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Sample records for wavelet compression algorithm

  1. Compression of fingerprint data using the wavelet vector quantization image compression algorithm. 1992 progress report

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.

    1992-04-11

    This report describes the development of a Wavelet Vector Quantization (WVQ) image compression algorithm for fingerprint raster files. The pertinent work was performed at Los Alamos National Laboratory for the Federal Bureau of Investigation. This document describes a previously-sent package of C-language source code, referred to as LAFPC, that performs the WVQ fingerprint compression and decompression tasks. The particulars of the WVQ algorithm and the associated design procedure are detailed elsewhere; the purpose of this document is to report the results of the design algorithm for the fingerprint application and to delineate the implementation issues that are incorporated in LAFPC. Special attention is paid to the computation of the wavelet transform, the fast search algorithm used for the VQ encoding, and the entropy coding procedure used in the transmission of the source symbols.

  2. Wavelet transform and Huffman coding based electrocardiogram compression algorithm: Application to telecardiology

    International Nuclear Information System (INIS)

    Chouakri, S A; Djaafri, O; Taleb-Ahmed, A

    2013-01-01

    We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly

  3. [A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

    Science.gov (United States)

    Zhang, Y; Liu, A; Yu, K

    1999-06-01

    A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

  4. PERFORMANCE ANALYSIS OF SET PARTITIONING IN HIERARCHICAL TREES (SPIHT ALGORITHM FOR A FAMILY OF WAVELETS USED IN COLOR IMAGE COMPRESSION

    Directory of Open Access Journals (Sweden)

    A. Sreenivasa Murthy

    2014-11-01

    Full Text Available With the spurt in the amount of data (Image, video, audio, speech, & text available on the net, there is a huge demand for memory & bandwidth savings. One has to achieve this, by maintaining the quality & fidelity of the data acceptable to the end user. Wavelet transform is an important and practical tool for data compression. Set partitioning in hierarchal trees (SPIHT is a widely used compression algorithm for wavelet transformed images. Among all wavelet transform and zero-tree quantization based image compression algorithms SPIHT has become the benchmark state-of-the-art algorithm because it is simple to implement & yields good results. In this paper we present a comparative study of various wavelet families for image compression with SPIHT algorithm. We have conducted experiments with Daubechies, Coiflet, Symlet, Bi-orthogonal, Reverse Bi-orthogonal and Demeyer wavelet types. The resulting image quality is measured objectively, using peak signal-to-noise ratio (PSNR, and subjectively, using perceived image quality (human visual perception, HVP for short. The resulting reduction in the image size is quantified by compression ratio (CR.

  5. Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

    Science.gov (United States)

    Hortos, William S.

    2008-04-01

    Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

  6. Evaluation of a wavelet-based compression algorithm applied to the silicon drift detectors data of the ALICE experiment at CERN

    International Nuclear Information System (INIS)

    Falchieri, Davide; Gandolfi, Enzo; Masotti, Matteo

    2004-01-01

    This paper evaluates the performances of a wavelet-based compression algorithm applied to the data produced by the silicon drift detectors of the ALICE experiment at CERN. This compression algorithm is a general purpose lossy technique, in other words, its application could prove useful even on a wide range of other data reduction's problems. In particular the design targets relevant for our wavelet-based compression algorithm are the following ones: a high-compression coefficient, a reconstruction error as small as possible and a very limited execution time. Interestingly, the results obtained are quite close to the ones achieved by the algorithm implemented in the first prototype of the chip CARLOS, the chip that will be used in the silicon drift detectors readout chain

  7. Wavelet-based audio embedding and audio/video compression

    Science.gov (United States)

    Mendenhall, Michael J.; Claypoole, Roger L., Jr.

    2001-12-01

    Watermarking, traditionally used for copyright protection, is used in a new and exciting way. An efficient wavelet-based watermarking technique embeds audio information into a video signal. Several effective compression techniques are applied to compress the resulting audio/video signal in an embedded fashion. This wavelet-based compression algorithm incorporates bit-plane coding, index coding, and Huffman coding. To demonstrate the potential of this audio embedding and audio/video compression algorithm, we embed an audio signal into a video signal and then compress. Results show that overall compression rates of 15:1 can be achieved. The video signal is reconstructed with a median PSNR of nearly 33 dB. Finally, the audio signal is extracted from the compressed audio/video signal without error.

  8. Multi-dimensional medical images compressed and filtered with wavelets

    International Nuclear Information System (INIS)

    Boyen, H.; Reeth, F. van; Flerackers, E.

    2002-01-01

    Full text: Using the standard wavelet decomposition methods, multi-dimensional medical images can be compressed and filtered by repeating the wavelet-algorithm on 1D-signals in an extra loop per extra dimension. In the non-standard decomposition for multi-dimensional images the areas that must be zero-filled in case of band- or notch-filters are more complex than geometric areas such as rectangles or cubes. Adding an additional dimension in this algorithm until 4D (e.g. a 3D beating heart) increases the geometric complexity of those areas even more. The aim of our study was to calculate the boundaries of the formed complex geometric areas, so we can use the faster non-standard decomposition to compress and filter multi-dimensional medical images. Because a lot of 3D medical images taken by PET- or SPECT-cameras have only a few layers in the Z-dimension and compressing images in a dimension with a few voxels is usually not worthwhile, we provided a solution in which one can choose which dimensions will be compressed or filtered. With the proposal of non-standard decomposition on Daubechies' wavelets D2 to D20 by Steven Gollmer in 1992, 1D data can be compressed and filtered. Each additional level works only on the smoothed data, so the transformation-time halves per extra level. Zero-filling a well-defined area alter the wavelet-transform and then performing the inverse transform will do the filtering. To be capable to compress and filter up to 4D-Images with the faster non-standard wavelet decomposition method, we have investigated a new method for calculating the boundaries of the areas which must be zero-filled in case of filtering. This is especially true for band- and notch filtering. Contrary to the standard decomposition method, the areas are no longer rectangles in 2D or cubes in 3D or a row of cubes in 4D: they are rectangles expanded with a half-sized rectangle in the other direction for 2D, cubes expanded with half cubes in one and quarter cubes in the

  9. Medical image compression based on vector quantization with variable block sizes in wavelet domain.

    Science.gov (United States)

    Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

    2012-01-01

    An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

  10. Wavelet compression algorithm applied to abdominal ultrasound images

    International Nuclear Information System (INIS)

    Lin, Cheng-Hsun; Pan, Su-Feng; LU, Chin-Yuan; Lee, Ming-Che

    2006-01-01

    We sought to investigate acceptable compression ratios of lossy wavelet compression on 640 x 480 x 8 abdominal ultrasound (US) images. We acquired 100 abdominal US images with normal and abnormal findings from the view station of a 932-bed teaching hospital. The US images were then compressed at quality factors (QFs) of 3, 10, 30, and 50 followed outcomes of a pilot study. This was equal to the average compression ratios of 4.3:1, 8.5:1, 20:1 and 36.6:1, respectively. Four objective measurements were carried out to examine and compare the image degradation between original and compressed images. Receiver operating characteristic (ROC) analysis was also introduced for subjective assessment. Five experienced and qualified radiologists as reviewers blinded to corresponding pathological findings, analysed paired 400 randomly ordered images with two 17-inch thin film transistor/liquid crystal display (TFT/LCD) monitors. At ROC analysis, the average area under curve (Az) for US abdominal image was 0.874 at the ratio of 36.6:1. The compressed image size was only 2.7% for US original at this ratio. The objective parameters showed the higher the mean squared error (MSE) or root mean squared error (RMSE) values, the poorer the image quality. The higher signal-to-noise ratio (SNR) or peak signal-to-noise ratio (PSNR) values indicated better image quality. The average RMSE, PSNR at 36.6:1 for US were 4.84 ± 0.14, 35.45 dB, respectively. This finding suggests that, on the basis of the patient sample, wavelet compression of abdominal US to a ratio of 36.6:1 did not adversely affect diagnostic performance or evaluation error for radiologists' interpretation so as to risk affecting diagnosis

  11. Spatial compression algorithm for the analysis of very large multivariate images

    Science.gov (United States)

    Keenan, Michael R [Albuquerque, NM

    2008-07-15

    A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

  12. Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2012-01-01

    Full Text Available An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

  13. Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

    OpenAIRE

    Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

    2012-01-01

    An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with vari...

  14. Coresident sensor fusion and compression using the wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Yocky, D.A.

    1996-03-11

    Imagery from coresident sensor platforms, such as unmanned aerial vehicles, can be combined using, multiresolution decomposition of the sensor images by means of the two-dimensional wavelet transform. The wavelet approach uses the combination of spatial/spectral information at multiple scales to create a fused image. This can be done in both an ad hoc or model-based approach. We compare results from commercial ``fusion`` software and the ad hoc, wavelet approach. Results show the wavelet approach outperforms the commercial algorithms and also supports efficient compression of the fused image.

  15. The wavelet/scalar quantization compression standard for digital fingerprint images

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.

    1994-04-01

    A new digital image compression standard has been adopted by the US Federal Bureau of Investigation for use on digitized gray-scale fingerprint images. The algorithm is based on adaptive uniform scalar quantization of a discrete wavelet transform image decomposition and is referred to as the wavelet/scalar quantization standard. The standard produces archival quality images at compression ratios of around 20:1 and will allow the FBI to replace their current database of paper fingerprint cards with digital imagery.

  16. Applications of wavelet-based compression to multidimensional earth science data

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.

    1993-01-01

    A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithm (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm axe reported, as are signal-to-noise ratio (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme.The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

  17. Applications of wavelet-based compression to multidimensional earth science data

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.

    1993-02-01

    A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithm (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm axe reported, as are signal-to-noise ratio (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme.The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

  18. Wavelet/scalar quantization compression standard for fingerprint images

    Energy Technology Data Exchange (ETDEWEB)

    Brislawn, C.M.

    1996-06-12

    US Federal Bureau of Investigation (FBI) has recently formulated a national standard for digitization and compression of gray-scale fingerprint images. Fingerprints are scanned at a spatial resolution of 500 dots per inch, with 8 bits of gray-scale resolution. The compression algorithm for the resulting digital images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition (wavelet/scalar quantization method). The FBI standard produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. The compression standard specifies a class of potential encoders and a universal decoder with sufficient generality to reconstruct compressed images produced by any compliant encoder, allowing flexibility for future improvements in encoder technology. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations.

  19. A hybrid video compression based on zerotree wavelet structure

    International Nuclear Information System (INIS)

    Kilic, Ilker; Yilmaz, Reyat

    2009-01-01

    A video compression algorithm comparable to the standard techniques at low bit rates is presented in this paper. The overlapping block motion compensation (OBMC) is combined with discrete wavelet transform which followed by Lloyd-Max quantization and zerotree wavelet (ZTW) structure. The novel feature of this coding scheme is the combination of hierarchical finite state vector quantization (HFSVQ) with the ZTW to encode the quantized wavelet coefficients. It is seen that the proposed video encoder (ZTW-HFSVQ) performs better than the MPEG-4 and Zerotree Entropy Coding (ZTE). (author)

  20. DSP accelerator for the wavelet compression/decompression of high- resolution images

    Energy Technology Data Exchange (ETDEWEB)

    Hunt, M.A.; Gleason, S.S.; Jatko, W.B.

    1993-07-23

    A Texas Instruments (TI) TMS320C30-based S-Bus digital signal processing (DSP) module was used to accelerate a wavelet-based compression and decompression algorithm applied to high-resolution fingerprint images. The law enforcement community, together with the National Institute of Standards and Technology (NISI), is adopting a standard based on the wavelet transform for the compression, transmission, and decompression of scanned fingerprint images. A two-dimensional wavelet transform of the input image is computed. Then spatial/frequency regions are automatically analyzed for information content and quantized for subsequent Huffman encoding. Compression ratios range from 10:1 to 30:1 while maintaining the level of image quality necessary for identification. Several prototype systems were developed using SUN SPARCstation 2 with a 1280 {times} 1024 8-bit display, 64-Mbyte random access memory (RAM), Tiber distributed data interface (FDDI), and Spirit-30 S-Bus DSP-accelerators from Sonitech. The final implementation of the DSP-accelerated algorithm performed the compression or decompression operation in 3.5 s per print. Further increases in system throughput were obtained by adding several DSP accelerators operating in parallel.

  1. A MODIFIED EMBEDDED ZERO-TREE WAVELET METHOD FOR MEDICAL IMAGE COMPRESSION

    Directory of Open Access Journals (Sweden)

    T. Celine Therese Jenny

    2010-11-01

    Full Text Available The Embedded Zero-tree Wavelet (EZW is a lossy compression method that allows for progressive transmission of a compressed image. By exploiting the natural zero-trees found in a wavelet decomposed image, the EZW algorithm is able to encode large portions of insignificant regions of an still image with a minimal number of bits. The upshot of this encoding is an algorithm that is able to achieve relatively high peak signal to noise ratios (PSNR for high compression levels. The EZW algorithm is to encode large portions of insignificant regions of an image with a minimal number of bits. Vector Quantization (VQ method can be performed as a post processing step to reduce the coded file size. Vector Quantization (VQ method can be reduces redundancy of the image data in order to be able to store or transmit data in an efficient form. It is demonstrated by experimental results that the proposed method outperforms several well-known lossless image compression techniques for still images that contain 256 colors or less.

  2. Hyperspectral image compressing using wavelet-based method

    Science.gov (United States)

    Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng

    2017-10-01

    Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

  3. Wavelet transform-vector quantization compression of supercomputer ocean model simulation output

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J N; Brislawn, C M

    1992-11-12

    We describe a new procedure for efficient compression of digital information for storage and transmission purposes. The algorithm involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The new vector quantizer design procedure optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The wavelet-vector quantization method, which originates in digital image compression. is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. In this paper we discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research and at the Los Alamos Advanced Computing Laboratory.

  4. A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2016-01-01

    Full Text Available Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS. It is composed of three successful components: (i exponential wavelet transform, (ii iterative shrinkage-thresholding algorithm, and (iii random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches.

  5. A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

    Science.gov (United States)

    Zhang, Yudong; Yang, Jiquan; Yang, Jianfei; Liu, Aijun; Sun, Ping

    2016-01-01

    Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. PMID:27066068

  6. A Compression Algorithm in Wireless Sensor Networks of Bearing Monitoring

    International Nuclear Information System (INIS)

    Zheng Bin; Meng Qingfeng; Wang Nan; Li Zhi

    2011-01-01

    The energy consumption of wireless sensor networks (WSNs) is always an important problem in the application of wireless sensor networks. This paper proposes a data compression algorithm to reduce amount of data and energy consumption during the data transmission process in the on-line WSNs-based bearing monitoring system. The proposed compression algorithm is based on lifting wavelets, Zerotree coding and Hoffman coding. Among of that, 5/3 lifting wavelets is used for dividing data into different frequency bands to extract signal characteristics. Zerotree coding is applied to calculate the dynamic thresholds to retain the attribute data. The attribute data are then encoded by Hoffman coding to further enhance the compression ratio. In order to validate the algorithm, simulation is carried out by using Matlab. The result of simulation shows that the proposed algorithm is very suitable for the compression of bearing monitoring data. The algorithm has been successfully used in online WSNs-based bearing monitoring system, in which TI DSP TMS320F2812 is used to realize the algorithm.

  7. Nuclear data compression and reconstruction via discrete wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Park, Young Ryong; Cho, Nam Zin [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

    1998-12-31

    Discrete Wavelet Transforms (DWTs) are recent mathematics, and begin to be used in various fields. The wavelet transform can be used to compress the signal and image due to its inherent properties. We applied the wavelet transform compression and reconstruction to the neutron cross section data. Numerical tests illustrate that the signal compression using wavelet is very effective to reduce the data saving spaces. 7 refs., 4 figs., 3 tabs. (Author)

  8. Nuclear data compression and reconstruction via discrete wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Park, Young Ryong; Cho, Nam Zin [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

    1997-12-31

    Discrete Wavelet Transforms (DWTs) are recent mathematics, and begin to be used in various fields. The wavelet transform can be used to compress the signal and image due to its inherent properties. We applied the wavelet transform compression and reconstruction to the neutron cross section data. Numerical tests illustrate that the signal compression using wavelet is very effective to reduce the data saving spaces. 7 refs., 4 figs., 3 tabs. (Author)

  9. Mammography image compression using Wavelet

    International Nuclear Information System (INIS)

    Azuhar Ripin; Md Saion Salikin; Wan Hazlinda Ismail; Asmaliza Hashim; Norriza Md Isa

    2004-01-01

    Image compression plays an important role in many applications like medical imaging, televideo conferencing, remote sensing, document and facsimile transmission, which depend on the efficient manipulation, storage, and transmission of binary, gray scale, or color images. In Medical imaging application such Picture Archiving and Communication System (PACs), the image size or image stream size is too large and requires a large amount of storage space or high bandwidth for communication. Image compression techniques are divided into two categories namely lossy and lossless data compression. Wavelet method used in this project is a lossless compression method. In this method, the exact original mammography image data can be recovered. In this project, mammography images are digitized by using Vider Sierra Plus digitizer. The digitized images are compressed by using this wavelet image compression technique. Interactive Data Language (IDLs) numerical and visualization software is used to perform all of the calculations, to generate and display all of the compressed images. Results of this project are presented in this paper. (Author)

  10. Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

    Science.gov (United States)

    Culik, Karel II; Kari, Jarkko

    1994-01-01

    Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

  11. JPEG and wavelet compression of ophthalmic images

    Science.gov (United States)

    Eikelboom, Robert H.; Yogesan, Kanagasingam; Constable, Ian J.; Barry, Christopher J.

    1999-05-01

    This study was designed to determine the degree and methods of digital image compression to produce ophthalmic imags of sufficient quality for transmission and diagnosis. The photographs of 15 subjects, which inclined eyes with normal, subtle and distinct pathologies, were digitized to produce 1.54MB images and compressed to five different methods: (i) objectively by calculating the RMS error between the uncompressed and compressed images, (ii) semi-subjectively by assessing the visibility of blood vessels, and (iii) subjectively by asking a number of experienced observers to assess the images for quality and clinical interpretation. Results showed that as a function of compressed image size, wavelet compressed images produced less RMS error than JPEG compressed images. Blood vessel branching could be observed to a greater extent after Wavelet compression compared to JPEG compression produced better images then a JPEG compression for a given image size. Overall, it was shown that images had to be compressed to below 2.5 percent for JPEG and 1.7 percent for Wavelet compression before fine detail was lost, or when image quality was too poor to make a reliable diagnosis.

  12. An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.

    Science.gov (United States)

    Kim, Jinkwon; Min, Se Dong; Lee, Myoungho

    2011-06-27

    Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

  13. An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

    Directory of Open Access Journals (Sweden)

    Min Se Dong

    2011-06-01

    Full Text Available Abstract Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. Results A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

  14. Wavelets: Applications to Image Compression-II

    Indian Academy of Sciences (India)

    Wavelets: Applications to Image Compression-II. Sachin P ... successful application of wavelets in image com- ... b) Soft threshold: In this case, all the coefficients x ..... [8] http://www.jpeg.org} Official site of the Joint Photographic Experts Group.

  15. Wavelet-LMS algorithm-based echo cancellers

    Science.gov (United States)

    Seetharaman, Lalith K.; Rao, Sathyanarayana S.

    2002-12-01

    This paper presents Echo Cancellers based on the Wavelet-LMS Algorithm. The performance of the Least Mean Square Algorithm in Wavelet transform domain is observed and its application in Echo cancellation is analyzed. The Widrow-Hoff Least Mean Square Algorithm is most widely used algorithm for Adaptive filters that function as Echo Cancellers. The present day communication signals are widely non-stationary in nature and some errors crop up when Least Mean Square Algorithm is used for the Echo Cancellers handling such signals. The analysis of non-stationary signals often involves a compromise between how well transitions or discontinuities can be located. The multi-scale or multi-resolution of signal analysis, which is the essence of wavelet transform, makes Wavelets popular in non-stationary signal analysis. In this paper, we present a Wavelet-LMS algorithm wherein the wavelet coefficients of a signal are modified adaptively using the Least Mean Square Algorithm and then reconstructed to give an Echo-free signal. The Echo Canceller based on this Algorithm is found to have a better convergence and a comparatively lesser MSE (Mean Square error).

  16. Wavelet-based compression of pathological images for telemedicine applications

    Science.gov (United States)

    Chen, Chang W.; Jiang, Jianfei; Zheng, Zhiyong; Wu, Xue G.; Yu, Lun

    2000-05-01

    In this paper, we present the performance evaluation of wavelet-based coding techniques as applied to the compression of pathological images for application in an Internet-based telemedicine system. We first study how well suited the wavelet-based coding is as it applies to the compression of pathological images, since these images often contain fine textures that are often critical to the diagnosis of potential diseases. We compare the wavelet-based compression with the DCT-based JPEG compression in the DICOM standard for medical imaging applications. Both objective and subjective measures have been studied in the evaluation of compression performance. These studies are performed in close collaboration with expert pathologists who have conducted the evaluation of the compressed pathological images and communication engineers and information scientists who designed the proposed telemedicine system. These performance evaluations have shown that the wavelet-based coding is suitable for the compression of various pathological images and can be integrated well with the Internet-based telemedicine systems. A prototype of the proposed telemedicine system has been developed in which the wavelet-based coding is adopted for the compression to achieve bandwidth efficient transmission and therefore speed up the communications between the remote terminal and the central server of the telemedicine system.

  17. REMOTELY SENSEDC IMAGE COMPRESSION BASED ON WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Heung K. Lee

    1996-06-01

    Full Text Available In this paper, we present an image compression algorithm that is capable of significantly reducing the vast mount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet trans-form to remove the spatial redundancy. The transformed images are than encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with KITSAT-1 image as well as the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by peak signal to noise ratio (PSNR and classification capability.

  18. Optimization of wavelet decomposition for image compression and feature preservation.

    Science.gov (United States)

    Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

    2003-09-01

    A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

  19. Optical image encryption using chaos-based compressed sensing and phase-shifting interference in fractional wavelet domain

    Science.gov (United States)

    Liu, Qi; Wang, Ying; Wang, Jun; Wang, Qiong-Hua

    2018-02-01

    In this paper, a novel optical image encryption system combining compressed sensing with phase-shifting interference in fractional wavelet domain is proposed. To improve the encryption efficiency, the volume data of original image are decreased by compressed sensing. Then the compacted image is encoded through double random phase encoding in asymmetric fractional wavelet domain. In the encryption system, three pseudo-random sequences, generated by three-dimensional chaos map, are used as the measurement matrix of compressed sensing and two random-phase masks in the asymmetric fractional wavelet transform. It not only simplifies the keys to storage and transmission, but also enhances our cryptosystem nonlinearity to resist some common attacks. Further, holograms make our cryptosystem be immune to noises and occlusion attacks, which are obtained by two-step-only quadrature phase-shifting interference. And the compression and encryption can be achieved in the final result simultaneously. Numerical experiments have verified the security and validity of the proposed algorithm.

  20. Comparison of JPEG and wavelet compression on intraoral digital radiographic images

    International Nuclear Information System (INIS)

    Kim, Eun Kyung

    2004-01-01

    To determine the proper image compression method and ratio without image quality degradation in intraoral digital radiographic images, comparing the discrete cosine transform (DCT)-based JPEG with the wavelet-based JPEG 2000 algorithm. Thirty extracted sound teeth and thirty extracted teeth with occlusal caries were used for this study. Twenty plaster blocks were made with three teeth each. They were radiographically exposed using CDR sensors (Schick Inc., Long Island, USA). Digital images were compressed to JPEG format, using Adobe Photoshop v. 7.0 and JPEG 2000 format using Jasper program with compression ratios of 5 : 1, 9 : 1, 14 : 1, 28 : 1 each. To evaluate the lesion detectability, receiver operating characteristic (ROC) analysis was performed by the three oral and maxillofacial radiologists. To evaluate the image quality, all the compressed images were assessed subjectively using 5 grades, in comparison to the original uncompressed images. Compressed images up to compression ratio of 14: 1 in JPEG and 28 : 1 in JPEG 2000 showed nearly the same the lesion detectability as the original images. In the subjective assessment of image quality, images up to compression ratio of 9 : 1 in JPEG and 14 : 1 in JPEG 2000 showed minute mean paired differences from the original images. The results showed that the clinically acceptable compression ratios were up to 9 : 1 for JPEG and 14 : 1 for JPEG 2000. The wavelet-based JPEG 2000 is a better compression method, comparing to DCT-based JPEG for intraoral digital radiographic images.

  1. 32Still Image Compression Algorithm Based on Directional Filter Banks

    OpenAIRE

    Chunling Yang; Duanwu Cao; Li Ma

    2010-01-01

    Hybrid wavelet and directional filter banks (HWD) is an effective multi-scale geometrical analysis method. Compared to wavelet transform, it can better capture the directional information of images. But the ringing artifact, which is caused by the coefficient quantization in transform domain, is the biggest drawback of image compression algorithms in HWD domain. In this paper, by researching on the relationship between directional decomposition and ringing artifact, an improved decomposition ...

  2. Image Compression using Haar and Modified Haar Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Mohannad Abid Shehab Ahmed

    2013-04-01

    Full Text Available Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and horizontal Haar filters are composed to construct four 2-dimensional filters, such filters applied directly to the image to speed up the implementation of the Haar wavelet transform. Modified Haar technique is studied and implemented for odd based numbers i.e. (N=3 & N=5 to generate many solution sets, these sets are tested using the energy function or numerical method to get the optimum one.The Haar transform is simple, efficient in memory usage due to high zero value spread (it can use sparse principle, and exactly reversible without the edge effects as compared to DCT (Discrete Cosine Transform. The implemented Matlab simulation results prove the effectiveness of DWT (Discrete Wave Transform algorithms based on Haar and Modified Haar techniques in attaining an efficient compression ratio (C.R, achieving higher peak signal to noise ratio (PSNR, and the resulting images are of much smoother as compared to standard JPEG especially for high C.R. A comparison between standard JPEG, Haar, and Modified Haar techniques is done finally which approves the highest capability of Modified Haar between others.

  3. Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.

    Science.gov (United States)

    Sharifahmadian, Ershad

    2006-01-01

    The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.

  4. Wavelets in scientific computing

    DEFF Research Database (Denmark)

    Nielsen, Ole Møller

    1998-01-01

    the FWT can be used as a front-end for efficient image compression schemes. Part II deals with vector-parallel implementations of several variants of the Fast Wavelet Transform. We develop an efficient and scalable parallel algorithm for the FWT and derive a model for its performance. Part III...... supported wavelets in the context of multiresolution analysis. These wavelets are particularly attractive because they lead to a stable and very efficient algorithm, namely the fast wavelet transform (FWT). We give estimates for the approximation characteristics of wavelets and demonstrate how and why...... is an investigation of the potential for using the special properties of wavelets for solving partial differential equations numerically. Several approaches are identified and two of them are described in detail. The algorithms developed are applied to the nonlinear Schrödinger equation and Burgers' equation...

  5. Study and analysis of wavelet based image compression techniques

    African Journals Online (AJOL)

    user

    Discrete Wavelet Transform (DWT) is a recently developed compression ... serve emerging areas of mobile multimedia and internet communication, ..... In global thresholding the best trade-off between PSNR and compression is provided by.

  6. DNABIT Compress - Genome compression algorithm.

    Science.gov (United States)

    Rajarajeswari, Pothuraju; Apparao, Allam

    2011-01-22

    Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, "DNABIT Compress" for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that "DNABIT Compress" algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases.

  7. Medical image compression by using three-dimensional wavelet transformation

    International Nuclear Information System (INIS)

    Wang, J.; Huang, H.K.

    1996-01-01

    This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB. In general, the smaller the slice distance, the better the 3-D compression performance

  8. Parallelization of one image compression method. Wavelet, Transform, Vector Quantization and Huffman Coding

    International Nuclear Information System (INIS)

    Moravie, Philippe

    1997-01-01

    Today, in the digitized satellite image domain, the needs for high dimension increase considerably. To transmit or to stock such images (more than 6000 by 6000 pixels), we need to reduce their data volume and so we have to use real-time image compression techniques. The large amount of computations required by image compression algorithms prohibits the use of common sequential processors, for the benefits of parallel computers. The study presented here deals with parallelization of a very efficient image compression scheme, based on three techniques: Wavelets Transform (WT), Vector Quantization (VQ) and Entropic Coding (EC). First, we studied and implemented the parallelism of each algorithm, in order to determine the architectural characteristics needed for real-time image compression. Then, we defined eight parallel architectures: 3 for Mallat algorithm (WT), 3 for Tree-Structured Vector Quantization (VQ) and 2 for Huffman Coding (EC). As our system has to be multi-purpose, we chose 3 global architectures between all of the 3x3x2 systems available. Because, for technological reasons, real-time is not reached at anytime (for all the compression parameter combinations), we also defined and evaluated two algorithmic optimizations: fix point precision and merging entropic coding in vector quantization. As a result, we defined a new multi-purpose multi-SMIMD parallel machine, able to compress digitized satellite image in real-time. The definition of the best suited architecture for real-time image compression was answered by presenting 3 parallel machines among which one multi-purpose, embedded and which might be used for other applications on board. (author) [fr

  9. Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

    Science.gov (United States)

    Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

    2009-05-01

    Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

  10. Methods of compression of digital holograms, based on 1-level wavelet transform

    International Nuclear Information System (INIS)

    Kurbatova, E A; Cheremkhin, P A; Evtikhiev, N N

    2016-01-01

    To reduce the size of memory required for storing information about 3D-scenes and to decrease the rate of hologram transmission, digital hologram compression can be used. Compression of digital holograms by wavelet transforms is among most powerful methods. In the paper the most popular wavelet transforms are considered and applied to the digital hologram compression. Obtained values of reconstruction quality and hologram's diffraction efficiencies are compared. (paper)

  11. Wavelet Compressed PCA Models for Real-Time Image Registration in Augmented Reality Applications

    OpenAIRE

    Christopher Cooper; Kent Wise; John Cooper; Makarand Deo

    2015-01-01

    The use of augmented reality (AR) has shown great promise in enhancing medical training and diagnostics via interactive simulations. This paper presents a novel method to perform accurate and inexpensive image registration (IR) utilizing a pre-constructed database of reference objects in conjunction with a principal component analysis (PCA) model. In addition, a wavelet compression algorithm is utilized to enhance the speed of the registration process. The proposed method is used to perform r...

  12. SPECTRUM analysis of multispectral imagery in conjunction with wavelet/KLT data compression

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.

    1993-12-01

    The data analysis program, SPECTRUM, is used for fusion, visualization, and classification of multi-spectral imagery. The raw data used in this study is Landsat Thematic Mapper (TM) 7-channel imagery, with 8 bits of dynamic range per channel. To facilitate data transmission and storage, a compression algorithm is proposed based on spatial wavelet transform coding and KLT decomposition of interchannel spectral vectors, followed by adaptive optimal multiband scalar quantization. The performance of SPECTRUM clustering and visualization is evaluated on compressed multispectral data. 8-bit visualizations of 56-bit data show little visible distortion at 50:1 compression and graceful degradation at higher compression ratios. Two TM images were processed in this experiment: a 1024 x 1024-pixel scene of the region surrounding the Chernobyl power plant, taken a few months before the reactor malfunction, and a 2048 x 2048 image of Moscow and surrounding countryside.

  13. DNABIT Compress – Genome compression algorithm

    Science.gov (United States)

    Rajarajeswari, Pothuraju; Apparao, Allam

    2011-01-01

    Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, “DNABIT Compress” for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that “DNABIT Compress” algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases. PMID:21383923

  14. Compression of seismic data: filter banks and extended transforms, synthesis and adaptation; Compression de donnees sismiques: bancs de filtres et transformees etendues, synthese et adaptation

    Energy Technology Data Exchange (ETDEWEB)

    Duval, L.

    2000-11-01

    Wavelet and wavelet packet transforms are the most commonly used algorithms for seismic data compression. Wavelet coefficients are generally quantized and encoded by classical entropy coding techniques. We first propose in this work a compression algorithm based on the wavelet transform. The wavelet transform is used together with a zero-tree type coding, with first use in seismic applications. Classical wavelet transforms nevertheless yield a quite rigid approach, since it is often desirable to adapt the transform stage to the properties of each type of signal. We thus propose a second algorithm using, instead of wavelets, a set of so called 'extended transforms'. These transforms, originating from the filter bank theory, are parameterized. Classical examples are Malvar's Lapped Orthogonal Transforms (LOT) or de Queiroz et al. Generalized Lapped Orthogonal Transforms (GenLOT). We propose several optimization criteria to build 'extended transforms' which are adapted the properties of seismic signals. We further show that these transforms can be used with the same zero-tree type coding technique as used with wavelets. Both proposed algorithms provide exact compression rate choice, block-wise compression (in the case of extended transforms) and partial decompression for quality control or visualization. Performances are tested on a set of actual seismic data. They are evaluated for several quality measures. We also compare them to other seismic compression algorithms. (author)

  15. Evaluation of the wavelet image two-line coder

    DEFF Research Database (Denmark)

    Rein, Stephan Alexander; Fitzek, Frank Hanns Paul; Gühmann, Clemens

    2015-01-01

    This paper introduces the wavelet image two-line (Wi2l) coding algorithm for low complexity compression of images. The algorithm recursively encodes an image backwards reading only two lines of a wavelet subband, which are read in blocks of 512 bytes from flash memory. It thus only requires very ...

  16. Framelets and wavelets algorithms, analysis, and applications

    CERN Document Server

    Han, Bin

    2017-01-01

    Marking a distinct departure from the perspectives of frame theory and discrete transforms, this book provides a comprehensive mathematical and algorithmic introduction to wavelet theory. As such, it can be used as either a textbook or reference guide. As a textbook for graduate mathematics students and beginning researchers, it offers detailed information on the basic theory of framelets and wavelets, complemented by self-contained elementary proofs, illustrative examples/figures, and supplementary exercises. Further, as an advanced reference guide for experienced researchers and practitioners in mathematics, physics, and engineering, the book addresses in detail a wide range of basic and advanced topics (such as multiwavelets/multiframelets in Sobolev spaces and directional framelets) in wavelet theory, together with systematic mathematical analysis, concrete algorithms, and recent developments in and applications of framelets and wavelets. Lastly, the book can also be used to teach on or study selected spe...

  17. Wavelet analysis

    CERN Document Server

    Cheng, Lizhi; Luo, Yong; Chen, Bo

    2014-01-01

    This book could be divided into two parts i.e. fundamental wavelet transform theory and method and some important applications of wavelet transform. In the first part, as preliminary knowledge, the Fourier analysis, inner product space, the characteristics of Haar functions, and concepts of multi-resolution analysis, are introduced followed by a description on how to construct wavelet functions both multi-band and multi wavelets, and finally introduces the design of integer wavelets via lifting schemes and its application to integer transform algorithm. In the second part, many applications are discussed in the field of image and signal processing by introducing other wavelet variants such as complex wavelets, ridgelets, and curvelets. Important application examples include image compression, image denoising/restoration, image enhancement, digital watermarking, numerical solution of partial differential equations, and solving ill-conditioned Toeplitz system. The book is intended for senior undergraduate stude...

  18. Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme

    Science.gov (United States)

    Li, Xianye; Meng, Xiangfeng; Yang, Xiulun; Wang, Yurong; Yin, Yongkai; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

    2018-03-01

    A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method.

  19. MEDICAL IMAGE COMPRESSION USING HYBRID CODER WITH FUZZY EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    K. Vidhya

    2011-02-01

    Full Text Available Medical imaging techniques produce prohibitive amounts of digitized clinical data. Compression of medical images is a must due to large memory space required for transmission and storage. This paper presents an effective algorithm to compress and to reconstruct medical images. The proposed algorithm first extracts edge information of medical images by using fuzzy edge detector. The images are decomposed using Cohen-Daubechies-Feauveau (CDF wavelet. The hybrid technique utilizes the efficient wavelet based compression algorithms such as JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT. The wavelet coefficients in the approximation sub band are encoded using tier 1 part of JPEG2000. The wavelet coefficients in the detailed sub bands are encoded using SPIHT. Consistent quality images are produced by this method at a lower bit rate compared to other standard compression algorithms. Two main approaches to assess image quality are objective testing and subjective testing. The image quality is evaluated by objective quality measures. Objective measures correlate well with the perceived image quality for the proposed compression algorithm.

  20. The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M. [Los Alamos National Lab., NM (United States); Hopper, T. [Federal Bureau of Investigation, Washington, DC (United States)

    1993-05-01

    The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite-length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBI`s Integrated Automated Fingerprint Identification System.

  1. DNABIT Compress – Genome compression algorithm

    OpenAIRE

    Rajarajeswari, Pothuraju; Apparao, Allam

    2011-01-01

    Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, “DNABIT Compress” for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our ...

  2. The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M. (Los Alamos National Lab., NM (United States)); Hopper, T. (Federal Bureau of Investigation, Washington, DC (United States))

    1993-01-01

    The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite-length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBI's Integrated Automated Fingerprint Identification System.

  3. Some applications of wavelets to physics

    International Nuclear Information System (INIS)

    Thompson, C.R.

    1992-01-01

    A thorough description of a fast wavelet transform algorithm (FWT) and its inverse (IFWT) are given. The effects of noise in the wavelet transform are studied, in particular the effects on signal reconstruction. A model for additive white noise on the coefficients is presented along with two methods that can help to suppress the effects of noise corruption of the signal. Problems of improper sampling are studied, including the propagation of uncertainty through the FWT and IFWT. Interpolation techniques and data compression are also studied. The FWT and IFWT are generalized for analysis of two dimensional images. Methods for edge detection are discussed as well as contrast improvement and data compression. Finally, wavelets are applied to electromagnetic wave propagation problems. Formulas relating the wavelet and Fourier transforms are given, and expansions of time-dependent electromagnetic fields using both fixed and moving wavelet bases are studied

  4. Multivariate wavelet frames

    CERN Document Server

    Skopina, Maria; Protasov, Vladimir

    2016-01-01

    This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...

  5. Full-frame compression of discrete wavelet and cosine transforms

    Science.gov (United States)

    Lo, Shih-Chung B.; Li, Huai; Krasner, Brian; Freedman, Matthew T.; Mun, Seong K.

    1995-04-01

    At the foreground of computerized radiology and the filmless hospital are the possibilities for easy image retrieval, efficient storage, and rapid image communication. This paper represents the authors' continuous efforts in compression research on full-frame discrete wavelet (FFDWT) and full-frame discrete cosine transforms (FFDCT) for medical image compression. Prior to the coding, it is important to evaluate the global entropy in the decomposed space. It is because of the minimum entropy, that a maximum compression efficiency can be achieved. In this study, each image was split into the top three most significant bit (MSB) and the remaining remapped least significant bit (RLSB) images. The 3MSB image was compressed by an error-free contour coding and received an average of 0.1 bit/pixel. The RLSB image was either transformed to a multi-channel wavelet or the cosine transform domain for entropy evaluation. Ten x-ray chest radiographs and ten mammograms were randomly selected from our clinical database and were used for the study. Our results indicated that the coding scheme in the FFDCT domain performed better than in FFDWT domain for high-resolution digital chest radiographs and mammograms. From this study, we found that decomposition efficiency in the DCT domain for relatively smooth images is higher than that in the DWT. However, both schemes worked just as well for low resolution digital images. We also found that the image characteristics of the `Lena' image commonly used in the compression literature are very different from those of radiological images. The compression outcome of the radiological images can not be extrapolated from the compression result based on the `Lena.'

  6. Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation

    Directory of Open Access Journals (Sweden)

    Schell Thomas

    2003-01-01

    Full Text Available In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.

  7. WAVELET-BASED ALGORITHM FOR DETECTION OF BEARING FAULTS IN A GAS TURBINE ENGINE

    Directory of Open Access Journals (Sweden)

    Sergiy Enchev

    2014-07-01

    Full Text Available Presented is a gas turbine engine bearing diagnostic system that integrates information from various advanced vibration analysis techniques to achieve robust bearing health state awareness. This paper presents a computational algorithm for identifying power frequency variations and integer harmonics by using wavelet-based transform. The continuous wavelet transform with  the complex Morlet wavelet is adopted to detect the harmonics presented in a power signal. The algorithm based on the discrete stationary wavelet transform is adopted to denoise the wavelet ridges.

  8. Rate-distortion analysis of directional wavelets.

    Science.gov (United States)

    Maleki, Arian; Rajaei, Boshra; Pourreza, Hamid Reza

    2012-02-01

    The inefficiency of separable wavelets in representing smooth edges has led to a great interest in the study of new 2-D transformations. The most popular criterion for analyzing these transformations is the approximation power. Transformations with near-optimal approximation power are useful in many applications such as denoising and enhancement. However, they are not necessarily good for compression. Therefore, most of the nearly optimal transformations such as curvelets and contourlets have not found any application in image compression yet. One of the most promising schemes for image compression is the elegant idea of directional wavelets (DIWs). While these algorithms outperform the state-of-the-art image coders in practice, our theoretical understanding of them is very limited. In this paper, we adopt the notion of rate-distortion and calculate the performance of the DIW on a class of edge-like images. Our theoretical analysis shows that if the edges are not "sharp," the DIW will compress them more efficiently than the separable wavelets. It also demonstrates the inefficiency of the quadtree partitioning that is often used with the DIW. To solve this issue, we propose a new partitioning scheme called megaquad partitioning. Our simulation results on real-world images confirm the benefits of the proposed partitioning algorithm, promised by our theoretical analysis. © 2011 IEEE

  9. Image Compression Based On Wavelet, Polynomial and Quadtree

    Directory of Open Access Journals (Sweden)

    Bushra A. SULTAN

    2011-01-01

    Full Text Available In this paper a simple and fast image compression scheme is proposed, it is based on using wavelet transform to decompose the image signal and then using polynomial approximation to prune the smoothing component of the image band. The architect of proposed coding scheme is high synthetic where the error produced due to polynomial approximation in addition to the detail sub-band data are coded using both quantization and Quadtree spatial coding. As a last stage of the encoding process shift encoding is used as a simple and efficient entropy encoder to compress the outcomes of the previous stage.The test results indicate that the proposed system can produce a promising compression performance while preserving the image quality level.

  10. Wavelet Approach to Data Analysis, Manipulation, Compression, and Communication

    National Research Council Canada - National Science Library

    Chui, Charles K

    2007-01-01

    ...; secondly, based on minimum-energy criteria, new data processing tools, particularly variational algorithms and optimal wavelet thresholding methods, with applications to image restoration, were introduced...

