1) The document discusses various pulse modulation techniques including pulse amplitude modulation (PAM), pulse width modulation (PWM), and pulse position modulation (PPM).
2) It provides details on the generation and detection of PAM and PWM signals, explaining the use of sampling, comparators, sawtooth waves, and filters.
3) The document compares different sampling techniques for PAM including natural sampling, flat top sampling, and discusses the need for analog to digital conversion in communication systems.
Communication engineering notes uniit iiManoj Kumar
This document discusses various types of pulse modulation techniques. It begins by introducing the low pass sampling theorem and quantization. It then describes several types of pulse modulation including PAM, PCM, DPCM, DM, ADPCM, ADM, and discusses time division and frequency division multiplexing. It provides details on pulse amplitude modulation (PAM) including its generation, detection of natural and flat top PAM, and advantages and disadvantages. It also provides an overview of pulse code modulation (PCM) including defining it as an analog to digital conversion process and describing the basic elements and operations of a PCM transmitter.
This document discusses various analog pulse modulation schemes, including pulse amplitude modulation (PAM), pulse width modulation (PWM), and pulse position modulation (PPM). PAM varies the amplitude of pulses in a carrier signal based on the modulating signal. PWM varies the width of pulses, while PPM varies the position of pulses. The document describes the generation and demodulation of these different modulation types. It also discusses sampling theory and the advantages and disadvantages of PAM.
The document discusses the sampling theorem, which states that a signal must be sampled at a rate at least twice the highest frequency present in the signal (fs > 2fa(max)) in order to perfectly reconstruct the original signal from the samples. If the sampling rate is lower than this Nyquist rate, aliasing distortion will occur as frequency components fold over each other. Digital transmission is preferable to analog as the signal is more robust to noise and can be easily recovered, corrected, and amplified. There are three main sampling methods: ideal sampling uses an impulse, natural sampling uses a short pulse, and flat top sampling "samples and holds" a single amplitude value.
The document discusses various types of pulse modulation techniques used to convert analog signals to digital signals. It describes pulse amplitude modulation (PAM), pulse width modulation (PWM), and pulse position modulation (PPM). PAM encodes information by varying the amplitude of pulses, PWM varies the width, and PPM varies the position. The key requirements for pulse modulation are that the sampling frequency must be at least twice the maximum input frequency to avoid aliasing, and techniques like flat top sampling and PWM help reduce the aperture effect by limiting high frequency roll-off. Pulse modulation allows analog signals to be transmitted and processed digitally.
communication concepts on sampling processNatarajVijapur
This document discusses principles of communication systems including sampling, quantization, pulse amplitude modulation (PAM), time division multiplexing (TDM), and pulse position modulation (PPM). It provides details on how analog signals are converted to digital through sampling and quantization. It explains the generation and detection of PAM, TDM, and PPM signals. Key advantages of digital transmission and various modulation techniques are summarized.
Pulse amplitude modulation (PAM) encodes information by varying the amplitude of pulses. There are two types: flat top PAM keeps pulse amplitudes flat, while natural PAM allows amplitudes to vary throughout each pulse. PAM works by sampling the input signal at regular intervals and making each pulse amplitude proportional to the input amplitude when sampled. The sampling frequency must be at least twice the highest input frequency to avoid aliasing. PAM can be generated using a modulating signal, carrier pulses, and a modulator circuit. It is demodulated using a low-pass filter and amplifier.
This document discusses various pulse modulation techniques including:
- Pulse Amplitude Modulation (PAM) where the amplitude of pulses varies with the message signal. PAM has simple modulation/demodulation but high noise and bandwidth.
- Pulse Width Modulation (PWM) where the width of pulses varies with the message signal. PWM has lower noise than PAM but requires more complex circuits.
- Pulse Position Modulation (PPM) where the position of pulses varies with the message signal. PPM has constant power but requires precise synchronization between transmitter and receiver.
Circuit diagrams and modulation/demodulation techniques are provided for generating and recovering signals for each pulse modulation method.
this lecture provide the different features of pulse code modulation it explains the concept using example and explained step by step shows the flat sampling and other type shows the advantage of pam provides the pcm system block diagram a brief introduction about delta modulation
Communication engineering notes uniit iiManoj Kumar
This document discusses various types of pulse modulation techniques. It begins by introducing the low pass sampling theorem and quantization. It then describes several types of pulse modulation including PAM, PCM, DPCM, DM, ADPCM, ADM, and discusses time division and frequency division multiplexing. It provides details on pulse amplitude modulation (PAM) including its generation, detection of natural and flat top PAM, and advantages and disadvantages. It also provides an overview of pulse code modulation (PCM) including defining it as an analog to digital conversion process and describing the basic elements and operations of a PCM transmitter.
