This document compares different window functions used for finite impulse response (FIR) filter design, including Hann, Hamming, Blackman, and Bartlett windows. It analyzes the performance of lowpass, highpass, bandpass, and bandstop filters designed with each window function. Hann window provides the narrowest main lobe width but Hamming window results in more side lobes. Blackman window achieves the highest side lobe attenuation of -70dB but with a wider main lobe. Bartlett window has the widest main lobe and highest side lobes. In conclusion, the appropriate window function depends on the specific filter design goals and tradeoffs between main lobe width and side lobe suppression.
Design of Low Pass Digital FIR Filter Using Cuckoo Search AlgorithmIJERA Editor
This paper presents a novel approach of designing linear phase FIR low pass filter using cuckoo Search Algorithm (CSA). FIR filter design is a multi-modal optimization problem. The conventional optimization techniques are not efficient for digital filter design. An iterative method is introduced to find the best solution of FIR filter design problem.Flat passband and high stopband attenuation are the major characteristics required in FIR filter design. To achieve these characteristics, a Cuckoo Search algorithm (CSA) is proposed in this paper. CSA have been used here for the design of linear phase finite impulse response (FIR) filters. Results are presented in this paper that seems to be promising tool for FIR filter design
Simulation Study of FIR Filter based on MATLABijsrd.com
First, the rapid design of FIR digital filter was completed by using the Signal Processing Toolbox FDA Tool, the case filter design of a composite signal by filtering, to prove that the content filter designed for filtering. MATLAB and Simulink programs of the filter were used to verify the performance of the filter in MATLAB. Experimental results show that the low-pass filter filters the high frequency component of input signals mixed. Comparison of two types of simulation, the latter method was more convenient quickly, and reduces the workload.
Design of fir filter using rife vincent window using ffd algorithmSubhadeep Chakraborty
Abstract: In Digital Signal Processing, The window techniques are used to design the FIR filter. Actually the window techniques can be applied on the IIR filter response to make it finite and so the FIR filter can be designed. Rife-Vincent window technique is one of the useful one to realize the FIR filter. The algorithm and the design method of Rife-Vincent window are shown in this paper with the realization and the simulation results where the advantage of the window is shown which is actually the minimization of the sidelobes. The simulation is done in Matlab 7 and it can be observed that the minimization of the sidelobes increase the efficiency of the filtering process as well as decreasing the power consumption. The other well known window functions such as the Blackman window, kaiser window, Hamming window, Hanning window etc. generates the sidelobes that are of higher Decibels compared to the Rife-Vincent window.
Design Technique of Bandpass FIR filter using Various Window FunctionIOSR Journals
Abstract: Filter is one of the most important part of communication system. Without digital filter we cannot think about proper communication because noise occurs in channel. For removing noise or cancellation of noise we use various type of digital filter. In this paper we propose design technique of bandpass FIR filter using various type of window function. Kaiser window is the best window function in FIR filter design. Using this window we can realize that FIR filter is simple and fast. Keywords: FIR filter, LTI, bandpass filter, MATLAB
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Design of Low Pass Digital FIR Filter Using Cuckoo Search AlgorithmIJERA Editor
This paper presents a novel approach of designing linear phase FIR low pass filter using cuckoo Search Algorithm (CSA). FIR filter design is a multi-modal optimization problem. The conventional optimization techniques are not efficient for digital filter design. An iterative method is introduced to find the best solution of FIR filter design problem.Flat passband and high stopband attenuation are the major characteristics required in FIR filter design. To achieve these characteristics, a Cuckoo Search algorithm (CSA) is proposed in this paper. CSA have been used here for the design of linear phase finite impulse response (FIR) filters. Results are presented in this paper that seems to be promising tool for FIR filter design
Simulation Study of FIR Filter based on MATLABijsrd.com
First, the rapid design of FIR digital filter was completed by using the Signal Processing Toolbox FDA Tool, the case filter design of a composite signal by filtering, to prove that the content filter designed for filtering. MATLAB and Simulink programs of the filter were used to verify the performance of the filter in MATLAB. Experimental results show that the low-pass filter filters the high frequency component of input signals mixed. Comparison of two types of simulation, the latter method was more convenient quickly, and reduces the workload.
