Discrete Multiwavelet Research Articles

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device

Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, traditional thickness measuring approaches of BT require complex operation, high-cost, and trigger biological stress response. Herein this study, an novel in vitro BT thickness measuring approach integrated with force test system (FST) and the discrete multiwavelet transform convolutional neural network (DMWA-CNN) prediction model based on deep learning are proposed. Simultaneously, several comprehensive experiments and model comparisons are conducted to demonstrate the superiority of the proposed approach. By establishing a DMWA-CNN demonstrates higher estimation accuracy than other traditional algorithm, achieving 100 % accuracy for artificial BT. Moreover, the experimental results indicate that proposed approach is robust to elastic modulus variation (E), external load variation (F), and small thickness differences (Ts). In addition, four kinds of the pork’ thickness are experimentally measured, and the accuracy value is not less than 98.2 %. The thickness of BT determined using the FST and DMWA-CNN algorithm demonstrate potential application in the biomechanical parameter prediction.

PERFORMANCE IMPROVEMENT OF MULTIPLE-INPUT/MULTIPLE-OUTPUT (MIMO) ORTHOGONAL FREQUENCY-DIVISION MULTIPLEXING (OFDM) SYSTEMS USING MIMO DISCRETE MULTIWAVELET TRANSFORM CODED OFDM

In the present work, we proposed to modify and improve multiple-input/multiple-output (MIMO) fast Fourier transform (FFT) coded orthogonal frequency-division multiplexing (COFDM) by MIMO discrete multiwavelet transform (DMWT) COFDM to reduce the interference level and increase the spectral efficiency and complexity of the system. Combining space division multiplexing with multiwavelet and error correction coding and the diversity of the orthogonal frequency division coding to improve the performance of wireless systems requires mastery of all techniques. This DMWT-COFDM system is new and has many applications in telecommunication systems. In this paper, we present the MIMO-FFT-COFDM and MIMO-DMWT-COFDM system block diagrams. Compared with the MIMO-FFT-COFDM system, the bit error rate of the system can be reduced by more than 10 dB.

Intelligent Sensing and Monitoring System for High-Voltage Transmission Line Status of Smart Grid Based on IoT Technology

This paper integrates the Internet of Things (IoT) technology and a smart grid to build an electric power IoT architecture and analyzes the intelligent sensing technology and wireless communication technology in this electric power IoT. Through the multi-channel data collection technology in power IoT technology and an orthogonal discrete multiwavelet transform algorithm of edge computing technology, the high-voltage transmission line status data of the smart grid are collected and processed. Then, the high-voltage transmission line condition monitoring system is designed using the node design of the high-voltage transmission line condition monitoring sensing network and the optimal sensor configuration for droop monitoring. The performance of the monitoring system is simulated and examined. The experimental results show that as the number of burst data nodes increases, the acceptance rate of the ODMT algorithm decreases from 99% to 98%, and the network survival time is over 2000. When the current exceeds 20% of the rated current, the overall measurement error is controlled at approx. 3%. At a height of 4 m, the ratio of the difference between the input voltage and output voltage sensing monitoring is approx. 5%. The error range of temperature sensing monitoring is within ±1 °C. The error rate of communication distance within 200 m is 0, and over 200 m, the error rate is approx. 7%. This system can monitor the transmission status of high-voltage lines very well.

Multiwavelet based-approach to detect shared congestion in computer networks

Internet paths sharing the same congested link can be identified using several shared congestion detection techniques. The new detection technique which is proposed in this paper depends on the previous novel technique (delay correlation with wavelet denoising (DCW) with new denoising method called Discrete Multiwavelet Transform (DMWT) as signal denoising to separate between queuing delay caused by network congestion and delay caused by various other delay variations. The new detection technique provides faster convergence (3 to 5 seconds less than previous novel technique) while using fewer probe packets approximately half numbers than the previous novel technique, so it will reduce the overload on the network caused by probe packets. Thus, new detection technique will improve the overall performance of computer network.

