Matlab

Page: 1 2 3 4

Thread / Author	Tags
dsp project ideas - electronics seminars	audio dsp projects, dsp projects using c, dsp projects for students, dsp project topics, dsp projects list, dsp projects using matlab, dsp projects matlab, dsp projects, dsp project ideas, ideas, project,

a. An Affine combination two LMS adaptive Filters Transient Mean Square Analysis b. A Time Varying Convergence parameter for LMS Algorithm in the presence white Gaussian noise c. Robust control approach to perfect reconstruction of digital signals d. Non parametric Linear time invariant system identification by DWT e. Adaptive DS-CDMA Receiver with code tracking in unknown phase environments f. Multi user detection in CDMA systems using PDA algorithm under AWGN g. Performance analysis of Iterative channel estimation and multi user detection in multi user CDMA System h. A Full rank regularization technique for MMSE detection in Multi user CDMA systems i. Implementation and analysis of Wide Band CDMA systems j. An efficient Resource allocation strategy for future wireless cellular systems k. Time-Domain signal detection using second order statistics for MIMO-OFDM systems l. Pre DFT processing for MIMO OFDM systems with space time frequency coding m. Channel estimation and prediction for adaptive OFDM downlinks n. Maximum Likelihood carrier frequency offset estimation for OFDM systems in fading channels o. Channel Estimation and prediction for adaptive OFDMA/TDMA uplinks based on non overlapping pilot signals p. Downlink BER simulation for IEEE 802.16e OFDM physical layer q. Channel Code tracking in wireless OFDM Systems r. Performance analysis of IEEE 802.11a physical layer s. Implementation of GMSK modem using Matlab t. Implementation of OFDM with variable data sets using Matlab GUI u. Multiuser Detection in DS-CDMA using MMSE approach v. DTMF detection using Goertzel algorithm with Matlab GUI w. Implementation Cellular Is-95 standard in CDMA systems x. Higher Order SVD for dynamic texture analysis in video y. Adaptive Bilateral filter for sharpness enhancement and noise removal z. Blind self authentication of images for robust watermarking using IWT aa. Comparision and improvement of wavelet based image fusion bb. Weighted Adaptive Lifting based wavelet transform for image coding cc. A spatial Median Filter for noise removal dd. Contourlet based image watermarking using optimum detector in noisy environment ee. Wavelet based palm print authentication system ff. A CMOS image sensor with focal plane SPIHT image compression gg. A visual information encryption scheme based on visual cryptography using DH method hh. Low power variable block size motion estimation using pixel truncation ii. Expansion embedding techniques for Reversible watermarking jj. Steganography using BPCS to the IWT image kk. Reconstruction of under water image by bi-spectrum ll. Wavelet based image authentication and recovery mm. Post processing low bit rate block DCT coded images based on fields of experts prior nn. Natural image compression based on modified SPIHT (Wavelet packets) oo. Image blur reduction for cell cameras via adaptive tonal correction pp. An improved visual cryptography for secret hiding qq. Curved wavelet transform for image coding rr. Sliced Ridge let transform for image denoising ss. Low complexity multi resolution image codec using lifting wavelet transform tt. Lossless compression of color map images by context tree modelling uu. Extended JPEG 2000 image compression systems vv. Data embedding scrambled digital video ww. Block Matching algorithm motion estimation for video codec xx. A high performance JPEG 2000 architecture yy. Video watermarking using discrete wavelet transforms zz. Morphological processing for color images aaa. Video surveillance with sum of absolute differences bbb. Content based image retrieval with realistic color images ccc. Implementation of IRIS Recognition system using HOUGH transforms ddd. Facial recognition system using PCA Analysis eee. Effective Fuzzy C means clustering algorithm for MRI Brain tumour detection fff. ECG signal denoising and baseline wander correction using empirical mode decomposition ggg. A wavelet based denoising technique for Ocular arti-fact correction of the EEG Signal hhh. Acoustic echo cancellation tolerable for double talk iii. A variable step size affine projection algorithm designed fro acoustic echo cancellation jjj. Variable step size NLMS algorithm for under modelling acoustic echo cancellation kkk. Adaptive algorithm for speech compression using discrete cosine packet transforms lll. Warped DCT based noisy speech enhancement mmm. Robust adaptive kalman filtering based speech enhancement algorithm nnn. An adaptive KLT approach for speech enhancement ooo. Speech compression using LPC/DWT ppp. Speech enhancement using adaptive wiener Filter qqq. Content based Speech watermarking using DWT rrr. Analyzing equalizer effects for a speech signals
image processing projects - electronics seminars	image processing projects java, digital image processing projects using matlab, image processing projects using matlab, image processing projects, image processing, projects, processing, image,

