Haar Discrete Wavelet Transform Research Articles

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

On a Hadamard–Haar Discrete Wavelet Transform and Its Inversion

On a Hadamard–Haar Discrete Wavelet Transform and Its Inversion

Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means

Induction motors need to be monitored regularly because it involves the company's productivity. The induction motor monitoring method in this study uses a motor current variable which is transformed using the Discrete Wavelet Transform. Discrete Wavelet Transform (DWT) is used in this study because the results are satisfactory for detecting a short circuit in the stator winding of an induction motor. Of the many types and levels of discrete wavelet transforms, the haar wavelet transform at the third level is used in this study. Furthermore, the results of the discrete wavelet transform are processed using the Fuzzy C-means method. Fuzzy C-Mean (FCM) is the grouping approach that each part has a member degree of cluster according to the fuzzy logic algorithm. Motor modeling is shown in this article as normal condition, final fault current, and initial fault current. For this analysis, a combination of wavelet transform and Fuzzy C-means is used to classify motor currents into three motor states. The motor current is processed by Haar DWT level 3 to generate a high frequency signal. Then the high frequency signal is processed to get the energy signal. The energy signal is then fed to Fuzzy C-means to identify its condition. The results show that fuzzy C-means produces an error of 0% for the normal case, 33.3% for the initial error case and 0% for the final error case.

Study and analysis of different segmentation methods for brain tumor MRI application.

Medical Resonance Imaging (MRI) is one of the preferred imaging methods for brain tumor diagnosis and getting detailed information on tumor type, location, size, identification, and detection. Segmentation divides an image into multiple segments and describes the separation of the suspicious region from pre-processed MRI images to make the simpler image that is more meaningful and easier to examine. There are many segmentation methods, embedded with detection devices, and the response of each method is different. The study article focuses on comparing the performance of several image segmentation algorithms for brain tumor diagnosis, such as Otsu’s, watershed, level set, K-means, HAAR Discrete Wavelet Transform (DWT), and Convolutional Neural Network (CNN). All of the techniques are simulated in MATLAB using online images from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset-2018. The performance of these methods is analyzed based on response time and measures such as recall, precision, F-measures, and accuracy. The measured accuracy of Otsu’s, watershed, level set, K-means, DWT, and CNN methods is 71.42%, 78.26%, 80.45%, 84.34%, 86.95%, and 91.39 respectively. The response time of CNN is 2.519 s in the MATLAB simulation environment for the designed algorithm. The novelty of the work is that CNN has been proven the best algorithm in comparison to all other methods for brain tumor image segmentation. The simulated and estimated parameters provide the direction to researchers to choose the specific algorithm for embedded hardware solutions and develop the optimal machine-learning models, as the industries are looking for the optimal solutions of CNN and deep learning-based hardware models for the brain tumor.

Power and Delay Efficient Haar Wavelet Transform for Image Processing Application

This paper presents a one-level decomposition Haar Discrete Wavelet Transform (DWT) architecture using a 4:2 compressor and carry propagate adder. In Haar DWT architecture, coefficient multiplication is an essential operation. The Haar coefficient multiplication [Formula: see text] is implemented with [Formula: see text] multiplier and the generated partial products are represented with sign power of two (SPT) terms. The addition of SPT terms is computed with a 4:2 compressor and the final sum is computed with CPA. A [Formula: see text] multiplier with 4:2 compressor technique is used to improve energy and delay. Compared to the previous architectures, the proposed architecture gives reduction in area, power, and delay. The proposed Haar wavelet architecture is implemented in gate-level Verilog HDL and synthesized with UMC 90-nm technology using Cadence RC compiler. When compared to the existing designs, the proposed architecture Haar DWT architecture synthesis results show reduction in latency of 32.32% and 31.46% of circuit area.

Collusion resistant secret sharing scheme for secure data storage and processing over cloud

Shamir secret sharing (SSS) is considered as a promising method for outsourcing the data securely due to its ability to support privacy preserving data processing while ensuring data availability. Major drawbacks of original SSS scheme are its susceptibility to collusion attack and high storage overhead. Hence in this paper, we first propose a modified SSS scheme (MSSS) which can resist collusion attack and provide adequate security even with two shares. However, the storage overhead of this scheme is high when it is extended to ensure data availability and integrity in cloud storage systems. Therefore, a modified ramp secret sharing (MRSS) with reduced storage overhead compared to MSSS scheme is also proposed in this paper. The proposed schemes can be employed for any privacy preserving data processing application which involve linear operations on the data. In this paper, in order to demonstrate the capability of proposed schemes to support privacy preserving data processing, Haar discrete wavelet transform (DWT) computation on medical images is considered as an example as DWT is widely used in feature extraction for disease diagnosis from pathological images. We present an algorithm for computing Haar DWT from medical image shares. The security of the proposed scheme is evaluated through mathematical cryptanalysis and resistance against various statistical attacks. The performance analysis shows that shared domain DWT offers same accuracy levels as that of plaintext domain.

