UAS ESP ENGLISH

Writer           :  Willy Riwaldi (2220201047)

Editor            : Willy Riwaldi (2220201047)

Reviewer       : Irfandi Bagus Fahrezi

 

List of Activities in ESP for Electrical Engineering

1. Short Article about Electrical Engineering

2. 5W+1H Sentences

3. Verbal and Nominal Sentences

4. Tenses and It’s Pattern

5. Active and Passive Construction

6. List of Vocabulary

7. Translation into Indonesia

8. Reason Why Writer Uses Tenses

 

Signals and Systems

The study of signals and systems is considered to be a classic subject in the curriculum of most engineering schools throughout the world. The theory of signals and systems is a coherent and elegant collection of mathematical results that date back to the work of Fourier and Laplace and many other famous mathematicians and engineers. Signals and systems theory has proven to be an extremely valuable tool for the past 70 years in many fields of science and engineering, including power systems, automatic control, communications, circuit design, filtering, and signal processing. Fantastic advances in these fields have brought revolutionary changes into our lives. The United States Department of Defense in 1969, through the ARPA project that developed a network related to the ARPANET (Advanced Research Project Agency Network), where they demonstrated how to hardware and software. At the heart of signals and systems theory is mankind’s historical curiosity and need to analyze the behavior of physical systems with simple mathematical models describing the cause-and-effect relationship between quantities. For example, Isaac Newton discovered the second law of rigid-body dynamics over 300 years ago and described it mathematically as a relationship between the resulting force applied on a body (the input) and its acceleration (the output), from which one can also obtain the body’s velocity and position with respect to time. The development of differential calculus by Leibniz and Newton provided a powerful tool for modeling physical systems in the form of differential equations implicitly relating the input variable to the output variable. A fundamental issue in science and engineering is to predict what the behavior, or output response, of a system will be for a given input signal. Where as science may seek to describe natural phenomena modeled as input-output systems, engineering seeks to design systems by modifying and analyzing such models. This issue is recurrent in the design of electrical or mechanical systems, where a system’s output signal must typically respond in an appropriate way to selected input signals. In this case, a mathematical input-output model of the system would be analyzed to predict the behavior of the output of the system. For example, in the design of a simple resistor-capacitor electrical circuit to be used as a filter, the engineer would first specify the desired attenuation of a sinusoidal input voltage of a given frequency at the output of the filter. Then, the design would proceed by selecting the appropriate resistance R and capacitance C in the differential equation model of the filter in order to achieve the attenuation specification. The filter can then be built using actual electrical components. A signal is defined as a function of time representing the evolution of a variable. Certain types of input and output signals have special properties with respect to linear time-invariant systems. Such signals include  sinusoidal and exponential functions of time. These signals can be linearly combined to form virtually any other signal, which is the basis of the Fourier series representation of periodic signals and the Fourier transform representation of aperiodic signals. The Fourier representation opens up a whole new interpretation of signals in terms of their frequency contents called the frequency spectrum. Furthermore, in the frequency domain, a linear time-invariant system acts as a filter on the frequency spectrum of the input signal, attenuating it at some frequencies while amplifying it at other frequencies. This effect is called the frequency response of the system. These frequency domain concepts are fundamental in electrical engineering, as they underpin the fields of communication systems, analog and digital filter design, feedback control, power engineering, etc. Well-trained electrical and computer engineers think of signals as being in the frequency domain probably just as much as they think of them as functions of time. The Fourier transform can be further generalized to the Laplace transform in continuous-time and the z-transform in discrete-time. The idea here is to define such transforms even for signals that tend to infinity with time. We chose to adopt the notation X( jω), instead of X(ω) or X( f ), for the Fourier transform of a continuous-time signal x(t). This is consistent with the Laplace transform of the signal denoted as X(s), since then X( jω) = X(s)|s = jω. The same remark goes for the discrete-time Fourier transform: X(ejω) = X(z)|z = e jω.

Signals

What is Signal?

Signal is a time varying physical phenomenon which is intended to convey information.

OR

Signal is a function of time.

OR

Signal is a function of one or more independent variables, which contain some information.

