1) Data can be either analog or digital, where analog data is continuous and digital data has discrete states. Signals that transmit data can also be analog or digital.
2) Periodic analog signals include sine waves and composite signals made of multiple sine waves. Sine waves are characterized by their frequency, amplitude, wavelength and phase.
3) Fourier analysis can decompose both periodic and non-periodic signals into their constituent sine waves in the frequency domain. This allows signals to be represented more compactly.
Ch3Data communication and networking by neha g. kuraleNeha Kurale
油
Data can exist in either analog or digital form. Analog data is continuous while digital data takes on discrete values. Both analog and digital signals can be periodic or non-periodic. Periodic signals can be decomposed into simpler sine waves using Fourier analysis. Non-periodic signals result in a combination of sine waves with continuous frequencies. The bandwidth of a signal is the difference between its highest and lowest frequencies.
This document discusses analog and digital data and signals. It defines analog data as continuous and digital data as discrete. Analog signals can have an infinite number of values while digital signals are limited. Periodic signals like sine waves can be simple or composite made of multiple sine waves. Frequency is the rate of change over time, while period is the inverse of frequency. Phase describes the position of a waveform. Composite signals can be periodic or nonperiodic. Bandwidth is the range of frequencies in a signal. Fourier analysis decomposes signals into sine waves to transform between time and frequency domains.
Data Communication And Networking - DATA & SIGNALSAvijeet Negel
油
This document discusses analog and digital data and signals. It defines analog data as continuous and taking on continuous values, while digital data is discrete and takes on discrete values. Signals can also be analog or digital. Periodic signals that repeat over time can be decomposed into simpler sine waves using Fourier analysis. This represents the signal in the frequency domain. Nonperiodic signals have a continuous range of frequencies. The bandwidth of a signal is the range between its highest and lowest frequencies. Examples are given of various signal types and their Fourier representations.
This document discusses analog and digital data and signals. It defines analog data as continuous and taking on continuous values, while digital data is discrete and takes on discrete values. Signals can also be analog or digital. Periodic signals that repeat over time can be decomposed into simpler sine waves using Fourier analysis. This represents the signal in the frequency domain. Nonperiodic signals have a continuous range of frequencies. The bandwidth of a signal is the range between its highest and lowest frequencies. Examples are given of various signal types and their Fourier representations.
This document discusses analog and digital signals. It begins by explaining that data can be either analog or digital. Analog data are continuous and take on continuous values, while digital data have discrete states and take discrete values. Signals can also be analog or digital. Analog signals can have an infinite number of values within a range, while digital signals can have only a limited number of discrete values. Periodic analog signals such as sine waves are discussed, along with their properties including frequency, period, amplitude, phase, and wavelength. Composite signals made up of multiple sine waves are also covered. The document then discusses digital signals and how they can be represented by analog signals.
Data can exist in either analog or digital form. Analog data is continuous while digital data takes on discrete values. Signals can also be analog or digital, with analog signals having an infinite number of possible values and digital signals having a finite set of values. For data to be transmitted, it must be converted into electromagnetic signals. Baseband transmission of digital signals is only possible if the channel has an extremely wide bandwidth to preserve the shape of the digital signal.
This document discusses analog and digital signals. It begins by explaining that signals can be either analog or digital. Analog signals are continuous and can have an infinite number of values, while digital signals are discrete and can have only a limited number of values. It then discusses periodic and nonperiodic signals, explaining that periodic signals repeat over time while nonperiodic signals do not. The document uses examples and diagrams to illustrate concepts like bandwidth, frequency, amplitude, and how signals can be represented in both the time and frequency domains.
This document discusses analog and digital signals and data. It defines analog data as continuous and taking on continuous values, while digital data is discrete and takes on discrete values. Signals can also be analog or digital. Analog signals have an infinite number of values in a range, while digital signals have a limited set of values. Periodic signals repeat consistently, while nonperiodic signals do not repeat. The bandwidth of a signal is the difference between the highest and lowest frequencies contained in the signal. For digital signals to be transmitted, they must be converted to analog signals using modulation.