a. IMPLEMENTATION OF BQ ALGORITHM & ARITHMETIC CODING FOR DATA COMPRESSION. b. NOISE CLEANING USING AVERAGING, MEDIAN AND ROTATING FILTERS & CONTRAST ENHANCEMENT USING GAMMA CORRECTION OF DIGITAL TRUE COLOR IMAGES c. IMPLEMENTATION OF EDGE DETECTION TECHNIQUES USING MATLAB d. IMPLEMENTATION OF SPATIAL AND FREQUENCY DOMAIN TECHNIQUES FOR IMAGE ENHANCEMENT e. IMPLEMENTATION OF HISTOGRAM EQUALIZATION TECHNIQUES f. IMAGE COMPRESSION USING BIORTHOGONAL 3.7 WAVELET TRANSFORMS g. IMPLEMENTAION OF IMAGE RESTORATION & IMAGE ENHANCEMENT TECHNIQUES USING MATLAB h. IMPLEMENTATION OF ALGORITHMS FOR SUCCESSIVE INTERFERENCE CANCELLATION IN CDMA USING MATLAB i. IMPLEMENTATION OF WCDMA USING VHDL j. IMAGE WATERMARKING USING WAVELETS & DIRECTIONAL FILTER BANKS k. MORPHOLOGICAL OPERATORS FOR COLOR IMAGE PROCESSING l. THE CURVELET TRANSFORM FOR IMAGE DENOISING m. CONTENT BASED IMAGE RETRIEVAL SYSTEM USING PCA
dsp projects matlab - electronics seminars	dsp projects for students, dsp projects using matlab, dsp projects matlab, matlab, projects,