 

A signal is a function of one or more variables that conveys information about some (usually physical) phenomenon. Some examples of signals include:

• a human voice

• a voltage in an electronic circuit

• the temperature of a room controlled by a thermostat system

• the position, velocity, and acceleration of an aircraft

• the acceleration measured by an accelerometer in a cell phone

• the force measured by a force sensor in a robotic system

• the electromagnetic waves used to transmit information in wireless computer networks

• a digitized photograph

• a digitized music recording

• the evolution of a stock market index over time

 

v Classification of Signals

Signals can be classified based on the number of independent variables with which they are associated. A signal that is a function of only one variable is said to be one dimensional. Similarly, a signal that is a function of two or more variables is said to be multi-dimensional. Human speech is an example of a one-dimensional signal. In this case, we have a signal associated with fluctuations in air pressure as a function of time. An example of a two-dimensional signal is a monochromatic image. In this case, we have a signal that corresponds to a measure of light intensity as a function of horizontal and vertical position. A signal can also be classified on the basis of whether it is a function of continuous or discrete variables. A signal that is a function of continuous variables (e.g., a real variable) is said to be continuous time. Similarly, a signal that is a function of discrete variables (e.g., an integer variable) is said to be discrete time. Although the independent variable need not represent time, for matters of convenience, much of the terminology is chosen as if this were so.

x( t) x[n]

For example, a digital image (which consists of a rectangular array of pixels) would be referred to as a discrete-time signal, even though the independent variables (i.e., horizontal and vertical position) do not actually correspond to time. If a signal is a function of discrete variables (i.e., discrete-time) and the value of the function itself is also discrete, the signal is said to be digital. Similarly, if a signal is a function of continuous variables, and the value of the function itself is also continuous, the signal is said to be analog.

Many phenomena in our physical world can be described in terms of continuous-time signals. Some examples of continuous-time signals include: voltage or current waveforms in an electronic circuit; electrocardiograms, speech, and music recordings; position, velocity, and acceleration of a moving body; forces and torques in a mechanical system; and flow rates of liquids or gases in a chemical process. Any signals processed by digital computers (or other digital devices) are discrete-time in nature. Some examples of discrete-time signals include digital video, digital photographs, and digital audio data. A discrete-time signal may be inherently discrete or correspond to a sampled version of a continuous-time signal. An example of the former would be a signal corresponding to the Dow Jones Industrial Average stock market index (which is only defined on daily intervals), while an example of the latter would be the sampled version of a (continuous-time) speech signal.

 

·       Notation and Graphical Representation of Signals

          In the case of discrete-time signals, we sometimes refer to the signal as a sequence. The nth element of a sequence x is denoted as either x(n) or xn. Figure 1.1 shows how continuous-time and discrete-time signals are represented graphically.

·       Examples of Signals

 A number of examples of signals have been suggested previously. Here, we provide some graphical representations of signals for illustrative purposes. Figure 1.2 depicts a digitized speech signal. Figure 1.3 shows an example of a monochromatic image. In this case, the signal represents light intensity as a function of two variables (i.e., horizontal and vertical position).

 

Systems

What is System?

System is a device or combination of devices, which can operate on signals and produces corresponding response. Input to a system is called as excitation and output from it is called as Response. For one or more inputs, the system can have one or more outputs. Example: Communication Systems.

A system is an entity that processes one or more input signals in order to produce one or more output signals, as shown in Figure 1.4. Such an entity is represented mathematically by a system of one or more equations. In a communication system, the input might represent the message to be sent, and the output might represent the received message. In a robotics system, the input might represent the desired position of the end effector (e.g., gripper), while the output could represent the actual position.

v Classification of Systems

A system can be classified based on the number of inputs and outputs it has. A system with only one input is described as single input, while a system with multiple inputs is described as multi-input. Similarly, a system with only one output is said to be single output, while a system with multiple outputs is said to be multi-output. Two commonly occurring types of systems are single-input single-output (SISO) and multi-input multi-output (MIMO). A system can also be classified based on the types of signals with which it interacts. A system that deals with continuous-time signals is called a continuous-time system. Similarly, a system that deals with discrete-time signals is said to be a discrete-time system. A system that handles both continuous- and discrete-time signals, is sometimes referred to as a hybrid system (or sampled-data system). Similarly, systems that deal with digital signals are referred to as digital, while systems that handle analog signals are referred to as analog. If a system interacts with one dimensional signals, the system is referred to as one-dimensional. Likewise, if a system handles multi-dimensional signals, the system is said to be multi-dimensional. Systems can manipulate signals in many different ways and serve many useful purposes. Sometimes systems serve to extract information from their input signals. For example, in the case of speech signals, systems can be used in order to perform speaker identification or voice recognition. A system might analyze electrocardiogram signals in order to detect heart abnormalities. Amplification and noise reduction are other functionalities that systems could offer.