  11. Effect of CT digital image compression on detection of coronary artery calcification

    International Nuclear Information System (INIS)

    Zheng, L.M.; Sone, S.; Itani, Y.; Wang, Q.; Hanamura, K.; Asakura, K.; Li, F.; Yang, Z.G.; Wang, J.C.; Funasaka, T.

    2000-01-01

    Purpose: To test the effect of digital compression of CT images on the detection of small linear or spotted high attenuation lesions such as coronary artery calcification (CAC). Material and methods: Fifty cases with and 50 without CAC were randomly selected from a population that had undergone spiral CT of the thorax for screening lung cancer. CT image data were compressed using JPEG (Joint Photographic Experts Group) or wavelet algorithms at ratios of 10:1, 20:1 or 40:1. Five radiologists reviewed the uncompressed and compressed images on a cathode-ray-tube. Observer performance was evaluated with receiver operating characteristic analysis. Results: CT images compressed at a ratio as high as 20:1 were acceptable for primary diagnosis of CAC. There was no significant difference in the detection accuracy for CAC between JPEG and wavelet algorithms at the compression ratios up to 20:1. CT images were more vulnerable to image blurring on the wavelet compression at relatively lower ratios, and 'blocking' artifacts occurred on the JPEG compression at relatively higher ratios. Conclusion: JPEG and wavelet algorithms allow compression of CT images without compromising their diagnostic value at ratios up to 20:1 in detecting small linear or spotted high attenuation lesions such as CAC, and there was no difference between the two algorithms in diagnostic accuracy

  12. The research of optimal selection method for wavelet packet basis in compressing the vibration signal of a rolling bearing in fans and pumps

    International Nuclear Information System (INIS)

    Hao, W; Jinji, G

    2012-01-01

    Compressing the vibration signal of a rolling bearing has important significance to wireless monitoring and remote diagnosis of fans and pumps which is widely used in the petrochemical industry. In this paper, according to the characteristics of the vibration signal in a rolling bearing, a compression method based on the optimal selection of wavelet packet basis is proposed. We analyze several main attributes of wavelet packet basis and the effect to the compression of the vibration signal in a rolling bearing using wavelet packet transform in various compression ratios, and proposed a method to precisely select a wavelet packet basis. Through an actual signal, we come to the conclusion that an orthogonal wavelet packet basis with low vanishing moment should be used to compress the vibration signal of a rolling bearing to get an accurate energy proportion between the feature bands in the spectrum of reconstructing the signal. Within these low vanishing moments, orthogonal wavelet packet basis, and 'coif' wavelet packet basis can obtain the best signal-to-noise ratio in the same compression ratio for its best symmetry.

  13. An NMR log echo data de-noising method based on the wavelet packet threshold algorithm

    International Nuclear Information System (INIS)

    Meng, Xiangning; Xie, Ranhong; Li, Changxi; Hu, Falong; Li, Chaoliu; Zhou, Cancan

    2015-01-01

    To improve the de-noising effects of low signal-to-noise ratio (SNR) nuclear magnetic resonance (NMR) log echo data, this paper applies the wavelet packet threshold algorithm to the data. The principle of the algorithm is elaborated in detail. By comparing the properties of a series of wavelet packet bases and the relevance between them and the NMR log echo train signal, ‘sym7’ is found to be the optimal wavelet packet basis of the wavelet packet threshold algorithm to de-noise the NMR log echo train signal. A new method is presented to determine the optimal wavelet packet decomposition scale; this is within the scope of its maximum, using the modulus maxima and the Shannon entropy minimum standards to determine the global and local optimal wavelet packet decomposition scales, respectively. The results of applying the method to the simulated and actual NMR log echo data indicate that compared with the wavelet threshold algorithm, the wavelet packet threshold algorithm, which shows higher decomposition accuracy and better de-noising effect, is much more suitable for de-noising low SNR–NMR log echo data. (paper)

  14. SeqCompress: an algorithm for biological sequence compression.

    Science.gov (United States)

    Sardaraz, Muhammad; Tahir, Muhammad; Ikram, Ataul Aziz; Bajwa, Hassan

    2014-10-01

    The growth of Next Generation Sequencing technologies presents significant research challenges, specifically to design bioinformatics tools that handle massive amount of data efficiently. Biological sequence data storage cost has become a noticeable proportion of total cost in the generation and analysis. Particularly increase in DNA sequencing rate is significantly outstripping the rate of increase in disk storage capacity, which may go beyond the limit of storage capacity. It is essential to develop algorithms that handle large data sets via better memory management. This article presents a DNA sequence compression algorithm SeqCompress that copes with the space complexity of biological sequences. The algorithm is based on lossless data compression and uses statistical model as well as arithmetic coding to compress DNA sequences. The proposed algorithm is compared with recent specialized compression tools for biological sequences. Experimental results show that proposed algorithm has better compression gain as compared to other existing algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Choice of word length in the design of a specialized hardware for lossless wavelet compression of medical images

    Science.gov (United States)

    Urriza, Isidro; Barragan, Luis A.; Artigas, Jose I.; Garcia, Jose I.; Navarro, Denis

    1997-11-01

    Image compression plays an important role in the archiving and transmission of medical images. Discrete cosine transform (DCT)-based compression methods are not suitable for medical images because of block-like image artifacts that could mask or be mistaken for pathology. Wavelet transforms (WTs) are used to overcome this problem. When implementing WTs in hardware, finite precision arithmetic introduces quantization errors. However, lossless compression is usually required in the medical image field. Thus, the hardware designer must look for the optimum register length that, while ensuring the lossless accuracy criteria, will also lead to a high-speed implementation with small chip area. In addition, wavelet choice is a critical issue that affects image quality as well as system design. We analyze the filters best suited to image compression that appear in the literature. For them, we obtain the maximum quantization errors produced in the calculation of the WT components. Thus, we deduce the minimum word length required for the reconstructed image to be numerically identical to the original image. The theoretical results are compared with experimental results obtained from algorithm simulations on random test images. These results enable us to compare the hardware implementation cost of the different filter banks. Moreover, to reduce the word length, we have analyzed the case of increasing the integer part of the numbers while maintaining constant the word length when the scale increases.

  16. EP-based wavelet coefficient quantization for linear distortion ECG data compression.

    Science.gov (United States)

    Hung, King-Chu; Wu, Tsung-Ching; Lee, Hsieh-Wei; Liu, Tung-Kuan

    2014-07-01

    Reconstruction quality maintenance is of the essence for ECG data compression due to the desire for diagnosis use. Quantization schemes with non-linear distortion characteristics usually result in time-consuming quality control that blocks real-time application. In this paper, a new wavelet coefficient quantization scheme based on an evolution program (EP) is proposed for wavelet-based ECG data compression. The EP search can create a stationary relationship among the quantization scales of multi-resolution levels. The stationary property implies that multi-level quantization scales can be controlled with a single variable. This hypothesis can lead to a simple design of linear distortion control with 3-D curve fitting technology. In addition, a competitive strategy is applied for alleviating data dependency effect. By using the ECG signals saved in MIT and PTB databases, many experiments were undertaken for the evaluation of compression performance, quality control efficiency, data dependency influence. The experimental results show that the new EP-based quantization scheme can obtain high compression performance and keep linear distortion behavior efficiency. This characteristic guarantees fast quality control even for the prediction model mismatching practical distortion curve. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

  17. Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System

    Directory of Open Access Journals (Sweden)

    Zhang Yulin

    2015-01-01

    Full Text Available To address the limitation of conventional adaptive algorithm used for active noise control (ANC system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE, which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS and Decomposition and Reconstruction LMS algorithm (DR-LMS based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.

  18. A Multiresolution Image Completion Algorithm for Compressing Digital Color Images

    Directory of Open Access Journals (Sweden)

    R. Gomathi

    2014-01-01

    Full Text Available This paper introduces a new framework for image coding that uses image inpainting method. In the proposed algorithm, the input image is subjected to image analysis to remove some of the portions purposefully. At the same time, edges are extracted from the input image and they are passed to the decoder in the compressed manner. The edges which are transmitted to decoder act as assistant information and they help inpainting process fill the missing regions at the decoder. Textural synthesis and a new shearlet inpainting scheme based on the theory of p-Laplacian operator are proposed for image restoration at the decoder. Shearlets have been mathematically proven to represent distributed discontinuities such as edges better than traditional wavelets and are a suitable tool for edge characterization. This novel shearlet p-Laplacian inpainting model can effectively reduce the staircase effect in Total Variation (TV inpainting model whereas it can still keep edges as well as TV model. In the proposed scheme, neural network is employed to enhance the value of compression ratio for image coding. Test results are compared with JPEG 2000 and H.264 Intracoding algorithms. The results show that the proposed algorithm works well.

  19. Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2015-01-01

    Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

  20. Joint Time-Frequency And Wavelet Analysis - An Introduction

    Directory of Open Access Journals (Sweden)

    Majkowski Andrzej

    2014-12-01

    Full Text Available A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in this paper. Unfortunately, there is no correlation between the width of the time-frequency window and its frequency content in the t/f analysis. This property is not valid in the case of a wavelet transform. A wavelet is a wave-like oscillation, which forms its own “wavelet window”. Compression of the wavelet narrows the window, and vice versa. Individual wavelet functions are well localized in time and simultaneously in scale (the equivalent of frequency. The wavelet analysis owes its effectiveness to the pyramid algorithm described by Mallat, which enables fast decomposition of a signal into wavelet components.

  1. JPEG2000 vs. full frame wavelet packet compression for smart card medical records.

    Science.gov (United States)

    Leehan, Joaquín Azpirox; Lerallut, Jean-Francois

    2006-01-01

    This paper describes a comparison among different compression methods to be used in the context of electronic health records in the newer version of "smart cards". The JPEG2000 standard is compared to a full-frame wavelet packet compression method at high (33:1 and 50:1) compression rates. Results show that the full-frame method outperforms the JPEG2K standard qualitatively and quantitatively.

  2. Video steganography based on bit-plane decomposition of wavelet-transformed video

    Science.gov (United States)

    Noda, Hideki; Furuta, Tomofumi; Niimi, Michiharu; Kawaguchi, Eiji

    2004-06-01

    This paper presents a steganography method using lossy compressed video which provides a natural way to send a large amount of secret data. The proposed method is based on wavelet compression for video data and bit-plane complexity segmentation (BPCS) steganography. BPCS steganography makes use of bit-plane decomposition and the characteristics of the human vision system, where noise-like regions in bit-planes of a dummy image are replaced with secret data without deteriorating image quality. In wavelet-based video compression methods such as 3-D set partitioning in hierarchical trees (SPIHT) algorithm and Motion-JPEG2000, wavelet coefficients in discrete wavelet transformed video are quantized into a bit-plane structure and therefore BPCS steganography can be applied in the wavelet domain. 3-D SPIHT-BPCS steganography and Motion-JPEG2000-BPCS steganography are presented and tested, which are the integration of 3-D SPIHT video coding and BPCS steganography, and that of Motion-JPEG2000 and BPCS, respectively. Experimental results show that 3-D SPIHT-BPCS is superior to Motion-JPEG2000-BPCS with regard to embedding performance. In 3-D SPIHT-BPCS steganography, embedding rates of around 28% of the compressed video size are achieved for twelve bit representation of wavelet coefficients with no noticeable degradation in video quality.

  3. A Wavelet-Based Algorithm for the Spatial Analysis of Poisson Data

    Science.gov (United States)

    Freeman, P. E.; Kashyap, V.; Rosner, R.; Lamb, D. Q.

    2002-01-01

    Wavelets are scalable, oscillatory functions that deviate from zero only within a limited spatial regime and have average value zero, and thus may be used to simultaneously characterize the shape, location, and strength of astronomical sources. But in addition to their use as source characterizers, wavelet functions are rapidly gaining currency within the source detection field. Wavelet-based source detection involves the correlation of scaled wavelet functions with binned, two-dimensional image data. If the chosen wavelet function exhibits the property of vanishing moments, significantly nonzero correlation coefficients will be observed only where there are high-order variations in the data; e.g., they will be observed in the vicinity of sources. Source pixels are identified by comparing each correlation coefficient with its probability sampling distribution, which is a function of the (estimated or a priori known) background amplitude. In this paper, we describe the mission-independent, wavelet-based source detection algorithm ``WAVDETECT,'' part of the freely available Chandra Interactive Analysis of Observations (CIAO) software package. Our algorithm uses the Marr, or ``Mexican Hat'' wavelet function, but may be adapted for use with other wavelet functions. Aspects of our algorithm include: (1) the computation of local, exposure-corrected normalized (i.e., flat-fielded) background maps; (2) the correction for exposure variations within the field of view (due to, e.g., telescope support ribs or the edge of the field); (3) its applicability within the low-counts regime, as it does not require a minimum number of background counts per pixel for the accurate computation of source detection thresholds; (4) the generation of a source list in a manner that does not depend upon a detailed knowledge of the point spread function (PSF) shape; and (5) error analysis. These features make our algorithm considerably more general than previous methods developed for the

  4. Study on characteristic points of boiling curve by using wavelet analysis and genetic algorithm

    International Nuclear Information System (INIS)

    Wei Huiming; Su Guanghui; Qiu Suizheng; Yang Xingbo

    2009-01-01

    Based on the wavelet analysis theory of signal singularity detection,the critical heat flux (CHF) and minimum film boiling starting point (q min ) of boiling curves can be detected and analyzed by using the wavelet multi-resolution analysis. To predict the CHF in engineering, empirical relations were obtained based on genetic algorithm. The results of wavelet detection and genetic algorithm prediction are consistent with experimental data very well. (authors)

  5. Orthonormal Wavelet Bases for Quantum Molecular Dynamics

    International Nuclear Information System (INIS)

    Tymczak, C.; Wang, X.

    1997-01-01

    We report on the use of compactly supported, orthonormal wavelet bases for quantum molecular-dynamics (Car-Parrinello) algorithms. A wavelet selection scheme is developed and tested for prototypical problems, such as the three-dimensional harmonic oscillator, the hydrogen atom, and the local density approximation to atomic and molecular systems. Our method shows systematic convergence with increased grid size, along with improvement on compression rates, thereby yielding an optimal grid for self-consistent electronic structure calculations. copyright 1997 The American Physical Society

  6. The FBI compression standard for digitized fingerprint images

    Energy Technology Data Exchange (ETDEWEB)

    Brislawn, C.M.; Bradley, J.N. [Los Alamos National Lab., NM (United States); Onyshczak, R.J. [National Inst. of Standards and Technology, Gaithersburg, MD (United States); Hopper, T. [Federal Bureau of Investigation, Washington, DC (United States)

    1996-10-01

    The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the current status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.

  7. Effect of high image compression on the reproducibility of cardiac Sestamibi reporting

    International Nuclear Information System (INIS)

    Thomas, P.; Allen, L.; Beuzeville, S.

    1999-01-01

    Full text: Compression algorithms have been mooted to minimize storage space and transmission times of digital images. We assessed the impact of high-level lousy compression using JPEG and wavelet algorithms on image quality and reporting accuracy of cardiac Sestamibi studies. Twenty stress/rest Sestamibi cardiac perfusion studies were reconstructed into horizontal short, vertical long and horizontal long axis slices using conventional methods. Each of these six sets of slices were aligned for reporting and saved (uncompressed) as a bitmap. This bitmap was then compressed using JPEG compression, then decompressed and saved as a bitmap for later viewing. This process was repeated using the original bitmap and wavelet compression. Finally, a second copy of the original bitmap was made. All 80 bitmaps were randomly coded to ensure blind reporting. The bitmaps were read blinded and by consensus of 2 experienced nuclear medicine physicians using a 5-point scale and 25 cardiac segments. Subjective image quality was also reported using a 3-point scale. Samples of the compressed images were also subtracted from the original bitmap for visual comparison of differences. Results showed an average compression ratio of 23:1 for wavelet and 13:1 for JPEG. Image subtraction showed only very minor discordance between the original and compressed images. There was no significant difference in subjective quality between the compressed and uncompressed images. There was no significant difference in reporting reproducibility of the identical bitmap copy, the JPEG image and the wavelet image compared with the original bitmap. Use of the high compression algorithms described had no significant impact on reporting reproducibility and subjective image quality of cardiac Sestamibi perfusion studies

  8. Application of wavelet transform in seismic signal processing

    International Nuclear Information System (INIS)

    Ghasemi, M. R.; Mohammadzadeh, A.; Salajeghe, E.

    2005-01-01

    Wavelet transform is a new tool for signal analysis which can perform a simultaneous signal time and frequency representations. Under Multi Resolution Analysis, one can quickly determine details for signals and their properties using Fast Wavelet Transform algorithms. In this paper, for a better physical understanding of a signal and its basic algorithms, Multi Resolution Analysis together with wavelet transforms in a form of Digital Signal Processing will be discussed. For a Seismic Signal Processing, sets of Orthonormal Daubechies Wavelets are suggested. when dealing with the application of wavelets in SSP, one may discuss about denoising from the signal and data compression existed in the signal, which is important in seismic signal data processing. Using this techniques, EL-Centro and Nagan signals were remodeled with a 25% of total points, resulted in a satisfactory results with an acceptable error drift. Thus a total of 1559 and 2500 points for EL-centro and Nagan seismic curves each, were reduced to 389 and 625 points respectively, with a very reasonable error drift, details of which are recorded in the paper. Finally, the future progress in signal processing, based on wavelet theory will be appointed

  9. Modelo de error en imágenes comprimidas con wavelets Error Model in Wavelet-compressed Images

    Directory of Open Access Journals (Sweden)

    Gloria Puetamán G.

    2007-06-01

    Full Text Available En este artículo se presenta la compresión de imágenes a través de la comparación entre el modelo Wavelet y el modelo Fourier, utilizando la minimización de la función de error. El problema que se estudia es específico, consiste en determinar una base {ei} que minimice la función de error entre la imagen original y la recuperada después de la compresión. Es de resaltar que existen muchas aplicaciones, por ejemplo, en medicina o astronomía, en donde no es aceptable ningún deterioro de la imagen porque toda la información contenida, incluso la que se estima como ruido, se considera imprescindible.In this paper we study image compression as a way to compare Wavelet and Fourier models, by minimizing the error function. The particular problem we consider is to determine basis {ei} minimizing the error function between the original image and the recovered one after compression. It is to be noted or remarked that there are many applications in such diverse fields as for example medicine and astronomy, where no image deteriorating is acceptable since even noise is considered essential.

  10. Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems.

    Science.gov (United States)

    Kumar, Ashish; Kumar, Manjeet; Komaragiri, Rama

    2018-04-19

    Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient's cardiac health. The device has been widely used to detect and monitor the patient's heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.

  11. WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Zhouzhou Liu

    2015-01-01

    Full Text Available For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times.

  12. Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

    Directory of Open Access Journals (Sweden)

    Jin Li

    2014-01-01

    Full Text Available Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC combined with image data compression (IDC approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE. Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS-based algorithm has better compression performance than the traditional compression approaches.

  13. Multispectral image compression based on DSC combined with CCSDS-IDC.

    Science.gov (United States)

    Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

    2014-01-01

    Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

  14. Comparison on Integer Wavelet Transforms in Spherical Wavelet Based Image Based Relighting

    Institute of Scientific and Technical Information of China (English)

    WANGZe; LEEYin; LEUNGChising; WONGTientsin; ZHUYisheng

    2003-01-01

    To provide a good quality rendering in the Image based relighting (IBL) system, tremendous reference images under various illumination conditions are needed. Therefore data compression is essential to enable interactive action. And the rendering speed is another crucial consideration for real applications. Based on Spherical wavelet transform (SWT), this paper presents a quick representation method with Integer wavelet transform (IWT) for the IBL system. It focuses on comparison on different IWTs with the Embedded zerotree wavelet (EZW) used in the IBL system. The whole compression procedure contains two major compression steps. Firstly, SWT is applied to consider the correlation among different reference images. Secondly, the SW transformed images are compressed with IWT based image compression approach. Two IWTs are used and good results are showed in the simulations.

  15. A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction

    Science.gov (United States)

    Siddeq, M. M.; Rodrigues, M. A.

    2015-09-01

    Image compression techniques are widely used on 2D image 2D video 3D images and 3D video. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level discrete cosine transform (DCT) and a two level discrete wavelet transform (DWT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of four steps. (1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix, respectively, (2) apply a second level DCT on the DC-Matrix to generate two arrays, namely nonzero-array and zero-array, (3) apply the Minimize-Matrix-Size algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT, (4) apply arithmetic coding to the output of previous steps. A novel decompression algorithm, Fast-Match-Search algorithm (FMS), is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined in one matrix followed by inverse two levels DCT with two levels DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D root-mean-square-error following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.

  16. A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless Sensor Networks.

    Science.gov (United States)

    Gu, Xiangping; Zhou, Xiaofeng; Sun, Yanjing

    2018-02-28

    Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings' spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node's residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets' sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.

  17. Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

    Directory of Open Access Journals (Sweden)

    Bekhtin Yury

    2016-01-01

    Full Text Available The new method for combining semisoft thresholding rules during wavelet-based data compression of images with multiplicative noise is suggested. The method chooses the best thresholding rule and the threshold value using the proposed criteria which provide the best nonlinear approximations and take into consideration errors of quantization. The results of computer modeling have shown that the suggested method provides relatively good image quality after restoration in the sense of some criteria such as PSNR, SSIM, etc.

  18. Image Quality Assessment for Different Wavelet Compression Techniques in a Visual Communication Framework

    Directory of Open Access Journals (Sweden)

    Nuha A. S. Alwan

    2013-01-01

    Full Text Available Images with subband coding and threshold wavelet compression are transmitted over a Rayleigh communication channel with additive white Gaussian noise (AWGN, after quantization and 16-QAM modulation. A comparison is made between these two types of compression using both mean square error (MSE and structural similarity (SSIM image quality assessment (IQA criteria applied to the reconstructed image at the receiver. The two methods yielded comparable SSIM but different MSE measures. In this work, we justify our results which support previous findings in the literature that the MSE between two images is not indicative of structural similarity or the visibility of errors. It is found that it is difficult to reduce the pointwise errors in subband-compressed images (higher MSE. However, the compressed images provide comparable SSIM or perceived quality for both types of compression provided that the retained energy after compression is the same.

  19. Optimisation algorithms for ECG data compression.

    Science.gov (United States)

    Haugland, D; Heber, J G; Husøy, J H

    1997-07-01

    The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.

  20. ERGC: an efficient referential genome compression algorithm.

    Science.gov (United States)

    Saha, Subrata; Rajasekaran, Sanguthevar

    2015-11-01

    Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression. We have done extensive experiments using five real sequencing datasets. The results on real genomes show that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem. It achieves compression ratios that are better than those of the currently best performing algorithms. The time to compress and decompress the whole genome is also very promising. The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/∼rajasek/ERGC.zip. rajasek@engr.uconn.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  1. HVS-based medical image compression

    Energy Technology Data Exchange (ETDEWEB)

    Kai Xie [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China)]. E-mail: xie_kai2001@sjtu.edu.cn; Jie Yang [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China); Min Zhuyue [CREATIS-CNRS Research Unit 5515 and INSERM Unit 630, 69621 Villeurbanne (France); Liang Lixiao [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China)

    2005-07-01

    Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.

  2. HVS-based medical image compression

    International Nuclear Information System (INIS)

    Kai Xie; Jie Yang; Min Zhuyue; Liang Lixiao

    2005-01-01

    Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time

  3. a pyramid algorithm for the haar discrete wavelet packet transform

    African Journals Online (AJOL)

    PROF EKWUEME

    computer-aided signal processing of non-stationary signals, this paper develops a pyramid algorithm for the discrete wavelet packet ... Edith T. Luhanga, School of Computational and Communication Sciences and Engineering, Nelson Mandela African. Institute of ..... Mathematics, Washington University. 134. EDITH T.

  4. Algorithm for Compressing Time-Series Data

    Science.gov (United States)

    Hawkins, S. Edward, III; Darlington, Edward Hugo

    2012-01-01

    An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

  5. Selecting a general-purpose data compression algorithm

    Science.gov (United States)

    Mathews, Gary Jason

    1995-01-01

    The National Space Science Data Center's Common Data Formate (CDF) is capable of storing many types of data such as scalar data items, vectors, and multidimensional arrays of bytes, integers, or floating point values. However, regardless of the dimensionality and data type, the data break down into a sequence of bytes that can be fed into a data compression function to reduce the amount of data without losing data integrity and thus remaining fully reconstructible. Because of the diversity of data types and high performance speed requirements, a general-purpose, fast, simple data compression algorithm is required to incorporate data compression into CDF. The questions to ask are how to evaluate and compare compression algorithms, and what compression algorithm meets all requirements. The object of this paper is to address these questions and determine the most appropriate compression algorithm to use within the CDF data management package that would be applicable to other software packages with similar data compression needs.

  6. A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

    Science.gov (United States)

    Gregoire, John M; Dale, Darren; van Dover, R Bruce

    2011-01-01

    Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

  7. Accelerometer North Finding System Based on the Wavelet Packet De-noising Algorithm and Filtering Circuit

    Directory of Open Access Journals (Sweden)

    LU Yongle

    2014-07-01

    Full Text Available This paper demonstrates a method and system for north finding with a low-cost piezoelectricity accelerometer based on the Coriolis acceleration principle. The proposed setup is based on the choice of an accelerometer with residual noise of 35 ng•Hz-1/2. The plane of the north finding system is aligned parallel to the local level, which helps to eliminate the effect of plane error. The Coriolis acceleration caused by the earth’s rotation and the acceleration’s instantaneous velocity is much weaker than the g-sensitivity acceleration. To get a high accuracy and a shorter time for north finding system, in this paper, the Filtering Circuit and the wavelet packet de-nosing algorithm are used as the following. First, the hardware is designed as the alternating currents across by filtering circuit, so the DC will be isolated and the weak AC signal will be amplified. The DC is interfering signal generated by the earth's gravity. Then, we have used a wavelet packet to filter the signal which has been done through the filtering circuit. Finally, compare the north finding results measured by wavelet packet filtering with those measured by a low-pass filter. Wavelet filter de-noise data shows that wavelet packet filtering and wavelet filter measurement have high accuracy. Wavelet Packet filtering has stronger ability to remove burst noise and higher engineering environment adaptability than that of Wavelet filtering. Experimental results prove the effectiveness and project implementation of the accelerometer north finding method based on wavelet packet de-noising algorithm.

  8. An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

    Science.gov (United States)

    Cui, Xinchun; Niu, Yuying; Zheng, Xiangwei; Han, Yingshuai

    2018-01-01

    In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

  9. Improved algorithms for circuit fault diagnosis based on wavelet packet and neural network

    International Nuclear Information System (INIS)

    Zhang, W-Q; Xu, C

    2008-01-01

    In this paper, two improved BP neural network algorithms of fault diagnosis for analog circuit are presented through using optimal wavelet packet transform(OWPT) or incomplete wavelet packet transform(IWPT) as preprocessor. The purpose of preprocessing is to reduce the nodes in input layer and hidden layer of BP neural network, so that the neural network gains faster training and convergence speed. At first, we apply OWPT or IWPT to the response signal of circuit under test(CUT), and then calculate the normalization energy of each frequency band. The normalization energy is used to train the BP neural network to diagnose faulty components in the analog circuit. These two algorithms need small network size, while have faster learning and convergence speed. Finally, simulation results illustrate the two algorithms are effective for fault diagnosis

  10. Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

    Science.gov (United States)

    Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

    2013-05-01

    A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

  11. NRGC: a novel referential genome compression algorithm.

    Science.gov (United States)

    Saha, Subrata; Rajasekaran, Sanguthevar

    2016-11-15

    Next-generation sequencing techniques produce millions to billions of short reads. The procedure is not only very cost effective but also can be done in laboratory environment. The state-of-the-art sequence assemblers then construct the whole genomic sequence from these reads. Current cutting edge computing technology makes it possible to build genomic sequences from the billions of reads within a minimal cost and time. As a consequence, we see an explosion of biological sequences in recent times. In turn, the cost of storing the sequences in physical memory or transmitting them over the internet is becoming a major bottleneck for research and future medical applications. Data compression techniques are one of the most important remedies in this context. We are in need of suitable data compression algorithms that can exploit the inherent structure of biological sequences. Although standard data compression algorithms are prevalent, they are not suitable to compress biological sequencing data effectively. In this article, we propose a novel referential genome compression algorithm (NRGC) to effectively and efficiently compress the genomic sequences. We have done rigorous experiments to evaluate NRGC by taking a set of real human genomes. The simulation results show that our algorithm is indeed an effective genome compression algorithm that performs better than the best-known algorithms in most of the cases. Compression and decompression times are also very impressive. The implementations are freely available for non-commercial purposes. They can be downloaded from: http://www.engr.uconn.edu/~rajasek/NRGC.zip CONTACT: rajasek@engr.uconn.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. A fully automated algorithm of baseline correction based on wavelet feature points and segment interpolation

    Science.gov (United States)

    Qian, Fang; Wu, Yihui; Hao, Peng

    2017-11-01

    Baseline correction is a very important part of pre-processing. Baseline in the spectrum signal can induce uneven amplitude shifts across different wavenumbers and lead to bad results. Therefore, these amplitude shifts should be compensated before further analysis. Many algorithms are used to remove baseline, however fully automated baseline correction is convenient in practical application. A fully automated algorithm based on wavelet feature points and segment interpolation (AWFPSI) is proposed. This algorithm finds feature points through continuous wavelet transformation and estimates baseline through segment interpolation. AWFPSI is compared with three commonly introduced fully automated and semi-automated algorithms, using simulated spectrum signal, visible spectrum signal and Raman spectrum signal. The results show that AWFPSI gives better accuracy and has the advantage of easy use.

  13. Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging.

    Directory of Open Access Journals (Sweden)

    Andrew M Huettner

    Full Text Available A new method for designing radiofrequency (RF pulses with numerical optimization in the wavelet domain is presented. Numerical optimization may yield solutions that might otherwise have not been discovered with analytic techniques alone. Further, processing in the wavelet domain reduces the number of unknowns through compression properties inherent in wavelet transforms, providing a more tractable optimization problem. This algorithm is demonstrated with simultaneous multi-slice (SMS spin echo refocusing pulses because reduced peak RF power is necessary for SMS diffusion imaging with high acceleration factors. An iterative, nonlinear, constrained numerical minimization algorithm was developed to generate an optimized RF pulse waveform. Wavelet domain coefficients were modulated while iteratively running a Bloch equation simulator to generate the intermediate slice profile of the net magnetization. The algorithm minimizes the L2-norm of the slice profile with additional terms to penalize rejection band ripple and maximize the net transverse magnetization across each slice. Simulations and human brain imaging were used to demonstrate a new RF pulse design that yields an optimized slice profile and reduced peak energy deposition when applied to a multiband single-shot echo planar diffusion acquisition. This method may be used to optimize factors such as magnitude and phase spectral profiles and peak RF pulse power for multiband simultaneous multi-slice (SMS acquisitions. Wavelet-based RF pulse optimization provides a useful design method to achieve a pulse waveform with beneficial amplitude reduction while preserving appropriate magnetization response for magnetic resonance imaging.

  14. Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

    Science.gov (United States)

    Ren, Zhong; Liu, Guodong; Huang, Zhen

    2012-11-01

    The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.

  15. Infinitely oscillating wavelets and a efficient implementation algorithm based the FFT

    Directory of Open Access Journals (Sweden)

    Marcela Fabio

    2015-01-01

    Full Text Available In this work we present the design of an orthogonal wavelet, infinitely oscillating, located in time with decay 1/|t|n and limited-band. Its appli- cation leads to the signal decomposition in waves of instantaneous, well defined frequency. We also present the implementation algorithm for the analysis and synthesis based on the Fast Fourier Transform (FFT with the same complexity as Mallat’s algorithm.

  16. A wavelet-based PWTD algorithm-accelerated time domain surface integral equation solver

    KAUST Repository

    Liu, Yang

    2015-10-26

    © 2015 IEEE. The multilevel plane-wave time-domain (PWTD) algorithm allows for fast and accurate analysis of transient scattering from, and radiation by, electrically large and complex structures. When used in tandem with marching-on-in-time (MOT)-based surface integral equation (SIE) solvers, it reduces the computational and memory costs of transient analysis from equation and equation to equation and equation, respectively, where Nt and Ns denote the number of temporal and spatial unknowns (Ergin et al., IEEE Trans. Antennas Mag., 41, 39-52, 1999). In the past, PWTD-accelerated MOT-SIE solvers have been applied to transient problems involving half million spatial unknowns (Shanker et al., IEEE Trans. Antennas Propag., 51, 628-641, 2003). Recently, a scalable parallel PWTD-accelerated MOT-SIE solver that leverages a hiearchical parallelization strategy has been developed and successfully applied to the transient problems involving ten million spatial unknowns (Liu et. al., in URSI Digest, 2013). We further enhanced the capabilities of this solver by implementing a compression scheme based on local cosine wavelet bases (LCBs) that exploits the sparsity in the temporal dimension (Liu et. al., in URSI Digest, 2014). Specifically, the LCB compression scheme was used to reduce the memory requirement of the PWTD ray data and computational cost of operations in the PWTD translation stage.

  17. Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo

    Energy Technology Data Exchange (ETDEWEB)

    Nakagami, K; Murayama, R; Matsuoka, T [Japan National Oil Corp., Tokyo (Japan)

    1996-05-01

    Introduced herein is the use of the wavelet transform in the field of seismic exploration. Among applications so far made, there are signal filtering, break point detection, data compression, and the solution of finite differential equations in the wavelet domain. In the field of data compression in particular, some examples of practical application have been introduced already. In seismic exploration, it is expected that the wavelet transform will separate signals and noises in data in a way different from the Fourier transform. The continuous wavelet transform displays time change in frequency easy to read, but is not suitable for the analysis and processing large quantities of data. On the other hand, the discrete wavelet transform, being an orthogonal transform, can handle large quantities of data. As compared with the conventional Fourier transform that handles only the frequency domain, the wavelet transform handles the time domain as well as the frequency domain, and therefore is more convenient in handling unsteady signals. 9 ref., 8 figs.

  18. VELOCITY FIELD OF COMPRESSIBLE MAGNETOHYDRODYNAMIC TURBULENCE: WAVELET DECOMPOSITION AND MODE SCALINGS

    International Nuclear Information System (INIS)

    Kowal, Grzegorz; Lazarian, A.

    2010-01-01

    We study compressible magnetohydrodynamic turbulence, which holds the key to many astrophysical processes, including star formation and cosmic-ray propagation. To account for the variations of the magnetic field in the strongly turbulent fluid, we use wavelet decomposition of the turbulent velocity field into Alfven, slow, and fast modes, which presents an extension of the Cho and Lazarian decomposition approach based on Fourier transforms. The wavelets allow us to follow the variations of the local direction of the magnetic field and therefore improve the quality of the decomposition compared to the Fourier transforms, which are done in the mean field reference frame. For each resulting component, we calculate the spectra and two-point statistics such as longitudinal and transverse structure functions as well as higher order intermittency statistics. In addition, we perform a Helmholtz- Hodge decomposition of the velocity field into incompressible and compressible parts and analyze these components. We find that the turbulence intermittency is different for different components, and we show that the intermittency statistics depend on whether the phenomenon was studied in the global reference frame related to the mean magnetic field or in the frame defined by the local magnetic field. The dependencies of the measures we obtained are different for different components of the velocity; for instance, we show that while the Alfven mode intermittency changes marginally with the Mach number, the intermittency of the fast mode is substantially affected by the change.

  19. Implementation in an FPGA circuit of Edge detection algorithm based on the Discrete Wavelet Transforms

    Science.gov (United States)

    Bouganssa, Issam; Sbihi, Mohamed; Zaim, Mounia

    2017-07-01

    The 2D Discrete Wavelet Transform (DWT) is a computationally intensive task that is usually implemented on specific architectures in many imaging systems in real time. In this paper, a high throughput edge or contour detection algorithm is proposed based on the discrete wavelet transform. A technique for applying the filters on the three directions (Horizontal, Vertical and Diagonal) of the image is used to present the maximum of the existing contours. The proposed architectures were designed in VHDL and mapped to a Xilinx Sparten6 FPGA. The results of the synthesis show that the proposed architecture has a low area cost and can operate up to 100 MHz, which can perform 2D wavelet analysis for a sequence of images while maintaining the flexibility of the system to support an adaptive algorithm.

  20. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  1. Parallel Algorithm for Wireless Data Compression and Encryption

    Directory of Open Access Journals (Sweden)

    Qin Jiancheng

    2017-01-01

    Full Text Available As the wireless network has limited bandwidth and insecure shared media, the data compression and encryption are very useful for the broadcasting transportation of big data in IoT (Internet of Things. However, the traditional techniques of compression and encryption are neither competent nor efficient. In order to solve this problem, this paper presents a combined parallel algorithm named “CZ algorithm” which can compress and encrypt the big data efficiently. CZ algorithm uses a parallel pipeline, mixes the coding of compression and encryption, and supports the data window up to 1 TB (or larger. Moreover, CZ algorithm can encrypt the big data as a chaotic cryptosystem which will not decrease the compression speed. Meanwhile, a shareware named “ComZip” is developed based on CZ algorithm. The experiment results show that ComZip in 64 b system can get better compression ratio than WinRAR and 7-zip, and it can be faster than 7-zip in the big data compression. In addition, ComZip encrypts the big data without extra consumption of computing resources.

  2. Efficient algorithms of multidimensional γ-ray spectra compression

    International Nuclear Information System (INIS)

    Morhac, M.; Matousek, V.

    2006-01-01

    The efficient algorithms to compress multidimensional γ-ray events are presented. Two alternative kinds of compression algorithms based on both the adaptive orthogonal and randomizing transforms are proposed. In both algorithms we employ the reduction of data volume due to the symmetry of the γ-ray spectra

  3. Highly Efficient Compression Algorithms for Multichannel EEG.

    Science.gov (United States)

    Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

    2018-05-01

    The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

  4. Hybrid sparse blind deconvolution: an implementation of SOOT algorithm to real data

    Science.gov (United States)

    Pakmanesh, Parvaneh; Goudarzi, Alireza; Kourki, Meisam

    2018-06-01

    Getting information of seismic data depends on deconvolution as an important processing step; it provides the reflectivity series by signal compression. This compression can be obtained by removing the wavelet effects on the traces. The recently blind deconvolution has provided reliable performance for sparse signal recovery. In this study, two deconvolution methods have been implemented to the seismic data; the convolution of these methods provides a robust spiking deconvolution approach. This hybrid deconvolution is applied using the sparse deconvolution (MM algorithm) and the Smoothed-One-Over-Two algorithm (SOOT) in a chain. The MM algorithm is based on the minimization of the cost function defined by standards l1 and l2. After applying the two algorithms to the seismic data, the SOOT algorithm provided well-compressed data with a higher resolution than the MM algorithm. The SOOT algorithm requires initial values to be applied for real data, such as the wavelet coefficients and reflectivity series that can be achieved through the MM algorithm. The computational cost of the hybrid method is high, and it is necessary to be implemented on post-stack or pre-stack seismic data of complex structure regions.

  5. The wavelet transform and the suppression theory of binocular vision for stereo image compression

    Energy Technology Data Exchange (ETDEWEB)

    Reynolds, W.D. Jr [Argonne National Lab., IL (United States); Kenyon, R.V. [Illinois Univ., Chicago, IL (United States)

    1996-08-01

    In this paper a method for compression of stereo images. The proposed scheme is a frequency domain approach based on the suppression theory of binocular vision. By using the information in the frequency domain, complex disparity estimation techniques can be avoided. The wavelet transform is used to obtain a multiresolution analysis of the stereo pair by which the subbands convey the necessary frequency domain information.

  6. Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2018-02-01

    Full Text Available Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA, Cuckoo Optimization Algorithm (COA, and Cuckoo Search (CS are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA and multiple linear regression (MLR. In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

  7. Image and video compression for multimedia engineering fundamentals, algorithms, and standards

    CERN Document Server

    Shi, Yun Q

    2008-01-01

    Part I: Fundamentals Introduction Quantization Differential Coding Transform Coding Variable-Length Coding: Information Theory Results (II) Run-Length and Dictionary Coding: Information Theory Results (III) Part II: Still Image Compression Still Image Coding: Standard JPEG Wavelet Transform for Image Coding: JPEG2000 Nonstandard Still Image Coding Part III: Motion Estimation and Compensation Motion Analysis and Motion Compensation Block Matching Pel-Recursive Technique Optical Flow Further Discussion and Summary on 2-D Motion Estimation Part IV: Video Compression Fundam

  8. LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

    Science.gov (United States)

    Cheng, Jun; Zhang, Jun; Tian, Jinwen

    2015-12-01

    Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.

  9. Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI)

    International Nuclear Information System (INIS)

    Delakis, Ioannis; Hammad, Omer; Kitney, Richard I

    2007-01-01

    Wavelet-based de-noising has been shown to improve image signal-to-noise ratio in magnetic resonance imaging (MRI) while maintaining spatial resolution. Wavelet-based de-noising techniques typically implemented in MRI require that noise displays uniform spatial distribution. However, images acquired with parallel MRI have spatially varying noise levels. In this work, a new algorithm for filtering images with parallel MRI is presented. The proposed algorithm extracts the edges from the original image and then generates a noise map from the wavelet coefficients at finer scales. The noise map is zeroed at locations where edges have been detected and directional analysis is also used to calculate noise in regions of low-contrast edges that may not have been detected. The new methodology was applied on phantom and brain images and compared with other applicable de-noising techniques. The performance of the proposed algorithm was shown to be comparable with other techniques in central areas of the images, where noise levels are high. In addition, finer details and edges were maintained in peripheral areas, where noise levels are low. The proposed methodology is fully automated and can be applied on final reconstructed images without requiring sensitivity profiles or noise matrices of the receiver coils, therefore making it suitable for implementation in a clinical MRI setting

  10. Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation

    Directory of Open Access Journals (Sweden)

    S.K. Saha

    2015-01-01

    Full Text Available This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA and Wavelet Mutation (WM strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM was adopted for the design of an 8th-order infinite impulse response (IIR filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP, high-pass (HP, band-pass (BP and band-stop (BS IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability.

  11. Fast reversible wavelet image compressor

    Science.gov (United States)

    Kim, HyungJun; Li, Ching-Chung

    1996-10-01

    We present a unified image compressor with spline biorthogonal wavelets and dyadic rational filter coefficients which gives high computational speed and excellent compression performance. Convolutions with these filters can be preformed by using only arithmetic shifting and addition operations. Wavelet coefficients can be encoded with an arithmetic coder which also uses arithmetic shifting and addition operations. Therefore, from the beginning to the end, the while encoding/decoding process can be done within a short period of time. The proposed method naturally extends form the lossless compression to the lossy but high compression range and can be easily adapted to the progressive reconstruction.