Design of fir filter using rife vincent window using ffd algorithmSubhadeep Chakraborty
Abstract: In Digital Signal Processing, The window techniques are used to design the FIR filter. Actually the window techniques can be applied on the IIR filter response to make it finite and so the FIR filter can be designed. Rife-Vincent window technique is one of the useful one to realize the FIR filter. The algorithm and the design method of Rife-Vincent window are shown in this paper with the realization and the simulation results where the advantage of the window is shown which is actually the minimization of the sidelobes. The simulation is done in Matlab 7 and it can be observed that the minimization of the sidelobes increase the efficiency of the filtering process as well as decreasing the power consumption. The other well known window functions such as the Blackman window, kaiser window, Hamming window, Hanning window etc. generates the sidelobes that are of higher Decibels compared to the Rife-Vincent window.
Design Technique of Bandpass FIR filter using Various Window FunctionIOSR Journals
Abstract: Filter is one of the most important part of communication system. Without digital filter we cannot think about proper communication because noise occurs in channel. For removing noise or cancellation of noise we use various type of digital filter. In this paper we propose design technique of bandpass FIR filter using various type of window function. Kaiser window is the best window function in FIR filter design. Using this window we can realize that FIR filter is simple and fast. Keywords: FIR filter, LTI, bandpass filter, MATLAB
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A survey report for performance analysis of finite impulse response digital f...eSAT Journals
Abstract
In the field of signal processing and communication, digital filter plays pivotal role. Digital FIR filter designed by different window
techniques perform better for reducing noise from signal. In this paper, we take an overview of various window methods such as
Rectangular, Triangular, Hanning, Hamming, Blackman, Kaiser and some modified windows. The magnitude responses are
demonstrated for different design methods at particular cut off frequency and different filter order. All these technique have their
merits and demerits. In this paper, we studied various techniques proposed earlier in literature for noise reduction from signal. This
paper also provides comparative study of various filters using different window. It has been seen that Kaiser window is better for
noise free signal.
Keywords: FIR filter, Rectangular window, Bartlett window, Hanning window, Hamming window, Blackman window
and Kaiser window etc.
In signal processing, a digital filter is a system that performs mathematical operations on a sampled, discrete-time signal to reduce or enhance certain aspects of that signal. This is in contrast to the other major type of electronic filter, the analog filter, which is an electronic circuit operating on continuous-time analog signals.
Design And Performance of Finite impulse Response Filter Using Hyperbolic Cos...IDES Editor
In this paper a proposed of design and analysis of
Finite impulse response filter using Hyperbolic Cosine
window (Cosh window for short). This window is very useful
for some applications such as beam forming, filter design,
and speech processing. Digital FIR filter designed by Kaiser
window has a better far-end stop-band attenuation than filter
designed by the other previously well known adjustable
windows such as Dolph-Chebyshev and Saramaki, which are
special cases of Ultraspherical windows, but obtaining a digital
filter which performs higher far-end stop band attenuation
than Kaiser window will be useful. In this paper, the design of
nonrecursive digital FIR filter has been proposed by using
Cosh window. It provides better side lobe roll-off ratio & farend
stop band attenuation than filter designed by well known
Kaiser window, which is the advantage of filter designed by
Cosh window over filter designed by Kaiser window. An
expression for the side lobe & far field level has been developed.
Simulation & experimental results showing a good agreement
with theory has been provided
Performance Analysis and Simulation of Decimator for Multirate ApplicationsIJEEE
In this paper, a decimator design has been presented for multirate digital signal processing. The decimator design has been analysed and simulated for performance comparison in terms of filter order and ripple factor. Direct form-I with decimation factor 2 have been used for performance and ripple analysis. The decimators have been designed & simulated using MATLAB. It can be observed from the simulated results that as we increase the filter order, ripple factor decreases, for the same filter structure. On the other hand, increasing filter order will increase its area and implementation cost.
