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integrating-sphere · 1 year ago

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Analysis of Lightning Surge generator Discharge Circuit

According to the working principle of the simulated surge generator used in electromagnetic compatibility tests and lightning surge generator test, combined with the commonly used 8/20 μs and 10/700 μs test waveforms in current standards, the composition and component parameters of the discharge circuit for simulating different waveforms of surge generator can be obtained through second-order differential equations and MATLAB simulation. These findings provide analytical methods and solutions for problems encountered in surge tests. Recent studies have shown that surge impulse observation devices, which combine computers and oscilloscopes, can record surge parameters in digital form. By using computer simulation software and nonlinear data fitting methods, numerical information can be transformed into corresponding simulated surge waveforms. Test personnel design surge generators based on the principle of capacitor charging and discharging, aiming to simulate overvoltage pulses generated by power system switches or lightning impulses. Understanding the composition and structure of the discharge circuit during the test process not only provides better control of the testing process, but also enables accurate judgment and in-depth analysis of problems encountered during the test. 1. Definition of Simulated Surge generator Waveform First, let us define the simulated surge generator waveform. Based on the single-pulse characteristics approximating the exponential rise and fall of a lightning pulse waveform, Bruce Godle summarized the double-exponential function of lightning current waveform. i（t）=I0k（e-at-e-βt），（ 1 ） In the formula (1), Io is the amounted to the current pulse, KA; α is attenuation before the waves Coefficient; β is the wavetail attenuation coefficient; K is the waveform correction coefficient. Similarly, voltage pulse waveforms can be represented u（t）=U0A（e-t/τ1-e-t/τ2），（ 2 ） In the formula (2), U0 is the voltage pulse amounted value, KV; A is the correction coefficient; Τ1 is a half -peak time constant; τ2 is the head time constant. Treatment of formula (1) and formula (2) can be obtained. I t)/u (t) = k (E-AT-E-βt). (3) Formula (3) is called a unit peak current/ voltage function equation. 8/20 μs The coefficient value corresponding to the wave shape of the 10/700 μS test. 2. 8/20 μS impact current generator Discharge circuit Mathematical analysis 2.1 current pulse wave micro -division equation and solution Next, we analyzed the mathematical analysis of the 8/20 μS impact current generator discharge circuit. First, we consider the differential equation of the current pulse wave and its solution. The equivalent of the impact current generator discharge circuit is shown in Figure 1. When the geometric size of the actual circuit is far less than the wavelength of the working signal, we call it a collection of total parameter circuits. The dynamic circuit composed of an independent power supply and resistance element and dynamic components, its circuit equation is a set of differential equations. The capacitance, inductance is related to the voltage and passing of the current. C -Main electric container; R -circuit impedance and wave resistance; L -circuit distribution inductance value and wave resistance. Through Kirhoff's law, we can list the relationship between the circuit and convert the differential equation of the circuit, and then solve the system's free response equation. Because the capacitor value is calculated from C × as a normalized parameter K, if the pulse current to obtain the corresponding amplitude value is to be obtained, the capacitor charging voltage must be equal to the pulse current value. However, this will increase the resistance level of charging capacitors and accelerate the aging of the capacitance. To solve this problem, in practical applications, we can appropriately increase the charging capacitor capacity through parallel capacitors and reduce the charging voltage amplitude. In addition, we can simulate through the Simulink component to obtain the discharge circuit composition and component parameters of different wave pulse waves, and to meet the standard requirements obtained by the combination of pulse waveforms. However, it should be noted that these models are established in an ideal environment, and in actual circuit design, we also need to consider the distribution parameters of components such as impedance loss, capacitance and inductors on the circuit, as well as The distributed parameters on the PEARSON coil. By fine -tuning different component parameter values, we can reach a relatively standard waveform. 3. Application of lightning surge generator: In the surge test, the application of the swarming pulse observer is very important. The surge pulse observer can record the swarming parameters in a digital form through the cooperation of the computer and oscilloscope. Through the non -linear fitting of digital information, these digital information can be converted into corresponding simulation waves. The test personnel can design the surge generator according to the principle of capacitor charging and discharge, simulation the power system switch or thunderbolt impact transients generated by transients. Through the application of surging pulse observations, test personnel can not only better grasp the test process, but also accurately judge and in -depth analysis of the problems in the test. Conclusion: (1) According to the component characteristics of the circuit (capacitive voltage, inductance current, etc.), the Cirhoff's law is used to list the circuit relationship, convert the differential equation of the circuit, and solve the system's free response equation. (2) Because the capacitance value is calculated as a normalized parameter K by the capacitor value is to obtain the pulse current with the corresponding amplitude value, the capacitor charging voltage must be equal to the pulse current value. This will increase the resistance level of the charging capacitor and accelerate the aging of the capacitance. In practical applications, because the U0C is a fixed value, it can appropriately increase the charging capacitor capacity through parallel capacitors and reduce the charging voltage amplitude. (3) Through the simulation of the Simulink component, the discharge circuit composition and component parameters of different wave pulse waves are obtained. The pulse waveform obtained by the combination meets the standard requirements. However, this is a model established in an ideal environment. In actual circuit design, it is necessary to consider the distribution parameters such as impedance loss, capacitance and inductors on the circuit, distributed parameters of the signs of the voltage of the circuit voltage, and circuit current Pearson Pearson The distributed parameters on the coil can be slightly adjusted to the values of different component to achieve a relatively standard waveform. (4) Through the inquiry of the working principle of simulated wave surges in the electromagnetic compatibility test and lightning surge generator test, and combined with the 8/20 μs and 10/700 μs test waveforms generally performed in the current standards, the second -order differential equation can be passed through the second order. Solution and Matlab calculation simulation to obtain the composition and component parameters of different waveform simulation surge generator discharge circuits. At the same time, the use of wave pulse observations can be used to observe and record, which can better grasp the test process and accurately analyze and solve the problems encountered in the test. The application of these methods and technologies will provide effective analysis methods and solutions for problems in electromagnetic compatibility tests and lightning impact tests. Lisun Instruments Limited was found by LISUN GROUP in 2003. LISUN quality system has been strictly certified by ISO9001:2015. As a CIE Membership, LISUN products are designed based on CIE, IEC and other international or national standards. All products passed CE certificate and authenticated by the third party lab. Our main products are Goniophotometer, Integrating Sphere, Spectroradiometer, Surge Generator, ESD Simulator Guns, EMI Receiver, EMC Test Equipment, Electrical Safety Tester, Environmental Chamber, Temperature Chamber, Climate Chamber, Thermal Chamber, Salt Spray Test, Dust Test Chamber, Waterproof Test, RoHS Test (EDXRF), Glow Wire Test and Needle Flame Test. Please feel free to contact us if you need any support. Tech Dep: [email protected], Cell/WhatsApp:+8615317907381 Sales Dep: [email protected], Cell/WhatsApp:+8618117273997 Read the full article