  12. Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

    Energy Technology Data Exchange (ETDEWEB)

    Anant, K.S.

    1997-06-01

    In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the P as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The `pick` time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the

  13. Evaluation of Algorithms for Compressing Hyperspectral Data

    Science.gov (United States)

    Cook, Sid; Harsanyi, Joseph; Faber, Vance

    2003-01-01

    With EO-1 Hyperion in orbit NASA is showing their continued commitment to hyperspectral imaging (HSI). As HSI sensor technology continues to mature, the ever-increasing amounts of sensor data generated will result in a need for more cost effective communication and data handling systems. Lockheed Martin, with considerable experience in spacecraft design and developing special purpose onboard processors, has teamed with Applied Signal & Image Technology (ASIT), who has an extensive heritage in HSI spectral compression and Mapping Science (MSI) for JPEG 2000 spatial compression expertise, to develop a real-time and intelligent onboard processing (OBP) system to reduce HSI sensor downlink requirements. Our goal is to reduce the downlink requirement by a factor > 100, while retaining the necessary spectral and spatial fidelity of the sensor data needed to satisfy the many science, military, and intelligence goals of these systems. Our compression algorithms leverage commercial-off-the-shelf (COTS) spectral and spatial exploitation algorithms. We are currently in the process of evaluating these compression algorithms using statistical analysis and NASA scientists. We are also developing special purpose processors for executing these algorithms onboard a spacecraft.

  14. Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB

    Directory of Open Access Journals (Sweden)

    Wei Jin

    2015-01-01

    Full Text Available An ideal compression method for neutron radiation image should have high compression ratio while keeping more details of the original image. Compressed sensing (CS, which can break through the restrictions of sampling theorem, is likely to offer an efficient compression scheme for the neutron radiation image. Combining wavelet transform with directional filter banks, a novel nonredundant multiscale geometry analysis transform named Wavelet Directional Filter Banks (WDFB is constructed and applied to represent neutron radiation image sparsely. Then, the block-based CS technique is introduced and a high performance CS scheme for neutron radiation image is proposed. By performing two-step iterative shrinkage algorithm the problem of L1 norm minimization is solved to reconstruct neutron radiation image from random measurements. The experiment results demonstrate that the scheme not only improves the quality of reconstructed image obviously but also retains more details of original image.

  15. Multiband CCD Image Compression for Space Camera with Large Field of View

    Directory of Open Access Journals (Sweden)

    Jin Li

    2014-01-01

    Full Text Available Space multiband CCD camera compression encoder requires low-complexity, high-robustness, and high-performance because of its captured images information being very precious and also because it is usually working on the satellite where the resources, such as power, memory, and processing capacity, are limited. However, the traditional compression approaches, such as JPEG2000, 3D transforms, and PCA, have the high-complexity. The Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC algorithm decreases the average PSNR by 2 dB compared with JPEG2000. In this paper, we proposed a low-complexity compression algorithm based on deep coupling algorithm among posttransform in wavelet domain, compressive sensing, and distributed source coding. In our algorithm, we integrate three low-complexity and high-performance approaches in a deeply coupled manner to remove the spatial redundant, spectral redundant, and bit information redundancy. Experimental results on multiband CCD images show that the proposed algorithm significantly outperforms the traditional approaches.

  16. BIND – An algorithm for loss-less compression of nucleotide ...

    Indian Academy of Sciences (India)

    constituting the FNA data set. Supplementary table 2. Original and compressed file sizes (obtained using various compression algorithms) for 2679 files constituting the FFN data set. Supplementary table 3. Original and compressed file sizes (obtained using various compression algorithms) for 25 files constituting the ...

  17. COMPRESSING BIOMEDICAL IMAGE BY USING INTEGER WAVELET TRANSFORM AND PREDICTIVE ENCODER

    OpenAIRE

    Anushree Srivastava*, Narendra Kumar Chaurasia

    2016-01-01

    Image compression has become an important process in today’s world of information exchange. It helps in effective utilization of high speed network resources. Medical image compression has an important role in medical field because they are used for future reference of patients. Medical data is compressed in such a way so that the diagnostics capabilities are not compromised or no medical information is lost. Medical imaging poses the great challenge of having compression algorithms that redu...

  18. Algorithms and data structures for grammar-compressed strings

    DEFF Research Database (Denmark)

    Cording, Patrick Hagge

    Textual databases for e.g. biological or web-data are growing rapidly, and it is often only feasible to store the data in compressed form. However, compressing the data comes at a price. Traditional algorithms for e.g. pattern matching requires all data to be decompressed - a computationally...... demanding task. In this thesis we design data structures for accessing and searching compressed data efficiently. Our results can be divided into two categories. In the first category we study problems related to pattern matching. In particular, we present new algorithms for counting and comparing...... substrings, and a new algorithm for finding all occurrences of a pattern in which we may insert gaps. In the other category we deal with accessing and decompressing parts of the compressed string. We show how to quickly access a single character of the compressed string, and present a data structure...

  19. Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

    Directory of Open Access Journals (Sweden)

    Yongjian Nian

    2013-01-01

    Full Text Available A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.

  20. Research on Wavelet-Based Algorithm for Image Contrast Enhancement

    Institute of Scientific and Technical Information of China (English)

    Wu Ying-qian; Du Pei-jun; Shi Peng-fei

    2004-01-01

    A novel wavelet-based algorithm for image enhancement is proposed in the paper. On the basis of multiscale analysis, the proposed algorithm solves efficiently the problem of noise over-enhancement, which commonly occurs in the traditional methods for contrast enhancement. The decomposed coefficients at same scales are processed by a nonlinear method, and the coefficients at different scales are enhanced in different degree. During the procedure, the method takes full advantage of the properties of Human visual system so as to achieve better performance. The simulations demonstrate that these characters of the proposed approach enable it to fully enhance the content in images, to efficiently alleviate the enhancement of noise and to achieve much better enhancement effect than the traditional approaches.

  1. Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

    International Nuclear Information System (INIS)

    Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

    2015-01-01

    Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach

  2. Resonance detection of EEG signals using two-layer wavelet analysis

    International Nuclear Information System (INIS)

    Abdallah, H. M; Odeh, F.S.

    2000-01-01

    This paper presents the hybrid quadrature mirror filter (HQMF) algorithm applied to the electroencephalogram (EEG) signal during mental activity. The information contents of this signal, i.e., its medical diagnosis, lie in its power spectral density (PSD). The HQMF algorithm is a modified technique that is based on the shape and the details of the signal. If applied efficiently, the HQMF algorithm will produce much better results than conventional wavelet methods in detecting (diagnosing) the information of the EEG signal from its PSD. This technique is applicable not only to EEG signals, but is highly recommended to compression analysis and de noising techniques. (authors). 16 refs., 9 figs

  3. Lossless medical image compression with a hybrid coder

    Science.gov (United States)

    Way, Jing-Dar; Cheng, Po-Yuen

    1998-10-01

    The volume of medical image data is expected to increase dramatically in the next decade due to the large use of radiological image for medical diagnosis. The economics of distributing the medical image dictate that data compression is essential. While there is lossy image compression, the medical image must be recorded and transmitted lossless before it reaches the users to avoid wrong diagnosis due to the image data lost. Therefore, a low complexity, high performance lossless compression schematic that can approach the theoretic bound and operate in near real-time is needed. In this paper, we propose a hybrid image coder to compress the digitized medical image without any data loss. The hybrid coder is constituted of two key components: an embedded wavelet coder and a lossless run-length coder. In this system, the medical image is compressed with the lossy wavelet coder first, and the residual image between the original and the compressed ones is further compressed with the run-length coder. Several optimization schemes have been used in these coders to increase the coding performance. It is shown that the proposed algorithm is with higher compression ratio than run-length entropy coders such as arithmetic, Huffman and Lempel-Ziv coders.

  4. An Introduction to Wavelet Theory and Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Miner, N.E.

    1998-10-01

    This report reviews the history, theory and mathematics of wavelet analysis. Examination of the Fourier Transform and Short-time Fourier Transform methods provides tiormation about the evolution of the wavelet analysis technique. This overview is intended to provide readers with a basic understanding of wavelet analysis, define common wavelet terminology and describe wavelet amdysis algorithms. The most common algorithms for performing efficient, discrete wavelet transforms for signal analysis and inverse discrete wavelet transforms for signal reconstruction are presented. This report is intended to be approachable by non- mathematicians, although a basic understanding of engineering mathematics is necessary.

  5. An analytical look at the effects of compression on medical images

    OpenAIRE

    Persons, Kenneth; Palisson, Patrice; Manduca, Armando; Erickson, Bradley J.; Savcenko, Vladimir

    1997-01-01

    This article will take an analytical look at how lossy Joint Photographic Experts Group (JPEG) and wavelet image compression techniques affect medical image content. It begins with a brief explanation of how the JPEG and wavelet algorithms work, and describes in general terms what effect they can have on image quality (removal of noise, blurring, and artifacts). It then focuses more specifically on medical image diagnostic content and explains why subtle pathologies, that may be difficult for...

  6. A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs

    Directory of Open Access Journals (Sweden)

    Yu Zheng

    2017-06-01

    Full Text Available In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm.

  7. LFQC: a lossless compression algorithm for FASTQ files

    Science.gov (United States)

    Nicolae, Marius; Pathak, Sudipta; Rajasekaran, Sanguthevar

    2015-01-01

    Motivation: Next Generation Sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large FASTQ files using innovative compression techniques. Results: We introduce a new lossless non-reference based FASTQ compression algorithm named Lossless FASTQ Compressor. We have compared our algorithm with other state of the art big data compression algorithms namely gzip, bzip2, fastqz (Bonfield and Mahoney, 2013), fqzcomp (Bonfield and Mahoney, 2013), Quip (Jones et al., 2012), DSRC2 (Roguski and Deorowicz, 2014). This comparison reveals that our algorithm achieves better compression ratios on LS454 and SOLiD datasets. Availability and implementation: The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/rajasek/lfqc-v1.1.zip. Contact: rajasek@engr.uconn.edu PMID:26093148

  8. A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

    Directory of Open Access Journals (Sweden)

    Meng Fan-Bo

    2016-01-01

    Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

  9. A splitting algorithm for the wavelet transform of cubic splines on a nonuniform grid

    Science.gov (United States)

    Sulaimanov, Z. M.; Shumilov, B. M.

    2017-10-01

    For cubic splines with nonuniform nodes, splitting with respect to the even and odd nodes is used to obtain a wavelet expansion algorithm in the form of the solution to a three-diagonal system of linear algebraic equations for the coefficients. Computations by hand are used to investigate the application of this algorithm for numerical differentiation. The results are illustrated by solving a prediction problem.

  10. Experimental scheme and restoration algorithm of block compression sensing

    Science.gov (United States)

    Zhang, Linxia; Zhou, Qun; Ke, Jun

    2018-01-01

    Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

  11. SCALCE: boosting sequence compression algorithms using locally consistent encoding.

    Science.gov (United States)

    Hach, Faraz; Numanagic, Ibrahim; Alkan, Can; Sahinalp, S Cenk

    2012-12-01

    The high throughput sequencing (HTS) platforms generate unprecedented amounts of data that introduce challenges for the computational infrastructure. Data management, storage and analysis have become major logistical obstacles for those adopting the new platforms. The requirement for large investment for this purpose almost signalled the end of the Sequence Read Archive hosted at the National Center for Biotechnology Information (NCBI), which holds most of the sequence data generated world wide. Currently, most HTS data are compressed through general purpose algorithms such as gzip. These algorithms are not designed for compressing data generated by the HTS platforms; for example, they do not take advantage of the specific nature of genomic sequence data, that is, limited alphabet size and high similarity among reads. Fast and efficient compression algorithms designed specifically for HTS data should be able to address some of the issues in data management, storage and communication. Such algorithms would also help with analysis provided they offer additional capabilities such as random access to any read and indexing for efficient sequence similarity search. Here we present SCALCE, a 'boosting' scheme based on Locally Consistent Parsing technique, which reorganizes the reads in a way that results in a higher compression speed and compression rate, independent of the compression algorithm in use and without using a reference genome. Our tests indicate that SCALCE can improve the compression rate achieved through gzip by a factor of 4.19-when the goal is to compress the reads alone. In fact, on SCALCE reordered reads, gzip running time can improve by a factor of 15.06 on a standard PC with a single core and 6 GB memory. Interestingly even the running time of SCALCE + gzip improves that of gzip alone by a factor of 2.09. When compared with the recently published BEETL, which aims to sort the (inverted) reads in lexicographic order for improving bzip2, SCALCE + gzip

  12. Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

    Science.gov (United States)

    Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

    2008-09-01

    In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

  13. Wavelet tree structure based speckle noise removal for optical coherence tomography

    Science.gov (United States)

    Yuan, Xin; Liu, Xuan; Liu, Yang

    2018-02-01

    We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.

  14. Considerations and Algorithms for Compression of Sets

    DEFF Research Database (Denmark)

    Larsson, Jesper

    We consider compression of unordered sets of distinct elements. After a discus- sion of the general problem, we focus on compressing sets of fixed-length bitstrings in the presence of statistical information. We survey techniques from previous work, suggesting some adjustments, and propose a novel...... compression algorithm that allows transparent incorporation of various estimates for probability distribution. Our experimental results allow the conclusion that set compression can benefit from incorporat- ing statistics, using our method or variants of previously known techniques....

  15. Composite Techniques Based Color Image Compression

    Directory of Open Access Journals (Sweden)

    Zainab Ibrahim Abood

    2017-03-01

    Full Text Available Compression for color image is now necessary for transmission and storage in the data bases since the color gives a pleasing nature and natural for any object, so three composite techniques based color image compression is implemented to achieve image with high compression, no loss in original image, better performance and good image quality. These techniques are composite stationary wavelet technique (S, composite wavelet technique (W and composite multi-wavelet technique (M. For the high energy sub-band of the 3rd level of each composite transform in each composite technique, the compression parameters are calculated. The best composite transform among the 27 types is the three levels of multi-wavelet transform (MMM in M technique which has the highest values of energy (En and compression ratio (CR and least values of bit per pixel (bpp, time (T and rate distortion R(D. Also the values of the compression parameters of the color image are nearly the same as the average values of the compression parameters of the three bands of the same image.

  16. Reconfigurable Hardware for Compressing Hyperspectral Image Data

    Science.gov (United States)

    Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua

    2010-01-01

    High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of

  17. Wavelets a primer

    CERN Document Server

    Blatter, Christian

    1998-01-01

    The Wavelet Transform has stimulated research that is unparalleled since the invention of the Fast Fourier Transform and has opened new avenues of applications in signal processing, image compression, radiology, cardiology, and many other areas. This book grew out of a short course for mathematics students at the ETH in Zurich; it provides a solid mathematical foundation for the broad range of applications enjoyed by the wavelet transform. Numerous illustrations and fully worked out examples enhance the book.

  18. Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms.

    Science.gov (United States)

    Khoje, Suchitra

    2018-02-01

    Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit

  19. Wavelet-Based Watermarking and Compression for ECG Signals with Verification Evaluation

    Directory of Open Access Journals (Sweden)

    Kuo-Kun Tseng

    2014-02-01

    Full Text Available In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user’s data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER, signal-to-noise ratio (SNR, compression ratio (CR, and compressed-signal to noise ratio (CNR methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

  20. BETTER FINGERPRINT IMAGE COMPRESSION AT LOWER BIT-RATES: AN APPROACH USING MULTIWAVELETS WITH OPTIMISED PREFILTER COEFFICIENTS

    Directory of Open Access Journals (Sweden)

    N R Rema

    2017-08-01

    Full Text Available In this paper, a multiwavelet based fingerprint compression technique using set partitioning in hierarchical trees (SPIHT algorithm with optimised prefilter coefficients is proposed. While wavelet based progressive compression techniques give a blurred image at lower bit rates due to lack of high frequency information, multiwavelets can be used efficiently to represent high frequency information. SA4 (Symmetric Antisymmetric multiwavelet when combined with SPIHT reduces the number of nodes during initialization to 1/4th compared to SPIHT with wavelet. This reduction in nodes leads to improvement in PSNR at lower bit rates. The PSNR can be further improved by optimizing the prefilter coefficients. In this work genetic algorithm (GA is used for optimizing prefilter coefficients. Using the proposed technique, there is a considerable improvement in PSNR at lower bit rates, compared to existing techniques in literature. An overall average improvement of 4.23dB and 2.52dB for bit rates in between 0.01 to 1 has been achieved for the images in the databases FVC 2000 DB1 and FVC 2002 DB3 respectively. The quality of the reconstructed image is better even at higher compression ratios like 80:1 and 100:1. The level of decomposition required for a multiwavelet is lesser compared to a wavelet.

  1. Denoising in Wavelet Packet Domain via Approximation Coefficients

    Directory of Open Access Journals (Sweden)

    Zahra Vahabi

    2012-01-01

    Full Text Available In this paper we propose a new approach in the wavelet domain for image denoising. In recent researches wavelet transform has introduced a time-Frequency transform for computing wavelet coefficient and eliminating noise. Some coefficients have effected smaller than the other's from noise, so they can be use reconstruct images with other subbands. We have developed Approximation image to estimate better denoised image. Naturally noiseless subimage introduced image with lower noise. Beside denoising we obtain a bigger compression rate. Increasing image contrast is another advantage of this method. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.100 images of LIVE Dataset were tested, comparing signal to noise ratios (SNR,soft thresholding was %1.12 better than hard thresholding, POAC was %1.94 better than soft thresholding and POAC with wavelet packet was %1.48 better than POAC.

  2. LOW COMPLEXITY HYBRID LOSSY TO LOSSLESS IMAGE CODER WITH COMBINED ORTHOGONAL POLYNOMIALS TRANSFORM AND INTEGER WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    R. Krishnamoorthy

    2012-05-01

    Full Text Available In this paper, a new lossy to lossless image coding scheme combined with Orthogonal Polynomials Transform and Integer Wavelet Transform is proposed. The Lifting Scheme based Integer Wavelet Transform (LS-IWT is first applied on the image in order to reduce the blocking artifact and memory demand. The Embedded Zero tree Wavelet (EZW subband coding algorithm is used in this proposed work for progressive image coding which achieves efficient bit rate reduction. The computational complexity of lower subband coding of EZW algorithm is reduced in this proposed work with a new integer based Orthogonal Polynomials transform coding. The normalization and mapping are done on the subband of the image for exploiting the subjective redundancy and the zero tree structure is obtained for EZW coding and so the computation complexity is greatly reduced in this proposed work. The experimental results of the proposed technique also show that the efficient bit rate reduction is achieved for both lossy and lossless compression when compared with existing techniques.

  3. Effect of JPEG2000 mammogram compression on microcalcifications segmentation

    International Nuclear Information System (INIS)

    Georgiev, V.; Arikidis, N.; Karahaliou, A.; Skiadopoulos, S.; Costaridou, L.

    2012-01-01

    The purpose of this study is to investigate the effect of mammographic image compression on the automated segmentation of individual microcalcifications. The dataset consisted of individual microcalcifications of 105 clusters originating from mammograms of the Digital Database for Screening Mammography. A JPEG2000 wavelet-based compression algorithm was used for compressing mammograms at 7 compression ratios (CRs): 10:1, 20:1, 30:1, 40:1, 50:1, 70:1 and 100:1. A gradient-based active contours segmentation algorithm was employed for segmentation of microcalcifications as depicted on original and compressed mammograms. The performance of the microcalcification segmentation algorithm on original and compressed mammograms was evaluated by means of the area overlap measure (AOM) and distance differentiation metrics (d mean and d max ) by comparing automatically derived microcalcification borders to manually defined ones by an expert radiologist. The AOM monotonically decreased as CR increased, while d mean and d max metrics monotonically increased with CR increase. The performance of the segmentation algorithm on original mammograms was (mean±standard deviation): AOM=0.91±0.08, d mean =0.06±0.05 and d max =0.45±0.20, while on 40:1 compressed images the algorithm's performance was: AOM=0.69±0.15, d mean =0.23±0.13 and d max =0.92±0.39. Mammographic image compression deteriorates the performance of the segmentation algorithm, influencing the quantification of individual microcalcification morphological properties and subsequently affecting computer aided diagnosis of microcalcification clusters. (authors)

  4. Wavelet-Based Poisson Solver for Use in Particle-in-Cell Simulations

    CERN Document Server

    Terzic, Balsa; Mihalcea, Daniel; Pogorelov, Ilya V

    2005-01-01

    We report on a successful implementation of a wavelet-based Poisson solver for use in 3D particle-in-cell simulations. One new aspect of our algorithm is its ability to treat the general (inhomogeneous) Dirichlet boundary conditions. The solver harnesses advantages afforded by the wavelet formulation, such as sparsity of operators and data sets, existence of effective preconditioners, and the ability simultaneously to remove numerical noise and further compress relevant data sets. Having tested our method as a stand-alone solver on two model problems, we merged it into IMPACT-T to obtain a fully functional serial PIC code. We present and discuss preliminary results of application of the new code to the modelling of the Fermilab/NICADD and AES/JLab photoinjectors.

  5. Wavelet-based Poisson Solver for use in Particle-In-Cell Simulations

    International Nuclear Information System (INIS)

    Terzic, B.; Mihalcea, D.; Bohn, C.L.; Pogorelov, I.V.

    2005-01-01

    We report on a successful implementation of a wavelet based Poisson solver for use in 3D particle-in-cell (PIC) simulations. One new aspect of our algorithm is its ability to treat the general(inhomogeneous) Dirichlet boundary conditions (BCs). The solver harnesses advantages afforded by the wavelet formulation, such as sparsity of operators and data sets, existence of effective preconditioners, and the ability simultaneously to remove numerical noise and further compress relevant data sets. Having tested our method as a stand-alone solver on two model problems, we merged it into IMPACT-T to obtain a fully functional serial PIC code. We present and discuss preliminary results of application of the new code to the modeling of the Fermilab/NICADD and AES/JLab photoinjectors

  6. Research on compressive sensing reconstruction algorithm based on total variation model

    Science.gov (United States)

    Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

    2017-12-01

    Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

  7. Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems.

    Science.gov (United States)

    Al-Busaidi, Asiya M; Khriji, Lazhar; Touati, Farid; Rasid, Mohd Fadlee; Mnaouer, Adel Ben

    2017-09-12

    One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.

  8. Wavelet based edge detection algorithm for web surface inspection of coated board web

    Energy Technology Data Exchange (ETDEWEB)

    Barjaktarovic, M; Petricevic, S, E-mail: slobodan@etf.bg.ac.r [School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11000 Belgrade (Serbia)

    2010-07-15

    This paper presents significant improvement of the already installed vision system. System was designed for real time coated board inspection. The improvement is achieved with development of a new algorithm for edge detection. The algorithm is based on the redundant (undecimated) wavelet transform. Compared to the existing algorithm better delineation of edges is achieved. This yields to better defect detection probability and more accurate geometrical classification, which will provide additional reduction of waste. Also, algorithm will provide detailed classification and more reliably tracking of defects. This improvement requires minimal changes in processing hardware, only a replacement of the graphic card would be needed, adding only negligibly to the system cost. Other changes are accomplished entirely in the image processing software.

  9. Efficient predictive algorithms for image compression

    CERN Document Server

    Rosário Lucas, Luís Filipe; Maciel de Faria, Sérgio Manuel; Morais Rodrigues, Nuno Miguel; Liberal Pagliari, Carla

    2017-01-01

    This book discusses efficient prediction techniques for the current state-of-the-art High Efficiency Video Coding (HEVC) standard, focusing on the compression of a wide range of video signals, such as 3D video, Light Fields and natural images. The authors begin with a review of the state-of-the-art predictive coding methods and compression technologies for both 2D and 3D multimedia contents, which provides a good starting point for new researchers in the field of image and video compression. New prediction techniques that go beyond the standardized compression technologies are then presented and discussed. In the context of 3D video, the authors describe a new predictive algorithm for the compression of depth maps, which combines intra-directional prediction, with flexible block partitioning and linear residue fitting. New approaches are described for the compression of Light Field and still images, which enforce sparsity constraints on linear models. The Locally Linear Embedding-based prediction method is in...

  10. Evaluation of onboard hyperspectral-image compression techniques for a parallel push-broom sensor

    Energy Technology Data Exchange (ETDEWEB)

    Briles, S.

    1996-04-01

    A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation coefficient error. Implementation issues are considered in algorithm development.

  11. A study of non-binary discontinuity wavelet

    International Nuclear Information System (INIS)

    Lin Hai; Liu Lianshou

    2006-01-01

    This paper gives a study of non-binary discontinuity wavelet, put forward the theory and method of constituting basic wavelet functions, and has constituted concretely a wavelet function using λ=3.4 as an example. It also conducts a theoretical inference on the decomposition algorithm and reconstruction algorithm of non-binary wavelet, and gives a concrete study of the change of matrix in connection with λ=3.4. In the end, it shows the future of application of the result to the study of high energy collision. (authors)

  12. [Research on electrocardiogram de-noising algorithm based on wavelet neural networks].

    Science.gov (United States)

    Wan, Xiangkui; Zhang, Jun

    2010-12-01

    In this paper, the ECG de-noising technology based on wavelet neural networks (WNN) is used to deal with the noises in Electrocardiogram (ECG) signal. The structure of WNN, which has the outstanding nonlinear mapping capability, is designed as a nonlinear filter used for ECG to cancel the baseline wander, electromyo-graphical interference and powerline interference. The network training algorithm and de-noising experiments results are presented, and some key points of the WNN filter using ECG de-noising are discussed.

  13. Parsimonious Wavelet Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wang Qin

    2015-11-01

    Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

  14. Visibility of wavelet quantization noise

    Science.gov (United States)

    Watson, A. B.; Yang, G. Y.; Solomon, J. A.; Villasenor, J.

    1997-01-01

    The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

  15. Wavelet theory and its applications

    Energy Technology Data Exchange (ETDEWEB)

    Faber, V.; Bradley, JJ.; Brislawn, C.; Dougherty, R.; Hawrylycz, M.

    1996-07-01

    This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). We investigated the theory of wavelet transforms and their relation to Laboratory applications. The investigators have had considerable success in the past applying wavelet techniques to the numerical solution of optimal control problems for distributed- parameter systems, nonlinear signal estimation, and compression of digital imagery and multidimensional data. Wavelet theory involves ideas from the fields of harmonic analysis, numerical linear algebra, digital signal processing, approximation theory, and numerical analysis, and the new computational tools arising from wavelet theory are proving to be ideal for many Laboratory applications. 10 refs.

  16. Analysing Music with Point-Set Compression Algorithms

    DEFF Research Database (Denmark)

    Meredith, David

    2016-01-01

    Several point-set pattern-discovery and compression algorithms designed for analysing music are reviewed and evaluated. Each algorithm takes as input a point-set representation of a score in which each note is represented as a point in pitch-time space. Each algorithm computes the maximal...... and sections in pieces of classical music. On the first task, the best-performing algorithms achieved success rates of around 84%. In the second task, the best algorithms achieved mean F1 scores of around 0.49, with scores for individual pieces rising as high as 0.71....

  17. Flaw characterization through nonlinear ultrasonics and wavelet cross-correlation algorithms

    Science.gov (United States)

    Bunget, Gheorghe; Yee, Andrew; Stewart, Dylan; Rogers, James; Henley, Stanley; Bugg, Chris; Cline, John; Webster, Matthew; Farinholt, Kevin; Friedersdorf, Fritz

    2018-04-01

    Ultrasonic measurements have become increasingly important non-destructive techniques to characterize flaws found within various in-service industrial components. The prediction of remaining useful life based on fracture analysis depends on the accurate estimation of flaw size and orientation. However, amplitude-based ultrasonic measurements are not able to estimate the plastic zones that exist ahead of crack tips. Estimating the size of the plastic zone is an advantage since some flaws may propagate faster than others. This paper presents a wavelet cross-correlation (WCC) algorithm that was applied to nonlinear analysis of ultrasonically guided waves (GW). By using this algorithm, harmonics present in the waveforms were extracted and nonlinearity parameters were used to indicate both the tip of the cracks and size of the plastic zone. B-scans performed with the quadratic nonlinearities were sensitive to micro-damage specific to plastic zones.

  18. Application specific compression : final report.

    Energy Technology Data Exchange (ETDEWEB)

    Melgaard, David Kennett; Byrne, Raymond Harry; Myers, Daniel S.; Harrison, Carol D.; Lee, David S.; Lewis, Phillip J.; Carlson, Jeffrey J.

    2008-12-01

    With the continuing development of more capable data gathering sensors, comes an increased demand on the bandwidth for transmitting larger quantities of data. To help counteract that trend, a study was undertaken to determine appropriate lossy data compression strategies for minimizing their impact on target detection and characterization. The survey of current compression techniques led us to the conclusion that wavelet compression was well suited for this purpose. Wavelet analysis essentially applies a low-pass and high-pass filter to the data, converting the data into the related coefficients that maintain spatial information as well as frequency information. Wavelet compression is achieved by zeroing the coefficients that pertain to the noise in the signal, i.e. the high frequency, low amplitude portion. This approach is well suited for our goal because it reduces the noise in the signal with only minimal impact on the larger, lower frequency target signatures. The resulting coefficients can then be encoded using lossless techniques with higher compression levels because of the lower entropy and significant number of zeros. No significant signal degradation or difficulties in target characterization or detection were observed or measured when wavelet compression was applied to simulated and real data, even when over 80% of the coefficients were zeroed. While the exact level of compression will be data set dependent, for the data sets we studied, compression factors over 10 were found to be satisfactory where conventional lossless techniques achieved levels of less than 3.

  19. EBLAST: an efficient high-compression image transformation 3. application to Internet image and video transmission

    Science.gov (United States)

    Schmalz, Mark S.; Ritter, Gerhard X.; Caimi, Frank M.

    2001-12-01

    A wide variety of digital image compression transforms developed for still imaging and broadcast video transmission are unsuitable for Internet video applications due to insufficient compression ratio, poor reconstruction fidelity, or excessive computational requirements. Examples include hierarchical transforms that require all, or large portion of, a source image to reside in memory at one time, transforms that induce significant locking effect at operationally salient compression ratios, and algorithms that require large amounts of floating-point computation. The latter constraint holds especially for video compression by small mobile imaging devices for transmission to, and compression on, platforms such as palmtop computers or personal digital assistants (PDAs). As Internet video requirements for frame rate and resolution increase to produce more detailed, less discontinuous motion sequences, a new class of compression transforms will be needed, especially for small memory models and displays such as those found on PDAs. In this, the third series of papers, we discuss the EBLAST compression transform and its application to Internet communication. Leading transforms for compression of Internet video and still imagery are reviewed and analyzed, including GIF, JPEG, AWIC (wavelet-based), wavelet packets, and SPIHT, whose performance is compared with EBLAST. Performance analysis criteria include time and space complexity and quality of the decompressed image. The latter is determined by rate-distortion data obtained from a database of realistic test images. Discussion also includes issues such as robustness of the compressed format to channel noise. EBLAST has been shown to perform superiorly to JPEG and, unlike current wavelet compression transforms, supports fast implementation on embedded processors with small memory models.

  20. Task-oriented lossy compression of magnetic resonance images

    Science.gov (United States)

    Anderson, Mark C.; Atkins, M. Stella; Vaisey, Jacques

    1996-04-01

    A new task-oriented image quality metric is used to quantify the effects of distortion introduced into magnetic resonance images by lossy compression. This metric measures the similarity between a radiologist's manual segmentation of pathological features in the original images and the automated segmentations performed on the original and compressed images. The images are compressed using a general wavelet-based lossy image compression technique, embedded zerotree coding, and segmented using a three-dimensional stochastic model-based tissue segmentation algorithm. The performance of the compression system is then enhanced by compressing different regions of the image volume at different bit rates, guided by prior knowledge about the location of important anatomical regions in the image. Application of the new system to magnetic resonance images is shown to produce compression results superior to the conventional methods, both subjectively and with respect to the segmentation similarity metric.

  1. Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2016-07-01

    Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

  2. Quasi Gradient Projection Algorithm for Sparse Reconstruction in Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Xin Meng

    2014-02-01

    Full Text Available Compressed sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. The existing recovery algorithms based on the gradient projection can either need prior knowledge or recovery the signal poorly. In this paper, a new algorithm based on gradient projection is proposed, which is referred as Quasi Gradient Projection. The algorithm presented quasi gradient direction and two step sizes schemes along this direction. The algorithm doesn’t need any prior knowledge of the original signal. Simulation results demonstrate that the presented algorithm cans recovery the signal more correctly than GPSR which also don’t need prior knowledge. Meanwhile, the algorithm has a lower computation complexity.

  3. Compressive sensing based algorithms for electronic defence

    CERN Document Server

    Mishra, Amit Kumar

    2017-01-01

    This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.

  4. A New Algorithm for the On-Board Compression of Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Raúl Guerra

    2018-03-01

    Full Text Available Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA, is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

  5. Wavelets for the stimulation of turbulent incompressible flows

    International Nuclear Information System (INIS)

    Deriaz, E.

    2006-02-01

    This PhD thesis presents original wavelet methods aimed at simulating incompressible fluids. In order to construct 2D and 3D wavelets designed for incompressible flows, we resume P-G Lemarie-Rieussets and K. Urbans works on divergence free wavelets. We show the existence of associated fast algorithms. In the following, we use divergence-free wavelet construction to define the Helmholtz decomposition of 2D and 3D vector fields. All these algorithms provide a new method for the numerical resolution of the incompressible Navier-Stokes equations. (author)

  6. Resource efficient data compression algorithms for demanding, WSN based biomedical applications.

    Science.gov (United States)

    Antonopoulos, Christos P; Voros, Nikolaos S

    2016-02-01

    During the last few years, medical research areas of critical importance such as Epilepsy monitoring and study, increasingly utilize wireless sensor network technologies in order to achieve better understanding and significant breakthroughs. However, the limited memory and communication bandwidth offered by WSN platforms comprise a significant shortcoming to such demanding application scenarios. Although, data compression can mitigate such deficiencies there is a lack of objective and comprehensive evaluation of relative approaches and even more on specialized approaches targeting specific demanding applications. The research work presented in this paper focuses on implementing and offering an in-depth experimental study regarding prominent, already existing as well as novel proposed compression algorithms. All algorithms have been implemented in a common Matlab framework. A major contribution of this paper, that differentiates it from similar research efforts, is the employment of real world Electroencephalography (EEG) and Electrocardiography (ECG) datasets comprising the two most demanding Epilepsy modalities. Emphasis is put on WSN applications, thus the respective metrics focus on compression rate and execution latency for the selected datasets. The evaluation results reveal significant performance and behavioral characteristics of the algorithms related to their complexity and the relative negative effect on compression latency as opposed to the increased compression rate. It is noted that the proposed schemes managed to offer considerable advantage especially aiming to achieve the optimum tradeoff between compression rate-latency. Specifically, proposed algorithm managed to combine highly completive level of compression while ensuring minimum latency thus exhibiting real-time capabilities. Additionally, one of the proposed schemes is compared against state-of-the-art general-purpose compression algorithms also exhibiting considerable advantages as far as the

  7. EEG Artifact Removal Using a Wavelet Neural Network

    Science.gov (United States)

    Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

    2011-01-01

    !n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

  8. Signal Recovery in Compressive Sensing via Multiple Sparsifying Bases

    DEFF Research Database (Denmark)

    Wijewardhana, U. L.; Belyaev, Evgeny; Codreanu, M.

    2017-01-01

    is sparse is the key assumption utilized by such algorithms. However, the basis in which the signal is the sparsest is unknown for many natural signals of interest. Instead there may exist multiple bases which lead to a compressible representation of the signal: e.g., an image is compressible in different...... wavelet transforms. We show that a significant performance improvement can be achieved by utilizing multiple estimates of the signal using sparsifying bases in the context of signal reconstruction from compressive samples. Further, we derive a customized interior-point method to jointly obtain multiple...... estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient....

  9. Adaptive algorithms for a self-shielding wavelet-based Galerkin method

    International Nuclear Information System (INIS)

    Fournier, D.; Le Tellier, R.

    2009-01-01

    The treatment of the energy variable in deterministic neutron transport methods is based on a multigroup discretization, considering the flux and cross-sections to be constant within a group. In this case, a self-shielding calculation is mandatory to correct sections of resonant isotopes. In this paper, a different approach based on a finite element discretization on a wavelet basis is used. We propose adaptive algorithms constructed from error estimates. Such an approach is applied to within-group scattering source iterations. A first implementation is presented in the special case of the fine structure equation for an infinite homogeneous medium. Extension to spatially-dependent cases is discussed. (authors)

  10. Empirical and Statistical Evaluation of the Effectiveness of Four Lossless Data Compression Algorithms

    Directory of Open Access Journals (Sweden)

    N. A. Azeez

    2017-04-01

    Full Text Available Data compression is the process of reducing the size of a file to effectively reduce storage space and communication cost. The evolvement in technology and digital age has led to an unparalleled usage of digital files in this current decade. The usage of data has resulted to an increase in the amount of data being transmitted via various channels of data communication which has prompted the need to look into the current lossless data compression algorithms to check for their level of effectiveness so as to maximally reduce the bandwidth requirement in communication and transfer of data. Four lossless data compression algorithm: Lempel-Ziv Welch algorithm, Shannon-Fano algorithm, Adaptive Huffman algorithm and Run-Length encoding have been selected for implementation. The choice of these algorithms was based on their similarities, particularly in application areas. Their level of efficiency and effectiveness were evaluated using some set of predefined performance evaluation metrics namely compression ratio, compression factor, compression time, saving percentage, entropy and code efficiency. The algorithms implementation was done in the NetBeans Integrated Development Environment using Java as the programming language. Through the statistical analysis performed using Boxplot and ANOVA and comparison made on the four algo

  11. A new modified fast fractal image compression algorithm

    DEFF Research Database (Denmark)

    Salarian, Mehdi; Nadernejad, Ehsan; MiarNaimi, Hossein

    2013-01-01

    In this paper, a new fractal image compression algorithm is proposed, in which the time of the encoding process is considerably reduced. The algorithm exploits a domain pool reduction approach, along with the use of innovative predefined values for contrast scaling factor, S, instead of searching...

  12. The compression algorithm for the data acquisition system in HT-7 tokamak

    International Nuclear Information System (INIS)

    Zhu Lin; Luo Jiarong; Li Guiming; Yue Dongli

    2003-01-01

    HT-7 superconducting tokamak in the Institute of Plasma Physics of the Chinese Academy of Sciences is an experimental device for fusion research in China. The main task of the data acquisition system of HT-7 is to acquire, store, analyze and index the data. The volume of the data is nearly up to hundreds of million bytes. Besides the hardware and software support, a great capacity of data storage, process and transfer is a more important problem. To deal with this problem, the key technology is data compression algorithm. In the paper, the data format in HT-7 is introduced first, then the data compression algorithm, LZO, being a kind of portable lossless data compression algorithm with ANSIC, is analyzed. This compression algorithm, which fits well with the data acquisition and distribution in the nuclear fusion experiment, offers a pretty fast compression and extremely fast decompression. At last the performance evaluation of LZO application in HT-7 is given

  13. MPEG-2 Compressed-Domain Algorithms for Video Analysis

    Directory of Open Access Journals (Sweden)

    Hesseler Wolfgang

    2006-01-01

    Full Text Available This paper presents new algorithms for extracting metadata from video sequences in the MPEG-2 compressed domain. Three algorithms for efficient low-level metadata extraction in preprocessing stages are described. The first algorithm detects camera motion using the motion vector field of an MPEG-2 video. The second method extends the idea of motion detection to a limited region of interest, yielding an efficient algorithm to track objects inside video sequences. The third algorithm performs a cut detection using macroblock types and motion vectors.

  14. A rapid compression technique for 4-D functional MRI images using data rearrangement and modified binary array techniques.

    Science.gov (United States)

    Uma Vetri Selvi, G; Nadarajan, R

    2015-12-01

    Compression techniques are vital for efficient storage and fast transfer of medical image data. The existing compression techniques take significant amount of time for performing encoding and decoding and hence the purpose of compression is not fully satisfied. In this paper a rapid 4-D lossy compression method constructed using data rearrangement, wavelet-based contourlet transformation and a modified binary array technique has been proposed for functional magnetic resonance imaging (fMRI) images. In the proposed method, the image slices of fMRI data are rearranged so that the redundant slices form a sequence. The image sequence is then divided into slices and transformed using wavelet-based contourlet transform (WBCT). In WBCT, the high frequency sub-band obtained from wavelet transform is further decomposed into multiple directional sub-bands by directional filter bank to obtain more directional information. The relationship between the coefficients has been changed in WBCT as it has more directions. The differences in parent–child relationships are handled by a repositioning algorithm. The repositioned coefficients are then subjected to quantization. The quantized coefficients are further compressed by modified binary array technique where the most frequently occurring value of a sequence is coded only once. The proposed method has been experimented with fMRI images the results indicated that the processing time of the proposed method is less compared to existing wavelet-based set partitioning in hierarchical trees and set partitioning embedded block coder (SPECK) compression schemes [1]. The proposed method could also yield a better compression performance compared to wavelet-based SPECK coder. The objective results showed that the proposed method could gain good compression ratio in maintaining a peak signal noise ratio value of above 70 for all the experimented sequences. The SSIM value is equal to 1 and the value of CC is greater than 0.9 for all

  15. Using general-purpose compression algorithms for music analysis

    DEFF Research Database (Denmark)

    Louboutin, Corentin; Meredith, David

    2016-01-01

    General-purpose compression algorithms encode files as dictionaries of substrings with the positions of these strings’ occurrences. We hypothesized that such algorithms could be used for pattern discovery in music. We compared LZ77, LZ78, Burrows–Wheeler and COSIATEC on classifying folk song...... in the input data, COSIATEC outperformed LZ77 with a mean F1 score of 0.123, compared with 0.053 for LZ77. However, when the music was processed a voice at a time, the F1 score for LZ77 more than doubled to 0.124. We also discovered a significant correlation between compression factor and F1 score for all...

  16. Wavelet Enhanced Appearance Modelling

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren; Cootes, Timothy F.

