Write one page essay to explain how you relate signals and systems theory with current technology advancement. Support your argument by providing a case study and identify signals and systems theory plays a major role on mentioned application. Please post the website or database you found information. Thank you! Solution A signal as referred to in communication systems, signal processing, and electrical engineering is a function that \"conveys information about the behavior or attributes of some phenomenon. In the physical world, any quantity exhibiting variation in time or variation in space (such as an image) is potentially a signal that might provide information on the status of a physical system, or convey a message between observers, among other possibilities. The IEEE Transactions on Signal Processing states that the term \"signal\" includes audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Typically, signals are provided by a sensor, and often the original form of a signal is converted to another form of energy using a transducer. For example, a microphone converts an acoustic signal to a voltage waveform, and a speaker does the reverse. The formal study of the information content of signals is the field of information theory. The information in a signal is usually accompanied by noise. The term noise usually means an undesirable random disturbance, but is often extended to include unwanted signals conflicting with the desired signal (such as crosstalk). The prevention of noise is covered in part under the heading of signal integrity. The separation of desired signals from a background is the field of signal recovery, one branch of which is estimation theory, a probabilistic approach to suppressing random disturbances. Engineering disciplines such as electrical engineering have led the way in the design, study, and implementation of systems involving transmission, storage, and manipulation of information. In the latter half of the 20th century, electrical engineering itself separated into several disciplines, specialising in the design and analysis of systems that manipulate physical signals; electronic engineering and computer engineering as examples; while design engineering developed to deal with functional design of man–machine interfaces. Definitions specific to sub-fields are common. For example, in information theory, a signal is a codified message, that is, the sequence of states in a communication channel that encodes a message. In the context of signal processing, arbitrary binary data streams are not considered as signals, but only analog and digital signals that are representations of analog physical quantities. In a communication system, a transmitter encodes a message to a signal, which is carried to a receiver by the communications channel. For example, the words \"Mary had a little lamb\" might be the message spoken into a telephone. The telephone transmitter converts th.

1. Write one page essay to explain how you relate signals and systems theory with current
technology advancement.
Support your argument by providing a case study and identify signals and systems theory plays a
major role on mentioned application.
Please post the website or database you found information. Thank you!
Solution
A signal as referred to in communication systems, signal processing, and electrical engineering is
a function that "conveys information about the behavior or attributes of some phenomenon. In
the physical world, any quantity exhibiting variation in time or variation in space (such as an
image) is potentially a signal that might provide information on the status of a physical system,
or convey a message between observers, among other possibilities. The IEEE Transactions on
Signal Processing states that the term "signal" includes audio, video, speech, image,
communication, geophysical, sonar, radar, medical and musical signals.
Typically, signals are provided by a sensor, and often the original form of a signal is converted to
another form of energy using a transducer. For example, a microphone converts an acoustic
signal to a voltage waveform, and a speaker does the reverse.
The formal study of the information content of signals is the field of information theory. The
information in a signal is usually accompanied by noise. The term noise usually means an
undesirable random disturbance, but is often extended to include unwanted signals conflicting
with the desired signal (such as crosstalk). The prevention of noise is covered in part under the
heading of signal integrity. The separation of desired signals from a background is the field of
signal recovery, one branch of which is estimation theory, a probabilistic approach to
suppressing random disturbances.
Engineering disciplines such as electrical engineering have led the way in the design, study, and
implementation of systems involving transmission, storage, and manipulation of information. In
the latter half of the 20th century, electrical engineering itself separated into several disciplines,
specialising in the design and analysis of systems that manipulate physical signals; electronic
engineering and computer engineering as examples; while design engineering developed to deal
with functional design of man–machine interfaces.
Definitions specific to sub-fields are common. For example, in information theory, a signal is a
codified message, that is, the sequence of states in a communication channel that encodes a
message.
In the context of signal processing, arbitrary binary data streams are not considered as signals,