Chapter 3 data and signals computer_networkDhairya Joshi
油
- Data can be analog or digital. Analog data is continuous while digital data has discrete states.
- Signals can also be analog or digital. Analog signals have an infinite range of values while digital signals have a limited set of values.
- For transmission, digital data must be converted to analog signals using modulation techniques if the available channel is a bandpass channel rather than a baseband channel.
- Data can be analog or digital. Analog data are continuous and take continuous values, while digital data have discrete states and take discrete values.
- Signals can be analog or digital. Analog signals can have an infinite number of values in a range, while digital signals can only have a limited number of discrete values.
- Periodic analog signals include simple sine waves and composite signals made up of multiple sine waves. Nonperiodic signals do not repeat.
- Data can be analog or digital. Analog data is continuous while digital data has discrete states.
- Signals can also be analog or digital. Analog signals have an infinite range of values while digital signals have a limited set of values.
- For transmission, digital data must be converted to analog signals using modulation techniques if the available channel is a bandpass channel rather than a baseband channel.
- Data can be analog or digital. Analog data is continuous while digital data has discrete states.
- Signals can also be analog or digital. Analog signals have an infinite range of values while digital signals have a limited set of values.
- Periodic signals repeat over time and can be decomposed into simpler sine waves. Nonperiodic signals do not repeat over time.
- The bandwidth of a signal is the difference between its highest and lowest frequencies. Bandwidth is important for data transmission.
This document discusses analog and digital signals and data. It begins by explaining that data can be either analog or digital. Analog data is continuous while digital data has discrete states. Signals can also be analog or digital. Analog signals can have an infinite number of values while digital signals are limited to a set number of values. Periodic analog signals include simple sine waves and composite signals made of multiple sine waves. Frequency and period are inversely related. Phase describes the position of a waveform relative to a reference time. Bandwidth is the difference between the highest and lowest frequencies in a composite signal. Digital signals represent information using discrete signal levels that can be transmitted using baseband or broadband transmission depending on the channel. Signals are impaired during transmission through
This document discusses analog and digital signals and data. It explains that:
- Analog data are continuous while digital data have discrete states and values.
- Analog signals can have an infinite number of values, while digital signals have a limited set of values.
- Periodic signals can be composed of simple sine waves or multiple sine waves. Nonperiodic signals have no repeating pattern.
- The bandwidth of a signal is the difference between the highest and lowest frequencies contained in it.
- Digital signals represent information using discrete signal levels that can encode binary digits (bits).
This document discusses analog and digital signals and data transmission. It begins by defining analog and digital data and signals, with analog being continuous and digital having discrete states. It then discusses periodic analog signals like sine waves. Digital signals encode information using different voltage levels. When transmitting signals, bandwidth and impairments like attenuation, distortion, and noise must be considered. The Nyquist theorem provides the maximum bit rate for a noiseless channel based on bandwidth and number of signal levels. The Shannon capacity theorem provides the maximum rate for a noisy channel.
This document summarizes key concepts about analog and digital data and signals. It discusses how data must be transformed into electromagnetic signals to be transmitted. Analog data and signals are continuous, while digital data and signals are discrete. Periodic signals can be simple sine waves or composed of multiple sine waves. Nonperiodic signals do not repeat. The bandwidth of a signal is the difference between the highest and lowest frequencies. Digital signals encode information using different voltage levels to represent bits. The bit rate is the number of bits transmitted per second.
This document discusses analog and digital data and signals. It explains that:
- Data can be analog (continuous) or digital (discrete). Analog signals can have an infinite number of values, while digital signals have a limited number of values.
- Periodic analog signals include sine waves and composite signals made of multiple sine waves. Frequency is the rate of change over time, while period is the inverse of frequency.
- Digital signals represent information as discrete voltage levels, allowing multiple bits to be encoded in each signal level. The required bit rate depends on factors like sampling rate and number of bits per sample.
This document discusses analog and digital signals. It begins by explaining that data must be transformed into electromagnetic signals to be transmitted. It then defines analog and digital data, noting that analog data is continuous while digital data has discrete states. Periodic and nonperiodic signals are also introduced. Specific topics covered include sine waves, frequency, phase, bandwidth, Fourier analysis, and digital signals. Examples are provided to illustrate key concepts such as calculating bandwidth and determining the number of bits needed to represent different signal levels.
