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Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at $t$ and $t-1$. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

Digital watermark technology has been proposed as an effective method to protect the copyright of multimedia data. An adaptive image watermark scheme based on fuzzy inference system that in discrete wavelet transform domain is proposed. By exploiting the brightness and texture characteristics of human visual system and considering properties of the original image wavelet coefficient, a fuzzy inference system is designed whose inputs are parameters of brightness and texture of the original image and output is the strength of embedded watermarks. It ensures that the watermark embedding strength is determined adaptively. The experimental results show that the watermarks embed by the proposed scheme are robust against attacks commonly used image processing methods such as JPEG compression, Gaussian noise, cropping, mean filtering, median filtering, rotation, and rescaling etc. Proposed scheme are shown to provide very good results in term of image imperceptibility, too.

Multiple Sclerosis (MS) is one of the debilitating disorders of the central nervous system. This disease causes lesions in the white matter of the brain tissue. It can also lead to many physical and psychological disorders in movement, vision, and memory. Lesion segmentation in MRI images to determine the number and size of lesions is one of the diagnostic problems for specialists. Using automated diagnostic tools as an aid can help professionals. Traditional image processing and deep learning methods are used to automate lesion segmentation. The U-Net is one of the most widely used deep learning architectures for MS lesion segmentation. The images are used in the Fourier domain in the U-Net network, which does not include all its features. Our proposed method combines the HAR wavelet transform and the Dense net-based U-Net. This makes local features and lesions of different sizes more prominent and leads to higher quality segmentation. The proposed method had a better Dice value than the compared methods in the experiments.

In this paper, we address the problem of Blind Audio Separation (BAS) by content evaluation of audio signals in the Time-Scale domain. Most of the proposed techniques rely on independence or at least uncorrelation assumption of the source signals exploiting mutual information or second/high order statistics. Here, we present a new algorithm, for instantaneous mixture, that considers only different time-scale source signature properties. Our approach lies in wavelet transformation advantages and proposes for this a new representation; Spatial Time Scale Distributions (STSD), to characterize energy and interference of the observed data. The BAS will be allowed by joint diagonalization, without a prior orthogonality constraint, of a set of selected diagonal STSD matrices. Several criteria will be proposed, in the transformed time-scale space, to assess the separated audio signal contents. We describe the logistics of the separation and the content rating, thus an exemplary implementation on synthetic signals and real audio recordings show the high efficiency of the proposed technique to restore the audio signal contents.

Edge-preserving image enhancement and noise removal are of great interest in medical imaging. This chapter describes schemes for noise suppression of magnetic resonance images using wavelet multiscale thresholding. To sufficiently exploit the wavelet interscale dependencies, we multiply the adjacent wavelet subbands of a Canny-edge-detector-like dyadic wavelet to form a multiscale product function where the significant features in images evolving with high magnitude across wavelet scales are amplified while noises are deteriorated, which facilitates an easy differentiation of edge structures from noise. Thereafter an adaptive threshold is calculated and imposed on the products, instead directly on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme outperforms other wavelet-thresholding denoising methods in suppressing noise and preserving edges.

Local reconstruction, metal artifact reduction and image denoising is the main problems in Computerized Tomography. In this paper, we chiefly introduce the wavelet methods in CT to deal with these problems. Furthermore, we propose the integration of wavelet methods.

Wavelet transformation is in the last few years an emerging science that develops rapidly. As a mathematics tool, the wavelet technique has been made use of in numerous fields quickly. The Virtual Reality Training System of Vehicle Driving that we developed has used the wavelet transformation technique in portrait handling.

This research paper originally build up the Wavelet — Kalman Filtering Hybrid Estimating and Forecasting Algorithm (WKHEFA), which incorporates the advantages of both Kalman filtering and wavelet analysis. And it successfully forecasts the 30-minute trading volume of Shanghai Stock Exchange based on WKHEFA.

In this paper, a novel method of feature extraction in non-stationary lung sound signals is put forward. Four kinds of lung sounds data (collected in the state of normal, tracheitis, pneumonia and asthma respectively) were sampled from various subjects. We use db5 wavelet to do fast arbitrary multi-scale wavelet transform (WT) and obtained each high-dimensional wavelet coefficient matrix. The equivalence relation in time domain between wavelet coefficients and signal energy was founded. The energy eigenvalue was used as the input feature vectors of artificial neural network (ANN) for classification. This greatly decreased the number of input vectors of ANN, and the recognition rate was 89.75%. The research findings show that the feature extraction method of lung sounds is highly efficient.

The character recognition is difficulty in the Vehicle License Plate Recognition system. Characteristic dot matching, wavelet and neural networks are used in the algorithm. The characteristic dot matching is used in the number and letter recognition, but wavelet transform and neural networks are used in the Chinese. This algorithm is not only efficient in character recognition, but also may be used in fragmentary character recognition. By this algorithm, the vehicle license plate can be recognized more quickly and more correctly.

To overcome the disadvantages of dyadic wavelet and extend the scope of wavelet applications, the multiresolution analysis of the rank M wavelet and the orthogonal condition of the rank M wavelet are studied. A new method for the construction of the filter coefficients corresponding to the rank 3 wavelet scaling function is presented. And the conclusion is drawn that the number of the FIR coefficients corresponding to the rank M wavelet scaling function is at least MN, where N is the vanishing moment of the wavelet function. Rank M wavelet has potential advantages over the traditional wavelets in image compression.

An image watermarking scheme is developed with echo-based hiding technique in the wavelet domain. In the embedding process, DWT coefficients with echo delay are utilized for watermarking. During the detection process, detect echo delay is first detected by the concept of image similarity and thus the embedded watermarks are extracted by the inverse procedure of embedding process. Experimental results confirm the watermarked image can keep high PSNR and high embedding capacity.