizer has better Bit Error Rate (BER) performance compared to multi-layer perceptron and least mean square equalizers. However, its BER performance is slightly poorer than that of radial basis function network and optimal Bayesian equalizer but is better in terms of structural complexity. Keywords: Channel equalizer, Hybrid learning algorithm, Intersymbol interference, Membership function, optimal Bayesian equalizer.
Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference SystemIOSR Journals
The rapidly increasing need for information communication requires higher speed data transmission over the existing channels. The data rate over these channels is limited mainly by Inter Symbol Interference (ISI). Channel equalizers are used to reduce the effect of ISI. In this paper, a new equalizer based on Adaptive Neuro-Fuzzy Inference System is presented. The performance of the proposed equalizer is evaluated for both linear as well as non-linear channels in terms of bit-error rate for different noise powers. Simulation results show that the proposed equalizer has better Bit Error Rate (BER) performance compared to multi-layer perceptron and least mean square equalizers. However, its BER performance is slightly poorer than that of radial basis function network and optimal Bayesian equalizer but is better in terms of structural complexity.
Comparative analysis on an exponential form of pulse with an integer and non-...IJERA Editor
The theme of this paper is to make a fundamental comparative analysis on time-bandwidth product of a short
duration pulse, whose amplitude is varied with an exponent as an integer and non-integer. The time-bandwidth
product is the most significant factor in pulse compression techniques which is used very often in radar systems
for better detection of the target and resolving the ambiguities in the both range and velocity with the help of
ambiguity function. In this paper, different exponents have been used and it is observed that the non-integer
exponents are giving slightly better quantitative parameters like time-bandwidth product, relative sidelobe level
and main lobe widths. To improve these quantitative parameters, phase variations have been incorporated with
the differentiated pulses of the original exponential signal. Finally, the modulation has also been applied on the
pulses to observe the results in real practical applications. From all these analysis, it is concluded that the
differentiated non-integer exponential pulse with bi-polar variations is giving better pulse compression
requirements (time-bandwidth product, peak sidelobe level, 3-dB beamwidth and main lobe widths) compared
to all other pulse forms. The outputs of the matched filter are also observed for each pulse shape.
Accurate Evaluation of Interharmonics of a Six Pulse, Full Wave - Three Phase...idescitation
Interharmonics are the non-integral multiples of
the system’s fundamental frequency. The interharmonic
components can be apprehended as the intermodulation of
the fundamental and harmonic components of the system with
any other frequency components introduced by the load. These
loads include static frequency converters, cyclo-converters,
induction motors, arc furnaces and all the loads not pulsating
synchronously with the fundamental frequency of the system.
The harmonic and interharmonic components inflict common
damage to the system and apart from these damages the
interharmonics also cause light flickering, sideband torques
on motor/generator and adverse effects on transformer and
motor components. To
filter/compensate the interharmonic
components, their accurate evaluation is essential and to
achieve the same the Iterative algorithm has been proposed.
The main cause of spectral leakage errors is the truncation of
the time-domain signal. The proposed adaptive approach
calculates the immaculate window width, eliminating the
spectral leakage errors in the frequency domain and thereby
the interharmonics/harmonics can be calculated accurately.
The algorithm does not require any inputs regarding the
system frequency and interharmonic constituents of the
system. The only parameter required is the signal sequence
obtained by sampling the analog signal at equidistant sampling
interval.
Spectrum Sensing Detection with Sequential Forward Search in Comparison to Kn...IJMTST Journal
FCC is currently working on the concept of white space users “borrowing” spectrum from free license
holders temporarily to improve the spectrum utilization.
This project provides a relation between a Pf and the SNR value of any spectrum detector to have a
certain performance. Previous spectrum sensing detection techniques are only suitable for Low SNR and
are based on signal information values. But these methods are purely narrow band spectrum applications
In order to overcome the above said drawbacks we propose a novel method of spectrum sensing method
and is suitable for low and high SNR values, the sensed spectrum applicable for wide band applications.
Our proposed method does not require signal information at the receiver and channel information, because
this flexibility sensing rate is very high compared to previous techniques.