Data Communications and Networking ch03ssuserdf9c52
油
This document discusses analog and digital data and signals. It explains that:
- Analog data are continuous while digital data have discrete states
- Analog signals can have an infinite number of values while digital signals are limited to a set number of values
- Periodic analog signals include sine waves and composite signals made of multiple sine waves
- Digital signals represent information as different voltage levels, with more levels allowing more bits per signal
COMPUTER NETWORKS DATAS AND SIGNALS.pptxKALPANAC20
油
This document discusses analog and digital signals. It begins by explaining that data can be either analog or digital. Analog data is continuous and takes on a continuous range of values, while digital data is discrete and takes on discrete states represented by numbers like 0s and 1s. It then discusses analog signals, which can have an infinite number of values in a range, and digital signals, which have a limited number of discrete values. The document provides examples of analog signals like human voice and digital signals like computer memory. It also compares periodic and nonperiodic signals.
- Data can be analog or digital, with analog being continuous and digital having discrete states. Analog signals are also continuous while digital signals have a limited set of values.
- Periodic analog signals like sine waves can be simple or composite, consisting of multiple sine waves. Nonperiodic signals are commonly used for digital data transmission.
- Digital signals represent information using discrete signal levels that can be encoded as voltages, with more levels allowing more bits to be sent per signal. The required bit rate depends on the data transmission rate and number of bits used per sample or character.
- Data can be analog or digital, with analog being continuous and digital having discrete states. Analog signals are also continuous while digital signals have a limited set of values.
- Periodic analog signals like sine waves can be simple or composite, with composite signals made up of multiple sine waves. Digital signals represent information as different voltage levels corresponding to bits.
- The bandwidth of a signal is the difference between its highest and lowest frequencies. It represents the range of frequencies the signal occupies.
Towards Scientific Foundation Models (Invited Talk)Steffen Staab
油
Foundation models are machine-learned models that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. Foundation models have been used successfully for question answering and text generation (ChatGPT), image understanding (Clip, VIT), or image generation. Recently, the basic idea underlying foundation models been considered for learning scientific foundation models that capture expectations about partial differential equations. Existing scientific foundation models have still been very much limited wrt. the type of PDEs or differential operators . In this talk, I present some of our recent work on paving the way towards scientific foundation models that aims at making them more robust and better generalisable.
Data can exist in either analog or digital form. Analog data is continuous while digital data takes on discrete values. Signals can also be analog or digital, with analog signals having an infinite number of possible values and digital signals having a finite set of values. For data to be transmitted, it must be converted into electromagnetic signals. Baseband transmission of digital signals is only possible if the channel has an extremely wide bandwidth to preserve the shape of the digital signal.
This document discusses analog and digital signals. It begins by explaining that signals can be either analog or digital. Analog signals are continuous and can have an infinite number of values, while digital signals are discrete and can have only a limited number of values. It then discusses periodic and nonperiodic signals, explaining that periodic signals repeat over time while nonperiodic signals do not. The document uses examples and diagrams to illustrate concepts like bandwidth, frequency, amplitude, and how signals can be represented in both the time and frequency domains.
This document discusses analog and digital signals and data. It defines analog data as continuous and taking on continuous values, while digital data is discrete and takes on discrete values. Signals can also be analog or digital. Analog signals have an infinite number of values in a range, while digital signals have a limited set of values. Periodic signals repeat consistently, while nonperiodic signals do not repeat. The bandwidth of a signal is the difference between the highest and lowest frequencies contained in the signal. For digital signals to be transmitted, they must be converted to analog signals using modulation.
Chapter 3 data and signals computer_networkDhairya Joshi
油
- Data can be analog or digital. Analog data is continuous while digital data has discrete states.
- Signals can also be analog or digital. Analog signals have an infinite range of values while digital signals have a limited set of values.
- For transmission, digital data must be converted to analog signals using modulation techniques if the available channel is a bandpass channel rather than a baseband channel.