This document discusses various analog pulse modulation schemes, including pulse amplitude modulation (PAM), pulse width modulation (PWM), and pulse position modulation (PPM). PAM varies the amplitude of pulses in a carrier signal based on the modulating signal. PWM varies the width of pulses, while PPM varies the position of pulses. The document describes the generation and demodulation of these different modulation types. It also discusses sampling theory and the advantages and disadvantages of PAM.
The document discusses the sampling theorem, which states that a signal must be sampled at a rate at least twice the highest frequency present in the signal (fs > 2fa(max)) in order to perfectly reconstruct the original signal from the samples. If the sampling rate is lower than this Nyquist rate, aliasing distortion will occur as frequency components fold over each other. Digital transmission is preferable to analog as the signal is more robust to noise and can be easily recovered, corrected, and amplified. There are three main sampling methods: ideal sampling uses an impulse, natural sampling uses a short pulse, and flat top sampling "samples and holds" a single amplitude value.
The document discusses various types of pulse modulation techniques used to convert analog signals to digital signals. It describes pulse amplitude modulation (PAM), pulse width modulation (PWM), and pulse position modulation (PPM). PAM encodes information by varying the amplitude of pulses, PWM varies the width, and PPM varies the position. The key requirements for pulse modulation are that the sampling frequency must be at least twice the maximum input frequency to avoid aliasing, and techniques like flat top sampling and PWM help reduce the aperture effect by limiting high frequency roll-off. Pulse modulation allows analog signals to be transmitted and processed digitally.
communication concepts on sampling processNatarajVijapur
This document discusses principles of communication systems including sampling, quantization, pulse amplitude modulation (PAM), time division multiplexing (TDM), and pulse position modulation (PPM). It provides details on how analog signals are converted to digital through sampling and quantization. It explains the generation and detection of PAM, TDM, and PPM signals. Key advantages of digital transmission and various modulation techniques are summarized.
Pulse amplitude modulation (PAM) encodes information by varying the amplitude of pulses. There are two types: flat top PAM keeps pulse amplitudes flat, while natural PAM allows amplitudes to vary throughout each pulse. PAM works by sampling the input signal at regular intervals and making each pulse amplitude proportional to the input amplitude when sampled. The sampling frequency must be at least twice the highest input frequency to avoid aliasing. PAM can be generated using a modulating signal, carrier pulses, and a modulator circuit. It is demodulated using a low-pass filter and amplifier.
This document discusses various pulse modulation techniques including:
- Pulse Amplitude Modulation (PAM) where the amplitude of pulses varies with the message signal. PAM has simple modulation/demodulation but high noise and bandwidth.
- Pulse Width Modulation (PWM) where the width of pulses varies with the message signal. PWM has lower noise than PAM but requires more complex circuits.
- Pulse Position Modulation (PPM) where the position of pulses varies with the message signal. PPM has constant power but requires precise synchronization between transmitter and receiver.
Circuit diagrams and modulation/demodulation techniques are provided for generating and recovering signals for each pulse modulation method.
this lecture provide the different features of pulse code modulation it explains the concept using example and explained step by step shows the flat sampling and other type shows the advantage of pam provides the pcm system block diagram a brief introduction about delta modulation
- Digital signal processing systems convert analog signals to digital signals for processing. They consist of anti-aliasing filters, analog-to-digital converters (ADCs), digital signal processors, digital-to-analog converters (DACs), and reconstruction filters.
- ADCs sample analog signals and convert them to discrete digital values. Sampling must occur at least twice the maximum frequency of the analog signal, as per the Nyquist-Shannon sampling theorem, to avoid aliasing.
- Aliasing occurs when the sampling rate is too low, causing high frequency signals to appear as lower frequencies. Anti-aliasing filters are used before sampling to remove frequencies above half the sampling rate.
This document provides an overview of digital transmission topics covered in a 10-hour syllabus, with a focus on Pulse Code Modulation (PCM). Key concepts include: PCM involves sampling analog signals and encoding samples into binary codes for transmission; the minimum sampling rate must be at least twice the highest analog frequency to avoid aliasing; and PCM is used widely in telephone networks by encoding voice signals using an analog-to-digital conversion process involving sampling, quantization, coding, transmission and decoding.
This document summarizes key concepts in sampling and pulse code modulation (PCM). It discusses:
- The sampling theorem which states that a signal can be reconstructed from samples if sampled at twice the bandwidth or higher.
