Frequency Subbands Research Articles

Various Image Processing Attacks for Image Watermarking in the Wavelet Domain Using Singular Value Decomposition and Discrete Cosine Transform

The dispersal of digital media due to the fast evolution of networked multimedia systems has created an essential need for copyright prompting technologies that can protect multimedia objects such as text, images, audio and videos from copyright ownership. This paper proposes digital image watermarking algorithm for copyright protection based on discrete wavelet transform, discrete cosine transform and singular value decomposition. In this method a watermark is embedded into the low frequency sub-band of a host image, after subjecting the watermarked image to various attacks like Gaussian noise, rotation sharpening, noise and pepper salt and speckle noise etc., we extract the originally inserted watermark images from LL sub-band by Truncated singular value decomposition and compare them on the basis of their mean square error, peak signal to noise ratio and normalized correlation values. Experimental results are provided to illustrate that the proposed scheme is the robustness of the technique on wide set of attacks.

Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study

Resting state functional magnetic resonance imaging studies of nocturnal enuresis have focused primarily on regional metrics in the blood oxygen level dependent (BOLD) signal ranging from 0.01 to 0.08 Hz. However, it remains unclear how local metrics show in sub-frequency band. 129 children with nocturnal enuresis (NE) and 37 healthy controls were included in this study. The patients were diagnosed by the pediatricians in Shanghai Children’s Medical Center affiliated to Shanghai Jiao Tong University School of Medicine, according to the criteria from International Children's Continence Society (ICCS). Questionnaires were used to evaluate the symptoms of enuresis and completed by the participants. In this study, fALFF, ReHo and PerAF were calculated within five different frequency bands: typical band (0.01–0.08 Hz), slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.25 Hz). In the typical band, ReHo increased in the left insula and the right thalamus, while fALFF decreased in the right insula in children with NE. Besides, PerAF was increased in the right middle temporal gyrus in these children. The results regarding ReHo, fALFF and PerAF in the typical band was similar to those in slow-5 band, respectively. A correlation was found between the PerAF value of the right middle temporal gyrus and scores of the urinary intention-related wakefulness. Results in other bands were either negative or in white matter. NE children might have abnormal intrinsic neural oscillations mainly on slow-5 bands.

Frequency discriminator design phase formula with experimental verification for frequency measurement systems with uniform sub‐band resolution

In this article, we propose a frequency discriminator (FD) design method to produce uniform frequency sub-bands. FDs are designed using an interferometer projected with the proposed formulated phase. For experimental verification that a uniform sub-band is formed using the proposed phase formula, 3-bit FDs are designed and fabricated to operate in the 2 to 3 GHz and 2 to 4 GHz bands, respectively. The FD consists of a power divider/combiner, reference line, and delay line. Scattering parameters of the FDs are analyzed using RF simulation and measurement results. RF characteristic results show that a 3-bit FD designed using the proposed phase formula can identify an unknown signal inside the defined frequency band.

Task specific Brain Synchronization in EEG based Speech and Speech Imagery Procedures

Speech being a complicated cognitive skill of the brain, retrieving verbal information from spoken and imagined speech using electrophysiological signal is widely under research to assist the impaired. Electroencephalography has been a successful medium in analyzing the temporal and behavioral changes happening in the brain during any task. In this work an endeavor has been made to distinguish such changes while speaking and envisioning speaking the phoneme of vowel ‘a’. The brain activity was recorded from volunteers while speaking and envisioning speaking the vowel ‘a’ and the basic signal processing routines were applied. The EEG sub-band frequencies were extracted using Fast Fourier Transform (fft). The synchronization indices were assessed for the segmented signals of various sub-band frequencies and the behavioral changes were enlisted. It was seen that the left hemisphere of the brain functioned synchronously during speech task and an inverse phenomenon was observed during the imagery task. Hence the possibility of reconstructing the broken or incomplete speech from the directly recorded brain activity has reached a step ahead towards the milestone. This work exhibits the analysis of brain connectivity parameters while speaking and imagining a vowel ‘a’. Speech imagery can be used to decode the thoughts for patients who are unable to express their thoughts, using non-invasive techniques.