A survey report for performance analysis of finite impulse response digital f...eSAT Journals
Abstract
In the field of signal processing and communication, digital filter plays pivotal role. Digital FIR filter designed by different window
techniques perform better for reducing noise from signal. In this paper, we take an overview of various window methods such as
Rectangular, Triangular, Hanning, Hamming, Blackman, Kaiser and some modified windows. The magnitude responses are
demonstrated for different design methods at particular cut off frequency and different filter order. All these technique have their
merits and demerits. In this paper, we studied various techniques proposed earlier in literature for noise reduction from signal. This
paper also provides comparative study of various filters using different window. It has been seen that Kaiser window is better for
noise free signal.
Keywords: FIR filter, Rectangular window, Bartlett window, Hanning window, Hamming window, Blackman window
and Kaiser window etc.
In signal processing, a digital filter is a system that performs mathematical operations on a sampled, discrete-time signal to reduce or enhance certain aspects of that signal. This is in contrast to the other major type of electronic filter, the analog filter, which is an electronic circuit operating on continuous-time analog signals.
Design And Performance of Finite impulse Response Filter Using Hyperbolic Cos...IDES Editor
In this paper a proposed of design and analysis of
Finite impulse response filter using Hyperbolic Cosine
window (Cosh window for short). This window is very useful
for some applications such as beam forming, filter design,
and speech processing. Digital FIR filter designed by Kaiser
window has a better far-end stop-band attenuation than filter
designed by the other previously well known adjustable
windows such as Dolph-Chebyshev and Saramaki, which are
special cases of Ultraspherical windows, but obtaining a digital
filter which performs higher far-end stop band attenuation
than Kaiser window will be useful. In this paper, the design of
nonrecursive digital FIR filter has been proposed by using
Cosh window. It provides better side lobe roll-off ratio & farend
stop band attenuation than filter designed by well known
Kaiser window, which is the advantage of filter designed by
Cosh window over filter designed by Kaiser window. An
expression for the side lobe & far field level has been developed.
Simulation & experimental results showing a good agreement
with theory has been provided
Performance Analysis and Simulation of Decimator for Multirate ApplicationsIJEEE
In this paper, a decimator design has been presented for multirate digital signal processing. The decimator design has been analysed and simulated for performance comparison in terms of filter order and ripple factor. Direct form-I with decimation factor 2 have been used for performance and ripple analysis. The decimators have been designed & simulated using MATLAB. It can be observed from the simulated results that as we increase the filter order, ripple factor decreases, for the same filter structure. On the other hand, increasing filter order will increase its area and implementation cost.
Analysis of Interfacial Microsstructure of Post Weld Heat Treated Dissimilar ...IOSR Journals
In Prototype Fast Breeder Reactor (PFBR), the main vessel which contains the primary sodium and supports the
core is suspended from the roof slab. The materials for construction for main vessel and roof slab are type 316LN austenitic
stainless steel and Carbon steel of grade A48P2, respectively. As the materials of construction are different, a transition joint
between austenitic stainless steel and C-steel is necessary. In this investigation the effect of post-weld heat treatment (PWHT) on the interfacial microstructure of as-welded and PWHTed type 316LN/C-steel joint welded with Inconel 182 was investigated. These joints were PWHTed to various temperatures between 898 to 973K for 1h and results were evaluated. From the above results, different methods to temper the martensitic structure or to change to an equilibrium structure without PWHT are also presented.
VHDL Implementation of DSDV Ad-Hoc Routing ProtocolIOSR Journals
Abstract: An Ad-Hoc network deals with the collection of mobile nodes without any centralized structure. This
can be well suited for environment where changes are frequent and establishment of infrastructure is not very
cost effective. In short it can turn the dream of ‘anytime anywhere’ into reality[1]. Here we deal with the VHDL
implementation of DSDV(destination sequenced distance vector) routing protocol to fulfill these requirements of
Ad-hoc network more effectively.