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matlabhwexperts-blog · 7 years ago

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Generate Matlab Waveforms Assignment Help

https://www.matlabhomeworkexperts.com/using-matlab-to-generate-waveforms.php

Waveform Generator uses Direct Digital Frequency Synthesis (DDS) technology to generate high accuracy and high stability waveforms. It can also generate programmable pulse signals as well as the standard wave functions. Electronic designs require a variety of stimulus signals during test. Additionally, the arbitrary waveform feature allows engineers to generate any desired waveform with Ultra Wave, our free waveform editing software. Actual signals can also be captured through an oscilloscope, and then downloaded to a signal generator for output. The digital sampling technology and the Direct Digital Frequency Synthesis technology enable engineers to build any required waveforms for circuit design verification. MATLAB can also be used to generate waveforms,. In most branches of Electronics Engineering, waves are studied both in the time domain and the frequency domain simultaneously. Our talented pool of Waveform Generator experts, Waveform Generator assignment tutors, Waveform Generator solvers, Waveform Generator professionals and Waveform Generator homework tutors can cater to your entire needs in the area of Digital Signal Processing such as Waveform Generator Homework Help, Undergraduate Waveform Generator Assignment Help and Graduate Waveform Generator Assignment Help, MATLAB Waveform Generator Project Paper Help and Waveform Generator Exam Preparation Help

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rojaswinther47-blog · 6 years ago

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My Favorite Stethoscopes

Littmann Classic III

Digital stethoscopes are computer aided products which can read sound, vibration, or acoustic signals. The list here don't actually only apply to medical students; it also applies to PGIs (post graduate interns) and even resident physicians. Since these things will go a long way from the time you start med school up until your early years of practicing the medical profession, it's smart to invest in quality items. Like for example, when it comes to stethoscopes you might want to consider investing in a 3M Littmann Classic II SE Stethoscope or the Master Cardiology stethoscope This is because this particular brand is very durable and could really go a long way. In fact, some of today's renowned doctors are even using the same Littmann units that they've been working with since med school. This entry-level Littmann Lightweight II S.E. Stethoscope is equipped with double-sided chest pieces and tunable diaphragm. Available in a variety of colors, including black, cell blue, green, lilac and sea foam green, the Littmann Lightweight II S.E. and similar to the Littmann II S.E. stethoscope, features a non-chill rim and diaphragm. This is one of the most commonly used stethoscopes among nurses and it is largely considered as one of the best stethoscope out there. The MATLAB user interface also met expectations, displaying cardiac waveforms in real time and when a stored waveform was played back as illustrated in the figure below. Several people were tested using the stethoscope to determine the accuracy of the BPM measurement algorithm. After obtaining a measurement using the digital stethoscope, the volunteers also determined their heart rate by holding two fingers to their necks and recording the number of seconds required for 10 heart beats. The results are included in the table below. The largest error between the two measurement techniques for the samples taken was around 6%, suggesting that the BPM measurements displayed on the plot were fairly accurate. It should be noted that there is a degree of uncertainty in measuring heart rate using the method with two fingers. Its flagship part is the sphygmomanometer which checks your blood pressure manually. You simply have to place it at your heart and breathe naturally. The rest will simply follow. Being devoid of any complex controls, it is likely to elicit any inconveniences at all. A vital element of the hospital beds mentioned above, are the mattress itself. One highly popular type of mattress available is the egg mattress These mattresses, so called due to their similarity in appearance to an egg box or carton, are placed on top of a standard mattress to decrease pressure on the skin, helping prevent sores and helping the blood flow of patients who spend a lot of time confined to bed. Their design also helps patients remain cool by allowing increased airflow around the patient. The stethoscope (from Greek στηθοσκόπιο, of στήθος, stéthos - chest and σκοπή, skopé - examination) is an acoustic medical device for auscultation, or listening to the internal sounds of an animal body. It is most often used to listen to heart sounds and breathing. It is also used to listen to intestines and blood flow in arteries and veins. Less commonly, "mechanic's stethoscopes" are used to listen to internal sounds made by machines, such as diagnosing a malfunctioning automobile engine by listening to the sounds of its internal parts. Stethoscopes can also be used to check scientific vacuum chambers for leaks, and for various other small-scale acoustic monitoring tasks.

25 Greates medical accessories To Start medical studies

Of course disposable stethoscopes have been around for some time, specifically for high risk areas. But at a cost of $3 or so, they are a somewhat expensive solution, and also for that cost, their quality (particularly acoustic quality, a key to Littmann brand performance) is quite poor. The main distinguishing design features in cardiology stethoscopes vs regular is the difference in chest piece (head design) and thicker tube. The tubing is made thicker to minimize interference as the sound waves are transmitted between the chest piece and the ear piece. The thickness is also meant to promote amplification of the sound as it travels. Product Details The 3M Littmann L5807RB-CAR Classic III Rainbow Finish Stethoscope features a Rainbow finish chestpiece and Brass finish eartubes. The single-piece diaphragm is easy to attach and easy to clean, with a smooth, crevice-free surface. littmann classic 3 reviews There are tunable diaphragms on both the adult and pediatric sides of the chestpiece. The pediatric side can convert to an open bell by just removing the single-piece diaphragm and replacing it with a non-chill rim. The open bell stays clear of dirt and debris when covered by the small diaphragm. The next-generation tubing lasts longer, with improved resistance to skin oils and alcohol. No natural rubber latex or phthalate plasticizers used in the tubing or any other component. Comes with a 5-year warranty. This dependable stethoscope comes from renown brand MDF. This stethoscope is widely popular with the medically related person not because of its high price but for its great services. It is made up such a way patient feel optimum comfort as well as a nurse also by using the stethoscope. Everybody's external acoustic meatus is not the same size and thinking this important fact three pairs of different sizes comfortSeal ear tips come with the package. The Listen-In Mobile App can improve the confidence of both the instructor and the students by ensuring that the sounds being heard Monitoring Stethoscope are accurate. Additionally, the application enforces correct anatomical positioning through both observation and interaction. The stethoscope traditionally used is the Sprague, developed more than 45 years ago. It is still considered to be the gold standard acoustic stethoscope, with an outstanding high to low range. However, that being said, once you have learnt to recognize what is simply the tube sound, you'd be hard pressed to get better acoustics from any scope.