·       Examples of Systems

Systems can manipulate signals in many different ways and serve many useful purposes. Sometimes systems serve to extract information from their input signals. For example, in the case of speech signals, systems can be used in order to perform speaker identification or voice recognition. A system might analyze electrocardiogram signals in order to detect heart abnormalities. Amplification and noise reduction are other functionalities that systems could offer.

One very basic system is the resistor-capacitor (RC) network shown in Figure 1.5. Here, the input would be the source voltage v_s and the output would be the capacitor voltage v_c.

Consider the signal-processing systems shown in Figure 1.6. The system in Figure 1.6(a) uses a discrete-time system (such as a digital computer) to process a continuous-time signal. The system in Figure 1.6(b) uses a continuoustime system (such as an analog computer) to process a discrete-time signal. The first of these types of systems is ubiquitous in the world today.

Consider the communication system shown in Figure 1.7. This system takes a message at one location and reproduces this message at another location. In this case, the system input is the message to be sent, and the output is the estimate of the original message. Usually, we want the message reproduced at the receiver to be as close as possible to the original message sent by the transmitter.

A system of the general form shown in Figure 1.8 frequently appears in control applications. Often, in such applications, we would like an output to track some reference input as closely as possible. Consider, for example, a robotics application. The reference input might represent the desired position of the end effector, while the output represents the actual position.

Ø 5W+1H SENTENCES

No	5W+1H	Sentence Building (Question and Answer)
1	Who	Who is the famous engineers?
	Answer	Fuorier and Laplace
2	Who	Who discovered the second law of dynamics of a rigid body?
	Answer	Isaac Newton
3	What	What is  Signal
	Answer	Signal is a time varying physical phenomenon which is intended to convey information
4	What	What is  Systems
	Answer	Systems is a device or combination of devices, which can operate on signals and produces corresponding response.
5	Where	Where they analyze the behaviorof physical systems with simple mathematical models?
	Answer	The United States Department of Defense in 1969, through the ARPA project that developed a network related to the ARPANET (Advanced Research Project Agency Network),
6	Where	Where can we find discrete time systems and continuous time systems on computers
	Answer	The first of these types of systems is ubiquitous in the world today.
7	Why	why the design would proceed by selecting the appropriate resistance R and capacitance C in the differential equation model of the filter
	Answer	in order to achieve the attenuation specification
8	Why	Why is signals are called discrete variables?
	Answer	If the signal is a function of a discrete variable (that is, discrete time) and the value of the function itself is also discrete, the signal is said to be digital.
9	When	When did Isac Newton discovered the second law of rigid-body dynamics
	Answer	It was discovered for 300 years
10	When	When was the ARPANET project established?
	Answer	The ARPANET project was founded in 1996
11	How	How is invented the signal?
	Answer	The United States Department of Defense in 1969, through the ARPA project that developed a network related to the ARPANET (Advanced Research Project Agency Network), where they demonstrated how to hardware and software
12	How	How do system entities work?
	Answer	that processes one or more input signals in order to produce one or more output signals

 

 

 

Ø List Verbal and Nominal sentences

 

Verbal Sentences

No	Sentences	V
01	Signal is a function of time.
02	The evolution of a stock market index over time.
03	System is a device or combination of devices.
04	Fantastic advances in these fields have brought revolutionary changes into our lives
05	A system of the general form shown in Figure 1.8 frequently appears in control applications
06	A system can be classified based on the number of inputs and outputs it has.
07	Many phenomena in our physical world can be described in terms of continuous-time signals
08	A signal is a function of one or more variables that conveys information about some (usually physical) phenomenon
09	Signals can be classified based on the number of independent variables with which they are associated
10	My wish is that the reader will enjoy learning the theory of signals and systems by using this book

 

Nominal Sentences

No	Sentences	N
01	Isaac Newton discovered the second law of rigid-body dynamics over 300 years ago.
02	The electromagnetic waves used to transmit information in wireless computer networks.
03	The development of differential calculus by Leibniz and Newton provided a powerful tool for modeling physical systems in the form of differential equations implicitly relating input variable to the output variable
04	The study of signals and systems is considered to be a classic subject in the curriculum of most engineering schools throughout the world.
05	A signal is defined as a function of time representing the evolution of a variable.
06	This issue is recurrent in the design of electrical or mechanical systems, where a system’s output signal must typically respond in an appropriate way to selected input signals
07	A number of examples of signals have been suggested previously
08	Learning about signals and systems and its applications is often the point at which an electrical or computer engineering student decides what she or he will specialize
09	Similarly, if a signal is a function of continuous variables, and the value of the function itself is also continuous, the signal is said to be analog.
10	Such an entity is represented mathematically by a system of one or more equations