Keywords: Adhoc, DSDV,FPGA,VHDL,Routing
Performance Analysis of FIR Filter using FDAToolijtsrd
The performance analysis of the FIR filter is presented by testing with different windowing methods. The FIR low pass filter was designed with the windowing system. It was simulated by setting different orders for comparing the performances of the filter. And then, it was also tested with different windowing methods. The performances of FIR low pass filter are analyzed by setting various order numbers such as 10, 20, 50 and 100. These identified FIR filters are designed with four windowing methods. They are Kaiser Window, Hamming Window, Blackman Window and Flat Top Window. The FIR filter is designed with FDATool and the results are edited with a filter visualization tool. The magnitude response, phase response, pole zero plot, time domain and frequency domain visualization of the filter are described in this paper. Especially, the comparison of the magnitude responses of different order filter design for Kaiser window, Hamming window, Blackman window and Flat Top window are described in this paper. San San Naing | Pann Ei San | Ma Ma Gyi "Performance Analysis of FIR Filter using FDATool" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26629.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26629/performance-analysis-of-fir-filter-using-fdatool/san-san-naing
Design Technique of Bandpass FIR filter using Various Window FunctionIOSR Journals
Abstract: Filter is one of the most important part of communication system. Without digital filter we cannot
think about proper communication because noise occurs in channel. For removing noise or cancellation of
noise we use various type of digital filter. In this paper we propose design technique of bandpass FIR filter
using various type of window function. Kaiser window is the best window function in FIR filter design. Using
this window we can realize that FIR filter is simple and fast.
Keywords: FIR filter, LTI, bandpass filter, MATLAB
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Higher Order Low Pass FIR Filter Design using IPSO Algorithmijtsrd
This paper presents an optimal design of digital low pass finite impulse response FIR filter using Improved Particle Swarm Optimization IPSO . The design target of FIR filter is to approximate the ideal filters on the request of a given designing specifications. The traditional based optimization techniques are not efficient for digital filter design. The filter specification to be realized IPSO algorithm generates the best coefficients and try to meet the ideal frequency response. Improved Particle swarm optimization PSO proposes a new equation for the velocity vector and updating the particle vectors and hence the solution quality is improved. The IPSO technique enhances its search capability that leads to a higher probability of obtaining the optimal solution. In this paper for the given problem the realization of the FIR filter has been performed. The simulation results have been performed by using the improved particle swarm optimization IPSO method. M. Santhanaraj | Rishikesh. S. S | Subramanian. A. N | Vijai Sooriya. Su ""Higher Order Low Pass FIR Filter Design using IPSO Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22899.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22899/higher-order-low-pass-fir-filter-design-using-ipso-algorithm/m-santhanaraj
Design of fir filter using rife vincent window using ffd algorithmSubhadeep Chakraborty
In Digital Signal Processing, The window techniques are used to design the FIR filter. Actually the window techniques can be applied on the IIR filter response to make it finite and so the FIR filter can be designed. Rife-Vincent window technique is one of the useful one to realize the FIR filter. The algorithm and the design method of Rife-Vincent window are shown in this paper with the realization and the simulation results where the advantage of the window is shown which is actually the minimization of the sidelobes. The simulation is done in Matlab 7 and it can be observed that the minimization of the sidelobes increase the efficiency of the filtering process as well as decreasing the power consumption. The other well known window functions such as the Blackman window, kaiser window, Hamming window, Hanning window etc. generates the sidelobes that are of higher Decibels compared to the Rife-Vincent window.
Design and performance analysis of low pass fir filter using hamming and kais...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm IJECEIAES
In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality.
Design and Performance Analysis of Filters Using a Proposed Window FunctionRSIS International
A new window function is presented which like the well known Hamming window offers a preferred property for use in signal spectrum analysis: the sum of window coefficients with its shifted version by half of the order is constant for the overlapped region in the time domain. In high orders, the new window has main-lobe width equal to Hamming window. For low orders, the window parameters are modified to have smaller main-lobe width compared to Hamming window, while maintaining smaller maximum side-lobe peak. The results indicate performance improvement of the proposed window compared to Kaiser and Gaussian windows. The FIR filters designed by windowing method show the efficiency of the new window.