Luigi Giubbolini | Time/Space-Probing Interferometer for Plasma DiagnosticsLuigi Giubbolini
By Luigi Giubbolini Published article about Rapid progress in plasma applications requires new instrumentation. Luigi Giubbolini has engineering experience in industrial, government laboratory & academic environments.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Development of fpga based system for neutron flux monitoring in fast breeder reactors
1. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol.4, No.8, 2013
30
Development of FPGA Based System for Neutron Flux
Monitoring in Fast Breeder Reactors
M.Sivaramakrishna, Dr. P.Chellapandi, IGCAR, Dr.S.V.G.Ravindranath (BARC),
IGCAR, Kalpakkam, India
(sivarama@igcar.gov.in)
Abstract
The project aims to calculate the frequency of the neutron flux by monitoring the signal from neutron detector
from shutdown to full power over 10 decades. This neutron flux signal is input to the FPGA based MODULE. A
mathematical relationship has been established between the neutron flux (frequency of the neutrons) and the area
under the signal. Variable amplitude and occurrence have been accounted for. White noise has also been added
and tested for. VHDL has been used to simplify the otherwise complicated logic gate design. Mathematical
modeling has been used as it is the most accurate of the available methods.
Index Terms -- Neutron flux monitoring, area, pulses
1. Need of the system
Currently, neutron flux is monitored in all states of the reactor by Neutron flux monitoring systems. The system
consists of several sets of detectors and instrument channels. For smooth transition from one set of instrument
channel to other, interlocks with auto inhibition in safety logic are provided. In each channel, there is a trade-off
between response time and accuracy. Volume of electronics involved is very high.
In addition, the pulses from the neutron detector are not periodic. Hence counting techniques do not result in
accurate prediction of frequency. It can also be seen that as the frequency goes up the pulses over lap. Hence
estimating the power using pulse counting can’t predict the power correctly.
Currently, the detector is operated in pulse and Campbell modes. Even in Campbell mode, there is a trade-off
between the accuracy and response time and linearity is obtained only for 4 decades.
FPGAs are currently the most user friendly and economically viable option for logic circuit design. They can be
programmed to match user’s requirements.
The work aims to find a relation between the frequency of the neutron flux signal and a mathematical function.
The code designed will be able to calculate frequency for signals with constant amplitude, random amplitude,
random occurrence and signals with noise from the samples supplied by the analog to digital convertor (ADC)
connected to the FPGA.
2. Data and assumptions
• The pulse width varies. However, a width of 100 ns is a good estimate. The signal rise time varies from
5 ns to 20 ns. The fall time varies from 50 ns to 120 ns
• The amplitude of the signal is varying 0.7 uA to 1.3 uA
• The individual signals might overlap resulting in a single larger pulse.
• The signals will be affected by noise, which is also random in nature.
• The occurrence of signals follows a Poisson distribution.
• There is almost no overlap of signals for a frequency of less than 104
Hz. Beyond this, pulses will
overlap. Due to this, the standard deviation will be proportional to the neutron flux, as per campbell
theorm (which is applicable to statistical random occurring, discrete overlapping incidents).
• At very high frequencies, the pulse over lap fully to give average DC current.
To solve the problems such as range, accuracy with the existing techniques of neutron detector instrumentation,
new techniques are investigated such as calculation of the area under the signal pattern (Curve).
Presently software simulation is completed to find out the relation between the above frequency parameters and
the incident neutron flux. Hardware simulation is being carried out. Finally FPGA based simple embedded
systems will be made for need of real time high computation required.
3. APPROACH
3.1 INITIAL STAGES
At first, sample signals were generated to mimic the neutron flux signals in terms of rise time, fall time, overlap,
noise etc. From this signal mathematical functions such as average, variance and area were calculated.
While considering the overlap, a linear relationship was taken. For each decade starting from 10^4 Hz, a 20%
overlap was considered upto 10^9 Hz.
2. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol.4, No.8, 2013
31
Initially samples were run for constant overlap. After the appropriate mathematical function was obtained, it was
extended to random amplitude and to noise.
Functions considered:
1) Average
We can see that for average, a straight line relation is obtained with frequency in case of both no overlap and
overlap in signals.
There is no discrepancy from the straight line at any frequency. However, calculation with such precision will
require very high sampling rates at the order of GHz. Hence, we search for a better option
Fig 1: plot of average value of neutron flux vs. frequency
2) Variance
For variance, at lower frequencies and no overlap, we get a straight line relationship. However as the frequency
and overlap increase, there is an exponential variation.
We also see that the rise of variance with frequency is sudden and large making it harder to distinguish between
the higher frequencies. Hence, this is not the best method to follow as accuracy will be low.
Fig 2: plot of variance of neutron flux vs. frequency
3) Area under curve
For area under curve we see that like variance and average, there is a straight line relationship at lower frequency
and in the absence of overlap. However, with increase in frequency and overlap, the area under curve increases
polynomially.
