NAVILIGHT ‘A GPS based Embedded System for Backtracking in Unfamiliar Environments’

ABSTRACT

             NAVILIGHT is an embedded system based, innovative flashlight used for navigation.  Its primary function is to record the user's starting point, trace the path taken and display the path back to the user.  This system uses pin-point precision if the user is receiving a GPS signal.  If the user does not receive this signal, the system uses dead-reckoning, where the NAVILIGHT uses software based backtracking to orient the user back to his/her starting point. 

The NAVILIGHT is a flashlight with special features primarily for military use. One of the two primary uses it has is a backtracking system that directs the user back through a maze from where he first began. An example can be if a person had to retrieve something from a dark area with numerous paths such as a cave.  The second use is a GPS system that shows the shortest path back to the given starting point. An example can be if a person were to travel across a forest at night, he can avoid misdirection on the way back by using the GPS system.

The NAVILIGHT will be controlled mainly by an 80C188EB CPU with the first primary function programmed in the C language.  This program will keep track of the direction taken and display the way back on the LCD display.  The embedded system will control the second function of the NAVILIGHT.  The embedded system interfaces with the GPS receiver and outputs the quickest direction or way back on the LCD display. It is also assumed that the user will operate the GPS receiver outside and will be able to acquire signals from at least three satellites.

            The NAVILIGHT also has minor functions that will make it as efficient as possible for the life of it's battery. It will have a sensor that detects the intensity of the light surrounding the NAVILIGHT and will adjust the power to the light bulbs as it sees fit. Depending on the light intensity being low or high, the sensor will increase or decrease the brightness of the bulbs. The NAVILIGHT will shut off automatically if it has been left idle over a period of time. And when there is a need to replace or recharge the battery, the NAVILIGHT will have a low battery indicator.

This project is important because it is a small portable device that is easily controllable. The possibilities for the use of the NAVILIGHT are countless. When the time comes for the NAVILIGHT to be used to its full potential it will have the ability to save lives.

Introduction:

The NAVILIGHT is a flashlight with special features primarily for military use. One of the two primary uses it has is a backtracking system that directs the user back through a maze from where he first began. An example can be if a person had to retrieve something from a dark area with numerous paths such as a cave.  The second use is a GPS system that shows the shortest path back to the given starting point. An example can be if a person were to travel across a forest at night, he can avoid misdirection on the way back by using the GPS system.

The NAVILIGHT also has minor functions that will make it as efficient as possible for the life of it's battery. It will have a sensor that detects the intensity of the light surrounding the NAVILIGHT and will adjust the power to the light bulbs as it sees fit. Depending on the light intensity being low or high, the sensor will increase or decrease the brightness of the bulbs. The NAVILIGHT will shut off automatically if it has been left idle over a period of time. And when there is a need to replace or recharge the battery, the NAVILIGHT will have a low battery indicator.

This project is important because it is a small portable device that is easily controllable. The possibilities for the use of the NAVILIGHT are countless. When the time comes for the NAVILIGHT to be used to its full potential it will have the ability to save lives.

Technical Description:

The NAVILIGHT will be controlled mainly by an 80C188EB CPU with the first primary function programmed in the C language.  This program will keep track of the direction taken and display the way back on the LCD display.  The embedded system will control the second function of the NAVILIGHT.  The embedded system interfaces with the GPS receiver and outputs the quickest direction or way back on the LCD display. It is also assumed that the user will operate the GPS receiver outside and will be able to acquire signals from at least three satellites.

             The intensity of the light will be controlled by a sensor, which supplies more voltage to the light bulbs as it gets darker or less voltage to make it dimmer as more light is present.  One of the minor functions includes a low battery indicator, which will be connected to a LED and will only be turned on when the battery life is low.

The other minor function is the auto shut off which is also controlled by a sensor. This sensor will turn the NAVILIGHT off if there is no activity for a certain amount of time.

The NAVILIGHT is designed to operate in normal weather conditions.