A. 3D Synthetic Environment. B. A High Performance Implementation of MPEG-1 Layer 3 Audio Encode. C. AC drive. D. Acoustic Echo Cancellation algorithm 7 Implementation using DSP. E. 5 Acoustic liquid level gauge. F. Adaptive channel equalization. G. Advanced LCR meter using DSP techniques. H. Alu design using VHDL. I. Audio signal processing. J. Automatic collision avoider. K. Binaural sound localization. L. Biologically plausible pitch perception. M. Biopmetric finger print system. N. Biometric smart camera. O. Cellular communication simulator b using MATLAB. P. Channel Implemention. Q. Character display . R. Class 5 accuracy data acuation. S. Cochlea Implants. T. Command and control using voice recognition. U. Counter design. V. CPU design. W. Data compression. X. Data transmission through power line. Y. Delta modulation. Z. Detection of human speech in structured Noise. AA. Digitale AC motor control. BB. Digital Scanner. CC. Digitale signal processing aid for labs. DD. 30 Doppler correction. EE. 31. DSP based data acquisition system. FF. 32. DSP based ECG Monitor. GG. 33. DSP based image processing. HH. 34. DSP based Karaoke system. II. 35. DSP based medical announcement system. JJ. 36. DSP based modems. KK. 37. DSP based multi channel monitoring system. LL. 38. DSP based signal analyzer. MM. 39. DSP based voice transmission system. NN. 40. DSP function generator. OO. 41. DSP based digital Equalizer. PP. 42. Digital signals processing demonstrator. QQ. 43. DTMF code generator & detection. RR. 44. Error correction coding using convoultion encoding and VITERBI decoding. SS. 45. Eror correction coding using trellis ecoding and VITERBI decoding. TT. 46. Estimation of aircraft trajectory from itsmotion using KALMAN TRACKING FILTER> UU. 47. Eseimation of energy using sub band coding. VV. 48. FFT design. WW. 49. FIR & IIR firlters design. XX. 50. FSK modulation & demodulation. YY. Robust DWT-SVD Domain Image Watermarking: Embedding Data in All Frequencies ZZ. Textural Features for Image Classification AAA. Implementation of a Wimax Simulator in Simulink BBB. MACAW: A Media Access Protocol for Wireless LAN’s CCC. Doubly Fed Induction Generator Using Back-To-Back PWM Converters and Its Application to Variable Speed Wind-Energy Generation DDD. Image Segmentation by Data Driven Markov Chain Monte Carlo EEE. Robust Image Watermarking Based On Multiband Wavelets and Empirical Mode Decomposition FFF. Grid Optical Burst Switched Networks (GOBS) GGG. A Lossless Data Compression and Decompression Algorithm and Its Hardware Architecture HHH. Colorization by Example III. A Comparative Study Between Wavelet And Contourlet Transform Features For Textural Image Classification. JJJ. Wireless LAN Medium Access KKK. Control (MAC) and Physical Layer LLL. (PHY) Specifications MMM. Achieving MAC Layer Fairness in Wireless Packet Network NNN. Colorization Using Optimization OOO. Hierarchical Contour Matching For Dental X-Ray Radiographs PPP. A System for Human Identification from X-Ray Dental Radiographs QQQ. A Review of Routing Protocols for Mobile Ad Hoc Networks RRR. Automatic Generation Control of Interconnected Power System Using Ann Technique Based On –Synthesis SSS. Discrete-Time Linear Parameter Varying Control Of Doubly-Fed Induction Generators TTT. Blocking Probabilities of Optical Burst Switching Networks Based On Reduced Load Fixed Point Approximations UUU. Automatic Recognition of Exudative Maculopathy Using Fuzzy Cmeans Clustering and Neural Networks VVV. Computer-Aided Shape Analysis and Classification of Weld Defects in Industrial Radiography Based Invariant Attributes and Neural Networks WWW. Optimized Software Implementation of A Full-Rate IEEE 802.11a Compliant Digital Baseband Transmitter on A Digital Signal Processor XXX. Performance of Optical Burst Switched Networks for Grid Applications YYY. A Hybrid Time Divisioning Scheme for Power Allocation In DMT-Based DSL Systems ZZZ. DUCHA: A New Dual-Channel MAC Protocol for Multihop Ad Hoc Networks AAAA. Active Noise Cancellation with a Fuzzy Adaptive Filtered-X Algorithm BBBB. Adaptive Routing In Dynamic Ad Hoc Networks CCCC. An FPGA-Based Architecture for Real Time Image Feature Extraction DDDD. Signal Adaptive Subband Decomposition for Adaptive Noise Cancellation EEEE. Implementation of IEEE802.1x in MATLAB FFFF. Profiling Delay and Throughput Characteristics of Interactive Multimedia Traffic over Wlans Using MATLAB GGGG. An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks HHHH. CIC Filter Introduction IIII. Call Admission Control Optimization in Wimax Networks JJJJ. Contention-Based Qos MAC Mechanisms for VBR Voip in IEEE 802.11e Wireless Lans KKKK. An Area-Efficient Universal Cryptography Processor for Smart Cards. LLLL. Design and Analysis of Bit Interleaved Coded MMMM. Space-Time Modulation NNNN. A Real-Time Adaptive Learning Method for Driver Eye Detection OOOO. Real-Time System for Monitoring Driver Vigilance PPPP. Erlang Reduced Load Model For Optical Burst Switched Grids QQQQ. The Contourlet Transform for Image De-Noising Using Cycle Spinning RRRR. Wavelet-Based Contourlet Transform and Its Application to Image Coding SSSS. Adaptive Nonlinear Congestion Controller for a Differentiated-Services Framework TTTT. Fast Handoff Support in IEEE 802.11 Wireless Networks UUUU. On The Design of A Multi-Mode Receive VVVV. Digital-Front-End for Cellular Terminal Rfics WWWW. Optical Burst Switching for Consumer Grids XXXX. Fuzzy Based PID Controller Using MATLAB for Transportation Application YYYY. An Improving Model Watermarking With Iris Biometric Code ZZZZ. Multiscale Feature Analysis Using Directional Filter Bank AAAAA. Image & Sound Compression Using BBBBB. Wavelet Transform CCCCC. Modeling Sigma-Delta Modulator Non-Idealities In Simulink DDDDD. Image Segmentation Using Iterative Watersheding Plus Ridge Detection EEEEE. Signal Processing-Oriented Topics (Spots) For MIMO Wireless Communications FFFFF. Mobility Management Techniques for the Next Generation Wireless Networks GGGGG. VLSI Architecture and Chip for Combined Invisible Robust and Fragile Watermarking HHHHH. Non-Symmetric Decompanding for Improved Performance of Companded OFDM Systems IIIII. Implementation of IEEE 802.11 A WLAN Baseband Processor JJJJJ. Content Based Image Retrieval Using Contourlet Transform KKKKK. VLSI Architecture and FPGA Prototyping of a Digital Camera for Image Security and Authentication LLLLL. A Performance Study of Mobile Handoff Delay in IEEE 802.11-Based Wireless Mesh Networks MMMMM. Backup Path Set Selection in Ad Hoc Wireless Network Using Link Expiration Time NNNNN. Impact of Heavy Traffic Beyond Communication Range in Multi-Hops Ad-Hoc Networks OOOOO. Performance Improvement in Wireless Networks Using Cross-Layer ARQ PPPPP. Analysis of IEEE 802.11e for Delay Sensitive Traffic in Wireless Lans QQQQQ. Sphere Detection in MIMO Communication Systems with Imperfect Channel State Information RRRRR. Novel Channel Interference Reduction in Optical Synchronous FSK-CDMA Network Using a Data-Free Reference SSSSS. An Adaptive Coherent Receiver for MPSK/MDPSK over the Nonselective Rayleigh Fading Channel with Unknown Characteristics TTTTT. Code Shift Keying Impulse Modulation for UWB Communications UUUUU. Wireless Mesh Networks: A Survey VVVVV. Performance of Coded Multi-Carrier DS-CDMA Systems in Multi-Path Fading Channels WWWWW. A VLSI Architecture for Visible Watermarking in a Secure Still Digital Camera (S2DC) Design
fractal image compression seminar report - applied electronics	fractal image compression matlab, fractal image compression theory and application, fractal image compression seminar report, report, seminar, compression, image, fractal,