- Data can be analog or digital. Analog data are continuous and take continuous values, while digital data have discrete states and take discrete values.
- Signals can be analog or digital. Analog signals can have an infinite number of values in a range, while digital signals can only have a limited number of discrete values.
- Periodic analog signals include simple sine waves and composite signals made up of multiple sine waves. Nonperiodic signals do not repeat.
- Data can be analog or digital. Analog data is continuous while digital data has discrete states.
- Signals can also be analog or digital. Analog signals have an infinite range of values while digital signals have a limited set of values.
- For transmission, digital data must be converted to analog signals using modulation techniques if the available channel is a bandpass channel rather than a baseband channel.
- Data can be analog or digital. Analog data is continuous while digital data has discrete states.
- Signals can also be analog or digital. Analog signals have an infinite range of values while digital signals have a limited set of values.
- Periodic signals repeat over time and can be decomposed into simpler sine waves. Nonperiodic signals do not repeat over time.
- The bandwidth of a signal is the difference between its highest and lowest frequencies. Bandwidth is important for data transmission.
This document discusses analog and digital signals and data. It begins by explaining that data can be either analog or digital. Analog data is continuous while digital data has discrete states. Signals can also be analog or digital. Analog signals can have an infinite number of values while digital signals are limited to a set number of values. Periodic analog signals include simple sine waves and composite signals made of multiple sine waves. Frequency and period are inversely related. Phase describes the position of a waveform relative to a reference time. Bandwidth is the difference between the highest and lowest frequencies in a composite signal. Digital signals represent information using discrete signal levels that can be transmitted using baseband or broadband transmission depending on the channel. Signals are impaired during transmission through
This document discusses analog and digital signals and data. It explains that:
- Analog data are continuous while digital data have discrete states and values.
- Analog signals can have an infinite number of values, while digital signals have a limited set of values.
- Periodic signals can be composed of simple sine waves or multiple sine waves. Nonperiodic signals have no repeating pattern.
- The bandwidth of a signal is the difference between the highest and lowest frequencies contained in it.
- Digital signals represent information using discrete signal levels that can encode binary digits (bits).
This document discusses analog and digital signals and data transmission. It begins by defining analog and digital data and signals, with analog being continuous and digital having discrete states. It then discusses periodic analog signals like sine waves. Digital signals encode information using different voltage levels. When transmitting signals, bandwidth and impairments like attenuation, distortion, and noise must be considered. The Nyquist theorem provides the maximum bit rate for a noiseless channel based on bandwidth and number of signal levels. The Shannon capacity theorem provides the maximum rate for a noisy channel.
This document summarizes key concepts about analog and digital data and signals. It discusses how data must be transformed into electromagnetic signals to be transmitted. Analog data and signals are continuous, while digital data and signals are discrete. Periodic signals can be simple sine waves or composed of multiple sine waves. Nonperiodic signals do not repeat. The bandwidth of a signal is the difference between the highest and lowest frequencies. Digital signals encode information using different voltage levels to represent bits. The bit rate is the number of bits transmitted per second.
This document discusses analog and digital data and signals. It explains that:
- Data can be analog (continuous) or digital (discrete). Analog signals can have an infinite number of values, while digital signals have a limited number of values.
- Periodic analog signals include sine waves and composite signals made of multiple sine waves. Frequency is the rate of change over time, while period is the inverse of frequency.
- Digital signals represent information as discrete voltage levels, allowing multiple bits to be encoded in each signal level. The required bit rate depends on factors like sampling rate and number of bits per sample.
This document discusses analog and digital signals. It begins by explaining that data must be transformed into electromagnetic signals to be transmitted. It then defines analog and digital data, noting that analog data is continuous while digital data has discrete states. Periodic and nonperiodic signals are also introduced. Specific topics covered include sine waves, frequency, phase, bandwidth, Fourier analysis, and digital signals. Examples are provided to illustrate key concepts such as calculating bandwidth and determining the number of bits needed to represent different signal levels.