- How PCM works by sampling an analog signal, quantizing the samples into digital values, coding the values into binary numbers, and transmitting the digital data.
- Key advantages of PCM include using inexpensive digital circuits, enabling all-digital transmission and further digital processing.
- Techniques like companding, differential PCM, and increasing bit depth can improve the quality and efficiency of PCM systems.
This document discusses sampling and quantization in digital communication. It introduces sampling as the process of converting a continuous-time analog signal into a discrete-time signal by taking samples at regular intervals. The sampling theorem states that if a signal is sampled at least twice the maximum frequency of the signal, it can be reconstructed without distortion. Quantization is the process of converting the discrete-time continuous amplitude samples into discrete amplitude values. The document covers topics such as the Nyquist rate, aliasing, ideal sampling, and methods of sampling like impulse sampling.
Pulse-amplitude modulation (PAM) encodes message information in the amplitude of signal pulses. A PAM-4 modulator takes two bits at a time and maps them to one of four amplitude levels, such as -3V, -1V, 1V, and 3V. Demodulation detects the amplitude level of each symbol period. PAM is widely used for baseband digital data transmission, though other modulation methods are now more common.
1. This document discusses non-linear signal processing and provides an overview of amplitude modulation (AM), frequency modulation (FM), and their generation and demodulation.
2. It defines key concepts such as modulation, carrier signal, bandwidth, and modulation index. For AM, it describes how the amplitude of the carrier wave is varied by the modulating signal.
3. Methods for generating and demodulating AM signals include square law diode modulation, collector modulation, and envelope detectors. FM varies the instantaneous frequency of the carrier proportionally to the modulating signal.
DSP_2018_FOEHU - Lec 02 - Sampling of Continuous Time SignalsAmr E. Mohamed
The document discusses sampling of continuous-time signals. It defines different types of signals and sampling methods. Ideal sampling involves multiplying the signal by a train of impulse functions to select sample values at regular intervals. For practical sampling, a train of rectangular pulses is used to approximate ideal sampling. Flat-top sampling is achieved by convolving the ideally sampled signal with a rectangular pulse, resulting in samples held at a constant height for the sample period. The Nyquist sampling theorem states that a signal must be sampled at least twice its maximum frequency to avoid aliasing when reconstructing the original signal from samples. An anti-aliasing filter can be used before sampling to prevent aliasing from high frequencies above half the sampling rate.
The document discusses digital communication systems and their advantages over analog communication. It describes how analog signals are digitized using sampling and quantization. Sampling must occur at least twice the maximum frequency of the signal to avoid aliasing, as stated by the Nyquist sampling theorem. Quantization converts continuous amplitudes to discrete levels, causing quantization error and noise. Digital communication provides benefits like lower distortion and easier signal processing. Companding is discussed as a technique used in pulse code modulation to improve signal-to-noise ratio by compressing higher amplitudes before transmission.
This section discusses two techniques for analog to digital conversion: pulse code modulation (PCM) and delta modulation. PCM involves sampling an analog signal, quantizing the sample amplitudes into discrete levels, and encoding the levels into binary digits. The sampling rate must be at least twice the highest signal frequency according to the Nyquist theorem. Delta modulation encodes the difference between samples rather than their absolute values, using one bit per sample. It has a simpler design than PCM but can result in larger errors for signals with large amplitude changes between samples.
The document discusses sampling and the Hilbert transform. It begins by defining sampling as the reduction of a continuous-time (CT) signal to a discrete-time (DT) signal using a sampler. It describes the Nyquist sampling theorem which specifies the minimum sampling rate to reconstruct the original signal. It then discusses different types of sampling including impulse, natural, and flat top sampling. The document also covers aliasing, the Hilbert transform, and properties and examples of using the Hilbert transform including on bandpass signals and for system representation.
Ch4 1 Data communication and networking by neha g. kuraleNeha Kurale
This document discusses analog-to-digital conversion techniques, specifically pulse code modulation (PCM) and delta modulation. PCM consists of sampling an analog signal, quantizing the sample amplitudes into discrete levels, and encoding the levels into binary codes. The sampling rate must be at least twice the highest frequency in the signal according to the Nyquist theorem. Quantization introduces error but more levels reduce the error. Delta modulation encodes changes in signal amplitude rather than absolute levels. Serial transmission can be asynchronous, synchronous, or isochronous depending on whether start/stop bits are used and if gaps between frames are of fixed duration.