    2004-01-01

    Generative segmentation methods such as the Active Appearance Models (AAM) establish dense correspondences by modelling variation of shape and pixel intensities. Alas, for 3D and high-resolution 2D images typical in medical imaging, this approach is rendered infeasible due to excessive storage......-7 wavelets on face images have shown that segmentation accuracy degrades gracefully with increasing compression ratio. Further, a proposed weighting scheme emphasizing edges was shown to be significantly more accurate at compression ratio 1:1, than a conventional AAM. At higher compression ratios the scheme...

  17. Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

    Science.gov (United States)

    Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

    2017-04-01

    Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

  18. Printed Persian Subword Recognition Using Wavelet Packet Descriptors

    Directory of Open Access Journals (Sweden)

    Samira Nasrollahi

    2013-01-01

    Full Text Available In this paper, we present a new approach to offline OCR (optical character recognition for printed Persian subwords using wavelet packet transform. The proposed algorithm is used to extract font invariant and size invariant features from 87804 subwords of 4 fonts and 3 sizes. The feature vectors are compressed using PCA. The obtained feature vectors yield a pictorial dictionary for which an entry is the mean of each group that consists of the same subword with 4 fonts in 3 sizes. The sets of these features are congregated by combining them with the dot features for the recognition of printed Persian subwords. To evaluate the feature extraction results, this algorithm was tested on a set of 2000 subwords in printed Persian text documents. An encouraging recognition rate of 97.9% is got at subword level recognition.

  19. Watermarking on 3D mesh based on spherical wavelet transform.

    Science.gov (United States)

    Jin, Jian-Qiu; Dai, Min-Ya; Bao, Hu-Jun; Peng, Qun-Sheng

    2004-03-01

    In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.

  20. A Near-Lossless Image Compression Algorithm Suitable for Hardware Design in Wireless Endoscopy System

    Directory of Open Access Journals (Sweden)

    Xie Xiang

    2007-01-01

    Full Text Available In order to decrease the communication bandwidth and save the transmitting power in the wireless endoscopy capsule, this paper presents a new near-lossless image compression algorithm based on the Bayer format image suitable for hardware design. This algorithm can provide low average compression rate ( bits/pixel with high image quality (larger than dB for endoscopic images. Especially, it has low complexity hardware overhead (only two line buffers and supports real-time compressing. In addition, the algorithm can provide lossless compression for the region of interest (ROI and high-quality compression for other regions. The ROI can be selected arbitrarily by varying ROI parameters. In addition, the VLSI architecture of this compression algorithm is also given out. Its hardware design has been implemented in m CMOS process.

  1. Dictionary Approaches to Image Compression and Reconstruction

    Science.gov (United States)

    Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

    1998-01-01

    This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as phi(sub gamma), are discrete time signals, where gamma represents the dictionary index. A dictionary with a collection of these waveforms is typically complete or overcomplete. Given such a dictionary, the goal is to obtain a representation image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

  2. Wavelet Radiosity on Arbitrary Planar Surfaces

    OpenAIRE

    Holzschuch , Nicolas; Cuny , François; Alonso , Laurent

    2000-01-01

    Colloque avec actes et comité de lecture. internationale.; International audience; Wavelet radiosity is, by its nature, restricted to parallelograms or triangles. This paper presents an innovative technique enabling wavelet radiosity computations on planar surfaces of arbitrary shape, including concave contours or contours with holes. This technique replaces the need for triangulating such complicated shapes, greatly reducing the complexity of the wavelet radiosity algorithm and the computati...

  3. Unified compression and encryption algorithm for fast and secure network communications

    International Nuclear Information System (INIS)

    Rizvi, S.M.J.; Hussain, M.; Qaiser, N.

    2005-01-01

    Compression and encryption of data are two vital requirements for the fast and secure transmission of data in the network based communications. In this paper an algorithm is presented based on adaptive Huffman encoding for unified compression and encryption of Unicode encoded textual data. The Huffman encoding weakness that same tree is needed for decoding is utilized in the algorithm presented as an extra layer of security, which is updated whenever the frequency change is above the specified threshold level. The results show that we get compression comparable to popular zip format and in addition to that data has got an additional layer of encryption that makes it more secure. Thus unified algorithm presented here can be used for network communications between different branches of banks, e- Government programs and national database and registration centers where data transmission requires both compression and encryption. (author)

  4. A real-time ECG data compression and transmission algorithm for an e-health device.

    Science.gov (United States)

    Lee, SangJoon; Kim, Jungkuk; Lee, Myoungho

    2011-09-01

    This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

  5. Wavelets and their uses

    International Nuclear Information System (INIS)

    Dremin, Igor M; Ivanov, Oleg V; Nechitailo, Vladimir A

    2001-01-01

    This review paper is intended to give a useful guide for those who want to apply the discrete wavelet transform in practice. The notion of wavelets and their use in practical computing and various applications are briefly described, but rigorous proofs of mathematical statements are omitted, and the reader is just referred to the corresponding literature. The multiresolution analysis and fast wavelet transform have become a standard procedure for dealing with discrete wavelets. The proper choice of a wavelet and use of nonstandard matrix multiplication are often crucial for the achievement of a goal. Analysis of various functions with the help of wavelets allows one to reveal fractal structures, singularities etc. The wavelet transform of operator expressions helps solve some equations. In practical applications one often deals with the discretized functions, and the problem of stability of the wavelet transform and corresponding numerical algorithms becomes important. After discussing all these topics we turn to practical applications of the wavelet machinery. They are so numerous that we have to limit ourselves to a few examples only. The authors would be grateful for any comments which would move us closer to the goal proclaimed in the first phrase of the abstract. (reviews of topical problems)

  6. Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms

    Science.gov (United States)

    2004-08-06

    wavelet transforms. Whereas the term “evolved” pertains only to the altered wavelet coefficients used during the inverse transform process. 2...words, the inverse transform produces the original signal x(t) from the wavelet and scaling coefficients. )()( ,, tdtx nk n nk k ψ...reconstruct the original signal as accurately as possible. The inverse transform reconstructs an approximation of the original signal (Burrus

  7. Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

    Science.gov (United States)

    Gong, Lihua; Deng, Chengzhi; Pan, Shumin; Zhou, Nanrun

    2018-07-01

    Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

  8. Application of Neuro-Wavelet Algorithm in Ultrasonic-Phased Array Nondestructive Testing of Polyethylene Pipelines

    Directory of Open Access Journals (Sweden)

    Reza Bohlouli

    2012-01-01

    Full Text Available Polyethylene (PE pipelines with electrofusion (EF joining is an essential method of transportation of gas energy. EF joints are weak points for leakage and therefore, Nondestructive testing (NDT methods including ultrasonic array technology are necessary. This paper presents a practical NDT method of fusion joints of polyethylene piping using intelligent ultrasonic image processing techniques. In the proposed method, to detect the defects of electrofusion joints, the NDT is applied based on an ANN-Wavelet method as a digital image processing technique. The proposed approach includes four steps. First an ultrasonic-phased array technique is used to provide real time images of high resolution. In the second step, the images are preprocessed by digital image processing techniques for noise reduction and detection of ROI (Region of Interest. Furthermore, to make more improvement on the images, mathematical morphology techniques such as dilation and erosion are applied. In the 3rd step, a wavelet transform is used to develop a feature vector containing 3-dimensional information on various types of defects. In the final step, all the feature vectors are classified through a backpropagation-based ANN algorithm. The obtained results show that the proposed algorithms are highly reliable and also precise for NDT monitoring.

  9. A novel high-frequency encoding algorithm for image compression

    Science.gov (United States)

    Siddeq, Mohammed M.; Rodrigues, Marcos A.

    2017-12-01

    In this paper, a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the discrete cosine transform (DCT) together with a high-frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) divide the image into blocks and apply DCT to each block; (2) apply a high-frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a minimized array; (3) build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) apply a delta or differential operator to the list of DC-components; and (5) apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high-frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000.

  10. Enhancement of Satellite Image Compression Using a Hybrid (DWT-DCT) Algorithm

    Science.gov (United States)

    Shihab, Halah Saadoon; Shafie, Suhaidi; Ramli, Abdul Rahman; Ahmad, Fauzan

    2017-12-01

    Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) image compression techniques have been utilized in most of the earth observation satellites launched during the last few decades. However, these techniques have some issues that should be addressed. The DWT method has proven to be more efficient than DCT for several reasons. Nevertheless, the DCT can be exploited to improve the high-resolution satellite image compression when combined with the DWT technique. Hence, a proposed hybrid (DWT-DCT) method was developed and implemented in the current work, simulating an image compression system on-board on a small remote sensing satellite, with the aim of achieving a higher compression ratio to decrease the onboard data storage and the downlink bandwidth, while avoiding further complex levels of DWT. This method also succeeded in maintaining the reconstructed satellite image quality through replacing the standard forward DWT thresholding and quantization processes with an alternative process that employed the zero-padding technique, which also helped to reduce the processing time of DWT compression. The DCT, DWT and the proposed hybrid methods were implemented individually, for comparison, on three LANDSAT 8 images, using the MATLAB software package. A comparison was also made between the proposed method and three other previously published hybrid methods. The evaluation of all the objective and subjective results indicated the feasibility of using the proposed hybrid (DWT-DCT) method to enhance the image compression process on-board satellites.

  11. Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

    Science.gov (United States)

    Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

    2015-05-01

    An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

  12. Image Encryption Algorithm Based on a Novel Improper Fractional-Order Attractor and a Wavelet Function Map

    Directory of Open Access Journals (Sweden)

    Jian-feng Zhao

    2017-01-01

    Full Text Available This paper presents a three-dimensional autonomous chaotic system with high fraction dimension. It is noted that the nonlinear characteristic of the improper fractional-order chaos is interesting. Based on the continuous chaos and the discrete wavelet function map, an image encryption algorithm is put forward. The key space is formed by the initial state variables, parameters, and orders of the system. Every pixel value is included in secret key, so as to improve antiattack capability of the algorithm. The obtained simulation results and extensive security analyses demonstrate the high level of security of the algorithm and show its robustness against various types of attacks.

  13. A novel algorithm for discrimination between inrush current and internal faults in power transformer differential protection based on discrete wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Eldin, A.A. Hossam; Refaey, M.A. [Electrical Engineering Department, Alexandria University, Alexandria (Egypt)

    2011-01-15

    This paper proposes a novel methodology for transformer differential protection, based on wave shape recognition of the discriminating criterion extracted of the instantaneous differential currents. Discrete wavelet transform has been applied to the differential currents due to internal fault and inrush currents. The diagnosis criterion is based on median absolute deviation (MAD) of wavelet coefficients over a specified frequency band. The proposed algorithm is examined using various simulated inrush and internal fault current cases on a power transformer that has been modeled using electromagnetic transients program EMTDC software. Results of evaluation study show that, proposed wavelet based differential protection scheme can discriminate internal faults from inrush currents. (author)

  14. A New Formula for the Inverse Wavelet Transform

    OpenAIRE

    Sun, Wenchang

    2010-01-01

    Finding a computationally efficient algorithm for the inverse continuous wavelet transform is a fundamental topic in applications. In this paper, we show the convergence of the inverse wavelet transform.

  15. Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning.

    Science.gov (United States)

    Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D

    2013-02-01

    Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

  16. A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

    International Nuclear Information System (INIS)

    Xiao Di; Cai Hong-Kun; Zheng Hong-Ying

    2015-01-01

    In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc. (paper)

  17. Application of content-based image compression to telepathology

    Science.gov (United States)

    Varga, Margaret J.; Ducksbury, Paul G.; Callagy, Grace

    2002-05-01

    Telepathology is a means of practicing pathology at a distance, viewing images on a computer display rather than directly through a microscope. Without compression, images take too long to transmit to a remote location and are very expensive to store for future examination. However, to date the use of compressed images in pathology remains controversial. This is because commercial image compression algorithms such as JPEG achieve data compression without knowledge of the diagnostic content. Often images are lossily compressed at the expense of corrupting informative content. None of the currently available lossy compression techniques are concerned with what information has been preserved and what data has been discarded. Their sole objective is to compress and transmit the images as fast as possible. By contrast, this paper presents a novel image compression technique, which exploits knowledge of the slide diagnostic content. This 'content based' approach combines visually lossless and lossy compression techniques, judiciously applying each in the appropriate context across an image so as to maintain 'diagnostic' information while still maximising the possible compression. Standard compression algorithms, e.g. wavelets, can still be used, but their use in a context sensitive manner can offer high compression ratios and preservation of diagnostically important information. When compared with lossless compression the novel content-based approach can potentially provide the same degree of information with a smaller amount of data. When compared with lossy compression it can provide more information for a given amount of compression. The precise gain in the compression performance depends on the application (e.g. database archive or second opinion consultation) and the diagnostic content of the images.

  18. An Implementation Of Elias Delta Code And ElGamal Algorithm In Image Compression And Security

    Science.gov (United States)

    Rachmawati, Dian; Andri Budiman, Mohammad; Saffiera, Cut Amalia

    2018-01-01

    In data transmission such as transferring an image, confidentiality, integrity, and efficiency of data storage aspects are highly needed. To maintain the confidentiality and integrity of data, one of the techniques used is ElGamal. The strength of this algorithm is found on the difficulty of calculating discrete logs in a large prime modulus. ElGamal belongs to the class of Asymmetric Key Algorithm and resulted in enlargement of the file size, therefore data compression is required. Elias Delta Code is one of the compression algorithms that use delta code table. The image was first compressed using Elias Delta Code Algorithm, then the result of the compression was encrypted by using ElGamal algorithm. Prime test was implemented using Agrawal Biswas Algorithm. The result showed that ElGamal method could maintain the confidentiality and integrity of data with MSE and PSNR values 0 and infinity. The Elias Delta Code method generated compression ratio and space-saving each with average values of 62.49%, and 37.51%.

  19. Fundamental papers in wavelet theory

    CERN Document Server

    Walnut, David F

    2006-01-01

    This book traces the prehistory and initial development of wavelet theory, a discipline that has had a profound impact on mathematics, physics, and engineering. Interchanges between these fields during the last fifteen years have led to a number of advances in applications such as image compression, turbulence, machine vision, radar, and earthquake prediction. This book contains the seminal papers that presented the ideas from which wavelet theory evolved, as well as those major papers that developed the theory into its current form. These papers originated in a variety of journals from differ

  20. An Improved Fast Compressive Tracking Algorithm Based on Online Random Forest Classifier

    Directory of Open Access Journals (Sweden)

    Xiong Jintao

    2016-01-01

    Full Text Available The fast compressive tracking (FCT algorithm is a simple and efficient algorithm, which is proposed in recent years. But, it is difficult to deal with the factors such as occlusion, appearance changes, pose variation, etc in processing. The reasons are that, Firstly, even if the naive Bayes classifier is fast in training, it is not robust concerning the noise. Secondly, the parameters are required to vary with the unique environment for accurate tracking. In this paper, we propose an improved fast compressive tracking algorithm based on online random forest (FCT-ORF for robust visual tracking. Firstly, we combine ideas with the adaptive compressive sensing theory regarding the weighted random projection to exploit both local and discriminative information of the object. The second reason is the online random forest classifier for online tracking which is demonstrated with more robust to the noise adaptively and high computational efficiency. The experimental results show that the algorithm we have proposed has a better performance in the field of occlusion, appearance changes, and pose variation than the fast compressive tracking algorithm’s contribution.

  1. A Comparative Study on Optimal Structural Dynamics Using Wavelet Functions

    Directory of Open Access Journals (Sweden)

    Seyed Hossein Mahdavi

    2015-01-01

    Full Text Available Wavelet solution techniques have become the focus of interest among researchers in different disciplines of science and technology. In this paper, implementation of two different wavelet basis functions has been comparatively considered for dynamic analysis of structures. For this aim, computational technique is developed by using free scale of simple Haar wavelet, initially. Later, complex and continuous Chebyshev wavelet basis functions are presented to improve the time history analysis of structures. Free-scaled Chebyshev coefficient matrix and operation of integration are derived to directly approximate displacements of the corresponding system. In addition, stability of responses has been investigated for the proposed algorithm of discrete Haar wavelet compared against continuous Chebyshev wavelet. To demonstrate the validity of the wavelet-based algorithms, aforesaid schemes have been extended to the linear and nonlinear structural dynamics. The effectiveness of free-scaled Chebyshev wavelet has been compared with simple Haar wavelet and two common integration methods. It is deduced that either indirect method proposed for discrete Haar wavelet or direct approach for continuous Chebyshev wavelet is unconditionally stable. Finally, it is concluded that numerical solution is highly benefited by the least computation time involved and high accuracy of response, particularly using low scale of complex Chebyshev wavelet.

  2. Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2012-01-01

    Full Text Available Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers’ gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm.

  3. A de-noising algorithm based on wavelet threshold-exponential adaptive window width-fitting for ground electrical source airborne transient electromagnetic signal

    Science.gov (United States)

    Ji, Yanju; Li, Dongsheng; Yu, Mingmei; Wang, Yuan; Wu, Qiong; Lin, Jun

    2016-05-01

    The ground electrical source airborne transient electromagnetic system (GREATEM) on an unmanned aircraft enjoys considerable prospecting depth, lateral resolution and detection efficiency, etc. In recent years it has become an important technical means of rapid resources exploration. However, GREATEM data are extremely vulnerable to stationary white noise and non-stationary electromagnetic noise (sferics noise, aircraft engine noise and other human electromagnetic noises). These noises will cause degradation of the imaging quality for data interpretation. Based on the characteristics of the GREATEM data and major noises, we propose a de-noising algorithm utilizing wavelet threshold method and exponential adaptive window width-fitting. Firstly, the white noise is filtered in the measured data using the wavelet threshold method. Then, the data are segmented using data window whose step length is even logarithmic intervals. The data polluted by electromagnetic noise are identified within each window based on the discriminating principle of energy detection, and the attenuation characteristics of the data slope are extracted. Eventually, an exponential fitting algorithm is adopted to fit the attenuation curve of each window, and the data polluted by non-stationary electromagnetic noise are replaced with their fitting results. Thus the non-stationary electromagnetic noise can be effectively removed. The proposed algorithm is verified by the synthetic and real GREATEM signals. The results show that in GREATEM signal, stationary white noise and non-stationary electromagnetic noise can be effectively filtered using the wavelet threshold-exponential adaptive window width-fitting algorithm, which enhances the imaging quality.

  4. Construction of wavelets with composite dilations

    International Nuclear Information System (INIS)

    Wu Guochang; Li Zhiqiang; Cheng Zhengxing

    2009-01-01

    In order to overcome classical wavelets' shortcoming in image processing problems, people developed many producing systems, which built up wavelet family. In this paper, the notion of AB-multiresolution analysis is generalized, and the corresponding theory is developed. For an AB-multiresolution analysis associated with any expanding matrices, we deduce that there exists a singe scaling function in its reducing subspace. Under some conditions, wavelets with composite dilations can be gotten by AB-multiresolution analysis, which permits the existence of fast implementation algorithm. Then, we provide an approach to design the wavelets with composite dilations by classic wavelets. Our way consists of separable and partly nonseparable cases. In each section, we construct all kinds of examples with nice properties to prove our theory.

  5. Option pricing from wavelet-filtered financial series

    Science.gov (United States)

    de Almeida, V. T. X.; Moriconi, L.

    2012-10-01

    We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most (≃99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.

  6. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    OpenAIRE

    Xiao, Zhihuai; He, Xinying; Fu, Xiangqian; Malik, O. P.

    2015-01-01

    Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO-) initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the fr...

  7. Detecting the quality of glycerol monolaurate: a method for using Fourier transform infrared spectroscopy with wavelet transform and modified uninformative variable elimination.

    Science.gov (United States)

    Chen, Xiaojing; Wu, Di; He, Yong; Liu, Shou

    2009-04-06

    Glycerol monolaurate (GML) products contain many impurities, such as lauric acid and glucerol. The GML content is an important quality indicator for GML production. A hybrid variable selection algorithm, which is a combination of wavelet transform (WT) technology and modified uninformative variable eliminate (MUVE) method, was proposed to extract useful information from Fourier transform infrared (FT-IR) transmission spectroscopy for the determination of GML content. FT-IR spectra data were compressed by WT first; the irrelevant variables in the compressed wavelet coefficients were eliminated by MUVE. In the MUVE process, simulated annealing (SA) algorithm was employed to search the optimal cutoff threshold. After the WT-MUVE process, variables for the calibration model were reduced from 7366 to 163. Finally, the retained variables were employed as inputs of partial least squares (PLS) model to build the calibration model. For the prediction set, the correlation coefficient (r) of 0.9910 and root mean square error of prediction (RMSEP) of 4.8617 were obtained. The prediction result was better than the PLS model with full-spectra data. It was indicated that proposed WT-MUVE method could not only make the prediction more accurate, but also make the calibration model more parsimonious. Furthermore, the reconstructed spectra represented the projection of the selected wavelet coefficients into the original domain, affording the chemical interpretation of the predicted results. It is concluded that the FT-IR transmission spectroscopy technique with the proposed method is promising for the fast detection of GML content.

  8. Algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales

    International Nuclear Information System (INIS)

    Li Tianduo; Xiao Gang; Di Yuming; Han Feng; Qiu Xiaoling

    1999-01-01

    The γ energy spectrum is expanded in allied energy-frequency space. By the different characterization of the evolution of wavelet transform modulus maxima across scales between energy spectrum and noise, the algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales is presented. The results show, in contrast to the methods in energy space or in frequency space, the method has the advantages that the peak of energy spectrum can be indicated accurately and the energy spectrum can be reconstructed with a good approximation

  9. On the use of harmony search algorithm in the training of wavelet neural networks

    Science.gov (United States)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2015-10-01

    Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

  10. Wavelet-based compression with ROI coding support for mobile access to DICOM images over heterogeneous radio networks.

    Science.gov (United States)

    Maglogiannis, Ilias; Doukas, Charalampos; Kormentzas, George; Pliakas, Thomas

    2009-07-01

    Most of the commercial medical image viewers do not provide scalability in image compression and/or region of interest (ROI) encoding/decoding. Furthermore, these viewers do not take into consideration the special requirements and needs of a heterogeneous radio setting that is constituted by different access technologies [e.g., general packet radio services (GPRS)/ universal mobile telecommunications system (UMTS), wireless local area network (WLAN), and digital video broadcasting (DVB-H)]. This paper discusses a medical application that contains a viewer for digital imaging and communications in medicine (DICOM) images as a core module. The proposed application enables scalable wavelet-based compression, retrieval, and decompression of DICOM medical images and also supports ROI coding/decoding. Furthermore, the presented application is appropriate for use by mobile devices activating in heterogeneous radio settings. In this context, performance issues regarding the usage of the proposed application in the case of a prototype heterogeneous system setup are also discussed.

  11. On system behaviour using complex networks of a compression algorithm

    Science.gov (United States)

    Walker, David M.; Correa, Debora C.; Small, Michael

    2018-01-01

    We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothesis tests. We demonstrate these ideas on systems with known dynamical behaviour and also show that our approach is capable of identifying behavioural transitions within electroencephalogram recordings as well as changes due to a bifurcation parameter of a chaotic system. The technique we propose is dependent on a coarse grained quantization of the original time series and therefore provides potential for a spatial scale-dependent characterization of the data. Finally the method is as computationally efficient as the underlying compression algorithm and provides a compression of the salient features of long time series.

  12. Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

    Science.gov (United States)

    Qian, Jinfang; Zhang, Changjiang

    2014-11-01

    An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.

  13. Alleviating Border Effects in Wavelet Transforms for Nonlinear Time-varying Signal Analysis

    Directory of Open Access Journals (Sweden)

    SU, H.

    2011-08-01

    Full Text Available Border effects are very common in many finite signals analysis and processing approaches using convolution operation. Alleviating the border effects that can occur in the processing of finite-length signals using wavelet transform is considered in this paper. Traditional methods for alleviating the border effects are suitable to compression or coding applications. We propose an algorithm based on Fourier series which is proved to be appropriate to the application of time-frequency analysis of nonlinear signals. Fourier series extension method preserves the time-varying characteristics of the signals. A modified signal duration expression for measuring the extent of border effects region is presented. The proposed algorithm is confirmed to be efficient to alleviate the border effects in comparison to the current methods through the numerical examples.

  14. Analysis on Behaviour of Wavelet Coefficient during Fault Occurrence in Transformer

    Science.gov (United States)

    Sreewirote, Bancha; Ngaopitakkul, Atthapol

    2018-03-01

    The protection system for transformer has play significant role in avoiding severe damage to equipment when disturbance occur and ensure overall system reliability. One of the methodology that widely used in protection scheme and algorithm is discrete wavelet transform. However, characteristic of coefficient under fault condition must be analyzed to ensure its effectiveness. So, this paper proposed study and analysis on wavelet coefficient characteristic when fault occur in transformer in both high- and low-frequency component from discrete wavelet transform. The effect of internal and external fault on wavelet coefficient of both fault and normal phase has been taken into consideration. The fault signal has been simulate using transmission connected to transformer experimental setup on laboratory level that modelled after actual system. The result in term of wavelet coefficient shown a clearly differentiate between wavelet characteristic in both high and low frequency component that can be used to further design and improve detection and classification algorithm that based on discrete wavelet transform methodology in the future.

  15. Numerical shaping of the ultrasonic wavelet

    International Nuclear Information System (INIS)

    Bonis, M.

    1991-01-01

    Improving the performance and the quality of ultrasonic testing requires the numerical control of the shape of the driving signal applied to the piezoelectric transducer. This allows precise shaping of the ultrasonic field wavelet and corrections for the physical defects of the transducer, which are mainly due to the damper or the lens. It also does away with the need for an accurate electric matching. It then becomes feasible to characterize, a priori, the ultrasonic wavelet by means of temporal and/or spectral specifications and to use, subsequently, an adaptative algorithm to calculate the corresponding driving wavelet. Moreover, the versatility resulting from the numerical control of this wavelet allows it to be changed in real time during a test

  16. A generalized wavelet extrema representation

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Jian; Lades, M.

    1995-10-01

    The wavelet extrema representation originated by Stephane Mallat is a unique framework for low-level and intermediate-level (feature) processing. In this paper, we present a new form of wavelet extrema representation generalizing Mallat`s original work. The generalized wavelet extrema representation is a feature-based multiscale representation. For a particular choice of wavelet, our scheme can be interpreted as representing a signal or image by its edges, and peaks and valleys at multiple scales. Such a representation is shown to be stable -- the original signal or image can be reconstructed with very good quality. It is further shown that a signal or image can be modeled as piecewise monotonic, with all turning points between monotonic segments given by the wavelet extrema. A new projection operator is introduced to enforce piecewise inonotonicity of a signal in its reconstruction. This leads to an enhancement to previously developed algorithms in preventing artifacts in reconstructed signal.

  17. The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

    Directory of Open Access Journals (Sweden)

    Jin-peng Liu

    2017-07-01

    Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.

  18. Freeing Space for NASA: Incorporating a Lossless Compression Algorithm into NASA's FOSS System

    Science.gov (United States)

    Fiechtner, Kaitlyn; Parker, Allen

    2011-01-01

    NASA's Fiber Optic Strain Sensing (FOSS) system can gather and store up to 1,536,000 bytes (1.46 megabytes) per second. Since the FOSS system typically acquires hours - or even days - of data, the system can gather hundreds of gigabytes of data for a given test event. To store such large quantities of data more effectively, NASA is modifying a Lempel-Ziv-Oberhumer (LZO) lossless data compression program to compress data as it is being acquired in real time. After proving that the algorithm is capable of compressing the data from the FOSS system, the LZO program will be modified and incorporated into the FOSS system. Implementing an LZO compression algorithm will instantly free up memory space without compromising any data obtained. With the availability of memory space, the FOSS system can be used more efficiently on test specimens, such as Unmanned Aerial Vehicles (UAVs) that can be in flight for days. By integrating the compression algorithm, the FOSS system can continue gathering data, even on longer flights.

  19. Comparison between wavelet and wavelet packet transform features for classification of faults in distribution system

    Science.gov (United States)

    Arvind, Pratul

    2012-11-01

    The ability to identify and classify all ten types of faults in a distribution system is an important task for protection engineers. Unlike transmission system, distribution systems have a complex configuration and are subjected to frequent faults. In the present work, an algorithm has been developed for identifying all ten types of faults in a distribution system by collecting current samples at the substation end. The samples are subjected to wavelet packet transform and artificial neural network in order to yield better classification results. A comparison of results between wavelet transform and wavelet packet transform is also presented thereby justifying the feature extracted from wavelet packet transform yields promising results. It should also be noted that current samples are collected after simulating a 25kv distribution system in PSCAD software.

  20. Contributions in compression of 3D medical images and 2D images; Contributions en compression d'images medicales 3D et d'images naturelles 2D

    Energy Technology Data Exchange (ETDEWEB)

    Gaudeau, Y

    2006-12-15

    The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)

  1. Development of information preserving data compression algorithm for CT images

    International Nuclear Information System (INIS)

    Kobayashi, Yoshio

    1989-01-01

    Although digital imaging techniques in radiology develop rapidly, problems arise in archival storage and communication of image data. This paper reports on a new information preserving data compression algorithm for computed tomographic (CT) images. This algorithm consists of the following five processes: 1. Pixels surrounding the human body showing CT values smaller than -900 H.U. are eliminated. 2. Each pixel is encoded by its numerical difference from its neighboring pixel along a matrix line. 3. Difference values are encoded by a newly designed code rather than the natural binary code. 4. Image data, obtained with the above process, are decomposed into bit planes. 5. The bit state transitions in each bit plane are encoded by run length coding. Using this new algorithm, the compression ratios of brain, chest, and abdomen CT images are 4.49, 4.34. and 4.40 respectively. (author)

  2. Advanced algorithms for information science

    International Nuclear Information System (INIS)

    Argo, P.; Brislawn, C.; Fitzgerald, T.J.; Kelley, B.; Kim, W.H.; Mazieres, B.; Roeder, H.; Strottman, D.

    1998-01-01

    This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). In a modern information-controlled society the importance of fast computational algorithms facilitating data compression and image analysis cannot be overemphasized. Feature extraction and pattern recognition are key to many LANL projects and the same types of dimensionality reduction and compression used in source coding are also applicable to image understanding. The authors have begun developing wavelet coding which decomposes data into different length-scale and frequency bands. New transform-based source-coding techniques offer potential for achieving better, combined source-channel coding performance by using joint-optimization techniques. They initiated work on a system that compresses the video stream in real time, and which also takes the additional step of analyzing the video stream concurrently. By using object-based compression schemes (where an object is an identifiable feature of the video signal, repeatable in time or space), they believe that the analysis is directly related to the efficiency of the compression

  3. Advanced algorithms for information science

    Energy Technology Data Exchange (ETDEWEB)

    Argo, P.; Brislawn, C.; Fitzgerald, T.J.; Kelley, B.; Kim, W.H.; Mazieres, B.; Roeder, H.; Strottman, D.

    1998-12-31

    This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). In a modern information-controlled society the importance of fast computational algorithms facilitating data compression and image analysis cannot be overemphasized. Feature extraction and pattern recognition are key to many LANL projects and the same types of dimensionality reduction and compression used in source coding are also applicable to image understanding. The authors have begun developing wavelet coding which decomposes data into different length-scale and frequency bands. New transform-based source-coding techniques offer potential for achieving better, combined source-channel coding performance by using joint-optimization techniques. They initiated work on a system that compresses the video stream in real time, and which also takes the additional step of analyzing the video stream concurrently. By using object-based compression schemes (where an object is an identifiable feature of the video signal, repeatable in time or space), they believe that the analysis is directly related to the efficiency of the compression.

  4. A study of biorthogonal multiple vector-valued wavelets

    International Nuclear Information System (INIS)

    Han Jincang; Cheng Zhengxing; Chen Qingjiang

    2009-01-01

    The notion of vector-valued multiresolution analysis is introduced and the concept of biorthogonal multiple vector-valued wavelets which are wavelets for vector fields, is introduced. It is proved that, like in the scalar and multiwavelet case, the existence of a pair of biorthogonal multiple vector-valued scaling functions guarantees the existence of a pair of biorthogonal multiple vector-valued wavelet functions. An algorithm for constructing a class of compactly supported biorthogonal multiple vector-valued wavelets is presented. Their properties are investigated by means of operator theory and algebra theory and time-frequency analysis method. Several biorthogonality formulas regarding these wavelet packets are obtained.

  5. Accurate reconstruction in digital holographic microscopy using Fresnel dual-tree complex wavelet transform

    Science.gov (United States)

    Zhang, Xiaolei; Zhang, Xiangchao; Yuan, He; Zhang, Hao; Xu, Min

    2018-02-01

    Digital holography is a promising measurement method in the fields of bio-medicine and micro-electronics. But the captured images of digital holography are severely polluted by the speckle noise because of optical scattering and diffraction. Via analyzing the properties of Fresnel diffraction and the topographies of micro-structures, a novel reconstruction method based on the dual-tree complex wavelet transform (DT-CWT) is proposed. This algorithm is shiftinvariant and capable of obtaining sparse representations for the diffracted signals of salient features, thus it is well suited for multiresolution processing of the interferometric holograms of directional morphologies. An explicit representation of orthogonal Fresnel DT-CWT bases and a specific filtering method are developed. This method can effectively remove the speckle noise without destroying the salient features. Finally, the proposed reconstruction method is compared with the conventional Fresnel diffraction integration and Fresnel wavelet transform with compressive sensing methods to validate its remarkable superiority on the aspects of topography reconstruction and speckle removal.

  6. Fast generation of computer-generated holograms using wavelet shrinkage.

    Science.gov (United States)

    Shimobaba, Tomoyoshi; Ito, Tomoyoshi

    2017-01-09

    Computer-generated holograms (CGHs) are generated by superimposing complex amplitudes emitted from a number of object points. However, this superposition process remains very time-consuming even when using the latest computers. We propose a fast calculation algorithm for CGHs that uses a wavelet shrinkage method, eliminating small wavelet coefficient values to express approximated complex amplitudes using only a few representative wavelet coefficients.

  7. Multiresolution signal decomposition transforms, subbands, and wavelets

    CERN Document Server

    Akansu, Ali N; Haddad, Paul R

    2001-01-01

    The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such ""hot"" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course

  8. Building nonredundant adaptive wavelets by update lifting

    NARCIS (Netherlands)

    H.J.A.M. Heijmans (Henk); B. Pesquet-Popescu; G. Piella (Gema)

    2002-01-01

    textabstractAdaptive wavelet decompositions appear useful in various applications in image and video processing, such as image analysis, compression, feature extraction, denoising and deconvolution, or optic flow estimation. For such tasks it may be important that the multiresolution representations

  9. Wavelet subband coding of computer simulation output using the A++ array class library

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.; Quinlan, D.J.; Zhang, H.D. [Los Alamos National Lab., NM (United States); Nuri, V. [Washington State Univ., Pullman, WA (United States). School of EECS

    1995-07-01

    The goal of the project is to produce utility software for off-line compression of existing data and library code that can be called from a simulation program for on-line compression of data dumps as the simulation proceeds. Naturally, we would like the amount of CPU time required by the compression algorithm to be small in comparison to the requirements of typical simulation codes. We also want the algorithm to accomodate a wide variety of smooth, multidimensional data types. For these reasons, the subband vector quantization (VQ) approach employed in has been replaced by a scalar quantization (SQ) strategy using a bank of almost-uniform scalar subband quantizers in a scheme similar to that used in the FBI fingerprint image compression standard. This eliminates the considerable computational burdens of training VQ codebooks for each new type of data and performing nearest-vector searches to encode the data. The comparison of subband VQ and SQ algorithms in indicated that, in practice, there is relatively little additional gain from using vector as opposed to scalar quantization on DWT subbands, even when the source imagery is from a very homogeneous population, and our subjective experience with synthetic computer-generated data supports this stance. It appears that a careful study is needed of the tradeoffs involved in selecting scalar vs. vector subband quantization, but such an analysis is beyond the scope of this paper. Our present work is focused on the problem of generating wavelet transform/scalar quantization (WSQ) implementations that can be ported easily between different hardware environments. This is an extremely important consideration given the great profusion of different high-performance computing architectures available, the high cost associated with learning how to map algorithms effectively onto a new architecture, and the rapid rate of evolution in the world of high-performance computing.

  10. Contributions in compression of 3D medical images and 2D images; Contributions en compression d'images medicales 3D et d'images naturelles 2D

    Energy Technology Data Exchange (ETDEWEB)

    Gaudeau, Y

    2006-12-15

    The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)

  11. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

  12. On-Line QRS Complex Detection Using Wavelet Filtering

    National Research Council Canada - National Science Library

    Szilagyi, L

    2001-01-01

    ...: first a wavelet transform filtering is applied to the signal, then QRS complex localization is performed using a maximum detection and peak classification algorithm The algorithm has been tested...

  13. Wavelets as basis functions in electronic structure calculations

    International Nuclear Information System (INIS)

    Chauvin, C.

    2005-11-01

    This thesis is devoted to the definition and the implementation of a multi-resolution method to determine the fundamental state of a system composed of nuclei and electrons. In this work, we are interested in the Density Functional Theory (DFT), which allows to express the Hamiltonian operator with the electronic density only, by a Coulomb potential and a non-linear potential. This operator acts on orbitals, which are solutions of the so-called Kohn-Sham equations. Their resolution needs to express orbitals and density on a set of functions owing both physical and numerical properties, as explained in the second chapter. One can hardly satisfy these two properties simultaneously, that is why we are interested in orthogonal and bi-orthogonal wavelets basis, whose properties of interpolation are presented in the third chapter. We present in the fourth chapter three dimensional solvers for the Coulomb's potential, using not only the preconditioning property of wavelets, but also a multigrid algorithm. Determining this potential allows us to solve the self-consistent Kohn-Sham equations, by an algorithm presented in chapter five. The originality of our method consists in the construction of the stiffness matrix, combining a Galerkin formulation and a collocation scheme. We analyse the approximation properties of this method in case of linear Hamiltonian, such as harmonic oscillator and hydrogen, and present convergence results of the DFT for small electrons. Finally we show how orbital compression reduces considerably the number of coefficients to keep, while preserving a good accuracy of the fundamental energy. (author)

  14. Exploiting of the Compression Methods for Reconstruction of the Antenna Far-Field Using Only Amplitude Near-Field Measurements

    Directory of Open Access Journals (Sweden)

    J. Puskely

    2010-06-01

    Full Text Available The novel approach exploits the principle of the conventional two-plane amplitude measurements for the reconstruction of the unknown electric field distribution on the antenna aperture. The method combines a global optimization with a compression method. The global optimization method (GO is used to minimize the functional, and the compression method is used to reduce the number of unknown variables. The algorithm employs the Real Coded Genetic Algorithm (RCGA as the global optimization approach. The Discrete Cosine Transform (DCT and the Discrete Wavelet Transform (DWT are applied to reduce the number of unknown variables. Pros and cons of methods are investigated and reported for the solution of the problem. In order to make the algorithm faster, exploitation of amplitudes from a single scanning plane is also discussed. First, the algorithm is used to obtain an initial estimate. Subsequently, the common Fourier iterative algorithm is used to reach global minima with sufficient accuracy. The method is examined measuring the dish antenna.

  15. Wavelet methods in mathematical analysis and engineering

    CERN Document Server

    Damlamian, Alain

    2010-01-01

    This book gives a comprehensive overview of both the fundamentals of wavelet analysis and related tools, and of the most active recent developments towards applications. It offers a stateoftheart in several active areas of research where wavelet ideas, or more generally multiresolution ideas have proved particularly effective. The main applications covered are in the numerical analysis of PDEs, and signal and image processing. Recently introduced techniques such as Empirical Mode Decomposition (EMD) and new trends in the recovery of missing data, such as compressed sensing, are also presented.

  16. Contributions in compression of 3D medical images and 2D images

    International Nuclear Information System (INIS)

    Gaudeau, Y.

    2006-12-01

    The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)

  17. Performance Analysis of Embedded Zero Tree and Set Partitioning in Hierarchical Tree

    OpenAIRE

    Pardeep Singh; Nivedita; Dinesh Gupta; Sugandha Sharma

    2012-01-01

    Compressing an image is significantly different than compressing raw binary data. For this different compression algorithm are used to compress images. Discrete wavelet transform has been widely used to compress the image. Wavelet transform are very powerful compared to other transform because its ability to describe any type of signals both in time and frequency domain simultaneously. The proposed schemes investigate the performance evaluation of embedded zero tree and wavelet based compress...

  18. Wavelet Based Diagnosis and Protection of Electric Motors

    OpenAIRE

    Khan, M. Abdesh Shafiel Kafiey; Rahman, M. Azizur

    2010-01-01

    In this chapter, a short review of conventional Fourier transforms and new wavelet based faults diagnostic and protection techniques for electric motors is presented. The new hybrid wavelet packet transform (WPT) and neural network (NN) based faults diagnostic algorithm is developed and implemented for electric motors. The proposed WPT and NN

  19. A review of lossless audio compression standards and algorithms

    Science.gov (United States)

    Muin, Fathiah Abdul; Gunawan, Teddy Surya; Kartiwi, Mira; Elsheikh, Elsheikh M. A.

    2017-09-01

    Over the years, lossless audio compression has gained popularity as researchers and businesses has become more aware of the need for better quality and higher storage demand. This paper will analyse various lossless audio coding algorithm and standards that are used and available in the market focusing on Linear Predictive Coding (LPC) specifically due to its popularity and robustness in audio compression, nevertheless other prediction methods are compared to verify this. Advanced representation of LPC such as LSP decomposition techniques are also discussed within this paper.

  20. Block-based wavelet transform coding of mammograms with region-adaptive quantization

    Science.gov (United States)

    Moon, Nam Su; Song, Jun S.; Kwon, Musik; Kim, JongHyo; Lee, ChoongWoong

    1998-06-01

    To achieve both high compression ratio and information preserving, it is an efficient way to combine segmentation and lossy compression scheme. Microcalcification in mammogram is one of the most significant sign of early stage of breast cancer. Therefore in coding, detection and segmentation of microcalcification enable us to preserve it well by allocating more bits to it than to other regions. Segmentation of microcalcification is performed both in spatial domain and in wavelet transform domain. Peak error controllable quantization step, which is off-line designed, is suitable for medical image compression. For region-adaptive quantization, block- based wavelet transform coding is adopted and different peak- error-constrained quantizers are applied to blocks according to the segmentation result. In view of preservation of microcalcification, the proposed coding scheme shows better performance than JPEG.

