This document discusses various techniques for digital-to-digital conversion in digital transmission, including line coding, block coding, and scrambling. It provides examples of common line coding schemes like NRZ, Manchester, and AMI. It also describes block coding techniques like 4B/5B and 8B/10B that add redundancy. Scrambling is discussed as a way to avoid long runs of zeros and enable the use of bipolar AMI coding over long distances. Diagrams and tables are included to illustrate these different coding techniques.
The document discusses various techniques for digital-to-digital conversion, including line coding, block coding, and scrambling. It explains line coding in more detail, covering topics such as signal element versus data element, line coding schemes like NRZ-L, NRZ-I, Manchester encoding, and multilevel coding schemes like 2B1Q. Worked examples are provided to illustrate concepts like calculating signal rate from data rate for different coding schemes.
This document discusses digital transmission techniques. It begins by explaining digital-to-digital conversion which involves line coding, block coding, and scrambling to represent digital data with digital signals. It then discusses analog-to-digital conversion techniques like pulse code modulation (PCM) and delta modulation. PCM samples an analog signal, quantizes the samples, and encodes the quantized values as a bit stream. The document provides details on various line coding schemes such as NRZ, Manchester, and scrambling techniques like B8ZS and HDB3. It also covers block coding, multilevel coding schemes, and the relationships between data rate and signal rate.
Digital transmission involves three main techniques: line coding, block coding, and scrambling. Line coding converts digital data to digital signals, such as NRZ and Manchester encoding. Block coding groups bits into blocks and replaces each block with another block, like 4B/5B encoding. Scrambling substitutes long runs of zeros to aid synchronization, using techniques like B8ZS and HDB3. Together these techniques allow reliable digital transmission over analog channels.
This document discusses digital transmission techniques, including:
1. Line coding is used to convert digital data into digital signals for transmission. Common line coding schemes like NRZ, Manchester, and multilevel codes are described.
2. Block coding is used to add redundancy to ensure synchronization and detect errors. Common block codes like 4B/5B and 8B/10B are discussed.
3. Analog to digital conversion techniques like pulse code modulation (PCM) and delta modulation are explained. PCM involves sampling, quantizing, and encoding analog signals into digital bits for transmission.
The key aspects of digital transmission covered are line coding, block coding, and analog to digital conversion methods. Common schemes
Line_Coding.ppt for engineering students for ug and pgHasanujJaman11
Line encoding is the process of converting digital data to digital signals. It involves representing each data bit as a signal element. There are various line coding schemes such as NRZ, RZ, and Manchester that use different signal patterns to represent bits. Block coding groups bits into blocks and encodes each block into a code word. This allows for error detection and synchronization. Pulse code modulation is used to convert analog signals to digital. It involves sampling, quantizing, and encoding the analog signal into binary code words. The sampling rate must be at least twice the highest frequency per the Nyquist theorem to perfectly reconstruct the original signal.
This document discusses digital line coding techniques. It begins with an introduction to digital-to-digital conversion, which involves line coding, block coding, and scrambling. Line coding converts digital data to digital signals, while block coding adds redundancy to improve line coding performance. Several common line coding schemes are described, including unipolar, polar, return-to-zero, Manchester, and bipolar coding. Block coding techniques like 4B/5B coding are also summarized, which divide data into blocks, substitute code values, and recombine the data with added redundancy. Key properties of line codes like voltage levels, transitions, bandwidth, synchronization and error detection are covered.
The document discusses various techniques for digital-to-digital conversion, including line coding, block coding, and scrambling. It explains line coding in more detail, covering topics such as signal element versus data element, line coding schemes like NRZ-L, NRZ-I, Manchester encoding, and multilevel coding schemes like 2B1Q. Worked examples are provided to illustrate concepts like calculating signal rate from data rate for different coding schemes.
This document discusses digital transmission techniques. It begins by explaining digital-to-digital conversion which involves line coding, block coding, and scrambling to represent digital data with digital signals. It then discusses analog-to-digital conversion techniques like pulse code modulation (PCM) and delta modulation. PCM samples an analog signal, quantizes the samples, and encodes the quantized values as a bit stream. The document provides details on various line coding schemes such as NRZ, Manchester, and scrambling techniques like B8ZS and HDB3. It also covers block coding, multilevel coding schemes, and the relationships between data rate and signal rate.
Digital transmission involves three main techniques: line coding, block coding, and scrambling. Line coding converts digital data to digital signals, such as NRZ and Manchester encoding. Block coding groups bits into blocks and replaces each block with another block, like 4B/5B encoding. Scrambling substitutes long runs of zeros to aid synchronization, using techniques like B8ZS and HDB3. Together these techniques allow reliable digital transmission over analog channels.
This document discusses digital transmission techniques, including:
1. Line coding is used to convert digital data into digital signals for transmission. Common line coding schemes like NRZ, Manchester, and multilevel codes are described.
2. Block coding is used to add redundancy to ensure synchronization and detect errors. Common block codes like 4B/5B and 8B/10B are discussed.