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harkcomringler4577 · 3 years ago

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Arbitrary Waveform Generators and Transceivers for Multi Channel RF Signal Generation

Tabor Electronics, a manufacturer of test and measurement equipment has added a new addition to its Proteus, Arbitrary Waveform generators / Transceivers series. The new RF AWG/AWT has a built-in IQ modulator with advanced capabilities for multi-channel RF signal generation. The system is based on a PXIe platform and enables you to transmit, receive and perform digital signal processing in a single instrument.

The Proteus series offers an integrated Numerically Controlled Oscillator (NCO), digital interpolator, and IQ modulator for the generation of complex RF signals directly from the Proteus instrument. The internal digital-up-converter enables the direct generation of IQ modulation signals eliminating limitations such as IQ mismatch, and in-band carrier feed-through that is present with external IQ modulators and mixers.

For multi-channel applications such as quantum physics and radar, needing high-performance synchronized, phase-coherent output, the Proteus RF AWG is an ideal, space-efficient, and cost-effective solution. These RF arbitrary waveform generators are available in three models – PXIe modules, Desktop, and Benchtop.

Key features of PXIe Module:

The Proteus RF Arbitrary Waveform Generator PXI module is based on PXI Express industry standard and can easily scale up to hundreds of channels, with the smallest footprint.

It enables up to 4 generator output channels and 2 digitizer input channels which occupy only 3 PXI slots. The Proteus PXIe AWG platform utilizes a PCI express Gen 3x8 lanes connection that enables up to 64Gb/s of data transfer speed. Two and Four channel 1.25GS/s & 2.5GS/s 16 bit, AWG & AWT configuration. Uses embedded IP and application-specific requirements. Real-time data streaming directly to the FPGA for continuous and infinite waveform generation. 8 GHz Bandwidth, 5.4 GS/s 12 bit digitizer option for feedback control system and conditional waveform generation. Innovative task-oriented sequence programming for maximum flexibility to generate any imaginable scenario. Up to 16 GS/s waveform memory with the ability to simultaneously generate and download waveforms. Excellent phase noise and spurious performance. Customizable FPGA for use embedded IP and application-specific requirements. Modular and space-efficient PXI Express platform, easily scalable to hundreds of channels. Key features of Desktop version:

The desktop version of the Proteus series offers up to 12 channels in a 4U, half 19" dedicated chassis. The compact form size and small footprint save valuable bench space.

Four, eight, and twelve channel 1.25 GS/s & 2.5 GS/s 16 bit, AWG & AWT configuration. Uses embedded IP and application-specific requirements. Real-time data streaming directly to the FPGA for continuous and infinite waveform generation. 8 GHz Bandwidth, 5.4 GS/s 12 bit digitizer option for feedback control system and conditional waveform generation. Innovative task-oriented sequence programming for maximum flexibility to generate any imaginable scenario. Up to 16 GS/s waveform memory with the ability to simultaneously generate and download waveforms. Excellent phase noise and spurious performance. Customizable FPGA for use embedded IP and application-specific requirements high-speed PCle Gen 3x8 lanes communication interface. Space efficient Desktop platform, with USB 3.0, 10G Ethernet, and Thunderbolt high-speed interfaces. Key features of Benchtop version:

The benchtop version of the Proteus Arbitrary Waveform Generator series offers up to 12 channels in a 4U. 19" benchtop box. With a 9" touch display and on-board PC the benchtop platform enables users to program the instrument without the need of an external PC. You can program the Proteus RF AWG benchtop from the onboard PC using the WDS Tabor's proprietary software or various programming environments such as Python, MATLAB, LabView, etc.

Four, eight, and twelve channel 1.25 GS/s & 2.5 GS/s 16 bit, AWG & AWT configuration. Uses embedded IP and application-specific requirements. Real-time data streaming directly to the FPGA for continuous and infinite waveform generation. 8 GHz Bandwidth, 5.4 GS/s 12 bit digitizer option for feedback control system and conditional waveform generation. Innovative task-oriented sequence programming for maximum flexibility to generate any imaginable scenario. Up to 16 GS/s waveform memory with the ability to simultaneously generate and download waveforms. Excellent phase noise and spurious performance. Customizable FPGA for use embedded IP and application-specific requirements. High-speed PCle GEN3x8 lanes communication interface. Space efficient Desktop platform, with USB 3.0, 10G Ethernet, and Thunderbolt high-speed interfaces.

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rosinathurfcb56 · 3 years ago

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Arbitrary Waveform Generators and Transceivers for Multi Channel RF Signal Generation

Key features of PXIe Module:

The Proteus RF Arbitrary Waveform Generator PXI module is based on PXI Express industry standard and can easily scale up to hundreds of channels, with the smallest footprint.

The desktop version of the Proteus series offers up to 12 channels in a 4U, half 19" dedicated chassis. The compact form size and small footprint save valuable bench space.

Four, eight, and twelve channel 1.25 GS/s & 2.5 GS/s 16 bit, AWG & AWT configuration. Uses embedded IP and application-specific requirements. Real-time data streaming directly to the FPGA for continuous and infinite waveform generation. 8 GHz Bandwidth, 5.4 GS/s 12 bit digitizer option for feedback control system and conditional waveform generation. Innovative task-oriented sequence programming for maximum flexibility to generate any imaginable scenario. Up to 16 GS/s waveform memory with the ability to simultaneously generate and download waveforms. Excellent phase noise and spurious performance. Customizable FPGA for use embedded IP and application-specific requirements. High-speed PCle GEN3x8 lanes communication interface. Space efficient Desktop platform, with USB 3.0, 10G Ethernet, and Thunderbolt high-speed interfaces.