  1. Study on data compression algorithm and its implementation in portable electronic device for Internet of Things applications

    Directory of Open Access Journals (Sweden)

    Khairi Nor Asilah

    2017-01-01

    Full Text Available An Internet of Things (IoT device is usually powered by a small battery, which does not last long. As a result, saving energy in IoT devices has become an important issue when it comes to this subject. Since power consumption is the primary cause of radio communication, some researchers have proposed several compression algorithms with the purpose of overcoming this particular problem. Several data compression algorithms from previous reference papers are discussed in this paper. The description of the compression algorithm in the reference papers was collected and summarized in a table form. From the analysis, MAS compression algorithm was selected as a project prototype due to its high potential for meeting the project requirements. Besides that, it also produced better performance regarding energy-saving, better memory usage, and data transmission efficiency. This method is also suitable to be implemented in WSN. MAS compression algorithm will be prototyped and applied in portable electronic devices for Internet of Things applications.

  2. Study on data compression algorithm and its implementation in portable electronic device for Internet of Things applications

    Science.gov (United States)

    Asilah Khairi, Nor; Bahari Jambek, Asral

    2017-11-01

    An Internet of Things (IoT) device is usually powered by a small battery, which does not last long. As a result, saving energy in IoT devices has become an important issue when it comes to this subject. Since power consumption is the primary cause of radio communication, some researchers have proposed several compression algorithms with the purpose of overcoming this particular problem. Several data compression algorithms from previous reference papers are discussed in this paper. The description of the compression algorithm in the reference papers was collected and summarized in a table form. From the analysis, MAS compression algorithm was selected as a project prototype due to its high potential for meeting the project requirements. Besides that, it also produced better performance regarding energy-saving, better memory usage, and data transmission efficiency. This method is also suitable to be implemented in WSN. MAS compression algorithm will be prototyped and applied in portable electronic devices for Internet of Things applications.

  3. Wavelet-based ground vehicle recognition using acoustic signals

    Science.gov (United States)

    Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.

    1996-03-01

    We present, in this paper, a wavelet-based acoustic signal analysis to remotely recognize military vehicles using their sound intercepted by acoustic sensors. Since expedited signal recognition is imperative in many military and industrial situations, we developed an algorithm that provides an automated, fast signal recognition once implemented in a real-time hardware system. This algorithm consists of wavelet preprocessing, feature extraction and compact signal representation, and a simple but effective statistical pattern matching. The current status of the algorithm does not require any training. The training is replaced by human selection of reference signals (e.g., squeak or engine exhaust sound) distinctive to each individual vehicle based on human perception. This allows a fast archiving of any new vehicle type in the database once the signal is collected. The wavelet preprocessing provides time-frequency multiresolution analysis using discrete wavelet transform (DWT). Within each resolution level, feature vectors are generated from statistical parameters and energy content of the wavelet coefficients. After applying our algorithm on the intercepted acoustic signals, the resultant feature vectors are compared with the reference vehicle feature vectors in the database using statistical pattern matching to determine the type of vehicle from where the signal originated. Certainly, statistical pattern matching can be replaced by an artificial neural network (ANN); however, the ANN would require training data sets and time to train the net. Unfortunately, this is not always possible for many real world situations, especially collecting data sets from unfriendly ground vehicles to train the ANN. Our methodology using wavelet preprocessing and statistical pattern matching provides robust acoustic signal recognition. We also present an example of vehicle recognition using acoustic signals collected from two different military ground vehicles. In this paper, we will

  4. ROI-based DICOM image compression for telemedicine

    Indian Academy of Sciences (India)

    ground and reconstruct the image portions losslessly. The compressed image can ... If the image is compressed by 8:1 compression without any perceptual distortion, the ... Figure 2. Cross-sectional view of medical image (statistical representation). ... The Integer Wavelet Transform (IWT) is used to have lossless processing.

  5. Multisensor data fusion algorithm development

    Energy Technology Data Exchange (ETDEWEB)

    Yocky, D.A.; Chadwick, M.D.; Goudy, S.P.; Johnson, D.K.

    1995-12-01

    This report presents a two-year LDRD research effort into multisensor data fusion. We approached the problem by addressing the available types of data, preprocessing that data, and developing fusion algorithms using that data. The report reflects these three distinct areas. First, the possible data sets for fusion are identified. Second, automated registration techniques for imagery data are analyzed. Third, two fusion techniques are presented. The first fusion algorithm is based on the two-dimensional discrete wavelet transform. Using test images, the wavelet algorithm is compared against intensity modulation and intensity-hue-saturation image fusion algorithms that are available in commercial software. The wavelet approach outperforms the other two fusion techniques by preserving spectral/spatial information more precisely. The wavelet fusion algorithm was also applied to Landsat Thematic Mapper and SPOT panchromatic imagery data. The second algorithm is based on a linear-regression technique. We analyzed the technique using the same Landsat and SPOT data.

  6. Sparse data structure design for wavelet-based methods

    Directory of Open Access Journals (Sweden)

    Latu Guillaume

    2011-12-01

    Full Text Available This course gives an introduction to the design of efficient datatypes for adaptive wavelet-based applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An answer will be given to the question: how to implement and efficiently use the discrete wavelet transform in computer applications? A focus will be made on time-evolution problems, and use of wavelet-based scheme for adaptively solving partial differential equations (PDE. One crucial issue is that the benefits of the adaptive method in term of algorithmic cost reduction can not be wasted by overheads associated to sparse data management.

  7. The parallel algorithm for the 2D discrete wavelet transform

    Science.gov (United States)

    Barina, David; Najman, Pavel; Kleparnik, Petr; Kula, Michal; Zemcik, Pavel

    2018-04-01

    The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme. As the lifting scheme consists of a small number of operations, it is preferred for processing using single-core CPUs. However, considering a parallel processing using multi-core processors, this scheme is inappropriate due to a large number of steps. On such architectures, the number of steps corresponds to the number of points that represent the exchange of data. Consequently, these points often form a performance bottleneck. Our approach appropriately rearranges calculations inside the transform, and thereby reduces the number of steps. In other words, we propose a new scheme that is friendly to parallel environments. When evaluating on multi-core CPUs, we consistently overcome the original lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and 8-core Intel Xeon processors.

  8. An introduction to random vibrations, spectral & wavelet analysis

    CERN Document Server

    Newland, D E

    2005-01-01

    One of the first engineering books to cover wavelet analysis, this classic text describes and illustrates basic theory, with a detailed explanation of the workings of discrete wavelet transforms. Computer algorithms are explained and supported by examples and a set of problems, and an appendix lists ten computer programs for calculating and displaying wavelet transforms.Starting with an introduction to probability distributions and averages, the text examines joint probability distributions, ensemble averages, and correlation; Fourier analysis; spectral density and excitation response relation

  9. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO- initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.

  10. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  11. A Coded Aperture Compressive Imaging Array and Its Visual Detection and Tracking Algorithms for Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Hanxiao Wu

    2012-10-01

    Full Text Available In this paper, we propose an application of a compressive imaging system to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system is proposed to reduce the needed high resolution coded mask requirements and facilitate the storage of the projection matrix. Random Gaussian, Toeplitz and binary phase coded masks are utilized to obtain the compressive sensing images. The corresponding motion targets detection and tracking algorithms directly using the compressive sampling images are developed. A mixture of Gaussian distribution is applied in the compressive image space to model the background image and for foreground detection. For each motion target in the compressive sampling domain, a compressive feature dictionary spanned by target templates and noises templates is sparsely represented. An l1 optimization algorithm is used to solve the sparse coefficient of templates. Experimental results demonstrate that low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz phase mask, motion detection algorithms using a random binary phase mask can yield better detection results. However using random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed image. Our tracking algorithm can achieve a real time speed that is up to 10 times faster than that of the l1 tracker without any optimization.

  12. Performance of a Discrete Wavelet Transform for Compressing Plasma Count Data and its Application to the Fast Plasma Investigation on NASA's Magnetospheric Multiscale Mission

    Science.gov (United States)

    Barrie, Alexander C.; Yeh, Penshu; Dorelli, John C.; Clark, George B.; Paterson, William R.; Adrian, Mark L.; Holland, Matthew P.; Lobell, James V.; Simpson, David G.; Pollock, Craig J.; hide

    2015-01-01

    Plasma measurements in space are becoming increasingly faster, higher resolution, and distributed over multiple instruments. As raw data generation rates can exceed available data transfer bandwidth, data compression is becoming a critical design component. Data compression has been a staple of imaging instruments for years, but only recently have plasma measurement designers become interested in high performance data compression. Missions will often use a simple lossless compression technique yielding compression ratios of approximately 2:1, however future missions may require compression ratios upwards of 10:1. This study aims to explore how a Discrete Wavelet Transform combined with a Bit Plane Encoder (DWT/BPE), implemented via a CCSDS standard, can be used effectively to compress count information common to plasma measurements to high compression ratios while maintaining little or no compression error. The compression ASIC used for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS) is used for this study. Plasma count data from multiple sources is examined: resampled data from previous missions, randomly generated data from distribution functions, and simulations of expected regimes. These are run through the compression routines with various parameters to yield the greatest possible compression ratio while maintaining little or no error, the latter indicates that fully lossless compression is obtained. Finally, recommendations are made for future missions as to what can be achieved when compressing plasma count data and how best to do so.

  13. A data compression algorithm for nuclear spectrum files

    International Nuclear Information System (INIS)

    Mika, J.F.; Martin, L.J.; Johnston, P.N.

    1990-01-01

    The total space occupied by computer files of spectra generated in nuclear spectroscopy systems can lead to problems of storage, and transmission time. An algorithm is presented which significantly reduces the space required to store nuclear spectra, without loss of any information content. Testing indicates that spectrum files can be routinely compressed by a factor of 5. (orig.)

  14. Study of Denoising in TEOAE Signals Using an Appropriate Mother Wavelet Function

    Directory of Open Access Journals (Sweden)

    Habib Alizadeh Dizaji

    2007-06-01

    Full Text Available Background and Aim: Matching a mother wavelet to class of signals can be of interest in signal analy­sis and denoising based on wavelet multiresolution analysis and decomposition. As transient evoked otoacoustic emissions (TEOAES are contaminated with noise, the aim of this work was to pro­vide a quantitative approach to the problem of matching a mother wavelet to TEOAE signals by us­ing tun­ing curves and to use it for analysis and denoising TEOAE signals. Approximated mother wave­let for TEOAE signals was calculated using an algorithm for designing wavelet to match a specified sig­nal.Materials and Methods: In this paper a tuning curve has used as a template for designing a mother wave­let that has maximum matching to the tuning curve. The mother wavelet matching was performed on tuning curves spectrum magnitude and phase independent of one another. The scaling function was calcu­lated from the matched mother wavelet and by using these functions, lowpass and highpass filters were designed for a filter bank and otoacoustic emissions signal analysis and synthesis. After signal analyz­ing, denoising was performed by time windowing the signal time-frequency component.Results: Aanalysis indicated more signal reconstruction improvement in comparison with coiflets mother wavelet and by using the purposed denoising algorithm it is possible to enhance signal to noise ra­tio up to dB.Conclusion: The wavelet generated from this algorithm was remarkably similar to the biorthogonal wave­lets. Therefore, by matching a biorthogonal wavelet to the tuning curve and using wavelet packet analy­sis, a high resolution time-frequency analysis for the otoacoustic emission signals is possible.

  15. Non-US data compression and coding research. FASAC Technical Assessment Report

    Energy Technology Data Exchange (ETDEWEB)

    Gray, R.M.; Cohn, M.; Craver, L.W.; Gersho, A.; Lookabaugh, T.; Pollara, F.; Vetterli, M.

    1993-11-01

    This assessment of recent data compression and coding research outside the United States examines fundamental and applied work in the basic areas of signal decomposition, quantization, lossless compression, and error control, as well as application development efforts in image/video compression and speech/audio compression. Seven computer scientists and engineers who are active in development of these technologies in US academia, government, and industry carried out the assessment. Strong industrial and academic research groups in Western Europe, Israel, and the Pacific Rim are active in the worldwide search for compression algorithms that provide good tradeoffs among fidelity, bit rate, and computational complexity, though the theoretical roots and virtually all of the classical compression algorithms were developed in the United States. Certain areas, such as segmentation coding, model-based coding, and trellis-coded modulation, have developed earlier or in more depth outside the United States, though the United States has maintained its early lead in most areas of theory and algorithm development. Researchers abroad are active in other currently popular areas, such as quantizer design techniques based on neural networks and signal decompositions based on fractals and wavelets, but, in most cases, either similar research is or has been going on in the United States, or the work has not led to useful improvements in compression performance. Because there is a high degree of international cooperation and interaction in this field, good ideas spread rapidly across borders (both ways) through international conferences, journals, and technical exchanges. Though there have been no fundamental data compression breakthroughs in the past five years--outside or inside the United State--there have been an enormous number of significant improvements in both places in the tradeoffs among fidelity, bit rate, and computational complexity.

  16. Specific features of NDT data and processing algorithms: new remedies to old ills; Caracteristiques specifiques des donnees de controle non destructif et algorithmes de traitement: nouveaux remedes aux vielles douleurs

    Energy Technology Data Exchange (ETDEWEB)

    Georgel, B

    1994-12-31

    Non destructive testing data from in-service inspections have specific features that require the most sophisticated techniques of signal and image processing. Each step in the overall information extraction process must be optimized by using recent approaches such like data decomposition and modelization, compression, sensor fusion and knowledge based systems. This can be achieved by means of wavelet transform, inverse problems formulation, standard compression algorithms, combined detection and estimation, neural networks and expert systems. These techniques are briefly presented through a number of Electricite de France applications or through recent literature results. (author). 1 fig., 20 refs.

  17. Adapted wavelet analysis from theory to software

    CERN Document Server

    Wickerhauser, Mladen Victor

    1994-01-01

    This detail-oriented text is intended for engineers and applied mathematicians who must write computer programs to perform wavelet and related analysis on real data. It contains an overview of mathematical prerequisites and proceeds to describe hands-on programming techniques to implement special programs for signal analysis and other applications. From the table of contents: - Mathematical Preliminaries - Programming Techniques - The Discrete Fourier Transform - Local Trigonometric Transforms - Quadrature Filters - The Discrete Wavelet Transform - Wavelet Packets - The Best Basis Algorithm - Multidimensional Library Trees - Time-Frequency Analysis - Some Applications - Solutions to Some of the Exercises - List of Symbols - Quadrature Filter Coefficients

  18. Object-Oriented Wavelet-Layered Digital Watermarking Technique

    Institute of Scientific and Technical Information of China (English)

    LIU Xiao-yun; YU Jue-bang; LI Ming-yu

    2005-01-01

    In this paper, an object-oriented digital watermarking technique is proposed in the wavelet domain for still images. According to the difference of recognition degree of the human eye to the different region of the image, the image is divided into the interested region and uninterested region of human eye vision in this scheme. Using the relativity of position and the difference to ocular sensitivity of the multiresolution wavelet among each subband, the image is processed with layered watermarking append technique. Experimental results show that the proposed technique successfully survives image processing operations, additive noise and JPEG compression.

  19. An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing

    KAUST Repository

    Han, Renmin

    2017-12-24

    Long-reads, point-of-care, and PCR-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the global mapping between the raw electrical current signal sequence and the expected signal sequence from the pore model serves as the key building block to base calling, reads mapping, variant identification, and methylation detection. However, the ultra-long reads of nanopore sequencing and an order of magnitude difference in the sampling speeds of the two sequences make the classical dynamic time warping (DTW) and its variants infeasible to solve the problem. Here, we propose a novel multi-level DTW algorithm, cwDTW, based on continuous wavelet transforms with different scales of the two signal sequences. Our algorithm starts from low-resolution wavelet transforms of the two sequences, such that the transformed sequences are short and have similar sampling rates. Then the peaks and nadirs of the transformed sequences are extracted to form feature sequences with similar lengths, which can be easily mapped by the original DTW. Our algorithm then recursively projects the warping path from a lower-resolution level to a higher-resolution one by building a context-dependent boundary and enabling a constrained search for the warping path in the latter. Comprehensive experiments on two real nanopore datasets on human and on Pandoraea pnomenusa, as well as two benchmark datasets from previous studies, demonstrate the efficiency and effectiveness of the proposed algorithm. In particular, cwDTW can almost always generate warping paths that are very close to the original DTW, which are remarkably more accurate than the state-of-the-art methods including FastDTW and PrunedDTW. Meanwhile, on the real nanopore datasets, cwDTW is about 440 times faster than FastDTW and 3000 times faster than the original DTW. Our program is available at https://github.com/realbigws/cwDTW.

  20. Wavelet denoising of multiframe optical coherence tomography data.

    Science.gov (United States)

    Mayer, Markus A; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y; Tornow, Ralf P

    2012-03-01

    We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.

  1. Specific features of NDT data and processing algorithms: new remedies to old ills

    International Nuclear Information System (INIS)

    Georgel, B.

    1994-01-01

    Non destructive testing data from in-service inspections have specific features that require the most sophisticated techniques of signal and image processing. Each step in the overall information extraction process must be optimized by using recent approaches such like data decomposition and modelization, compression, sensor fusion and knowledge based systems. This can be achieved by means of wavelet transform, inverse problems formulation, standard compression algorithms, combined detection and estimation, neural networks and expert systems. These techniques are briefly presented through a number of Electricite de France applications or through recent literature results. (author). 1 fig., 20 refs

  2. Image quality (IQ) guided multispectral image compression

    Science.gov (United States)

    Zheng, Yufeng; Chen, Genshe; Wang, Zhonghai; Blasch, Erik

    2016-05-01

    Image compression is necessary for data transportation, which saves both transferring time and storage space. In this paper, we focus on our discussion on lossy compression. There are many standard image formats and corresponding compression algorithms, for examples, JPEG (DCT -- discrete cosine transform), JPEG 2000 (DWT -- discrete wavelet transform), BPG (better portable graphics) and TIFF (LZW -- Lempel-Ziv-Welch). The image quality (IQ) of decompressed image will be measured by numerical metrics such as root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural Similarity (SSIM) Index. Given an image and a specified IQ, we will investigate how to select a compression method and its parameters to achieve an expected compression. Our scenario consists of 3 steps. The first step is to compress a set of interested images by varying parameters and compute their IQs for each compression method. The second step is to create several regression models per compression method after analyzing the IQ-measurement versus compression-parameter from a number of compressed images. The third step is to compress the given image with the specified IQ using the selected compression method (JPEG, JPEG2000, BPG, or TIFF) according to the regressed models. The IQ may be specified by a compression ratio (e.g., 100), then we will select the compression method of the highest IQ (SSIM, or PSNR). Or the IQ may be specified by a IQ metric (e.g., SSIM = 0.8, or PSNR = 50), then we will select the compression method of the highest compression ratio. Our experiments tested on thermal (long-wave infrared) images (in gray scales) showed very promising results.

  3. Wavelet analysis for nonstationary signals

    International Nuclear Information System (INIS)

    Penha, Rosani Maria Libardi da

    1999-01-01

    Mechanical vibration signals play an important role in anomalies identification resulting of equipment malfunctioning. Traditionally, Fourier spectral analysis is used where the signals are assumed to be stationary. However, occasional transient impulses and start-up process are examples of nonstationary signals that can be found in mechanical vibrations. These signals can provide important information about the equipment condition, as early fault detection. The Fourier analysis can not adequately be applied to nonstationary signals because the results provide data about the frequency composition averaged over the duration of the signal. In this work, two methods for nonstationary signal analysis are used: Short Time Fourier Transform (STFT) and wavelet transform. The STFT is a method of adapting Fourier spectral analysis for nonstationary application to time-frequency domain. To have a unique resolution throughout the entire time-frequency domain is its main limitation. The wavelet transform is a new analysis technique suitable to nonstationary signals, which handles the STFT drawbacks, providing multi-resolution frequency analysis and time localization in a unique time-scale graphic. The multiple frequency resolutions are obtained by scaling (dilatation/compression) the wavelet function. A comparison of the conventional Fourier transform, STFT and wavelet transform is made applying these techniques to: simulated signals, arrangement rotor rig vibration signal and rotate machine vibration signal Hanning window was used to STFT analysis. Daubechies and harmonic wavelets were used to continuos, discrete and multi-resolution wavelet analysis. The results show the Fourier analysis was not able to detect changes in the signal frequencies or discontinuities. The STFT analysis detected the changes in the signal frequencies, but with time-frequency resolution problems. The wavelet continuos and discrete transform demonstrated to be a high efficient tool to detect

  4. Image Registration Using Redundant Wavelet Transforms

    National Research Council Canada - National Science Library

    Brown, Richard

    2001-01-01

    .... In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency...

  5. The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms

    OpenAIRE

    Dai, Qi; Sha, Wei

    2009-01-01

    The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the different forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.

  6. The CCSDS Lossless Data Compression Algorithm for Space Applications

    Science.gov (United States)

    Yeh, Pen-Shu; Day, John H. (Technical Monitor)

    2001-01-01

    In the late 80's, when the author started working at the Goddard Space Flight Center (GSFC) for the National Aeronautics and Space Administration (NASA), several scientists there were in the process of formulating the next generation of Earth viewing science instruments, the Moderate Resolution Imaging Spectroradiometer (MODIS). The instrument would have over thirty spectral bands and would transmit enormous data through the communications channel. This was when the author was assigned the task of investigating lossless compression algorithms for space implementation to compress science data in order to reduce the requirement on bandwidth and storage.

  7. Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Syed Zulqadar Hassan

    2017-03-01

    Full Text Available An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

  8. A wavelet multiscale denoising algorithm for magnetic resonance (MR) images

    International Nuclear Information System (INIS)

    Yang, Xiaofeng; Fei, Baowei

    2011-01-01

    Based on the Radon transform, a wavelet multiscale denoising method is proposed for MR images. The approach explicitly accounts for the Rician nature of MR data. Based on noise statistics we apply the Radon transform to the original MR images and use the Gaussian noise model to process the MR sinogram image. A translation invariant wavelet transform is employed to decompose the MR 'sinogram' into multiscales in order to effectively denoise the images. Based on the nature of Rician noise we estimate noise variance in different scales. For the final denoised sinogram we apply the inverse Radon transform in order to reconstruct the original MR images. Phantom, simulation brain MR images, and human brain MR images were used to validate our method. The experiment results show the superiority of the proposed scheme over the traditional methods. Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities

  9. A wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry

    Science.gov (United States)

    Wang, Jianhua; Yang, Yanxi

    2018-05-01

    We present a new wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry (2-D WTP). First of all, the maximum value point is extracted from two-dimensional wavelet transform coefficient modulus, and the local extreme value points over 90% of maximum value are also obtained, they both constitute wavelet ridge candidates. Then, the gradient of rotate factor is introduced into the Abid's cost function, and the logarithmic Logistic model is used to adjust and improve the cost function weights so as to obtain more reasonable value estimation. At last, the dynamic programming method is used to accurately find the optimal wavelet ridge, and the wrapped phase can be obtained by extracting the phase at the ridge. Its advantage is that, the fringe pattern with low signal-to-noise ratio can be demodulated accurately, and its noise immunity will be better. Meanwhile, only one fringe pattern is needed to projected to measured object, so dynamic three-dimensional (3-D) measurement in harsh environment can be realized. Computer simulation and experimental results show that, for the fringe pattern with noise pollution, the 3-D surface recovery accuracy by the proposed algorithm is increased. In addition, the demodulation phase accuracy of Morlet, Fan and Cauchy mother wavelets are compared.

  10. Wavelet Based Hilbert Transform with Digital Design and Application to QCM-SS Watermarking

    Directory of Open Access Journals (Sweden)

    S. P. Maity

    2008-04-01

    Full Text Available In recent time, wavelet transforms are used extensively for efficient storage, transmission and representation of multimedia signals. Hilbert transform pairs of wavelets is the basic unit of many wavelet theories such as complex filter banks, complex wavelet and phaselet etc. Moreover, Hilbert transform finds various applications in communications and signal processing such as generation of single sideband (SSB modulation, quadrature carrier multiplexing (QCM and bandpass representation of a signal. Thus wavelet based discrete Hilbert transform design draws much attention of researchers for couple of years. This paper proposes an (i algorithm for generation of low computation cost Hilbert transform pairs of symmetric filter coefficients using biorthogonal wavelets, (ii approximation to its rational coefficients form for its efficient hardware realization and without much loss in signal representation, and finally (iii development of QCM-SS (spread spectrum image watermarking scheme for doubling the payload capacity. Simulation results show novelty of the proposed Hilbert transform design and its application to watermarking compared to existing algorithms.

  11. APPLICATIONS OF WAVELETS IN INDUCTION MACHINE FAULT DETECTION APLICACIONES DE WAVELETS EN LA DETECCIÓN DE FALLAS DE MÁQUINAS DE INDUCCIÓN

    Directory of Open Access Journals (Sweden)

    Erick Schmitt

    2010-08-01

    Full Text Available This paper presents a new wavelet-based algorithm for three-phase induction machine fault detection. This new method uses the standard deviation of wavelet coefficients, obtained from n-level decomposition of each phase voltage and current, to identify single-phasing faults or unbalanced stator resistance faults in induction machines. The proposed algorithm can operate independent of the operational frequency, fault type and loading conditions. Results show that this algorithm has better detection response than the Fourier transform-based techniques.Este trabajo presenta un nuevo algoritmo basado en wavelets para la detección de fallas en máquinas de inducción de tres fases. Este nuevo método utiliza la desviación estándar de los coeficientes wavelet, que se obtiene de la descomposición de n-niveles de cada fase, para identificar fallas en el voltaje en una fase o fallas en la resistencia del estator en máquinas de inducción. El algoritmo propuesto puede funcionar independiente de la frecuencia de operación, tipo de falla y condiciones de carga. Los resultados muestran que este algoritmo tiene una mejor respuesta de detección que las técnicas basadas en la transformada de Fourier.

  12. A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter

    Science.gov (United States)

    Chatterjee, Krishnendu; Roy, Deboshree; Tuli, Suneet

    2017-05-01

    This paper proposes a novel pulse compression algorithm, in the context of frequency modulated thermal wave imaging. The compression filter is derived from a predefined reference pixel in a recorded video, which contains direct measurement of the excitation signal alongside the thermal image of a test piece. The filter causes all the phases of the constituent frequencies to be adjusted to nearly zero value, so that on reconstruction a pulse is obtained. Further, due to band-limited nature of the excitation, signal-to-noise ratio is improved by suppressing out-of-band noise. The result is similar to that of a pulsed thermography experiment, although the peak power is drastically reduced. The algorithm is successfully demonstrated on mild steel and carbon fibre reference samples. Objective comparisons of the proposed pulse compression algorithm with the existing techniques are presented.

  13. Signal-dependent independent component analysis by tunable mother wavelets

    International Nuclear Information System (INIS)

    Seo, Kyung Ho

    2006-02-01

    The objective of this study is to improve the standard independent component analysis when applied to real-world signals. Independent component analysis starts from the assumption that signals from different physical sources are statistically independent. But real-world signals such as EEG, ECG, MEG, and fMRI signals are not statistically independent perfectly. By definition, standard independent component analysis algorithms are not able to estimate statistically dependent sources, that is, when the assumption of independence does not hold. Therefore before independent component analysis, some preprocessing stage is needed. This paper started from simple intuition that wavelet transformed source signals by 'well-tuned' mother wavelet will be simplified sufficiently, and then the source separation will show better results. By the correlation coefficient method, the tuning process between source signal and tunable mother wavelet was executed. Gamma component of raw EEG signal was set to target signal, and wavelet transform was executed by tuned mother wavelet and standard mother wavelets. Simulation results by these wavelets was shown

  14. General purpose graphic processing unit implementation of adaptive pulse compression algorithms

    Science.gov (United States)

    Cai, Jingxiao; Zhang, Yan

    2017-07-01

    This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

  15. Three-Dimensional Inverse Transport Solver Based on Compressive Sensing Technique

    Science.gov (United States)

    Cheng, Yuxiong; Wu, Hongchun; Cao, Liangzhi; Zheng, Youqi

    2013-09-01

    According to the direct exposure measurements from flash radiographic image, a compressive sensing-based method for three-dimensional inverse transport problem is presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. It is always very expensive to obtain enough measurements. With limited measurements, compressive sensing sparse reconstruction technique orthogonal matching pursuit is applied to obtain the sparse coefficients by solving an optimization problem. A three-dimensional inverse transport solver is developed based on a compressive sensing-based technique. There are three features in this solver: (1) AutoCAD is employed as a geometry preprocessor due to its powerful capacity in graphic. (2) The forward projection matrix rather than Gauss matrix is constructed by the visualization tool generator. (3) Fourier transform and Daubechies wavelet transform are adopted to convert an underdetermined system to a well-posed system in the algorithm. Simulations are performed and numerical results in pseudo-sine absorption problem, two-cube problem and two-cylinder problem when using compressive sensing-based solver agree well with the reference value.

  16. Two-level image authentication by two-step phase-shifting interferometry and compressive sensing

    Science.gov (United States)

    Zhang, Xue; Meng, Xiangfeng; Yin, Yongkai; Yang, Xiulun; Wang, Yurong; Li, Xianye; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

    2018-01-01

    A two-level image authentication method is proposed; the method is based on two-step phase-shifting interferometry, double random phase encoding, and compressive sensing (CS) theory, by which the certification image can be encoded into two interferograms. Through discrete wavelet transform (DWT), sparseness processing, Arnold transform, and data compression, two compressed signals can be generated and delivered to two different participants of the authentication system. Only the participant who possesses the first compressed signal attempts to pass the low-level authentication. The application of Orthogonal Match Pursuit CS algorithm reconstruction, inverse Arnold transform, inverse DWT, two-step phase-shifting wavefront reconstruction, and inverse Fresnel transform can result in the output of a remarkable peak in the central location of the nonlinear correlation coefficient distributions of the recovered image and the standard certification image. Then, the other participant, who possesses the second compressed signal, is authorized to carry out the high-level authentication. Therefore, both compressed signals are collected to reconstruct the original meaningful certification image with a high correlation coefficient. Theoretical analysis and numerical simulations verify the feasibility of the proposed method.

  17. Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.

    Science.gov (United States)

    Rezaee, Kh; Haddadnia, J

    2013-09-01

    Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters' number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

  18. Scalable Atomistic Simulation Algorithms for Materials Research

    Directory of Open Access Journals (Sweden)

    Aiichiro Nakano

    2002-01-01

    Full Text Available A suite of scalable atomistic simulation programs has been developed for materials research based on space-time multiresolution algorithms. Design and analysis of parallel algorithms are presented for molecular dynamics (MD simulations and quantum-mechanical (QM calculations based on the density functional theory. Performance tests have been carried out on 1,088-processor Cray T3E and 1,280-processor IBM SP3 computers. The linear-scaling algorithms have enabled 6.44-billion-atom MD and 111,000-atom QM calculations on 1,024 SP3 processors with parallel efficiency well over 90%. production-quality programs also feature wavelet-based computational-space decomposition for adaptive load balancing, spacefilling-curve-based adaptive data compression with user-defined error bound for scalable I/O, and octree-based fast visibility culling for immersive and interactive visualization of massive simulation data.

  19. Decompositions of bubbly flow PIV velocity fields using discrete wavelets multi-resolution and multi-section image method

    International Nuclear Information System (INIS)

    Choi, Je-Eun; Takei, Masahiro; Doh, Deog-Hee; Jo, Hyo-Jae; Hassan, Yassin A.; Ortiz-Villafuerte, Javier

    2008-01-01

    Currently, wavelet transforms are widely used for the analyses of particle image velocimetry (PIV) velocity vector fields. This is because the wavelet provides not only spatial information of the velocity vectors, but also of the time and frequency domains. In this study, a discrete wavelet transform is applied to real PIV images of bubbly flows. The vector fields obtained by a self-made cross-correlation PIV algorithm were used for the discrete wavelet transform. The performances of the discrete wavelet transforms were investigated by changing the level of power of discretization. The images decomposed by wavelet multi-resolution showed conspicuous characteristics of the bubbly flows for the different levels. A high spatial bubble concentrated area could be evaluated by the constructed discrete wavelet transform algorithm, in which high-leveled wavelets play dominant roles in revealing the flow characteristics

  20. Research on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model

    Science.gov (United States)

    Zhang, Jin-Yu; Meng, Xiang-Bing; Xu, Wei; Zhang, Wei; Zhang, Yong

    2014-01-01

    This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method. PMID:24696649

  1. Research on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model

    Directory of Open Access Journals (Sweden)

    Jin-Yu Zhang

    2014-01-01

    Full Text Available This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method.

  2. Surface quality monitoring for process control by on-line vibration analysis using an adaptive spline wavelet algorithm

    Science.gov (United States)

    Luo, G. Y.; Osypiw, D.; Irle, M.

    2003-05-01

    The dynamic behaviour of wood machining processes affects the surface finish quality of machined workpieces. In order to meet the requirements of increased production efficiency and improved product quality, surface quality information is needed for enhanced process control. However, current methods using high price devices or sophisticated designs, may not be suitable for industrial real-time application. This paper presents a novel approach of surface quality evaluation by on-line vibration analysis using an adaptive spline wavelet algorithm, which is based on the excellent time-frequency localization of B-spline wavelets. A series of experiments have been performed to extract the feature, which is the correlation between the relevant frequency band(s) of vibration with the change of the amplitude and the surface quality. The graphs of the experimental results demonstrate that the change of the amplitude in the selective frequency bands with variable resolution (linear and non-linear) reflects the quality of surface finish, and the root sum square of wavelet power spectrum is a good indication of surface quality. Thus, surface quality can be estimated and quantified at an average level in real time. The results can be used to regulate and optimize the machine's feed speed, maintaining a constant spindle motor speed during cutting. This will lead to higher level control and machining rates while keeping dimensional integrity and surface finish within specification.

  3. The Modified Frequency Algorithm of Digital Watermarking of Still Images Resistant to JPEG Compression

    Directory of Open Access Journals (Sweden)

    V. A. Batura

    2015-01-01

    Full Text Available Digital watermarking is an effective copyright protection for multimedia products (in particular, still images. Digital marking represents process of embedding into object of protection of a digital watermark which is invisible for a human eye. However there is rather large number of the harmful influences capable to destroy the watermark which is embedded into the still image. The most widespread attack is JPEG compression that is caused by efficiency of this format of compression and its big prevalence on the Internet.The new algorithm which is modification of algorithm of Elham is presented in the present article. The algorithm of digital marking of motionless images carries out embedding of a watermark in frequency coefficients of discrete Hadamard transform of the chosen image blocks. The choice of blocks of the image for embedding of a digital watermark is carried out on the basis of the set threshold of entropy of pixels. The choice of low-frequency coefficients for embedding is carried out on the basis of comparison of values of coefficients of discrete cosine transformation with a predetermined threshold, depending on the product of the built-in watermark coefficient on change coefficient.Resistance of new algorithm to compression of JPEG, noising, filtration, change of color, the size and histogram equalization is in details analysed. Research of algorithm consists in comparison of the appearance taken from the damaged image of a watermark with the introduced logo. Ability of algorithm to embedding of a watermark with a minimum level of distortions of the image is in addition analysed. It is established that the new algorithm in comparison by initial algorithm of Elham showed full resistance to compression of JPEG, and also the improved resistance to a noising, change of brightness and histogram equalization.The developed algorithm can be used for copyright protection on the static images. Further studies will be used to study the

  4. An Algorithm to Compress Line-transition Data for Radiative-transfer Calculations

    Science.gov (United States)

    Cubillos, Patricio E.

    2017-11-01

    Molecular line-transition lists are an essential ingredient for radiative-transfer calculations. With recent databases now surpassing the billion-line mark, handling them has become computationally prohibitive, due to both the required processing power and memory. Here I present a temperature-dependent algorithm to separate strong from weak line transitions, reformatting the large majority of the weaker lines into a cross-section data file, and retaining the detailed line-by-line information of the fewer strong lines. For any given molecule over the 0.3-30 μm range, this algorithm reduces the number of lines to a few million, enabling faster radiative-transfer computations without a significant loss of information. The final compression rate depends on how densely populated the spectrum is. I validate this algorithm by comparing Exomol’s HCN extinction-coefficient spectra between the complete (65 million line transitions) and compressed (7.7 million) line lists. Over the 0.6-33 μm range, the average difference between extinction-coefficient values is less than 1%. A Python/C implementation of this algorithm is open-source and available at https://github.com/pcubillos/repack. So far, this code handles the Exomol and HITRAN line-transition format.

  5. WAVELET TRANSFORM AND LIP MODEL

    Directory of Open Access Journals (Sweden)

    Guy Courbebaisse

    2011-05-01

    Full Text Available The Fourier transform is well suited to the study of stationary functions. Yet, it is superseded by the Wavelet transform for the powerful characterizations of function features such as singularities. On the other hand, the LIP (Logarithmic Image Processing model is a mathematical framework developed by Jourlin and Pinoli, dedicated to the representation and processing of gray tones images called hereafter logarithmic images. This mathematically well defined model, comprising a Fourier Transform "of its own", provides an effective tool for the representation of images obtained by transmitted light, such as microscope images. This paper presents a Wavelet transform within the LIP framework, with preservation of the classical Wavelet Transform properties. We show that the fast computation algorithm due to Mallat can be easily used. An application is given for the detection of crests.

  6. ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

    Science.gov (United States)

    Kora, Padmavathi; Sri Rama Krishna, K.

    2016-12-01

    Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

  7. An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems.

    Science.gov (United States)

    Cho, Gyoun-Yon; Lee, Seo-Joon; Lee, Tae-Ro

    2015-01-01

    Recent medical information systems are striving towards real-time monitoring models to care patients anytime and anywhere through ECG signals. However, there are several limitations such as data distortion and limited bandwidth in wireless communications. In order to overcome such limitations, this research focuses on compression. Few researches have been made to develop a specialized compression algorithm for ECG data transmission in real-time monitoring wireless network. Not only that, recent researches' algorithm is not appropriate for ECG signals. Therefore this paper presents a more developed algorithm EDLZW for efficient ECG data transmission. Results actually showed that the EDLZW compression ratio was 8.66, which was a performance that was 4 times better than any other recent compression method widely used today.

  8. Innovative hyperchaotic encryption algorithm for compressed video

    Science.gov (United States)

    Yuan, Chun; Zhong, Yuzhuo; Yang, Shiqiang

    2002-12-01

    It is accepted that stream cryptosystem can achieve good real-time performance and flexibility which implements encryption by selecting few parts of the block data and header information of the compressed video stream. Chaotic random number generator, for example Logistics Map, is a comparatively promising substitute, but it is easily attacked by nonlinear dynamic forecasting and geometric information extracting. In this paper, we present a hyperchaotic cryptography scheme to encrypt the compressed video, which integrates Logistics Map with Z(232 - 1) field linear congruential algorithm to strengthen the security of the mono-chaotic cryptography, meanwhile, the real-time performance and flexibility of the chaotic sequence cryptography are maintained. It also integrates with the dissymmetrical public-key cryptography and implements encryption and identity authentification on control parameters at initialization phase. In accord with the importance of data in compressed video stream, encryption is performed in layered scheme. In the innovative hyperchaotic cryptography, the value and the updating frequency of control parameters can be changed online to satisfy the requirement of the network quality, processor capability and security requirement. The innovative hyperchaotic cryprography proves robust security by cryptoanalysis, shows good real-time performance and flexible implement capability through the arithmetic evaluating and test.

  9. Spatial correlation genetic algorithm for fractal image compression

    International Nuclear Information System (INIS)

    Wu, M.-S.; Teng, W.-C.; Jeng, J.-H.; Hsieh, J.-G.

    2006-01-01

    Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In this paper, a spatial correlation genetic algorithm (SC-GA) is proposed to speed up the encoder. There are two stages for the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfied. With the aid of spatial correlation in images, the encoding time is 1.5 times faster than that of traditional genetic algorithm method, while the quality of the retrieved image is almost the same. Moreover, about half of the matched blocks come from the correlated space, so fewer bits are required to represent the fractal transform and therefore the compression ratio is also improved

  10. Certain problems concerning wavelets and wavelets packets

    Energy Technology Data Exchange (ETDEWEB)

    Siddiqi, A H

    1995-09-01

    Wavelets is the outcome of the synthesis of ideas that have emerged in different branches of science and technology, mainly in the last decade. The concept of wavelet packets, which are superpositions of wavelets, has been introduced a couple of years ago. They form bases which retain many properties of wavelets like orthogonality, smoothness and localization. The Walsh orthornomal system is a special case of wavelet packet. The wavelet packets provide at our disposal a library of orthonormal bases, each of which can be used to analyze a given signal of finite energy. The optimal choice is decided by the entropy criterion. In the present paper we discuss results concerning convergence, coefficients, and approximation of wavelet packets series in general and wavelets series in particular. Wavelet packet techniques for solutions of differential equations are also mentioned. (author). 117 refs.

  11. Certain problems concerning wavelets and wavelets packets

    International Nuclear Information System (INIS)

    Siddiqi, A.H.

    1995-09-01

    Wavelets is the outcome of the synthesis of ideas that have emerged in different branches of science and technology, mainly in the last decade. The concept of wavelet packets, which are superpositions of wavelets, has been introduced a couple of years ago. They form bases which retain many properties of wavelets like orthogonality, smoothness and localization. The Walsh orthornomal system is a special case of wavelet packet. The wavelet packets provide at our disposal a library of orthonormal bases, each of which can be used to analyze a given signal of finite energy. The optimal choice is decided by the entropy criterion. In the present paper we discuss results concerning convergence, coefficients, and approximation of wavelet packets series in general and wavelets series in particular. Wavelet packet techniques for solutions of differential equations are also mentioned. (author). 117 refs

  12. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  13. OpenCL-based vicinity computation for 3D multiresolution mesh compression

    Science.gov (United States)

    Hachicha, Soumaya; Elkefi, Akram; Ben Amar, Chokri

    2017-03-01

    3D multiresolution mesh compression systems are still widely addressed in many domains. These systems are more and more requiring volumetric data to be processed in real-time. Therefore, the performance is becoming constrained by material resources usage and an overall reduction in the computational time. In this paper, our contribution entirely lies on computing, in real-time, triangles neighborhood of 3D progressive meshes for a robust compression algorithm based on the scan-based wavelet transform(WT) technique. The originality of this latter algorithm is to compute the WT with minimum memory usage by processing data as they are acquired. However, with large data, this technique is considered poor in term of computational complexity. For that, this work exploits the GPU to accelerate the computation using OpenCL as a heterogeneous programming language. Experiments demonstrate that, aside from the portability across various platforms and the flexibility guaranteed by the OpenCL-based implementation, this method can improve performance gain in speedup factor of 5 compared to the sequential CPU implementation.