 

Ø Identify tenses and it’s pattern

No	Sentences	Pattern
01	Isaac Newton discovered the second law of rigid-body dynamics over 300 years ago.	Simple past tense (S + Verb II)
02	The electromagnetic waves used to transmit information in wireless computer networks.	Simple past tense (S + Verb II)
03	We have a signal associated with fluctuations in air pressure as a function of time.	Simple past tense (S + Verb II)
04	Leibniz developed differential calculus.	Simple past tense (S + Verb II)
05	We provide some graphical representations of signals for illustrative purposes.	Simple present tenses (S + Verb I)
06	A signal is defined as a function of time representing the evolution of a variable	Simple past tense (S + Verb II)
07	A signal is a function of one or more variables that conveys information about some (usually physical) phenomenon	Simple present tense (S + Verb I)
08	The evolution of a stock market index over time.	Simple present tense (S + Verb I )
09	A number of examples of signals have been suggested previously	Simple present tense (S + Verb I)
10	Signals can be classified based on the number of independent variables with which they are associated	Simple past tense (S + Verb II)

 

Ø Change sentences either into active or passive construction

No	Sentences	Active	Passive
1	Isaac Newton discovered the second law of rigid-body dynamics over 300 years ago.	√
2	The second law of rigid body dynamics was discovered 300 years ago by Isaac Newton.		√
3	Leibniz developed differential calculus.	√
4	Differential calculus developed by Leibniz		√
5	Thermostat system controlled the temperature.	√
6	The temperature of a room controlled by a thermostat system.		√
7	Electrocardiogram signals might be analyzed by a system in order to detect heart abnormalities.		√
8	We provide some graphical representations of signals for illustrative purposes.	√
9	A signal is defined as a function of time representing the evolution of a variable		√
10	A number of examples of signals have been suggested previously	√

 

 

 

Ø List of Vocabulary

 

No	Vocabulary	Pronoun Spelling	Meaning
1	Use	/juːs/	Menggunakan
2	Work	/wɜːk/	Kerja
3	Time	/taɪm/	Waktu
4	Send	/sɛnd/	Kirim
5	Famous	/ˈfeɪməs/	Terkenal
6	Give	/ɡɪv/	Memberi
7	Design	/di’zain/	Merancang
8	Stock	/sta:k/	Menstok
9	Learn	/lɜːn/	Belajar
10	Order	/ˈɔːdə(r)/	Pesanan

 

 

Translate

 