Filtering is an important mitigation technique for suppressing undesired conducted electromagnetic interference, when a system incorporates shielding, undesired coupling caused by radiated EMI is reduced. Conventional filter analysis and design assumes idealized and simplified conditions. These assumptions are not completely valid in many EMI filter because of unavoidable and severe impedance mismatch. Classical passive filter theory is well developed for communication circuits, where one can operate under impedance-matched conditions. Such filter characteristics are evaluated with 50Ω terminations. Filter evaluated with this procedure may behave differently when used in a circuit, where the impedance presented by the circuit to the filters is not exactly 50Ω. Now a day, digital signals are mostly used to avoid such EMI effects. These are caused by the capacitors, inductors, which are also part of the filtering circuits. Filter design using software, like MATLAB is very useful in avoiding hardware, is highly immune to noise and possesses considerable parameter stability, can be operated over a wide range of frequencies. The frequency response can be changed by changing the filter coefficients and can minimize the Insertion loses (IL).
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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
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/
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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.
1. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 4, Ver. III (Jul - Aug .2015), PP 15-20
www.iosrjournals.org
DOI: 10.9790/2834-10431520 www.iosrjournals.org 15 | Page
FIR window method: A comparative Analysis
Gopika P
Assistant Professor, Department of Electronics, S.E.A.S, Rai Technology University, Bangalore, India.
Abstract : This paper includes a concise record of data windows along with their significant performance
parameters from which the different windows can be compared. Windows impact on many attributes of a signal
processor which includes detectability , resolution, dynamic range, confidence and ease of implementation.
Various Computer Aided Design tools are available for this purpose. The Filter Design and Analysis tool
available with MATLAB provides a method to design. In a signal when we compute the frequency content,
usually we tend to pick a limited-duration snapshot which results in errors because the signal actually lasts for
a longer time. Windowing is a way to reduce these errors, though it cannot eliminate them completely. The
problem identified in this paper is to allow performance comparisons between different FIR windows; mainly
Hamming, Hann and Blackman window; based on some major parameters.
Keywords - FIR windows , MATLAB Signal processing ,Window function
I. Introduction
Windowing method uses a function called window function. It is also called tapering function. A
window function is a mathematical function that is zero-valued outside of some chosen interval. When another
function is multiplied by some window function , the product is also zero-valued outside the interval. In signal
processing a filter removes unwanted component from the signal. The filters can be classified as lowpass,
highpass, bandpass and bandstop based on their frequency characteristics. Window method is the most
conventional method used for designing FIR filters. During frequency analysis we assume that the snapshot
repeats. It is often found that the ends of the snapshots also does not smoothly blend. We can suppress the
discontinuities and the resulting spurious high frequencies in the frequency analysis , by tapering the signal to
zero at the start and end of the recording period. This is the basis of windowing[1][2]. Different filters are used
based on the end effect they make on the input signal so that they alter the signal in some useful way. In the
present day signal processing finds its application in telecommunication, military, space research , image
processing , pattern recognition and in all these areas filters are an unavoidable electronic device. Digital filters
are having obvious advantage over the olden days filters designed with passive components. In addition the
digital filters provide more flexibility as their design can be changed easily using software[3].
Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters are among the two
fundamental types of digital filters. The classification of the filters indicates that impulse response of FIR filter
is finite. But an IIR filter has an impulse response of infinite duration. While implementing an FIR filter needs
no feedback. It is not a recursive filter. For this reason FIR filters are much modest than IIR filters. Among the
various methods used to implement an FIR filter window methods are widely used. This paper presents a
comparative study of the commonly used window techniques.
II. Problem Formulation
The initial step towards filtering technique is to identify the filter coefficients. Digital filter design
involves usually the following basic steps. Determine a desired or a set of desired responses. Select a class of
filters for approximating the desired responses. Establish a criterion of ‘goodness’ for the responses of a filter in
the selected class compared to the desired response. Develop a method for finding the best member in the filter
class. Analyse the filter performance.
III. Finite Impulse Response Filter
The generalized form of an N-tap FIR filter is
Y(n) = x(n)*h(k)
=
1
0
)()(
N
k
knxkh
Where h(k) is the filter coefficient array and x(n-k) is the input data array to the filter.The number of taps relates
to the filter performance. An N-tap filer requires N multiply-accumulate cycles.The output signal is determined
as
Y(n) = x(0)h(n) + x(1)h(n-1) + x(2)h(n-2) + ………..+ x(n)h(0)
2. FIR window method: A comparative Analysis
DOI: 10.9790/2834-10431520 www.iosrjournals.org 16 | Page
The transfer function of a causal FIR filter is obtained by taking the z-transform of impulse
response of FIR filter h(n)[4].