Digital modulation involves transmitting digitally modulated analog signals between points in a communication system. It offers advantages over analog systems like ease of processing, multiplexing, and noise immunity. Digital modulation techniques include pulse amplitude modulation, pulse width modulation, and pulse position modulation. Pulse code modulation is a digital pulse modulation technique where analog signals are sampled, quantized into discrete levels, and encoded into binary code for transmission. The minimum sampling rate required by the Nyquist theorem is twice the maximum signal frequency to avoid aliasing during reconstruction.
Pulse modulation schemes aim to transfer an analog signal over an analog channel as a two-level signal by modulating a pulse wave. Some schemes also allow digital transfer of the analog signal with a fixed bit rate. Pulse modulation includes analog-over-analog methods like PAM, PWM, and PPM as well as analog-over-digital methods like PCM, DPCM, ADPCM, DM, and delta-sigma modulation. Sampling is the reduction of a continuous signal to a discrete signal by taking values at points in time. The Nyquist-Shannon sampling theorem states that a bandlimited signal can be perfectly reconstructed from samples if the sampling rate is at least twice the highest frequency in the signal.
The document discusses various types of pulse modulation techniques including pulse amplitude modulation (PAM), pulse width modulation (PWM), pulse position modulation (PPM), and pulse code modulation (PCM). It provides details on the basic principles, components, and advantages of each technique. PCM is described as the digital form of pulse modulation where the analog signal is converted to digital pulses by sampling, quantizing, and encoding the signal. The minimum sampling rate required by the Nyquist theorem and examples of calculating bit rates for PCM are also covered.
- Obtained the Fast Fourier Transform of signals.
- Designed and Validated Low Pass, High Pass, and Band Pass filters in compliance with the specifications.
- Produced and compared graphs of the results upon processing.
This document describes a circuit designed to extract a message signal from a frequency modulated (FM) wave. The circuit uses differentiation, envelope detection, and bandpass filtering to extract the message signal. Simulation results showed the circuit could successfully extract a 1000 Hz message but not a 100 Hz message, likely due to incorrect high-pass filter settings. Reducing the high-pass filter's natural frequency may improve low frequency message extraction.
This presentation discusses digitization and digital communication. It provides advantages of digital communication like reliability and error detection. The key components of digital communication are described including the source, encoder, modulator, channel, demodulator, decoder and output. Pulse code modulation is explained as a process to convert analog signals to binary digital signals using sampling, quantization and encoding. The presentation also covers sampling rate, Nyquist rate, aliasing, quantization levels, quantization error and companding techniques used in pulse code modulation.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
- Digital signal processing systems convert analog signals to digital signals for processing. They consist of anti-aliasing filters, analog-to-digital converters (ADCs), digital signal processors, digital-to-analog converters (DACs), and reconstruction filters.
- ADCs sample analog signals and convert them to discrete digital values. Sampling must occur at least twice the maximum frequency of the analog signal, as per the Nyquist-Shannon sampling theorem, to avoid aliasing.
- Aliasing occurs when the sampling rate is too low, causing high frequency signals to appear as lower frequencies. Anti-aliasing filters are used before sampling to remove frequencies above half the sampling rate.
This document provides an overview of digital transmission topics covered in a 10-hour syllabus, with a focus on Pulse Code Modulation (PCM). Key concepts include: PCM involves sampling analog signals and encoding samples into binary codes for transmission; the minimum sampling rate must be at least twice the highest analog frequency to avoid aliasing; and PCM is used widely in telephone networks by encoding voice signals using an analog-to-digital conversion process involving sampling, quantization, coding, transmission and decoding.
This document summarizes key concepts in sampling and pulse code modulation (PCM). It discusses:
- The sampling theorem which states that a signal can be reconstructed from samples if sampled at twice the bandwidth or higher.
- How PCM works by sampling an analog signal, quantizing the samples into digital values, coding the values into binary numbers, and transmitting the digital data.
- Key advantages of PCM include using inexpensive digital circuits, enabling all-digital transmission and further digital processing.
- Techniques like companding, differential PCM, and increasing bit depth can improve the quality and efficiency of PCM systems.
This document discusses sampling and quantization in digital communication. It introduces sampling as the process of converting a continuous-time analog signal into a discrete-time signal by taking samples at regular intervals. The sampling theorem states that if a signal is sampled at least twice the maximum frequency of the signal, it can be reconstructed without distortion. Quantization is the process of converting the discrete-time continuous amplitude samples into discrete amplitude values. The document covers topics such as the Nyquist rate, aliasing, ideal sampling, and methods of sampling like impulse sampling.