FUNCTIONAL REQUIREMENTS:

Vector 2X Digital Compass

MAGNETO-INDUCTIVE SENSORS

These sensors change inductance with different applied magnetic fields.  It employs a single solenoid winding for each axis.  The frequency of the oscillation changes with the magnetic field.  The output of the sensor circuit is inherently digital, and can be fed directly into a microprocessor.

The Earth's magnetic field is three-dimensional. In the Northern hemisphere the X-Y component of magnetic field vector lies parallel to the Earth's surface and points towards the magnetic north pole, but the majority of the Earth's field vector lies along the Z axis, which points into the ground. Because of this, compasses need to be held level to the Earth's surface in order to be accurate.

The sensors are assumed to be at right angles with respect to each other. Upon initiation of the calibration sequence take two readings from the compass at 180 degrees apart from each other.

We chose the Vector 2X Sensor, shown below, because of its ease of calibration and low power consumption (less than 10 milliamp draw as compared to the 30 milliamp draw for Dinsmore's sensor).  The module itself is 1.5" X 1.3" X 0.3". The pins are on standard

0.1  spacing.  The Vector 2X (V2X) compass delivers high accuracy, low power consumption, and low cost in a small package.  The module delivers 2-degree accuracy with 1-degree resolution, outperforming all compasses in its price range.  In the end, the Vector 2X (V2X) was by far the best choice. The V2X is a 2-axis magnetometer that measures the magnetic field in a single plane. This plane is the plane created by its two sensors, which are perpendicular to each other on the board.

80C188EB CPU (MICRO-CONTROLLER):

The 80C186EB is a second generation CHMOS High-Integration microprocessor. It has features that are new to the 80C186 family and include a STATIC CPU core, an enhanced Chip Select decode unit, two independent Serial Channels, I/O ports, and the capability of Idle or Powerdown low power modes. This micro-controller is having a Turbo Debugger. So the codings can be written in C Language.

M12 Oncore GPS Receiver (Antenna)

 

       Motorola M12 Oncore Global Positioning System Receiver

The M12 Oncore adds more features at both a lower cost and smaller size.  This GPS unit measures 40mm X 60mm X 10mm and draws between 220 milliamps and 250 milliamps.  The M12 can be easily interfaced to a serial receiver.  Once again, this met our requirement of low power consumption. GPS Receiver is operated outside and is able to aquire signals from atleast three satellites.

MPC 201 LCD & Keypad Interface

            LCD interface is required to produce output to the user.  The MPC201 LCD and Keypad Interface features alphanumeric and sub-VGA graphic LCD display interface, pinouts for popular LCD panels, and PC/104 format so it can attach directly to our computer.  This unit was also chosen because it is directly compatible to the SBC1190 microcontroller and the

           The MPC201 LCD and keypad interface allows direct connection to alphanumeric LCD displays (i.e. 2 line by 40 character, etc.). In addition, sub-VGA graphic LCD displays can also be driven if they employ an 8-bit parallel interface. Two LCD connectors are supplied, one normal and one with mirror image pinout. This provides the option of cabling into the top or bottom of the LCD units in tight places, for instance against front panels. The keypad scanner drives rows of switches and senses inputs from columns. Key codes reflect the intersection of row and column. Debounce is provided on-board, so any type of switch can be used in connecting a row line to a column line. Two connectors are provided, each for up to a 4 x 5 matrix of 20 keys.

LIGHT SENSOR

A Smart Q Light Sensor uses a photodiode which produces a voltage proportional to light intensity. It is sensitive to light in the range from 350nm to 700nm. The sensor has a built in infrared rejection filter, giving it a spectral response similar to that of the human eye.

         The Smart Q Light sensors is having high accuracy, precision and consistency. They are supplied calibrated and the stored calibration (in Lux) is automatically loaded when the light sensor is connected.

APPROACH AND DESIGN:

Compass Calibration

Let (Xe,Ye) represent the Earth's magnetic field in any given direction as measured with no interfering magnetic field. The host system will superimpose its additive interfering field, which we can call (Xo,Yo), on top of (Xe,Ye). Let (Xn,Yn) represent the field of a given heading as measured by the sensors in the operating environment, such that it contains the interfering fields as generated by the host system.

