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Context: The proposed technique uses the edge-preserving capabilities of cross-bilateral filter (CBF) and artificial intelligence technique adaptive neuro-fuzzy inference system (ANFIS) to fuse multi-modality medical images. Aims: The aim is to present the unlike information onto a single image as each modality of medical image contains the unalike domain of information. Settings and Design: First, the multi-modality medical images are decomposed using CBF by tuning its parameters: radiometric and geometric sigma producing CBF component and detail component. This detail is fed to ANFIS for fusion. On the other hand, the sub-bands obtained from DWT are fused using average rule. Reconstruction method gives final image. Subjects and Methods: ANFIS is used to train the Sugeno systems using neuro-adaptive learning. The fuzzy inference system in the ANFIS is used to define fuzzy rules for fusion. On the other hand, bior2.2 is used to decompose the source images. Statistical Analysis Used: The performance is verified on the Harvard database with five cases, and the results are equated with conventional metrics, objective metrics as well as visual inspection. The statistics of the metrics values is visualized in the form of column chart. Results: In Case 1, better results are obtained for all conventional metrics except for average gradient (AG) and spatial frequency (SF). It also achieved preferred objective metric values. In Case 2, all metrics except AG, mutual information, fusion symmetry, and SF are better values among all methods. In Cases 3, 4, and 5, all the metrics have achieved desired values. Conclusions: Experiments conclude that conventional, objective, visual evaluation shows best results for Cases 1, 3, 4, and 5.

At present, network image communication is still restricted by channel coding, image and multimedia transmission and other key technologies. Therefore, the transmission process needs to convert the image signal into a digital signal, and then use the relevant band compression technology to reduce these signals to narrow the occupied frequency band, that is, to reduce the amount of information to be transmitted in synchronous transmission. Wavelet transform is a powerful tool for image compression because of its low entropy, multi-resolution, decorrelation and flexible base selection. In this paper, a method of multimedia image compression based on biorthogonal wavelet packet is proposed, which includes the establishment of linear phase biorthogonal wavelet basis, the selection of 3 or 4 levels of wavelet decomposition and reconstruction stage, and the combination of improved band division and preservation strategy. Finally, a compression test is performed based on the selected wavelet basis function and the optimal decomposition and reconstruction layers of the standard test image, which enables to obtain a more ideal compression ratio.

Context: The proposed technique uses the edge-preserving capabilities of cross-bilateral filter (CBF) and artificial intelligence technique adaptive neuro-fuzzy inference system (ANFIS) to fuse multi-modality medical images. Aims: The aim is to present the unlike information onto a single image as each modality of medical image contains the unalike domain of information. Settings and Design: First, the multi-modality medical images are decomposed using CBF by tuning its parameters: radiometric and geometric sigma producing CBF component and detail component. This detail is fed to ANFIS for fusion. On the other hand, the sub-bands obtained from DWT are fused using average rule. Reconstruction method gives final image. Subjects and Methods: ANFIS is used to train the Sugeno systems using neuro-adaptive learning. The fuzzy inference system in the ANFIS is used to define fuzzy rules for fusion. On the other hand, bior2.2 is used to decompose the source images. Statistical Analysis Used: The performance is verified on the Harvard database with five cases, and the results are equated with conventional metrics, objective metrics as well as visual inspection. The statistics of the metrics values is visualized in the form of column chart. Results: In Case 1, better results are obtained for all conventional metrics except for average gradient (AG) and spatial frequency (SF). It also achieved preferred objective metric values. In Case 2, all metrics except AG, mutual information, fusion symmetry, and SF are better values among all methods. In Cases 3, 4, and 5, all the metrics have achieved desired values. Conclusions: Experiments conclude that conventional, objective, visual evaluation shows best results for Cases 1, 3, 4, and 5. [ABSTRACT FROM AUTHOR]

To provide a significantly richer representation of non‐stationary signals appearing in various disciplines of science and engineering, we introduce a novel fractional nonuniform multiresolution analysis (FrNUMRA) on the spectrum Λ given by Λ=0,rN+2ℤ, where N≧1 is an integer and r is an odd integer with 1≦r≦2N−1, such that r and N are relatively prime. The necessary and sufficient condition for the existence of nonuniform wavelets of fractional order is derived and an algorithm is also presented for the construction of fractional NUMRA starting from a fractional low‐pass filter h0α with appropriate conditions. Besides, we provide a complete characterization for the biorthogonality of the translates of the scaling functions of two fractional nonuniform multiresolution analyses and the associated fractional biorthogonal wavelet families. [ABSTRACT FROM AUTHOR]

A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) the inverse of the transpose of JPEG-wavelet transform without involving matrices which are computationally cumbersome. The algorithm is suitable for the widely-spread situations where the original data distribution is unobservable such as in cases where there is deficient representation of the entire population in the training data (in machine learning) and thus the covariate shift assumption is violated. The proposed estimator corrects for both biases, the one generated by endogenous truncation and the one generated by endogenous covariates. Results from utilizing 2 000 000 different distribution functions verify the applicability and high accuracy of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed.

