Noise Distortion Research Articles

Optimization and analysis of Si/SiGe strained vertically stacked heterostructure on insulator FeFinFET for high performance analog and RF applications

As semiconductor technology advances, the exploration of novel materials and device architectures becomes imperative to meet the growing demands of integrated circuits for analog and radio-frequency (RF) applications. In this paper, various advanced technologies have been amalgamated such as integration of ferroelectric layer in multigate FinFET along with the adaptation of SOI technology. Further strain technology is also used which employs a tri-layered strained-silicon channel system with the help of SiGe to form Vertically Stacked Heterostructure on Insulator Ferroelectric based FinFET (VS-HOI-FeFinFET) and on comparison with baseline FeFinFET, it is found to show remarkable improvements in terms of various measured parameters such as drain current, switching ratio, threshold voltage and subthreshold swing. Subsequently, gate stacking architecture is incorporated in VS-HOI-FeFinFET to further optimize the device performance. The four different configurations C1 to C4 are taken in terms of four different combinations of gate stack materials considered for gate oxide such as C1(SiO2+Al2O3), C2(SiO2+HfO2), C3(Al2O3), and C4(Al2O3+HfO2). It is found that the static and analog performance of VS-HOI-GS-FeFinFET enhance sequentially from configuration C1 to C4 such as switching ratio is enhanced upto around 5 times, DIBL and quality factor are improved by around 41% and 58% respectively along with significant improvement in device efficiency, early voltage, intrinsic gain, output conductance and output resistance. Subsequently performance optimization of VS-HOI-GS-FeFinFET with variation in mole fraction of germanium is also explored for various analog metrics. Further, several RF parameters are also explored and it is observed that the gain frequency product (GFP) and gain transconductance frequency product (GTFP) are augmented by around three times in magnitude along with 16% reduction in the unity gain cut off frequency in C4 configuration, exhibiting its ability of high frequency amplification with minimized noise distortion thus makes the device suitable for various high performance Analog and RF applications.

Vehicle noise characteristics in magnetotelluric data and vehicle noise removal using waveform fitting

Magnetic field fluctuations due to vehicle noise were observed in magnetotelluric (MT) time-series data measured near roads. The observed vehicle noise had magnitudes ranging from tens to thousands of μA/m, whereas the observed weak natural MT signal magnitudes were approximately tens of μA/m. A small signal-to-noise ratio made it difficult to apply robust processing for removing vehicle noise. In addition, vehicle noise severely distorts the MT response in the MT deadband from 0.01 Hz to 0.3 Hz, where the MT signal is very weak, and methods to remove it are required for deep structure imaging. In this study, magnetic field fluctuations due to moving vehicles were simulated with a magnetic dipole and attempted to be removed using a waveform fitting method. A total of 378 vehicle noises were extracted from the near-road MT data and synthesized with the remote MT data without vehicle noises to investigate the effect of vehicle noise on the MT response. Removal of vehicle noise from synthesized remote MT data resulted in substantial restoration of the apparent resistivity and phase curves around the MT deadband and below 0.001 Hz. In the MT field data, the vehicle noise was simulated and removed with two moving dipoles; the magnitude of the remaining vehicle noise was reduced by approximately half compared to a single dipole, and very stable apparent resistivity and phase curves were obtained. Although electromagnetic noise distortion remains after vehicle noise removal, the waveform fitting method significantly improves the apparent resistivity and phase curve response in the 0.01–0.3 Hz frequency band.

Beamforming design for Star-RIS assisted secure wireless communication system under hardware impairments

Simultaneous transmission and reflection reconfigurable intelligent surface (Star-RIS) has been recently introduced as one of the hopeful technologies in future communications. In this work, we investigate a beamforming design for Star-RIS-assisted secure wireless communications with two practical protocols, namely energy splitting (ES) and mode switching (MS). We assume that our transceivers are not ideal and have residual hardware impairments that lead to distortion noise. We aim to maximize the total secrecy rate by optimizing the beamforming in BS and Star-RIS under the limitation of transmission power consumption. The proposed non-convex optimization problem is solved via successive convex approximation and alternating optimization.

