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Electrocardiogram (ECG) is a noninvasive, effective and economical biomedical signal that is vital in diagnosing cardiovascular diseases. However, the acquiring process contaminates the ECG signal with several types of noises like Motion Artifacts, Power Line Interference and Baseline Wander. Hence, this paper proposes a new approach to detect and suppress the noises from ECG signals. The complete methodology comprises two stages: noise detection and noise suppression. The former stage applies Improved Complete Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noisy ECG into Intrinsic Mode Functions (IMFs). Next, Maximum Absolute Amplitude (MAA) and Auto-Correlation Maximum Amplitude (AMA) are extracted and used to classify the type of noises from ECG. Then, the noisy ECG segments are processed through the second stage and decomposed into sub-bands through Discrete Wavelet Transform (DWT). Then, the sub-bands are categorized into noise-dominant and signal-dominant frequency bins, and only noise-dominant frequency bins are subjected to noise suppression through a newly proposed adaptive soft thresholding mechanism. The effectiveness of the proposed method is assessed by contaminating the ECG signals acquired from the MIT-BIH arrhythmia database with different noises at different Signal-Noise Ratios (SNRs). Three performance metrics, namely Output SNR, Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC), are employed to explore the superiority of the proposed method over state-of-the-art methods, which considered EMD and CEEMD as decomposition filters. The proposed method improved by an average of 3.5 dB in output SNR and 0.0290 in RMSE.

Due to the existence of cloud shadows and the clouds, it restricted the development of optical remote sensing information. Presently, various cloud shifting mechanisms are concentrated on regenerating the remote sensing information which is corrupted by the thin or small cloud layer or cover. Therefore, automated removal and detection techniques are needed in the complex environment which helps to prevent the important data of the image in the remote sensing. Owing to this, the resolution of the remote sensing image gets affected which fails to provide the clear representations. Thus, it losses the information and makes the system more complicated. Therefore, an improved deep learning-based reconstruction method is implemented to rebuild the lost data of the remote sensing information corrupted by the broad and large clouds. The proposed reconstruction model is developed by a Discrete Wavelet Transform with Adaptive Deep Dilated Residual DenseNet (DWT-ADDi-RD). By employing the bicubic interpolation-based down-sampling and up-sampling, the overall High Resolution (HR) images are transformed into Low-Resolution (LR) images. After that, the DWT structure helps to produce LR wavelet Sub-Bands (SBs) for LR images and HR wavelet Sub-Band (SB) for the HR images. By taking the differences between HR and LR wavelength, the residual image is produced. Training and testing phases are performed in this developed model. During the training stage, the remaining image of the entire image is trained by ADDi-RD with LR wavelet SBs as the input and the remaining image as the goal. In the testing module, the LR wavelet SBs query image is fed into ADDi-RD, which acquires the corresponding remaining image. Thus, the produced remaining image with LR wavelet SBs is applied to the Inverse Discrete Wavelet Transform (IDWT) to acquire the resultant super-resolution image. The significant goal of this proposed model is to enrich the efficiency of the ADDi-RD by optimising the parameters using a Random Value Enhanced Single Candidate Optimiser (RVE-SCO). In the end, the implemented system attains enhanced premium outcomes that are contrasted to the conventional approaches.

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person’s life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time–frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.

Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance.

Cellular automata (CA) can be considered as discrete dynamical systems exhibiting a rich intrinsic behavior both in space and time. Starting from disordered initial configurations and according to different local evolution rules, CA can evolve into steady states showing regular or complex space–time structures. These structures have been shown to have fractal and multifractal properties. Here, the multifractal properties of linear one-dimensional cellular automata with complex spatio-temporal behaviors are calculated using discrete wavelets transforms.

A novel approach for feature extraction of fingerprint matching is proposed by using two-dimensional (2D) rotated wavelet filters (RWF). 2D RWF are used to capture the characterization of diagonally oriented information present in fingerprint image. Proposed method extracts the significant information from small area of fingerprint image. Experimental results conducted on standard database of Bologna University and FVC2002 indicate that the proposed method improves the genuine acceptance rate (GAR) from 92.14% to 96.12% and reduces false acceptance rate (FAR) from 25.2% to 21.2% on Bologna University database and it reduces FAR from 36.71% to 22.79% on FVC2002 database compared with discrete wavelet transform-based approach.

Face recognition in constraint conditions is no longer a further challenge. However, even the best method is not able to cope with real world situations. In this paper, a robust method is proposed such that the performance of the face recognition system is still highly reliable even if the face undergoes large head rotation. Our proposed method considers local regions from half side of face rather than using the holistic face approach since in the former approach the "linearity" of features within the limited region is somewhat preserved regardless of the pose variation. Discrete wavelet transform is then utilized onto these patches in order to form face feature vectors. We train our recognizer using linear regression algorithm to interpret the relationship between a face vector for a specific pose and its corresponding frontal face feature vector. We demonstrate that our proposed method is able to recognize a non-frontal face with high accuracy even under low-resolution image by relying only on single frontal face in the database.