Pulse-amplitude modulation (PAM) encodes message information in the amplitude of signal pulses. A PAM-4 modulator takes two bits at a time and maps them to one of four amplitude levels, such as -3V, -1V, 1V, and 3V. Demodulation detects the amplitude level of each symbol period. PAM is widely used for baseband digital data transmission, though other modulation methods are now more common.
1. This document discusses non-linear signal processing and provides an overview of amplitude modulation (AM), frequency modulation (FM), and their generation and demodulation.
2. It defines key concepts such as modulation, carrier signal, bandwidth, and modulation index. For AM, it describes how the amplitude of the carrier wave is varied by the modulating signal.
3. Methods for generating and demodulating AM signals include square law diode modulation, collector modulation, and envelope detectors. FM varies the instantaneous frequency of the carrier proportionally to the modulating signal.
DSP_2018_FOEHU - Lec 02 - Sampling of Continuous Time SignalsAmr E. Mohamed
The document discusses sampling of continuous-time signals. It defines different types of signals and sampling methods. Ideal sampling involves multiplying the signal by a train of impulse functions to select sample values at regular intervals. For practical sampling, a train of rectangular pulses is used to approximate ideal sampling. Flat-top sampling is achieved by convolving the ideally sampled signal with a rectangular pulse, resulting in samples held at a constant height for the sample period. The Nyquist sampling theorem states that a signal must be sampled at least twice its maximum frequency to avoid aliasing when reconstructing the original signal from samples. An anti-aliasing filter can be used before sampling to prevent aliasing from high frequencies above half the sampling rate.
The document discusses digital communication systems and their advantages over analog communication. It describes how analog signals are digitized using sampling and quantization. Sampling must occur at least twice the maximum frequency of the signal to avoid aliasing, as stated by the Nyquist sampling theorem. Quantization converts continuous amplitudes to discrete levels, causing quantization error and noise. Digital communication provides benefits like lower distortion and easier signal processing. Companding is discussed as a technique used in pulse code modulation to improve signal-to-noise ratio by compressing higher amplitudes before transmission.
This section discusses two techniques for analog to digital conversion: pulse code modulation (PCM) and delta modulation. PCM involves sampling an analog signal, quantizing the sample amplitudes into discrete levels, and encoding the levels into binary digits. The sampling rate must be at least twice the highest signal frequency according to the Nyquist theorem. Delta modulation encodes the difference between samples rather than their absolute values, using one bit per sample. It has a simpler design than PCM but can result in larger errors for signals with large amplitude changes between samples.
The document discusses sampling and the Hilbert transform. It begins by defining sampling as the reduction of a continuous-time (CT) signal to a discrete-time (DT) signal using a sampler. It describes the Nyquist sampling theorem which specifies the minimum sampling rate to reconstruct the original signal. It then discusses different types of sampling including impulse, natural, and flat top sampling. The document also covers aliasing, the Hilbert transform, and properties and examples of using the Hilbert transform including on bandpass signals and for system representation.
Ch4 1 Data communication and networking by neha g. kuraleNeha Kurale
This document discusses analog-to-digital conversion techniques, specifically pulse code modulation (PCM) and delta modulation. PCM consists of sampling an analog signal, quantizing the sample amplitudes into discrete levels, and encoding the levels into binary codes. The sampling rate must be at least twice the highest frequency in the signal according to the Nyquist theorem. Quantization introduces error but more levels reduce the error. Delta modulation encodes changes in signal amplitude rather than absolute levels. Serial transmission can be asynchronous, synchronous, or isochronous depending on whether start/stop bits are used and if gaps between frames are of fixed duration.
Digital modulation involves transmitting digitally modulated analog signals between points in a communication system. It offers advantages over analog systems like ease of processing, multiplexing, and noise immunity. Digital modulation techniques include pulse amplitude modulation, pulse width modulation, and pulse position modulation. Pulse code modulation is a digital pulse modulation technique where analog signals are sampled, quantized into discrete levels, and encoded into binary code for transmission. The minimum sampling rate required by the Nyquist theorem is twice the maximum signal frequency to avoid aliasing during reconstruction.
Pulse modulation schemes aim to transfer an analog signal over an analog channel as a two-level signal by modulating a pulse wave. Some schemes also allow digital transfer of the analog signal with a fixed bit rate. Pulse modulation includes analog-over-analog methods like PAM, PWM, and PPM as well as analog-over-digital methods like PCM, DPCM, ADPCM, DM, and delta-sigma modulation. Sampling is the reduction of a continuous signal to a discrete signal by taking values at points in time. The Nyquist-Shannon sampling theorem states that a bandlimited signal can be perfectly reconstructed from samples if the sampling rate is at least twice the highest frequency in the signal.