H(Z) =
1
0
)(
N
n
n
Znh
= h(0) + h(1)Z-1
+ h(2)Z-2
+ h(3)Z-3
+ ..... + h(N-1)Z-(N-1)
FIR filters can be designed in different ways. There are many straightforward techniques for designing
FIR digital filters to meet arbitrary frequency and phase response specifications, such as window design method
or frequency sampling techniques. The Window method is the most popular and effective method because this
method is simple, convenient, fast and easy to understand. The main advantage of this design technique is that
the impulse response coefficient can be obtained in closed form without the need for solving complex
optimization problems.
There are essentially three well-known methods for FIR filter design namely:
(a) The window method
(b) The frequency sampling technique
(c) Optimal filter design methods
The window method is one of the simplest methods of designing FIR digital filters. It is well suited for
designing filters with simple frequency response shapes, such as ideal lowpass filters. Mostly used Fixed
window functions are; Rectangular window, Hann window, Hamming window and Blackman window. On the
other hand the Kaiser window is a kind of adjustable window function.
The most commonly used windows are discussed in this paper. The most important parameters in window
weighting in FFT analysis are highest side-lobe level and the worst case processing loss. In both cases the lower
the better[5].
3.1. HANN Window
This is also known as a cosine taper. It starts at 0, rises to 1 in the middle of the period, and then goes
smoothly back down to zero at the end.
The window function of a causal Hann window is given by,
The window function of a non-causal Hann window is given by,
While the Hann window does a good job of forcing the ends to zero, it also adds distortion to
the wave form being analyzed in the form of amplitude modulation; i.e., the variation in amplitude of
the signal over the time record. The Hann window should always be used with continuous signals, but must
never be used with transients. The reason is that the window shape will distort the shape of the transient, and
the frequency and phase content of a transient is intimately connected with its shape. The measured level will
also be greatly distorted. Even if the transient were in the center of the Hann window, the measured level would
be twice as great as the actual level because of the amplitude correction the analyzer applies when using the
Hann weighting.
A Hann window is used to design Lowpass filter, Highpass filter, Bandpass and Bandstop filter. Since
speech signal can be considered for filtering , we use the sampling frequency here as 8000Hz. The simulation of
filters are done using FDA tool of Signal Processing toolbox in MATLAB.
3. FIR window method: A comparative Analysis
DOI: 10.9790/2834-10431520 www.iosrjournals.org 17 | Page
Figure.1 Hann window analysis
3.2. Hamming Window
Like Hann window the Hamming window is also one period of a raised cosine. However, the cosine is
raised so high that its negative peaks are above zero, and the window has a discontinuity in amplitude leaving
the window (stepping discontinuously from 0.08 to 0). This makes the side-lobe roll-off rate very slow. This
modified cosine taper starts at 0.08, rises to 1 in the middle of the period, and then goes smoothly back down to
0.08 at the end.
The Hamming window is defined as
Where α,β are coefficients. For Hamming window the value of coefficients are
α = 0.54 and β = 1- α= 1-0.54=0.46.
Now the modified equation comes as,
Analysis is done for Hamming window for Lowpass filter, Highpass filter , Bandpass Filter and Bandstop filter.