We can see that at each level of overlap, the frequencies follow a linear pattern. Overall, when we look at the
curve, we see the increase is more gradual and a more distinguishable pattern is observed.
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Fig 3: plot of area under curve of neutron flu
A clearer pattern is observed, when the curve is spilt into different portions and more values are taken in each
range.
Hence, we decide to go ahead with area under curve as it is best suited for the situation.
Now we tried the pattern for random amplitude.
Initially we taook only 3 amplitudes at 0.7, 1 and 1.3 times the constant amplitude considered (1V)
Fig 4. Area under curve vs. frequency (for amplitude of 0.7, 1, 1.3 V)
We see that due to the randomness in amplitude, the patter
waveform.
However, if we take this further and completely the randomize the amplitude to all values between 0.7 to 1.3 ( a
3 sigma Poisson variation), we notice that the amplitudes average out to give a
This linear pattern is observed at all frequencies. However the error is lower at higher frequencies because more
the pulses, higher are the chances of the averaging of the values to the mean level.
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Fig 3: plot of area under curve of neutron flux vs. frequency
A clearer pattern is observed, when the curve is spilt into different portions and more values are taken in each
Hence, we decide to go ahead with area under curve as it is best suited for the situation.
for random amplitude.
only 3 amplitudes at 0.7, 1 and 1.3 times the constant amplitude considered (1V)
Fig 4. Area under curve vs. frequency (for amplitude of 0.7, 1, 1.3 V)
We see that due to the randomness in amplitude, the pattern immediately disappears and is replaced by a random
However, if we take this further and completely the randomize the amplitude to all values between 0.7 to 1.3 ( a
3 sigma Poisson variation), we notice that the amplitudes average out to give a linear pattern
This linear pattern is observed at all frequencies. However the error is lower at higher frequencies because more
the pulses, higher are the chances of the averaging of the values to the mean level.
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A clearer pattern is observed, when the curve is spilt into different portions and more values are taken in each
only 3 amplitudes at 0.7, 1 and 1.3 times the constant amplitude considered (1V)
Fig 4. Area under curve vs. frequency (for amplitude of 0.7, 1, 1.3 V)
n immediately disappears and is replaced by a random
However, if we take this further and completely the randomize the amplitude to all values between 0.7 to 1.3 ( a
linear pattern
This linear pattern is observed at all frequencies. However the error is lower at higher frequencies because more
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Fig 5. Area under curve vs. frequency
3.2 PATTERNS OBSERVED
Once area under curve was finalized as the function to be considered, more tests were run with multiple values in
all ranges. Pattern was identified for each range of frequency and ov
Fig 6. Area under curve vs. frequency (0 to 10^4 Hz)
Below are the plots obtained for each range. In the curve, the equation corresponding and the variation of points
from the plot are mentioned.
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Fig 5. Area under curve vs. frequency (for completely randomized amplitude 0.7
Once area under curve was finalized as the function to be considered, more tests were run with multiple values in
all ranges. Pattern was identified for each range of frequency and overlap.
Fig 6. Area under curve vs. frequency (0 to 10^4 Hz)
Below are the plots obtained for each range. In the curve, the equation corresponding and the variation of points
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(for completely randomized amplitude 0.7-1.3 V)
Once area under curve was finalized as the function to be considered, more tests were run with multiple values in
Below are the plots obtained for each range. In the curve, the equation corresponding and the variation of points
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3.3 DEVELOPMENT OF CODE
After all patterns were identified, code was developed on VHDL for calculation of frequency from sampled
signal.
Value will be read from a text file containing 107
values per second. The output frequency will be stored in
another text file. A signal of ‘1’ is output if frequency is below threshold else ‘0’ is output.
The VHDL code for identifying mathematical, for sample signal generation, test values and for frequency
calculation is in Appendix.
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On changing the values in the places commented in the code, sample signal is generated for any frequency and
corresponding value calculated by VHDL simulation is obtained.
4. RESULTS
Below are the results obtained from the simulation of the VHDL simulation.
Table 1 shows the value of frequency used for sample signal generation on matlab and the corresponding value
obtained from VHDL for constant amplitude, random amplitude and occurrence and signals with noise. We see
error increases slightly with very high overlap and frequency and is more in case of overlap with noise.
For constant amplitude, 80 more samples were taken, distributed equally in every decade, and tested to check
deviation.The average error comes to 2.50699%.