INTRODUCTION The subject of this work is image compression with fractals. Today JPEG has become an industrial standard in image compression. Further researches are held in two areas, wavelet based compression and fractal image compression. The fractal scheme was introduced by Michael F Barnsley in the year 1945.His idea was that images could be compactly stored as iterated functions which led to the development of the IFS scheme which forms the basis of fractal image compression. Further work in this area was conducted by A.Jacquin, a student of Barnsley who published several papers on this subject. He was the first to publish an efficient algorithm based on local fractal system. Fractal image compression has the following features: • Compression has high complexity. • Fast image decoding • High compression ratios can be achieved. These features enable applications such as computer encyclopedias, like the Microsoft Atlas that came with this technology. The technology is relatively new. OVERVIEW OF IMAGE COMPRESSION Images are stored as pixels or picture forming elements. Each pixel requires a certain amount of computer memory to be stored on. Suppose we had to store a family album with say a hundred photos. To store this on a computer memory would require say a few thousands of dollars. This problem can be solved by image compression. The number of pixels involved in the picture can be drastically reduced by employing image compression techniques. The human eye is insensitive to a wide variety of information loss. The redundancies in images cannot be easily detected and certain minute details in pictures can also be eliminated while storing so as to reduce the number of pixels. These can be further incorporated while reconstructing the image for minimum error. This is the basic idea behind image compression. Most of the image compression techniques are said to be lossy as they reduce the information being stored. The present method being employed consists of storing the image by eliminating the high frequency Fourier co-efficients and storing only the low frequency coefficients. This is the principle behind the DCT transformation which forms the basis of the JPEG scheme of image compression. Barnsley suggested that storing of images as iterated functions of parts of itself leads to efficient image compression.In the middle of the 80’s this concept of IFS became popular. Barnsley and his colleagues in the Georgia University first observed the use of IFS in computerized graphics applications. They found that IFS was able to cress colour images upto 10000 times. The compression contained two phases. First the image was partitioned to segments that were self-similar as possible. Then each part was described as IFS with probabilities. The key to compression was the collage theory, which gave a criterion to select the parameters in the transformation. Image decoding was done by iterative repeat of the transformation. While the decoding was done automatically the encoding required human iterations, at least in the image segmentation. In 1989, Jacquin proposed a full automatic algorithm for fractal image compression based on affine transforms that work locally rather than globally. Ever since the release of this paper several strides have been made in this area. FRACTALS Consider the photocopying machine as illustrated. This machine reduces the image input to it by one-third and triplicates it in the copy. Thus the image finally obtained has been scaled by a factor of three as well as increased in number . However on careful observation of the image we can find that the final image is self-similar to the original image and contains detail at every stage. It is thus a fractal. If we were to put this image in a feedback loop and repeat the process iteratively say infinite number of times we would get a highly complex image with perfect self-similarity at any scale. The final image obtained is called the attractor for the final output. This is the process followed to obtain many complex figures such as the fern leaf image, Cantor dust, Sierpenski triangle etc. Natural fractals are difficult to detect. No object in nature can be infinitely reduced to obtain a self-similar portion. However every image will contain some self-similar portion which can yield a fractal at a certain scale. Thus every image may be considered to be formed of self-transformed parts of itself. These transformations which the image undergoes are affine transforms. PROPERTIES OF FRACTALS A set F is said to be a fractal if it possesses the following poperties. 1. F is found to contain detail at every scale. 2. F is self-similar. 3. The fractal dimension of F is greater than it’s topological dimension. 4. F has got a simple algorithmic description. AFFINE TRANSFORMATIONS These are combinations of rotation, scaling and translation of the co-ordinate axis in an N-dimensional space. The figure shows an example of an affine transformation W which moves towards W(f)-that is it is a contractive transformation. FRACTAL IMAGE COMPRESSION Let us consider the case of the photocopying machine which we saw earlier. Suppose we were to feed the output of this machine back as input and continue the process iteratively. We can find that all the output images seem to be converging back to the same output image and also that the final image is not changed by the process. We call this image the attractor for the copying machine. Because the copying machine reduces the input image the copies of the initial image will be reduced to a point as we repeatedly feed the output back as input; there will be more and more copies but the copies at each stage get smaller and smaller. So the initial image doesn’t affect the final attractor ; in fact, it is only the position and orientation of the copies that determines what the final image will look like. The final result is determined by the way the input image is transformed , we only describe these transformations. Different transformations lead to different attractors with the technical limitation that the images must be contractive; i.e, a given transformation applied to any point in the input image must bring them closer in the copy. This technical condition is very natural since if the points in the copy were to be spread out the attractor might have to be of infinite size. A common feature of these attractors thus formed is that in the position of each of these images of the original square there is a transformed copy of the whole image. Thus the images thus formed are all fractals. This method of creating fractals was put forward by John Hutchinson. M.Barnsley suggested that perhaps storing images as collections of transformations could lead to image compression. The complex figure of a fern is generated from just four affine transforms. Each affine transform is defined by six numbers a,b,c,d,e and f. Storing these on a computer do not require much memory. Suppose we are required to store the image of a face. If a small number of transformations could generate that face then it could be stored compactly. If we scale the transformations defining any image the resulting attractor will also be scaled. This is the implication of fractal image compression. The extra detail needed for decoding at larger sizes is generated automatically by the encoding transforms. However in some cases the detail is realistic at low magnification and this is a useful feature of the method. Magnification of the original shows pixelation; the dots that make up the image are clearly discernable. This is because of the magnification produced. Standard image compression methods can be evaluated using their compression ratios : the ratio of the memory required to store an image as a collection of pixels and the memory require to store a representation of the image in compressed form. The compression ratio of fractal is easy to misunderstand since the image can be decoded at any scale. In practice it is important to either give the initial and decompressed image sizes or use the same sizes for a proper evaluation. Because the decoded image is not exactly the same as the original such schemes are said to be lossy. ITERATED FUNCTION SYSTEM A mathematical model for the copying machine described earlier is called an Iterative Function System (IFS). An IFS system consists of a collection of contractive transformations wi , this collection defines a map, W(s) = U wi(s) , where I = 1, 2, ….,n. Where s is the input and W(s) is the output of the copier. Whatever the initial image S, the image after infinite interactions will tend to the attractor Xw. This Xw is unique for that particular W(s). Two important facts are now listed. • When the wi are contractive in the plane then W is contractive in a ste of the plane. This was proved by Hutchinson. • If we are given a contractive map W on a space of images then there is a special image called the attractor denoted by Xw with the following properties. 1. The attractor is called the fixed point of W 2. Xw is unique. If we find any set S and an image transformation w satisfying W(s) =S, then S is the attractor of W. SELF SIMILARITY IN IMAGES Natural images are not self-similar. A typical image of a face does not contain the type of self similarity found in the fractals. The image does not appear to contain affine transformations of itself. But this image contains a different type of self-similarity. Here the image is formed of properly transformed parts of itself. These transformed parts do not fit together in general to form an exact copy of the original image and so we must allow some amount of error in our representation of an image as a set of self-transformations. This means that an image that we encode as a set of transformations will not be an identical copy but an approximation. To get a mathematical model of an image we express it as a function z = f(x,y) where z is the grayscale. METRIC ON IMAGES If we want to know whether W transformation is contractive we will have to define a distance between two images. A metric is a function that measures the distance between two images. There are metrics that measure the distance between two images , the distance between two points , distance between two sets etc. Two of the most commonly used metrics are Dsup(f,g) = Sup(f(x,y) – g(x,y) ) And the rms metric Drms(f.g) = ^0.5 The supremium metric finds the position where two images f and g differ the most and sets this value as the distance between the two points. The rms metric is more convenient in applications. PARTITIONED IFS The individual transformations described earlier are performed on parts of the image and not on the whole.This forms a partitioned IFS also called a PIFS. There are two spatial dimensions and the grey level adds a third dimension. A portion of the original image called domain (Di) is mapped to a part of the produced image called range (Ri) by the transformation Wi. Since W(f) is an image the Ri covers the whole square page and are adjacent but not overlapping. In the PIFS case, a fixed point or attractor is an image f that satisfies W(f) =f. Thus if we apply the transformations to the image we get back the original image.Thus if we are required to code a natural image this is first partitioned to several domain blocks and iterative function systems are formed of the different parts of the image to yield the output image. AN ILLUSTRATIVE EXAMPLE Suppose we are dealing with a 256x256 pixel image in which each pixel can be one of 256 level of gray. Let R1, R2,….. be the 8x8 pixel non-overlapping subsquares of the image and let D be the collection of all 16x16 pixel (overlapping subsequences of the image). For each Ri search through all of D to find a Di E D that looks like the image above Ri, i.e. minimizes the function. That is, we find pieces Di and maps wi so that when we apply a wi to a part of the image over Di we get something that is very close to the part of image over Ri. There are 8 ways to map one square onto another so that this means comparing 8x242x241 = 464648 squares with each of 1024 range squares. Also, a square D has 4 times as many pixels as an Ri, we must either subsample or average the 2x2 subsquares corresponding to each pixel of Ri, when we minimize the above function. Minimizing the above equation means two things. Firstly, finding a good choice for the Di and secondly finding good contrast and brightness setting si and oi for wi. A choice of Di alongwith a corresponding si and oi determines a map wi. Once we have a collection w1, w2, … we can decode the image by estimating Xw. It is to be noted that the choice of the metric used affects the contractivity of the transformation and the fidelity of the system. PARTITIONING OF IMAGES The basic idea behind partitioning of images is to first partition the image by some collection of ranges Ri, then for each Ri seeek from some collection of image pieces a Di that has a low rms error when mapped to Ri. If we know Ri and Di then we can determine the remaining co-efficients. The various partitioning schemes used are as given under QUADTREE PARTITIONING Quadtree partitioning is based on the generalization of the fixed range sizes. In a quadtree partition a square is broken up into four equal-sized sub-squares when it is not covered well enough.This process repeats recursively starting from the whole image and continuing till the squares are small enough to be covered by some specific rms tolerance.Small squares are covered better than larger ones because contiguous pixels in an image tend to be highly correlated. Let us assume the image size to be 256x256 pixels. Choose for the collection D of permissible domains all the sub-squares in the image of size 8, 12, 16, 32, 48 and 64.Partition the image recursively by a quadtree method until the squares are of size 32.Attempt to cover each square by a larger domain. If a predetermined tolerance value is met then call the square Ri and the covering domain Di. Else repeat the entire process. HV PARTITIONING A weakness of the quadtree based scheme is that it makes no attempt to select the domain pool in a content-dependent way. The collection must be chosen to be very large so that a good fit to a given range can be obtained. A way to remedy this while increasing the flexibility of the range partition is to use a HV partition. In an HV partition a rectangular image is recursively partitioned either horizontally or vertically to form two new rectangles,The partitioning repeats recursively until a covering tolerance is satisfied. This scheme is more flexible since the position of the partition is variable. We can try to make the partitions in such a way that they share some self-similar structure. For example, we can try to arrange the partitions so that the edges in the image tend to run diagonally through them. It is then possible to use the larger partitions to cover the smaller ones with a reasonable expectation of a good cover. TRIANGULAR PARTITIONING Here the partitioning is not restricted to the horizontal and vertical directions. It is flexible so that triangles in the scheme can be chosen to share self-similar properties as before. This scheme is still in its infancy. These partitioning schemes are adaptive since they use a range size that depends on the local image complexity. For a fixed image more transformations lead to better fidelity but worse compression. The tradeoff between compression and fidelity leads to two different approaches to encoding an image. If fidelity is to be maximized partitioned should be continued until the rms error falls below a particular value. If the compression is to be increased we should limit the number of transformations. OTHER PARTITIONING SCHEMES Partitioning schemes come in various varieties. In the triangular scheme a rectangular image is divided diagonally into two triangles. This scheme has several potential advantages. It is flexible in that the triangles can be chosen to share self similar properties. The artifacts arising from the covering do not run horizontally and vertically which is less distracting. Also the triangles can have any orientation so we break away from the rigid 90 degree rotation schemes. Fixed partitioning schemes are also being developed. LEAST REGRESSION METHOD In practice we compare a domain and range using an rms metric. Using this metric also allows easy computation of optimal values for contrast and brightness. Given two squares of pixel intensities a1, a2,…an (from Di) and b1, b2,…bn (from Ri) we can seek s and o to minimize R. APPLICATIONS OF FRACTALS Creating a fractal requires just a few lines of software. The resulting picture could be quite rich in details and would require large memory if stored as such. This forms the basis of fractal compression of pictures. Given any arbitrary picture one has to find out which of the portions of images could be thought of as self-similar versions of other portions. Self-affine transformations can then be found out. The image can then be displayed quickly and at any magnification with infinite levels of fractal detail. Genetic algorithms in MATLAB are generally used for the efficient encoding of fractals. FUTURE DEVELOPMENTS Several new schemes of image compression are being developed day-by-day. The most notable of these is the fractal scheme. It provides endless scope and is under research. Further new partitioning schemes are also being developed. Another recent advance in this field has been through wavelet transformations. This is a highly efficient method based on the Fourier representation of an image. This scheme comes as a competitive development to the fractal approach. CONCLUSION The power of fractal encoding is shown by its ability to outperform the DCT, which forms the basis of the JPEG scheme of image compression. This method has had the benefit of thousands of man hours of research, optimization and general tweaking. Thus the fractal scheme has won for itself more than the attention of the average engineer. REFERENCES 1. FRACTAL IMAGE COMPRESSION By Yuval Fischer 2. ISTE-AICTE STTP ADVANCES IN SIGNAL COMPRESSION TECHNOLOGY (January 2003) 3. IMAGE COMPRESSION TECHNIQUES By Raouf Hamzaoui ABSTRACT Storing an image on a computer requires a very large memory. This problem can be averted by the use of various image compression techniques. Most images contain some amount of redundancy that can be removed when the image is stored and then replaced when it is reconstructed. Fractal image compression is a recent technique based on the representation of an image. The self-transformability property of an image is assumed and exploited in fractal coding. It provides high compression ratios and fast decoding. Apart from this it is also simple and is an easily executable technique. ACKNOWLEDGEMENT I express my sincere gratitude to Dr.Nambissan, Prof. & Head, Department of Electrical and Electronics Engineering, MES College of Engineering, Kuttippuram, for his cooperation and encouragement. I would also like to thank my seminar guide and Staff in-charge Asst. Prof. Gylson Thomas. (Department of EEE) for his invaluable advice and wholehearted cooperation without which this seminar would not have seen the light of day. Gracious gratitude to all the faculty of the department of EEE & friends for their valuable advice and encouragement. CONTENTS 1. INTRODUCTION 1 2. OVERVIEW OF IMAGE COMPRESSION 2 3. FRACTALS 4 4. PROPERTIES OF FRACTALS 5 5. FRACTAL IMAGE COMPRESSION 6 6. ITERATED FUNCTION SYSTEM 9 7. SELF SIMILARITY IN IMAGES 10 8. PARTITIONED IFS 12 9. PARTITIONING OF IMAGES 14 10. HV PARTITIONING 15 11. TRIANGULAR PARTITIONING 16 12. APPLICATIONS OF FRACTALS 18 13. FUTURE DEVELOPMENTS 19 14. CONCLUSION 20 15. REFERENCES 21
Matlab Based CONTROL SYSTEM PROJECTS Ideas - electronics seminars	Ideas, PROJECTS, SYSTEM, CONTROL, Based, Matlab, Matlab Based CONTROL SYSTEM PROJECTS Ideas,