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simone0123 · 4 years ago

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Signal Processing Assignment Help

What is a signal?

A signal can be defined as an envelope or stream of information encoded in the form of waves. It is generally in the form of continuous signals or electrical pulses. The branch of Electrical and Electronics Engineering has evolved which is basically designed to focus on how the information extraction and information propagation takes place. This branch is sometimes called as Signal Processing.

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juniperpublishers-robotics-blog · 6 years ago

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Research on Electromagnetic Driving Robot Fish-Juniper Publishers

Abstract

Based on the actual physical appearance of the tuna and the wave equation derived from observing its swimming locomotion, this paper proposes a new idea on the simulation and modeling of the robot fish and a novel way of thinking on kinetic analysis. The motion law for each joint of the robot fish was first obtained through the discrete fitting method and the motion law was subsequently used as the basis for the robot fish electromagnetic drive control signal. The fluent flow field analysis software was utilized. The meshing of the fluid around the fish body was performed by using the dynamic grid technique. The surface pressure values of the fish body during the steady state forward movement were analyzed, and the values of the driving force of each part of the fish were obtained. It was then determined that the electromagnetic drive caudal fin robot fish was the optimal design. Based on the idea of digital-analog conversion, the drive control signal waveform was digitally discretized, then the C51 single-chip microcomputer and DAC digital-to-analog converter was used for conversion. The OPA544 op amp chip was then used to simulate the amplified output in accordance with the control signal waveform. Electromagnetic coil drive signal with variable frequency and voltage can thus be achieved.

Keywords: Robot fish; Electromagnetic drive; Fluent flow field analysis; Kinetic analysis; Control signal; Swimming experiment

Construction of the Robot Fish Model

Determination ofthe external physical characteristics of the fish body and the number of joints

A real tuna fish was selected, the total length was measured and the external curve of the fish body was fitted; the following data can be obtained:

Total length l = 260mm

Where R(x) is the function of the longitudinal fish body curve, r(x) is the function of the transverse fish body curve

In the selection of the number of joints of the robot fish, the higher the number of joints, the higher the degree of fit between the swimming curve and the known fish body wave equation, the closer it is to the real fish-swimming situation. On the other hand, the cumulative error of the series structure and the complexity of the structure should also be considered. Usually the range for the number of robot joints N is 2-10. The number of robot fish joints designed in this paper is N = 6.

In the design of the length of each joint, considering that the movement of the fish body is mainly concentrated in the rear half of the fish body, therefore the first half 130 mm of the fish can be viewed as not swinging. Also, according to the actual measurement, it is known that the fish tail length is 47 mm. Based on the joint size parameter optimization design method proposed by Chai Zhikun [1], except for the tail joint, the proportion of the remaining five joints should be 83: 67: 59: 53: 48. The length of each joint after calculation and rounding is: joint 1 is 22mm, joint 2 is 18mm, joint 3 is 16mm, joint 4 is 14mm, joint 5 is 13mm, and the joint 6 (caudal fin) is 47mm long.

Calculation of the mass and moment of inertia of each part

Based on the above geometric parameters of the fish, the mass of the various parts of the fish can be calculated:

Where, ρ is the fish density (kg/m3), which can be approximated as the density of water, a, b are the abscissa values of the starting point and the end of each joint.

During the swimming process of the fish, due to interaction with the surrounding fluid, the fish has a virtual mass. According to Lighthill's "Large-amplitude elongated-body theory of fish locomotion,” the equation for calculation of the virtual mass of the fish body cross-section is [2]:

Where ρf is the fluid density, β is the virtual mass coefficient; when there is no fish fins on the fish cross-section, β = 1.

As the robot fish cross-section can be regarded as an oval with the long axis of 2R (x), the short axis of 2r (x), the inertia of each part around the z-axis can be calculated according to the following formula:

Where m is the sum of the mass of each joint and the virtual mass, and do is the length of each joint. As this robot fish has six joints, therefore there is seven parts in total. The following is the calculated mass of each joint, virtual mass, and moment of inertia:

Kinematical Analysis of the Robot Fish

According to the observations of the tuna fish by Donley and Sepulveda and others [3], the fish body wave equation corresponding to the swimming locomotion of this type of fish is: h (x, t )= H (x) sin (ωt - kx) , where, h (x, t) is the lateral displacement of the fish. H (x) is the envelope equation of the fish’s swimming locomotion. ω is the swing frequency of each fish joint, k is the wave number of the fish body. k = 5.7/1 . H (x) = α1 + α2 + α3x2 , Where α1 = 0.02/ α2 =-0.12 α3 = 0.3/1.

When the frequency of each joint is 2Hz, the swing period is 0.5s. The swing cycle is divided into 10 moments. With the starting point of each joint as the center, the joint length is the intersection of the radius and the fish wave equation at that moment. The coordinate position of the six joint starting points at each moment was obtained (In this coordinate system, the front end of the fish head is the coordinate origin, the forward direction of the swimming direction is the negative direction of the x axis, and the direction of the lateral displacement is the y direction, dynamic body coordinate system). The following Table 1 is the calculated coordinates of each joint starting point at each moment.

The swing movement law of each joint can be obtained by using curve fitting toolbox cftool of Matlab, As shown in Figure 1.

Fluent Flow Field Simulation and Kinetic Analysis of Robot Fish

The Cartesian coordinate system was established: the negative direction of the x-axis was the advancing direction of the fish, the z-axis was the transverse displacement direction of the fish, and the y-axis was the longitudinal displacement direction of the fish. Ignoring the force in the y-axis direction of the fish (gravity, buoyancy, lift due to pectoral fins, and others), and considering only the x-z plane force on the body during the advancing process, the fish is only subjected to two forces: the resulting thrust due to swing water and the resistance in the forward movement.