3. Analog to digital conversion techniques like pulse code modulation (PCM) and delta modulation are explained. PCM involves sampling, quantizing, and encoding analog signals into digital bits for transmission.
The key aspects of digital transmission covered are line coding, block coding, and analog to digital conversion methods. Common schemes
Line_Coding.ppt for engineering students for ug and pgHasanujJaman11
Line encoding is the process of converting digital data to digital signals. It involves representing each data bit as a signal element. There are various line coding schemes such as NRZ, RZ, and Manchester that use different signal patterns to represent bits. Block coding groups bits into blocks and encodes each block into a code word. This allows for error detection and synchronization. Pulse code modulation is used to convert analog signals to digital. It involves sampling, quantizing, and encoding the analog signal into binary code words. The sampling rate must be at least twice the highest frequency per the Nyquist theorem to perfectly reconstruct the original signal.
This document discusses digital line coding techniques. It begins with an introduction to digital-to-digital conversion, which involves line coding, block coding, and scrambling. Line coding converts digital data to digital signals, while block coding adds redundancy to improve line coding performance. Several common line coding schemes are described, including unipolar, polar, return-to-zero, Manchester, and bipolar coding. Block coding techniques like 4B/5B coding are also summarized, which divide data into blocks, substitute code values, and recombine the data with added redundancy. Key properties of line codes like voltage levels, transitions, bandwidth, synchronization and error detection are covered.
The document discusses the physical layer of computer networks. It describes how the physical layer interacts with hardware, defines signaling mechanisms, and converts digital data to electrical pulses for transmission. The physical layer provides encoding, signaling, transmission, and topology design. Digital data is converted to digital or analog signals using techniques like line coding, block coding, and modulation. Analog to digital conversion involves sampling, quantization, and encoding analog signals. Data can be transmitted serially or in parallel.
The document discusses digital transmission of data and signals. It describes how analog data and signals can be converted to digital formats using techniques like pulse code modulation (PCM) and delta modulation. It also discusses how digital data can be represented as digital signals using line coding schemes like NRZ and block coding. Additionally, it covers analog-to-digital conversion methods like PCM and delta modulation for converting analog signals to digital data. Finally, it examines different modes for transmitting data serially or in parallel, including asynchronous, synchronous, and isochronous serial transmission.
This document discusses digital transmission techniques for converting digital data into digital signals. It covers line coding, which maps binary data bits to signal levels. Common line coding schemes include NRZ, RZ, Manchester, and AMI. Multilevel coding schemes are also introduced, which encode multiple data bits as a single signal element to increase data rates. Key considerations for line coding include baseline wandering, DC components, self-synchronization, error detection, noise immunity, and complexity. Worked examples calculate baud rates and minimum bandwidths for different schemes.
1. The document discusses various techniques for encoding digital and analog data into digital and analog signals for transmission, including NRZ, Manchester, and scrambling techniques.
2. Digital modulation techniques like ASK, FSK, and PSK are described for converting digital data into analog signals. ASK represents values by amplitude, FSK uses frequency, and PSK shifts the carrier phase.
3. Encoding digital data into a digital signal is simpler than analog, but converting analog data like voice to digital allows use of modern transmission. Encoding impacts bandwidth, error rates, synchronization and more.
Ch4 Data communication and networking by neha g. kuraleNeha Kurale
This document discusses digital-to-digital conversion techniques for representing digital data as digital signals during transmission. It covers line coding, which converts binary data to voltage levels; block coding, which adds redundancy by replacing data bit groups with longer bit groups; and scrambling, which dynamically creates bit sequences with desirable transmission characteristics. Line coding is always used and involves choosing schemes like NRZ, Manchester, or AMI that map bits to signal levels. Block coding like 4B/5B or 8B/10B adds bits to improve synchronization and error detection. Scrambling removes problematic bit runs to prevent issues like baseline wandering.
Digital signals can be encoded in various ways:
1) NRZ-L uses two voltage levels to represent 0s and 1s, maintaining a constant level during each bit.
2) NRZI represents 1s as transitions and 0s as no transitions at the start of each bit.
3) Bipolar-AMI represents 0s as no signal and alternating positive and negative pulses for 1s, eliminating long runs of the same signal.
The document discusses digital-to-digital and analog-to-digital conversion techniques. It covers line coding schemes such as unipolar NRZ, polar, bipolar, multilevel, and multiline transmission. It also discusses block coding, scrambling, and pulse code modulation for analog-to-digital conversion. The key steps of PCM encoding are sampling, quantization, and encoding the analog signal into digital data.
Data Communication & Computer Networks:Digital Signal EncodingDr Rajiv Srivastava
These slides cover the fundamentals of data communication & networking. It covers Digital signal Encoding which are used in communication of data over transmission medium. it is useful for engineering students & also for the candidates who want to master data communication & computer networking.