Data Communications and Networking ch03ssuserdf9c52
油
This document discusses analog and digital data and signals. It explains that:
- Analog data are continuous while digital data have discrete states
- Analog signals can have an infinite number of values while digital signals are limited to a set number of values
- Periodic analog signals include sine waves and composite signals made of multiple sine waves
- Digital signals represent information as different voltage levels, with more levels allowing more bits per signal
COMPUTER NETWORKS DATAS AND SIGNALS.pptxKALPANAC20
油
This document discusses analog and digital signals. It begins by explaining that data can be either analog or digital. Analog data is continuous and takes on a continuous range of values, while digital data is discrete and takes on discrete states represented by numbers like 0s and 1s. It then discusses analog signals, which can have an infinite number of values in a range, and digital signals, which have a limited number of discrete values. The document provides examples of analog signals like human voice and digital signals like computer memory. It also compares periodic and nonperiodic signals.
- Data can be analog or digital, with analog being continuous and digital having discrete states. Analog signals are also continuous while digital signals have a limited set of values.
- Periodic analog signals like sine waves can be simple or composite, consisting of multiple sine waves. Nonperiodic signals are commonly used for digital data transmission.
- Digital signals represent information using discrete signal levels that can be encoded as voltages, with more levels allowing more bits to be sent per signal. The required bit rate depends on the data transmission rate and number of bits used per sample or character.
- Data can be analog or digital, with analog being continuous and digital having discrete states. Analog signals are also continuous while digital signals have a limited set of values.
- Periodic analog signals like sine waves can be simple or composite, with composite signals made up of multiple sine waves. Digital signals represent information as different voltage levels corresponding to bits.
- The bandwidth of a signal is the difference between its highest and lowest frequencies. It represents the range of frequencies the signal occupies.
Towards Scientific Foundation Models (Invited Talk)Steffen Staab
油
Foundation models are machine-learned models that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. Foundation models have been used successfully for question answering and text generation (ChatGPT), image understanding (Clip, VIT), or image generation. Recently, the basic idea underlying foundation models been considered for learning scientific foundation models that capture expectations about partial differential equations. Existing scientific foundation models have still been very much limited wrt. the type of PDEs or differential operators . In this talk, I present some of our recent work on paving the way towards scientific foundation models that aims at making them more robust and better generalisable.
Discovery of a dwarf planet candidate in an extremely wide orbit: 2017 OF201S辿rgio Sacani
油
We report the discovery of a dwarf planet candidate, 2017 OF201, currently located at a distance of 90.5 au. Its orbit is extremely wide and extends to the inner Oort cloud, with a semi-major axis of 838 au andaperihelion of 44.9 au precisely determined from 19 observations over seven years. Assuming a typical albedo of 0.15, we estimate a diameter 700 km, making it the second-largest known object in this dynamical population and a likely dwarf planet. Its high eccentricity suggests that it is part of a broader, unseen population of similar objects totaling 1% of Earths mass. Notably, the longitude of perihelion of 2017 OF201 lies well outside the clustering observed in extreme trans-Neptunian objects, which has been proposed as dynamical evidence for a distant, undetected planet.
Thermodynamic concepts of zinc availability in soil and recent advances.pptxArchana Verma
油
Zinc (Zn) deficiency, which is a common micronutrient disorder in plants, reduces crop yields and nutritional quality. About 50% of cereal crops are cultivated on soils with low Zn availability worldwide (Alloway, 2009). The general mechanisms involved in the transformation of zinc ions in the soil lead to retention (mediated by sorption, precipitation, and complexation reactions) or loss (plant uptake, leaching) of zinc (Seshadri et al. 2015). Negative value of G (kJ mol-1) concluded the overall biosorption of Zn(II) ion on the surface of CDS biomass as spontaneous at liquid solid interface during the sorption of Zn(II) ion (Mishra et al. 2012). The properties of rhizosphere vary according to the plant species, where the width has been shown to extend from 280 mm from the root surface. Concentration of root exudates and extent of microbial activity are useful indicators of demarcation of rhizosphere and bulk soil zone (Seshadri et al. 2015). Kinetic studies are required to find out the rate and mechanism of reaction coupled with the determination of rate controlling step, while mechanistic model consists of equations describing nutrient influx are combined with equations describing plant growth in order to describe nutrient uptake (Adhikari and Rattan, 2000). Several factors influence Zn adsorption, desorption, and equilibrium between the solid and solution phases. These factors include soil pH, clay content, organic matter (OM), cation exchange capacity (CEC), and Fe/Al oxides (Gaudalix and Pardo, 1995), among which, soil pH is one of the most important factors (Barrow, 1987).