Approximate Pruned and Truncated Haar Discrete Wavelet Transform VLSI Hardware for Energy-Efficient ECG Signal Processing

The approximate computing paradigm emerged as a key alternative for trading off accuracy and energy efficiency. Error-tolerant applications, such as multimedia and signal processing, can process the information with lower-than-standard accuracy at the circuit level while still fulfilling a good and acceptable service quality at the application level. The automatic detection of R-peaks in an electrocardiogram (ECG) signal is the essential step preceding ECG processing and analysis. The Haar discrete wavelet transform (HDWT) is a low-complexity pre-processing filter suitable to detect ECG R-peaks in embedded systems like wearable devices, which are incredibly energy-constrained. This work presents an approximate HDWT hardware architecture for ECG processing at very high energy efficiency. Our best-proposal employing pruning within the approximate HDWT hardware architecture requires just seven additions. The use of a truncation technique to improve energy efficiency is also investigated herein by observing the evolution of the signal-to-noise ratio and the ultimate impact in the ECG peak-detection application. This research finds that our HDWT approximate hardware architecture proposal accepts higher truncation levels than the original HDWT. In summary: Our results show about 9 times energy reduction when combining our HDWT matrix approximation proposal with the pruning and the highest acceptable level of truncation while still maintaining the R-peak detection performance accuracy of 99.68% on average.

Hybrid Detection for Cooperative Cognitive Radio Using AWT and HDWT

A lot of research shows that spectrum is not effectively used in wireless communications. There are several gaps in spectrum that are unused. Cognitive radio (CR) is a smart technique of addressing inefficient usage of spectrum resources. Spectrum sensing is a vital task in cognitive radio systems. The objective of the spectrum sensing process is to investigate the presence and absence of primary user signals in different spectrum holes. To increase the feasibility of the spectrum sensing process, it can be implemented in a cooperative mode. The cooperative spectrum sensing depends on a decision making task at a group of secondary users. One of the most popular spectrum sensing techniques is the energy detection. Unfortunately, this technique fails in low SNR scenarios to give high detection results. So, there is a need for cooperative spectrum sensing in addition to noise reduction techniques to enhance the performance of the CR system. This paper presents noise reduction techniques based on additive wavelet transform (AWT) with homomorphic decomposition and Haar discrete wavelet transform (HDWT) with homomorphic decomposition to reduce the noise prior to the decision making process. This scenario is investigated with several fusion rules that are used for the cooperative spectrum sensing with fixed-time sensing. Simulation results prove that with OR fusion rule, the proposed scenario increases the probability of detection in the range of SNR from − 20 to − 15 dB using the AWT with homomorphic decomposition. The achievable throughput increases using the HDWT with homomorphic decomposition. The AND fusion rule is not recommended with the HDWT, but the probability of detection is high with the AWT. The majority fusion rule achieves high throughput with the HDWT and a high probability of detection with the AWT.

Real-time FPGA implementation of a secure chaos-based digital crypto-watermarking system in the DWT domain using co-design approach

In this paper a new approach for designing invisible non-blind full crypto-watermarking system targeting images security on FPGA platform is presented. This new design is based on the Hardware-Software co-design approach using the High-Level Synthesis (HLS) tool of Xilinx which allows a good compromise between development time and performances. For a better authentication and robustness of the proposed system, the Discrete Wavelet Transform (DWT) is employed. To more enhance the security level, a new chaos-based generator proposed is integrated into a stream cipher algorithm in order to encrypt and decrypt the watermark during the insertion and extraction phases.This approach allows a better secure access at the positions of the watermark and to distribute the watermark evenly throughout the image. Three novel customized Intellectual Property (IP) cores designed under HLS tool, implementing Haar DWT and the new chaos-based key generator, have been generated, tested, and validated. The generated Register Transfer Level-IP (RTL-IP) cores are integrated into a Vivado library that achieves real-time secured watermarking operations for both embedding and extraction processes. The system has been evaluated using the main metrics in terms of imperceptibility of the produced watermarked images achieving a Peak Signal to Noise Ratio (PSNR) of 47 dB, robustness against most geometric and image processing attacks achieving a Normalized Cross-Correlation (NCC) of 0.99. The proposed crypt-watermarking system allows a good solution against brute force attack which produce a huge key-space of $$2^{768}$$ . Finally, the implementation offers a good efficiency value of 0.19 MHz/LUT in terms of FPGA resource consumption and speed, making the system a reliable choice for real sensitive embedded applications.