1. Backlash Estimation With Application to Automotive Power trains 2. Control of Integrated Power train With Electronic Throttle and Automatic Transmission 3. Reset Integral-Derivative Control for HDD Servo Systems 4. A Fuzzy Logic-Controlled Superconducting Magnetic Energy Storage For Transient Stability Augmentation 5. An Online Rotor Time Constant Estimator for the Induction Machine 6. An Interative Learning Controlling for Reduction of Repetitive in Disk Drivers 7. Multirate Digital Control System Design & Its Application to Computer Disk 8. A Wavelet Network Control Method for Disk Drives 9. Design Of Delayed Resonator Vibration Absorber 10. Nonlinearity Estimation and Compensation of PWM VSI for PMSM Under Resistance and Flux Linkage Uncertainty 11. Load Frequency Control by Hybrid Evolutionary Fuzzy PI Controller 12. Control of Hot Rolling Mill 13. A New Control Scheme for Nonlinear Systems With Disturbances 14. Experimental Frequency-Domain Analysis of Nonlinear Controlled Optical Storage Drives 15. Steady-State Bump less Transfer Under Controller Uncertainty Using The State/Output Feedback Topology 16. Multirate Control for Computation Saving 17. Accepting Performance Degradation in Fault-Tolerant Control System Design
Matlab Based COMMUNICATION SYSTEM PROJECTS Ideas - electronics seminars	Ideas, PROJECTS, SYSTEM, COMMUNICATION, Based, Matlab, Matlab Based COMMUNICATION SYSTEM PROJECTS Ideas,