Research on the Aesthetic Features and Technology of Computer Graphics

Computer graphics is a new science produced with the popularization of computers. The processing technology of graphics and images in computer graphics has attracted much attention. How to make reasonable use of them has become an important subject in the field of modern scientific applications. For this research background, this paper proposes an image recognition algorithm based on wavelet and DFB-PCA. After decomposing the recognition image with wavelet transform, DFB-PCA recognition is performed on the low frequency subband. The experimental results show that the method is beneficial to reduce the influence of factors such as illumination, posture and expression, reduce the dimensionality of noise and image recognition, and has better robustness. Compared with directly using PCA recognition on the original image, the calculation speed and recognition efficiency of the image can be improved.

Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network

Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography (CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the disease. The network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease.

Feature fusion for improving performance of motor imagery brain-computer interface system

A brain-computer interface (BCI) is a system that makes communication between an external device and the brain based on the brain’s neural activity. This communication is conducted by analyzing brain signals, so extracting and selecting those features of the brain signals that distinguish between humans’ different activities are essentially important. In this study, first, the brain signal is divided into frequency sub-bands using Constant-Q filters, which allows achieving better frequency resolution in lower frequencies and also the better temporal resolution in higher frequencies. Then, appropriate features in temporal, spatial, and spectral domains are extracted from the considered frequency sub-bands to improve the motor imagery classification. Three different ranking methods including Fisher’s method, ReliefF, and mRMR are used to select the features; due to their specific criteria, they can help the best selection for the motor imagery classification. Finally, the results obtained from the selection stage are fused using the Dempster-Shafer evidence method. The proposed technique is applied to the BCI 2008−2b competition dataset, which achieves a Kappa score of 0.718. The results show the capability and excellent performance of the proposed method in comparison with the state-of-the-art studies.

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain’s behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information.

Impaired intrinsic functional connectivity among medial temporal lobe and sub-regions related to memory deficits in intracranial dural arteriovenous fistula.

The functional changes concerning memory deficits in dural arteriovenous fistula (dAVF) brain are inadequately understood. This study aimed to understand the functional connectivity alterations of brain regions widely affirmed for explicit and implicit memory functions in dAVF patients (DP) and look into the frequency effects of the altered functional networks. Resting-state functional magnetic resonance imaging (rsfMRI) analysis was done in the memory-associated regions of 30 DP and 30 healthy controls (HC). Frequency decomposition was used to determine potential frequency-dependent functional connectivity changes. They underwent neuropsychological tests and were correlated with changes in memory networks compared with HC. The results showed weaker functional connectivity among the medial temporal lobe and sub-regions in DP suggestive of dysfunction of explicit and implicit memory functions, which corroborated with the positive correlation between memory scores and hippocampal-parahippocampal connectivity of DP, along with a significant group difference of lower memory and cognitive performance in DP assessed by neuropsychological tests. A frequency-dependent study of the altered rsFC revealed lower functional connectivity strength and impaired neural coupling manifested at some sub-band frequencies indicative of disturbed cortical rhythm in DP. This pilot study gives insights into significant intrinsic functional connectivity changes in the memory regions of the dAVF brain. The results may have clinical implications in the choice of interventional management of dAVF and can impact clinical decision making for realizable prevention of progressive memory impairment and irreversible brain damage in such patients.