Xn = Xe + Xo

Yn = Ye + Yo

So:

Xe = Xn – Xo

Ye = Yn – Yo

(Xe,Ye) represents the true Earth's magnetic field – which is the field that the correct heading can be computed from. Therefore, the offset value (Xo,Yo) needs to be calculated and subtracted from (Xn,Yn) in order to obtain the correct heading information.

Because the compass is fixed with respect to the host system, the readings (X₁,Y₁) and

(X₂,Y₂) taken during calibration will both contain the same (Xo,Yo) values. Since the calibration points are 180° apart, the Earth's magnetic field contained within (X₁,Y₁) and (X₂,Y₂) will be equal but opposite in sign. So we can write:

X₁ = Xe + Xo

Y₁ = Ye + Yo

X₂ = -Xe + Xo

Y₂ = -Ye + Yo

Thus if we add the appropriate equations above and solve for Xo and Yo we find

Xo = (X₁ + X₂)/2

Yo = (Y₁ + Y₂)/2

Xo and Yo are generally constant values that can should be stored and subtracted for each

heading computation performed. Their values will change if the magnetic field of the host

system either increases through magnetization or decreases through demagnetization.

Heading is calculated from Xe and Ye, as follows:

Ae = arctangent(Ye / Xe)

Depending upon the arctangent function implemented, Ae will need to be map into the

correct quadrant. The (Xe,Ye) values correspond to the following quadrants:

  +X, +Y à Ae is between 0° and 90°

-X, +Y à Ae is between 90° and 180°

-X, -Y à Ae is between 180° and 270°     +X, -Y à Ae is between 270° and 360°

GLOBAL POSITIONING SYSTEM:

The Global Positioning System (GPS) is a worldwide radio-navigation system formed from a constellation of 24 satelllites and their ground stations

The 24 GPS satellites (21 active, 3 spare) are in orbit at 10,600 miles above the earth. The satellites are spaced so that from any point on earth, four satellites will be above the horizon. Each satellite contains a computer, an atomic clock, and a radio. With an understanding of its own orbit and the clock, the satellite continually broadcasts its changing position and time. On the ground, any GPS receiver contains a computer that "triangulates" its own position by getting bearings from three of the four satellites. The result is provided in the form of a geographic position - longitude and latitude - to, for most receivers, within a few meters.

EMBEDDED SYSTEM:

        Embedded systems involve the miniaturization of electronics so that it can fit into compact devices. It also deals with the software required to drive the associated hardware. Embedded systems contain programmed instructions running via processor chips. We can define as a system where a computing device I embedded into a non-computing device meant for doing some computations. These tasks may range from acquiring (or) transferring data about the work done by mother device to displaying information (or) controlling mother device. Embedded system contains a processor, memory, I/O ports and sensors and many more. 

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DIGITAL IMAGE PROCESSING Retinal Image Analysis to Detect and Quantify Lesions Associated with Diabetic Retinopathy

Retinal Image Analysis to Detect and Quantify Lesions Associated                 with Diabetic Retinopathy

Abstract—An automatic method to detect hard exudates, lesion associated with diabetic retinopathy, is proposed. The algorithm found on their color, using a statistical classification, and their sharp edges, applying an edge detector, to localize them. A sensitivity of 79.62%  with a mean number of  3 false positives per image is obtained in a database of 20 retinal image with variable color, brightness and quality. In that way, we evaluate the robustness of the method in order to make adequate to a clinical environment. Further efforts will be done to improve its performance.Keywords— Diabetic retinopathy, hard exudates, image processing, retinal images.