Line coding refers to converting digital data into digital signals for transmission. There are several characteristics line coding schemes should have, such as low complexity, noise tolerance, no DC component, error detection capability, and self-synchronization. Common line coding techniques include unipolar, polar, and bipolar coding. Specific techniques discussed include non-return to zero (NRZ), return to zero (RZ), Manchester, and differential Manchester coding. These techniques vary in their voltage levels, presence of a DC component, synchronization capabilities, and bandwidth requirements.
This document discusses base-band digital data transmission. It begins by defining the components of data communication, including analog and digital data and signals. It then discusses the need for digital transmission over analog. The main topics covered are line coding techniques, including the important characteristics of line coding like number of levels, bit vs baud rate, DC components, signal spectrum, synchronization and cost of implementation. Specific line coding techniques discussed include unipolar, polar, NRZ, RZ, Manchester, differential Manchester and bipolar coding. The modulation rate relationship to data rate and number of signal elements is also covered.
This document discusses various techniques for digital-to-digital conversion, including line coding, block coding, and scrambling. It describes several common line coding schemes such as NRZ-L, NRZ-I, Manchester, and AMI. Block coding techniques like 4B/5B and 8B/10B are also summarized. The purpose of these coding methods is to convert digital bits into signals while preventing long runs of identical signal levels and enabling synchronization. Scrambling can further be used to create self-clocking bit streams without DC components or wide bandwidth needs.
The document discusses different types of line coding schemes used to convert digital data to digital signals, including unipolar, polar, and bipolar schemes. It also covers techniques for digital to analog conversion, including amplitude shift keying, frequency shift keying, and phase shift keying. The key differences between these schemes involve the number of signal levels used (unipolar uses one, polar uses two, bipolar uses three) and whether the signal crosses or remains on one side of the reference axis. Common modulation techniques for digital to analog conversion are amplitude shift keying, frequency shift keying, and phase shift keying, which vary the amplitude, frequency, or phase of an analog carrier signal to represent digital data.
This document discusses line coding techniques used in data communication. It describes three main categories of line coding: unipolar, polar, and bipolar. Unipolar coding uses one signal level, polar uses two levels, and bipolar uses three levels including positive, negative and zero. Specific techniques like NRZ, NRZ-L, and AMI are discussed along with their advantages and disadvantages. The document also covers encapsulation and decapsulation in networking, where data is packaged at each layer of transmission and unpacked at the receiving end.
This document summarizes digital transmission techniques including digital-to-digital conversion, analog-to-digital conversion, and transmission modes. Digital-to-digital conversion involves line coding, block coding, and scrambling to convert digital data to digital signals. Analog-to-digital conversion uses pulse code modulation to sample, quantize, and encode analog signals as digital bits. Transmission can be done in parallel or serial modes, with serial transmission occurring asynchronously, synchronously, or isochronously.
The document discusses different methods of encoding and modulating digital and analog signals for transmission. It covers digital-to-digital encoding techniques like unipolar, polar, Manchester and differential Manchester encoding. It also discusses analog-to-digital conversion techniques like PAM and PCM. Finally, it discusses analog-to-analog modulation techniques like AM, FM and PM and how they modulate parameters of a carrier signal to transmit an analog signal.
This document provides a summary of a 2-hour lecture on communication basics including modulation and encoding techniques for digital-to-digital, digital-to-analog, and analog-to-digital signals. It discusses common modulation techniques like ASK, FSK, PSK and line codes like Manchester encoding, AMI, and 4B/5B. It also covers topics like digitization of analog signals, constellation diagrams, and multilevel FSK to improve bit rates. The document includes examples, diagrams, and a short quiz to test understanding of key concepts covered in the lecture.
This document provides a summary of a 2-hour lecture on communication basics including modulation and encoding techniques for digital-to-digital, digital-to-analog, and analog-to-digital signals. It discusses common modulation techniques like ASK, FSK, PSK and line codes like Manchester encoding, AMI, and 4B/5B. It also covers topics like digitization of analog signals, constellation diagrams, and multilevel FSK to improve bit rates. The document includes examples, diagrams, and a short quiz to test understanding of key concepts covered in the lecture.
This document provides an overview of a lecture on data communications and networking. It discusses various topics related to line coding, including conversion methods, characteristics of line coding, and different line coding schemes. Specifically, it defines line coding as the process of converting binary data to a digital signal, and discusses signal level, data rate, pulse rate, DC component, and self-synchronization in the context of line coding. It also explains different line coding schemes such as unipolar, polar, NRZ, RZ, Manchester, and bipolar encoding.
This document provides an outline and overview of key topics in digital transmission covered in Chapter 4, including:
- Digital-to-digital conversion techniques like line coding, block coding, and scrambling.
- Analog-to-digital conversion using pulse code modulation (PCM) and delta modulation to convert analog signals to digital data.