Image multi‐encryption architecture based on hybrid keystream sequence interspersed with Haar discrete wavelet transform

A novel image multi-encryption architecture based on hybrid keystream sequence generated by a single hyperchaotic system and Haar discrete wavelet transform (HDWT) is proposed. The architecture consists of a pre-cipher stage, first encryption operation, Haar discrete wavelet decomposition stage and a second encryption operation. In the pre-cipher stage, the algorithm applies two-level pixel position permutation of the image. The first encryption operation is accomplished by diffusing the pixel's numbers with keystream sequence generated by a sequence generator SG-1. The resulting cipher image is decomposed using two-dimensional HDWT decomposition technique. The decomposed image is further encrypted through operation between keystream sequence generated by sequence generator SG-2 and bytes of the decomposed image which are selected with the aid of a novel pseudo – 4 bit Boolean truth table-based byte selection mechanism. SG-1 and SG-2 are special cases of the hyperchaotic system and are evolved online by separately nullifying selected parameters of the hyperchaotic system. The novelty of the proposed architecture lies in the possibility of increasing the number of sequence generators, which can result in exponentially huge key space and fast encryption speed. A comprehensive complexity analysis of performance, security and robustness to attacks, confirmed the feasibility of the architecture.

FPGA Implementation of Digital Images Watermarking System Based on Discrete Haar Wavelet Transform

In this paper we propose a novel and efficient hardware implementation of an image watermarking system based on the Haar Discrete Wavelet Transform (DWT). DWT is used in image watermarking to hide secret pieces of information into a digital content with a good robustness. The main advantage of Haar DWT is the frequencies separation into four subbands (LL, LH, HL, and HH) which can be treated independently. This permits ensuring a better compromise between robustness and visibility factors. A Field Programmable Gate Array (FPGA) that is based on a very large scale integration architecture of the watermarking algorithm is developed to accelerate media authentication. A hardware cosimulation strategy using the Matlab-Xilinx system generator (XSG) was applied to prove the validity of the suggested implementation. The hardware cosimulation results show the effectiveness of the developed architecture in terms of visibility and robustness against several attacks. The proposed hardware system presents also a high performance in terms of the operating speed.

Identification of mineralization pattern in high frequencies of geochemical data by using the new approach of DWT-PCA

summary The Dalli area has been introduced as copper–gold probably porphyry mineralized area in the central part of Iran. In this study, in order to determine the mineralization pattern, a new method based on coupling discrete wavelet transform (DWT) and principal component analysis (PCA) has been used. The surface geochemical data of 30 elements were transformed to position – scale domain using 2DWT and were decomposed to high and low frequencies in one level then PCA was performed on vertical and horizontal detail components separately. In the final, the elements of Au and Cu have been classified clearly using the combination of mineralization factors obtained of vertical and horizontal detail components. The results of this study demonstrate that the DWT – PCA combined approach is a modern method for geochemical data processing. Introduction In this paper, the position - scale domain of geochemical data, using 2dimensional discrete wavelet transform has been represented and analyzed on Cu–Au porphyry deposit in northern Dalli area and the results have been discussed. Wavelet analyses have led to very successful results in numerous scientific and engineering fields such as signal analysis and numerical applications. Methodology and Approaches WT is a tool for the analysis of signals. Wavelets are used as the basis functions for signal representation such as sines and cosines. In the DWT, detailed and approximation coefficients are obtained with the wavelet algorithm based on high-pass and low-pass filters. In this paper, a new method is proposed based on coupling Haar DWT and Principal component analysis (PCA) for mineralization elements forecasting applications. PCA is a multivariate statistical method for geo-information identification of geo-datasets. Results and Conclusions The wavelet coefficients of geochemical data in vertical and horizontal detail components have appropriate exploratory information. The results of this analysis on the Haar WT have desirably identified the mineralizing elements. Hence, the Haar wavelet is a suitable mother wavelet for interpretation of geochemical data. The surface information and the exploration drillings in the study area confirm the results of WT.

A Fast Discrete Wavelet Transform Using Hybrid Parallelism on GPUs

Wavelet transform has been widely used in many signal and image processing applications. Due to its wide adoption for time-critical applications, such as streaming and real-time signal processing, many acceleration techniques were developed during the past decade. Recently, the graphics processing unit (GPU) has gained much attention for accelerating computationally-intensive problems and many solutions of GPU-based discrete wavelet transform (DWT) have been introduced, but most of them did not fully leverage the potential of the GPU. In this paper, we present various state-of-the-art GPU optimization strategies in DWT implementation, such as leveraging shared memory, registers, warp shuffling instructions, and thread- and instruction-level parallelism (TLP, ILP), and finally elaborate our hybrid approach to further boost up its performance. In addition, we introduce a novel mixed-band memory layout for Haar DWT, where multi-level transform can be carried out in a single fused kernel launch. As a result, unlike recent GPU DWT methods that focus mainly on maximizing ILP, we show that the optimal GPU DWT performance can be achieved by hybrid parallelism combining both TLP and ILP together in a mixed-band approach. We demonstrate the performance of our proposed method by comparison with other CPU and GPU DWT methods.