1. Reliable Adaptive Modulation and Interference Mitigation for Mobile Radio Slow Frequency Hopping Channels 2. End-to-End BER Analysis for Dual-Hop OSTBC Transmissions over Raleigh Fading Channels 3. Low-Complexity Localized Walsh Decoding For CDMA Systems 4. Frequency Offset Estimation for MB-OFDM-based UWB Systems 5. Design and Analysis of Bit Interleaved Coded Space-Time Modulation 6. Performance Analysis of Iterative Channel Estimation and Multiuser Detection In Multi path DS-CDMA Channels 7. Training Signal Design for Estimation of Correlated MIMO Channels with Colored Interference 8. Time-Domain Signal Detection Based on Second-Order Statistics for MIMO-OFDM Systems 9. On Minimum-BER Linear Multiuser Detection for DS-CDMA Channels 10. Blind Identification of MIMO Channels in Zero Padding Block Transmission system 11. Quasi-Orthogonal Time-Reversal Space–Time Block Coding for Frequency- Selective Fading Channels 12. Pre coded Orthogonal Space–Time Block Codes over Correlated Ricean MIMO Channels 13. Blind Channel Estimation for MIMO OFDM Systems via Non redundant Linear Pre coding 14. Multinode Cooperative Communications in Wireless Networks 15. Orthogonal pace time Block Coded Rate Adaptive Modulation with Feedback 16. Energy Efficient Secured Pattern Based Data Aggregation for wireless sensor Networks
Matlab Based DIGITAL IMAGE PROCESSING PROJECTS Ideas - electronics seminars	Ideas, PROJECTS, PROCESSING, IMAGE, DIGITAL, Based, Matlab, Matlab Based DIGITAL IMAGE PROCESSING PROJECTS Ideas,