- Transmission modes for sending digital data, including parallel transmission of multiple bits at once, and serial transmission of single bits in asynchronous, synchronous, or isochronous formats.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
The document discusses the physical layer of computer networks. It describes how the physical layer interacts with hardware, defines signaling mechanisms, and converts digital data to electrical pulses for transmission. The physical layer provides encoding, signaling, transmission, and topology design. Digital data is converted to digital or analog signals using techniques like line coding, block coding, and modulation. Analog to digital conversion involves sampling, quantization, and encoding analog signals. Data can be transmitted serially or in parallel.
The document discusses digital transmission of data and signals. It describes how analog data and signals can be converted to digital formats using techniques like pulse code modulation (PCM) and delta modulation. It also discusses how digital data can be represented as digital signals using line coding schemes like NRZ and block coding. Additionally, it covers analog-to-digital conversion methods like PCM and delta modulation for converting analog signals to digital data. Finally, it examines different modes for transmitting data serially or in parallel, including asynchronous, synchronous, and isochronous serial transmission.
This document discusses digital transmission techniques for converting digital data into digital signals. It covers line coding, which maps binary data bits to signal levels. Common line coding schemes include NRZ, RZ, Manchester, and AMI. Multilevel coding schemes are also introduced, which encode multiple data bits as a single signal element to increase data rates. Key considerations for line coding include baseline wandering, DC components, self-synchronization, error detection, noise immunity, and complexity. Worked examples calculate baud rates and minimum bandwidths for different schemes.
1. The document discusses various techniques for encoding digital and analog data into digital and analog signals for transmission, including NRZ, Manchester, and scrambling techniques.
2. Digital modulation techniques like ASK, FSK, and PSK are described for converting digital data into analog signals. ASK represents values by amplitude, FSK uses frequency, and PSK shifts the carrier phase.
3. Encoding digital data into a digital signal is simpler than analog, but converting analog data like voice to digital allows use of modern transmission. Encoding impacts bandwidth, error rates, synchronization and more.
Ch4 Data communication and networking by neha g. kuraleNeha Kurale
This document discusses digital-to-digital conversion techniques for representing digital data as digital signals during transmission. It covers line coding, which converts binary data to voltage levels; block coding, which adds redundancy by replacing data bit groups with longer bit groups; and scrambling, which dynamically creates bit sequences with desirable transmission characteristics. Line coding is always used and involves choosing schemes like NRZ, Manchester, or AMI that map bits to signal levels. Block coding like 4B/5B or 8B/10B adds bits to improve synchronization and error detection. Scrambling removes problematic bit runs to prevent issues like baseline wandering.
Digital signals can be encoded in various ways:
1) NRZ-L uses two voltage levels to represent 0s and 1s, maintaining a constant level during each bit.
2) NRZI represents 1s as transitions and 0s as no transitions at the start of each bit.
3) Bipolar-AMI represents 0s as no signal and alternating positive and negative pulses for 1s, eliminating long runs of the same signal.
The document discusses digital-to-digital and analog-to-digital conversion techniques. It covers line coding schemes such as unipolar NRZ, polar, bipolar, multilevel, and multiline transmission. It also discusses block coding, scrambling, and pulse code modulation for analog-to-digital conversion. The key steps of PCM encoding are sampling, quantization, and encoding the analog signal into digital data.
Data Communication & Computer Networks:Digital Signal EncodingDr Rajiv Srivastava
These slides cover the fundamentals of data communication & networking. It covers Digital signal Encoding which are used in communication of data over transmission medium. it is useful for engineering students & also for the candidates who want to master data communication & computer networking.
Line coding refers to converting digital data into digital signals for transmission. There are several characteristics line coding schemes should have, such as low complexity, noise tolerance, no DC component, error detection capability, and self-synchronization. Common line coding techniques include unipolar, polar, and bipolar coding. Specific techniques discussed include non-return to zero (NRZ), return to zero (RZ), Manchester, and differential Manchester coding. These techniques vary in their voltage levels, presence of a DC component, synchronization capabilities, and bandwidth requirements.
This document discusses base-band digital data transmission. It begins by defining the components of data communication, including analog and digital data and signals. It then discusses the need for digital transmission over analog. The main topics covered are line coding techniques, including the important characteristics of line coding like number of levels, bit vs baud rate, DC components, signal spectrum, synchronization and cost of implementation. Specific line coding techniques discussed include unipolar, polar, NRZ, RZ, Manchester, differential Manchester and bipolar coding. The modulation rate relationship to data rate and number of signal elements is also covered.
This document discusses various techniques for digital-to-digital conversion, including line coding, block coding, and scrambling. It describes several common line coding schemes such as NRZ-L, NRZ-I, Manchester, and AMI. Block coding techniques like 4B/5B and 8B/10B are also summarized. The purpose of these coding methods is to convert digital bits into signals while preventing long runs of identical signal levels and enabling synchronization. Scrambling can further be used to create self-clocking bit streams without DC components or wide bandwidth needs.