I.  INTRODUCTION

DIABETIC retinopathy (DR) is a severe eye disease at affects many diabetic patients. It remains one of the leading causes of blindness and vision defects in developed countries. There exist effective treatments that inhibit the progression of the disease provided that it would-be diagnosed early enough. But DR is usually asymptomatic in its beginning, so diabetic patients do not undergo any eye examination until it is already too late for an optimal treatment and severe retinal damages have been caused. Regular retinal examinations for diabetic patient’s guarantee an early detection of DR reducing significantly the incidence of blindness cases. Because of great prevalence of diabetes, mass screening is time consuming and requires many trained graders to examine the fundus photographs searching retinal lesions. A reliable method for automated assessment of the presence of lesions in fundus images will be a valuable tool in assisting the limited number of professional and reducing the examination time. This paper focuses only in the automatic detection of one of the lesions associated with DR: hard exudates. They usually appear in the fundus photographs as small yellow-white patches with sharp margins and different shapes. Among lesions caused by DR, exudates are one of the mostoccurring early lesions [1]. So the detection and quantification of them will contribute to the mass screening and assessing of DR.Some investigations in the  past have identified retinal exudates in fundus images based on their gray level [2], [3],their high contrast [4-7] or their color [8],[9]. Because the brightness, contrast and color of exudates vary a lot among different patients and, therefore, different photographs, these method would not work in all the images used in clinical environment. The main improvement introduced by the technique described in this paper is its robustness to the variable appearance of retinal fundus images to obtain an optimal performance in all types of images, in contrast to these other approaches.

II. METHODOLOGY

The method attempts to detect hard exudates using two features of this lesion: its color and its sharp edges. So hard exudates extraction is carried out in the following stages:

·         Detection of the optic disk and the blood vessels

·         Detection of yellowish objects in the image.

·         Detection of objects in the image with sharp edges.

·         Combination of the previous steps to detect Yellowish objects with sharp edges.

A.    Detection of the optic disk and the blood vessels.

 In order to localize these main features, we build on some works developed by other authors. We follow the method proposed in [7] to detect the center of the optic disk (OD). This method determined a number of candidate regions with the brightest pixels in intensity image. Then the PCA based model approach is applied to the candidate regions to give the final location of the OD. We also detect the disk boundary using a snake driven by an external fieldv(x,y)=[u(x,y),v(x,y)] called Gradient Vector Flow (GVF) [10] over the image

  (1)In this work the snake is initialized automatically as a circle placed in the center of the OD localized previously. The blood vessels are segmented applying the matchedfilter method described in [11] to enhance blood vessels and thresholding the image obtained.

B.  Detection of yellowish objects

The detection of this kind of objects is carried outperforming color segmentation based on the statistical classification method described in [8] and [9]. This method found on the fact that if a group of features can be defined so that the objects in an image map to nonintersecting classes in the feature space, then we can easily identify different objects classifying them into corresponding classes by a certain rule. For our algorithm, we have to discriminate between two classes: yellowish objects and background, which are perfectly characterized using only three color features (the luminance of the pixels in each plane (R,G,B)).  In order to map all the pixels in the image to one of these classes, an appropriate discriminant function has to be defined. Using the posterior probability and Bayes’ theory, we can obtained the minimum distance discriminant

Where i=1,...,N and N is the number of classes (in our caseN=2).So for each pixel X(xR,xG,xB), the distances Dyell(X) and Dback(X) are calculated. If Dyell(X) is less than Dback(X), then the pixel X is classified as yellowish lesion, otherwise it is classified as background. Cyell and Cback denote the center of each class in RGB space, which characterize the color of the yellowish objects and the background respectively. Therefore, one problem has to be resolved before applying this method: the definition of the features center Cyell and Cback. In [8] and [9], they are selected as a global value after obtaining them from different windows in training samples. In that way, it is taken for granted that all the images have the same fundus color, and that the exudates and the background appear with the same illumination and color. In practice, there is a wide variation in the color of fundus from different patient, strongly correlated to skin pigmentation and iris color. So, global values for Cyell and Cback can work in some images but fail in others. This problem can be resolved using specified feature centers for each image. To define them avoiding user interaction, we have to find pixels belonging to both classes in all the images. For background, we select a group of pixels that surrounds the contour of the OD obtaining in section A. And because of the fact that the OD usually has the same color as the exudates, the pixels that belong to the OD are used to identify the color of the yellowish objects. So we obtain for each fundus photograph the values of Cyell and Cback:

where m and  n are the number of pixels in yellowish and background region respectively that are used to calculate these centers and Yi and Bi are the vectors of the three color features in the different region Because of lighting variation, decreasing color saturation, skin pigmentation, etc, the color of lesions in some regions of an image may appear dimmer than the background color that is located in another region and would be wrongly classified. So it is of crucial importance to perform an adjustment for non-uniformity of illumination. But if a general method to avoid this phenomenon is applied, the color in some fundus photograph, due to the wide variation of this feature in different patients, could be modified introducing some strange effects. In this work, we use a new color image. This image is obtained performing an operation of the channels (N1, N2, N3) of the NTSC color space

and then converting the image obtained (N1´,N2, N3) into the RGB color space again. In that way, we improve both contrasting attributes of lesions and the overall color saturation in the image, achieving that the OD and the exudates appear with the same color independently of their location (Fig. 1.(b)). Hard exudates and other yellowish objects can be detected applying the minimum distance discriminant to all the pixels of this image, as shown in Fig. 1.(c). As well as hard exudates, other yellowish regions are detected, as the optic disk, other lesions (cotton wool spots and drusen), artifacts, etc.

C.  Detection of objects with sharp edges

An edge-finding operator can characterize the edge strength of the objects of an image. So, in our case, Kirsch’s mask (5) and different rotations of it are applied to the green component of the color fundus and the maximum response of them is selected to detect the edges in the fundus photograph.

Thresholding this image at grey level T1, we obtain objects with sharpest edges (Fig. 1.(d)). T1 is a parameter of the algorithm. If T1 is chosen too low, the sensitivity increases but the specificity decreases. Other objects in the images with sharp edges are also detected, as the optic disk, blood vessels, hemorrhages’, etc

D.  Combination of the two previous images

To detect only hard exudates and remove all the false positives introduces in the previous stages, we combine the two images obtained using a  Boolean operation, feature-based AND. In feature-based AND, ON pixels in one binary image are used to select objects (connected groups of ON pixels) in another image. Here we use the image with objects with sharp edges to select object in the image with yellowish elements, because in the last one the lesions are detected completely, not only their contours. In this way, we obtain lesions characterized by the two desired features: yellowish color and sharp edge. After that, we also get some false positives due to the papillary region and some artifacts near the vessels (because the reflection in young people). To reduce them, we remove a dilated version of the segmentation result of the detection of the OD and of the vessel in section. Fig.2.  shows the final image.

Fig.1.Images obtained applying the method to (a), (b) image after the enhancement, (c) detection of the yellowish objects, (d) detection of the objects with sharp edges.

Fig.2. Detection of hard exudates presented in Fig. 1. (a).

III RESULTS

We have tested the algorithm on an data base of twenty576x768 digital images taken with a TopCon TRC-NW6SNon-Mydriatic Retinal Camera and have compared the results obtained by the algorithm with the performance of a specialist who marked the exudates on these images. For evaluation of the detection performance of the system the number of true and false positive clusters has to be determined for each image in the test set, while the segmentation threshold T1 is varied. In this way the true positive (TP) rate can be plotted as a function of the number of false positive (FP)  

Detections per image, using free-response receiver operating characteristic (FROC) curve. Each decision threshold results in a corresponding operating point on a curve. We believe that FROC analysis is an appropriate measure for our detection system, because there will be a trade-off between the TP rate and the number of FP detections per image. A true exudates is considered detected if the detected cluster overlaps at least 50% of its area. All findings outside the criterion are considered as false detections. The curve obtained is shown in Fig. 3., obtaining a T1=0.8 a sensitivity of  79.62%.

