Differences Between Analog and Digital Signal Processing and Control - Literature review Example

Differences Between Analog and Digital Signal Processing and Control Signal processing protocols are present in numerous applications such as communication and control frameworks, musical composition, and biomedical signal processing. These protocols can be implemented in two different manners: ASP (Analog Signal Processing) and DSP (Digital Signal Processing). This paper explains the differences between analog and digital signal processing and controls. Analog signal processing and control can be carried out in real-time and takes up less bandwidth while digital signal processing and control does not assure real-time processing, but takes up more bandwidth to convey the same data. Analog and Digital Signal Processing ASP applies analog circuit components like rheostats, capacitors, and transistors. ASP has been in use for numerous years. With the arrival of the digital processor and later microchip, DSP has turned into a more dominant approach for signal processing. ASP is founded on the natural capacity of the analog framework to decipher differential equations. This way, ASP defines a physical framework. Solutions for these equations can be acquired in real-time by using identical hardware to carry out a variety of signal processing protocols in DSP while designing a framework for every type of protocol in the core of ASP. Similar signal processing protocols can be repeated several times to produce the same outcomes while facing a parameter variation in ASP because of a difference in temperature or power supply. Differences in Analog and Digital Signals Any time-uninterrupted signal with a time differing trait is a representation of another time differing measure in an analog signal. This signal is different from a digital one because of small, significant fluctuations that are evident in figure 1. This understanding of the analog signal is important for understanding the difference between ASP and DSP (Tektronix, 2001, p. 5). ASP largely occurs in an electrical setting but mechanical, air-filled, hydraulic, and other frameworks can transmit analog signals as well. An analog signal applies some feature of the channel to transmit the signal’s data. For instance, an aneroid barometer applies a rotational position as the signal for transmitting pressure data. The feature most widely used in ASP is electric energy, then frequency, electric current, and charge. An analog signal might transmit any data. Such a signal is treated as a calculated response to variations in physical phenomena like sound, light, heat, location, and weight. To do this, a response by use of a transducer is required. For instance, during sound recording, variations in air compression hit the diaphragm of a microphone and an equivalent variation in electric energy or current in an electric circuit occurs (Lopez, 2002, p. 3). This equivalent change in electric energy or current is the “analog” of the sound received. Figure 1: An illustration of analog and digital signals before and after processing Obstacles and Issues Faced When Using Analog and Digital Signals Respectively A digital signal is a quantized, separate-time signal while a separate-time signal is a tested analog signal. In the digital era, the application of digital signals has risen substantially (Pentek, n.d., p. 2). Numerous contemporary multimedia gadgets, particularly those capable of linking with computers, use digital signals to embody signals that were embodied as uninterrupted-time signals conventionally. Examples of such gadgets are smartphones, mp3 players, tablets, digital cameras, and camcorders. In a majority of these applications, binary digits embody digital signals so their accuracy of quantization is calculated in bits (Samson, 1999, p. 10). For instance, calculating a signal is necessary for two substantial decimal numerals. Seven binary numbers can record up to 128 separate values. These seven numbers are more than adequate for expressing a variety of one hundred values. The Quantization Process One can use the term digital signal to mean several theories. First, a digital signal can mean a digitized, separate-time signal. Second, a digital signal can be the waveform signals produced within a digital framework (Pentek, n.d., p. 1). In figure 2, digital signals are digital demonstrations of separate-time signals that frequently come from analog signals. As a result, a separate-time signal is a tested version of an analog signal, which means recording the value of the analog signal’s datum at fixed intermissions is important instead of constantly. If estimating the respective time values of the separate-time signal to a given accuracy is required instead of precise measurements, the system would need a particular number of digits (Lopez, 2002, p. 4). The subsequent data flow is a digital signal. The procedure of estimating the