Computation of Wavelet and Multiwavelet Transforms Using Fast Fourier Transform

A novel fast and efficient algorithm was proposed that uses the Fast Fourier Transform (FFT) as a tool to compute the Discrete Wavelet Transform (DWT) and Discrete Multiwavelet Transform. The Haar Wavelet Transform and the GHM system are shown to be a special case of the proposed algorithm, where the discrete linear convolution will adapt to achieve the desired approximation and detail coefficients. Assuming that no intermediate coefficients are canceled and no approximations are made, the algorithm will give the exact solution. Hence the proposed algorithm provides an efficient complexity verses accuracy tradeoff. The main advantages of the proposed algorithm is that high band and the low band coefficients can be exploited for several classes of signals resulting in very low computation.

Orthonormalized basic of fractal stepped multiwavelets – a new multiwavelet technology for signal and image processing

A new class of fractal step functions with linear and nonlinear changes in values is described, and on their basis a recurrent method for constructing functions of a new class of fractal step multiwavelets (FSMW) of various shapes with linear and nonlinear changes in values is developed. A method and an algorithm for constructing a whole family of basic FSMW systems have been developed. An algorithm for calculating the coefficients of a discrete multiwavelet transform based on a multiwavelet packet without performing convolution and decimated sampling operations, in contrast to the classical method, is presented. A method and algorithm for fast multiwavelet transform of low computational complexity has been developed, which, in comparison with the well-known classical Mall's algorithm, is 70 times less in multiplicative complexity, and 20 times less in additive complexity.

Novel Efficient Multiwavelet-Based Modulation for Downlink NOMA Systems

A new modulation scheme using multiwavelets for downlink non-orthogonal multiple-access (NOMA) transceivers is presented in this work. Multiwavelets leading to remarkable spectral diversity are exploited to modulate/demodulate the superimposed multi-user signals in downlink NOMA systems, where the discrete multiwavelet transform and the inverse discrete multiwavelet transform are invoked in the new demodulator of the user equipment and the new modulator of the base station, respectively. The performance evaluation and the computational-complexity analysis of our proposed new NOMA scheme are also conducted. Simulation results demonstrate that the proposed new scheme can significantly increase the system capacity while dramatically suppressing the peak-to-average-power ratio (PAPR) compared to the conventional OFDM-based NOMA approach. Meanwhile, our new scheme can achieve the same system capacity as the scalar-wavelet-based NOMA approach but with a lower PAPR at little extra cost of computational-complexity. All of these three aforementioned NOMA schemes can result in the identical bit-error-rate under the same signal-to-noise ratio condition.

Banks of filters for implementation of DMWT of an image

Abstract We describee some possible options for implementation of the Discrete multiwavelet transform (DMWT) of an image by using filter banks. DMWT can be implemented by two channel bank of vector filters which are made by cross-connected scalar filters. The properties of DGHM, CL, BiHermite and SA4 multiwavelets are here analyzed, and compression analysis for output normalization of DMWT is performed. A procedure is design of equivalent replacing of 2 channel multifilters bank by 4 channel bank of single scalar filters. Finally, we deal with a possible reduction and combinations of subbands and suggest their use.

Image compression using fractal multiwavelet transform

Multiwavelets occur in wavelet theory for more general multiresolution analysis (MRA) using several scaling functions. The muliwavelets feature orthogonality, short support, symmetry, approximation order and regularity at a time which is not possible by using only a single wavelet system. In the present work, theory of MRA and wavelet using several scaling functions is reviewed to define Discrete Multiwavelet Transform and Fractal Multiwavelet Transform in matrix form, using a multifilter. As an application the defined transforms are applied to an image for its compression and the resulting entropy and PSNR values are compared with those occurring with earlier used transforms like Haar and D4 transforms.

IMAGE COMPRESSION ALGORITHMS BASED ON DISCRETE MULTIWAVELET TRANSFORM

In the last two decades, the communication and Internet industry has seen a rapid development, which caused an increase needed to develop an efficient compression image algorithm to reduce the volumes of the information transmitted through the communication system. In this work, the discrete multi wavelet transform is used to build an efficient image compression algorithm. The proposed algorithm is built under MATLAB environment and it is tested using two different images. The experimental results stated that the proposed algorithm has good result with very small loss of resolution when applied on the reconstructed images.