Video coding is an imperative part of the modern day communication system. Furthermore, it has vital roles in the fields of video streaming, multimedia, video conferencing and much more. Scalable Video Coding (SVC) is an emerging research area, due to its extensive application in most of the multimedia devices as well as public demand. The proposed coding technique is capable of eliminating the Spatio-temporal regularity of a video sequence. In Discrete Bandelet Transform (DBT), the directions are modeled by a three-directional vector field, known as structural flow. Regularity is decided by this flow where the data entropy is low. The wavelet vector decomposition of geometrically ordered data results in a lesser extent of significant coefficients. The directions of geometrical regularity are interpreted with a two-dimensional vector, and the approximation of these directions is found with spline functions. This paper deals with a novel SVC technique by exploiting the DBT. The bandelet coefficients are further encoded by utilizing Set Partitioning in Hierarchical Trees (SPIHT) encoder, followed by global thresholding mechanism. The proposed method is verified with several benchmark datasets using the performance measures which gives enhanced performance. Thus, the experimental results bring out the superiority of the proposed technique over the state-of-arts.

This manuscript presents a VLSI architecture and its design rule, called embedded instruction code (EIC), to realize discrete wavelet transform (DWT) codec in a single chip. Since the essential computation of DWT is convolution, we build a set of multiplication instruction, MUL, and the addition instruction, ADD, to complete the work. We segment the computation paths of DWT according to the multiplication and addition, and apply the instruction codes to execute the operators. Besides, we offer a parallel arithmetic logic unit (PALU) organization that is composed of two multipliers and four adders (2M4A) in our design. Thus, the instruction codes programmed by EIC control the PALU to compute efficiently. Additionally, we establish a few necessary registers in PALU, and the number of registers depends on the wavelet filters' length and the decomposition level. Yet, the numbers of multipliers and adders do not increase as we execute the DWT or the inverse DWT (IDWT) in multilevel decomposition. Furthermore, we deduce the similarity between DWT and IDWT, so the functions can be integrated in the same architecture. Besides, we schedule the instructions; thus, the execution of the multilevel processes can be achieved without superfluous PALU in a single chip. Moreover, we solve the boundary problem of DWT by using the symmetric extension. Therefore, the perfect reconstruction (PR) condition for DWT requirement can be accomplished. Through EIC, we can systematically generate a flexible instruction codes while we adopt different filters. Our chip supports up to six levels of decomposition, and versatile image specifications, e.g., VGA, MPEG-1, MPEG-2, and 1024×1024 image sizes. The processing speed is 7.78 Mpixel/s when the operation frequency, for normal case, is 100 MHz.

The real-time rendering of high-quality, non-uniform scenes based on viewpoint has always been one of the most difficult problems in the CG area. In this paper, we propose one efficient algorithm to solve this problem with the help of merging texture synthesis and discrete wavelet transform (DWT) techniques. Using a single normal-sized image input, we can efficiently obtain texture sizes with different resolutions and update these in real-time rendering with the help of DWT. The results of our experiments prove that our algorithm can smoothly and efficiently render the non-uniform scenes based on viewpoint.

Detecting epileptic seizure is a very time consuming and costly task if a support vector machine (SVM) hardware processor is used. In this paper, an automated seizure detection scheme is developed by combining discrete wavelet transform (DWT), sample entropy (SampEn) and a novel classification algorithm based on each wavelet coefficient and voting strategy. In order to save circuit area, a Daubechies order 4 (db4) filter of lattice structure is introduced in DWT, only half elements of the symmetric distance matrix in the SampEn are stored and module reusing strategy is used. To speed up the detection, intermediate results are reused by reasonably organizing the SampEn calculation procedures. The seizure detection scheme is implemented in a field-programmable gate array (FPGA) and its classification performance is tested with publicly available epilepsy dataset.

Medical image fusion is the process of deriving vital information from multimodality medical images. Some important applications of image fusion are medical imaging, remote control sensing, personal computer vision and robotics. For medical diagnosis, computerized tomography (CT) gives the best information about denser tissue with a lesser amount of distortion and magnetic resonance image (MRI) gives the better information on soft tissue with little higher distortion. The main scheme is to combine CT and MRI images for getting most significant information. The need is to focus on less power consumption and less occupational area in the implementations of the applications involving image fusion using discrete wavelet transform (DWT). To design the DWT processor with low power and area, a low power multiplier and shifter are incorporated in the hardware. This low power DWT improves the spatial resolution of fused image and also preserve the color appearance. Also, the adaptation of the lifting scheme in the 2D DWT process further improves the power reduction. In order to implement this 2D DWT processor in field-programmable gate array (FPGA) architecture as a very large scale integration (VLSI)-based design, the process is simulated with Xilinx 14.1 tools and also using MATLAB. When comparing the performance of this low power DWT and other available methods, this high performance processor has 24%, 54% and 53% of improvements on the parameters like standard deviation (SD), root mean square error (RMSE) and entropy. Thus, we are obtaining a low power, low area and good performance FPGA architecture suited for VLSI, for extracting the needed information from multimodality medical images with image fusion.