Sinyal dan Sistem

Studi tentang sinyal dan sistem dianggap sebagai mata pelajaran klasik dalam kurikulum sebagian besar sekolah teknik di seluruh dunia. Teori sinyal dan sistem adalah kumpulan hasil matematika yang koheren dan elegan yang berasal dari karya Fourier dan Laplace serta banyak matematikawan dan insinyur terkenal lainnya. Teori sinyal dan sistem telah terbukti menjadi alat yang sangat berharga selama 70 tahun terakhir di banyak bidang sains dan teknik, termasuk sistem tenaga, kontrol otomatis, komunikasi, desain sirkuit, penyaringan, dan pemrosesan sinyal. Kemajuan fantastis di bidang ini telah membawa perubahan revolusioner ke dalam hidup kita. Departemen Pertahanan Amerika Serikat pada tahun 1969, melalui proyek ARPA yang mengembangkan jaringan yang berhubungan dengan ARPANET (Advanced Research Project Agency Network), dimana mereka mendemonstrasikan bagaimana hardware dan software. Inti dari teori sinyal dan sistem adalah keingintahuan historis umat manusia dan kebutuhan untuk menganalisis perilaku sistem fisik dengan model matematika sederhana yang menjelaskan hubungan sebab-akibat antara kuantitas. Misalnya, Isaac Newton menemukan hukum kedua dinamika benda tegar lebih dari 300 tahun yang lalu dan menggambarkannya secara matematis sebagai hubungan antara gaya yang dihasilkan yang diterapkan pada benda (input) dan percepatannya (output), dari mana seseorang juga dapat mendapatkan kecepatan tubuh dan posisi terhadap waktu. Pengembangan kalkulus diferensial oleh Leibniz dan Newton menyediakan alat yang ampuh untuk memodelkan sistem fisik dalam bentuk persamaan diferensial yang secara implisit menghubungkan variabel masukan dengan variabel keluaran. Masalah mendasar dalam sains dan teknik adalah memprediksi seperti apa perilaku, atau respons keluaran, dari suatu sistem untuk sinyal input yang diberikan. Sementara ilmu pengetahuan berusaha untuk menggambarkan fenomena alam yang dimodelkan sebagai sistem input-output, teknik berusaha untuk merancang sistem dengan memodifikasi dan menganalisis model tersebut. Masalah ini berulang dalam desain sistem kelistrikan atau mekanis, di mana sinyal keluaran sistem biasanya harus merespons dengan cara yang tepat terhadap sinyal masukan yang dipilih. Dalam hal ini, model input-output matematis dari sistem akan dianalisis untuk memprediksi perilaku output sistem. Misalnya, dalam desain rangkaian listrik resistor-kapasitor sederhana untuk digunakan sebagai filter, insinyur pertama-tama akan menentukan pelemahan tegangan input sinusoidal yang diinginkan dari frekuensi tertentu pada output filter. Efek ini disebut respons frekuensi sistem. Konsep domain frekuensi ini sangat mendasar dalam teknik kelistrikan, karena mereka mendukung bidang sistem komunikasi, desain filter analog dan digital, kontrol umpan balik, teknik tenaga, dll. Insinyur listrik dan komputer yang terlatih dengan baik menganggap sinyal sebagai domain frekuensi mungkin sama seperti mereka menganggapnya sebagai fungsi waktu. Transformasi Fourier dapat digeneralisasi lebih lanjut ke transformasi Laplace dalam waktu kontinu dan transformasi z dalam waktu diskrit. Idenya di sini adalah untuk mendefinisikan transformasi tersebut bahkan untuk sinyal yang cenderung tak terhingga dengan waktu. Kami memilih untuk mengadopsi notasi X( jω), daripada X(ω) atau X( f ), untuk transformasi Fourier dari sinyal waktu kontinu x(t). Hal ini konsisten dengan transformasi Laplace dari sinyal yang dilambangkan sebagai X(s), karena itu X(jω) = X(s)|s = jω. Pernyataan yang sama berlaku untuk transformasi Fourier waktu-diskrit: X(ejω) = X(z)|z = e jω.

 

 

Sinyal

Apa itu Sinyal?

Sinyal adalah fenomena fisik yang bervariasi waktu yang dimaksudkan untuk menyampaikan informasi.

ATAU

Sinyal adalah fungsi waktu.

ATAU

Sinyal adalah fungsi dari satu atau lebih variabel independen, yang mengandung beberapa informasi.

Sinyal adalah fungsi dari satu atau lebih variabel yang menyampaikan informasi tentang beberapa fenomena (biasanya fisik). Beberapa contoh sinyal antara lain:

• suara manusia

• tegangan dalam sirkuit elektronik

• suhu ruangan yang dikendalikan oleh sistem termostat

• posisi, kecepatan, dan percepatan pesawat terbang

• percepatan diukur dengan accelerometer di ponsel

• gaya yang diukur oleh sensor gaya dalam sistem robot

• gelombang elektromagnetik yang digunakan untuk mengirimkan informasi dalam jaringan komputer nirkabel

• foto digital

• rekaman musik digital

• evolusi indeks pasar saham dari waktu ke waktu

 

v Klasifikasi Sinyal

Sinyal dapat diklasifikasikan berdasarkan jumlah variabel independen yang terkait dengannya. Sinyal yang merupakan fungsi dari satu variabel saja dikatakan satu dimensi. Demikian pula, sinyal yang merupakan fungsi dari dua atau lebih variabel dikatakan multidimensi. Ucapan manusia adalah contoh dari sinyal satu dimensi. Dalam hal ini, kami memiliki sinyal yang terkait dengan fluktuasi tekanan udara sebagai fungsi waktu. Contoh sinyal dua dimensi adalah gambar monokromatik. Dalam hal ini, kami memiliki sinyal yang sesuai dengan ukuran intensitas cahaya sebagai fungsi dari posisi horizontal dan vertikal. Suatu sinyal juga dapat diklasifikasikan berdasarkan apakah itu fungsi dari variabel kontinu atau diskrit. Sinyal yang merupakan fungsi dari variabel kontinu (misalnya, variabel nyata) disebut waktu kontinu. Demikian pula, sinyal yang merupakan fungsi dari variabel diskrit (misalnya, variabel bilangan bulat) dikatakan sebagai waktu diskrit. Meskipun variabel independen tidak perlu mewakili waktu, untuk kenyamanan, banyak terminologi yang dipilih seolah-olah demikian.