  14. End-point detection in potentiometric titration by continuous wavelet transform.

    Science.gov (United States)

    Jakubowska, Małgorzata; Baś, Bogusław; Kubiak, Władysław W

    2009-10-15

    The aim of this work was construction of the new wavelet function and verification that a continuous wavelet transform with a specially defined dedicated mother wavelet is a useful tool for precise detection of end-point in a potentiometric titration. The proposed algorithm does not require any initial information about the nature or the type of analyte and/or the shape of the titration curve. The signal imperfection, as well as random noise or spikes has no influence on the operation of the procedure. The optimization of the new algorithm was done using simulated curves and next experimental data were considered. In the case of well-shaped and noise-free titration data, the proposed method gives the same accuracy and precision as commonly used algorithms. But, in the case of noisy or badly shaped curves, the presented approach works good (relative error mainly below 2% and coefficients of variability below 5%) while traditional procedures fail. Therefore, the proposed algorithm may be useful in interpretation of the experimental data and also in automation of the typical titration analysis, specially in the case when random noise interfere with analytical signal.

  15. Accelerating wavelet lifting on graphics hardware using CUDA

    NARCIS (Netherlands)

    Laan, van der W.J.; Roerdink, J.B.T.M.; Jalba, A.C.

    2011-01-01

    The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as

  16. Accelerating Wavelet Lifting on Graphics Hardware Using CUDA

    NARCIS (Netherlands)

    Laan, Wladimir J. van der; Jalba, Andrei C.; Roerdink, Jos B.T.M.

    The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as

  17. Reconstruction algorithm in compressed sensing based on maximum a posteriori estimation

    International Nuclear Information System (INIS)

    Takeda, Koujin; Kabashima, Yoshiyuki

    2013-01-01

    We propose a systematic method for constructing a sparse data reconstruction algorithm in compressed sensing at a relatively low computational cost for general observation matrix. It is known that the cost of ℓ 1 -norm minimization using a standard linear programming algorithm is O(N 3 ). We show that this cost can be reduced to O(N 2 ) by applying the approach of posterior maximization. Furthermore, in principle, the algorithm from our approach is expected to achieve the widest successful reconstruction region, which is evaluated from theoretical argument. We also discuss the relation between the belief propagation-based reconstruction algorithm introduced in preceding works and our approach

  18. MAP-MRF-Based Super-Resolution Reconstruction Approach for Coded Aperture Compressive Temporal Imaging

    Directory of Open Access Journals (Sweden)

    Tinghua Zhang

    2018-02-01

    Full Text Available Coded Aperture Compressive Temporal Imaging (CACTI can afford low-cost temporal super-resolution (SR, but limits are imposed by noise and compression ratio on reconstruction quality. To utilize inter-frame redundant information from multiple observations and sparsity in multi-transform domains, a robust reconstruction approach based on maximum a posteriori probability and Markov random field (MAP-MRF model for CACTI is proposed. The proposed approach adopts a weighted 3D neighbor system (WNS and the coordinate descent method to perform joint estimation of model parameters, to achieve the robust super-resolution reconstruction. The proposed multi-reconstruction algorithm considers both total variation (TV and ℓ 2 , 1 norm in wavelet domain to address the minimization problem for compressive sensing, and solves it using an accelerated generalized alternating projection algorithm. The weighting coefficient for different regularizations and frames is resolved by the motion characteristics of pixels. The proposed approach can provide high visual quality in the foreground and background of a scene simultaneously and enhance the fidelity of the reconstruction results. Simulation results have verified the efficacy of our new optimization framework and the proposed reconstruction approach.

  19. Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

    Science.gov (United States)

    Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

    2010-03-01

    Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

  20. A Robust Parallel Algorithm for Combinatorial Compressed Sensing

    Science.gov (United States)

    Mendoza-Smith, Rodrigo; Tanner, Jared W.; Wechsung, Florian

    2018-04-01

    In previous work two of the authors have shown that a vector $x \\in \\mathbb{R}^n$ with at most $k Parallel-$\\ell_0$ decoding algorithm, where $\\mathrm{nnz}(A)$ denotes the number of nonzero entries in $A \\in \\mathbb{R}^{m \\times n}$. In this paper we present the Robust-$\\ell_0$ decoding algorithm, which robustifies Parallel-$\\ell_0$ when the sketch $Ax$ is corrupted by additive noise. This robustness is achieved by approximating the asymptotic posterior distribution of values in the sketch given its corrupted measurements. We provide analytic expressions that approximate these posteriors under the assumptions that the nonzero entries in the signal and the noise are drawn from continuous distributions. Numerical experiments presented show that Robust-$\\ell_0$ is superior to existing greedy and combinatorial compressed sensing algorithms in the presence of small to moderate signal-to-noise ratios in the setting of Gaussian signals and Gaussian additive noise.

  1. Optimal Image Data Compression For Whole Slide Images

    Directory of Open Access Journals (Sweden)

    J. Isola

    2016-06-01

    Differences in WSI file sizes of scanned images deemed “visually lossless” were significant. If we set Hamamatsu Nanozoomer .NDPI file size (using its default “jpeg80 quality” as 100%, the size of a “visually lossless” JPEG2000 file was only 15-20% of that. Comparisons to Aperio and 3D-Histech files (.svs and .mrxs at their default settings yielded similar results. A further optimization of JPEG2000 was done by treating empty slide area as uniform white-grey surface, which could be maximally compressed. Using this algorithm, JPEG2000 file sizes were only half, or even smaller, of original JPEG2000. Variation was due to the proportion of empty slide area on the scan. We anticipate that wavelet-based image compression methods, such as JPEG2000, have a significant advantage in saving storage costs of scanned whole slide image. In routine pathology laboratories applying WSI technology widely to their histology material, absolute cost savings can be substantial.  

  2. Texture analysis using Gabor wavelets

    Science.gov (United States)

    Naghdy, Golshah A.; Wang, Jian; Ogunbona, Philip O.

    1996-04-01

    Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for texture classification. The 'rosette' spans 360 degrees of orientation and covers frequencies from dc. In the proposed algorithm, the texture images are decomposed by the Gabor wavelet configuration and the feature vectors corresponding to the mean of the outputs of the multi-channel filters extracted. A minimum distance classifier is used in the classification procedure. As a comparison the Gabor filter has been used to classify the same texture images from the Brodatz album and the results indicate the superior discriminatory characteristics of the Gabor wavelet. With the test images used it can be concluded that the Gabor wavelet model is a better approximation of the cortical cell receptive field profiles.

  3. Content Adaptive Lagrange Multiplier Selection for Rate-Distortion Optimization in 3-D Wavelet-Based Scalable Video Coding

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2018-03-01

    Full Text Available Rate-distortion optimization (RDO plays an essential role in substantially enhancing the coding efficiency. Currently, rate-distortion optimized mode decision is widely used in scalable video coding (SVC. Among all the possible coding modes, it aims to select the one which has the best trade-off between bitrate and compression distortion. Specifically, this tradeoff is tuned through the choice of the Lagrange multiplier. Despite the prevalence of conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to 3-D wavelet-based SVC where the explicit values of the quantization step are not available, with on consideration of the content features of input signal. In this paper, an efficient content adaptive Lagrange multiplier selection algorithm is proposed in the context of RDO for 3-D wavelet-based SVC targeting quality scalability. Our contributions are two-fold. First, we introduce a novel weighting method, which takes account of the mutual information, gradient per pixel, and texture homogeneity to measure the temporal subband characteristics after applying the motion-compensated temporal filtering (MCTF technique. Second, based on the proposed subband weighting factor model, we derive the optimal Lagrange multiplier. Experimental results demonstrate that the proposed algorithm enables more satisfactory video quality with negligible additional computational complexity.

  4. ITERATION FREE FRACTAL COMPRESSION USING GENETIC ALGORITHM FOR STILL COLOUR IMAGES

    Directory of Open Access Journals (Sweden)

    A.R. Nadira Banu Kamal

    2014-02-01

    Full Text Available The storage requirements for images can be excessive, if true color and a high-perceived image quality are desired. An RGB image may be viewed as a stack of three gray-scale images that when fed into the red, green and blue inputs of a color monitor, produce a color image on the screen. The abnormal size of many images leads to long, costly, transmission times. Hence, an iteration free fractal algorithm is proposed in this research paper to design an efficient search of the domain pools for colour image compression using Genetic Algorithm (GA. The proposed methodology reduces the coding process time and intensive computation tasks. Parameters such as image quality, compression ratio and coding time are analyzed. It is observed that the proposed method achieves excellent performance in image quality with reduction in storage space.

  5. An Edge-Sensing Predictor in Wavelet Lifting Structures for Lossless Image Coding

    Directory of Open Access Journals (Sweden)

    Ömer N. Gerek

    2007-02-01

    Full Text Available The introduction of lifting implementations for image wavelet decomposition generated possibilities of several applications and several adaptive decomposition variations. The prediction step of a lifting stage constitutes the interesting part of the decomposition since it aims to reduce the energy of one of the decomposition bands by making predictions using the other decomposition band. In that aspect, more successful predictions yield better efficiency in terms of reduced energy in the lower band. In this work, we present a prediction filter whose prediction domain pixels are selected adaptively according to the local edge characteristics of the image. By judicuously selecting the prediction domain from pixels that are expected to have closer relation to the estimated pixel, the prediction error signal energy is reduced. In order to keep the adaptation rule symmetric for the encoder and the decoder sides, lossless compression applications are examined. Experimental results show that the proposed algorithm provides good compression results. Furthermore, the edge calculation is computationally inexpensive and comparable to the famous Daubechies 5/3 lifting implementation.

  6. Analysis and removing noise from speech using wavelet transform

    Science.gov (United States)

    Tomala, Karel; Voznak, Miroslav; Partila, Pavol; Rezac, Filip; Safarik, Jakub

    2013-05-01

    The paper discusses the use of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) wavelet in removing noise from voice samples and evaluation of its impact on speech quality. One significant part of Quality of Service (QoS) in communication technology is the speech quality assessment. However, this part is seriously overlooked as telecommunication providers often focus on increasing network capacity, expansion of services offered and their enforcement in the market. Among the fundamental factors affecting the transmission properties of the communication chain is noise, either at the transmitter or the receiver side. A wavelet transform (WT) is a modern tool for signal processing. One of the most significant areas in which wavelet transforms are used is applications designed to suppress noise in signals. To remove noise from the voice sample in our experiment, we used the reference segment of the voice which was distorted by Gaussian white noise. An evaluation of the impact on speech quality was carried out by an intrusive objective algorithm Perceptual Evaluation of Speech Quality (PESQ). DWT and SWT transformation was applied to voice samples that were devalued by Gaussian white noise. Afterwards, we determined the effectiveness of DWT and SWT by means of objective algorithm PESQ. The decisive criterion for determining the quality of a voice sample once the noise had been removed was Mean Opinion Score (MOS) which we obtained in PESQ. The contribution of this work lies in the evaluation of efficiency of wavelet transformation to suppress noise in voice samples.

  7. Neural network wavelet technology: A frontier of automation

    Science.gov (United States)

    Szu, Harold

    1994-01-01

    Neural networks are an outgrowth of interdisciplinary studies concerning the brain. These studies are guiding the field of Artificial Intelligence towards the, so-called, 6th Generation Computer. Enormous amounts of resources have been poured into R/D. Wavelet Transforms (WT) have replaced Fourier Transforms (FT) in Wideband Transient (WT) cases since the discovery of WT in 1985. The list of successful applications includes the following: earthquake prediction; radar identification; speech recognition; stock market forecasting; FBI finger print image compression; and telecommunication ISDN-data compression.

  8. Fast, large-scale hologram calculation in wavelet domain

    Science.gov (United States)

    Shimobaba, Tomoyoshi; Matsushima, Kyoji; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, Marie; Hirayama, Ryuji; Kakue, Takashi; Ito, Tomoyoshi

    2018-04-01

    We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of 65 , 536 × 65 , 536 pixels and a pixel pitch of 1 μm. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.

  9. Evaluation of the Use of Second Generation Wavelets in the Coherent Vortex Simulation Approach

    Science.gov (United States)

    Goldstein, D. E.; Vasilyev, O. V.; Wray, A. A.; Rogallo, R. S.

    2000-01-01

    The objective of this study is to investigate the use of the second generation bi-orthogonal wavelet transform for the field decomposition in the Coherent Vortex Simulation of turbulent flows. The performances of the bi-orthogonal second generation wavelet transform and the orthogonal wavelet transform using Daubechies wavelets with the same number of vanishing moments are compared in a priori tests using a spectral direct numerical simulation (DNS) database of isotropic turbulence fields: 256(exp 3) and 512(exp 3) DNS of forced homogeneous turbulence (Re(sub lambda) = 168) and 256(exp 3) and 512(exp 3) DNS of decaying homogeneous turbulence (Re(sub lambda) = 55). It is found that bi-orthogonal second generation wavelets can be used for coherent vortex extraction. The results of a priori tests indicate that second generation wavelets have better compression and the residual field is closer to Gaussian. However, it was found that the use of second generation wavelets results in an integral length scale for the incoherent part that is larger than that derived from orthogonal wavelets. A way of dealing with this difficulty is suggested.

  10. A Wavelet-Based Approach to Pattern Discovery in Melodies

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David; Weyde, Tillman

    2016-01-01

    We present a computational method for pattern discovery based on the application of the wavelet transform to symbolic representations of melodies or monophonic voices. We model the importance of a discovered pattern in terms of the compression ratio that can be achieved by using it to describe...

  11. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  12. Application of wavelets to image coding in an rf-link communication system

    Science.gov (United States)

    Liou, C. S. J.; Conners, Gary H.; Muczynski, Joe

    1995-04-01

    The joint University of Rochester/Rochester Institute of Technology `Center for Electronic Imaging Systems' (CEIS) is designed to focus on research problems of interest to industrial sponsors, especially the Rochester Imaging Consortium. Compression of tactical images for transmission over an rf link is an example of this type of research project which is being worked on in collaboration with one of the CEIS sponsors, Harris Corporation/RF Communications. The Harris digital video imagery transmission system (DVITS) is designed to fulfill the need to transmit secure imagery between unwired locations at real-time rates. DVITS specializes in transmission systems for users who rely on hf equipment operating at the low end of the frequency spectrum. However, the inherently low bandwidth of hf combined with transmission characteristics such as fading and dropout severely restrict the effective throughput. The problem at designing a system such as DVITS is particularly challenging because of bandwidth and signal/noise limitations, and because of the dynamic nature of the operational environment. In this paper, a novel application of wavelets in tactical image coding is proposed to replace the current DCT compression algorithm in the DVITS system. THe effects of channel noise on the received image are determined and various design strategies combining image segmentation, compression, and error correction are described.

  13. Controlled wavelet domain sparsity for x-ray tomography

    Science.gov (United States)

    Purisha, Zenith; Rimpeläinen, Juho; Bubba, Tatiana; Siltanen, Samuli

    2018-01-01

    Tomographic reconstruction is an ill-posed inverse problem that calls for regularization. One possibility is to require sparsity of the unknown in an orthonormal wavelet basis. This, in turn, can be achieved by variational regularization, where the penalty term is the sum of the absolute values of the wavelet coefficients. The primal-dual fixed point algorithm showed that the minimizer of the variational regularization functional can be computed iteratively using a soft-thresholding operation. Choosing the soft-thresholding parameter \

  14. Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning

    Science.gov (United States)

    Li, Hao; Ma, Yong; Liang, Kun; Tian, Yong; Wang, Rui

    2012-01-01

    Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.

  15. High-resolution time-frequency representation of EEG data using multi-scale wavelets

    Science.gov (United States)

    Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

    2017-09-01

    An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

  16. Detection of Heart Sounds in Children with and without Pulmonary Arterial Hypertension--Daubechies Wavelets Approach.

    Directory of Open Access Journals (Sweden)

    Mohamed Elgendi

    Full Text Available Automatic detection of the 1st (S1 and 2nd (S2 heart sounds is difficult, and existing algorithms are imprecise. We sought to develop a wavelet-based algorithm for the detection of S1 and S2 in children with and without pulmonary arterial hypertension (PAH.Heart sounds were recorded at the second left intercostal space and the cardiac apex with a digital stethoscope simultaneously with pulmonary arterial pressure (PAP. We developed a Daubechies wavelet algorithm for the automatic detection of S1 and S2 using the wavelet coefficient 'D6' based on power spectral analysis. We compared our algorithm with four other Daubechies wavelet-based algorithms published by Liang, Kumar, Wang, and Zhong. We annotated S1 and S2 from an audiovisual examination of the phonocardiographic tracing by two trained cardiologists and the observation that in all subjects systole was shorter than diastole.We studied 22 subjects (9 males and 13 females, median age 6 years, range 0.25-19. Eleven subjects had a mean PAP < 25 mmHg. Eleven subjects had PAH with a mean PAP ≥ 25 mmHg. All subjects had a pulmonary artery wedge pressure ≤ 15 mmHg. The sensitivity (SE and positive predictivity (+P of our algorithm were 70% and 68%, respectively. In comparison, the SE and +P of Liang were 59% and 42%, Kumar 19% and 12%, Wang 50% and 45%, and Zhong 43% and 53%, respectively. Our algorithm demonstrated robustness and outperformed the other methods up to a signal-to-noise ratio (SNR of 10 dB. For all algorithms, detection errors arose from low-amplitude peaks, fast heart rates, low signal-to-noise ratio, and fixed thresholds.Our algorithm for the detection of S1 and S2 improves the performance of existing Daubechies-based algorithms and justifies the use of the wavelet coefficient 'D6' through power spectral analysis. Also, the robustness despite ambient noise may improve real world clinical performance.

  17. A new approach to voltage sag detection based on wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Gencer, Oezguer; Oeztuerk, Semra; Erfidan, Tarik [Kocaeli University, Faculty of Engineering, Department of Electrical Engineering, Veziroglu Kampuesue, Eski Goelcuek Yolu, Kocaeli (Turkey)

    2010-02-15

    In this work, a new voltage sag detection method based on wavelet transform is developed. Voltage sag detection algorithms, so far have proved their efficiency and computational ability. Using several windowing techniques take long computational times for disturbance detection. Also researchers have been working on separating voltage sags from other voltage disturbances for the last decade. Due to increasing power quality standards new high performance disturbance detection algorithms are necessary to obtain high power quality standards. For this purpose, the wavelet technique is used for detecting voltage sag duration and magnitude. The developed voltage sag detection algorithm is implemented with high speed microcontroller. Test results show that, the new approach provides very accurate and satisfactory voltage sag detection. (author)

  18. Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

    Science.gov (United States)

    Zheng, Yang; Chen, Xihao; Zhu, Rui

    2017-07-01

    Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

  19. A new chest compression depth feedback algorithm for high-quality CPR based on smartphone.

    Science.gov (United States)

    Song, Yeongtak; Oh, Jaehoon; Chee, Youngjoon

    2015-01-01

    Although many smartphone application (app) programs provide education and guidance for basic life support, they do not commonly provide feedback on the chest compression depth (CCD) and rate. The validation of its accuracy has not been reported to date. This study was a feasibility assessment of use of the smartphone as a CCD feedback device. In this study, we proposed the concept of a new real-time CCD estimation algorithm using a smartphone and evaluated the accuracy of the algorithm. Using the double integration of the acceleration signal, which was obtained from the accelerometer in the smartphone, we estimated the CCD in real time. Based on its periodicity, we removed the bias error from the accelerometer. To evaluate this instrument's accuracy, we used a potentiometer as the reference depth measurement. The evaluation experiments included three levels of CCD (insufficient, adequate, and excessive) and four types of grasping orientations with various compression directions. We used the difference between the reference measurement and the estimated depth as the error. The error was calculated for each compression. When chest compressions were performed with adequate depth for the patient who was lying on a flat floor, the mean (standard deviation) of the errors was 1.43 (1.00) mm. When the patient was lying on an oblique floor, the mean (standard deviation) of the errors was 3.13 (1.88) mm. The error of the CCD estimation was tolerable for the algorithm to be used in the smartphone-based CCD feedback app to compress more than 51 mm, which is the 2010 American Heart Association guideline.

  20. Image Enhancement In HSI Space Using Wavelet Transform

    Science.gov (United States)

    Bansal, Sonia; Malhotra, Deepti

    2010-11-01

    Image processing modifies images to improve them (enhancement, restoration), extract information (analysis, recognition), and change their structure (composition, image editing). Image Enhancement is simple and most appealing area among all the digital image processing techniques. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image [1]. The color restoration functions of some real color image enhancement algorithms are greatly at random and not proved , and the real color images enhanced which are based on illumination-reflectance model have the loss of details and the `halos', we proposed a new algorithm to overcome these disadvantages. Firstly, we transform the real color image from RGB space to HSI space which is approximately orthonormal system. Secondly, the illumination and the reflectance of value are separated by homomorphic filtering based on illumination-reflectance model. We have discovered that the high dynamic range of image including high bright lights is mainly caused by the reflectance. Thirdly, the details of reflectance are preserved by wavelet transform. Fourthly, the dynamic range of reflectance is compressed by Butterworth filtering. Lastly, the energy of the saturation of real color image in HSI space is attenuated according to the spectral sensitivity of most human vision.

  1. Discovering Wavelets

    CERN Document Server

    Aboufadel, Edward

    1999-01-01

    An accessible and practical introduction to wavelets. With applications in image processing, audio restoration, seismology, and elsewhere, wavelets have been the subject of growing excitement and interest over the past several years. Unfortunately, most books on wavelets are accessible primarily to research mathematicians. Discovering Wavelets presents basic and advanced concepts of wavelets in a way that is accessible to anyone with only a fundamental knowledge of linear algebra. The basic concepts of wavelet theory are introduced in the context of an explanation of how the FBI uses wavelets

  2. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  3. CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks

    Science.gov (United States)

    Lee, HyungJune; Kim, HyunSeok; Chang, Ik Joon

    2014-01-01

    We propose a technique to optimize the energy efficiency of data collection in sensor networks by exploiting a selective data compression. To achieve such an aim, we need to make optimal decisions regarding two aspects: (1) which sensor nodes should execute compression; and (2) which compression algorithm should be used by the selected sensor nodes. We formulate this problem into binary integer programs, which provide an energy-optimal solution under the given latency constraint. Our simulation results show that the optimization algorithm significantly reduces the overall network-wide energy consumption for data collection. In the environment having a stationary sink from stationary sensor nodes, the optimized data collection shows 47% energy savings compared to the state-of-the-art collection protocol (CTP). More importantly, we demonstrate that our optimized data collection provides the best performance in an intermittent network under high interference. In such networks, we found that the selective compression for frequent packet retransmissions saves up to 55% energy compared to the best known protocol. PMID:24721763

  4. Secured Data Transmission Using Wavelet Based Steganography and cryptography

    OpenAIRE

    K.Ravindra Reddy; Ms Shaik Taj Mahaboob

    2014-01-01

    Steganography and cryptographic methods are used together with wavelets to increase the security of the data while transmitting through networks. Another technology, the digital watermarking is the process of embedding information into a digital (image) signal. Before embedding the plain text into the image, the plain text is encrypted by using Data Encryption Standard (DES) algorithm. The encrypted text is embedded into the LL sub band of the wavelet decomposed image using Le...

  5. [A new peak detection algorithm of Raman spectra].

    Science.gov (United States)

    Jiang, Cheng-Zhi; Sun, Qiang; Liu, Ying; Liang, Jing-Qiu; An, Yan; Liu, Bing

    2014-01-01

    The authors proposed a new Raman peak recognition method named bi-scale correlation algorithm. The algorithm uses the combination of the correlation coefficient and the local signal-to-noise ratio under two scales to achieve Raman peak identification. We compared the performance of the proposed algorithm with that of the traditional continuous wavelet transform method through MATLAB, and then tested the algorithm with real Raman spectra. The results show that the average time for identifying a Raman spectrum is 0.51 s with the algorithm, while it is 0.71 s with the continuous wavelet transform. When the signal-to-noise ratio of Raman peak is greater than or equal to 6 (modern Raman spectrometers feature an excellent signal-to-noise ratio), the recognition accuracy with the algorithm is higher than 99%, while it is less than 84% with the continuous wavelet transform method. The mean and the standard deviations of the peak position identification error of the algorithm are both less than that of the continuous wavelet transform method. Simulation analysis and experimental verification prove that the new algorithm possesses the following advantages: no needs of human intervention, no needs of de-noising and background removal operation, higher recognition speed and higher recognition accuracy. The proposed algorithm is operable in Raman peak identification.

  6. Wavelet and Spectral Analysis of Some Selected Problems in Reactor Diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Sunde, Carl

    2004-12-01

    Both spectral and wavelet analysis were successfully used in various diagnostic problems involving non-stationary core processes in nuclear power reactors. Three different problems were treated: two-phase flow identification, detector tube impacting and core-barrel vibrations. The first two problems are of non-stationary nature, whereas the last one is not. In the first problem, neutron radiographic and visible light images of four different vertical two-phase flow regimes, bubbly, slug, chum and annular flow, were analysed and classified with a neuro-wavelet algorithm. The algorithm consists of a wavelet part, using the 2-D discrete wavelet transform and of an artificial neural network. It classifies the different flow regimes with up to 99% efficiency. Detector tubes in a Boiling Water Reactor may execute vibrations and may also impact on nearby fuel-assemblies. Signals from in-core neutron detectors in Ringhals-1 were analysed, for detection of impacting, with both a classical spectral method and wavelet-based methods. The wavelet methods include both the discrete and the continuous 1-D wavelet transform. It was found that there is agreement between the different methods as well as with visual inspections made during the outage at the plant. However, the wavelet technique has the advantage that it does not require expert judgement for the interpretation of the analysis. In the last part two analytical calculations of the neutron noise, induced by shell-mode core-barrel vibrations, were carried out. The results are in good agreement with calculations from a numerical simulator. An out-of-phase behaviour between in-core and ex-core positions was found, which is in agreement with earlier measurements from the Pressurised Water Reactor Ringhals-3. The results from these calculations are planned to be used when diagnosing the shell-mode core-barrel vibrations in an operating plant.

  7. Wavelet and Spectral Analysis of Some Selected Problems in Reactor Diagnostics

    International Nuclear Information System (INIS)

    Sunde, Carl

    2004-12-01

    Both spectral and wavelet analysis were successfully used in various diagnostic problems involving non-stationary core processes in nuclear power reactors. Three different problems were treated: two-phase flow identification, detector tube impacting and core-barrel vibrations. The first two problems are of non-stationary nature, whereas the last one is not. In the first problem, neutron radiographic and visible light images of four different vertical two-phase flow regimes, bubbly, slug, chum and annular flow, were analysed and classified with a neuro-wavelet algorithm. The algorithm consists of a wavelet part, using the 2-D discrete wavelet transform and of an artificial neural network. It classifies the different flow regimes with up to 99% efficiency. Detector tubes in a Boiling Water Reactor may execute vibrations and may also impact on nearby fuel-assemblies. Signals from in-core neutron detectors in Ringhals-1 were analysed, for detection of impacting, with both a classical spectral method and wavelet-based methods. The wavelet methods include both the discrete and the continuous 1-D wavelet transform. It was found that there is agreement between the different methods as well as with visual inspections made during the outage at the plant. However, the wavelet technique has the advantage that it does not require expert judgement for the interpretation of the analysis. In the last part two analytical calculations of the neutron noise, induced by shell-mode core-barrel vibrations, were carried out. The results are in good agreement with calculations from a numerical simulator. An out-of-phase behaviour between in-core and ex-core positions was found, which is in agreement with earlier measurements from the Pressurised Water Reactor Ringhals-3. The results from these calculations are planned to be used when diagnosing the shell-mode core-barrel vibrations in an operating plant

  8. Accelerating wavelet-based video coding on graphics hardware using CUDA

    NARCIS (Netherlands)

    Laan, van der W.J.; Roerdink, J.B.T.M.; Jalba, A.C.; Zinterhof, P.; Loncaric, S.; Uhl, A.; Carini, A.

    2009-01-01

    The DiscreteWavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory and computation efficient way on modern, programmable GPUs, which can be regarded as massively

  9. Accelerating Wavelet-Based Video Coding on Graphics Hardware using CUDA

    NARCIS (Netherlands)

    Laan, Wladimir J. van der; Roerdink, Jos B.T.M.; Jalba, Andrei C.; Zinterhof, P; Loncaric, S; Uhl, A; Carini, A

    2009-01-01

    The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory mid computation efficient way on modern, programmable GPUs, which can be regarded as massively

  10. Iris image recognition wavelet filter-banks based iris feature extraction schemes

    CERN Document Server

    Rahulkar, Amol D

    2014-01-01

    This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis, and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provide an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc.  that will...

  11. Novel prediction- and subblock-based algorithm for fractal image compression

    International Nuclear Information System (INIS)

    Chung, K.-L.; Hsu, C.-H.

    2006-01-01

    Fractal encoding is the most consuming part in fractal image compression. In this paper, a novel two-phase prediction- and subblock-based fractal encoding algorithm is presented. Initially the original gray image is partitioned into a set of variable-size blocks according to the S-tree- and interpolation-based decomposition principle. In the first phase, each current block of variable-size range block tries to find the best matched domain block based on the proposed prediction-based search strategy which utilizes the relevant neighboring variable-size domain blocks. The first phase leads to a significant computation-saving effect. If the domain block found within the predicted search space is unacceptable, in the second phase, a subblock strategy is employed to partition the current variable-size range block into smaller blocks to improve the image quality. Experimental results show that our proposed prediction- and subblock-based fractal encoding algorithm outperforms the conventional full search algorithm and the recently published spatial-correlation-based algorithm by Truong et al. in terms of encoding time and image quality. In addition, the performance comparison among our proposed algorithm and the other two algorithms, the no search-based algorithm and the quadtree-based algorithm, are also investigated

  12. A Novel Error Resilient Scheme for Wavelet-based Image Coding Over Packet Networks

    OpenAIRE

    WenZhu Sun; HongYu Wang; DaXing Qian

    2012-01-01

    this paper presents a robust transmission strategy for wavelet based scalable bit stream over packet erasure channel. By taking the advantage of the bit plane coding and the multiple description coding, the proposed strategy adopts layered multiple description coding (LMDC) for the embedded wavelet coders to improve the error resistant capability of the important bit planes in the meaning of D(R) function. Then, the post-compression rate-distortion (PCRD) optimization process is used to impro...

  13. Secure Hashing of Dynamic Hand Signatures Using Wavelet-Fourier Compression with BioPhasor Mixing and Discretization

    Directory of Open Access Journals (Sweden)

    Wai Kuan Yip

    2007-01-01

    Full Text Available We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific discretization acts both as an error correction step as well as a real-to-binary space converter. We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT and discrete fourier transform (DFT. Without the conventional use of dynamic time warping, the proposed method avoids storage of user's hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs of and for random and skilled forgeries for stolen token (worst case scenario, and for both forgeries in the genuine token (optimal scenario.

  14. Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    2010-02-01

    This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

  15. Wavelet representation of the nuclear dynamics

    International Nuclear Information System (INIS)

    Jouault, B.; Sebille, F.; Mota, V. de la.

    1997-01-01

    The study of transport phenomena in nuclear matter is addressed in a new approach named DYWAN, based on the projection methods of statistical physics and on the mathematical theory of wavelets. Strongly compressed representations of the nuclear systems are obtained with an accurate description of the wave functions and of their antisymmetrization. The results of the approach are illustrated for the ground state description as well as for the dissipative dynamics of nuclei at intermediate energies. (K.A.)

  16. Detecting microcalcifications in digital mammogram using wavelets

    International Nuclear Information System (INIS)

    Yang Jucheng; Park Dongsun

    2004-01-01

    delegates of different kinds of the family wavelets. Among them, the Biothgonal wavelet is bi-orthogonal, but it is not orthogonal and symmetric. While other three wavelets are not only bi-orthogonal but also orthogonal, besides, they are near symmetric. These different characteristics will affect their detecting results. We first decompose the mammogram by using db4, bior3.7, coif3 and sym2 wavelet respectably, and for each family wavelet, the image is decompose into 4 levels (the first level is the original image). The detection of microcalcifications is accomplished by setting the wavelet coefficients of upper-left sub-band to zero in order to suppress the image background information before the reconstruction of the image. The reconstructed mammogram is expected to contain only high-frequency components, which include the microcalcifications. After the wavelets transform process, the third step is to locate microcalcifications through a thresholding operation. The labeling operation with a threshold changes each reconstructed image into a binary image. The threshold is determined through a series of simulation study. The final step of the proposed detection algorithm is the post-processing to eliminate tiny isolated points using binary morphological closing and opening operators. The digital mammogram database used in this work is the MIAS (Mammographic Image Analysis Society) database. The images in this database were scanned with a Joyce-Loebl microdensitometer SCANDIG-3, which has a linear response in the optical density range 0-3.2. Each pixel is 8-bits deep and at a resolution of 50 um x 50 um. And regions of microcalcifications have marked by the veteran diagnostician. Twenty five images (twelve with benign and thirteen with malignant microcalcifications) are selected for this experiment. The performance of the proposed algorithm is evaluated by a free-response receiver operating characteristic (FROC) in terms of true-positive (TP) fraction for a given number of

  17. Simultaneous optical image compression and encryption using error-reduction phase retrieval algorithm

    International Nuclear Information System (INIS)

    Liu, Wei; Liu, Shutian; Liu, Zhengjun

    2015-01-01

    We report a simultaneous image compression and encryption scheme based on solving a typical optical inverse problem. The secret images to be processed are multiplexed as the input intensities of a cascaded diffractive optical system. At the output plane, a compressed complex-valued data with a lot fewer measurements can be obtained by utilizing error-reduction phase retrieval algorithm. The magnitude of the output image can serve as the final ciphertext while its phase serves as the decryption key. Therefore the compression and encryption are simultaneously completed without additional encoding and filtering operations. The proposed strategy can be straightforwardly applied to the existing optical security systems that involve diffraction and interference. Numerical simulations are performed to demonstrate the validity and security of the proposal. (paper)

  18. Handling the data management needs of high-throughput sequencing data: SpeedGene, a compression algorithm for the efficient storage of genetic data

    Science.gov (United States)

    2012-01-01

    Background As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs. Conclusions The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary. PMID:22591016

  19. Detecting fine scratches on smooth surfaces with multiscale wavelet representation

    International Nuclear Information System (INIS)

    Yao, Li; Wan, Yan; Yao, Ming; Xu, Bugao

    2012-01-01

    This paper presents a set of image-processing algorithms for automatic detection of fine scratches on smooth surfaces, such as automobile paint surfaces. The scratches to be detected have random directions, inconspicuous gray levels and background noise. The multiscale wavelet transform was used to extract texture features, and a controlled edge fusion model was employed to merge the detailed (horizontal, vertical and diagonal) wavelet coefficient maps. Based on the fused detail map, multivariate statistics were applied to synthesize features in multiple scales and directions, and an optimal threshold was set to separate scratches from the background. The experimental results of 24 automobile paint surface showed that the presented algorithms can effectively suppress background noise and detect scratches accurately. (paper)

  20. Detection of seismic phases by wavelet transform. Dependence of its performance on wavelet functions; Wavelet henkan ni yoru jishinha no iso kenshutsu. Wavelet ni yoru sai

    Energy Technology Data Exchange (ETDEWEB)

    Zeng, X; Yamazaki, K [Tokyo Gakugei University, Tokyo (Japan); Oguchi, Y [Hosei University, Tokyo (Japan)

    1997-10-22

    A study has been performed on wavelet analysis of seismic waves. In the wavelet analysis of seismic waves, there is a possibility that the results according to different wavelet functions may come out with great difference. The study has carried out the following analyses: an analysis of amplitude and phase using wavelet transform which uses wavelet function of Morlet on P- and S-waves generated by natural earthquakes and P-wave generated by an artificial earthquake, and an analysis using continuous wavelet transform, which uses a constitution of complex wavelet function constructed by a completely diagonal scaling function of Daubechies and the wavelet function. As a result, the following matters were made clear: the result of detection of abnormal components or discontinuity depends on the wavelet function; if the Morlet wavelet function is used to properly select angular frequency and scale, equiphase lines in a phase scalogram concentrate on the discontinuity; and the result of applying the complex wavelet function is superior to that of applying the wavelet function of Morlet. 2 refs., 5 figs.

  1. Efficient random access high resolution region-of-interest (ROI) image retrieval using backward coding of wavelet trees (BCWT)

    Science.gov (United States)

    Corona, Enrique; Nutter, Brian; Mitra, Sunanda; Guo, Jiangling; Karp, Tanja

    2008-03-01

    Efficient retrieval of high quality Regions-Of-Interest (ROI) from high resolution medical images is essential for reliable interpretation and accurate diagnosis. Random access to high quality ROI from codestreams is becoming an essential feature in many still image compression applications, particularly in viewing diseased areas from large medical images. This feature is easier to implement in block based codecs because of the inherent spatial independency of the code blocks. This independency implies that the decoding order of the blocks is unimportant as long as the position for each is properly identified. In contrast, wavelet-tree based codecs naturally use some interdependency that exploits the decaying spectrum model of the wavelet coefficients. Thus one must keep track of the decoding order from level to level with such codecs. We have developed an innovative multi-rate image subband coding scheme using "Backward Coding of Wavelet Trees (BCWT)" which is fast, memory efficient, and resolution scalable. It offers far less complexity than many other existing codecs including both, wavelet-tree, and block based algorithms. The ROI feature in BCWT is implemented through a transcoder stage that generates a new BCWT codestream containing only the information associated with the user-defined ROI. This paper presents an efficient technique that locates a particular ROI within the BCWT coded domain, and decodes it back to the spatial domain. This technique allows better access and proper identification of pathologies in high resolution images since only a small fraction of the codestream is required to be transmitted and analyzed.

  2. ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

    Science.gov (United States)

    El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

    2017-02-07

    Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

  3. Wavelet analysis of epileptic spikes

    Science.gov (United States)

    Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-05-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  4. Wavelet analysis of epileptic spikes

    CERN Document Server

    Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-01-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  5. Application of Cubic Box Spline Wavelets in the Analysis of Signal Singularities

    Directory of Open Access Journals (Sweden)

    Rakowski Waldemar

    2015-12-01

    Full Text Available In the subject literature, wavelets such as the Mexican hat (the second derivative of a Gaussian or the quadratic box spline are commonly used for the task of singularity detection. The disadvantage of the Mexican hat, however, is its unlimited support; the disadvantage of the quadratic box spline is a phase shift introduced by the wavelet, making it difficult to locate singular points. The paper deals with the construction and properties of wavelets in the form of cubic box splines which have compact and short support and which do not introduce a phase shift. The digital filters associated with cubic box wavelets that are applied in implementing the discrete dyadic wavelet transform are defined. The filters and the algorithme à trous of the discrete dyadic wavelet transform are used in detecting signal singularities and in calculating the measures of signal singularities in the form of a Lipschitz exponent. The article presents examples illustrating the use of cubic box spline wavelets in the analysis of signal singularities.

  6. A New Wavelet-Based Document Image Segmentation Scheme

    Institute of Scientific and Technical Information of China (English)

    赵健; 李道京; 俞卞章; 耿军平

    2002-01-01

    The document image segmentation is very useful for printing, faxing and data processing. An algorithm is developed for segmenting and classifying document image. Feature used for classification is based on the histogram distribution pattern of different image classes. The important attribute of the algorithm is using wavelet correlation image to enhance raw image's pattern, so the classification accuracy is improved. In this paper document image is divided into four types: background, photo, text and graph. Firstly, the document image background has been distingusished easily by former normally method; secondly, three image types will be distinguished by their typical histograms, in order to make histograms feature clearer, each resolution' s HH wavelet subimage is used to add to the raw image at their resolution. At last, the photo, text and praph have been devided according to how the feature fit to the Laplacian distrbution by -X2 and L. Simulations show that classification accuracy is significantly improved. The comparison with related shows that our algorithm provides both lower classification error rates and better visual results.

  7. Wavelet representation of the nuclear dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Jouault, B.; Sebille, F.; Mota, V. de la

    1997-12-31

    The study of transport phenomena in nuclear matter is addressed in a new approach named DYWAN, based on the projection methods of statistical physics and on the mathematical theory of wavelets. Strongly compressed representations of the nuclear systems are obtained with an accurate description of the wave functions and of their antisymmetrization. The results of the approach are illustrated for the ground state description as well as for the dissipative dynamics of nuclei at intermediate energies. (K.A.). 52 refs.

  8. Wavelet analysis of MR functional data from the cerebellum

    Energy Technology Data Exchange (ETDEWEB)

    Karen, Romero Sánchez, E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Vásquez Reyes Marcos, A., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; González Gómez Dulce, I., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Hernández López, Javier M., E-mail: javierh@fcfm.buap.mx [Faculty of Physics and Mathematics, BUAP, Puebla, Pue (Mexico); Silvia, Hidalgo Tobón, E-mail: shidbon@gmail.com [Infant Hospital of Mexico, Federico Gómez, Mexico DF. Mexico and Physics Department, Universidad Autónoma Metropolitana. Iztapalapa, Mexico DF. (Mexico); Pilar, Dies Suarez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx [Infant Hospital of Mexico, Federico Gómez, Mexico DF. (Mexico); Benito, De Celis Alonso, E-mail: benileon@yahoo.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. Mexico and Foundation for Development Carlos Sigüenza. Puebla, Pue. (Mexico)

    2014-11-07

    The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis was found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD.

  9. Wavelet analysis of MR functional data from the cerebellum

    International Nuclear Information System (INIS)

    Karen, Romero Sánchez; Vásquez Reyes Marcos, A.; González Gómez Dulce, I.; Hernández López, Javier M.; Silvia, Hidalgo Tobón; Pilar, Dies Suarez; Eduardo, Barragán Pérez; Benito, De Celis Alonso

    2014-01-01

    The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis was found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD

  10. A REVIEW WAVELET TRANSFORM AND FUZZY K-MEANS BASED IMAGE DE-NOISING METHOD

    OpenAIRE

    Nidhi Patel*, Asst. Prof. Pratik Kumar Soni

    2017-01-01

    The research area of image processing technique using fuzzy k-means and wavelet transform. The enormous amount of data necessary for images is a main reason for the growth of many areas within the research field of computer imaging such as image processing and compression. In order to get this in requisites of the concerned research work, wavelet transforms and k-means clustering is applied. This can be done in order to discover more possible combinations that may lead to the finest de-noisin...