1. A synopsis of recent work in edge detection using the DWT 2. Automated estimation of the upper surface of the diaphragm in 3-D CT images 3. Adaptive bilateral filter for sharpness enhancement and noise removal 4. Phase shifting for Non-separable 2D Hear wavelets 5. Universal Impulse noise filter based on Genetic programming 6. Efficient Non-local mean for De-noising of Textural patterns 7. Dynamic De-noising of tracking sequences 8. Analysis and compensation of rolling shutter effect 9. Coherent multi-scale image processing using dual tree quaternion wavelets 10. Texture analysis and classification with linear regression model based wavelet Transform 11. Wavelet, Ridge lets and curve lets for Poisson noise removal 12. Image Processing vision system- standard image sensor and retinas 13. An EEG Based Approach for Pattern Recognition of Precise Hand Activities with Data Fusion Technology 14. Lossless Video Sequence Compression Using Adaptive Prediction 15. Estimating 3-D Human Body Poses from 2-D Static Images 16. A Facial-Skin Condition Classification System in Wavelet Domain 17. Processing of Low Resolution Metal Transfer Images 18. Ultrasound Speckle Image Process Using Wiener Pseudo-inverse Filtering 19. Simple Face-detection Algorithm Based on Minimum Facial Features 20. Target of Imaging Observation Based on The Wavelet Transform and GPR 21. Touch-less Fingerprint Recognition System 22. A Novel Iris Recognition System based on Micro-Features 23. Face Identification by SIFT-based Complete Graph Topology 24. Feature Selection Based on Genetic Algorithms for On-Line Signature Verification 25. Moving Vehicle Registration and Super-Resolution 26. Impact of Age Groups on Fingerprint Recognition Performance 27. A Robust Method for Multiple Face Tracking Using Kalman Filter 28. Image Differencing Approaches to Medical Image Classification 29. Facial expression recognition under illumination variation 30. Iris Recognition Based on FFG 31. Palm print Recognition with Multiple Correlation Filters Using Edge Detection for Class-Specific Segmentation 32. An Improved Face Recognition Algorithm through Gabor Filter Adaptation 33. Protecting Iris Images through Asymmetric Digital Watermarking 34. Region-Level Motion-Based Background Modeling and Subtraction Using MRFs 35. A Hybrid Algorithm With Artifact Detection Mechanism for Region Filling After Object Removal From a Digital Photograph 36. On Rate-Distortion Models for Natural Images and Wavelet Coding Performance 37. Extended Analysis of Motion-Compensated Frame Difference for Block-Based Motion Prediction Error 38. Reversible Integer Color Transform 39. De blurring of Color Images Corrupted by Impulsive Noise 40. Vehicle Detection Using Normalized Color and Edge Map 41. Expansion Embedding Techniques for Reversible Watermarking 42. Stochastic View Registration of Overlapping Cameras Based on Arbitrary Motion 43. Application of Wavelet Energy Feature in Facial Expression Recognition 44. A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution 45. A novel watermark arithmetic for video coding 46. Image Denoising by Averaging of Piecewise Constant Simulations of Image Partitions 47. Under sampled Boundary Pre-/Post filters for Low Bit-Rate DCT-Based Block Coders 48. Matching Pursuit-Based Region-of-Interest Image Coding 49. Adaptive Directional Lifting-Based Wavelet Transform for Image Coding 50. Example-Based Color Transformation of Image and Video Using Basic Color Categories 51. Demosaicing With Directional Filtering and a posteriori Decision
Matlab Based POWER ELECTRONICS PROJECT Ideas - electronics seminars	Ideas, PROJECT, ELECTRONICS, POWER, Based, Matlab, Matlab Based POWER ELECTRONICS PROJECT Ideas,