x( t) x[n]

 

Misalnya, gambar digital (yang terdiri dari susunan piksel persegi panjang) akan disebut sebagai sinyal waktu diskrit, meskipun variabel independen (yaitu, posisi horizontal dan vertikal) sebenarnya tidak sesuai dengan waktu. Jika sebuah sinyal adalah fungsi dari variabel diskrit (yaitu, waktu diskrit) dan nilai dari fungsi itu sendiri juga diskrit, sinyal tersebut dikatakan digital. Demikian pula, jika sinyal adalah fungsi dari variabel kontinu, dan nilai fungsi itu sendiri juga kontinu, sinyal tersebut dikatakan analog.

Banyak fenomena di dunia fisik kita dapat dijelaskan dalam bentuk sinyal waktu kontinu. Beberapa contoh sinyal waktu kontinu meliputi: bentuk gelombang tegangan atau arus dalam sirkuit elektronik; rekaman elektrokardiogram, ucapan, dan musik; posisi, kecepatan, dan percepatan benda yang bergerak; gaya dan torsi dalam sistem mekanis; dan laju aliran cairan atau gas dalam proses kimia. Setiap sinyal yang diproses oleh komputer digital (atau perangkat digital lainnya) bersifat waktu diskrit. Beberapa contoh sinyal waktu diskrit meliputi video digital, foto digital, dan data audio digital. Sinyal waktu-diskrit mungkin secara inheren diskrit atau sesuai dengan versi sampel dari sinyal waktu-kontinu. Contoh yang pertama adalah sinyal yang sesuai dengan indeks pasar saham Dow Jones Industrial Average (yang hanya ditentukan pada interval harian), sedangkan contoh yang terakhir adalah versi sampel dari sinyal ucapan (waktu berkelanjutan).

 

• Notasi dan Representasi Grafis Sinyal

          Dalam kasus sinyal waktu diskrit, terkadang kita menyebut sinyal sebagai urutan. Elemen ke-n dari suatu barisan x dinotasikan sebagai x(n) atau xn. Gambar 1.1 menunjukkan bagaimana sinyal waktu kontinu dan waktu diskrit direpresentasikan secara grafis.

• Contoh Sinyal

          Sejumlah contoh sinyal telah disarankan sebelumnya. Di sini, kami menyediakan beberapa representasi grafis dari sinyal untuk tujuan ilustrasi. Gambar 1.2 menggambarkan sinyal ucapan digital. Gambar 1.3 menunjukkan contoh gambar monokromatik. Dalam hal ini, sinyal mewakili intensitas cahaya sebagai fungsi dari dua variabel (yaitu posisi horizontal dan vertikal).

 

Sistem

Apa itu Sistem?

Sistem adalah perangkat atau kombinasi perangkat, yang dapat beroperasi pada sinyal dan menghasilkan respons yang sesuai. Input ke sistem disebut sebagai eksitasi dan output darinya disebut sebagai Respons. Untuk satu atau lebih input, sistem dapat memiliki satu atau lebih output. Contoh: Sistem Komunikasi.

Sistem adalah entitas yang memproses satu atau lebih sinyal input untuk menghasilkan satu atau lebih sinyal output, seperti yang ditunjukkan pada Gambar 1.4. Entitas seperti itu diwakili secara matematis oleh sistem satu atau lebih persamaan. Dalam sistem komunikasi, masukan dapat mewakili pesan yang akan dikirim, dan keluaran dapat mewakili pesan yang diterima. Dalam sistem robotika, input dapat mewakili posisi yang diinginkan dari efektor akhir (misalnya, gripper), sedangkan output dapat mewakili posisi sebenarnya.