The presentation "C-O, C-N and C-S Bond Formation Methods" delves into the pivotal role of carbon-heteroatom (C-O, C-N, C-S) bond formation in synthetic organic and medicinal chemistry, focusing on their significance in natural products and pharmaceuticals. It surveys key synthetic strategies, from historical methods like Williamson ether synthesis to advanced transition metal-catalyzed reactions such as Chan-Lam and Buchwald-Hartwig couplings, emphasizing their efficiency and practical applications. The discussion highlights the importance of these bonds in drug-receptor interactions and molecular synthesis, while addressing challenges and recent innovations in eco-friendly, cost-effective catalytic systems. The presentation concludes by affirming the enduring value of these reactions as essential tools for synthesizing diverse, critical compounds across agrochemical, pharmaceutical, and fine chemical industries.
Bioplastics, derived from renewable sources like corn starch or sugarcane, offer a sustainable alternative to conventional plastics, aiding the global green transition. They reduce dependence on fossil fuels and lower greenhouse gas emissions during production and degradation. Some bioplastics are biodegradable or compostable, lessening long-term environmental pollution. However, their widespread adoption faces challenges such as high production costs, limited industrial composting facilities, land use concerns, and inconsistent biodegradability under natural conditions. Additionally, bioplastics may compete with food production and still contribute to microplastic pollution. Thus, while innovative, bioplastics are not a complete solution to plastic pollution.
A review on simple heterocyclics involved in chemical ,biochemical and metabo...DrAparnaYeddala
油
Heterocyclics play crucial role in the drug discovery process and exhibit various
biological activities. Among aromatic heterocycles, the prevalent moieties are five membered
rings.The role and utility of heterocycles in organic synthesis paved the way to develop
precursors for aminoacids, medicinaldrugs and other chemical componetnts.For an organic
molecule the potency is measured based on its non toxic nature, lower dosage and inhibition
of microbial cellwall growth.
Also for evaluating their potential to be used as drugs, pharmaceuticals, special
chemicals and agrochemicals.
Heterocyclic chemistry credits for nearly thirty percent of contemporary
publications. In fact seventy five percent of organic compounds are heterocyclic compounds.
The alkaloids with nitrogen atoms like ergotamine show antimigraine activity, cinchonine,
and display antimalarial activity. The loaded activity of these compounds was explored by
many researchers in medicinal, insecticidal, pesticidal and naturally occurring aminoacids.
Nucleic acid strands contain heterocylic compounds as major components. Also they display
their major role as central nervous system activators, insecticidal, pesticidal and physiological
processes like antiinflammation activity and antitumor activity.
Ch 1 Powerpoint - Introduction to Earth Science [Savvas].pptjoshscally027
油
Title: Earths Extremes: Glaciers and Deserts
Description:
Explore the powerful forces shaping Earths surface in this exciting slideshow about glaciers and deserts. Learn how icy giants slowly carve out valleys and how winds sculpt stunning desert landscapes. Through vivid images and simple explanations, discover the processes of erosion, deposition, and climate impact in two of Earths most extreme environments. This presentation helps students understand the surprising similarities and key differences between frozen and arid ecosystems.
This slideshow takes you on a journey through two of Earths most dramatic landscapes: glaciers and deserts. Discover how glaciers grind down mountains, transport massive amounts of rock, and shape U-shaped valleys, while deserts, shaped by wind and minimal rainfall, form dunes, mesas, and canyons. Learn how both environments reveal clues about Earths climate, water cycles, and geologic history. Perfect for classrooms or anyone curious about Earth science.