The document discusses different types of line coding schemes used to convert digital data to digital signals, including unipolar, polar, and bipolar schemes. It also covers techniques for digital to analog conversion, including amplitude shift keying, frequency shift keying, and phase shift keying. The key differences between these schemes involve the number of signal levels used (unipolar uses one, polar uses two, bipolar uses three) and whether the signal crosses or remains on one side of the reference axis. Common modulation techniques for digital to analog conversion are amplitude shift keying, frequency shift keying, and phase shift keying, which vary the amplitude, frequency, or phase of an analog carrier signal to represent digital data.
This document discusses line coding techniques used in data communication. It describes three main categories of line coding: unipolar, polar, and bipolar. Unipolar coding uses one signal level, polar uses two levels, and bipolar uses three levels including positive, negative and zero. Specific techniques like NRZ, NRZ-L, and AMI are discussed along with their advantages and disadvantages. The document also covers encapsulation and decapsulation in networking, where data is packaged at each layer of transmission and unpacked at the receiving end.
This document summarizes digital transmission techniques including digital-to-digital conversion, analog-to-digital conversion, and transmission modes. Digital-to-digital conversion involves line coding, block coding, and scrambling to convert digital data to digital signals. Analog-to-digital conversion uses pulse code modulation to sample, quantize, and encode analog signals as digital bits. Transmission can be done in parallel or serial modes, with serial transmission occurring asynchronously, synchronously, or isochronously.
The document discusses different methods of encoding and modulating digital and analog signals for transmission. It covers digital-to-digital encoding techniques like unipolar, polar, Manchester and differential Manchester encoding. It also discusses analog-to-digital conversion techniques like PAM and PCM. Finally, it discusses analog-to-analog modulation techniques like AM, FM and PM and how they modulate parameters of a carrier signal to transmit an analog signal.
This document provides a summary of a 2-hour lecture on communication basics including modulation and encoding techniques for digital-to-digital, digital-to-analog, and analog-to-digital signals. It discusses common modulation techniques like ASK, FSK, PSK and line codes like Manchester encoding, AMI, and 4B/5B. It also covers topics like digitization of analog signals, constellation diagrams, and multilevel FSK to improve bit rates. The document includes examples, diagrams, and a short quiz to test understanding of key concepts covered in the lecture.
This document provides a summary of a 2-hour lecture on communication basics including modulation and encoding techniques for digital-to-digital, digital-to-analog, and analog-to-digital signals. It discusses common modulation techniques like ASK, FSK, PSK and line codes like Manchester encoding, AMI, and 4B/5B. It also covers topics like digitization of analog signals, constellation diagrams, and multilevel FSK to improve bit rates. The document includes examples, diagrams, and a short quiz to test understanding of key concepts covered in the lecture.
This document provides an overview of a lecture on data communications and networking. It discusses various topics related to line coding, including conversion methods, characteristics of line coding, and different line coding schemes. Specifically, it defines line coding as the process of converting binary data to a digital signal, and discusses signal level, data rate, pulse rate, DC component, and self-synchronization in the context of line coding. It also explains different line coding schemes such as unipolar, polar, NRZ, RZ, Manchester, and bipolar encoding.
This document provides an outline and overview of key topics in digital transmission covered in Chapter 4, including:
- Digital-to-digital conversion techniques like line coding, block coding, and scrambling.
- Analog-to-digital conversion using pulse code modulation (PCM) and delta modulation to convert analog signals to digital data.
- Transmission modes for sending digital data, including parallel transmission of multiple bits at once, and serial transmission of single bits in asynchronous, synchronous, or isochronous formats.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
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.
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.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
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.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
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4-1 DIGITAL-TO-DIGITAL CONVERSION
In this section, we see how we can represent digital
data by using digital signals. The conversion involves
three techniques: line coding, block coding, and
scrambling. Line coding is always needed; block
coding and scrambling may or may not be needed.
Line Coding
Line Coding Schemes
Block Coding
Scrambling
Topics discussed in this section:
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Signal Element versus Data Element
• Data element
– The smallest entity that can represent a piece of
information: this is bit.
• Signal element
– The shortest unit (timewise) of a digital signal.
• In other words
– Data element are what we need to send.
– Signal elements are what we can send.
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Data Rate versus Signal Rate
• Data rate
– The number of data elements (bits) sent in 1s
– The unit is bits per second (bps)
– Called bit rate
• Signal rate
– The number of signal elements sent in 1s
– The unit is the baud
– Signal rate is sometimes called the pulse rate, the modulation rate,
or the baud rate
• Relationship between data rate and signal rate
• S: number of signal elements, c: the case factor, N: data
rate (bps), r: data elements per signal elements
r
N
c
S
1
baud
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A signal is carrying data in which one data element is
encoded as one signal element (r = 1). If the bit rate is
100 kbps, what is the average value of the baud rate if c is
between 0 and 1?
Solution
We assume that the average value of c is 1/2 . The baud
rate is then
Example 4.1
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The maximum data rate of a channel (see Chapter 3) is
Nmax = 2 × B × log2 L (defined by the Nyquist formula).
Does this agree with the previous formula for Nmax?