According to the "large-amplitude elongated-body theory of fish locomotion,” the fish's thrust is the component of the hydrodynamic force generated by the fish's swing in the forward direction [4]. However, due to the complexity of the flow field, there is no standard and uniform thrust formula. It is difficult to obtain relatively accurate simulation results. Therefore, the fluent flow field analysis method was used. The dynamic grid technology was used for the meshing of the fluid around a certain area of the fish, and turbulence model was used to obtain the solution. Hence, the forces exerted by the surrounding fluid on the fish body during the steady state forward movement can be obtained by simulation (surface pressure and viscous force). Further, the thrust formula can be obtained.

As shown in Figure 2a is a three-dimensional model of the extracted robot fish, Figure 2b is a plane view intercepted at y = 0 after the static grid division is completed. In the choice of computing domain, on the one hand, the length of the calculation area must be large enough to meet the needs of the continuous advance of the robot fish; on the other hand, the smaller calculation area can improve the calculation efficiency. After several trials, the calculation domain was selected as rectangular, the length, width, and height were selected to be 10 times of the corresponding maximum size of the biomimetic robot fish.

In the above model, the triangular/tetrahedral mesh is used to divide the area/body domain of the region. Since the grid boundary changes with time according to the variation trend of the fish wave equation, it is necessary to define the grid as the dynamic grid in the computing domain. Also, the spring smooth method and the local mesh reconstruction method were used to ensure the quality of dynamic grid. The following is the dynamic grid parameters: the spring coefficient is 0.2, the boundary node relaxation factor is 0.4, the maximum length of the local reconstruction is 0.021, the minimum length is 0.0004, the maximum cell skew rate is 0.76, and the size function is activated, and the remaining values are default.

On the boundary condition, the boundary conditions of the six surfaces of the rectangular parallelepiped and the surfaces of the biomimetic robot fish are all non-slip condition on the wall surface, but the dynamic region is defined by the UDF (User Defined Functions) function. For the robot fish model, DEFINE-GRID-MOTION Macro was used to define the movement of fish. Take the movement at the end of the joint as an example; the custom program of this dynamic region can be defined as follows:

#include "udf.h”

#define PI 3.1415926

DEFINE_GRID_MOTION (guanjie6,domain,dt,time,dtime)

{

Thread *tf = DT_THREAD(dt);

face_t f;

Node *v;

real NV_VEC(omega), NV_VEC(axis), NV_VEC(dx);

real NV_VEC(origin), NVVEC(rvec);

int n;

SET_DEFORMING_THREAD_FLAG(THREAD_T0(tf));

begin_f_loop(f,tf)

{

f_node_loop(f,tf,n)

{

v = F_NODE(f,tf,n);

if (NODE_POS_NEED_UPDATE (v))

{

NODE_POS_UPDATED(v);

NV_S(omega, =, 0.0);

NV_D(axis, =, 0.0, 1.0, 0.0);

origin[0] = 0.213;

origin[1] = NODE_Y(v);

origin[2] = 0;

omega[1] = 0.2587*PI*PI*sin(4*PI*time-0.3848+PI/2);

NV_VV(rvec, =, NODE_COORD(v), -, origin);

NV_CROSS(dx, omega, rvec);

NV_S(dx, *=, dtime);

NV_V(NODE_COORD(v), +=, dx);

}

end_f_loop(f,t)

}

In the choice of the calculation model, because the robot fish movement belongs to the large Reynolds number movement mode, therefore the transient three-dimensional turbulence model was used for solution. Due to the wide range of applications (especially the situation of high Reynolds number), economic, reasonable accuracy, and other characteristics, k-epsilon turbulence model was used.

Since the surface of the fish is divided into seven parts according to the joint position when drawing the three-dimensional map, the force component of the wall area of the fish body in the specified direction (the advancing direction of the robot fish) can be obtained in the processing of the results.

In Table 2, x(p) represents the fish surface pressure value, and x (v) represents the viscous force value. It can be seen from Figure 3 and Table 2 that the positive and negative surface pressure of the fish in the z-direction is cancelled in one cycle, and the viscous force is usually in the resistance state, and its value is small relative to the surface pressure, the difference is 2-3 orders of magnitude. Consequently, it can be neglected when fitting the thrust formula. The thrust value of each part of the fish body can be obtained as follows (N):

Fish front end: T0 = 0.1094sin(4nt + 5.098)-0.0410 ;

Joint 1: T1 = 0.0875sin (8πt - 0.3221)-0.0477 ;

Joint 2: T2 = 0.1639sin(4πt + 0.9005)-0.0835 ;

Joint 3: T3 = 0.2328sin(4πt + 0.2085)-0.1045 ;

Joint 4: T4 = 0.2410sin(4πt-0.3612)-0.1170 ;

Joint 5: T5 = 0.2247sin(4πt + 5.575)-0.1360 ;

Joint 6: T6 = 1.638sin (8πt +1.881)-1.571 .

The influence of viscous resistance (frictional resistance) is small and negligible in the case of fish swimming locomotion that has a large Reynolds number (As demonstrated in Table 2). The pressure resistance, which played a major role, can be calculated by the standard Newton equation [5]:

Dp = 0.5CpSpU 2ρ

WhereCp is the resistance coefficient, usually 1.2; Sp is the effective area, in the practical application ( is the displacement) can be used as the effective area of action as the fish moves in the water, because the density of fish and water are basically the same, Sp =(m/ρ)2/3 [6].U is the average speed of swimming locomotion; ρ is the density of water.

It can be concluded that the rear joint (caudal fin) provides the maximum thrust, accounting for more than 80% of the total thrust, so the caudal fin drive mode is selected; electromagnetic drive has advantages of compact structure, pollution-free, high reliability, and large driving force, and accurate process control can be achieved. The cyclic oscillation-driving mode is selected to be the electromagnetic drive.

Research on Control Signal of Electromagnetic Drive Robot Fish

Signal transformation

The standard electromagnetic coil current waveform was digitized, and then through the MCU processing the signal was transferred to the DAC module for digital-analog transformation to reproduce the analog control signal. The realization of the control signal was the acquisition, storage, and reproduction of the standard control waveform, which was used to drive the electromagnetic coil that further drove the caudal fin movement. The basic flow of the control drive circuit is shown in Figure 4. The components used in the Multisim simulation are described in Table 3.

Simulation circuit has been completed. A waveform generator and an oscilloscope were used as waveform generation and acquisition circuit (sinusoidal wave as an example) in Multisim. The amplitude was set to 255V, the cycle was 1s, as shown in Figure 4 & 5.