Convolution implementation with a novel approach of DGHM multiwavelet image transform

Abstract The purpose of this paper is to develop convolution implementation of DGHM (Donovan, Geronimo, Harding, Massopust) multiwavelet image transform using a new approach of ordering wavelet coefficients at the second and higher levels. Firstly, the method of implementation of one-dimensional discrete multiwavelet transform (1D DMWT) for DGHM multiwavelet using discrete convolution and scalar filters is presented. Then, convolution implementation of DGHM multiwavelet image transform by application of 1D DMWT for two dimensions (2D) in a separable way is proposed. Next, the second level of 2D DMWT is performed in three possible ways. The novelty of the proposed implementation is in reordering of L subband wavelet coefficients at the first level into one subimage. The results are evaluated as the energy ratios between the transformed images in L subband at the second level and the input original image. According to the experimental results, the developed implementation of 2D DMWT is approximately 5% more effective in energy compression than the ones most commonly mentioned in the literature. This paper shows a possibility of convolution implementation of 2D DMWT with higher energy compression.

Face Recognition Employing DMWT Followed by FastICA

Face recognition becomes a challenging topic in several fields since images of faces are varied by changing illuminations, facial rotations, facial expressions, etc. In this paper, two dimensional discrete multiwavelet transform (2D DMWT) and fast independent component analysis (FastICA) are proposed for face recognition. Preprocessing, feature extraction, and classification are the main steps in the proposed system. In the preprocessing step, each pose in the database is divided into six parts to reduce the effect of unnecessary facial features and highlight the local features in each part. For feature extraction, the 2D DMWT is applied to each part for dimensionality reduction and features extraction. This results in two facial representations. Then FastICA followed by $$\ell _2$$ -norm is applied to each representation, which produces six and three different techniques for the first and second representation, respectively. This results in features that are more discriminating, less dependent, and more compressed. In the recognition step, the resulted compressed features from the two representations are fed to a neural network-based classifier for training and testing. The proposed techniques are extensively evaluated using five databases, namely ORL, YALE, FERET, FEI, and LFW, which have different facial variations, such as illuminations, rotations, facial expressions, etc. The results are analyzed using K-fold cross-validation. Sample results and comparison with a large number of recently proposed approaches are provided. The proposed approach is shown to yield significant improvement compared with the other approaches.

Performance Enhancement of Discrete Multiwavelet Critical-Sampling Transform-Based OFDM System Using Hybrid Technique Under Different Channel Models

A hybrid technique, which is a combination of the Fast Fourier Transform (FFT) and Sliding Window (SW) technique, is proposed for the development of new structure for Discrete Multiwavelet Critical-Sampling Transform based Orthogonal Frequency Division Multiplexing (DMWCST-OFDM) system under multipath fading channels with the Doppler frequency effect. FFT is utilized to increase the orthogonality of subcarriers against the multipath frequency-selective fading channels. SW technique reduces the fluctuation of signal amplitude, thereby reducing the Doppler frequency effect. This hybrid technique offers a good tradeoff between performance and complexity, where this technique needs more computations. However, it performs better than the standard DMWCST-OFDM system. The performance of the proposed system, called SW-DMWCST-OFDM, was compared to that of the standard DMWCST-OFDM, SW-FFT-OFDM, and FFT-OFDM systems under three channels, namely, additive white Gaussian noise, flat fading, and frequency-selective fading. The simulation results show that the proposed model performs better than the three other models in all types of channels.

Enhancement the Performance of OFDM based on Multiwavelets Using Turbo Codes

In wireless communication systems, the main challenge is to provide a high data rate and reliable transmission over a frequency selective fading channel. Orthogonal Frequency Division Multiplexing (OFDM) is a very attractive technique for high data rate transmission with better bandwidth efficiency. In this paper, the effectiveness of turbo codes is utilized to develop a new approach for an OFDM system based on a Discrete Multiwavelet Critical-Sampling Transform (OFDM-DMWCST). The use of turbo coding in an OFDM-DMWCST system is useful in providing the desired performance at higher data rates. Two types of turbo codes were used in this work, i.e., Parallel Concatenated Convolutional Codes (PCCCs) and Serial Concatenated Convolutional Codes (SCCCs). In both types, the decoding is performed by the iterative decoding algorithm based on the log-MAP (Maximum A Posteriori) algorithm. The simulation results showed that, the turbo-coded OFDM-DMWCST system achieves large coding gain with lower Bit-Error-Rate (BER), therefore, offering a higher data rate under different channel conditions. In addition, the PCCCs offer better performance than SCCCs.