The classification of electroencephalogram (EEG) signals is a key technique of brain–computer interface (BCI) system. In view of the complexity of EEG signals and the low accuracy in EEG signals recognition, a motor imagery EEG signals classification method with multi-domain fusion based on Dempster–Shafer (D-S) evidence theory is presented in this paper. Firstly, time domain statistics (TDS), autoregressive (AR) model and discrete wavelet transform (DWT) are used to extract features from EEG signals, respectively, and three probabilistic output support vector machine (SVM) classification models are trained based on these three feature sets. Secondly, using the output of each SVM, we construct basic probability assignment (BPA) function and get fusion BPA through D-S evidence theory. Finally, determining the class of test samples based on decision rules. Four databases from BCI competition are employed to evaluate the proposed approach, and the highest classification accuracy reaches 92.83%. Results show that this method acquires higher accuracy and has strong individual adaptability.

This work describes a new scheme for denoising noisy electrocardiogram (ECG) signals. In the first step, the noise variance is estimated using the well-known DONOHO’s estimator followed by the wavelet-based baseline wander removing. In the second step, the estimated variance is employed in the adaptive 1D Wiener filter to reduce the additive noise. Next, a Low Pass filter, based on the FFT, is applied on the resulting denoised signal. Furthermore, a cascaded Savitzky–Golay (SG) smoother filter is applied to refine the restoration process. The final step consists in the recovering of the $R$-peaks and the surrounding areas. It can be reported that the suggested algorithm is optimal for the additive Gaussian noise and is useful for other types of noises. Both qualitative and quantitative results, achieved from several experimental tests, establish high-quality restoration ability and the efficiency of the proposed method. Thus, when compared to some powerful techniques recently published, the designed algorithm demonstrates very competitive performances.

In order to protect audio files, we propose in this paper a new integration scheme for blind audio file watermarking. The goal is to find a compromise between capacity and imperceptibility in order to hide as much data as possible while minimizing file degradation. This integration scheme is implemented in the three insertion domains: spatial, frequency and multi-resolution domains. For the spatial-domain integration, the mark is inserted directly into the data samples. For the frequency-domain integration, a Discrete Cosine Transform is applied to the audio frames; after the thresholding and quantification step, the watermark is inserted into the Discrete Cosine Transform coefficients to obtain the watermarked file. For the multi-resolution-domain insertion, a single-level Discrete Wavelet Transform is applied using the scaling low-pass filter and wavelet high-pass filter. The watermark integration is then performed using the obtained AC coefficients. The proposed concealment process combines three values to integrate two bits and only one may be modified, which reduces the probability of change unlike other approaches. This implies less modification and therefore less distortion of the host file; this explains the good Signal-to-Noise Ratio obtained of more than 59dB for the spatial-domain integration and therefore a reasonable imperceptibility. An evaluation of the watermark’s robustness demonstrates that the proposed schemes generate reasonably robust watermarked samples against various attacks with a high-quality watermark with normalized cross-correlation greater than 0.9 for the three insertion domains.

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘$l$’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.

In this work, we proposed a robust and blind watermarking approach to adequately secure medical images exchanged in telemedicine. This approach ensures the traceability and integrity of the medical and essential image for data security in the field of telemedicine. In this paper, a blind watermarking method is proposed to adequately secure the electronic patient records. The integration of the watermark will be carefully performed by combining the parity of the successive values. This innovative approach will be typically implemented in the three insertion domains: spatial, frequency and multi-resolution. For the spatial domain, the watermark will be integrated into the colorimetric values of the image. In the frequency domain, the watermark bits will be substituted to the DCT coefficient’s least significant bit. For the multi-resolution domain insertion, after calculating a DWT, the obtained LL sub-band coefficients will be used for the integration process. After comparing our approaches to the various recent works in the three domains, the obtained results demonstrate that our proposed approach offers a good imperceptibility for the frequency and spatial domains insertion.

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help to achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

Since the fetus is not available for direct observations, only indirect information can guide the obstetrician in charge. Electronic Fetal Monitoring (EFM) is widely used for assessing fetal well being. EFM involves detection of the Fetal Heart Rate (FHR) signal and the Uterine Activity (UA) signal. The most serious fetal incident is the hypoxic injury leading to cerebral palsy or even death, which is a condition that must be predicted and avoided. This research work proposes a new integrated method for feature extraction and classification of the FHR signal able to associate FHR with umbilical artery pH values at delivery. The proposed method introduces the use of the Discrete Wavelet Transform (DWT) to extract time-scale dependent features of the FHR signal and the use of Support Vector Machines (SVMs) for the categorization. The proposed methodology is tested on a data set of intrapartum recordings were the FHR categories are associated with umbilical artery pH values, This proposed approach achieved high overall classification performance proving its merits.