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Keyword: Wavelet Transform (274) | 18 Feb 2025 | Run |
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Visual Question Answering (VQA) is one of the attractive topics in the field of multimedia, affective, and empathic computing to garner user interest. Unlike existing models which aim at addressing challenges of VQA for the scene images, this work aims at developing a new model for Personality Trait Question Answering (PQA). It uses Twitter account information, which includes shared images, profile pictures, banners, text in the images, and descriptions of the images. Motivated by the accomplishments of the transformer, for encoding visual features of the images, a new InfoGain Multi-Axial Wavelet Vision Transformer (IgMaWaViT) is explored here. For encoding textual features in the images and descriptions, a new Information Gain BERT (InfoBert) method is introduced, which can handle the variable length encoding of text by choosing the optimal discriminator. Furthermore, the model fuses encodings of images and text according to the questions on different personality traits for question answering. The model is called InfoGain Multi-Axial Wavelet Vision Transformer for Personality Traits Question Answering (IgMaWaViT-PQA). To validate the efficacy of the proposed model, a dataset has been constructed, and it is used along with standard datasets for experimentation. Comprehensive experiments show that the proposed model is better than the state-of-the-art models. The code is available at the link: https://github.com/biswaskunal29/InfoGain_MultiAxial_PQA.
Tomatoes are edible berries that are utilized extensively around the world. Their scientific name is solanum lycopersicum. Many tomato varietals are primarily produced in temperate climates. Even though tomatoes are used as ingredients in food, they are botanically categorized as berries. Bacterial spot, Black mold, Gray spot, Late blight, and powdery mildew (PM) are the primary diseases that affect tomato plants. The most challenging process in this decade is also the early diagnosis of these disorders. In this study, the suggested method is applied to the assessment of disease severity and disease diagnosis in tomato plant images. There are various stages to the procedure. The tomato plant image is initially pre-processed using the Wavelet technique, and then the modified Watershed algorithm is used to segment the image using improved Double Sigmoid and Erosion (WSA-IDS&E). The segmented image is then processed in the feature extraction stage in order to extract features. The Gray Level Co-occurrence Matrix (GLCM), histogram, illness area, and pixel-based retrieved characteristics are fed into the CNN model, a deep learning method for identifying diseases. In addition, a novel training method, WUDHOA, which adjusts the CNN’s weight, is introduced in this study to improve the performance of deep learning techniques. In addition, the severity of the tomato plant illness is measured in order to improve disease identification outcomes, where this severity assessment is taken into account using a fresh evaluation. Finally, the performance of the detection outcome is compared with other existing works to prove its efficiency. The conventional techniques have the lowest specificity ratings, such as AEO=0.8473, SSO=0.8249, SHO=0.8672, MFO=0.8394, BES=0.8962, WOA=0.8749, and DHO=0.8846, whereas the WUDHOA achieved a specificity of 0.9384.
To represent Clifford-valued signals more efficiently in the time–frequency domain, we establish the notion of novel integral transform known as Clifford quadratic-phase wavelet transform (CQPWT) by invoking the convolution theory associated with the Clifford quadratic-phase Fourier transform (CQPFT). We begin our discussion by establishing the definition of CQPWT and some fundamental properties, few of them include linearity, translation, and parity. We then proceed to the derivation of some mathematical formulae including the orthogonality relation, inversion formula, and reproducing kernel by formulating the relationship between the CQPFT and Clifford Fourier transform (CFT) of an analyzing function. We then investigate the Heisenberg’s and logarithmic uncertainty principles corresponding to the proposed transform. Finally, we conclude our discussion by displaying the validity of transform via illustrative examples.
Due to their propensity for stripe noise distortions, infrared remote sensing images present substantial difficulty for interpretation. Our ability to address this issue by offering an easy, efficient, and fast technique for image stripe noise correction is what makes our work unique. Our proposed solution tackles stripe noise by subtracting the mean value along the stripes from the noisy image. Additionally, we leverage the wavelet transform on the average signal to exploit the inherent sparsity of noise in the wavelet domain. This approach not only enhances denoising performance without introducing blurring effects but also enables us to recover image details with remarkable precision, all without the need for intricate algorithms, iterative processes, or training models. To validate the effectiveness of our approach, we conducted evaluations using a dataset of real-world infrared remote sensing images. This dataset encompasses a wide range of examples, featuring both real and artificially induced noise scenarios.