This comprehensive slideshow explores the contrasting but equally dynamic geomorphic systems of glaciers and deserts. Examine the mechanisms of glacial erosion (plucking, abrasion), the formation of features like moraines and glacial troughs, and the role of aeolian processes in desert environments. Case studies include the Sahara Desert, the Atacama, and the Antarctic ice sheet. This presentation also touches on climate influences, past glaciations, and desertification trends. Ideal for advanced Earth science courses.
Title Management of Food Hazards for Ensuring Food Safety.pdfSUTITHI HAZRA
油
Management of Food Hazards to Ensure Food Safety" is an informative presentation that explores the critical aspects of identifying and controlling food hazards to protect public health. The content delves into the three major categories of food hazardsbiological, chemical, and physicaland explains how they can enter the food chain at various stages, from production to consumption.
The presentation also highlights key food safety practices, including Good Manufacturing Practices (GMP), Hazard Analysis and Critical Control Points (HACCP), and other preventive strategies to minimize the risk of contamination. Special emphasis is given to the role of food handlers, regulatory bodies, and consumers in maintaining food safety standards.
This resource is ideal for students, educators, food industry professionals, and anyone interested in food safety and public health. It aims to raise awareness about the importance of proper food hazard management to ensure safe and nutritious food for all.
2nd International Conference on Life Sciences (LiSci 2025)gisellejose582
油
2nd International Conference on Life Sciences (LiSci 2025) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Life Science Research. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to met and share cutting-edge development in the field of Life Sciences.
3. 3.3
3-1 ANALOG AND DIGITAL
Data can be analog or digital. The term analog data refers
to information that is continuous; digital data refers to
information that has discrete states. Analog data take on
continuous values. Digital data take on discrete values.
Analog and Digital Data
Analog and Digital Signals
Periodic and Nonperiodic Signals
Topics discussed in this section:
4. 3.4
Analog and Digital Data
Data can be analog or digital.
Analog data are continuous and take
continuous values.
Digital data have discrete states and take
discrete values.
5. 3.5
Analog and Digital Signals
Signals can be analog or digital.
Analog signals can have an infinite number
of values in a range.
Digital signals can have only a limited
number of values.
7. 3.7
3-2 PERIODIC ANALOG SIGNALS
In data communications, we commonly use periodic
analog signals and nonperiodic digital signals.
Periodic analog signals can be classified as simple or
composite. A simple periodic analog signal, a sine wave,
cannot be decomposed into simpler signals. A composite
periodic analog signal is composed of multiple sine
waves.
Sine Wave
Wavelength
Time and Frequency Domain
Composite Signals
Bandwidth
Topics discussed in this section:
13. 3.13
The power we use at home has a frequency of 60 Hz.
The period of this sine wave can be determined as
follows:
Example 3.1
14. 3.14
The period of a signal is 100 ms. What is its frequency in
kilohertz?
Example 3.2
Solution
First we change 100 ms to seconds, and then we
calculate the frequency from the period (1 Hz = 103
kHz).
15. 3.15
Frequency
Frequency is the rate of change with respect
to time.
Change in a short span of time means high
frequency.
Change over a long span of
time means low frequency.
16. 3.16
If a signal does not change at all, its
frequency is zero.
If a signal changes instantaneously, its
frequency is infinite.
Note
18. 3.18
Figure 3.5 Three sine waves with the same amplitude and frequency,
but different phases
19. 3.19
A sine wave is offset 1/6 cycle with respect to time 0.
What is its phase in degrees and radians?
Example 3.3
Solution
We know that 1 complete cycle is 360属. Therefore, 1/6
cycle is
22. 3.22
A complete sine wave in the time
domain can be represented by one
single spike in the frequency domain.
Note
23. 3.23
The frequency domain is more compact and
useful when we are dealing with more than one
sine wave. For example, Figure 3.8 shows three
sine waves, each with different amplitude and
frequency. All can be represented by three
spikes in the frequency domain.
Example 3.7
25. 3.25
Signals and Communication
A single-frequency sine wave is not
useful in data communications
We need to send a composite signal, a
signal made of many simple sine
waves.
According to Fourier analysis, any
composite signal is a combination of
simple sine waves with different
frequencies, amplitudes, and phases.