IV. DISCUSSION

The best performance is achieved at the operation point with a sensitivity of 79.62% with a mean number of 3.2false detections per image. Some exudates are not detected due to their proximity to blood vessels or because they appear very faint, even after the proposed enhancement. Missing faint exudates has not a crucial importance since even human experts are not sure about some ambiguous regions. In the present work we have evaluated the system on an independent database of retinal images with variable characteristics to investigate its robustness. Due to the lack of a common database and a reliable way to measure the performance, it is difficult to compare the performance of our method related to those reported in the literature. Although some work [5], [7] show superior performance than our algorithm, the main improvement is that a good performance is obtained overall independently on the color, illumination, size, etc, keeping FPs low. This independence on the aspect of the image is obtained using a particular method for each image (to enhance them, to obtain the color of the background and exudates), unlike other authors which use global approaches for all of them. So the behavior of our algorithm is appropriate for a clinical environment. But there are some problems that deserve comment. First of all, the algorithm depends on other detection tasks, as the detection of the OD and blood vessels, making the results dependent of the successful of these methods. This indicates the further necessity of improving the robustness of these tasks. On the other hand, we have used the color of the OD to characterize yellowish regions but this cannot represent its real color. It could be a good idea to localize some exudates firstly and then use their color.

Other issues concerning ADDR

One of the issues arising from the use of digital images for diabetic retinopathy screening is the time and space involved in capture and storage of the files. Currently, the use of image compression using utilities such as Joint Photographic Experts Group (JPEG) have not been recommended, although there is some evidence that while large file compression significantly reduces the ability of automated detection programs, a compression ratio of 1:12 or 1:20 would produce little reduction insensitivity Another consideration for diabetic screening is the use of routine mydriasis. Hansen et al. (2004a) address the impact of pharmaco-logically dilated pupils on ADDR. They report a change in sensitivity before and after pupil dilatation of 90%and 97%,respectively,for detection of ‘red lesions’(hemorrhages’/micro aneurysms) and specificity before and after pupil dilatation was reported as 86% and75%,respectively (n ¼ 165 eyes of 83 patients). The use of routine mydrias is for diabetic screening is controversial. Currently, the National Screening Committee in England and Wales have recommended routine my-driasis for all screened patients, whereas the Health Technology Assessment Board for Scotland  only recommend mydriasis under certain defined circumstances.

Whilst the detection of sight-threatening diabetic retinopathy has received the most attention with respect to automated digital image analysis, other pathologies offer potential to use this tool as well, including morphological evaluations of the optic nerve in glaucoma and themacular region in age-related macular degenerationand retinopathy of pre-maturity (ROP) Table 1 summarises sensitivities and specificities of selected studies of ADDR.

V.  CONCLUSION

In this work we have evaluated an automated detection scheme for one of the primary signs of DR: hard exudates. This lesion was identified by its color, using a statistical classification, and its sharpness of its edges, applying a Kirsch operator. After applying our method to 20 fundus photograph, the detection sensitivity for the hard exudates jumped from 65% to 85% when the number of FPs was kept low (3/image). Our results suggest that the system is competent to complement the screening of DR of ophthalmologists in their daily practice because it is very robust in the face of changes of the characteristic of the images. Future work will address the issue of improving the sensitivity by improving the  results of other tasks, as the detection of OD and blood vessels, and trying to localize faint and small hard exudates.

Fig.3.  FROC curve for a database of 20 retinal images using the developed method.

REFERENCES

[1] D. Klein, B. E.. Klein, S. E. Moss et al, “The Wisconsinepidemiologic study of diabetic retinopathy. VII. Diabeticnonproliferative retinal lesions,” Ophthalmol., vol. 94, pp. 1389–1400,  1986.

[2] N. P. Ward, S. Tomlinson, and C. J. Taylor, “Image analysis of fundus photographs – The detection and measurement ofexudates associated with diabetic retinopathy,” Ophthalmol., vol.96, pp. 80–86,  1989.

[3] R. Philips, J. Forrester, and P. Sharp, “Automated detection and quantification of retinal exudates,” Graefe’s Arch. Clin. Exp.Ophthalmol, vol. 231, pp. 90–94, 1993.

