Frequency Subbands Research Articles

Image enhancement method for paint defects based on polarization fusion technique

The highly reflective nature of paint surfaces poses a challenge for defect detection. In this paper, a polarization fusion based image enhancement algorithm for paint defects is proposed to improve the image quality and complement the masked defect details. Firstly, the linearly polarized photometric (DOLP) image is initially fused with the angle of polarization (AOP) image. The light intensity images and polarization feature images were enhanced using a single scale Retinex algorithm and these images were decomposed and reconstructed using wavelet transform to integrate the low and high frequency subbands based on saliency maps and regional energy features. Experiments were performed on defects at different locations and compared with other classical methods. The results show that the proposed fusion algorithm improves the contrast while providing more complete defect information, clearer texture details and significant advantages over other algorithms.

Frequency-Specific Alternations in the Amplitude of Fluctuations in Tension-Type Headache: A Machine Learning Study.

Brain neural signal at different frequency bands relates to different functions. However, the frequency-specific properties of spontaneous brain activity in tension-type headache (TTH)-the most rampant primary headache-remain largely unknown. We investigated the local neural activity of 33 TTH patients and 31 healthy controls (HCs) in the conventional frequency band and two sub-frequency bands (slow-4 and slow-5 frequency band), employing fractional amplitude of low-frequency fluctuations (fALFF), percent amplitude fluctuations (PerAF) and Wavelet-ALFF analytic methods. Using age as covariate, we performed two sample t-test to compare the between-group differences of each metrics in each frequency band. Support vector machine (SVM) was conducted to classify TTH patients and HCs on the basis of altered spontaneous brain activities. TTH patients showed lower fALFF values in the left cerebellar lobule X, left parahippocampal gyrus, and right supplementary motor area in slow-5 band. TTH patients showed lower PerAF in the left fusiform and cerebellar regions in three bands. Altered Wavelet-ALFF values in the right thalamus, left anterior cingulum gyrus, superior parietal gyrus and middle and parietal frontal regions in three frequency bands were detected. And the SVM classifier obtained an overall accuracy of 77.38%, 82.38%, and 95% based on fALFF, PerAF, and Wavelet ALFF values, respectively. TTH patients exhibited abnormal neural activity in various brain regions. The abnormal brain activities serve as powerful features for distinguishing TTH patients. This preliminary exploration provides a novel insight into the underlying mechanism of TTH.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

Linear Monotonic Inter-electrode Associations as Quantitative EEG for Alcoholism Diagnosis

Alcohol use disorders (AUDs) are associated with alterations in EEG patterns that reflect underlying neural dysfunctions, such as impaired cognitive processing, reduced neural connectivity, and disrupted brain rhythms. These EEG alterations can be indicative of several brain disorders, including cognitive deficits, mood disorders, and neurodegenerative diseases, which are often exacerbated by chronic alcohol abuse. Cognitive impairment in alcoholic subjects is caused due to altered functional connectivity between brain regions that can be quantified using statistical measures such as correlation. This paper proposes novel Quantitative EEG (QEEG) features comprising the band-wise absolute value of inter-electrode correlations as a measure of brain functional connectivity to classify alcoholic and non-alcoholic EEG. The EEG signal is first decomposed into five frequency sub-bands. In each sub-band, the absolute value of the linear Pearson product-moment correlation coefficient is computed between time-series EEG recorded by electrode pairs placed over different brain regions. To reduce the dimensionality of the feature vector, an ensemble feature selection approach is adopted, utilizing ANOVA and Chi-square, in conjunction with greedy forward feature selection wrapper algorithm. Four classifiers, namely SVM, KNN, ANN and RF are utilized for the classification. Among them, the SVM classifier achieves the best classification accuracies of 100% on validation with test data and 99.58% on cross-validation with train data, respectively.

A Novel Chirp-Z Transform Algorithm for Multi-Receiver Synthetic Aperture Sonar Based on Range Frequency Division

When a synthetic aperture sonar (SAS) system operates under low-frequency broadband conditions, the azimuth range coupling of the point target reference spectrum (PTRS) is severe, and the high-resolution imaging range is limited. To solve the above issue, we first convert multi-receivers’ signal into the equivalent monostatic signal and then divide the equivalent monostatic signal into range subblocks and the range frequency subbands within each range subblock in order. The azimuth range coupling terms are converted into linear terms based on piece-wise linear approximation (PLA), and the phase error of the PTRS within each subband is less than π/4. Then, we use the chirp-z transform (CZT) to correct range cell migration (RCM) to obtain low-resolution results for different subbands. After RCM correction, the subbands’ signals are coherently summed in the range frequency domain to obtain a high-resolution image. Finally, different subblocks are concatenated in the range time domain to obtain the final result of the whole swath. The processing of different subblocks and different subbands can be implemented in parallel. Computer simulation experiments and field data have verified the superiority of the proposed method over existing methods.