The document discusses various types of pulse modulation techniques including pulse amplitude modulation (PAM), pulse width modulation (PWM), pulse position modulation (PPM), and pulse code modulation (PCM). It provides details on the basic principles, components, and advantages of each technique. PCM is described as the digital form of pulse modulation where the analog signal is converted to digital pulses by sampling, quantizing, and encoding the signal. The minimum sampling rate required by the Nyquist theorem and examples of calculating bit rates for PCM are also covered.
- Obtained the Fast Fourier Transform of signals.
- Designed and Validated Low Pass, High Pass, and Band Pass filters in compliance with the specifications.
- Produced and compared graphs of the results upon processing.
This document describes a circuit designed to extract a message signal from a frequency modulated (FM) wave. The circuit uses differentiation, envelope detection, and bandpass filtering to extract the message signal. Simulation results showed the circuit could successfully extract a 1000 Hz message but not a 100 Hz message, likely due to incorrect high-pass filter settings. Reducing the high-pass filter's natural frequency may improve low frequency message extraction.
This presentation discusses digitization and digital communication. It provides advantages of digital communication like reliability and error detection. The key components of digital communication are described including the source, encoder, modulator, channel, demodulator, decoder and output. Pulse code modulation is explained as a process to convert analog signals to binary digital signals using sampling, quantization and encoding. The presentation also covers sampling rate, Nyquist rate, aliasing, quantization levels, quantization error and companding techniques used in pulse code modulation.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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Mechanical Engineering on AAI Summer Training Report-003.pdf
Unit 4 Pulse Modulation.pdf
1. Presented by
Mr. S. P. Bangal
Assistant Professor
Department of Electronics & Computer Engineering
P.R.E.C., Loni
SE ECE 2019 Course
Subject: Principles of Communication Systems
Unit No.4: Pulse Modulation
1
2. Unit No.4. Pulse Modulation
Theory Practicals
Need of analog to digital conversion
sampling theorem for low pass
signal in time domain, and Nyquist
criteria.
Types of sampling- natural and flat
top.
Pulse amplitude modulation &
concept of TDM: Channel bandwidth
for PAM,
equalization, Signal Recovery
through holding.
Pulse Width Modulation (PWM)
and Pulse Position Modulation (PPM):
Generation & Detection.
Experiment No.3
Verification of Sampling Theorem,
PAM Techniques, (Flat top & Natural
sampling), reconstruction of original
signal, Observe Aliasing Effect in
frequency domain.
Experiment No.4
Generation and Detection of PWM
using IC 555
Experiment No.13
Verify Sampling Theorem using
simulation.
3. Need of Analog to Digital conversion
Analog-to-Digital converters translate analog signals, real
world signals like temperature, pressure, voltage, current,
distance, or light intensity, into a digital representation of that
signal.
This digital representation can then be processed,
computed, transmitted or stored.
Que.1) : Discuss the Need of analog to digital conversion.
4. Digital signal is easy to perform mathematical manipulation to
the data. So the computers and microprocessors can store, analyse,
understand, process, and display the results, since microprocessors
deal with digital information.
Once the data is in digital format, it can be statistically analysed
such as mathematically process it to extract, isolate, manipulate
information as desired.
It can be compressed for faster transmission and minimize
hardware storage capacity.
It can be encrypted for security.
With analog, it is pretty easy to tap into a private phone
conversation; digital encryption secured privacy.
Need of Analog to Digital conversion
5. It is easy to search and compare stored digital data.
Better Noise immunity.
Supports high signal fidelity.
Channel coding can be possible.
Flexible hardware implementation.
Use of regenerative repeaters is possible.
Digital signal is more reliable as compared to analog circuits.
Need of Analog to Digital conversion
6. Sampling Process
It is the process of converting continuous a analog signal to discrete
analog signal. Hence sampled signal is discrete time representation
of original analog signal.
7. Sampling theorem states that “continues form of a time-variant
signal can be represented in the discrete form of a signal with help of
samples and the sampled (discrete) signal can be recovered to
original form when the sampling signal frequency Fs having the
greater frequency value than or equal to the input signal frequency
Fm.
Fs≥2Fm
If the sampling frequency (Fs) equals twice the input signal
frequency (Fm), then such a condition is called the Nyquist Criteria
for sampling. When sampling frequency equals twice the input signal
frequency is known as “Nyquist rate”.
Fs=2Fm
If the sampling frequency (Fs) is less than twice the input signal
frequency, such criteria called an Aliasing effect.
Fs<2Fm
So, there are three conditions that are possible from the sampling
frequency criteria. They are sampling, Nyquist and aliasing states.