26. 3.26
Composite Signals and
Periodicity
If the composite signal is periodic, the
decomposition gives a series of signals
with discrete frequencies.
If the composite signal is nonperiodic, the
decomposition gives a combination of
sine waves with continuous frequencies.
27. 3.27
Figure 3.9 shows a periodic composite signal with
frequency f. This type of signal is not typical of those
found in data communications. We can consider it to be
three alarm systems, each with a different frequency.
The analysis of this signal can give us a good
understanding of how to decompose signals.
Example 3.4
30. 3.30
Figure 3.11 shows a nonperiodic composite signal. It
can be the signal created by a microphone or a telephone
set when a word or two is pronounced. In this case, the
composite signal cannot be periodic, because that
implies that we are repeating the same word or words
with exactly the same tone.
Example 3.5
32. 3.32
Bandwidth and Signal
Frequency
The bandwidth of a composite signal is
the difference between the highest and the
lowest frequencies contained in that
signal.
34. 3.34
If a periodic signal is decomposed into five sine waves
with frequencies of 100, 300, 500, 700, and 900 Hz, what
is its bandwidth? Draw the spectrum, assuming all
components have a maximum amplitude of 10 V.
Solution
Let fh be the highest frequency, fl the lowest frequency,
and B the bandwidth. Then
Example 3.6
The spectrum has only five spikes, at 100, 300, 500, 700,
and 900 Hz (see Figure 3.13).
36. 3.36
A periodic signal has a bandwidth of 20 Hz. The highest
frequency is 60 Hz. What is the lowest frequency? Draw
the spectrum if the signal contains all frequencies of the
same amplitude.
Solution
Let fh be the highest frequency, fl the lowest frequency,
and B the bandwidth. Then
Example 3.7
The spectrum contains all integer frequencies. We show
this by a series of spikes (see Figure 3.14).
38. 3.38
A nonperiodic composite signal has a bandwidth of 200
kHz, with a middle frequency of 140 kHz and peak
amplitude of 20 V. The two extreme frequencies have an
amplitude of 0. Draw the frequency domain of the
signal.
Solution
The lowest frequency must be at 40 kHz and the highest
at 240 kHz. Figure 3.15 shows the frequency domain
and the bandwidth.
Example 3.8
40. 3.40
An example of a nonperiodic composite signal is the
signal propagated by an AM radio station. In the United
States, each AM radio station is assigned a 10-kHz
bandwidth. The total bandwidth dedicated to AM radio
ranges from 530 to 1700 kHz. We will show the rationale
behind this 10-kHz bandwidth in Chapter 5.
Example 3.9
41. 3.41
Another example of a nonperiodic composite signal is
the signal propagated by an FM radio station. In the
United States, each FM radio station is assigned a 200-
kHz bandwidth. The total bandwidth dedicated to FM
radio ranges from 88 to 108 MHz. We will show the
rationale behind this 200-kHz bandwidth in Chapter 5.
Example 3.10
42. 3.42
Another example of a nonperiodic composite signal is
the signal received by an old-fashioned analog black-
and-white TV. A TV screen is made up of pixels. If we
assume a resolution of 525 700, we have 367,500
pixels per screen. If we scan the screen 30 times per
second, this is 367,500 30 = 11,025,000 pixels per
second. The worst-case scenario is alternating black and
white pixels. We can send 2 pixels per cycle. Therefore,
we need 11,025,000 / 2 = 5,512,500 cycles per second, or
Hz. The bandwidth needed is 5.5125 MHz.
Example 3.11
43. 3.43
Fourier analysis is a tool that changes a
time domain signal to a frequency
domain signal and vice versa.
Note
Fourier Analysis
44. 3.44
Fourier Series
Every composite periodic signal can be
represented with a series of sine and cosine
functions.
The functions are integral harmonics of the
fundamental frequency f of the composite
signal.
Using the series we can decompose any
periodic signal into its harmonics.
51. 3.51
Time limited and Band limited
Signals
A time limited signal is a signal for which
the amplitude s(t) = 0 for t > T1 and t < T2
A band limited signal is a signal for which
the amplitude S(f) = 0 for f > F1 and f < F2