Cavitation phenomena are accompanied by harsh cavitation noise. Studying cavitation noise is an effective method to monitor cavitation conditions. In order to study the characteristics of cavitation noise, a Venturi test bench experiment platform is built, the cavitation flow field and cavitation noise of Venturi test bench are simulated. The frequency domain analysis of cavitation noise is carried out, and the relationship between cavitation region, flow rate and sound pressure level of cavitation noise is verified by comparison between simulation and experiment. Because the cavitation noise is easily polluted by ambient noise, wavelet transform is used to analyze the cavitation noise and background noise, and the characteristics of cavitation noise are studied by comparing the difference between them.
In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.
We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.
Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact ‘footprint of uncertainty’ (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of 94%±1 for the clinical 64Hz sampled EEG and 97%±1 for the high resolution 1024Hz sampled EEG that is improved upon over conventional standard wavelet 67%±5 and 82%±3, respectively, and fuzzy approaches 88%±2 and 90%±3, respectively, when performed in isolation.
Brain–computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10s of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10s each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of 75.9%±11.4 was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for p<0.05). The online classification of yes versus no yielded an average accuracy of 69.3%±14.1, with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.
Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time–frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80–250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.
Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group’s observations from in utero fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI micro-scale epileptiform transients emerge along suppressed EEG/ECoG background during a latent phase of 6–7h post-insult. However, having to observe for the whole of the latent phase disqualifies any chance of clinical intervention. A precise automatic identification of these transients can help for a well-timed diagnosis of the HIE and to stop the spread of the injury before it becomes irreversible. This paper reports fusion of Reverse-Biorthogonal Wavelets with Type-1 Fuzzy classifiers, for the accurate real-time automatic identification and quantification of high-frequency HI spike transients in the latent phase, tested over seven in utero preterm sheep. Considerable high performance of 99.78 ± 0.10% was obtained from the Rbio-Wavelet Type-1 Fuzzy classifier for automatic identification of HI spikes tested over 42h of high-resolution recordings (sampling-freq:1024Hz). Data from post-insult automatic time-localization of high-frequency HI spikes reveals a promising trend in the average rate of the HI spikes, even in the animals with shorter occlusion periods, which highlights considerable higher number of transients within the first 2h post-insult.
In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of 76.21±3.77%.
Let D(Ω,Φ) be the unbounded realization of the classical domain of type one. In general, its Šilov boundary
is a nilpotent Lie group of step two. In this article we define the Radon transform on
, and obtain an inversion formula
in terms of a determinantal differential operator. Moreover, we characterize a subspace of
on which the Radon transform is a bijection. By use of the suitable continuous wavelet transform we establish a new inversion formula of the Radon transform in weak sense without the assumption of differentiability.
In this paper, we study in detail the multifractal features of the main matrices of an encryption system based on a rule-90 cellular automaton. For this purpose, we consider the scaling method known as the wavelet transform multifractal detrended fluctuation analysis (WT-MFDFA). In addition, we analyze the multifractal structure of the matrices of different dimensions, and find that there are minimal differences in all the examined multifractal quantities such as the multifractal support, the most frequent singularity exponent, and the generalized Hurst exponent.
The time series of the states of several well-known hyperchaotic systems are analyzed numerically using the detrended fluctuation analysis based on the discrete wavelet transform. We report the finding of significant scaling behaviors (power-law like) in some of these time series, which can be used as an additional characteristic distinguishing the dynamical evolution of such systems.