Basic Sampling Theorem Statement
8. Que.2) State and prove sampling theorem for band limited signal
Statement:-“A continuous time signal x(t) can be completely
represented in its sampled form and recovered back from the sampled
form if sampling frequency Fs≥2W where W is the maximum frequency
of the continuous time signal x(t)”
15. what is aliasing how can it be avoided.
Alising: This phenomenon of a high frequency in the spectrum of the original signal x
(t), taking on the identity of lower frequency in the spectrum of the sampled signal x
ɗ(t) is called as aliasing or fold over error.
how can it be avoided:
• Aliasing can be completely eliminated if we take the following action: 1) Use a
bandlimiting low pass filter and pass the signal x (t) through it before sampling as
shown in Fig.
• This filter has a cutoff frequency at fc = W therefore it will strictly bandlimit the
signal x (t) before sampling takes place.
• This filter is also called as antialiasing filter or prealias filter.
• Increase the sampling frequency f, to a great extent i.e. fs>>> 2W. Due to this,
even though x (t) is not strictly bandlimited, the spectrums will not overlap. A
guard band is created between the adjacent spectrums as shown in Fig.
• Thus aliasing can be prevented by:
1. Using an antialiasing or prealiasing filter and
2. Using the sampling frequency f,>2W.
16. Pulse Modulation
Pulse modulation consists essentially of sampling
analog information signals and then converting
those samples into discrete pulses and transporting
the pulses from a source to a destination over a
physical transmission medium.
The four predominant methods of pulse modulation:
1) Pulse Amplitude Modulation (PAM)
2) Pulse Width Modulation (PWM)
3) Pulse Position Modulation (PPM)
4) Pulse Code Modulation (PCM).
Introduction
17. Analog Pulse Modulation Digital Pulse Modulation
Pulse Amplitude (PAM)
Pulse Width (PWM)
Pulse Position (PPM)
Pulse Code (PCM)
Pulse Modulation
Pulse Amplitude Modulation (PAM):
* The signal is sampled at regular intervals such that each sample is
proportional to the amplitude of the signal at that sampling instant. This
technique is called “sampling”.
* For minimum distortion, the sampling rate should be more than twice the
signal frequency.
19. • The continuous modulating signal x(t), is passed through a low pass filter.
• The LPF will bandlimit this signal to fm. That means all the frequency
components higher than the frequency fm are removed.
• Bandlimiting is necessary to avoid the "aliasing" effect in the sampling
process.
• The pulse train generator generates a pulse train at a frequency fs, such that
fs≥ 2fm. Thus the Nyquist criteria is satisfied.
• The carrier pulses generated by the pulse train generator would carry out the
uniform "sampling" in the multiplier block, to generate the PAM signal
Que.4) Explain How PAM (Pulse Amplitude Modulation) signal may generated
20. Que.5) : Draw and explain a circuit for Natural sampling or
Draw and explain a circuit for Natural PAM
1) Pulse Amplitude Modulation:
Natural PAM /Sampling or Chopper Sampling:-
22. Natural Sampling/ Natural PAM Working:
• The continuous modulating signal x(t) is applied to Chopper
switch/ MOSFET.
• The pulse train generator generates a pulse train c(t) at a
frequency fs,, such that fs, ≥ 2fm Thus the Nyquist criteria is
satisfied.
• Pulse train c(t) is applied to Chopper switch/ MOSFET
Chopper switch/ MOSFET act as Switch.
• If switch is on then we get Natural PAM Signal
• .If switch is off then we can not get Natural PAM Signal
23. Que.6) : Draw and explain a circuit for Flat top sampling.
Or Draw and explain a circuit for Flat top PAM
Flat Top Sampling/ Flat Top PAM
25. Working:
• The sample and hold circuit consists of two FET switches and
a capacitor.
• The analog signal x (t) is applied at the input of this circuit and
the sampled signal s(t) is obtained across the capacitor.
• A gate pulse will be applied to gate G1 at the instant of
sampling for a very short time. The sampling switch will turn
on and the capacitor charges through it to the sample value x
(nTs).
• The sampling switch is then turned off. Both the FETs will
remain OFF for a duration of “T" seconds and the capacitor
will hold the voltage across it constant for this period. Thus the
pulse is stretched to “T" seconds.
• At the end of the pulse interval (t), a pulse is applied to G₂ i.e.
gate terminal of discharge FET. This will turn on the discharge
FET and short circuit the capacitor. The output voltage then
reduces to zero.