Solution
A signal with L levels actually can carry log2L bits per
level. If each level corresponds to one signal element and
we assume the average case (c = 1/2), then we have
Example 4.2
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Baseline Wandering
• In decoding a digital signal, the receiver calculates
a running average of the received signal power.
• This average is called the baseline.
• The incoming signal power is evaluated against
this baseline to determine the value of the data
element.
• A long string of 0s or 1s can cause a drift in the
baseline (baseline wandering) and make it
difficult for the receiver to decode correctly.
• A good line coding scheme needs to prevent
baseline wandering.
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DC Components
• DC Components
– When the voltage level in a digital signal is constant for a
while, the spectrum creates very low frequencies (results
of Fourier analysis).
– These frequencies around zero, call DC (direct-current)
components, present problems for a system that cannot
pass low frequencies or a system that uses electrical
coupling (via a transformer).
– For example, a telephone line cannot pass frequencies
below 200 Hz.
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Self-synchronization
• To correctly interpret the signals received from the
sender, the receiver’s bit intervals must correspond
exactly to the sender’s bit intervals. If the receiver
clock is faster or slower, the bit intervals are not
matched and the receiver might misinterpret the
signals.
• Self-synchronization
– Digital signal includes timing information in the data
being transmitted.
– This can be achieved if there are transitions in the signal
that alert the receiver to the beginning, middle, or end of
the pulse.
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In a digital transmission, the receiver clock is 0.1 percent
faster than the sender clock. How many extra bits per
second does the receiver receive if the data rate is
1 kbps? How many if the data rate is 1 Mbps?
Solution
At 1 kbps, the receiver receives 1001 bps instead of 1000
bps.
Example 4.3
At 1 Mbps, the receiver receives 1,001,000 bps instead of
1,000,000 bps.
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Figure 4.5 Unipolar NRZ scheme
Non-Return-to-Zero (NRZ)
It is called NRZ because the signal does not return to zero at the middle of the bit.
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In NRZ-L the level of the voltage
determines the value of the bit.
In NRZ-I the inversion
or the lack of inversion
determines the value of the bit.
Note
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A system is using NRZ-I to transfer 10-Mbps data. What
are the average signal rate and minimum bandwidth?
Solution
The average signal rate is S = N/2 = 500 kbaud. The
minimum bandwidth for this average baud rate is Bmin =
S = 500 kHz.
Example 4.4
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Bipolar Schemes
• Bipolar encoding (sometimes called multilevel
binary)
– Three voltage levels: positive, negative, and zero
• Two variations of bipolar encoding
– AMI (alternate mark inversion)
• 0: neutral zero voltage
• 1: alternating positive and negative voltages
– Pseudoternary
• 1: neutral zero voltage
• 0: alternating positive and negative voltages
• Bipolar schemes have no DC component problem
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AMI and Pseudoternary
• AMI (alternate mark inversion)
– The work mark comes from telegraphy and means 1.
– AMI means alternate 1 inversion
– The neutral zero voltage represents binary 0.
– Binary 1s are represented by alternating positive and
negative voltages.
• Pesudotenary
– Same as AMI, but 1 bit is encoded as a zero voltage and
the 0 bit is encoded as alternating positive and negative
voltages.
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Multilevel Schemes
• The desire to increase the data speed or decrease the
required bandwidth has resulted in the creation of many
schemes.
• The goal is to increase the number of bits per baud by
encoding a pattern of m data elements into a pattern of n
signal elements.
• Different types of signal elements can be allowing different
signal levels.
• If we have L different levels, then we can produce Ln
combinations of signal patterns.
• The data element and signal element relation is
• mBnL coding, where m is the length of the binary pattern, B
means binary data, n is the length of the signal pattern, and
L is the number of levels in the signaling.
• B (binary, L=2), T (tenary, L=3), and Q (quaternary, L=4).
n
m
L
2
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In mBnL schemes, a pattern of m data
elements is encoded as a pattern of n
signal elements in which 2m ≤ Ln.
Note
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2B1Q
• 2B1Q (two binary, one quaternary)
– m=2, n=1, and L=4
– The signal rate (baud rate)
• 2B1Q is used in DSL (digital subscriber line) technology to
provide a high-speed connection to the Internet by using
subscriber telephone lines.
4
2
1
2
1
1 N
N
r
cN
S
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8B6T
• Eight binary, six ternary (8B6T)
– This code is used with 100BASE-4T cable.
– Encode a pattern of 8 bits as a pattern of 6 signal elements, where
the signal has three levels (ternary).
– 28=256 different data patterns and 36=478 different signal patterns.
(The mapping is shown in Appendix D.)
– There are 478-256=222 redundant signal elements that provide
synchronization and error detection.
– Part of the redundancy is also used to provide DC (direct-current)
balance.
• + (positive signal), - (negative signal), and 0 (lack of signal)
notation.
• To make whole stream DC-balanced, the sender keeps
track of the weight
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4D-PAM5
• Four-dimensional five-level pulse amplitude modulation
(4D-PAM5)
– 4D means that data is sent over four wires at the same time.