In this article, we introduce the notion of biorthgonoal nonuniform multiresolution analysis on the spectrum Λ=0,r/N+2ℤ, where N ≥ 1 is an integer and r is an odd integer with 1 ≤ r ≤ 2N − 1 such that r and N are relatively prime. We first establish the necessary and sufficient conditions for the translates of a single function to form the Riesz bases for their closed linear span. We provide the complete characterization for the biorthogonality of the translates of scaling functions of two nonuniform multiresolution analysis and the associated biorthogonal wavelet families. Furthermore, under the mild assumptions on the scaling functions and the corresponding wavelets associated with nonuniform multiresolution analysis, we show that the wavelets can generate Reisz bases. [ABSTRACT FROM AUTHOR]

Abstract In order to get divergence free and curl free wavelets, one introduced the smoothed pseudo spline by using the convolution method. The smoothed pseudo splines can be considered as an extension of pseudo splines. In this paper, we first show that the shifts of a smoothed pseudo spline are linearly independent. The linear independence of the shifts of a pseudo spline is a necessary and sufficient condition for the construction of the biorthogonal wavelet system. Based on this result, we generalize the results of Riesz wavelets and derive biorthogonal wavelets from smoothed pseudo splines. Furthermore, by applying the unitary extension principle, we construct tight frame systems associated with smoothed pseudo splines with desired approximation order.

Recently, wavelet-based numerical methods have been newly developed in the areas of science and engineering. In this paper, we proposed a full-approximation scheme for the numerical solution of Burgers' equation using biorthogonal wavelet filter coefficients as prolongation and restriction operators. The presented scheme gives higher accuracy in terms of higher convergence in less CPU time, which has been illustrated through the test problem. [ABSTRACT FROM AUTHOR]

Extracting arterial pulse wave from the video, also known as Imaging Photoplethysmography (IPPG), has emerged as an efficient health monitoring approach. The detail-preserving arterial pulse wave can significantly improve healthcare efficiency through the provision of physiological and pathological information. However, the accurate measurement of arterial pulse wave by IPPG is still challenged by the fact that the pulsation is weakly expressed at the skin surface and extremely vulnerable to interferences. This work proposes a novel approach to measure the detail-preserving arterial pulse wave via interference isolation from facial video. A skin reflection model is introduced to guide the design of the IPPG measurement system, where the IPPG signal is pre-filtered to obtain the variation related to the pulse wave, and a Biorthogonal wavelet is further employed to distinguish the unique pattern of arterial subtle variation from interferences. Experimental results validate the efficiency of proposed method with a correlation coefficient of 0.92. • The skin reflection model reveals the detailed composition of IPPG signal. • Accurate pulse wave measurement using IPPG is achieved with Biorthogonal wavelet. • Proposed algorithm is hardware-independent for most remote physiological measurement. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%.

This paper deals with new approaches to analyse electrocardiogram (ECG) signals for extracting useful diagnostic features. Initially, elimination of different types of noise is carried out using maximal overlap discrete wavelet transform (MODWT) and universal thresholding. Next, R-peak fiducial points are detected from these noise free ECG signals using discrete wavelet transform along with thresholding. Then, extraction of other features, viz., Q waves, S waves, P waves, T waves, P wave onset and offset points, T wave onset and offset points, QRS onset and offset points are identified using some rule based algorithms. Eventually, other important features are computed using the above extracted features. The software developed for this purpose has been validated by extensive testing of ECG signals acquired from the MIT-BIH database. The resulting signals and tabular results illustrate the performance of the proposed method. The sensitivity, predictivity and error of beat detection are 99.98%, 99.97% and 0.05%, respectively. The performance of the proposed beat detection method is compared to other existing techniques, which shows that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]

Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost. An ideal image compression system must yield highquality compressed image with high compression ratio. In this article, a neural network is implemented for image compression using the feature of wavelet transform. The idea is that a back-propagation neural network can be trained to relate the image contents to its ideal compression method between two different wavelet transforms: orthogonal (Haar) and biorthogonal (bior4.4). [ABSTRACT FROM AUTHOR]