Traffic speed is an essential indicator for measuring traffic conditions. Real-time and accurate traffic speed prediction is an essential part of building intelligent transportation systems (ITS). Currently, speed prediction methods are characterized by insufficient short-term prediction accuracy and stability, nonlinear, nonstationary, strong fluctuation and relatively small sample size. To better explore the traffic characteristics of the road networks, a hybrid prediction model based on wavelet transform (WT) of the autoregressive moving average model (ARIMA) and gate recurrent unit (GRU) was constructed. First, this model decomposes the original traffic speed data into low-frequency data, and high-frequency data by WT. Second, the ARIMA and GRU models are used to model data predictions in two frequency bands, respectively. Finally, the prediction result of the predicted value is fused. In addition, in this paper, traffic speed data of four sections in Guangzhou from 1 August to 31 September 2016 are taken as examples to test the validity, applicability, and practicability of the model. The results show that compared with ARIMA, LSTM, GRU, RNN, and other single models and hybrid models, the prediction method proposed in this paper has higher prediction accuracy and can provide a more scientific decision-making basis for urban traffic management.
In order to overcome the problems of low detection accuracy and long detection time of traditional fault detection methods for CNC machine tools, a new fault detection method for CNC machine tools based on wavelet transform is proposed in this paper. In order to improve the effectiveness of running fault detection of CNC machine tools, a wavelet transform method is used to extract the features of the running fault signals of CNC machine tools. According to the feature extraction results, the convolution calculation of the continuous wavelet transform is used to complete the fault detection of CNC machine tool according to the scale result of fault signal. The experimental results show that, compared with traditional fault detection methods, the detection accuracy and efficiency of this method is significantly better: the highest detection accuracy is 97%, and the lowest detection time is only 1.1s.
We shortly recall the mathematical and physical aspects of Talbot's self-imaging effect occurring in near-field diffraction. In the rational paraxial approximation, the Talbot images are formed at distances ζ = p/q, where p and q are coprimes, and are superpositions of q equally spaced images of the original binary transmission (Ronchi) grating. This interpretation offers the possibility to express the Talbot effect through Gauss sums. Here, we pay attention to the Talbot effect in the case of dispersion in optical fibers presenting our considerations based on the close relationships of the mathematical representations of diffraction and dispersion. Although dispersion deals with continuous functions, such as gaussian and supergaussian pulses, whereas in diffraction one frequently deals with discontinuous functions, the mathematical correspondence enables one to characterize the Talbot effect in the two cases with minor differences. In addition, we apply, for the first time to our knowledge, the wavelet transform to the fractal Talbot effect in both diffraction and fiber dispersion. In the first case, the self similar character of the transverse paraxial field at irrational multiples of the Talbot distance is confirmed, whereas in the second case it is shown that the field is not self similar for supergaussian pulses. Finally, a high-precision measurement of irrational distances employing the fractal index determined with the wavelet transform is pointed out.
Electrostatic Discharge (ESD) effects have been identified as one of the most dangerous causes of giant magnetoresistive (GMR) recording head damage. These phenomena have been studied at all levels of hard-disk drive manufacturing 1. The head gimbal assembly (HGA) is mainly studied because of its exposure to the environment. The standard models are typically based on the human body model (HBM), the machine model (MM) and the charged device model (CDM) where research and practical tests are incompatible. In production, one or more ESD models are normally effective while the other is undergone under a separate model. In addition, tests in the time domain are more accurate than those in the frequency domain. However, picosecond measurements are taken with difficulty where the frequency domain measurement provides non-real time results. Therefore, this is the first report of serial ESD detection using the new technique of wavelet transform. It has been found that the glitch occurs when the ESD level of HBM – MM and HBM – CDM serial ESD on GMR heads are in the ranges of 1.2-2.6 V and 12-15 V respectively.
In this paper, we investigate the relation between generalized and phase synchronization for time-delayed systems. Two different systems are considered, namely, Logistic and Mackey–Glass systems. Sufficient conditions for determining the generalized synchronization are derived analytically for scalar and modulated time-delay and tested for correctness by numerical simulations. We propose an example that phase synchronization is stronger than generalized synchronization for scalar time-delay and the opposite situation happens for modulated delay time.
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