  11. Recursive Pyramid Algorithm-Based Discrete Wavelet Transform for Reactive Power Measurement in Smart Meters

    Directory of Open Access Journals (Sweden)

    Mahin K. Atiq

    2013-09-01

    Full Text Available Measurement of the active, reactive, and apparent power is one of the most fundamental tasks of smart meters in energy systems. Recently, a number of studies have employed the discrete wavelet transform (DWT for power measurement in smart meters. The most common way to implement DWT is the pyramid algorithm; however, this is not feasible for practical DWT computation because it requires either a log N cascaded filter or O (N word size memory storage for an input signal of the N-point. Both solutions are too expensive for practical applications of smart meters. It is proposed that the recursive pyramid algorithm is more suitable for smart meter implementation because it requires only word size storage of L × Log (N-L, where L is the length of filter. We also investigated the effect of varying different system parameters, such as the sampling rate, dc offset, phase offset, linearity error in current and voltage sensors, analog to digital converter resolution, and number of harmonics in a non-sinusoidal system, on the reactive energy measurement using DWT. The error analysis is depicted in the form of the absolute difference between the measured and the true value of the reactive energy.

  12. Modeling of video traffic in packet networks, low rate video compression, and the development of a lossy+lossless image compression algorithm

    Science.gov (United States)

    Sayood, K.; Chen, Y. C.; Wang, X.

    1992-01-01

    During this reporting period we have worked on three somewhat different problems. These are modeling of video traffic in packet networks, low rate video compression, and the development of a lossy + lossless image compression algorithm, which might have some application in browsing algorithms. The lossy + lossless scheme is an extension of work previously done under this grant. It provides a simple technique for incorporating browsing capability. The low rate coding scheme is also a simple variation on the standard discrete cosine transform (DCT) coding approach. In spite of its simplicity, the approach provides surprisingly high quality reconstructions. The modeling approach is borrowed from the speech recognition literature, and seems to be promising in that it provides a simple way of obtaining an idea about the second order behavior of a particular coding scheme. Details about these are presented.

  13. A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

    Energy Technology Data Exchange (ETDEWEB)

    Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch [University of Applied Sciences and Arts Northwestern Switzerland FHNW, 5210 Windisch (Switzerland)

    2017-11-01

    One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

  14. A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

    Science.gov (United States)

    Felix, Simon; Bolzern, Roman; Battaglia, Marina

    2017-11-01

    One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

  15. FPGA compression of ECG signals by using modified convolution scheme of the Discrete Wavelet Transform Compresión de señales ECG sobre FPGA utilizando un esquema modificado de convolución de la Transformada Wavelet Discreta

    Directory of Open Access Journals (Sweden)

    Dora M Ballesteros

    2012-04-01

    Full Text Available This paper presents FPGA design of ECG compression by using the Discrete Wavelet Transform (DWT and one lossless encoding method. Unlike the classical works based on off-line mode, the current work allows the real-time processing of the ECG signal to reduce the redundant information. A model is developed for a fixed-point convolution scheme which has a good performance in relation to the throughput, the latency, the maximum frequency of operation and the quality of the compressed signal. The quantization of the coefficients of the filters and the selected fixed-threshold give a low error in relation to clinical applications.Este documento presenta el diseño basado en FPGA para la compresión de señales ECG utilizando la Transformada Wavelet Discreta y un método de codificación sin pérdida de información. A diferencia de los trabajos clásicos para modo off-line, el trabajo actual permite la compresión en tiempo real de la señal ECG por medio de la reducción de la información redundante. Se propone un modelo para el esquema de convolución en formato punto fijo, el cual tiene buen desempeño en relación a la tasa de salida, la latencia del sistema, la máxima frecuencia de operación y la calidad de la señal comprimida. La arquitectura propuesta, la cuantización utilizada y el método de codificación proporcionan un PRD que es apto para el análisis clínico.

  16. SIRFING: Sparse Image Reconstruction For INterferometry using GPUs

    Science.gov (United States)

    Cranmer, Miles; Garsden, Hugh; Mitchell, Daniel A.; Greenhill, Lincoln

    2018-01-01

    We present a deconvolution code for radio interferometric imaging based on the compressed sensing algorithms in Garsden et al. (2015). Being computationally intensive, compressed sensing is ripe for parallelization over GPUs. Our compressed sensing implementation generates images using wavelets, and we have ported the underlying wavelet library to CUDA, targeting the spline filter reconstruction part of the algorithm. The speedup achieved is almost an order of magnitude. The code is modular but is also being integrated into the calibration and imaging pipeline in use by the LEDA project at the Long Wavelength Array (LWA) as well as by the Murchinson Widefield Array (MWA).

  17. Wavelet Filter Banks for Super-Resolution SAR Imaging

    Science.gov (United States)

    Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

    2011-01-01

    This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

  18. Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

    Directory of Open Access Journals (Sweden)

    Enliang Song

    2013-07-01

    Full Text Available One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM, by addressing the neighborhood preserving embedding (NPE algorithm of manifold learning on the wavelet packet node energy (WPNE of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity.

  19. Towards the compression of parton densities through machine learning algorithms

    CERN Document Server

    Carrazza, Stefano

    2016-01-01

    One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.

  20. The formation of spiral galaxies: adiabatic compression with Young's algorithm and the relation of dark matter haloes to their primordial antecedents

    NARCIS (Netherlands)

    Katz, Harley; McGaugh, Stacy S.; Sellwood, J. A.; de Blok, W. J. G.

    We utilize Young's algorithm to model the adiabatic compression of the dark matter haloes of galaxies in the THINGS survey to determine the relationship between the halo fit to the rotation curve and the corresponding primordial halo prior to compression. Young's algorithm conserves radial action

  1. Fringe pattern information retrieval using wavelets

    Science.gov (United States)

    Sciammarella, Cesar A.; Patimo, Caterina; Manicone, Pasquale D.; Lamberti, Luciano

    2005-08-01

    Two-dimensional phase modulation is currently the basic model used in the interpretation of fringe patterns that contain displacement information, moire, holographic interferometry, speckle techniques. Another way to look to these two-dimensional signals is to consider them as frequency modulated signals. This alternative interpretation has practical implications similar to those that exist in radio engineering for handling frequency modulated signals. Utilizing this model it is possible to obtain frequency information by using the energy approach introduced by Ville in 1944. A natural complementary tool of this process is the wavelet methodology. The use of wavelet makes it possible to obtain the local values of the frequency in a one or two dimensional domain without the need of previous phase retrieval and differentiation. Furthermore from the properties of wavelets it is also possible to obtain at the same time the phase of the signal with the advantage of a better noise removal capabilities and the possibility of developing simpler algorithms for phase unwrapping due to the availability of the derivative of the phase.

  2. Enhanced ATM Security using Biometric Authentication and Wavelet Based AES

    Directory of Open Access Journals (Sweden)

    Sreedharan Ajish

    2016-01-01

    Full Text Available The traditional ATM terminal customer recognition systems rely only on bank cards, passwords and such identity verification methods are not perfect and functions are too single. Biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. This paper presents a highly secured ATM banking system using biometric authentication and wavelet based Advanced Encryption Standard (AES algorithm. Two levels of security are provided in this proposed design. Firstly we consider the security level at the client side by providing biometric authentication scheme along with a password of 4-digit long. Biometric authentication is achieved by considering the fingerprint image of the client. Secondly we ensure a secured communication link between the client machine to the bank server using an optimized energy efficient and wavelet based AES processor. The fingerprint image is the data for encryption process and 4-digit long password is the symmetric key for the encryption process. The performance of ATM machine depends on ultra-high-speed encryption, very low power consumption, and algorithmic integrity. To get a low power consuming and ultra-high speed encryption at the ATM machine, an optimized and wavelet based AES algorithm is proposed. In this system biometric and cryptography techniques are used together for personal identity authentication to improve the security level. The design of the wavelet based AES processor is simulated and the design of the energy efficient AES processor is simulated in Quartus-II software. Simulation results ensure its proper functionality. A comparison among other research works proves its superiority.

  3. Image encryption using the fractional wavelet transform

    International Nuclear Information System (INIS)

    Vilardy, Juan M; Useche, J; Torres, C O; Mattos, L

    2011-01-01

    In this paper a technique for the coding of digital images is developed using Fractional Wavelet Transform (FWT) and random phase masks (RPMs). The digital image to encrypt is transformed with the FWT, after the coefficients resulting from the FWT (Approximation, Details: Horizontal, vertical and diagonal) are multiplied each one by different RPMs (statistically independent) and these latest results is applied an Inverse Wavelet Transform (IWT), obtaining the encrypted digital image. The decryption technique is the same encryption technique in reverse sense. This technique provides immediate advantages security compared to conventional techniques, in this technique the mother wavelet family and fractional orders associated with the FWT are additional keys that make access difficult to information to an unauthorized person (besides the RPMs used), thereby the level of encryption security is extraordinarily increased. In this work the mathematical support for the use of the FWT in the computational algorithm for the encryption is also developed.

  4. Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms

    International Nuclear Information System (INIS)

    Tang Jie; Nett, Brian E; Chen Guanghong

    2009-01-01

    Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

  5. Wavelet based analysis of multi-electrode EEG-signals in epilepsy

    Science.gov (United States)

    Hein, Daniel A.; Tetzlaff, Ronald

    2005-06-01

    For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

  6. A New Method for Multisensor Data Fusion Based on Wavelet Transform in a Chemical Plant

    Directory of Open Access Journals (Sweden)

    Karim Salahshoor

    2014-07-01

    Full Text Available This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT and extended Kalman filter (EKF. Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR and minimum square error (MSE criteria.

  7. Implementation of the 2-D Wavelet Transform into FPGA for Image

    Energy Technology Data Exchange (ETDEWEB)

    Leon, M; Barba, L; Vargas, L; Torres, C O, E-mail: madeleineleon@unicesar.edu.co [Laboratorio de Optica e Informatica, Universidad Popular del Cesar, Sede balneario Hurtado, Valledupar, Cesar (Colombia)

    2011-01-01

    This paper presents a hardware system implementation of the of discrete wavelet transform algorithm in two dimensions for FPGA, using the Daubechies filter family of order 2 (db2). The decomposition algorithm of this transform is designed and simulated with the Hardware Description Language VHDL and is implemented in a programmable logic device (FPGA) XC3S1200E reference, Spartan IIIE family, by Xilinx, take advantage the parallels properties of these gives us and speeds processing that can reach them. The architecture is evaluated using images input of different sizes. This implementation is done with the aim of developing a future images encryption hardware system using wavelet transform for security information.

  8. HVS scheme for DICOM image compression: Design and comparative performance evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Prabhakar, B. [Biomedical and Engineering Division, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu (India)]. E-mail: prabhakarb@iitm.ac.in; Reddy, M. Ramasubba [Biomedical and Engineering Division, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu (India)

    2007-07-15

    Advanced digital imaging technology in medical domain demands efficient and effective DICOM image compression for progressive image transmission and picture archival. Here a compression system, which incorporates sensitivities of HVS coded with SPIHT quantization, is discussed. The weighting factors derived from luminance CSF are used to transform the wavelet subband coefficients to reflect characteristics of HVS in best possible manner. Mannos et al. and Daly HVS models have been used and results are compared. To evaluate the performance, Eskicioglu chart metric is considered. Experiment is done on both Monochrome and Color Dicom images of MRI, CT, OT, and CR, natural and benchmark images. Reconstructed image through our technique showed improvement in visual quality and Eskicioglu chart metric at same compression ratios. Also the Daly HVS model based compression shows better performance perceptually and quantitatively when compared to Mannos et el. model. Further 'bior4.4' wavelet filter provides better results than 'db9' filter for this compression system. Results give strong evidence that under common boundary conditions; our technique achieves competitive visual quality, compression ratio and coding/decoding time, when compared with jpeg2000 (kakadu)

  9. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

    Science.gov (United States)

    Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

    2016-09-22

    To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

  10. The design and implementation of signal decomposition system of CL multi-wavelet transform based on DSP builder

    Science.gov (United States)

    Huang, Yan; Wang, Zhihui

    2015-12-01

    With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.

  11. Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • Four algorithms [EMD/FEEMD/WD/WPD] are proposed for the wind speed decomposition. • Two new hybrid forecasting algorithms [FEEMD-MLP/ANFIS] are presented. • The contributions of the FEEMD/WPD algorithms are both significant. • The MLP has better forecasting performance than the ANFIS in these cases. • All the proposed hybrid algorithms are suitable for the wind speed predictions. - Abstract: The technology of wind speed prediction is important to guarantee the safety of wind power utilization. Compared to the single algorithms, the hybrid ones always have better performance in the wind speed predictions. In this paper, three most important decomposing algorithms [Wavelet Decomposition – WD/Wavelet Packet Decomposition – WPD/Empirical Mode Decomposition – EMD] and a latest decomposing algorithm [Fast Ensemble Empirical Mode Decomposition – FEEMD] are all adopted to realize the wind speed high-precision predictions with two representative networks [MLP Neural Network/ANFIS Neural Network]. Based on the hybrid forecasting framework, two new wind speed forecasting methods [FEEMD-MLP and FEEMD-ANFIS] are proposed. Additionally, a series of performance comparison is provided, which includes EMD-MLP, FEEMD-MLP, EDM-ANFIS, FEEMD-ANFIS, WD-MLP, WD-ANFIS, WPD-MLP and WPD-ANFIS. The aim of the study is to investigate the decomposing and forecasting performance of the different hybrid models. Two experimental results show that: (1) Due to the inclusion of the decomposing algorithms, the hybrid ANN algorithms have better performance than their corresponding single ANN algorithms; (2) the proposed new FEEMD-MLP hybrid model has the best performance in the three-step predictions while the WPD-MLP hybrid model has the best performance in the one-step predictions; (3) among the decomposing algorithms, the FEEMD and WPD have better performance than the EMD and WD, respectively; (4) in the forecasting neural networks, the MLP has better performance

  12. WASP (Wavelet Analysis of Secondary Particles Angular Distributions) package. Version 1.0. User's guide

    International Nuclear Information System (INIS)

    Solov'ev, A.G.

    2001-01-01

    WASP package is a C++ program aimed to analyze angular distributions of secondary particles generated in nuclear interactions. (WASP is designed for data analysis of the STAR and ALICE experiments). It uses a wavelet analysis for this purpose and the vanishing momentum or gaussian wavelets are chosen for transformations. WASP provides an user-friendly Graphical User Interface (GUI) which makes it quite simple to use. WASP design, a brief description of the used wavelet transformation algorithm and GUI are presented in this user's guide

  13. A Wavelet-Based Approach to Fall Detection

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2015-05-01

    Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.

  14. Supplementary Material for: Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano; Genton, Marc G.

    2016-01-01

    algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific datasets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a

  15. Forced Ignition Study Based On Wavelet Method

    Science.gov (United States)

    Martelli, E.; Valorani, M.; Paolucci, S.; Zikoski, Z.

    2011-05-01

    The control of ignition in a rocket engine is a critical problem for combustion chamber design. Therefore it is essential to fully understand the mechanism of ignition during its earliest stages. In this paper the characteristics of flame kernel formation and initial propagation in a hydrogen-argon-oxygen mixing layer are studied using 2D direct numerical simulations with detailed chemistry and transport properties. The flame kernel is initiated by adding an energy deposition source term in the energy equation. The effect of unsteady strain rate is studied by imposing a 2D turbulence velocity field, which is initialized by means of a synthetic field. An adaptive wavelet method, based on interpolating wavelets is used in this study to solve the compressible reactive Navier- Stokes equations. This method provides an alternative means to refine the computational grid points according to local demands of the physical solution. The present simulations show that in the very early instants the kernel perturbed by the turbulent field is characterized by an increased burning area and a slightly increased rad- ical formation. In addition, the calculations show that the wavelet technique yields a significant reduction in the number of degrees of freedom necessary to achieve a pre- scribed solution accuracy.

  16. A New RTL Design Approach for a DCT/IDCT-Based Image Compression Architecture using the mCBE Algorithm

    Directory of Open Access Journals (Sweden)

    Rachmad Vidya Wicaksana Putra

    2012-09-01

    Full Text Available In the literature, several approaches of designing a DCT/IDCT-based image compression system have been proposed. In this paper, we present a new RTL design approach with as main focus developing a DCT/IDCT-based image compression architecture using a self-created algorithm. This algorithm can efficiently minimize the amount of shifter-adders to substitute multipliers. We call this new algorithm the multiplication from Common Binary Expression (mCBE Algorithm. Besides this algorithm, we propose alternative quantization numbers, which can be implemented simply as shifters in digital hardware. Mostly, these numbers can retain a good compressed-image quality compared to JPEG recommendations. These ideas lead to our design being small in circuit area, multiplierless, and low in complexity. The proposed 8-point 1D-DCT design has only six stages, while the 8-point 1D-IDCT design has only seven stages (one stage being defined as equal to the delay of one shifter or 2-input adder. By using the pipelining method, we can achieve a high-speed architecture with latency as a trade-off consideration. The design has been synthesized and can reach a speed of up to 1.41ns critical path delay (709.22MHz.

  17. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

    Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

    2016-10-01

    Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

  18. Efficient JPEG 2000 Image Compression Scheme for Multihop Wireless Networks

    Directory of Open Access Journals (Sweden)

    Halim Sghaier

    2011-08-01

    Full Text Available When using wireless sensor networks for real-time data transmission, some critical points should be considered. Restricted computational power, reduced memory, narrow bandwidth and energy supplied present strong limits in sensor nodes. Therefore, maximizing network lifetime and minimizing energy consumption are always optimization goals. To overcome the computation and energy limitation of individual sensor nodes during image transmission, an energy efficient image transport scheme is proposed, taking advantage of JPEG2000 still image compression standard using MATLAB and C from Jasper. JPEG2000 provides a practical set of features, not necessarily available in the previous standards. These features were achieved using techniques: the discrete wavelet transform (DWT, and embedded block coding with optimized truncation (EBCOT. Performance of the proposed image transport scheme is investigated with respect to image quality and energy consumption. Simulation results are presented and show that the proposed scheme optimizes network lifetime and reduces significantly the amount of required memory by analyzing the functional influence of each parameter of this distributed image compression algorithm.

  19. Significance tests for the wavelet cross spectrum and wavelet linear coherence

    Directory of Open Access Journals (Sweden)

    Z. Ge

    2008-12-01

    Full Text Available This work attempts to develop significance tests for the wavelet cross spectrum and the wavelet linear coherence as a follow-up study on Ge (2007. Conventional approaches that are used by Torrence and Compo (1998 based on stationary background noise time series were used here in estimating the sampling distributions of the wavelet cross spectrum and the wavelet linear coherence. The sampling distributions are then used for establishing significance levels for these two wavelet-based quantities. In addition to these two wavelet quantities, properties of the phase angle of the wavelet cross spectrum of, or the phase difference between, two Gaussian white noise series are discussed. It is found that the tangent of the principal part of the phase angle approximately has a standard Cauchy distribution and the phase angle is uniformly distributed, which makes it impossible to establish significance levels for the phase angle. The simulated signals clearly show that, when there is no linear relation between the two analysed signals, the phase angle disperses into the entire range of [−π,π] with fairly high probabilities for values close to ±π to occur. Conversely, when linear relations are present, the phase angle of the wavelet cross spectrum settles around an associated value with considerably reduced fluctuations. When two signals are linearly coupled, their wavelet linear coherence will attain values close to one. The significance test of the wavelet linear coherence can therefore be used to complement the inspection of the phase angle of the wavelet cross spectrum. The developed significance tests are also applied to actual data sets, simultaneously recorded wind speed and wave elevation series measured from a NOAA buoy on Lake Michigan. Significance levels of the wavelet cross spectrum and the wavelet linear coherence between the winds and the waves reasonably separated meaningful peaks from those generated by randomness in the data set. As

  20. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Donghao Wang

    2016-09-01

    Full Text Available To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

  1. An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform, chaotic maps and substitution box

    Science.gov (United States)

    Khan, Muazzam A.; Ahmad, Jawad; Javaid, Qaisar; Saqib, Nazar A.

    2017-03-01

    Wireless Sensor Networks (WSN) is widely deployed in monitoring of some physical activity and/or environmental conditions. Data gathered from WSN is transmitted via network to a central location for further processing. Numerous applications of WSN can be found in smart homes, intelligent buildings, health care, energy efficient smart grids and industrial control systems. In recent years, computer scientists has focused towards findings more applications of WSN in multimedia technologies, i.e. audio, video and digital images. Due to bulky nature of multimedia data, WSN process a large volume of multimedia data which significantly increases computational complexity and hence reduces battery time. With respect to battery life constraints, image compression in addition with secure transmission over a wide ranged sensor network is an emerging and challenging task in Wireless Multimedia Sensor Networks. Due to the open nature of the Internet, transmission of data must be secure through a process known as encryption. As a result, there is an intensive demand for such schemes that is energy efficient as well as highly secure since decades. In this paper, discrete wavelet-based partial image encryption scheme using hashing algorithm, chaotic maps and Hussain's S-Box is reported. The plaintext image is compressed via discrete wavelet transform and then the image is shuffled column-wise and row wise-wise via Piece-wise Linear Chaotic Map (PWLCM) and Nonlinear Chaotic Algorithm, respectively. To get higher security, initial conditions for PWLCM are made dependent on hash function. The permuted image is bitwise XORed with random matrix generated from Intertwining Logistic map. To enhance the security further, final ciphertext is obtained after substituting all elements with Hussain's substitution box. Experimental and statistical results confirm the strength of the anticipated scheme.

  2. Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Deyun Wang

    2018-04-01

    Full Text Available Natural gas consumption has increased with an average annual growth rate of about 10% between 2012 and 2017. Total natural gas consumption accounted for 6.4% of consumed primary energy resources in 2016, up from 5.4% in 2012, making China the world’s third-largest gas user. Therefore, accurately predicting natural gas consumption has become very important for market participants to organize indigenous production, foreign supply contracts and infrastructures in a better way. This paper first presents the main factors affecting China’s natural gas consumption, and then proposes a hybrid forecasting model by combining the particle swarm optimization algorithm and wavelet neural network (PSO-WNN. In PSO-WNN model, the initial weights and wavelet parameters are optimized using PSO algorithm and updated through a dynamic learning rate to improve the training speed, forecasting precision and reduce fluctuation of WNN. The experimental results show the superiority of the proposed model compared with ANN and WNN based models. Then, this study conducts the scenario analysis of the natural gas consumption from 2017 to 2025 in China based on three scenarios, namely low scenario, reference scenario and high scenario, and the results illustrate that the China’s natural gas consumption is going to be 342.70, 358.27, 366.42 million tce (“standard” tons coal equivalent in 2020, and 407.01, 437.95, 461.38 million tce in 2025 under the low, reference and high scenarios, respectively. Finally, this paper provides some policy suggestions on natural gas exploration and development, infrastructure construction and technical innovations to promote a sustainable development of China’s natural gas industry.

  3. Discrete wavelet transform: a tool in smoothing kinematic data.

    Science.gov (United States)

    Ismail, A R; Asfour, S S

    1999-03-01

    Motion analysis systems typically introduce noise to the displacement data recorded. Butterworth digital filters have been used to smooth the displacement data in order to obtain smoothed velocities and accelerations. However, this technique does not yield satisfactory results, especially when dealing with complex kinematic motions that occupy the low- and high-frequency bands. The use of the discrete wavelet transform, as an alternative to digital filters, is presented in this paper. The transform passes the original signal through two complementary low- and high-pass FIR filters and decomposes the signal into an approximation function and a detail function. Further decomposition of the signal results in transforming the signal into a hierarchy set of orthogonal approximation and detail functions. A reverse process is employed to perfectly reconstruct the signal (inverse transform) back from its approximation and detail functions. The discrete wavelet transform was applied to the displacement data recorded by Pezzack et al., 1977. The smoothed displacement data were twice differentiated and compared to Pezzack et al.'s acceleration data in order to choose the most appropriate filter coefficients and decomposition level on the basis of maximizing the percentage of retained energy (PRE) and minimizing the root mean square error (RMSE). Daubechies wavelet of the fourth order (Db4) at the second decomposition level showed better results than both the biorthogonal and Coiflet wavelets (PRE = 97.5%, RMSE = 4.7 rad s-2). The Db4 wavelet was then used to compress complex displacement data obtained from a noisy mathematically generated function. Results clearly indicate superiority of this new smoothing approach over traditional filters.

  4. Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Shafqat Ullah Khan

    2016-01-01

    Full Text Available A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA, parallel coordinate descent (PCD algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.

  5. Blind assessment of image blur using the Haar wavelet

    CSIR Research Space (South Africa)

    Bachoo, A

    2010-10-01

    Full Text Available algorithms. We present an intuitive quality metric for characterizing the amount of blur in an image, through blind image assessment, using the Haar discrete wavelet transform. Thus, the method does not require a reference image or any prior information...

  6. Human Body Image Edge Detection Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    李勇; 付小莉

    2003-01-01

    Human dresses are different in thousands way.Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to tte peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.

  7. A New Approach for Fingerprint Image Compression

    Energy Technology Data Exchange (ETDEWEB)

    Mazieres, Bertrand

    1997-12-01

    The FBI has been collecting fingerprint cards since 1924 and now has over 200 million of them. Digitized with 8 bits of grayscale resolution at 500 dots per inch, it means 2000 terabytes of information. Also, without any compression, transmitting a 10 Mb card over a 9600 baud connection will need 3 hours. Hence we need a compression and a compression as close to lossless as possible: all fingerprint details must be kept. A lossless compression usually do not give a better compression ratio than 2:1, which is not sufficient. Compressing these images with the JPEG standard leads to artefacts which appear even at low compression rates. Therefore the FBI has chosen in 1993 a scheme of compression based on a wavelet transform, followed by a scalar quantization and an entropy coding : the so-called WSQ. This scheme allows to achieve compression ratios of 20:1 without any perceptible loss of quality. The publication of the FBI specifies a decoder, which means that many parameters can be changed in the encoding process: the type of analysis/reconstruction filters, the way the bit allocation is made, the number of Huffman tables used for the entropy coding. The first encoder used 9/7 filters for the wavelet transform and did the bit allocation using a high-rate bit assumption. Since the transform is made into 64 subbands, quite a lot of bands receive only a few bits even at an archival quality compression rate of 0.75 bit/pixel. Thus, after a brief overview of the standard, we will discuss a new approach for the bit-allocation that seems to make more sense where theory is concerned. Then we will talk about some implementation aspects, particularly for the new entropy coder and the features that allow other applications than fingerprint image compression. Finally, we will compare the performances of the new encoder to those of the first encoder.

  8. An compression algorithm for medical images and a display with the decoding function

    International Nuclear Information System (INIS)

    Gotoh, Toshiyuki; Nakagawa, Yukihiro; Shiohara, Morito; Yoshida, Masumi

    1990-01-01

    This paper describes and efficient image compression method for medical images, a high-speed display with the decoding function. In our method, an input image is divided into blocks, and either of Discrete Cosine Transform coding (DCT) or Block Truncation Coding (BTC) is adaptively applied on each block to improve image quality. The display, we developed, receives the compressed data from the host computer and reconstruct images of good quality at high speed using four decoding microprocessors on which our algorithm is implemented in pipeline. By the experiments, our method and display were verified to be effective. (author)

  9. Wavelet Decomposition of the Financial Market

    Czech Academy of Sciences Publication Activity Database

    Vošvrda, Miloslav; Vácha, Lukáš

    2007-01-01

    Roč. 16, č. 1 (2007), s. 38-54 ISSN 1210-0455 R&D Projects: GA ČR GA402/04/1026; GA ČR(CZ) GA402/06/1417 Grant - others:GA UK(CZ) 454/2004/A-EK FSV Institutional research plan: CEZ:AV0Z10750506 Keywords : agents' trading strategies * heterogeneous agents model with stochastic memory * worst out algorithm * wavelet Subject RIV: AH - Economics

  10. Comparisons between two wavelet functions in extracting coherent structures from solar wind time series

    International Nuclear Information System (INIS)

    Bolzani, M.J.A.; Guarnieri, F.L.; Vieira, Paulo Cesar

    2009-01-01

    Nowadays, wavelet analysis of turbulent flows have become increasingly popular. However, the study of geometric characteristics from wavelet functions is still poorly explored. In this work we compare the performance of two wavelet functions in extracting the coherent structures from solar wind velocity time series. The data series are from years 1996 to 2002 (except 1998 and 1999). The wavelet algorithm decomposes the annual time-series in two components: the coherent part and non-coherent one, using the daubechies-4 and haar wavelet function. The threshold assumed is based on a percentage of maximum variance found in each dyadic scale. After the extracting procedure, we applied the power spectral density on the original time series and coherent time series to obtain spectral indices. The results from spectral indices show higher values for the coherent part obtained by daubechies-4 than those obtained by the haar wavelet function. Using the kurtosis statistical parameter, on coherent and non-coherent time series, it was possible to conjecture that the differences found between two wavelet functions may be associated with their geometric forms. (author)

  11. JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding

    Directory of Open Access Journals (Sweden)

    Thomas André

    2007-03-01

    Full Text Available We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted bit allocation procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.

  12. Image-adaptive and robust digital wavelet-domain watermarking for images

    Science.gov (United States)

    Zhao, Yi; Zhang, Liping

    2018-03-01

    We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.

  13. JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding

    Directory of Open Access Journals (Sweden)

    André Thomas

    2007-01-01

    Full Text Available We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted bit allocation procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.

  14. A new approach to pre-processing digital image for wavelet-based watermark

    Science.gov (United States)

    Agreste, Santa; Andaloro, Guido

    2008-11-01

    The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.

  15. Detection and classification of Breast Cancer in Wavelet Sub-bands of Fractal Segmented Cancerous Zones.

    Science.gov (United States)

    Shirazinodeh, Alireza; Noubari, Hossein Ahmadi; Rabbani, Hossein; Dehnavi, Alireza Mehri

    2015-01-01

    Recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. It is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. In this paper, for fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of cancerous zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of breast cancer regions by applying an appropriate threshold. For detection of cancerous zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and

  16. Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy.

    Science.gov (United States)

    Wang, Yishan; Doleschel, Sammy; Wunderlich, Ralf; Heinen, Stefan

    2016-07-01

    In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization.

  17. 3D Scan-Based Wavelet Transform and Quality Control for Video Coding

    Directory of Open Access Journals (Sweden)

    Parisot Christophe

    2003-01-01

    Full Text Available Wavelet coding has been shown to achieve better compression than DCT coding and moreover allows scalability. 2D DWT can be easily extended to 3D and thus applied to video coding. However, 3D subband coding of video suffers from two drawbacks. The first is the amount of memory required for coding large 3D blocks; the second is the lack of temporal quality due to the sequence temporal splitting. In fact, 3D block-based video coders produce jerks. They appear at blocks temporal borders during video playback. In this paper, we propose a new temporal scan-based wavelet transform method for video coding combining the advantages of wavelet coding (performance, scalability with acceptable reduced memory requirements, no additional CPU complexity, and avoiding jerks. We also propose an efficient quality allocation procedure to ensure a constant quality over time.

  18. Compression of surface myoelectric signals using MP3 encoding.

    Science.gov (United States)

    Chan, Adrian D C

    2011-01-01

    The potential of MP3 compression of surface myoelectric signals is explored in this paper. MP3 compression is a perceptual-based encoder scheme, used traditionally to compress audio signals. The ubiquity of MP3 compression (e.g., portable consumer electronics and internet applications) makes it an attractive option for remote monitoring and telemedicine applications. The effects of muscle site and contraction type are examined at different MP3 encoding bitrates. Results demonstrate that MP3 compression is sensitive to the myoelectric signal bandwidth, with larger signal distortion associated with myoelectric signals that have higher bandwidths. Compared to other myoelectric signal compression techniques reported previously (embedded zero-tree wavelet compression and adaptive differential pulse code modulation), MP3 compression demonstrates superior performance (i.e., lower percent residual differences for the same compression ratios).

  19. Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

    Science.gov (United States)

    Elahi, Sana; kaleem, Muhammad; Omer, Hammad

    2018-01-01

    Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

  20. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

    Science.gov (United States)

    Boix, Macarena; Cantó, Begoña

    2013-04-01

    Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

  1. Speech Data Compression using Vector Quantization

    OpenAIRE

    H. B. Kekre; Tanuja K. Sarode

    2008-01-01

    Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table s...

  2. A Novel Fractional-Order PID Controller for Integrated Pressurized Water Reactor Based on Wavelet Kernel Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Yu-xin Zhao

    2014-01-01

    Full Text Available This paper presents a novel wavelet kernel neural network (WKNN with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system.

  3. Grating geophone signal processing based on wavelet transform

    Science.gov (United States)

    Li, Shuqing; Zhang, Huan; Tao, Zhifei

    2008-12-01

    Grating digital geophone is designed based on grating measurement technique benefiting averaging-error effect and wide dynamic range to improve weak signal detected precision. This paper introduced the principle of grating digital geophone and its post signal processing system. The signal acquisition circuit use Atmega 32 chip as core part and display the waveform on the Labwindows through the RS232 data link. Wavelet transform is adopted this paper to filter the grating digital geophone' output signal since the signal is unstable. This data processing method is compared with the FIR filter that widespread use in current domestic. The result indicates that the wavelet algorithm has more advantages and the SNR of seismic signal improve obviously.

  4. Towards discrete wavelet transform-based human activity recognition

    Science.gov (United States)

    Khare, Manish; Jeon, Moongu

    2017-06-01

    Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

  5. Transformer Protection Using the Wavelet Transform

    OpenAIRE

    ÖZGÖNENEL, Okan; ÖNBİLGİN, Güven; KOCAMAN, Çağrı

    2014-01-01

    This paper introduces a novel approach for power transformer protection algorithm. Power system signals such as current and voltage have traditionally been analysed by the Fast Fourier Transform. This paper aims to prove that the Wavelet Transform is a reliable and computationally efficient tool for distinguishing between the inrush currents and fault currents. The simulated results presented clearly show that the proposed technique for power transformer protection facilitates the a...

  6. Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Nibaldo Rodriguez

    2017-10-01

    Full Text Available The behavioural diagnostics of bearings play an essential role in the management of several rotation machine systems. However, current diagnostic methods do not deliver satisfactory results with respect to failures in variable speed rotational phenomena. In this paper, we consider the Shannon entropy as an important fault signature pattern. To compute the entropy, we propose combining stationary wavelet transform and singular value decomposition. The resulting feature extraction method, that we call stationary wavelet singular entropy (SWSE, aims to improve the accuracy of the diagnostics of bearing failure by finding a small number of high-quality fault signature patterns. The features extracted by the SWSE are then passed on to a kernel extreme learning machine (KELM classifier. The proposed SWSE-KELM algorithm is evaluated using two bearing vibration signal databases obtained from Case Western Reserve University. We compare our SWSE feature extraction method to other well-known methods in the literature such as stationary wavelet packet singular entropy (SWPSE and decimated wavelet packet singular entropy (DWPSE. The experimental results show that the SWSE-KELM consistently outperforms both the SWPSE-KELM and DWPSE-KELM methods. Further, our SWSE method requires fewer features than the other two evaluated methods, which makes our SWSE-KELM algorithm simpler and faster.

  7. Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression

    KAUST Repository

    Halim Boukaram, Wajih

    2017-09-14

    We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.

  8. Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression

    KAUST Repository

    Halim Boukaram, Wajih; Turkiyyah, George; Ltaief, Hatem; Keyes, David E.

    2017-01-01

    We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.

  9. A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

    DEFF Research Database (Denmark)

    Cesari, Matteo; Mehlsen, Jesper; Mehlsen, Anne-Birgitte

    2017-01-01

    T-wave amplitude (TWA) has been proposed as a marker of the innervation of the myocardium. Until now, TWA has been calculated manually or with poor algorithms, thus making its use not efficient in a clinical environment. We introduce a new wavelet-based algorithm for the delineation QRS complexes...

  10. A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging

    International Nuclear Information System (INIS)

    Chaari, L.; Pesquet, J.Ch.; Chaari, L.; Ciuciu, Ph.; Benazza-Benyahia, A.

    2011-01-01

    To reduce scanning time and/or improve spatial/temporal resolution in some Magnetic Resonance Imaging (MRI) applications, parallel MRI acquisition techniques with multiple coils acquisition have emerged since the early 1990's as powerful imaging methods that allow a faster acquisition process. In these techniques, the full FOV image has to be reconstructed from the resulting acquired under sampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used Sensitivity Encoding (SENSE) method. However, the reconstructed image generally presents artifacts when perturbations occur in both the measured data and the estimated coil sensitivity profiles. In this paper, we aim at achieving accurate image reconstruction under degraded experimental conditions (low magnetic field and high reduction factor), in which neither the SENSE method nor the Tikhonov regularization in the image domain give convincing results. To this end, we present a novel method for SENSE-based reconstruction which proceeds with regularization in the complex wavelet domain by promoting sparsity. The proposed approach relies on a fast algorithm that enables the minimization of regularized non-differentiable criteria including more general penalties than a classical l 1 term. To further enhance the reconstructed image quality, local convex constraints are added to the regularization process. In vivo human brain experiments carried out on Gradient-Echo (GRE) anatomical and Echo Planar Imaging (EPI) functional MRI data at 1.5 T indicate that our algorithm provides reconstructed images with reduced artifacts for high reduction factors. (authors)

  11. On exploiting wavelet bases in statistical region-based segmentation

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren

    2002-01-01

    Statistical region-based segmentation methods such as the Active Appearance Models establish dense correspondences by modelling variation of shape and pixel intensities in low-resolution 2D images. Unfortunately, for high-resolution 2D and 3D images, this approach is rendered infeasible due to ex...... 9-7 wavelet on cardiac MRIs and human faces show that the segmentation accuracy is minimally degraded at compression ratios of 1:10 and 1:20, respectively....

  12. Smart-phone based electrocardiogram wavelet decomposition and neural network classification

    International Nuclear Information System (INIS)

    Jannah, N; Hadjiloucas, S; Hwang, F; Galvão, R K H

    2013-01-01

    This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

  13. Optical Flow of Small Objects Using Wavelets, Bootstrap Methods, and Synthetic Discriminant Filters

    National Research Council Canada - National Science Library

    Hewer, Gary

    1997-01-01

    ...) targets in highly cluttered and noisy environments. In this paper; we present a novel wavelet detection algorithm which incorporates adaptive CFAR detection statistics using the bootstrap method...

  14. Wavelet basics

    CERN Document Server

    Chan, Y T

    1995-01-01

    Since the study of wavelets is a relatively new area, much of the research coming from mathematicians, most of the literature uses terminology, concepts and proofs that may, at times, be difficult and intimidating for the engineer. Wavelet Basics has therefore been written as an introductory book for scientists and engineers. The mathematical presentation has been kept simple, the concepts being presented in elaborate detail in a terminology that engineers will find familiar. Difficult ideas are illustrated with examples which will also aid in the development of an intuitive insight. Chapter 1 reviews the basics of signal transformation and discusses the concepts of duals and frames. Chapter 2 introduces the wavelet transform, contrasts it with the short-time Fourier transform and clarifies the names of the different types of wavelet transforms. Chapter 3 links multiresolution analysis, orthonormal wavelets and the design of digital filters. Chapter 4 gives a tour d'horizon of topics of current interest: wave...

  15. Optical image encryption scheme with multiple light paths based on compressive ghost imaging

    Science.gov (United States)

    Zhu, Jinan; Yang, Xiulun; Meng, Xiangfeng; Wang, Yurong; Yin, Yongkai; Sun, Xiaowen; Dong, Guoyan

    2018-02-01

    An optical image encryption method with multiple light paths is proposed based on compressive ghost imaging. In the encryption process, M random phase-only masks (POMs) are generated by means of logistic map algorithm, and these masks are then uploaded to the spatial light modulator (SLM). The collimated laser light is divided into several beams by beam splitters as it passes through the SLM, and the light beams illuminate the secret images, which are converted into sparse images by discrete wavelet transform beforehand. Thus, the secret images are simultaneously encrypted into intensity vectors by ghost imaging. The distances between the SLM and secret images vary and can be used as the main keys with original POM and the logistic map algorithm coefficient in the decryption process. In the proposed method, the storage space can be significantly decreased and the security of the system can be improved. The feasibility, security and robustness of the method are further analysed through computer simulations.

  16. A STUDY OF WAVELET ENTROPY MEASURE DEFINITION AND ITS APPLICATION FOR FAULT FEATURE PICK-UP AND CLASSIFICATION

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Shannon entropy in time domain is a measure of signal or system uncertainty. When based on spectrum entropy, Shannon entropy can be taken as a measure of signal or system complexity.Therefore, wavelet analysis based on wavelet entropy measure can signify the complexity of non-steady signal or system in both time and frequency domain. In this paper, in order to meet the requirements of post-analysis on abundant wavelet transform result data and the need of information mergence, the basic definition of wavelet entropy measure is proposed, corresponding algorithms of several wavelet entropies, such as wavelet average entropy, wavelet time-frequency entropy, wavelet distance entropy,etc. are put forward, and the physical meanings of these entropies are analyzed as well. The application principle of wavelet entropy measure in ElectroEncephaloGraphy (EEG) signal analysis, mechanical fault diagnosis, fault detection and classification in power system are analyzed. Finally, take the transmission line fault detection in power system for example, simulations in two different systems, a 10kV automatic blocking and continuous power transmission line and a 500kV Extra High Voltage (EHV) transmission line, are carried out, and the two methods, wavelet entropy and wavelet modulus maxima, are compared, the results show feasibility and application prospect of the six wavelet entropies.

  17. Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation

    Directory of Open Access Journals (Sweden)

    R. Hajiabadi

    2016-10-01

    Full Text Available Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes estimation of these phenomena become easier. Preprocessing in the data driven models such as artificial neural network, gene expression programming, support vector machine, is more effective because the quality of data in these models is important. Present study, by considering diagnosing and data transformation as two different preprocessing, tries to improve the results of intelligent models. In this study two different intelligent models, Artificial Neural Network and Gene Expression Programming, are applied to estimation of daily suspended sediment load. Wavelet transforms and logarithmic transformation is used for diagnosing and data transformation, respectively. Finally, the impacts of preprocessing on the results of intelligent models are evaluated. Materials and Methods In this study, Gene Expression Programming and Artificial Neural Network are used as intelligent models for suspended sediment load estimation, then the impacts of diagnosing and logarithmic transformations approaches as data preprocessor are evaluated and compared to the result improvement. Two different logarithmic transforms are considered in this research, LN and LOG. Wavelet transformation is used to time series denoising. In order to denoising by wavelet transforms, first, time series can be decomposed at one level (Approximation part and detail part and second, high-frequency part (detail will be removed as noise. According to the ability of gene expression programming and artificial neural network to analysis nonlinear systems; daily values of suspended sediment load of the Skunk River in USA, during a 5-year period, are investigated and then estimated.4 years of

  18. Rolling bearings control by comparing the wavelet of scaling ...

    African Journals Online (AJOL)

    The method for bearing monitoring is proposed, which makes it possible to automate the process of defect detection and to increase the resolving power during vibrationacoustic control performance. The result of the study showed that the application of analysis algorithms with the use of wavelet transformation allows to ...