[4] K. Akita and H. Kuga, “A computer method of understanding ocular fundus images,” Pattern Recogn., vol. 21, no. 6, pp. 431–443, 1982.

[5] T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “Acontribution of image processing to the diagnosis of diabetic retinopathy – Detection of exudates in color fundus images of the human retina,”  IEEE. Trans. Med. Imag., vol. 21, no. 10, pp.1236–1243, Oct. 2002.

[6] H. Li and O. Chutatape, “Fundus image features extraction,” in Proc. 22nd Annual Int. Conf. of the IEEE Engin. Med. Biol. Soc., EMBS’00, Chicago, IL, pp. 3071–3073.

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Noise Reduction Using LMS Algorithm

Noise Reduction Using LMS Algorithm

AIM:

This paper describes one of the noise reduction techniques, which is widely used in reducing the noise of audio signal. This paper also describes practical implementation of LMS algorithm in both Software and Hardware (On Texas Instrument Processor).

INTRODUCTION

As we know that Noise is a very big problem for communication system. Due to the noise, the message signal can’t be easily retrieved. Hence for a good communication system, it is very important to reduce the noise as much as possible. Coming to digital communication, various noise reduction techniques are used for this purpose. One of widely used technique is Least Mean Square (LMS) techniques, which will be discussed here.

THE LMS ALGORITHM:

This was invented in 1960 by Stanford University professor Bernard Widrow and his first Ph.D. student, Ted Hoff.

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal). It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time.

 

The Adaptive Filter is a Finite Impulse Response Filter (FIR), with N variable coefficients w.

The Least Mean Squares Algorithm (LMS) updates each coefficient on a sample-by-sample basis based on the error e(n).

The value of µ (mu) is critical.

If µ is too small, the filter reacts slowly.

If µ is too large, the filter resolution is poor.

The selected value of µ is a compromise.

SIMULATION:

Any of the simulation tools can be used for this purpose, either MATLAB or CODE COMPOSER STUDIO.

For realising the input and output waveform, MATLAB simulation tools is going to be used.

Steps:

1)        Open the following Simulink model: “AcousticNoiseCancellation”.(This model is already designed in newer version of MATLAB ,if not so, then you can make this model)

2)        Setting the Step size (mu)

The rate of convergence of the LMS Algorithm is controlled by the “Step size (mu)”.

This is the critical variable.

3)        Trace of Input to Model

INPUT= SIGNAL + NOISE

4)        Trace of LMS Filter Output

5)        Trace of LMS Filter error.

The step by step MATLAB is shown in figure

                                    STEP1           

                    STEP2

                                                                            STEP3

                       STEP4

STEP5

          STEP3                                     STEP4                                          STEP5

INTRODUCTION TO LABORATORY:

To implement the LMS Algorithm, we can use Texas Instrument DSP Processor i.e. c6713. First

We should make the model on simulink. then we  will interface with processor.

We will build the model “AcousticNoiseReductionDSKC6713”

STEP 2: Using Frames

1).This model uses frames of data rather than individual bytes.

2).The “Samples per frame” is set to 64.

 

                                  STEP2                                                  

3)      When the model is built, the frames are shown as double lines.

                     STEP3

Setting up the C6713 DSK

•         Plug an microphone and computer loudspeakers / headphones into the C6713 DSK.

•         Put the microphone next to a source of random noise e.g. an off-station radio.

•         Speak into the microphone.

•         Listen to the output.

Then run the model, and you can analyze the output in headphone.

CONCLUSION

Thus, we wind up this session by concluding that this LMS technique of noise reduction is easiest technique and waiting for more future application. Hence we can use this technique for innovative applications, where noise reduction is more important. As an ECE engineer, I hope that we will use this technique in many applications.

REFRENCES:

1) Digital Signal Processing, A Practical Approach by Emmanuel C. Ifeachor and Barrie W. Jervis. ISBN 0201-59619-9.

2)Digital Signal Processing with C and the TMS320C30 by Rulph Chassaing. ISBN 0-471-55780-3

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