After the standard sine wave was obtained, the collection and digital discretization was required. As shown in Figure 6, the oscilloscope in Multisim provided the function of waveform sampling. Click "Save” on the oscilloscope in the figure, the settings of the sampling time interval and number of sampling points of the oscilloscope will be displayed. To ensure signal accuracy, 120 points were sampled in a cycle. The sampled data was set as a binary output text file.

After the sampling data was obtained, start the singlechip microcomputer-programming interface of the completed circuit simulation platform. The collected discrete value's output was through the single-chip microcomputer one by one cyclically. The programming interface and the program are shown in Figure 7.

After programming, the program is successfully downloaded to the single-chip microcomputer, click on the "start simulation,” back to Figure 5 interface and open the two oscilloscopes in the figure, waveform as shown in Figure 8 can be observed (Figure 9).

Oscilloscope XSC2 displays the 8-bit DAC direct output of the control signal; the oscilloscope XSC1 displays the output control signal of the DAC output after passing through the polarity converter. For the frequency of the waveform, delay value in the parentheses of the delay statement in the program can be adjusted. When calling the delay program, the singlechip microcomputer will adjust the output hold time of each digit according to the delay value in parentheses, and thus the frequency of the whole waveform can be adjusted. The amplitude needs to be matched based on the characteristics of the power amplifier module in the actual circuit and the DAC module in the actual circuit has a reference voltage setting [7-10].

The simulation program as shown in Figure 3 & 4 was then transplanted to the Keil single-chip microcomputer programming software. After the program was successfully compiled, the crystal resonator frequency was set to be the crystal resonating frequency of the single-chip microcomputer, which was 12Mhz. The Keil debugging interface was started, break points out of the cycles were set, and the delay parameter in the brackets of the "delay ()” statement was adjusted continuously until the output cycle reached 1s, namely, control signal so that the caudal fin swing cycle was 1Hz. The debugging interface of Keil as shown in Figure 4 & 5 was entered. In every click of "run”, the program executed a cycle, and "sec” increased accordingly, and the added value was the time required to output a complete waveform [11-14].

After debugging, the program was downloaded to the single-chip microcomputer. The DAC output control waveform can be observed by connecting the DAC module output with the oscilloscope. It can be seen that the actual circuit output control signal and the simulation circuit output signal were exactly the same.

Signal adjustment

The DAC module was connected with the STC89C52 development board with the downloaded test program. The output of the DAC module was connected to the input of the power amplifier module. The DAC module and the power amplifier module shared the DC ± 15V power supply. The overall circuit of the control platform is shown in Figure 10. The "delay ()” delay parameter was modified in the Keil programming debugging interface, the signal frequency was the electromagnetic coil operating frequency.

Experimental Study

Development of experimental prototype of robot fish

The external fish body shape curve was imported into the Pro/E three-dimensional modelling software; closed fish surface shape can be obtained by the use of "variable crosssection scanning” tool. Then, "thickening” operation was performed on the surface, with the set thickness of 1.8 mm. The closed fish body shell was thus obtained. Considering the fish body assembly, weight allocation, fish body was designed into a removable form, as shown in Figure 11.

After modeling, the fish body model was obtained with the use of precision 3D printing, the printing material was resin. The polypropylene caudal fins were tailored to thin caudal fins according to the caudal fins of the actual tuna fish, and the flexible caudal fins were then fixed on the connecting parts as shown in Figure 12. Underwater experiments are shown in Figure 13.

Electromagnetic drive robot fish underwater experiment

Fish body linear curve swimming experiment: In the experiment, the swing frequency of the fish when swimming along straight lines is set within 1-8Hz. The amplitude of the drive voltage is set between 2-6V. After adjustment of the fish weight, the control signal waveform acquisition was used to generate 1-8Hz sinusoidal signal, the signal waveform are shown in Figure 14. By adjusting the frequency of the control signal through adjusting the delay time of the sinusoidal output program, the obtained experimental data is recorded in Table 4, the distribution diagram of velocity - frequency under different voltage amplitude is shown in Figure 15.

The above data is shown in Figure 15. The greater the amplitude of the driving voltage signal, the faster the fish swims. At different drive voltage amplitude, the best swing frequency was basically the same; at about 2Hz, the fastest swimming speed was achieved. As the high drive voltage heats up the coil, it is desirable to operate at low voltage, hence at 2 V voltage, 2Hz, the swimming speed of 81.74mm/s was selected as the best electromagnetic drive control signal [15-18].

Conclusion

Based on the actual physical appearance of the tuna and the wave equation derived from observing its swimming locomotion, the motion law of each joint of the robot fish is obtained through the discrete fitting method, and the motion law is utilized as the basis of the control signal of the electromagnetically driven robot fish. With the utilization of the fluent flow field analysis software, a dynamic grid technique was used to mesh the fluid around the fish body. By analyzing the surface pressure value of the simulated fish body in the steady state of forward movement, the thrust values of each part of the fish body were obtained. Hence, the electromagnetic-driven caudal fin robot fish was determined as the optimal design. Based on the idea of digital-analog conversion, the drive control signal waveform was first digitally discretized. Then, it was converted through the C51 single-chip microcomputer and DAC digital-to-analog converter. Finally, the simulated amplified analog output was obtained through the OPA544 op amp chip in accordance with the waveform of the control signal. Electromagnetic coil drive signal with variable frequency and voltage was thus achieved. An electromagnetic driven caudal fin robot fish prototype was successfully produced, a number of robot fish underwater experiments were designed, and a series of experimental data was obtained. Based on the linear swimming experiment of the robot fish prototype, it was obtained that the optimal value of the electromagnetic drive signal is 2V, 2HZ when the electromagnetic driven caudal fin robot fish was at a relatively high speed. Based on this, the next steps of the study lays on the following two aspects:

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juniperpublishers-etoaj · 6 years ago

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Juniper Publishers - Open Access Journal of Engineering Technology

Removal of the Power Line Interference from ECG Signal Using Different Adaptive Filter Algorithms and Cubic Spline Interpolation for Missing Data Points of ECG in Telecardiology System