exact value inside a stable number of numerals or bits is known as quantization. Figure 2: The Quantization Procedure When Analog and Digital Signal Processing are Used DSP receives true, digitized signals like voice, imagery, temperature, pressure, or location then controls them arithmetically (BEARCOM, 2010, p. 4). DSP is made to carry out arithmetic functions like add, deduct, multiply, and divide very fast. Digital signal processors have to process signals so that a user can show, examine, or transform data into another kind of signal that might be useful. Analog commodities find signals like sound, light, heat, or pressure and works on them. Afterwards, instruments like the analog-to-digital converter receive the true signals and convert them into binary numbers. The instrument then dominates the entire process by acquiring the digitized data and processes it (Samson, 1999, p. 10). The converter then conveys this data back into an analog signal for use in reality. The device achieves this last conversion through digital and analog formats. These conversions and transmissions occur at extremely fast speeds. To demonstrate this concept, figure 3 depicts how an mp3 player applies DSP (BEARCOM, 2010, p. 4). In the course of the recoding stage, analog sound waves go through a receiver or another source. Afterwards, a converting instrument changes the analog signal into a digital one then transmits it to the digital signal processor. This processor carries out the mp3 encrypting and stores the file to a hard drive. While playing back, the processor retrieves the file from the memory, decrypts it, and lastly transforms it back to an analog signal through a converting instrument. This final conversion ensures emits the analog signal through the speaker (Zhu, Yuan, and Khan, 2013, p. 2). Examples that are more complicated entail digital signal processors that can change volumes and equalize the sounds emitted through the speaker. Figure 3: Conversion of sound as an analog signal into a digital one as binary digits and back into sound in an mp3 player (BEARCOM, 2010, p. 5). In DSP, linear and nonlinear arithmetic practices are effective over a broad active variety of signals for average floating points. In addition, DSP facilitates a set of mathematical operations accessible. With DSP, applying higher order filters with a more or less reduced incremental expense and extra memory and calculations can be helpful, which is evident in figure 4 (Manolakis and Ingle, 2011, p. 23). Methods for filter design in DSP also offer a more or less high level of freedom in spectral modeling such as the Frequency Sampling technique. DSP does not including the tuning of analog mechanisms in the course of production or preservation. DSP has good version control where a computer integrates filter coefficients into memory that will by no means alter from a single unit to another (Zwavashe, Duri, & Mutandavari, 2014, p. 4237). Software-oriented applications in DSP do not need any special hardware, only average signal I/O panels and special programming. Figure 4: The design of a DSP framework An analog signal has a hypothetically endless resolution. In figure 5, the analog signal has a higher solution than any digital framework where the solution is in distinct steps at all times. As analog frameworks practically advance into complicated systems, consequences like nonlinearity and sound eventually degrade analog resolution for digital frameworks to outdo it (Lopez, 2002, p. 3). It is hard to determine when such degradation takes place in analog frameworks. However, degradation cannot be simply determined, but corrected in digital frameworks as well. Figure 5: Conversion of an analog signal into a digital one through an analog-to-digital converter Advantages Of Analog and Digital Signal Processing and Control DSP has two major merits over ASP: adaptability and repeatability (Zwavashe, Duri, & Mutandavari, 2014, p. 4236). A computer can use data in a digital signal processor to regulate matters like security, cell phone, entertainment systems, and motion picture compression (Zwavashe, Duri, & Mutandavari, 2014, p. 4237). Computers and cellphones might compress digital signals to enable rapid and effective transmission from one location to another. For instance, videoconferencing can convey speech and imagery through telephone cables. Certain instruments can boost or influence signals to better their quality or supply data that human beings cannot collect like echo alleviation for telephones and computer-improved imagery for medical purposes. Even though true signals can be processed while in their analog format, converting them to digital offers the benefits of increased speed and accuracy. Due to its programmability, a digital signal processor can be used for a broad array of uses. A designer or engineer can make his or her individual software or apply software offered by ADI and its intermediaries to