EEE 113 Comms_ Lecture 1_1st Sem 2024-2025 PDF

EEE 113 Introduction to Electrical and Electronics Engineering Systems 1st Sem AY 2023-2024 Energy conversion Information transfer 1  Instructors for this semester Jaybie de Guzman, Ph.D. Smart Systems Lab jaybie.de.gu...

EEE 113 Introduction to Electrical and Electronics Engineering Systems 1st Sem AY 2023-2024 Energy conversion Information transfer 1  Instructors for this semester Jaybie de Guzman, Ph.D. Smart Systems Lab [email protected]  Instructors for this semester Rhandley D. Cajote, PhD Digital Signal Processing Laboratory [email protected] 3  "Attention the Universe by Kingdoms Right Wheel” Samuel F.B. Morse https://www.pafa.org/collection/samuel-fb-morse-study-attention-universe-kingdoms-right-wheel-page-66 January 24, 1838 was the time that Samuel F.B. Morse first demonstrated the technology of the telegraph system. “Attention the Universe by Kingdoms Right Wheel” was the message that was sent. Morse Code was revolutionary as it was the first time that we used electrical signals to transmit information. 4  Communication Systems Today How to transform information? How to transmit the input signal? What happens to the signal as it travels the medium? How to recover the transmitted information? For this session we will try to answer on a very high-level the following questions. You might have some questions like what is information? and what is a signal? From wikipedia “In terms of communication, information is expressed either as the content of a message or through direct or indirect observation. That which is perceived can be construed as a message in its own right, and in that sense, information is always conveyed as the content of a message.” This information therefore can be transmitted as a signal. From wikipedia “In signal processing, a signal is a function that conveys information about a phenomenon. In electronics and telecommunications, it refers to any time varying voltage, current or electromagnetic wave that carries information. A signal may also be defined as an observable change in a quality such as quantity.” A signal therefore is a means to convey the information. For today, we’ll discuss how to transform information and how to transmit signals. 5  Human Communication What is communication? Why do people communicate? What are the barriers to human communication? How has electronic communication changed society? Morse has enabled communication at a distance: why do we need to communicate over a long distance? why do people communicate? We attempt to answer the above questions. 6  Human Communication What is communication? – Process of exchanging information Why do people communicate? – Convey thoughts, ideas, feelings using open vocal system and larger bank of symbols What are the barriers to human communication? – distance, language How has electronic communication changed society? – Enabled access and application of information in a timely way Key differences between human communication and that of other primates are that (1) humans have an open vocal system while other primates have a closed vocal system, and (2) humans have a larger bank of symbols to use in communication. Supplement: Figure 1-1 Principles of Electronics Comms, Frenzel 7  Milestones in Electronic Communication http://www.wanowandthen.com/website/telegraph.jpg Telegraph http://www.antiquetelephones.co.uk/ http://www.hammondmuseumofradio.org/images/hb- 3coils.jpg contents/media/img_4746.jpg Samuel Morse, invention 1834 Telephone Radio Alexander Bell, Guglielmo Marconi, invention 1876 demo 1887 Long distance comms: drum beats, smoke signals. In this slide are some of the early inventions in electronic communication. voltage and current are used as signals to convey the information. 8 How do modern communication systems work? Modern communication systems nowadays are sophisticated enough that you can communicate over long distances using a mobile device. You can send over voice, image, video and data over long distances, you can also communicate with more than one person at a time: video conferences and voice conferences. We are unaware of the underlying technologies that we use for everyday communications, we will try to identify and understand some of those underlying technologies. 9 How do modern communication systems work? Modern communications work through a vast network of sophisticated communications infrastructures that are connected over a wide-range (locally and globally). 1. For example the voice communication over modern cellular communication can be broken into segments: point-to-point communications from mobile device to base station. 2. Then there is mechanism for multiple access to a single base-station, how do users share a communication channel. 3. Then base-stations communicate or relay information to each other over a network. 10  Point-to-Point Communication Converting information to bits We start with the point-to-point communication on how information is converted into bits, a process known as information encoding or source coding. Point-to-point communication refers to the mode of communication where there is only one sender of information and one receiver of information over a relatively short-distance. 11  Point-to-Point Communication Show in this figure is a typical block diagram of a point-to-point communications system. The information “sender” sometimes also called the transmitter or the information Source transmits the encoder “bits” of information over a communication channel. To add resiliency to channel errors redundancy bits are added by the “Error Correcting Coding” block. The bits are converted into analog form as waveforms that can be transmitted via electromagnetic waves using an antenna. As the information is carried through the air, noise in the form of atmospheric disturbances may affect the integrity of the information, then the information is received by the receiver to reverse the process and convert the information back to bits. 12 How? If we disregard the effects of the channel coding for now, and assume that the channel is perfect and without noise. We can model the transmission of voice encoded as bits over a channel and sent to a receiver and converted back to voice. 