– It uses five voltage levels, such as -2, -1, 0, 1, and 2.
– The level 0 is used only for forward error detection.
– If we assume that the code is just one-dimensional, the four levels
create something similar to 8B4Q.
– The worst signal rate for this imaginary one-dimensional version is
Nx4/8, or N/2.
– 4D-PAM5 sends data over four channels (four wires). This means
the signal rate can be reduced to N/8.
– All 8 bits can be fed into a wire simultaneously and sent by using
one signal element.
– Gigabit Ethernet use this technique to send 1-Gbps data over four
copper cables that can handle 1Gbps/8 = 125Mbaud
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Multiline Transmission: MLT-3
• The multiline transmission, three level (MLT-3)
• Three levels (+V, 0, and –V) and three transition rules to
move the levels
– If the next bit is 0, there is no transition
– If the next bit is 1 and the current level is not 0, the next level is 0.
– If the next bit is 1 and the current level is 0, the next level is the
opposite of the last nonzero level.
• Why do we need to use MLT-3?
– The signal rate for MLT-3 is one-fourth the bit rate (N/4).
– This makes MLT-3 a suitable choice when we need to send 100
Mbps on a copper wire that cannot support more than 32 MHz
(frequencies above this level create electromagnetic emission).
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Block Coding
• Use redundancy to ensure synchronization and to
provide some kind of inherent error detecting.
• In general, block coding changes a block of m
bits into a block of n bits, where n is larger than m.
• Block coding is referred to as an mB/nB encoding
technique.
• For example:
– 4B/5B encoding means a 4-bit code for a 5-bit group.
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4B/5B Encoding
• 5-bit output that replaces the 4-bit input
• No more than one leading zero (left bit) and no
more than two trailing zeros (right bits).
• There are never more than three consecutive 0s.
• If a 5-bit group arrives that belongs to the unused
portion of the table, the receiver knows that there is
an error in the transmission.
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We need to send data at a 1-Mbps rate. What is the
minimum required bandwidth, using a combination of
4B/5B and NRZ-I or Manchester coding?
Solution
First 4B/5B block coding increases the bit rate to 1.25
Mbps. The minimum bandwidth using NRZ-I is N/2 or
625 kHz. The Manchester scheme needs a minimum
bandwidth of 1 MHz. The first choice needs a lower
bandwidth, but has a DC component problem; the second
choice needs a higher bandwidth, but does not have a DC
component problem.
Example 4.5
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Scrambling
• Biphase schemes that are suitable for dedicated links
between stations in a LAN are not suitable for long-
distance communication because of their wide bandwidth
requirement.
• The combination of block coding and NRZ line coding is not
suitable for long-distance encoding either, because of the
DC component problem.
• Bipolar AMI encoding, on the other hand, has a narrow
bandwidth and does not create a DC component.
• However, a long sequence of 0s upsets the synchronization.
• If we can find a way to avoid a long sequence of 0s in the
original stream, we can use bipolar AMI for long distances.
• One solution is called scrambling.
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B8ZS
• Bipolar with 8-zero substitution (B8ZS)
– Commonly used in North America
– Eight consecutive zero-level voltages are replaced by
the sequence 000VB0VB.
– The V in the sequence denotes violation; that is a
nonzero voltage that breaks an AMI rule of encoding
(opposite polarity from the previous).
– The B in the sequence denotes bipolar, which means a
nonzero level voltage in accordance with the AMI rule.
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HDB3
• High-density bipolar 3-zero (HDB3)
– Used outside of North America
– Four consecutive zero-level voltages are replaced with a
sequence of 000V or B00V.
– 1. If the number of nonzero pulses after the last
substitution is odd, the substitution pattern will be 000V,
which makes the total number of nonzero pulses even.
– 2. If the number of nonzero pulses after the last
substitution is even, the substitution pattern will be B00V,
which means makes the total number of nonzero pulses
even.
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4-2 ANALOG-TO-DIGITAL CONVERSION
We have seen in Chapter 3 that a digital signal is
superior to an analog signal. The tendency today is to
change an analog signal to digital data. In this section
we describe two techniques, pulse code modulation
and delta modulation.
Pulse Code Modulation (PCM)
Delta Modulation (DM)
Topics discussed in this section:
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According to the Nyquist theorem, the
sampling rate must be
at least 2 times the highest frequency
contained in the signal.
Note
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For an intuitive example of the Nyquist theorem, let us
sample a simple sine wave at three sampling rates: fs = 4f
(2 times the Nyquist rate), fs = 2f (Nyquist rate), and
fs = f (one-half the Nyquist rate). Figure 4.24 shows the
sampling and the subsequent recovery of the signal.
It can be seen that sampling at the Nyquist rate can create
a good approximation of the original sine wave (part a).
Oversampling in part b can also create the same
approximation, but it is redundant and unnecessary.
Sampling below the Nyquist rate (part c) does not produce
a signal that looks like the original sine wave.