Numerical Solution of Integral Equations by using Discrete GHM Multiwavelet

Numerical Solution of Integral Equations by using Discrete GHM Multiwavelet

Discrete Multiwavelet–Based Video Watermarking Scheme Using SURF

This paper proposes a robust, imperceptible block-based digital video watermarking algorithm that makes use of the Speeded Up Robust Feature (SURF) technique. The SURF technique is used to extract the most important features of a video. A discrete multiwavelet transform (DMWT) domain in conjunction with a discrete cosine transform is used for embedding a watermark into feature blocks. The watermark used is a binary image. The proposed algorithm is further improved for robustness by an error-correction code to protect the watermark against bit errors. The same watermark is embedded temporally for every set of frames of an input video to improve the decoded watermark correlation. Extensive experimental results demonstrate that the proposed DMWT domain video watermarking using SURF features is robust against common image processing attacks, motion JPEG2000 compression, frame averaging, and frame swapping attacks. The quality of a watermarked video under the proposed algorithm is high, demonstrating the imperceptibility of an embedded watermark.

Secure GPRS Data Systems Based on 3D-Discrete Wavelet and Multiwavelet Transformations

This paper shows a new efficient data delivery method based on three dimensional discrete Multiwavelet transformations Critically Sampled (3D-DMWTCS). This method was been done by get the data from any data source then catted the stream data to a packets of A certain length then each of these packet will convert to a square matrix, and then collected these square matrices in three dimensional matrix form. The 3D - discrete Multiwavelet transforms Critically Sampled (3D-DMWTCS) will be applied to each three dimensional matrix form and the output of this procedure will be also in three dimensional matrix forms and contain 3D-DMWTCS coefficient as real numbers. After this stage, the 3D matrix will be converting to square matrix and then to serial data stream of samples, then will be sending through USB port to SIM908 GSM GPRS module that must secure a connection between two or more communication points. As well as this encryption method can be done using three dimensional discrete wavelet transformations (3D-DWT) and (2D-DMWTCS) and (2D-DWT). The purposed of these methods is to maintain the confidentiality of the information sent, whether it is written texts, or data, or talk speech, and it is done with a high data rate and excellent coding. It is impossible or difficult to decrypt these methods without knowing how these encryption systems are build. The decryption is done by extracting the encrypted data from the Multiwavelet decomposition vector using the algorithm of inverse of 3D-Discrete Multiwavelet Transformation. The encrypted messages that send to other points consists 3D-DMWTCS decomposition vector. The encryption key that used in this method is 3D-DMWTCS method so the decryption key will be 3D-IDMWTCS. Due to the GSM GPRS mobile communications systems mostly use OFDM technology, therefore these technology has been used in the designed of wireless communication transceivers system using MATLAB2014a to simulate GSM GPRS under multichannel types and run the simulations to determine the specifications of these cryptographic systems under various conditions. The simulation results proved that these methods are retrieving the security and improve the performance of the communications system.

Image Steganography by Using Multiwavelet Transform

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego images. The proposed algorithm is compared with wavelet based algorithm which shows a favorable results in terms of PSNR reaches to 18 dB.

Image Steganography by Using Multiwavelet Transform

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego images. The proposed algorithm is compared with wavelet based algorithm which shows a favorable results in terms of PSNR reaches to 18 dB.

The Research of Remote Sensing Image Fusion Technology

Remote sensing image fusion is an important branch in the field of image fusion. Remote sensing image fusion mainly researches how to use different aerial remote sensor to obtain relevant image information. In view of the present research status in this field and the full analysis of remote sensing image fusion method and wavelet transform, the paper aims at proposing the image fusion technology of combining discrete multiwavelet transform and HIS method, then the paper analyses the experimental results with the help of MATLAB. It is proved that the analysis method of wavelet has more advantages than traditional methods: Wavelet transform fusion method can keep maximized resolution and multispectral image.