·       Klasifikasi Sistem

Suatu sistem dapat diklasifikasikan berdasarkan jumlah input dan output yang dimilikinya. Sebuah sistem dengan hanya satu input digambarkan sebagai input tunggal, sedangkan sistem dengan banyak input digambarkan sebagai multi-input. Demikian pula, sebuah sistem dengan hanya satu output dikatakan single output, sedangkan sistem dengan banyak output dikatakan multi-output. Dua jenis sistem yang umum terjadi adalah single-input single-output (SISO) dan multi-input multi-output (MIMO). Suatu sistem juga dapat diklasifikasikan berdasarkan jenis sinyal yang berinteraksi dengannya. Suatu sistem yang berurusan dengan sinyal waktu kontinu disebut sistem waktu kontinu. Demikian pula, sistem yang berhubungan dengan sinyal waktu diskrit dikatakan sebagai sistem waktu diskrit. Suatu sistem yang menangani sinyal waktu kontinu dan diskrit, kadang-kadang disebut sebagai sistem hibrid (atau sistem data sampel). Demikian pula, sistem yang menangani sinyal digital disebut digital, sedangkan sistem yang menangani sinyal analog disebut analog. Jika suatu sistem berinteraksi dengan sinyal satu dimensi, sistem tersebut disebut sebagai satu dimensi. Demikian juga, jika suatu sistem menangani sinyal multidimensi, sistem tersebut dikatakan multidimensi. Sistem dapat memanipulasi sinyal dengan berbagai cara dan melayani banyak tujuan yang berguna. Terkadang sistem berfungsi untuk mengekstraksi informasi dari sinyal inputnya. Misalnya, dalam hal sinyal ucapan, sistem dapat digunakan untuk melakukan identifikasi pembicara atau pengenalan suara. Suatu sistem mungkin menganalisis sinyal elektrokardiogram untuk mendeteksi kelainan jantung. Amplifikasi dan pengurangan noise adalah fungsi lain yang dapat ditawarkan sistem.

• Contoh Sistem

Sistem dapat memanipulasi sinyal dengan berbagai cara dan melayani banyak tujuan yang berguna. Terkadang sistem berfungsi untuk mengekstraksi informasi dari sinyal inputnya. Misalnya, dalam hal sinyal ucapan, sistem dapat digunakan untuk melakukan identifikasi pembicara atau pengenalan suara. Suatu sistem mungkin menganalisis sinyal elektrokardiogram untuk mendeteksi kelainan jantung. Amplifikasi dan pengurangan noise adalah fungsi lain yang dapat ditawarkan sistem.

Salah satu sistem yang sangat mendasar adalah jaringan resistor-kapasitor (RC) yang ditunjukkan pada Gambar 1.5. Di sini, inputnya adalah tegangan sumber vs dan outputnya adalah tegangan kapasitor vc.

Pertimbangkan sistem pemrosesan sinyal yang ditunjukkan pada Gambar 1.6. Sistem pada Gambar 1.6(a) menggunakan sistem waktu diskrit (seperti komputer digital) untuk memproses sinyal waktu kontinu. Sistem pada Gambar 1.6(b) menggunakan sistem waktu kontinu (seperti komputer analog) untuk memproses sinyal waktu diskrit. Yang pertama dari jenis sistem ini ada di mana-mana di dunia saat ini.

Pertimbangkan sistem komunikasi yang ditunjukkan pada Gambar 1.7. Sistem ini mengambil pesan di satu lokasi dan mereproduksi pesan ini di lokasi lain. Dalam hal ini, masukan sistem adalah pesan yang akan dikirim, dan keluarannya adalah perkiraan pesan asli. Biasanya, kami ingin pesan yang direproduksi di penerima sedekat mungkin dengan pesan asli yang dikirim oleh pemancar.

Suatu sistem dengan bentuk umum yang ditunjukkan pada Gambar 1.8 sering muncul dalam aplikasi kontrol. Seringkali, dalam aplikasi seperti itu, kami menginginkan keluaran untuk melacak beberapa masukan referensi sedekat mungkin. Pertimbangkan, misalnya, aplikasi robotika. Input referensi mungkin mewakili posisi yang diinginkan dari efektor akhir, sedangkan output mewakili posisi sebenarnya.