1. Digital load current feed-forward control method for a dc-dc converter 2. RB-IGBT gate drive circuit and its application in two-stage matrix Converter 3. Digital carrier modulation and sampling issues of matrix converters 4. A simple analytical switching loss model for buck voltage regulators 5. High-efficiency regulated gate driver for power MOSFET 6. Self-tuning digital current estimator for low-power switching converters 7. A single switch dual output non-isolated boost converter 8. Simulation of a novel ZVT technique based boost PFC converter with EMI filter 9. A Modified ZVS Flyback Resonant Inverter for Induction Cooking Applications 10. Synchronization Technique for Random Switching Frequency Pulse-Width Modulation 11. Current-Fed Dual-Bridge DC–DC Converter 12. Comparison of Voltage-Source and Current-Source Shunt Active Power Filters 13. A Single Phase Voltage Regulator Module (VRM) With Stepping Inductance For Fast Transient Response 14. Design and Comparison of High Performance Stationary-Frame controller for DVR Implementation 15. A Single-Phase Boost Rectifier System for Wide Range of Load Variations 16. Boost Converter for Vibration Power Generator System 17. Detecting Rotor Faults in Low Power Permanent Magnet Synchronous Machine 18. An Induction Generator System Using Fuzzy Modeling and Recurrent Fuzzy Neural Network 19. Performance Improvement of Shunt Active Power Filter With Dual Parallel Topology 20. Adaptive Neuro-Wavelet Control for Switching Power Supplies Protections 21. Realization of Parasitic in State-Space Average-Value Modeling of PWM DC-DC Converters 22. An Improved Reliability Cuk Based Solar Inverter With Sliding Mode Control 23. A Novel Design of Line-Interactive Uninterruptible Power Supplies Without Load Current sensor 24. Unified Flux and Torque Control Method for DTC Based Induction-Motor Drivers 25. Analytical Loss Model of Power MOSFET 26. Feed forward Current Control of Boost Single -Phase PFC converter 27. Characterization and Performance comparison of Ripple-Based Control for Voltage Regulator Modules 28. Current-Mode Variable-Frequency Control Architecture for High-Current Low-Voltage Dc to Dc Converters 29. Direct ZVS Start-up of a Current-Fed Resonant Inverter 30. Sensor less Optimization of Dead Times in DC-DC Converters with Synchronous Rectifiers 31. Numerical State-Space Average-Value Modeling of PWM DC-DC Converters Operating in DCM and CCM 32. Dynamic Control and Performance of a Unified Power Flow Controller for Stabilizing n AC Transmission System 33. A Grid Interfacing Power Quality Compensator for Three-Phase Three Wire Micro Grid Application 34. Lateral Power MOSFET for Megahertz -Frequency, High-Density
project topic for "digital image processing in matlab" - Electrical Fan	matlab\, processing, image, \digital, topic, project,

you can try in biometrics and authentication using matlab ,eg:iris recognition
MATLab Project List - computer science crazy	List, Project, MATLab,

please post in http://www.college-seminars.com/newthread.php?fid=46 otherwise this list may destroy.....

Similar Thread Tags

Page: 1 2 3 4

Archive

Current time: 03-10-2010, 03:49 AM

Please Upload A Seminar Or Project Report/Material/Presentation Here