Ultrasound 3D beam-forming with distributed electrodes and frequency sub-band compounding

Abstract Array probes are commonly used in ultrasound equipment. These are transducers arranged in an array. This system requires multiple transmitter and receiver circuits, making it an expensive system; three-dimensional ultrasound imaging requires a two-dimensional array, which is even more expensive and difficult to popularize. We propose an imaging system with a single transducer and a single transmitter/receiver circuit for low-cost implementation of ultrasound imaging. The system uses a distributed electrode, which consists of multiple electrodes on a single transducer. Each electrode is connected to a single transmit/receive circuit. In this paper, we evaluate the performance of the proposed and existing array probe systems and systems by simulation.

Epileptic network identification: insights from Dynamic Mode Decomposition of sEEG data.

For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial recordings to identify brain networks participating in early seizure organization and propagation (i.e., the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high dimensional sEEG data. We apply here an unsupervised data-driven algorithm, Dynamic Mode Decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free. DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ("modes") defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce Dynamic Modal Maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the Higher-Frequency Mode-based Norm Index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery. DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ. This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

New oscillatory features of electrodermal activity signals evaluated in automated mental workload monitoring

Oscillatory components are discriminative features of mental workload monitoring in electrodermal activity (EDA) signals. Currently, most feature extraction techniques mainly focus on time domain or linear techniques but rarely consider the non-stationary nature, individual differences, and recording noise. This study has proposed a new time–frequency feature extraction method based on Tunable Q-factor Wavelet Transform providing an effective quantification of oscillatory behavior of a single transient. The extracted features have been classified using a recurrent neural network with the advantage of dynamic mapping procedure. The proposed method has been evaluated using EDA data acquired during an arithmetic task of five workload levels with both subtle and distinct differences. In a comparative study, the effects of several decomposition parameters and cognitive factors have been explored. Experimental results have shown that it achieves an average accuracy rate of 98.52% for three workload levels discrimination by optimizing the number of features to 3 (features extracted from a low frequency sub-band) from 15 (features extracted from five frequency sub-bands), i.e., 80% feature reduction. The greatest contribution coming fromthe smallest frequency sub-bands has also been obtained. This method is superior to other existing methods in separating 3 to 5 workload levels with both distinct and subtle differences extracting effective oscillatory characteristics. Considering the merits of a simple feature extraction procedure and cost-effectiveness of a single lead EDA signal as well as high discriminability between several workload levels, it provides a trade-off between performance and computational complexity, thus demonstrating its potential for online human-error prevention systems.

Frequency-specific static and dynamic neural activity indices in children with different attention deficit hyperactivity disorder subtypes: a resting-state fMRI study.

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood. Numerous resting-state functional magnetic resonance imaging (rs-fMRI) studies in ADHD have been performed using traditional low-frequency bands (0.01-0.08 Hz). However, the neural activity patterns of frequency subbands in ADHD still require further investigation. The purpose of this study is to explore the frequency-dependent characteristics and neural activity patterns of ADHD subtypes. We selected the ADHD combined type (ADHD-C, N = 25), ADHD inattentive type (ADHD-I, N = 26) and typically developing (TD, N = 28) children from the ADHD-200 Consortium. Based on the slow-5 band (0.01-0.027 Hz) and slow-4 band (0.027-0.073 Hz), we generated static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) maps for each participant. A flexible-factorial analysis of variance model was performed on static and temporal dynamic rs-fMRI measurements within two subbands. Results revealed that the orbital-frontal gyrus, precuneus, superior temporal gyrus and angular gyrus were found to have obvious frequency band and group interaction effects. The intrinsic neural activity differences among three groups were more prominent in the slow-5 frequency band compared to the slow-4 band. In addition, the indices of significant interaction regions showed correlations with the progression of the disease and the features in slow-5 showed an advantageous diagnostic performance compared with those in slow-4. The results suggested the intrinsic neural activities of ADHD subtypes were frequency-dependent. The frequency-specific analysis of static and dynamic brain activity may provide a deeper understanding of neurophysiological dysfunction patterns in ADHD subtypes and provide supplementary information for assessing ADHD subtypes.

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong compound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the electromagnetic interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, Common Mode Rejection Ratio, gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain–Computer Interfaces and neurofeedback training systems.

MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals

Approximately 65 million individuals experience epilepsy globally. Surgery or medication cannot cure more than 30% of epilepsy patients.However, through therapeutic intervention, anticipating a seizure can help us avoid it. According to previous studies, aberrant activity inside the brain begins a few minutes before the onset of a seizure, known as a pre-ictal state. Many researchers have attempted to anticipate the pre-ictal condition of a seizure; however, achieving high sensitivity and specificity remains challenging. Therefore, deep learning-based early diagnostic tools for epilepsy therapies using electroencephalogram (EEG) signals are urgently needed. Traditional methods perform well in binary epilepsy scenarios, such as normal vs. ictal, but poorly in ternary situations, such as ictal vs. normal vs. inter-ictal. This study proposes a multi-scale dilated convolution-based network (MD-DCNN) to predict seizures or epilepsy. Traditional DCNNs for epilepsy classification overfit due to insufficient training data (fewer subjects). Windowing 2-sec EEG recordings and extracting the frequency sub-band from each window prevents overfitting in deep networks, which lack training data. We convert each segmented window and its sub-bands into scalogram images and input them into MD-DCNN. The proposed MD-DCNN combines data from several scales without narrowing the acquisition domain. Integrating detailed information into high-level semantic features improves network interpretation and classification. The proposed MD-DCNN is evaluated for two-class, three-class, and cross-database strategy problems using three publicly accessible databases. Experiments show that the MD-DCNN statistically performs better than 13 other current approaches. This demonstrates its potential for developing equipment capable of measuring, monitoring, and recording EEG signals to diagnose epilepsy.

A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.

Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study

BackgroundInternet gaming disorder (IGD) has become increasingly prevalent worldwide and is recognized as a significant public health concern because of its negative consequences on individuals mental and physical health, social relationships, academic performance and overall well-being. While research on IGD has gained significant momentum in the past decade, the neural substrates underlying this disorder remains unclear. This study aims to investigate resting-state cortical activation in male subjects with IGD using a concurrent functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) hybrid system. MethodsTwenty-two male individuals with IGD (18–23 years old) and twenty-two male healthy, age-matched healthy controls were included in the study. Mean oxygenation of the prefrontal cortex (PFC) and whole head neural activity were measured using fNIRS and EEG respectively, during eyes-open and eyes-closed conditions at the resting state. EEG signals were decomposed into distinct frequency sub-bands with wavelet transform, followed by the analysis of the power spectral density within each band. Mean oxygenation of PFC is measured using a multichannel fNIRS system. ResultsResults revealed that the individuals with IGD had significantly higher beta power in the frontal region compared to the control group. Individuals with IGD showed significantly increased PFC oxygenation compared to healthy controls. Additionally, both frontal beta power and PFC oxygenation were significantly correlated with IGD severity. However, there were no significant correlations observed between oxygenation and beta powers. ConclusionThis study is the first to examine resting-state cortical activation using multimodal EEG-fNIRS system in young adults with IGD. Moreover, it provides an important contribution to the understanding of the underlying neural mechanisms of IGD and offer new insights for the diagnosis and intervention of the disorder using multimodal EEG-fNIRS system. Further studies should aim to replicate the findings of this study using a larger and more culturally diverse sample to support the neurophysiological basis of IGD.

DCFNet: Infrared and Visible Image Fusion Network Based on Discrete Wavelet Transform and Convolutional Neural Network.

Aiming to address the issues of missing detailed information, the blurring of significant target information, and poor visual effects in current image fusion algorithms, this paper proposes an infrared and visible-light image fusion algorithm based on discrete wavelet transform and convolutional neural networks. Our backbone network is an autoencoder. A DWT layer is embedded in the encoder to optimize frequency-domain feature extraction and prevent information loss, and a bottleneck residual block and a coordinate attention mechanism are introduced to enhance the ability to capture and characterize the low- and high-frequency feature information; an IDWT layer is embedded in the decoder to achieve the feature reconstruction of the fused frequencies; the fusion strategy adopts the l1-norm fusion strategy to integrate the encoder's output frequency mapping features; a weighted loss containing pixel loss, gradient loss, and structural loss is constructed for optimizing network training. DWT decomposes the image into sub-bands at different scales, including low-frequency sub-bands and high-frequency sub-bands. The low-frequency sub-bands contain the structural information of the image, which corresponds to the important target information, while the high-frequency sub-bands contain the detail information, such as edge and texture information. Through IDWT, the low-frequency sub-bands that contain important target information are synthesized with the high-frequency sub-bands that enhance the details, ensuring that the important target information and texture details are clearly visible in the reconstructed image. The whole process is able to reconstruct the information of different frequency sub-bands back into the image non-destructively, so that the fused image appears natural and harmonious visually. Experimental results on public datasets show that the fusion algorithm performs well according to both subjective and objective evaluation criteria and that the fused image is clearer and contains more scene information, which verifies the effectiveness of the algorithm, and the results of the generalization experiments also show that our network has good generalization ability.

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants are facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.09% and 35FPS.

Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach

In recent years, numerous image encryption schemes have been developed that demonstrate different levels of effectiveness in terms of robust security and real-time applications. While a few of them outperform in terms of robust security, others perform well for real-time applications where less processing time is required. Balancing these two aspects poses a challenge, aiming to achieve efficient encryption without compromising security. To address this challenge, the proposed research presents a robust data security approach for encrypting grayscale images, comprising five key phases. The first and second phases of the proposed encryption framework are dedicated to the generation of secret keys and the confusion stage, respectively. While the level-1, level-2, and level-2 diffusions are performed in phases 3, 4, and 5, respectively, The proposed approach begins with secret key generation using chaotic maps for the initial pixel scrambling in the plaintext image, followed by employing the Fibonacci Transformation (FT) for an additional layer of pixel shuffling. To enhance security, Tribonacci Transformation (TT) creates level-1 diffusion in the permuted image. Level-2 diffusion is introduced to further strengthen the diffusion within the plaintext image, which is achieved by decomposing the diffused image into eight-bit planes and implementing XOR operations with corresponding bit planes that are extracted from the key image. After that, the discrete wavelet transform (DWT) is employed to develop secondary keys. The DWT frequency sub-band (high-frequency sub-band) is substituted using the substitution box process. This creates further diffusion (level 3 diffusion) to make it difficult for an attacker to recover the plaintext image from an encrypted image. Several statistical tests, including mean square error analysis, histogram variance analysis, entropy assessment, peak signal-to-noise ratio evaluation, correlation analysis, key space evaluation, and key sensitivity analysis, demonstrate the effectiveness of the proposed work. The proposed encryption framework achieves significant statistical values, with entropy, correlation, energy, and histogram variance values standing at 7.999, 0.0001, 0.0156, and 6458, respectively. These results contribute to its robustness against cyberattacks. Moreover, the processing time of the proposed encryption framework is less than one second, which makes it more suitable for real-world applications. A detailed comparative analysis with the existing methods based on chaos, DWT, Tribonacci transformation (TT), and Fibonacci transformation (FT) reveals that the proposed encryption scheme outperforms the existing ones.

Acute intracardiac electrogram changes and their relation to pulsed field ablation dose in porcine left ventricle

Abstract Background Pulsed field ablation (PFA) is an emerging cardiac ablation modality based on irreversible electroporation with promising preclinical and clinical results. Acute reduction of intracardiac electrograms (bipolar iEGMs) is consistently observed, similar to other ablation modalities, e.g. radiofrequency ablation. We and others have previously reported lesion size dependence on PFA dosing. However, the relevance of iEGM changes and their dependence on the PFA dose is not established. Furthermore, our study shows that unipolar iEGM signals may contain additional information about the effects of PFA in cardiac tissue. Purpose To quantify the effect of different PFA dosing on changes observed in bipolar and unipolar iEGMs within 5 minutes after the ablation. Methods In total, PFA was applied endocardially at 40 sites in the left ventricles of 8 pigs via a 5 mm tip catheter using a modified decapolar catheter in the inferior vena cava for current return (all electrodes). The following PFA doses (voltage and number of pulse trains) were used: 1000 V, 4 trains; 1300 V, 4 trains; 1500 V, 1 train; 1500 V, 4 trains; 1500 V, 8 trains; and 1500 V, 16 trains. For standard bipolar iEGMs (30 Hz – 500 Hz) the reduction in peak-to-peak amplitude after PFA was considered as a measure of the effect. In addition, unipolar iEGMs (0.5 Hz – 500 Hz) were decomposed into non-overlapping frequency sub-bands with Daubechies order 6 discrete wavelet transform (Figure 1A) allowing separate analysis in high-frequency (HF) and low-frequency (LF) ranges (63 Hz – 500 Hz and 0.5 Hz – 8 Hz, respectively). Results In total, 30 standard bipolar and 35 unipolar iEGMs (with at least 3.5 minutes of artefact-free post-ablation duration) were analysed. No dose dependency was observed for standard bipolar iEGMs within 5 minutes post-ablation (data not shown). However, the HF and LF content of unipolar iEGMs revealed opposing effects: a decrease in the HF and an increase in the LF content. Correlation with the dose was clearer in the LF content 3.5 minutes post-ablation, the difference being significant (p<0.05) between the lowest and the highest doses (Figure 1B and C). Discussion The decrease in HF content could reflect electrical silencing, ie. stunning of locally affected cardiomyocytes, most likely caused by irreversibly electroporated (ablated) cells located close to the catheter tip and by reversibly electroporated or otherwise temporarily silenced cells in adjacent tissue. The increase in LF content could reflect the current-of-injury effect, which is a broader area phenomenon derived from electrical potential differences between affected (reversibly and irreversibly electroporated) and non-affected regions. Conclusion Unipolar iEGMs acquired with a broadened frequency range and decomposed into HF and LF content provide new insight into the effects of PFA and correlate with PFA dosing in this pre-clinical porcine model.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.