Example 4.6
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Consider the revolution of a hand of a clock. The second
hand of a clock has a period of 60 s. According to the
Nyquist theorem, we need to sample the hand every 30 s
(Ts = T or fs = 2f ). In Figure 4.25a, the sample points, in
order, are 12, 6, 12, 6, 12, and 6. The receiver of the
samples cannot tell if the clock is moving forward or
backward. In part b, we sample at double the Nyquist rate
(every 15 s). The sample points are 12, 3, 6, 9, and 12.
The clock is moving forward. In part c, we sample below
the Nyquist rate (Ts = T or fs = f ). The sample points are
12, 9, 6, 3, and 12. Although the clock is moving forward,
the receiver thinks that the clock is moving backward.
Example 4.7
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An example related to Example 4.7 is the seemingly
backward rotation of the wheels of a forward-moving car
in a movie. This can be explained by under-sampling. A
movie is filmed at 24 frames per second. If a wheel is
rotating more than 12 times per second, the under-
sampling creates the impression of a backward rotation.
Example 4.8
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A complex low-pass signal has a bandwidth of 200 kHz.
What is the minimum sampling rate for this signal?
Solution
The bandwidth of a low-pass signal is between 0 and f,
where f is the maximum frequency in the signal.
Therefore, we can sample this signal at 2 times the
highest frequency (200 kHz). The sampling rate is
therefore 400,000 samples per second.
Example 4.10
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A complex bandpass signal has a bandwidth of 200 kHz.
What is the minimum sampling rate for this signal?
Solution
We cannot find the minimum sampling rate in this case
because we do not know where the bandwidth starts or
ends. We do not know the maximum frequency in the
signal.
Example 4.11
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Quantization
• Quantization Levels
– Choosing lower values of L increases the quantization
error if there is a lot of fluctuation in the signal.
• Quantization Error
– If the input value is also at the middle of the zone, there
is no quantization error; otherwise, there is an error.
– It can be proven that the contribution of the quantization
error to the SNRdB of the signal depends on the number
of quantization levels L, or the bits per sample nb, as
shown in the following formula:
SNRdB = 6.02nb + 1.76 dB
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What is the SNRdB in the example of Figure 4.26?
Solution
We can use the formula to find the quantization. We have
eight levels and 3 bits per sample, so
SNRdB = 6.02(3) + 1.76 = 19.82 dB
Increasing the number of levels increases the SNR.
Example 4.12
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A telephone subscriber line must have an SNRdB above
40. What is the minimum number of bits per sample?
Solution
We can calculate the number of bits as
Example 4.13
Telephone companies usually assign 7 or 8 bits per
sample.
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Quantization
• Uniform versus nonuniform quantization
– Changes in amplitude often occur more frequently in the
lower amplitudes than in the higher ones.
– Nonuniform quantization
• Companding
• Expanding
• Encoding
– nb = log2L
– Bit rate = sampling rate x number of bits per sample
= fs x nb
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We want to digitize the human voice. What is the bit rate,
assuming 8 bits per sample?
Solution
The human voice normally contains frequencies from 0
to 4000 Hz. So the sampling rate and bit rate are
calculated as follows:
Example 4.14
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PCM Bandwidth
• The minimum bandwidth of the channel that can pass the
digitized signal:
• When 1/r = 1 (for NRZ or bipolar signal) and c = ½
r
B
n
c
r
f
n
c
r
N
c
B b
s
b
1
2
1
1
analog
min
analog
min B
n
B b
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Maximum data rate of a channel
• Maximum data rate of a channel
• The data rate is
• Minimum required bandwidth
L
B
N 2
max log
2
bps
L
B
n
f
N b
s 2
log
2
L
N
B
2
min
log
2
Hz
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We have a low-pass analog signal of 4 kHz. If we send the
analog signal, we need a channel with a minimum
bandwidth of 4 kHz. If we digitize the signal and send 8
bits per sample, we need a channel with a minimum
bandwidth of 8 × 4 kHz = 32 kHz.
Example 4.15
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Adaptive DM
• Adaptive delta modulation
– A better performance can be achieved if the value of δ is
not fixed.
– The value of δ changes according to the amplitude of the
analog signal.
• Quantization Error
– DM is not perfect.
– Quantization error is always introduced in the process.
– Much less than that for PCM.
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4-3 TRANSMISSION MODES
The transmission of binary data across a link can be
accomplished in either parallel or serial mode. In
parallel mode, multiple bits are sent with each clock
tick. In serial mode, 1 bit is sent with each clock tick.
While there is only one way to send parallel data, there
are three subclasses of serial transmission:
asynchronous, synchronous, and isochronous.
Parallel Transmission
Serial Transmission
Topics discussed in this section:
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In asynchronous transmission, we send
1 start bit (0) at the beginning and 1 or
more stop bits (1s) at the end of each
byte. There may be a gap between
each byte.
Note
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In synchronous transmission, we send
bits one after another without start or
stop bits or gaps. It is the responsibility
of the receiver to group the bits.
Note