Processing of the Passive Drilling-bit Ranging Signal Based on Wavelet Packet

Recent technological advances have increased interest to acoustic ranging especially the passive acoustic ranging which is easier to operate in the oilfield. For analyzing the non-stationary drilling-bit signal, the conventional discrete Fourier Transform (DFT) has been known to be less efficient. A new extraction method based on wavelet packet (WP) is presented. In the signal processing, WP is used to decompose the original drilling-bit signal into the sub-frequency bands (SFBs). By analyzing energy spectrum of the SFBs, the first four SFBs are found that contain the characteristic of the drilling-bit, and the specific frequency band of the drilling-bit signal is determined after power spectrum analyzing. The field test shows that when the bit is near to the casing of the adjacent well, the reconstruction drilling-bit signal changes significantly which can give warning accurately.

Baseline EEG-correlates of responders/non-responders to combined antidepressive treatment including transcranial magnetic stimulation

IntroductionUse of combined antidepressive treatment included high-frequency rhythmic transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) is one of the ways for overcoming of pharmaco-resistance in depressive patients.ObjectivesThe aim of the study was the search for possible EEG predictors of antidepressive effects of rTMS of the left DLPFC in combined treatment of depression.Methods30 female in-patients (F31.3, F33.0, F33.1, by ICD-10; 20-50 years, mean age 36.9±10.3) with pharmaco-resistant depression were enrolled in the study. Treatment included antidepressants (mainly SSRI) and a 3-week course of rTMS (20 Hz) of the left DLPFC. Correlations between pre-treatment EEG spectral power values, and post-treatment quantitative clinical assessments of patients were analyzed. Responders/non-responders were determined by standard criteria of 50% decrease in HDRS-17 scale total scores after treatment course.ResultsResponders (23 out of 30) revealed significant (p<0.05) negative correlations between post-treatment HDRS-17 scores and pre-treatment EEG spectral power in theta-2 (6-8 Hz) and alpha-1 (8-9 Hz) frequency sub-bands in the parietal-occipital-posterior temporal leads. Non-responders (7 out of 30) showed negative correlations between the post-treatment HDRS-17 scores and pre-treatment theta-2 EEG spectral power in the frontal-central-temporal regions of the right hemisphere.ConclusionsEven brief course of rTMS of the left DLPFC enhances the action of antidepressants, and allows overcoming partially the pharmaco-resistance in depressive patients. Baseline values of theta-2 and alpha-1 EEG spectral power may serve as possible predictors of the effects of combined antidepressive therapy including rTMS. The study supported by RBRF grant No.18-01-00029a.DisclosureNo significant relationships.

Eeg coherence in depressive female adolescents with different types of auto-aggressive behavior

IntroductionIntracortical interactions reflected in EEG coherence (Coh) play an important role in the control of behavior in both norm and mental disorders. EEG Coh in depression is less than in the norm. Non-suicidal self-injuries (NSSI) are widespread in adolescents and represent a significant risk factor for consequent suicidal attempts (SA).ObjectivesThe aim of the study was to identify the differences in EEG coherence between depressive female adolescents who have NSSI or NSSI and SA in their history compared with healthy controls.Methods75 depressive female adolescents (16–25 years old) were enrolled in the study and divided into two subgroups: NSSI (n=38) and NSSI+SA (n=37). The control group included 20 healthy subjects (HC) matched by age and gender. Baseline EEG was recorded, and EEG coherence was analyzed in 8 narrow frequency sub-bands.ResultsIn the NSSI subgroup, the number of “high coherent connections” (pairs of EEG leads with Coh>0.80) was the lowest in comparison with the NSSI+SA subgroup (intermediate values) and HC (the highest values) in theta-1 (4-6 Hz), theta-2 (6-8 Hz), alpha-1 (8-9 Hz) and alpha-2 (9-11 Hz) EEG frequency sub-bands, especially in frontal-central-parietal regions.ConclusionsThe lowest EEG coherence in the NSSI subgroup suggests that this subgroup is “more depressive” than the NSSI+SA subgroup, while the NSSI+SA subgroup is “more normal” but has increased suicidal risk. The results obtained suggested the use of EEG Coh data to clarify the degree of suicidal risk in depressive adolescents with different types of auto-aggressive behavior. The study supported by RBRF grant No.20-013-00129а.DisclosureNo significant relationships.