Authored by : Shekh Md Mahmudul Islam

Abstract

Maintaining one's health is a fundamental human right although one billion people do not have access to quality healthcare services. Telemedicine can help medical facilities reach their previously inaccessible target community. The Telecardiology system designed and implemented in this research work is based on the use of local market electronics. In this research work we tested three algorithms named as LMS (Least Mean Square), NLMS (Normalized Mean Square), and RLS (Recursive Least Square). We have used 250 mV amplitude ECG signal from MIT- BIH database, 25mV (10% of original ECG signal) of random noise, white Gaussian noise and 100mV (40% of original ECG signal) of 50 Hz power signal noise is added with ECG signal in different combinations and Adaptive filter with three different algorithms have been used to reduce the noise that is added during transmission through the telemedicine system. Normalized mean square error was calculated and our MATLAB simulation results suggest that RLS performs better than other two algorithms to reduce the noise from ECG. During analog transmission of ECG signal through existing Telecommunication network some data points may be lost and we have theoretically used Cubic Spline interpolation to regain missing data points. We have taken 5000 data points of ECG Signal from MIT -BIH database. The normalized mean square error was calculated for regaining missing data points of the ECG signal and it was very less in all the conditions. Cubic Spline Interpolation function on MATLAB platform could be a good solution for regaining missing data points of original ECG signal sent through our proposed Telecardiology system but practically it may not be efficient one.

Keywords: Telemedicine; Power line interference (PLI); ECG; Adaptive filter; LMS; NLMS; RLS

Abbreviations: EMG: Electromyography; ECG: Electrocardiogram; EEG: Electroencephalogram; NLMS: Normalized Least Mean Square; RLS: Recursive Least Square; SD: Storage Card

Introduction

The ECG signal measured with an electrocardiograph, is a biomedical electrical signal occurring on the surface of the body related to the contraction and relaxation of the heart. This signal represents an extremely important measure for doctors as it provides vital information about a patient cardiac condition and general health. Generally the frequency band of the ECG signal is 0.05 to 100 Hz [1]. Inside the heart there is a specialized electrical conduction system that ensures the heart to relax and contracts in a coordinated and effective fashion. ECG recordings may have common artifacts with noise caused by factors such as power line interference, external electromagnetic field, random body movements, other biomedical signals and respiration. Different types of digital filters may be used to remove signal components from unwanted frequency ranges [2].

The Power line interference 50/60 Hz is the source of interference in bio potential measurement and it corrupt the biomedical signal's recordings such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG) which are extremely important for the diagnosis of patients. It is hard to find out the problem because the frequency range of ECG signal is nearly same as the frequency of power line interference. The QRS complex is a waveform which is most important in all ECG's waveforms and it comes into view in usual and unusual signals in ECG [3]. As it is difficult to apply filters with fixed coefficients to reduce biomedical signal noises because human behaviour is not exact depending on time, an adaptive filtering technique is required to overcome the problem. Adaptive filer is designed using different algorithms such as least mean square (LMS), Normalized least mean square (NLMS), Recursive least square (RLS) [4]. Least square algorithms aims at minimization of the sum of the squares of the difference between the desired signal and model filter output when new samples of the incoming signals are received at every iteration, the solution for the least square problem can be computed in recursive form resulting in the recursive least square algorithm. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts so as to present an ECG that facilitates an easy and accurate interpretation.

The basic idea for adaptive filter is to predict the amount of noise in the primary signal and then subtract noise from it. In this research work a Telecardiology system has been designed and implemented using instrumentation amplifier, band pass filter and Arduino interfacing between Smartphone and Arduino board. First of all raw ECG signal has been amplified and filtered by Band pass filter. Analog signal has been digitized using Arduino board and then interfacing between Arduino board and smart phone has been implemented and Digitized value of analog signal has been sent from Arduino board to smart phone and digitized value of analog signal has been stored in SD storage card of smart phone. Using Bluetooth or existing Telecommunication Network. As sinusoidal signals are known to be corrupted during transmission it is expected that similarly an ECG signal will be corrupted.

We have therefore designed an adaptive filter with three different algorithms and simulated in MATLAB platform to compare the performance of denoising of ECG signal. During transmission of ECG signals through existing Telecommunication networks some data pints may be lost. In this research work we have used cubic spline interpolation to regain missing data points. If more data points are missing then reconstruction of an ECG signal becomes impossible and doctor can not accurately interpret a patient's ECG in an efficient manner. Cubic spline interpolation has been implemented for various missing data points of original ECG signal taken from MIT-BIH database. The normalized mean square error of cubic spline interpolation was very low. Cubic Spline interpolation in Matlab platform could be a better solution for regaining missing data points of ECG signal theoretically.

Related Works and Literature Review

The extraction of high-resolution ECG signals from recordings contaminated with system noise is an important issue to investigate in Telecardiology system. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts, so as to present an ECG that facilitates easy and accurate interpretation.

The work of this research paper is the development of our previous research work "Denoising ECG Signal using Different Adaptive Filter Algorithms and Cubic Spline Interpolation for Missing data points of ECG in Telecardiology System” [5]. Many approaches have been reported in the literature to address ECG enhancement using adaptive filters [6-9], which permit to detect time varying potentials and to track the dynamic variations of the signals. In Md. Maniruzzaman et al [7,10,11] proposed wavelet packet transform, Least-Mean-Square (LMS) normalized least-mean-square (NLMS) and recursive-least-square (RLS), and the results are compared with a conventional notch filter both in time and frequency domain. In these papers, power line interference noise is denoised by NLMS or LMS or RLS algorithms and performed by MTLAB or LABVIEW. But in our research work we have developed our previous work. We denoised ECG signal by removing power line interference, Random noise and White Gaussian noise. In our previous research paper [12] we denoised ECG signal from random noise and white Gaussian noise. In our present research work we have added power line interference with Pure ECG signal individually and added in mixed of power line interference, random noise and white Gaussian noise in different combinations. Finally in our research work we have used cubic spline interpolation for regaining missing data points of ECG signal sent through telecommunication network.