Figure.2 Hamming window analysis
4. FIR window method: A comparative Analysis
DOI: 10.9790/2834-10431520 www.iosrjournals.org 18 | Page
3.3. Blackman Window
This modified cosine taper uses two cosines. The Blackman window is defined as
Where are coefficients. The coefficients can be explained with the help of
;
The value of α for Blackman window is 0.16 and substituting this value in the expression for the window we get
;
0 0.2 0.4 0.6 0.8
-100
-50
0
50
Magnitude Response of LPF
GainindB---->
Normalised Frequency ---->
0 0.2 0.4 0.6 0.8
-100
-50
0
50
Magnitude Response of HPF
GainindB---->
Normalised Frequency ---->
0 0.2 0.4 0.6 0.8
-100
-50
0
Magnitude Response of BPF
GainindB---->
Normalised Frequency ---->
0 0.2 0.4 0.6 0.8
-10
-5
0
5
Magnitude Response of BSF
GainindB---->
Normalised Frequency ---->
Figure.3 Blackman window analysis
3.4. Barlett Window
It is also called triangular window. Often applied discreetly to sample correlations of finite data.The
expression for Barlett window is given by,
0 0.2 0.4 0.6 0.8
-30
-20
-10
0
Magnitude Response of LPF
GainindB---->
Normalised Frequency ---->
0 0.2 0.4 0.6 0.8
-20
-10
0
10
Magnitude Response of HPF
GainindB---->
Normalised Frequency ---->
0 0.2 0.4 0.6 0.8
-40
-20
0
20
Magnitude Response of BPF
GainindB---->
Normalised Frequency ---->
0 0.2 0.4 0.6 0.8
-5
0
5
Magnitude Response of BSF
GainindB---->
Normalised Frequency ---->
Figure.4 Barlett window analysis
5. FIR window method: A comparative Analysis
DOI: 10.9790/2834-10431520 www.iosrjournals.org 19 | Page
IV. Analysis Of Simulation Results
In order to study window method different filters were considered. The major parameters which
decides the quality of filters is the width of the main lobe , number of side lobes and the roll off. The desirable
character is that width of main lobe should be high whereas the side lobes should be least and roll off should be
high. For analysis Lowpass filter using Hann window, Hamming window , Blackman window and Barlett
window are carried out.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-120
-100
-80
-60
-40
-20
0
20
Magnitude Response of Low Pass Filter
GainindB
Normalised Frequency
hanning
hamming
blackman
bartlett
Figure.5 A comparative simulation of windws for Lowwpass filter
Table.1Comparison between different window techniques
V. Conclusion
A study on different Finite Impulse Response windows were carried out. All the literature available on
this subject helps us to select the different parameters required for comparing the varied windowing
techniques[6][7][8][9].A comparative study of Barlett, Hann, Hamming and Blackman window was carried out
using the FDA tool in MATLAB.Using the windows a Lowpass filter was similated.ReferFig.5.As it can be
concluded that Hann window has the least main lobe width. For Hamming window the number of sidelobes are
very high when compared with other all other windows. Future scope of the paper lies in the application of
windows on a possible input signal say a speech signal and analyzing how effective they filter out the undesired
components.
Acknowledgements
I would like to thank the management and Vice Chancellor of Rai Technology University to give me
this opportunity to publish this paper.
References
Journal Papers:
[1] Fredric J Harris, On the use of Windows for Harmonic Analysis with the Discrete Fourier Transform,
Proceedings of the IEEE,
Vol 66 ,No.1 , Jan 1978.
[2] Sonika Gupta, Aman Panghal Performance Analysis of FIR Filter Design by Using Rectangular,
Hanning and Hamming Windows Methods,IJARCSSE, Vol-2,Issue-6,Jun2012,pp273-278
[3] Manira Khatun, Implement a new Window function and Design FIR filters by using this new window,
IJECS,Volume-3,Issue-3, March 2014, pp4087-4090
Window Method Peak side lobe
level(db)
Number of side
lobes
First null(KHz)
Hann window -39 5 1.65
Hamming window -45 4 1.73
Blackman window -70 3 2.08
Barlett window -25 2 1.66
6. FIR window method: A comparative Analysis
DOI: 10.9790/2834-10431520 www.iosrjournals.org 20 | Page
[4] Gopika P, Dr.Supriya Subash, Implementation of Space Time Adaptive Processing in Active SONAR
detection,IOSR,Vol 9,Issue 3,May 2014 , pp 73-76.
[5] Walt Kester ,Digital filters
[6] HESC686 Mathematics and Signal Processing for Biomechanics
[7] Window function , Wikipedia, the free encyclopedia,https://en.wikipedia.org/wiki/Window_function
Books:
[8] Sanjit K Mitra, James F. Kaiser, Handbook for Digital Signal Processing, John Wiley & Sons, Inc, 1993.
[9] John G. Proakis and Dimitris G Manolakis, Introduction to Digital Signal Processing, MacMillian,1988.
[10] Opprnhiem , R Schafer, J.Buck, Discrete-Time Signal Processing, Second Edition , Prentice-Hall , 1999.