Non-invasive determination of blood glucose level using narrowband microwave sensor

The diabetic patients use invasive technique to monitor their Blood Glucose Level (BGL) on a regular basis. This paper mainly presents a system that estimates BGL noninvasively using microwave techniques. A narrowband microstrip antenna resonating at 1.3 GHz is used as a microwave sensor. If human finger containing a specific value of the BGL is placed on a radiating patch of narrowband microstrip patch antenna, microwave sensor structure, then the near field of this radiating patch antenna structure gets interact with human finger and results change in electrical characteristics of the antenna. These electric changes are in relation to blood permittivity changes due to variation in BGL value. The change in electric characteristics of narrow band antenna microwave structure results corresponding frequency shift. The dataset of more than 200 individuals are generated where corresponding frequency shifts for various BGLs are recorded. The prepared dataset provided linear relationship between reference BGL and corresponding frequency shift. Regression analysis for BGL estimation resulted coefficient of determination with value 0.75. The significant improvement in this coefficient of determination is obtained by subband frequency analysis, which resulted in close approximation with expected value. The Surveillance Error Grid (SEG) and Mean Absolute Relative Difference (MARD) analyses are performed on prepared dataset to validate and verify clinical acceptance of microwave based non-invasive BGL estimation system. The developed antenna model has the MARD value of 4.204% and existing split ring resonator non-invasive antenna has 12.5% MARD value for the whole dataset.

Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.

SVM CLASSIFIER FOR EPILEPTIC SEIZURE PREDICTION USING SUB-BANDING OF IEEG SIGNALS

Epileptic seizure in the brain affects the day-to-day life of any individual due to its unexpected nature of occurrence. It has affected more than 50 million people worldwide. Drug resistance of patients is an important factor which leads to failure of epilepsy treatments using medications in 30% of patients. Surgery is also not a viable option in a substantial number of patients. In such cases, a new kind of seizure forecasting system is necessary to help those people. In our work, various sub-frequency bands of EEG signals are produced from the originally recorded Intracranial Electroencephalogram (IEEG) signals of five canines and two persons to identify possible low complex and less intense EEG features from each sub-band of the entire spectrum. Support Vector Machine (SVM) with different Kernel-based classifiers are used to categorize features into preictal and interictal data. Epileptic Seizures forecasting accuracy of 99% has been achieved for data from canine and human. Employed wavelet filter for noise removal and found that it improved the seizure prediction accuracy in some subjects and reduced the accuracy in some subjects. Similarly, the feature selection technique also improved the preictal detection accuracy in some patients/subjects and reduced the accuracy in some data. From this work, we identified that seizure prediction is possible in at least one sub-frequency band especially in high gamma sub-band generated from the originally recorded signal using a high-pass filter. This work demonstrates an algorithm for seizure forecasting or identifying the preictal region which identifies the suitable best sub-frequency band for predicting the seizure of the originally recorded EEG data by using the computationally less intense EEG features and employing the best classifying SVM kernel.

IMAGE RESOLUTION ENHANCEMENT OF SATELLITE IMAGES USING DYADIC INTEGER COEFFICIENT WAVELET FILTER FOR WAVELET TRANSFORM

In this paper, image resolution enhancements for satellite images are proposed using dyadic integer coefficients based wavelet filter (DICWF). We proposes a technique in which discrete wavelet transform and stationary wavelet transform using DICWF which is used to obtain a high resolution image and this image is derived from frequency subbands. The satellite images play a very vital role now days in the development of technical aspects which needs to be enhanced. These satellite images are superresolved with the help of dyadic integer coefficient-based wavelet filters, which reduces the hardware complexity and computational difficulties due to the rational and integer coefficients of these filter banks. The value of the peak signal-to-noise ratio (PSNR) of the proposed method and the resultant visual images of the proposed method show the effectiveness of this algorithm over other existing algorithms using discrete wavelet transform. Noise can be minimized by applying thresholding on different frequency subbands which obtained by the application of DICWF to the noisy, blurred input images.