There are certain clinical applications of ECG signal processing that require adaptive filters with large number of taps. In such applications the conventional LMS algorithm is computationally expensive to implement The LMS algorithm and NLMS (normalized LMS) algorithm require few computations, and are, therefore, widely applied for acoustic echo cancellers. However, there is a strong need to improve the convergence speed of the LMS and NLMS algorithms. The RLS (recursive least-squares) algorithm, whose convergence does not depend on the input signal, is the fastest of all conventional adaptive algorithms. The major drawback of the RLS algorithm is its large computational cost. However, fast (small computational cost) RLS algorithms have been studied recently In this paper we aim to obtain a comparative study of faster algorithm by incorporating knowledge of the room impulse response into the RLS algorithm. Unlike the NLMS and projection algorithms, the RLS algorithm does not have a scalar step size.

Therefore, the variation characteristics of an ECG signal cannot be reflected directly in the RLS algorithm. Here, we study the RLS algorithm from the viewpoint of the adaptive filter because

a. The RLS algorithm can be regarded as a special version of the adaptive filter and

b. Each parameter of the adaptive filter has physical meaning.

Computer simulations demonstrate that this algorithm converges twice as fast as the conventional algorithm. These characteristics may plays a vital role in biotelemetry, where extraction of noise free ECG signal for efficient diagnosis and fast computations, high data transfer rate are needed to avoid overlapping of pulses and to resolve ambiguities. To the best of our knowledge, transform domain has not been considered previously within the context of filtering artifacts in ECG signals.

In this paper we compare the performances of LMS, NLMS and RLS algorithms to remove the artifacts from ECG. This algorithm enjoys less computational complexity and good filtering capability. To study the performance of the algorithms to effectively remove the noise from the ECG signal, we carried out simulations on MIT-BIH database. During transmission of ECG signal through existing Telecommunication network it may be corrupted or some data points may be lost. Linear Spline interpolation was popular method for regaining missing data points of ECG signal [13]. Cubic Spline interpolation has gained popularity very recently [6]. In our previous research work we used cubic spline interpolation. The development of our previous research work i.e., in the present research work, in this research paper we have also used cubic spline interpolation for regaining missing data points of ECG signal sent through telecommunication network.

Adaptive Filter Algorithms

In this research work a Telecardiology system has been implemented and proposed for sending ECG signal through smart phone (Figure 1).

The raw ECG signal will be taken from patient electrode and passed through instrumentation amplifier and band pass filter to amplify the signal and to reduce the noise coming from electrodes. Then that amplified and filtered analog ECG signal will be converted into digital signal by using Arduino AVR microcontroller based system. Then that digital value of ECG signal will be sent to smart phone by using Arduino interfacing with smart phone and digitized signal values will be sent to smart phone SD card. After that digital value will be sent to another smart phone by using Bluetooth technology. Digitized ECG value will be received to smart phone via Bluetooth .During transmission of ECG signal through Telecommunication network it may be corrupted by random noise or white Gaussian noise of the network. Adaptive filter using different algorithms have been used to reduce noise of the transmitted ECG signal. A MATLAB coding has been done to reduce the noise of the ECG signal and for reducing noise of digitized ECG signal, transmitted noisy ECG signal needs to be loaded in MATLAB and then it is filtered suing adaptive filter with different algorithms and performances of different algorithms are measured based on their de-noising capabilities. During transmission of ECG signal some data points may be missing and MATLAB spline interpolation algorithm will get them back so that ECG signal can be transmitted reliably (Figure 2).

Least Mean Square (LMS), Normalized Mean Square Algorithm (NLMS) and Recursive Least Square Algorithm (RLS) has been designed and implemented for denoising ECG signal in MATLAB platform [4,12-14]. Cubic Spline Interpolation has been used for regaining missing data points of ECG signal during transmission through existing Telecommunication network. The normalized mean square error was calculated for regaining missing data points of ECG signal and it was very less and so Cubic spline interpolation could be a better solution in MATLAB platform for regaining missing data points of ECG signal.

Result

In this work we have taken pure ECG signal from MIT-BIH database. The amplitude of our taken ECG signal was 250 mV which is amplified from.5mV (2 % of original ECG signal), 10 mV (4% of original ECG) 15mV (6% of original ECG), 20 mV (8% of original ECG signal) and 25mV (10% of original ECG signal) of random noise and white Gaussian noise is added with ECG signal. Three different algorithms of Adaptive filter were implemented and tested their performances of denosing ECG signal. We have taken ECG signal with 250 mV amplitude and 5000 samples were taken from MIT-BIH database (Figure 3). In our simulation work we have denoised 100mV of 50 Hz power signal noise.

Recursive Least Square (RLS) Algorithms

We have taken 5000 data points of ECG signal from MIT- BIH database. In our simulation 11data points (from 689 to 699 of original data points of ECG), 201 data points (from 800 to 1000 of original data points of ECG), 300 data points (from 1600 to 1900 of original data points of ECG), 500 data points (from 2000 to 5000) and 6 data points (from 4095 to 5000) are made zero and cubic spline interpolation function was called in MATLAB platform and it could regain the original data points of ECG signal (Figure 15-20). The MATLAB coding result of Spline Interpolation is given below Table 2

The above simulation result suggests that Recursive algorithms. RLS could be the best option for Telemedicine system Least Square algorithm (RLS) performs better than other two to denoise ECG signal during transmission (Table 1).

Normalized mean square error calculation suggests that Cubic Spline performs satisfactorily for regaining missing data points of ECG signal.

Conclusion

During transmission of ECG signal it may be corrupted due to random noise and Gaussian noise. So we have tested the performances of LMS, NLMS and RLS algorithm of adaptive filter. Our simulation result suggest that RLS could be the best option for recovering ECG signal or denoising EEG signal during transmission through Telemedicine system.

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programmingsolver · 5 years ago

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Lab 2 DC Motor Characterization Solution

Objective

The primary focus of this experiment is to write a MATLAB script to automate the sweep of a DC voltage signal from the arbitrary waveform generator (AWG) which is supplied to an Arduino and used to generate a PWM signal of proportional duty cycle, which causes a wheel attached to a DC Motor to rotate. The goal of this experiment is to generate, using automation, the RPM vs. duty cycle…

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myprogrammingsolver · 5 years ago

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Lab 2 DC Motor Characterization Solution

Objective

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zanghonghuaer-blog · 7 years ago

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