Below is the plot of obtained value vs. average value. We see the pattern is almost linear and there is very slight
deviation in pattern
Fig 16. Obtained frequency vs. actual frequency
5. CONCLUSIONS
We see that the frequency calculation for constant amplitude is almost accurate with very small error. The error
falls and then increases again at very high frequency.
For random amplitude, the error is larger at lower frequencies but reduces significantly at larger frequencies as
the amplitudes average out.
For signals with noise, the error is lower at lower frequencies but is high at higher frequencies. However, here
we have considered the noise to be mixed with the signal. In practicality, however, the noise will be separated
out first. Hence error will be significantly lower. In this project, noise signals were analysed using the same
pattern as constant amplitude case. If patterns for this are analysed like for the other cases, the error can be
decreased. Due to lack of time this was not attempted.
BIBLIOGRAPHY
1. Perry, Douglas 1998, VHDL by Examples, 3rd
edition, Singapore: Mc GrawHill
2. Bhasker, Jayaram, 1998, VHDL Primer, New Jersey: P T R Prentice Hall
3. Ashden, Peter J, 1990, The VHDL Cookbook, Ashenden Designs
4. Chu, Pong P, 2008, FPGA prototyping by VHDL examples, New Jersey: John Wiley & Sons Inc.
5. http://www.cs.umbc.edu/portal/help/VHDL/math_real.vhdl
6. http://www.velocityreviews.com/forums/t22430-random-number-generator.html
7. http://verificationguild.com/dload/utils/vhdl/random1.vhd
8. http://www.freemodelfoundry.com/converters_vhdl.php
9. http://www.jjmk.dk/MMMI/Exercises/05_Counters_Shreg/No7_PWM_vs_SigmaDelta/index.htm
10. Singh, Om Pal, 2007, Interfacing Analog to Digital Converters to FPGAs
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Table I: Values obtained for random test samples
Test
Frequency
With Constant
amplitude
% error With Random
amplitude
% error With noise % error
3 2.774 7.66 2.48 17.3 2.77 7.5
70 66.59 4.8 65.39 6.5 66.6 4.87
800 761.9 4.76 759.2 5.09 763 4.68
3300 3142. 4.78 3144 4.69 3140 4.75
22000 21703 1.34 20992 4.58 19300 12
864000 856085 0.915 823105 4.73 757000 12.4
6123400 5428491 11.3 5832320 4.75 4560000 25
49200000 45283660 7.96 36200000 26
729000000 706756500 3.05 644000000 11.6
Annexure:
VHDL CODE
1) Constant amplitude
--for constant amplitude
library ieee;
use ieee.std_logic_1164.all;
use std.textio.all;
use ieee.math_real.all;
entity area is
end area;
architecture freq_calc of area is
signal clk: std_logic;
begin
clockgen:process
begin
clk<='1';
wait for 1ns;
clk<='0';
wait for 1ns;
end process;
process
variable area:real:=0.0;
variable freq:real:=0.0;
FILE infile: TEXT is in "C:testsamples_const_9.txt"; --enter file name with samples here
FILE outfile: TEXT is out "C:/test/freq9.txt"; --enter file name to store result here
variable in_val,out_val:line;
variable val:real;
variable a:real:=0.0;
variable b:real;
variable c:real;
variable d:real;
variable flag:integer:=0;
variable check: std_logic:='0';
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begin
wait until clk'EVENT and clk='1';
while not(endfile(infile)) loop
check:='1';
readline(infile,in_val); -- each line from file being read
read(in_val,val); -- value from each line being read
area:=area+val*1.0E-7; -- area being calculated.