2. but only analog and digital signals that are representations of analog physical quantities.
In a communication system, a transmitter encodes a message to a signal, which is carried to a
receiver by the communications channel. For example, the words "Mary had a little lamb"
might be the message spoken into a telephone. The telephone transmitter converts the sounds
into an electrical voltage signal. The signal is transmitted to the receiving telephone by wires; at
the receiver it is reconverted into sounds.
In telephone networks, signalling, for example common-channel signaling, refers to phone
number and other digital control information rather than the actual voice signal.
Signals can be categorized in various ways. The most common distinction is between discrete
and continuous spaces that the functions are defined over, for example discrete and continuous
time domains. Discrete-time signals are often referred to as time series in other fields.
Continuous-time signals are often referred to as continuous signals even when the signal
functions are not continuous; an example is a square-wave signal.
A second important distinction is between discrete-valued and continuous-valued. Particularly in
digital signal processing a digital signal is sometimes defined as a sequence of discrete values,
that may or may not be derived from an underlying continuous-valued physical process. In other
contexts, digital signals are defined as the continuous-time waveform signals in a digital system,
representing a bit-stream. In the first case, a signal that is generated by means of a digital
modulation method is considered as converted to an analog signal, while it is considered as a
digital signal in the second case.
Another important property of a signal (actually, of a statistically defined class of signals) is its
entropy or information content.
Signals and Images: Advances and Results in Speech, Estimation, Compression, Recognition,
Filtering, and Processing cohesively combines contributions from field experts to deliver a
comprehensive account of the latest developments in signal processing. These experts detail the
results of their research related to audio and speech enhancement, acoustic image estimation,
video compression, biometric recognition, hyperspectral image analysis, tensor decomposition
with applications in communications, adaptive sparse-interpolated filtering, signal processing for
power line communications, bio-inspired signal processing, seismic data processing, arithmetic
transforms for spectrum computation, particle filtering in cooperative networks, three-
dimensional television, and more.
This book not only shows how signal processing theory is applied in current and emerging
technologies, but also demonstrates how to tackle key problems such as how to enhance speech
in the time domain, improve audio quality, and meet the desired electrical consumption target for
controlling carbon emissions.
Signals and Images: Advances and Results in Speech, Estimation, Compression, Recognition,

3. Filtering, and Processing serves as a guide to the next generation of signal processing solutions
for speech and video coding, hearing aid devices, big data processing, smartphones, smart digital
communications, acoustic sensors, and beyond.
A signal is a description of how one parameter varies with another parameter. For instance,
voltage changing over time in an electronic circuit, or brightness varying with distance in an
image. A system is any process that produces an output signal in response to an input signal.
This is illustrated by the block diagram in Fig. 5-1. Continuous systems input and output
continuous signals, such as in analog electronics. Discrete systems input and output discrete
signals, such as computer programs that manipulate the values stored in arrays.
Several rules are used for naming signals. These aren't always followed in DSP, but they are
very common and you should memorize them. The mathematics is difficult enough without a
clear notation. First, continuoussignals use parentheses, such as: x(t) and y(t), while discrete
signals use brackets, as in: x[n] and y[n]. Second, signals use lower case letters. Upper case
letters are reserved for the frequency domain,. Third, the name given to a signal is usually
descriptive of the parameters it represents. For example, a voltage depending on time might be
called: v(t), or a stock market pricemeasured each day could be: p[d].
Signals and systems are frequently discussed without knowing the exact parameters being
represented. This is the same as using xand y in algebra, without assigning a physical meaning to
the variables. This brings in a fourth rule for naming signals. If a more descriptive name is not
available, the input signal to a discrete system is usually called: x[n], and the output signal: y[n].
For continuous systems, the signals: x(t) and y(t) are used.
There are many reasons for wanting to understand a system. For example, you may want to
design a system to remove noise in an electrocardiogram, sharpen an out-of-focus image, or
remove echoes in an audio recording. In other cases, the system might have a distortion or
interfering effect that you need to characterize or measure. For instance, when you speak into a
telephone, you expect the other person to hear something that resembles your voice.
Unfortunately, the input signal to a transmission line is seldom identical to the output signal. If
you understand how the transmission line (the system) is changing the signal, maybe you can
compensate for its effect. In still other cases, the system may represent some physical process
that you want to study or analyze. Radar and sonar are good examples of this. These methods
operate by comparing the transmitted and reflected signals to find the characteristics of a remote
object. In terms of system theory, the problem is to find the system that changes the transmitted
signal into the received signal.
At first glance, it may seem an overwhelming task to understand all of the possible systems in
the world. Fortunately, most useful systems fall into a category called linear systems. This fact is
extremely important. Without the linear system concept, we would be forced to examine the

4. individual characteristics of many unrelated systems. With this approach, we can focus on the
traits of the linear system category as a whole. Our first task is to identify what properties make a
system linear, and how they fit into the everyday notion of electronics, software, and other signal
processing systems.