create a DSP solution for use. ASP does not require computer processing. As a result, it is more suitable for high frequency implementations than DSP when accessible time for the transformation of information or for calculations is too finite (BEARCOM, 2010, p. 5). ASP requires simpler application when overall mechanisms matter. Unlike DSP, ASP is relatively cheap as long as computers are not necessary. During ASP, no items correspond with the issue of limit phases that take place in digital filters. A limit phase is an endless low amplitude fluctuation in the resultant value of a filter induced by rotating effects brought in by limited accuracy arithmetic operations (Tektronix, 2001, p. 9). ASP undergo smoother changes of low-degree analog signals into the noise level contrary to the output of DSP that produces a low responsibility phase and square wave when transmitting a digital value in binary form. ASP does not need to enforce certain criteria like particular limits affiliated with the digitization procedure. With numerous physical factors like electricity, analog inconsistency is simple to come across. If a physical factor is used as a signal medium, it will be capable of embodying inconsistencies of data with nearly limitless resolution (Lopez, 2002, p. 4). During the industrial instrumentation, researchers and industrialists used pressurized air as a signaling intermediate for sending data from measuring tools to showing and regulating devices positioned distantly. The quantity of air pressure matched the scale of the variable being determined. Clean air without humidity was supplied at an estimated 20lbs/PSI from an air compressor through tubes to the measuring tool (Lopez, 2002, p. 5). Here, the measuring tool, in line with the amount being measured, then controlled the air to produce an equivalent resultant signal. For instance, an air-filled level antennae device established to determine the height of water inside a huge container would produce reduced air pressure when the container was empty, average air pressure when the container was half full, and high pressure when the container was full. Disadvantages Of Analog and Digital Signal Processing and Control The major disadvantage of ASP over DSP is the prominence of noise, which are arbitrary fluctuations of sound. As a processor replicates an analog signal repeatedly or conveyed over lengthy distances, these arbitrary fluctuations become overriding (Tektronix, 2001, p. 9). These losses can lessen electronically by guarding, proper connections, and some types of wiring like coaxial or twisted pair. The requirements for high quality digital filters are high accuracy coefficients and calculations for ASP while those for ASP filters are high accuracy components. The optimal separate amplitude and the resolution of the analog-digital converter ascertain the SNR of a digital signal (Zhu, Yuan, and Khan, 2013, p. 2). On the other hand, electrical supply circuits and noise levels ascertain the SNR of an analog signal. Active spectral modeling in a digital filter occurs through online coefficient modifications and active spectral modeling in an analog filter occurs through swapped capacitor rails and the quality of a band pass filter. A swapped capacitor circuit can modify endpoint frequencies and the quality of a bandpass filter is altered by differing inputs (BEARCOM, 2010, p. 5). Conclusion Analog signal processing and control can be carried out in real-time and takes up less bandwidth while digital signal processing and control does not assure real-time processing, but takes up more bandwidth to convey the same data. The impacts of noise include losing and interfering with signals, which makes it impossible to recover. This is because magnifying the signal for recovery purposes weakened sections of the signal magnifies the noise too. Even when the solution of an analog signal is more than a relative digital signal, noise mostly eclipses this difference in the signal. References BEARCOM. (2010). White Paper: Comparing and Contrasting Analog and Digital Two-Way Radios. Dallas, TX: BEARCOM Wireless Worldwide. Lopez, A. B. (2002). Comparative Study Of Analog And Digital Hearing Aids. Louisiana: Louisiana State University. Manolakis, D. and Ingle, V. (2011). Applied Digital Signal Processing. London: Cambridge University Press. Pentek. (n.d.). Digital Receivers Bring DSP to Radio Frequencies. Upper Saddle River, NJ: Pentek Inc. Samson, A. G. (1999). Digital Signals. Frankfurt: Samson AG. Tektronix. (2001). XYZs of Oscilloscopes. New York, NY: Tektronix, Inc. Zhu, G., Yuan, F., and Khan, G. (2013). Time-Mode Approach for Mixed Analog-Digital Signal Processing. Journal of Electrical and Electronics, 2(1), 1-4. Zwavashe, T., Duri, R., & Mutandavari, M. (2014). Towards More Efficient DSP Implementations: An Analysis into the Sources of Error in DSP Design. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4235-41. Read More