An Image Steganography Algorithm for Hiding Data Based on HDWT, LZW and OPAP

Image steganography is the art of information hiding, which embeds a secret data into a cover image. However, high capacity of secret data and high quality of stego image are key issues in image steganography. In this paper, an image steganography technique based on Haar Discrete Wavelet Transform (HDWT), Lempel Ziv Welch (LZW) algorithm and Optimal Pixel Adjustment Process (OPAP) is proposed. The HDWT is used to increase the robustness of stego image against attacks. To increase the hidden capacity, LZW algorithm is performed on the secret message. The OPAP is then applied to reduce the embedding error between the cover image and stego image. The experimental results are evaluated by four standard images as covers, and with two types of secret messages. The results demonstrate high visual quality of stego image with large Hidden Capacity (HC) of secret data compared with recent techniques

An Image Steganography Algorithm for Hiding Data Based on HDWT, LZW and OPAP

Image steganography is the art of information hiding, which embeds a secret data into a cover image. However, high capacity of secret data and high quality of stego image are key issues in image steganography. In this paper, an image steganography technique based on Haar Discrete Wavelet Transform (HDWT), Lempel Ziv Welch (LZW) algorithm and Optimal Pixel Adjustment Process (OPAP) is proposed. The HDWT is used to increase the robustness of stego image against attacks. To increase the hidden capacity, LZW algorithm is performed on the secret message. The OPAP is then applied to reduce the embedding error between the cover image and stego image. The experimental results are evaluated by four standard images as covers, and with two types of secret messages. The results demonstrate high visual quality of stego image with large Hidden Capacity (HC) of secret data compared with recent techniques

A Automatic Vehicle License Plate Location Based on Sobel Edge Method

This thesis is to approach license-plate location using Sobel mask operator for license-plate location. This thesis consists of three main parts. The first part is to gray and HSI transform and cut image to reduce image scan time. The second part is to use Sobel edge function and use scan line scanning image to find license-plate. The third part is to locate and extract the license-plate. The last step is license plate and through 2D Haar Discrete Wavelet Transform two times, after transform only selected LL low-frequency parts and will the data arrange into a row when license plate the feature values. The experimental result can be high location rate. Hence, Sobel edge image is a good each of methods.

Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

Wavelet based ILU preconditioners for the numerical solution by PUFEM of high frequency elastic wave scattering

Daubechies family of wavelets combined to the Incomplete Lower–Upper (ILU) factorization are considered as preconditioners for a block sparse linear system arising from the approximation of the time harmonic elastic wave equations by the Partition of Unity Finite Element Method (PUFEM). After applying the discrete wavelet transform (DWT) to each dense block in the final matrix and the known right-hand side, due to the local enrichment by pressure ( P) and shear ( S) plane waves, the resulting linear system is solved by the restarted Generalized Minimum RESidual method (GMRES) with ILU preconditioners allowing fill-in elements in the L and U matrix factors. A reordering algorithm of the vertices based on Gibbs method is also introduced. It leads to a significant reduction of the bandwidth of the wave based Finite Element (FE) matrix and enables the ILU preconditioners to be more effective. To study the performance of the proposed preconditioners, a problem of a horizontal S plane wave scattered by a rigid circular body in an infinite elastic medium is considered. The numerical tests show the good performance of the DWT based ILU preconditioners in improving the rate of convergence of GMRES, for high numbers of approximating P and S plane waves, on coarse mesh grids containing multi-wavelength sized elements. Moreover, the Haar DWT enhances the scalability with respect to the problem size, when the number of the nodal points increases, of the ILU preconditioner which uses the threshold strategy in the control of the fill-in elements. Despite the high level of the conditioning, a good accuracy may be achieved for a discretization level of around 1.9 degrees of freedom per S wavelength, which is far below the rule of thumb of 10 nodal points per wavelength, adopted for the conventional FE.

Digital Watermarking System based on Cascading Haar Wavelet Transform and Discrete Wavelet Transform

Digital Watermarking System based on Cascading Haar Wavelet Transform and Discrete Wavelet Transform

An efficient VLSI architecture and FPGA implementation of the Finite Ridgelet Transform

In this paper, an efficient architecture for the Finite Ridgelet Transform (FRIT) suitable for VLSI implementation based on a parallel, systolic Finite Radon Transform (FRAT) and a Haar Discrete Wavelet Transform (DWT) sub-block, respectively is presented. The FRAT sub-block is a novel parametrisable, scalable and high performance core with a time complexity of O(p 2), where p is the block size. Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) implementations are carried out to analyse the performance of the FRIT core developed.