Performance Analysis of Data Transmission for Joint Radar and Communication Systems

This paper investigates the problem of data transmission for the joint radar and communication systems (JRCSs). The performance of the JRCS is characterized by data throughput related to the radar echo data (RED) and communication data rate (CDR). Two spectral coexistence schemes are proposed based on the degree of spectrum sharing for radar and communication, i.e., the isolated subfrequency band (ISFB) and mix-used frequency band (MUFB) schemes. Firstly, the signals of radar and communication are operated on the isolated subcarriers, enabling the received signals to be processed independently and bringing the advantage of interference avoidance. Secondly, the signals of radar and communication can be jointly operated on the same subcarriers for the MUFB scheme, which enhances the spectrum efficiency. Unlike the ISFB scheme, the CDR of the MUFB scheme is maximized along with the interference from the radar signal, and meanwhile, the allocated radar power on each subcarrier is derived by maximizing the radar mutual information. Numerical results show that the MUFB scheme significantly improves the performance of data transmission over the ISFB scheme, and a significant performance gain in the data transmission can be achieved, compared to the average power allocation case.

A high-resolution minimum variance algorithm based on optimal frequency-domain segmentation

In order to enhance the adaptability of minimum variance (MV) algorithm to broadband ultrasound signals, a high-resolution MV beamforming algorithm based on optimal frequency-domain segmentation is proposed. By an improved time-frequency concentration criterion based on logarithmic window energy, the optimal window length of Short-time Fourier Transform (STFTMV) is obtained, and the ultrasound signals are converted into narrowband sub-signals through optimal frequency-domain segmentation. Then, the sub-signals in frequency domain are reconstructed according to the non-overlapping characteristic of window, which further improves the imaging quality and extends the limitations of traditional broadband MV near-field imaging. Moreover, according to the symmetry of STFTMV, the computation is reduced by half, which further improves the efficiency. The Field II results indicate that the mainlobe width of the STFTMV is reduced to 38.09 % of MV and 47.05 % of eigenspace-based minimum variance (ESBMV), and the imaging efficiency of STFTMV is almost 2.5 times higher than that of ESBMV. In addition, the contrast of the proposed STFTMV can be further improved by combining with the improved coherence factor (FCF) based on sub-band frequency signals.

A secure video steganography scheme using DWT based on object tracking

ABSTRACT Videos are nowadays the most frequent and easy mode of communication over the internet; the reason behind the growth is the accessibility to video processing software available on the internet. Video steganography is a field where the data is embedded in video keeping the visual quality of the video intact. This study presents a new video steganography scheme with a stable trade-off between robustness and imperceptibility using 2D-DWT (Discrete Wavelet Transform) based on object detection and tracking. The main contribution of this paper includes embedding of secret data in the moving objects after applying object detection for the video frames where the secret bits are embedded in middle frequency sub-bands after applying 2D-DWT. To highlight the effectiveness of the proposed scheme, experimental results are carried out both quantitatively and qualitatively where quantitative analysis is done using different metrics such as PSNR, SSIM, BER and qualitative analysis is done using visual results of the frames. The experimental results illustrated that the proposed approach outperforms existing techniques in terms of qualitative and quantitative evaluation with high imperceptibility and robustness against noise attack. Eventually, the scheme has also been tested against existing steganalysis techniques to ensure the security of the proposed scheme.

Data hiding in encryption–compression domain

This paper introduces a robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain. First, host image is transformed using lifting wavelet transform, Hessenberg decomposition and redundant singular value decomposition. Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. We also use suitable encryption-then-compression scheme to improve the security of the image. Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. Experimental results verify the effectiveness of our scheme, including embedding capacity, robustness, invisibility, and security. Further, it is established that our scheme has a better ability to recover concealed mark than conventional ones at low cost.