26. Working: Reconstruction of Flat Top PAM:
10
Fig. Recovering the original message signal m(t) from PAM signal
An amplitude distortion as well as a delay is introduced in the flat
top sampled signal.
This distortion can be corrected by connecting an equalizer after the
reconstruction filter (low pass filter) as shown in
29. Que.9) Draw and explain with the neat schematic of generation
and Detection of PWM signal.
2. Pulse Width Modulation (PWM generation):
30. Working:-
• A sawtooth generates a sawtooth signal. It is applied to the inverting
terminal of a comparator.
• The modulating signal x (t) is applied to the non-inverting terminal
of the same comparator.
• The comparator output will remain high as long as the instantaneous
amplitude of x (t) is higher than that of the ramp signal c (t).
• This will generate the PWM signal at the comparator output.
• Note that the leading edges of the PWM waveform coincide with
the falling edges of the ramp signal.
• Thus the leading edges of PWM signal are always generated at fixed
time instants.
• Trailing edges will be dependent on the instantaneous amplitude of
x(t).
• Therefore this PWM signal is said to be trail edge modulated PWM.
31. PWM detection:
Detection of PWM
Working:-
• The PWM signal received at the input of the detection circuit is mixed with noise.
• This signal is applied to pulse generator circuit which regenerates the PWM signal. Thus
some of the noise is removed and the pulses are squared up.
• The regenerated pulses are applied to a reference pulse generator.
• It produces a train of constant amplitude, constant width pulses.
• The regenerated PWM pulses are also applied to a ramp generator. At the output ramp
generator we get a constant slope ramp signal.
• The height of the ramp is thus proportional to the widths of the PWM pulses.
• The constant amplitude pulses at the output of reference pulse generator and the ramp
signal are then added to in adder
• The output of the adder is then clipped off at a threshold level to generate a PAM signal at
the output of the clipper.
• A low pass filter is used to recover the original modulating signal from the PAM signal.
33. Que.10) Explain the generation and detection of PPM wave with waveforms.
3. Pulse Position Modulation Generation:
34. Working:-
• A sawtooth signal generator generates a sawtooth signal. It is applied to the
inverting terminal of a comparator.
• The modulating signal x (t) is applied to the non-inverting terminal of the same
comparator.
• The comparator will compare modulating signal x (t) as well as sawtooth signal
• The comparator output will remain high as long as the instantaneous amplitude
of x (t) is higher than that of the ramp signal c (t).
• Comparator will generate the PWM signal at the output.
• PWM pulses obtained at the comparator output are applied to a monostable
multivibrator.
• The monostable multivibrator is negative edge triggered. Hence corresponding to
each trailing edge of PWM signal, the monostable multivibrator. output goes
high.
• It remains high for a fixed time decided by its own RC components.
• At the output of monostable multivibrator we get PPM pulses.
• Thus as the trailing edges of the PWM signal keep shifting in proportion with the
modulating signal x(t), the PPM pulses also keep shifting
35. PPM Detection:
Working:-
• The PPM Pulses with noise are received by the PPM demodulator circuit.
• The pulse generator develops a pulsed signal waveform at its output and apply
these pulses to the reset pin (R) of a SR flip-flop.
• Reference pulse generator develop fixed period reference pulse signal and
applied to set pin (R) of a SR flip-flop.
• Due to the set and reset signals applied to the flip-flop, we get a PWM signal
at its output.
• The PWM signal can be demodulated using the PWM demodulator.
• At the output of PWM demodulator we get modulating signal.
37. f
t
c
k2 k3 k4 k5 k6
k1
Time Division Multiplexing (TDM)
Channels ki
TDM is a technique used for
transmitting several message
signal
over single communication
channel by dividing the time
frame in to slots, one slot for
each message signal.
Definition: Time Division Multiplexing
(TDM) is the time interleaving of samples
from several sources so that the
information from these sources can be
transmitted serially over a single
communication channel.
39. Que.12) Explain PAM –TDM System (Time Division Multiplexing) in detail.
PAM –TDM System
• Bandlimited signals are applied Low pass Filter. LPF is called as antialising Filter for to avoid
Aliasing Effect. LPF output is applid to the contact point of a rotary switch.
• All the signals are sampled in a sequential manner when the rotary arm of switch swings.
• Pulse modulater is used for to modulate PAM signal and applied to communication channel.
• communication channel is used for transpar the signal from one place to another place.
• Receiver commutator switch is in synchronism with that of transmitter communicator switch.
• With each revolution of the switch, one sample is taken as input and provided to receiver.
• At the receiver, train of pulses pass through filter and original signal m(t) reconstructed.