13 HOW DO WE CONVERT INFORMATION TO BITS? 14 Analog-to-Digital Conversion (ADC) analog ADC digital signal signal sampling quantization coding 10011 First we have to convert the analog voice to bits, this is done by sampling the analog voice signal and converting the sampled information to binary information. This process is known as analog-to-digital conversion of ADC. The ADC has three main blocks, the sampling quantization and coding. Before the sampling process can begin, we have to convert the analog signal into electrical signals, this is done using transducers. An example of a transducer is a microphone. 15  Signal Signal: any physical quantity that varies with time, space, or any other independent variable(s) – e.g. temperature, pressure, light intensity, etc. Electrical transducer: physical device capable of converting the physical quantity to proportional electrical quantity – e.g. thermocouple, piezoelectric, photovoltaic, etc. The microphone convert Sound Pressure Level or SPL into Electromotive force of EMF, also known as voltage. microphone: SPL to EMF (voltage) 16  Example: Microphone Faraday's Law: a changing magnetic flux causes an electromotive force (emf in volts). Ohm's Law: voltage acts as a kind of potential energy that will cause charge to flow if there is a path (a circuit). The moving diaphragm cause by the vibrating air generated by our voice causes a changing magnetic flux. The changing magnetic flux in turn generates voltage according to Faraday's Law. The technical definition of Faraday's law says that a changing magnetic flux (Φ) causes an electromotive force (emf in volts). Similar principle used in generator When you connect a wire across the diaphragm, the generated voltage will cause current to flow through the wire because of Ohm’s Law, the wire then carries the information contained in the voice. The voice is thus converted into electrical signal. At this point the signal is still in analog form since it has not been sampled yet, a process called sampling converts any continuous analog signal into discrete time signal. 17  Signal Continuous-Time vs Discrete-Time Signals – Continuous-time (analog) signals are defined for every value of time and value – Discrete-time signal can be derived by selecting values of an analog signal at discrete-time instants, process known as sampling Analog signals are continuous-time signals, they have values for all time, and their value can be any real-number. Quantities like the temperature in a room is an example of an analog signal. Real world physical signals that we can sense are mostly analog signals. Discrete-time signals are analog signals that are sampled in time through a sampling process. Examples of a discrete-time signals is the display of a digital clock, the display changes only at certain time instants. 18  Periodic Sampling analog signal discrete-time signal Shown in the figure is an illustration of the periodic sampling process. The sampler is a switch, that opens and closes at periodic time intervals, each time the switch is closed, the analog signal on the left is sampled (transmitted) on the right at discrete-time intervals. The rate of sampling is known as the sampling frequency,Fs. The units of sampling frequency is Hertz or per second /s, or samples per second. Examples of Fs are 8000 Hz for voice, and 44,100 Hz for CD-quality audio. The inverse of the sampling rate is the sampling period, or Ts, the units of the sampling period is seconds. The discrete time signal on the right figure has the same shape contour as the analog signal, same values but only at discrete time instants that are multiples of the sampling period Ts. The next question is, can we recover the original analog process from the discrete time samples. The answer is yes, but we have to select the proper sampling rate. 19  Sampling Theorem How do we select the sampling rate Fs? If the highest frequency content of an analog signal is Fmax, the sampling rate is selected such that Fs>2Fmax. This guarantees that the original analog signal can be exactly recovered from the sampled signal. The sampling rate FN=2Fmax is called the Nyquist rate. The sampling theorem determines the minimum sampling frequency needed in order to recover the original analog signal from the discrete-time samples. The sampling theorem, is stated as above. If the highest frequency content of a signal is Fmax, then the minimum sampling rate must be greater than twice the maximum frequency content of the signal. This guarantees that the analog signal can be recovered from the sampled signals. The frequency that is twice the maximum frequency content of the signal is called the Nyquist rate. For example, for a voice signal the maximum frequency of voice is 4000 Hz, then the Nyquist rate is 8000 Hz. The minimum sampling frequency is 8000 Hz used in telephone voice applications. The human auditory signals can perceive sounds up to a maximum of 22,050 Hz, the Nyquist rate is 44,100 Hz, so CD-quality audio signals are sampled at 44,100 Hz. 20  Number of Samples Sampling a signal of length Tw with a sample period Ts results in N samples where Ts TW Tradeoff: A higher sample frequency is Good: Less information lost since less time between samples Bad: More storage needed since more samples for a given length of time The number of samples can be computed if we know the signal duration and the sampling frequency. For example, for a 10 second signal, sampled at 0.01 second sample period (or a sampling frequency of 100 Hz). The total number of samples is 10 / 0.01 = 1000 samples. That is the number of signal samples in a 10 second duration. Higher sampling rate for example 200 Hz, will results in 2,000 samples for the same signal duration. Increasing the sampling rate or oversampling, or equivalently reducing the sample duration, will result in more samples per unit time, this improves the discrete-time signal fidelity, and less information is lost. But the trade-off is increased storage capacity. For sophisticated mobile devices with a lot of memory, maybe increasing the sampling rate is not an issue. But for IoT or environmental sensors that are deployed in the thousand covering a wide-area such as weather stations, humidity sensors, temperature and proximity sensors. Those sensors run on small limited capacity storage devices, the sampling rate must be chosen so as not to exceed the storage capacity of the device. 21  Sampling Theorem What happens when Fs