wait until clk'EVENT and clk='1'; --sampling time=10^-7 seconds
end loop;
if (area<0.00756) then --frequency calculation
freq:=(area-5.0E-9)/(6.0E-8);
elsif(area<0.625) then
a:=5.0E-15;
b:=1.0E-7;
c:=-0.0034-area;
d:=b**2-4.0*a*c;
freq:=(-b+sqrt(d))/(2.0*a);
elsif(area<13.1) then
a:=2.0E-15;
b:=2.0E-7;
c:=-0.3114-area;
d:=b**2-4.0*a*c;
freq:=(-b+sqrt(d))/(2.0*a);
elsif (area<306.0) then
a:=1.0E-15;
b:=1.0E-7;
c:=7.4409-area;
d:=b**2-4.0*a*c;
freq:=(-b+sqrt(d))/(2.0*a);
elsif (area<386.103) then
freq:=(area+1.0962)/(8.0E-7);
elsif (area<498.0) then
freq:=(area+1.1679)/(9.0E-7);
elsif (area<671.0) then
freq:=(area+0.9811)/(1.0E-6);
elsif (area<950.0) then
freq:=(area+0.9252)/(1.0E-6);
elsif (area<1532.0) then
freq:=(area+0.6058)/(2.0E-6);
else
freq:=(area+0.3535)/(4.0E-6);
end if;
wait until clk'EVENT and clk='1';
write(out_val,freq); --writing value into file
writeline(outfile,out_val);
wait ;
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end process;
end freq_calc;
2) For randomized amplitude
--for randomly varying amplitude
--solving linear equation
library ieee;
use ieee.std_logic_1164.all;
use std.textio.all;
entity area is
end area;
architecture freq_calc of area is
signal clk: std_logic;
begin
clockgen:process
begin
clk<='1';
wait for 1ns;
clk<='0';
wait for 1ns;
end process;
process
variable area:real:=0.0;
variable freq:real:=0.0;
FILE infile: TEXT is in "C:testsamples_rand_8.txt";--enter file with ADC samples here
FILE outfile: TEXT is out "C:/test/freq_r_8.txt"; --enter file to store results here
variable in_val,out_val:line;
variable val:real;
variable check: std_logic:='0';
begin
wait until clk'EVENT and clk='1';
while not(endfile(infile)) loop
check:='1';
readline(infile,in_val);
read(in_val,val);
area:=area+val*(1.0E-7); --area calculation
wait until clk'EVENT and clk='1';
end loop;
freq:=(area-2.0E-8)/(6.0E-8); --frequency calculation
wait until clk'EVENT and clk='1';
write(out_val,freq);
writeline(outfile,out_val);
wait;
end process;
end freq_calc;
3) For signals with noise
--for constant amplitude
--with noise
library ieee;
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use ieee.std_logic_1164.all;
use std.textio.all;
use ieee.math_real.all;
entity area is
end area;
architecture freq_calc of area is
signal clk: std_logic;
begin
clockgen:process
begin
clk<='1';
wait for 1ns;
clk<='0';
wait for 1ns;
end process;
process
variable area:real:=0.0;
variable freq:real:=0.0;
FILE infile: TEXT is in "H:testresult81.txt"; --enter file name with samples here
FILE outfile: TEXT is out "H:/test/a81.txt"; -- enter file to store results here
variable in_val,out_val:line;
variable val:real;
variable a:real:=0.0;
variable b:real;
variable c:real;
variable d:real;
variable flag:integer:=0;
variable check: std_logic:='0';
begin
wait until clk'EVENT and clk='1';
while not(endfile(infile)) loop
check:='1';
readline(infile,in_val);
read(in_val,val);
area:=area+val*1.0E-7; --sampling interval of 10^-7 seconds
wait until clk'EVENT and clk='1';
end loop;
area:=area*2.0/3.0
if (area<0.00756) then --frequency calculation
freq:=(area-5.0E-9)/(6.0E-8);
elsif(area<0.625) then
a:=5.0E-15;
b:=1.0E-7;
c:=-0.0034-area;
d:=b**2-4.0*a*c;
freq:=(-b+sqrt(d))/(2.0*a);
elsif(area<13.1) then
a:=2.0E-15;
b:=2.0E-7;
c:=-0.3114-area;
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Vol.4, No.8, 2013
41
d:=b**2-4.0*a*c;
freq:=(-b+sqrt(d))/(2.0*a);
elsif (area<306.0) then
a:=1.0E-15;
b:=1.0E-7;
c:=7.4409-area;
d:=b**2-4.0*a*c;
freq:=(-b+sqrt(d))/(2.0*a);
elsif (area<386.103) then
freq:=(area+1.0962)/(8.0E-7);
elsif (area<498.0) then
freq:=(area+1.1679)/(9.0E-7);
elsif (area<671.0) then
freq:=(area+0.9811)/(1.0E-6);
elsif (area<950.0) then
freq:=(area+0.9252)/(1.0E-6);
elsif (area<1532.0) then
freq:=(area+0.6058)/(2.0E-6);
else
freq:=(area+0.3535)/(4.0E-6);
end if;
wait until clk'EVENT and clk='1';
write(out_val,freq); --writing value into file
writeline(outfile,out_val);
wait ;
end process;
end freq_calc;
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