  19. DE-BLURRING SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY IMAGES USING WAVELET DECOMPOSITION

    Directory of Open Access Journals (Sweden)

    Neethu M. Sasi

    2016-02-01

    Full Text Available Single photon emission computed tomography imaging is a popular nuclear medicine imaging technique which generates images by detecting radiations emitted by radioactive isotopes injected in the human body. Scattering of these emitted radiations introduces blur in this type of images. This paper proposes an image processing technique to enhance cardiac single photon emission computed tomography images by reducing the blur in the image. The algorithm works in two main stages. In the first stage a maximum likelihood estimate of the point spread function and the true image is obtained. In the second stage Lucy Richardson algorithm is applied on the selected wavelet coefficients of the true image estimate. The significant contribution of this paper is that processing of images is done in the wavelet domain. Pre-filtering is also done as a sub stage to avoid unwanted ringing effects. Real cardiac images are used for the quantitative and qualitative evaluations of the algorithm. Blur metric, peak signal to noise ratio and Tenengrad criterion are used as quantitative measures. Comparison against other existing de-blurring algorithms is also done. The simulation results indicate that the proposed method effectively reduces blur present in the image.

  20. A Wavelet-Enhanced PWTD-Accelerated Time-Domain Integral Equation Solver for Analysis of Transient Scattering from Electrically Large Conducting Objects

    KAUST Repository

    Liu, Yang; Yucel, Abdulkadir C.; Bagci, Hakan; Gilbert, Anna C.; Michielssen, Eric

    2018-01-01

    requirement and computational cost of the PWTD algorithm by representing the PWTD ray data using local cosine wavelet bases (LCBs) and performing PWTD operations in the wavelet domain. The memory requirement and computational cost of the LCB-enhanced PWTD

  1. Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted ℓ₁ minimization reconstruction.

    Science.gov (United States)

    Zhang, Jun; Gu, Zhenghui; Yu, Zhu Liang; Li, Yuanqing

    2015-03-01

    Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

  2. Wavelets in neuroscience

    CERN Document Server

    Hramov, Alexander E; Makarov, Valeri A; Pavlov, Alexey N; Sitnikova, Evgenia

    2015-01-01

    This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade. Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics. Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural ...

  3. A Novel Robust Audio Watermarking Algorithm by Modifying the Average Amplitude in Transform Domain

    Directory of Open Access Journals (Sweden)

    Qiuling Wu

    2018-05-01

    Full Text Available In order to improve the robustness and imperceptibility in practical application, a novel audio watermarking algorithm with strong robustness is proposed by exploring the multi-resolution characteristic of discrete wavelet transform (DWT and the energy compaction capability of discrete cosine transform (DCT. The human auditory system is insensitive to the minor changes in the frequency components of the audio signal, so the watermarks can be embedded by slightly modifying the frequency components of the audio signal. The audio fragments segmented from the cover audio signal are decomposed by DWT to obtain several groups of wavelet coefficients with different frequency bands, and then the fourth level detail coefficient is selected to be divided into the former packet and the latter packet, which are executed for DCT to get two sets of transform domain coefficients (TDC respectively. Finally, the average amplitudes of the two sets of TDC are modified to embed the binary image watermark according to the special embedding rule. The watermark extraction is blind without the carrier audio signal. Experimental results confirm that the proposed algorithm has good imperceptibility, large payload capacity and strong robustness when resisting against various attacks such as MP3 compression, low-pass filtering, re-sampling, re-quantization, amplitude scaling, echo addition and noise corruption.

  4. An Improved Split-Step Wavelet Transform Method for Anomalous Radio Wave Propagation Modelling

    Directory of Open Access Journals (Sweden)

    A. Iqbal

    2014-12-01

    Full Text Available Anomalous tropospheric propagation caused by ducting phenomenon is a major problem in wireless communication. Thus, it is important to study the behavior of radio wave propagation in tropospheric ducts. The Parabolic Wave Equation (PWE method is considered most reliable to model anomalous radio wave propagation. In this work, an improved Split Step Wavelet transform Method (SSWM is presented to solve PWE for the modeling of tropospheric propagation over finite and infinite conductive surfaces. A large number of numerical experiments are carried out to validate the performance of the proposed algorithm. Developed algorithm is compared with previously published techniques; Wavelet Galerkin Method (WGM and Split-Step Fourier transform Method (SSFM. A very good agreement is found between SSWM and published techniques. It is also observed that the proposed algorithm is about 18 times faster than WGM and provide more details of propagation effects as compared to SSFM.

  5. Image Steganography of Multiple File Types with Encryption and Compression Algorithms

    Directory of Open Access Journals (Sweden)

    Ernest Andreigh C. Centina

    2017-05-01

    Full Text Available The goals of this study were to develop a system intended for securing files through the technique of image steganography integrated with cryptography by utilizing ZLIB Algorithm for compressing and decompressing secret files, DES Algorithm for encryption and decryption, and Least Significant Bit Algorithm for file embedding and extraction to avoid compromise on highly confidential files from exploits of unauthorized persons. Ensuing to this, the system is in acc ordance with ISO 9126 international quality standards. Every quality criteria of the system was evaluated by 10 Information Technology professionals, and the arithmetic Mean and Standard Deviation of the survey were computed. The result exhibits that m ost of them strongly agreed that the system is excellently effective based on Functionality, Reliability, Usability, Efficiency, Maintainability and Portability conformance to ISO 9126 standards. The system was found to be a useful tool for both governmen t agencies and private institutions for it could keep not only the message secret but also the existence of that particular message or file et maintaining the privacy of highly confidential and sensitive files from unauthorized access.

  6. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  7. Weighted least squares phase unwrapping based on the wavelet transform

    Science.gov (United States)

    Chen, Jiafeng; Chen, Haiqin; Yang, Zhengang; Ren, Haixia

    2007-01-01

    The weighted least squares phase unwrapping algorithm is a robust and accurate method to solve phase unwrapping problem. This method usually leads to a large sparse linear equation system. Gauss-Seidel relaxation iterative method is usually used to solve this large linear equation. However, this method is not practical due to its extremely slow convergence. The multigrid method is an efficient algorithm to improve convergence rate. However, this method needs an additional weight restriction operator which is very complicated. For this reason, the multiresolution analysis method based on the wavelet transform is proposed. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels and an equivalent equation system with better convergence condition can be obtained. Fast convergence in separate coarse resolution levels speeds up the overall system convergence rate. The simulated experiment shows that the proposed method converges faster and provides better result than the multigrid method.

  8. Comparative Survey of Ultrasound Images Compression Methods Dedicated to a Tele-Echography Robotic System

    National Research Council Canada - National Science Library

    Delgorge, C

    2001-01-01

    .... For the purpose of this work, we selected seven compression methods : Fourier Transform, Discrete Cosine Transform, Wavelets, Quadtrees Transform, Fractals, Histogram Thresholding, and Run Length Coding...

  9. A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

    Directory of Open Access Journals (Sweden)

    Suyi Li

    2017-01-01

    Full Text Available The noninvasive peripheral oxygen saturation (SpO2 and the pulse rate can be extracted from photoplethysmography (PPG signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects’ PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

  10. Identification of speech transients using variable frame rate analysis and wavelet packets.

    Science.gov (United States)

    Rasetshwane, Daniel M; Boston, J Robert; Li, Ching-Chung

    2006-01-01

    Speech transients are important cues for identifying and discriminating speech sounds. Yoo et al. and Tantibundhit et al. were successful in identifying speech transients and, emphasizing them, improving the intelligibility of speech in noise. However, their methods are computationally intensive and unsuitable for real-time applications. This paper presents a method to identify and emphasize speech transients that combines subband decomposition by the wavelet packet transform with variable frame rate (VFR) analysis and unvoiced consonant detection. The VFR analysis is applied to each wavelet packet to define a transitivity function that describes the extent to which the wavelet coefficients of that packet are changing. Unvoiced consonant detection is used to identify unvoiced consonant intervals and the transitivity function is amplified during these intervals. The wavelet coefficients are multiplied by the transitivity function for that packet, amplifying the coefficients localized at times when they are changing and attenuating coefficients at times when they are steady. Inverse transform of the modified wavelet packet coefficients produces a signal corresponding to speech transients similar to the transients identified by Yoo et al. and Tantibundhit et al. A preliminary implementation of the algorithm runs more efficiently.

  11. A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals.

    Science.gov (United States)

    Li, Suyi; Jiang, Shanqing; Jiang, Shan; Wu, Jiang; Xiong, Wenji; Diao, Shu

    2017-01-01

    The noninvasive peripheral oxygen saturation (SpO 2 ) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects' PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO 2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

  12. A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

    Science.gov (United States)

    Jiang, Shanqing; Jiang, Shan; Wu, Jiang; Xiong, Wenji

    2017-01-01

    The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects' PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis. PMID:29250135

  13. Adaptive Wavelet Transforms

    Energy Technology Data Exchange (ETDEWEB)

    Szu, H.; Hsu, C. [Univ. of Southwestern Louisiana, Lafayette, LA (United States)

    1996-12-31

    Human sensors systems (HSS) may be approximately described as an adaptive or self-learning version of the Wavelet Transforms (WT) that are capable to learn from several input-output associative pairs of suitable transform mother wavelets. Such an Adaptive WT (AWT) is a redundant combination of mother wavelets to either represent or classify inputs.

  14. Ambiguity attacks on robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2017-12-01

    Full Text Available Among emergent applications of digital watermarking are copyright protection and proof of ownership. Recently, Makbol and Khoo (2013 have proposed for these applications a new robust blind image watermarking scheme based on the redundant discrete wavelet transform (RDWT and the singular value decomposition (SVD. In this paper, we present two ambiguity attacks on this algorithm that have shown that this algorithm fails when used to provide robustness applications like owner identification, proof of ownership, and transaction tracking. Keywords: Ambiguity attack, Image watermarking, Singular value decomposition, Redundant discrete wavelet transform

  15. Embedded wavelet-based face recognition under variable position

    Science.gov (United States)

    Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

    2015-02-01

    For several years, face recognition has been a hot topic in the image processing field: this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B*), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform: results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).

  16. Wavelet-Smoothed Interpolation of Masked Scientific Data for JPEG 2000 Compression

    Energy Technology Data Exchange (ETDEWEB)

    Brislawn, Christopher M. [Los Alamos National Laboratory

    2012-08-13

    How should we manage scientific data with 'holes'? Some applications, like JPEG 2000, expect logically rectangular data, but some sources, like the Parallel Ocean Program (POP), generate data that isn't defined on certain subsets. We refer to grid points that lack well-defined, scientifically meaningful sample values as 'masked' samples. Wavelet-smoothing is a highly scalable interpolation scheme for regions with complex boundaries on logically rectangular grids. Computation is based on forward/inverse discrete wavelet transforms, so runtime complexity and memory scale linearly with respect to sample count. Efficient state-of-the-art minimal realizations yield small constants (O(10)) for arithmetic complexity scaling, and in-situ implementation techniques make optimal use of memory. Implementation in two dimensions using tensor product filter banks is straighsorward and should generalize routinely to higher dimensions. No hand-tuning required when the interpolation mask changes, making the method aeractive for problems with time-varying masks. Well-suited for interpolating undefined samples prior to JPEG 2000 encoding. The method outperforms global mean interpolation, as judged by both SNR rate-distortion performance and low-rate artifact mitigation, for data distributions whose histograms do not take the form of sharply peaked, symmetric, unimodal probability density functions. These performance advantages can hold even for data whose distribution differs only moderately from the peaked unimodal case, as demonstrated by POP salinity data. The interpolation method is very general and is not tied to any particular class of applications, could be used for more generic smooth interpolation.

  17. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  18. Wavelet based Image Registration Technique for Matching Dental x-rays

    OpenAIRE

    P. Ramprasad; H. C. Nagaraj; M. K. Parasuram

    2008-01-01

    Image registration plays an important role in the diagnosis of dental pathologies such as dental caries, alveolar bone loss and periapical lesions etc. This paper presents a new wavelet based algorithm for registering noisy and poor contrast dental x-rays. Proposed algorithm has two stages. First stage is a preprocessing stage, removes the noise from the x-ray images. Gaussian filter has been used. Second stage is a geometric transformation stage. Proposed work uses two l...

  19. An adaptive wavelet-network model for forecasting daily total solar-radiation

    International Nuclear Information System (INIS)

    Mellit, A.; Benghanem, M.; Kalogirou, S.A.

    2006-01-01

    The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet-networks are feed-forward networks using wavelets as activation functions. Wavelet-networks have been used successfully in various engineering applications such as classification, identification and control problems. In this paper, the use of adaptive wavelet-network architecture in finding a suitable forecasting model for predicting the daily total solar-radiation is investigated. Total solar-radiation is considered as the most important parameter in the performance prediction of renewable energy systems, particularly in sizing photovoltaic (PV) power systems. For this purpose, daily total solar-radiation data have been recorded during the period extending from 1981 to 2001, by a meteorological station in Algeria. The wavelet-network model has been trained by using either the 19 years of data or one year of the data. In both cases the total solar radiation data corresponding to year 2001 was used for testing the model. The network was trained to accept and handle a number of unusual cases. Results indicate that the model predicts daily total solar-radiation values with a good accuracy of approximately 97% and the mean absolute percentage error is not more than 6%. In addition, the performance of the model was compared with different neural network structures and classical models. Training algorithms for wavelet-networks require smaller numbers of iterations when compared with other neural networks. The model can be used to fill missing data in weather databases. Additionally, the proposed model can be generalized and used in different locations and for other weather data, such as sunshine duration and ambient temperature. Finally, an application using the model for sizing a PV-power system is presented in order to confirm the validity of this model

  20. On Normalized Compression Distance and Large Malware

    OpenAIRE

    Borbely, Rebecca Schuller

    2015-01-01

    Normalized Compression Distance (NCD) is a popular tool that uses compression algorithms to cluster and classify data in a wide range of applications. Existing discussions of NCD's theoretical merit rely on certain theoretical properties of compression algorithms. However, we demonstrate that many popular compression algorithms don't seem to satisfy these theoretical properties. We explore the relationship between some of these properties and file size, demonstrating that this theoretical pro...

  1. Contextual Compression of Large-Scale Wind Turbine Array Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Gruchalla, Kenny M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Brunhart-Lupo, Nicholas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Potter, Kristin C [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Clyne, John [National Center for Atmospheric Research (NCAR)

    2017-12-04

    Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interative visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contextualized representation is a valid approach and encourages contextual data management.

  2. Image compression with Iris-C

    Science.gov (United States)

    Gains, David

    2009-05-01

    Iris-C is an image codec designed for streaming video applications that demand low bit rate, low latency, lossless image compression. To achieve compression and low latency the codec features the discrete wavelet transform, Exp-Golomb coding, and online processes that construct dynamic models of the input video. Like H.264 and Dirac, the Iris-C codec accepts input video from both the YUV and YCOCG colour spaces, but the system can also operate on Bayer RAW data read directly from an image sensor. Testing shows that the Iris-C codec is competitive with the Dirac low delay syntax codec which is typically regarded as the state-of-the-art low latency, lossless video compressor.

  3. Wavelet Transforms using VTK-m

    Energy Technology Data Exchange (ETDEWEB)

    Li, Shaomeng [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sewell, Christopher Meyer [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-27

    These are a set of slides that deal with the topics of wavelet transforms using VTK-m. First, wavelets are discussed and detailed, then VTK-m is discussed and detailed, then wavelets and VTK-m are looked at from a performance comparison, then from an accuracy comparison, and finally lessons learned, conclusion, and what is next. Lessons learned are the following: Launching worklets is expensive; Natural logic of performing 2D wavelet transform: Repeat the same 1D wavelet transform on every row, repeat the same 1D wavelet transform on every column, invoke the 1D wavelet worklet every time: num_rows x num_columns; VTK-m approach of performing 2D wavelet transform: Create a worklet for 2D that handles both rows and columns, invoke this new worklet only one time; Fast calculation, but cannot reuse 1D implementations.

  4. Wavelets as basis functions in electronic structure calculations; Les ondelettes comme fonction de base dans le calcul de structures electroniques

    Energy Technology Data Exchange (ETDEWEB)

    Chauvin, C

    2005-11-15

    This thesis is devoted to the definition and the implementation of a multi-resolution method to determine the fundamental state of a system composed of nuclei and electrons. In this work, we are interested in the Density Functional Theory (DFT), which allows to express the Hamiltonian operator with the electronic density only, by a Coulomb potential and a non-linear potential. This operator acts on orbitals, which are solutions of the so-called Kohn-Sham equations. Their resolution needs to express orbitals and density on a set of functions owing both physical and numerical properties, as explained in the second chapter. One can hardly satisfy these two properties simultaneously, that is why we are interested in orthogonal and bi-orthogonal wavelets basis, whose properties of interpolation are presented in the third chapter. We present in the fourth chapter three dimensional solvers for the Coulomb's potential, using not only the preconditioning property of wavelets, but also a multigrid algorithm. Determining this potential allows us to solve the self-consistent Kohn-Sham equations, by an algorithm presented in chapter five. The originality of our method consists in the construction of the stiffness matrix, combining a Galerkin formulation and a collocation scheme. We analyse the approximation properties of this method in case of linear Hamiltonian, such as harmonic oscillator and hydrogen, and present convergence results of the DFT for small electrons. Finally we show how orbital compression reduces considerably the number of coefficients to keep, while preserving a good accuracy of the fundamental energy. (author)

  5. A network security situation prediction model based on wavelet neural network with optimized parameters

    Directory of Open Access Journals (Sweden)

    Haibo Zhang

    2016-08-01

    Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

  6. Quantum computation of multifractal exponents through the quantum wavelet transform

    International Nuclear Information System (INIS)

    Garcia-Mata, Ignacio; Giraud, Olivier; Georgeot, Bertrand

    2009-01-01

    We study the use of the quantum wavelet transform to extract efficiently information about the multifractal exponents for multifractal quantum states. We show that, combined with quantum simulation algorithms, it enables to build quantum algorithms for multifractal exponents with a polynomial gain compared to classical simulations. Numerical results indicate that a rough estimate of fractality could be obtained exponentially fast. Our findings are relevant, e.g., for quantum simulations of multifractal quantum maps and of the Anderson model at the metal-insulator transition.

  7. Wavelet domain image restoration with adaptive edge-preserving regularization.

    Science.gov (United States)

    Belge, M; Kilmer, M E; Miller, E L

    2000-01-01

    In this paper, we consider a wavelet based edge-preserving regularization scheme for use in linear image restoration problems. Our efforts build on a collection of mathematical results indicating that wavelets are especially useful for representing functions that contain discontinuities (i.e., edges in two dimensions or jumps in one dimension). We interpret the resulting theory in a statistical signal processing framework and obtain a highly flexible framework for adapting the degree of regularization to the local structure of the underlying image. In particular, we are able to adapt quite easily to scale-varying and orientation-varying features in the image while simultaneously retaining the edge preservation properties of the regularizer. We demonstrate a half-quadratic algorithm for obtaining the restorations from observed data.

  8. Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage

    Directory of Open Access Journals (Sweden)

    Yan-long Chen

    2014-01-01

    Full Text Available The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.

  9. EIT Imaging Regularization Based on Spectral Graph Wavelets.

    Science.gov (United States)

    Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

    2017-09-01

    The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.

  10. Wavelet analysis of the impedance cardiogram waveforms

    Science.gov (United States)

    Podtaev, S.; Stepanov, R.; Dumler, A.; Chugainov, S.; Tziberkin, K.

    2012-12-01

    Impedance cardiography has been used for diagnosing atrial and ventricular dysfunctions, valve disorders, aortic stenosis, and vascular diseases. Almost all the applications of impedance cardiography require determination of some of the characteristic points of the ICG waveform. The ICG waveform has a set of characteristic points known as A, B, E ((dZ/dt)max) X, Y, O and Z. These points are related to distinct physiological events in the cardiac cycle. Objective of this work is an approbation of a new method of processing and interpretation of the impedance cardiogram waveforms using wavelet analysis. A method of computer thoracic tetrapolar polyrheocardiography is used for hemodynamic registrations. Use of original wavelet differentiation algorithm allows combining filtration and calculation of the derivatives of rheocardiogram. The proposed approach can be used in clinical practice for early diagnostics of cardiovascular system remodelling in the course of different pathologies.

  11. Wavelet analysis of the impedance cardiogram waveforms

    International Nuclear Information System (INIS)

    Podtaev, S; Stepanov, R; Dumler, A; Chugainov, S; Tziberkin, K

    2012-01-01

    Impedance cardiography has been used for diagnosing atrial and ventricular dysfunctions, valve disorders, aortic stenosis, and vascular diseases. Almost all the applications of impedance cardiography require determination of some of the characteristic points of the ICG waveform. The ICG waveform has a set of characteristic points known as A, B, E ((dZ/dt) max ) X, Y, O and Z. These points are related to distinct physiological events in the cardiac cycle. Objective of this work is an approbation of a new method of processing and interpretation of the impedance cardiogram waveforms using wavelet analysis. A method of computer thoracic tetrapolar polyrheocardiography is used for hemodynamic registrations. Use of original wavelet differentiation algorithm allows combining filtration and calculation of the derivatives of rheocardiogram. The proposed approach can be used in clinical practice for early diagnostics of cardiovascular system remodelling in the course of different pathologies.

  12. Lecture notes on wavelet transforms

    CERN Document Server

    Debnath, Lokenath

    2017-01-01

    This book provides a systematic exposition of the basic ideas and results of wavelet analysis suitable for mathematicians, scientists, and engineers alike. The primary goal of this text is to show how different types of wavelets can be constructed, illustrate why they are such powerful tools in mathematical analysis, and demonstrate their use in applications. It also develops the required analytical knowledge and skills on the part of the reader, rather than focus on the importance of more abstract formulation with full mathematical rigor.  These notes differs from many textbooks with similar titles in that a major emphasis is placed on the thorough development of the underlying theory before introducing applications and modern topics such as fractional Fourier transforms, windowed canonical transforms, fractional wavelet transforms, fast wavelet transforms, spline wavelets, Daubechies wavelets, harmonic wavelets and non-uniform wavelets. The selection, arrangement, and presentation of the material in these ...

  13. Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

    International Nuclear Information System (INIS)

    Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

    2016-01-01

    Highlights: • We propose a spatio-temporal approach for wind speed forecasting. • The method is based on a combination of Wavelet decomposition and structured-sparse recovery. • Our analyses confirm that low-dimensional structures govern the interactions between stations. • Our method particularly shows improvements for profiles with high ramps. • We examine our approach on real data and illustrate its superiority over a set of benchmark models. - Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.

  14. A nonlinear relaxation/quasi-Newton algorithm for the compressible Navier-Stokes equations

    Science.gov (United States)

    Edwards, Jack R.; Mcrae, D. S.

    1992-01-01

    A highly efficient implicit method for the computation of steady, two-dimensional compressible Navier-Stokes flowfields is presented. The discretization of the governing equations is hybrid in nature, with flux-vector splitting utilized in the streamwise direction and central differences with flux-limited artificial dissipation used for the transverse fluxes. Line Jacobi relaxation is used to provide a suitable initial guess for a new nonlinear iteration strategy based on line Gauss-Seidel sweeps. The applicability of quasi-Newton methods as convergence accelerators for this and other line relaxation algorithms is discussed, and efficient implementations of such techniques are presented. Convergence histories and comparisons with experimental data are presented for supersonic flow over a flat plate and for several high-speed compression corner interactions. Results indicate a marked improvement in computational efficiency over more conventional upwind relaxation strategies, particularly for flowfields containing large pockets of streamwise subsonic flow.

  15. Wavelet decomposition based principal component analysis for face recognition using MATLAB

    Science.gov (United States)

    Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

    2016-03-01

    For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

  16. R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

    Science.gov (United States)

    Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

    2017-01-01

    Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

  17. Wavelet analysis of hemispheroid flow separation toward understanding human vocal fold pathologies

    Science.gov (United States)

    Plesniak, Daniel H.; Carr, Ian A.; Bulusu, Kartik V.; Plesniak, Michael W.

    2014-11-01

    Physiological flows observed in human vocal fold pathologies, such as polyps and nodules, can be modeled by flow over a wall-mounted protuberance. The experimental investigation of flow separation over a surface-mounted hemispheroid was performed using particle image velocimetry (PIV) and measurements of surface pressure in a low-speed wind tunnel. This study builds on the hypothesis that the signatures of vortical structures associated with flow separation are imprinted on the surface pressure distributions. Wavelet decomposition methods in one- and two-dimensions were utilized to elucidate the flow behavior. First, a complex Gaussian wavelet was used for the reconstruction of surface pressure time series from static pressure measurements acquired from ports upstream, downstream, and on the surface of the hemispheroid. This was followed by the application of a novel continuous wavelet transform algorithm (PIVlet 1.2) using a 2D-Ricker wavelet for coherent structure detection on instantaneous PIV-data. The goal of this study is to correlate phase shifts in surface pressure with Strouhal numbers associated with the vortex shedding. Ultimately, the wavelet-based analytical framework will be aimed at addressing pulsatile flows. This material is based in part upon work supported by the National Science Foundation under Grant Number CBET-1236351, and GW Center for Biomimetics and Bioinspired Engineering (COBRE).

  18. Value at risk estimation with entropy-based wavelet analysis in exchange markets

    Science.gov (United States)

    He, Kaijian; Wang, Lijun; Zou, Yingchao; Lai, Kin Keung

    2014-08-01

    In recent years, exchange markets are increasingly integrated together. Fluctuations and risks across different exchange markets exhibit co-moving and complex dynamics. In this paper we propose the entropy-based multivariate wavelet based approaches to analyze the multiscale characteristic in the multidimensional domain and improve further the Value at Risk estimation reliability. Wavelet analysis has been introduced to construct the entropy-based Multiscale Portfolio Value at Risk estimation algorithm to account for the multiscale dynamic correlation. The entropy measure has been proposed as the more effective measure with the error minimization principle to select the best basis when determining the wavelet families and the decomposition level to use. The empirical studies conducted in this paper have provided positive evidence as to the superior performance of the proposed approach, using the closely related Chinese Renminbi and European Euro exchange market.

  19. Intelligent transportation systems data compression using wavelet decomposition technique.

    Science.gov (United States)

    2009-12-01

    Intelligent Transportation Systems (ITS) generates massive amounts of traffic data, which posts : challenges for data storage, transmission and retrieval. Data compression and reconstruction technique plays an : important role in ITS data procession....

  20. Optimal wavelet transform for the detection of microaneurysms in retina photographs.

    Science.gov (United States)

    Quellec, Gwénolé; Lamard, Mathieu; Josselin, Pierre Marie; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian

    2008-09-01

    In this paper, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalities: there are color photographs, green filtered photographs, and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24%, and 93.74% and a positive predictive value of respectively 89.50%, 89.75%, and 91.67%, which is better than previously published methods.

  1. Harmonic analysis of traction power supply system based on wavelet decomposition

    Science.gov (United States)

    Dun, Xiaohong

    2018-05-01

    With the rapid development of high-speed railway and heavy-haul transport, AC drive electric locomotive and EMU large-scale operation in the country on the ground, the electrified railway has become the main harmonic source of China's power grid. In response to this phenomenon, the need for timely monitoring of power quality problems of electrified railway, assessment and governance. Wavelet transform is developed on the basis of Fourier analysis, the basic idea comes from the harmonic analysis, with a rigorous theoretical model, which has inherited and developed the local thought of Garbor transformation, and has overcome the disadvantages such as window fixation and lack of discrete orthogonally, so as to become a more recently studied spectral analysis tool. The wavelet analysis takes the gradual and precise time domain step in the high frequency part so as to focus on any details of the signal being analyzed, thereby comprehensively analyzing the harmonics of the traction power supply system meanwhile use the pyramid algorithm to increase the speed of wavelet decomposition. The matlab simulation shows that the use of wavelet decomposition of the traction power supply system for harmonic spectrum analysis is effective.

  2. Surpassing the Theoretical 1-Norm Phase Transition in Compressive Sensing by Tuning the Smoothed L0 Algorithm

    DEFF Research Database (Denmark)

    Oxvig, Christian Schou; Pedersen, Patrick Steffen; Arildsen, Thomas

    2013-01-01

    Reconstruction of an undersampled signal is at the root of compressive sensing: when is an algorithm capable of reconstructing the signal? what quality is achievable? and how much time does reconstruction require? We have considered the worst-case performance of the smoothed ℓ0 norm reconstruction...... algorithm in a noiseless setup. Through an empirical tuning of its parameters, we have improved the phase transition (capabilities) of the algorithm for fixed quality and required time. In this paper, we present simulation results that show a phase transition surpassing that of the theoretical ℓ1 approach......: the proposed modified algorithm obtains 1-norm phase transition with greatly reduced required computation time....

  3. Wavelets in quantification of liver tumors in contrasted computed tomography images; Wavelets na quantificacao de tumores de figado em exames contrastados de tomografia computadorizada

    Energy Technology Data Exchange (ETDEWEB)

    Rodrigues, Bruna T.; Alvarez, Matheus; Souza, Rafael T.F.; Miranda, Jose R.A., E-mail: matheus@ibb.unesp.br [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Botucatu, SP (Brazil). Instituto de Biociencias. Departamento de Fisica e Biofisica; Romeiro, Fernando G. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Botucatu, SP (Brazil). Fac de Mediciana. Departamento de Clinica Medica; Pina, Diana R. de; Trindade, Andre Petean [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Botucatu, SP (Brazil). Fac. de Medicina. Departamento de Doencas Tropicais e Diagnostico por Imagem

    2012-12-15

    This paper presents an original methodology of liver tumors segmentation, based on wavelet transform. A virtual phantom was constructed with the same mean and standard deviation of the intensity of gray presented by the measured liver tissue. The optimized algorithm had a sensitivity ranging from 0.81 to 0.83, with a specificity of 0.95 for differentiation of hepatic tumors from normal tissues. We obtained a 96% agreement between the pixels segmented by an experienced radiologist and the algorithm presented here. According to the results shown in this work, the algorithm is optimal for the beginning of the tests for quantification of liver tumors in retrospective surveys. (author)

  4. A wavelet-based ECG delineation algorithm for 32-bit integer online processing.

    Science.gov (United States)

    Di Marco, Luigi Y; Chiari, Lorenzo

    2011-04-03

    Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.

  5. WAVELET ANALYSIS OF ABNORMAL ECGS

    Directory of Open Access Journals (Sweden)

    Vasudha Nannaparaju

    2014-02-01

    Full Text Available Detection of the warning signals by the heart can be diagnosed from ECG. An accurate and reliable diagnosis of ECG is very important however which is cumbersome and at times ambiguous in time domain due to the presence of noise. Study of ECG in wavelet domain using both continuous Wavelet transform (CWT and discrete Wavelet transform (DWT, with well known wavelet as well as a wavelet proposed by the authors for this investigation is found to be useful and yields fairly reliable results. In this study, Wavelet analysis of ECGs of Normal, Hypertensive, Diabetic and Cardiac are carried out. The salient feature of the study is that detection of P and T phases in wavelet domain is feasible which are otherwise feeble or absent in raw ECGs.

  6. Fractional Calculus and Shannon Wavelet

    Directory of Open Access Journals (Sweden)

    Carlo Cattani

    2012-01-01

    Full Text Available An explicit analytical formula for the any order fractional derivative of Shannon wavelet is given as wavelet series based on connection coefficients. So that for any 2(ℝ function, reconstructed by Shannon wavelets, we can easily define its fractional derivative. The approximation error is explicitly computed, and the wavelet series is compared with Grünwald fractional derivative by focusing on the many advantages of the wavelet method, in terms of rate of convergence.

  7. Study on the effects of sample selection on spectral reflectance reconstruction based on the algorithm of compressive sensing

    International Nuclear Information System (INIS)

    Zhang, Leihong; Liang, Dong

    2016-01-01

    In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)

  8. Wavelets, vibrations and scalings

    CERN Document Server

    Meyer, Yves

    1997-01-01

    Physicists and mathematicians are intensely studying fractal sets of fractal curves. Mandelbrot advocated modeling of real-life signals by fractal or multifractal functions. One example is fractional Brownian motion, where large-scale behavior is related to a corresponding infrared divergence. Self-similarities and scaling laws play a key role in this new area. There is a widely accepted belief that wavelet analysis should provide the best available tool to unveil such scaling laws. And orthonormal wavelet bases are the only existing bases which are structurally invariant through dyadic dilations. This book discusses the relevance of wavelet analysis to problems in which self-similarities are important. Among the conclusions drawn are the following: 1) A weak form of self-similarity can be given a simple characterization through size estimates on wavelet coefficients, and 2) Wavelet bases can be tuned in order to provide a sharper characterization of this self-similarity. A pioneer of the wavelet "saga", Meye...

  9. A Motion Estimation Algorithm Using DTCWT and ARPS

    Directory of Open Access Journals (Sweden)

    Unan Y. Oktiawati

    2013-09-01

    Full Text Available In this paper, a hybrid motion estimation algorithm utilizing the Dual Tree Complex Wavelet Transform (DTCWT and the Adaptive Rood Pattern Search (ARPS block is presented. The proposed algorithm first transforms each video sequence with DTCWT. The frame n of the video sequence is used as a reference input and the frame n+2 is used to find the motion vector. Next, the ARPS block search algorithm is carried out and followed by an inverse DTCWT. The motion compensation is then carried out on each inversed frame n and motion vector. The results show that PSNR can be improved for mobile device without depriving its quality. The proposed algorithm also takes less memory usage compared to the DCT-based algorithm. The main contribution of this work is a hybrid wavelet-based motion estimation algorithm for mobile devices. Other contribution is the visual quality scoring system as used in section 6.

  10. Method and algorithm for efficient calibration of compressive hyperspectral imaging system based on a liquid crystal retarder

    Science.gov (United States)

    Shecter, Liat; Oiknine, Yaniv; August, Isaac; Stern, Adrian

    2017-09-01

    Recently we presented a Compressive Sensing Miniature Ultra-spectral Imaging System (CS-MUSI)1 . This system consists of a single Liquid Crystal (LC) phase retarder as a spectral modulator and a gray scale sensor array to capture a multiplexed signal of the imaged scene. By designing the LC spectral modulator in compliance with the Compressive Sensing (CS) guidelines and applying appropriate algorithms we demonstrated reconstruction of spectral (hyper/ ultra) datacubes from an order of magnitude fewer samples than taken by conventional sensors. The LC modulator is designed to have an effective width of a few tens of micrometers, therefore it is prone to imperfections and spatial nonuniformity. In this work, we present the study of this nonuniformity and present a mathematical algorithm that allows the inference of the spectral transmission over the entire cell area from only a few calibration measurements.

  11. A New RTL Design Approach for a DCT/IDCT-Based Image Compression Architecture using the mCBE Algorithm

    Directory of Open Access Journals (Sweden)

    Rachmad Vidya Wicaksana Putra

    2013-09-01

    Full Text Available In  the  literature, several approaches  of  designing  a  DCT/IDCT-based image compression system have been proposed.  In this paper,  we present a new RTL design approach with as main  focus developing a  DCT/IDCT-based image compression  architecture  using  a  self-created  algorithm.  This  algorithm  can efficiently  minimize  the  amount  of  shifter -adders  to  substitute  multiplier s.  We call  this  new  algorithm  the  multiplication  from  Common  Binary  Expression (mCBE  Algorithm. Besides this algorithm, we propose alternative quantization numbers,  which  can  be  implemented  simply  as  shifters  in  digital  hardware. Mostly, these numbers can retain a good compressed-image quality  compared to JPEG  recommendations.  These  ideas  lead  to  our  design  being  small  in  circuit area,  multiplierless,  and  low  in  complexity.  The  proposed  8-point  1D-DCT design  has  only  six  stages,  while  the  8-point  1D-IDCT  design  has  only  seven stages  (one  stage  being  defined as  equal  to  the  delay  of  one  shifter  or  2-input adder. By using the pipelining method, we can achieve a high-speed architecture with latency as    a  trade-off consideration. The  design has been synthesized and can reach a speed of up to 1.41ns critical path delay (709.22MHz. 

  12. Adaptive compressive ghost imaging based on wavelet trees and sparse representation.

    Science.gov (United States)

    Yu, Wen-Kai; Li, Ming-Fei; Yao, Xu-Ri; Liu, Xue-Feng; Wu, Ling-An; Zhai, Guang-Jie

    2014-03-24

    Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.

  13. An image compression method for space multispectral time delay and integration charge coupled device camera

    International Nuclear Information System (INIS)

    Li Jin; Jin Long-Xu; Zhang Ran-Feng

    2013-01-01

    Multispectral time delay and integration charge coupled device (TDICCD) image compression requires a low-complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems (CCSDS) has proposed an image data compression (CCSDS-IDC) algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in multispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC (ICCSDS-IDC)-based distributed source coding (DSC) scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian—Wolf (SW) coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band

  14. Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Gruchalla, Kenny M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Brunhart-Lupo, Nicholas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Potter, Kristin C [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Clyne, John [National Center for Atmospheric Research

    2017-11-03

    Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.

  15. An improved wavelet-Galerkin method for dynamic response reconstruction and parameter identification of shear-type frames

    Science.gov (United States)

    Bu, Haifeng; Wang, Dansheng; Zhou, Pin; Zhu, Hongping

    2018-04-01

    An improved wavelet-Galerkin (IWG) method based on the Daubechies wavelet is proposed for reconstructing the dynamic responses of shear structures. The proposed method flexibly manages wavelet resolution level according to excitation, thereby avoiding the weakness of the wavelet-Galerkin multiresolution analysis (WGMA) method in terms of resolution and the requirement of external excitation. IWG is implemented by this work in certain case studies, involving single- and n-degree-of-freedom frame structures subjected to a determined discrete excitation. Results demonstrate that IWG performs better than WGMA in terms of accuracy and computation efficiency. Furthermore, a new method for parameter identification based on IWG and an optimization algorithm are also developed for shear frame structures, and a simultaneous identification of structural parameters and excitation is implemented. Numerical results demonstrate that the proposed identification method is effective for shear frame structures.

  16. Astronomical Image Compression Techniques Based on ACC and KLT Coder

    Directory of Open Access Journals (Sweden)

    J. Schindler

    2011-01-01

    Full Text Available This paper deals with a compression of image data in applications in astronomy. Astronomical images have typical specific properties — high grayscale bit depth, size, noise occurrence and special processing algorithms. They belong to the class of scientific images. Their processing and compression is quite different from the classical approach of multimedia image processing. The database of images from BOOTES (Burst Observer and Optical Transient Exploring System has been chosen as a source of the testing signal. BOOTES is a Czech-Spanish robotic telescope for observing AGN (active galactic nuclei and the optical transient of GRB (gamma ray bursts searching. This paper discusses an approach based on an analysis of statistical properties of image data. A comparison of two irrelevancy reduction methods is presented from a scientific (astrometric and photometric point of view. The first method is based on a statistical approach, using the Karhunen-Loeve transform (KLT with uniform quantization in the spectral domain. The second technique is derived from wavelet decomposition with adaptive selection of used prediction coefficients. Finally, the comparison of three redundancy reduction methods is discussed. Multimedia format JPEG2000 and HCOMPRESS, designed especially for astronomical images, are compared with the new Astronomical Context Coder (ACC coder based on adaptive median regression.

  17. Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification.

    Science.gov (United States)

    Lu, Zhao; Sun, Jing; Butts, Kenneth

    2016-02-03

    A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

  18. Comparative data compression techniques and multi-compression results

    International Nuclear Information System (INIS)

    Hasan, M R; Ibrahimy, M I; Motakabber, S M A; Ferdaus, M M; Khan, M N H

    2013-01-01

    Data compression is very necessary in business data processing, because of the cost savings that it offers and the large volume of data manipulated in many business applications. It is a method or system for transmitting a digital image (i.e., an array of pixels) from a digital data source to a digital data receiver. More the size of the data be smaller, it provides better transmission speed and saves time. In this communication, we always want to transmit data efficiently and noise freely. This paper will provide some compression techniques for lossless text type data compression and comparative result of multiple and single compression, that will help to find out better compression output and to develop compression algorithms

  19. Hermitian Mindlin Plate Wavelet Finite Element Method for Load Identification

    Directory of Open Access Journals (Sweden)

    Xiaofeng Xue

    2016-01-01

    Full Text Available A new Hermitian Mindlin plate wavelet element is proposed. The two-dimensional Hermitian cubic spline interpolation wavelet is substituted into finite element functions to construct frequency response function (FRF. It uses a system’s FRF and response spectrums to calculate load spectrums and then derives loads in the time domain via the inverse fast Fourier transform. By simulating different excitation cases, Hermitian cubic spline wavelets on the interval (HCSWI finite elements are used to reverse load identification in the Mindlin plate. The singular value decomposition (SVD method is adopted to solve the ill-posed inverse problem. Compared with ANSYS results, HCSWI Mindlin plate element can accurately identify the applied load. Numerical results show that the algorithm of HCSWI Mindlin plate element is effective. The accuracy of HCSWI can be verified by comparing the FRF of HCSWI and ANSYS elements with the experiment data. The experiment proves that the load identification of HCSWI Mindlin plate is effective and precise by using the FRF and response spectrums to calculate the loads.

  20. Efficient Compression of Far Field Matrices in Multipole Algorithms based on Spherical Harmonics and Radiating Modes

    Directory of Open Access Journals (Sweden)

    A. Schroeder

    2012-09-01

    Full Text Available This paper proposes a compression of far field matrices in the fast multipole method and its multilevel extension for electromagnetic problems. The compression is based on a spherical harmonic representation of radiation patterns in conjunction with a radiating mode expression of the surface current. The method is applied to study near field effects and the far field of an antenna placed on a ship surface. Furthermore, the electromagnetic scattering of an electrically large plate is investigated. It is demonstrated, that the proposed technique leads to a significant memory saving, making multipole algorithms even more efficient without compromising the accuracy.