Author's Message

My wish is that the reader will enjoy learning the theory of signals and systems by using this book. One of my goals is to present the theory in a direct and straightforward manner. Another goal is to instill interest in different areas of specialization of electrical and computer engineering

EDITORIAL SKILL IN ENGLISH PUBLIPRENEUR-BASED LANGUAGE LEARNING

(PBLL-Editing)

INSTRUCTIONS
1.           Use the red ink pen to mark your editorial findings 2.           Write the name of the manuscript’s writer within the box 3.           Write your name as an editor within the editor’s box 4.           Write the title of the manuscript 5.           Treat the draft as an accepted manuscript to the Editorial Department. 6.           Edit the manuscript by using the editorial signs 7.           Put the number of your editorial findings (mechanical, substantive, pictorial) within the box right- side 8.           Write your verbal verification of suggestion, comment, or input for the improvement of the manuscript. 9.           Give your editorial judgment about the manuscript from the perspective of prewriting, drafting, revising, editing, publishing, marketing, and delivering) 10.        Good Luck..be your best.
Students’ Identity				Writer			Editor
Name				Willy Riwaldi			Irfandi Bagus Fahrezi
Study Program				Electrical Engineering			Electrical Engineering
Title of Manuscript				Why do countries have different Signal and Systems?
C=Competence : NC= Non Competence
No			Editorial Findings		Number	Key Word		C	NC
A			Mechanical Editing						types
			·          Types		-	-		-	-
			·          Words		-	-		-	phrase
			·          Phrase		-	-		-	-
			·          Clause		-	-		-	-
			·          Punctuations		-	-		-	-
			·          Comma		-			-	-
			·          Colon		-	-		-	-
			·         Semi Colon		-	-		-	-
			·         Preposition		-	-		-	-
			·         Dictions		-	-		-	-
B			Substantive Editing
			·          Content Accuracy		-	-		-	-
			·          Language Consistency		-	-		-	-
			·          Message Originality		-	-		-	-
			·          Reader’s Interest		-	-		-	-
			·          Coherence		-	-		-	-
C			Pictorial Editing
			·          Harmony		-	-		-	-
			·          Balancing		-	-		-	-
			·          White Space		-	-		-	-
		·          Color			-	-		-	-
	Verbal Verification:   There are several author errors in writing, namely the wrong placement of   spaces and the use of capital letters.
	Editorial Judgment   I think the sentence-by-sentence that the author wrote is very good and very easy to understand for readers to read, but in writing, there are some words that I need to improve, such as example, describing, rigid body, etc. but overall I appreciate the author in choosing sentence by sentence.

QUESTIONERS OF PUBLIPRENEUR-BASED LANGUAGE LEARNING (PBLL) USED TO TEACH ENGLISH FOR SPECIFIC PURPOSES OF

ELECTRO ENGINEERING

AT MUHAMMADIYAH UNIVERSITY

Name		Willy Riwaldi
Study Program		Electrical Engineering
Lecturer		Dr. Zalzulifa, M.Pd
No	Questioners	Much (M); Enough (E); Less (L)			Reasons
		M	E	L
1	How far do you know about the concept of the Publipreneur-Based Language Learning (PBLL) approach in language teaching	√			The application of Problem-Based Learning in Physical Therapy courses begins by raising real cases that students face when implementing them. After selecting one case, then a theoretical study of the case was carried out both from textbooks and from the results of a study of similar cases. The internet can be a means which is very helpful. Cases that have been completed with theoretical studies are then presented for criticism in terms of the accuracy of diagnosis, effectiveness of therapy, and continuation of rehabilitation. required readiness of all discussion participants to listen, reflect and express logically and systematically.
2	Do you think that the Publipreneur-Based Language Learning (PBLL) approach applicable used to teach English for Specific Purposes (ESP)	√			Yes of course, in addition to course content, PBL can promote the development of critical thinking skills, problem-solving abilities, and communication skills. It can also provide opportunities for working in groups, finding and evaluating research materials, and lifelong learning.
3	How far does Publipreneur-Based Language Learning (PBLL) influence your English Reading skill in Electro Engineering Business		√		Pretty good, but I'm still a little difficult to understand how many words and accent pronunciation.
4	How far does Publipreneur-Based Language Learning (PBLL) influence your English Writing skill in Electro Engineering Business		√		Maybe the influence is quite big because in today's modern era computers allow large amounts of information and of course can facilitate trends as a means of learning. It can also provide instant feedback to learners to improve their writing skills.
5	How far does Publipreneur-Based Language Learning (PBLL) influence your English Listening skill in Electro Engineering Business		√		Quite helpful because there are so many factors that affect us besides studying Publippreneur-Based Language Learning (PBLL). such as motivation, attitude, age, intelligence, talent, cognitive style, and personality are considered factors that greatly influence a person in the process of mastering his second language.
6	How far does Publipreneur-Based Language Learning (PBLL) influence your English Speaking skill in Electro Engineering Business		√		Very lacking, because I often stammer when speaking english.