An efficient Transformer with neighborhood contrastive tokenization for hyperspectral images classification

The success of vision Transformers (ViTs) relies heavily on the self-attention mechanism, which requires support from appropriate patch tokenization. However, hyperspectral image (HSI) often suffer from significant noise distortions and spectral uncertainty, which result in unstable attention patterns and overfitting due to equivocal tokenization. In this paper, we propose a neighborhood contrastive tokenization task (NeiCoT) to learn compact, semantically meaningful, and context-sensitive tokens for efficient Transformer encoding. Specifically, we employ a predictor on patch embedding to maximize the mutual information between local individuals and their global average anchor. This encourages neighboring tokens’ relevance and active participation in feature learning. Next, we revise a token-level contrastive loss to align predictions with local individuals and distinguish them from other samples in a mini-batch to enhance tokens rich in contextual semantics. Furthermore, we apply a Gaussian weighting to the tokens’ contrastive loss to balance the neighborhood contribution. Finally, we propose a sequence-specific MAE framework with NeiCoT to achieve HSI representation, and additionally validate NeiCoT on a supervised Transformer backbone. The results demonstrate that NeiCoT consistently enhances the robustness and generalization of the Transformer, achieving accurate object recognition and boundary localization even with limited training samples. Our code will be available at https://github.com/zoegnov07/NeiCoT.

Application of Unet-SE-Bisru Algorithm for Music Signal Processing in Music Source Separation

At present, the use of time-domain deep learning for end-to-end neural network models has the problem of long training time and poor performance in music source separation. To address this issue, a U-network squeezing excitation bidirectional simple recursive unit model was proposed based on the deep extractor model. Replace Unet SE Bisru with Unet SE Bisru in the following text. This model improves the bidirectional long short-term memory network into a bidirectional simple recurrent unit, and then introduces attention mechanisms in the generalized encoding and decoding layers. The squeezing excitation block is used to selectively extract features based on the type of audio to be separated. Finally, group normalization is added after onedimensional convolution, And its effectiveness was verified. The experimental results show that the signal noise distortion ratio in the improved model is 5.68 decibels compared to the bidirectional simple recursive unit value, which is higher than the 5.55 decibels of bidirectional long short-term memory. After adding the squeezing excitation module, the overall increase is about 0.1-0.5 decibels. In addition, in the model comparison, the three indicators of the improved model with the same number of channels were 5.68 decibels, 5.91 decibels, and 11.28 decibels, respectively, higher than the benchmark model. Compared with other music source classification models, the improved model has better comprehensive separation performance. Although some indicators are lower than the comparison model, the signal noise distortion ratio of drum and bass is 6.11 decibels and 6.36 decibels, which is better than the comparison model. Overall, the improved model has high performance in music source separation for music signal processing and can be effectively applied in practical music source separation.

Synchronization and Equalization for Joint Close-In and Reciprocal Mixing Phase Noise Distortion Compensation in Packet-Based OFDM Systems

Synchronization and Equalization for Joint Close-In and Reciprocal Mixing Phase Noise Distortion Compensation in Packet-Based OFDM Systems

Influence of Noise Distortions of the Spectral Phase on the Profile of an Ultrashort Pulse

Influence of Noise Distortions of the Spectral Phase on the Profile of an Ultrashort Pulse

An improved robust algorithm for optimisation-based colour medical image watermarking

Recently, significant attention has been focused on copyright protection of medical images, causing watermarking to become a prevalent research topic. In this paper, we propose an optimisation-based robust watermarking algorithm for the copyright protection of colour medical images. Initially, we decompose a host medical image by lifting wavelet transform (LWT), followed by discrete cosine transform (DCT) into transformed coefficients, and then embed multiple watermarks into the transformed host image by using hybrid optimisation algorithms. To further improve security and reduce the channel noise distortion, we then apply hashing and Hamming code to image and text mark, respectively, before the embedding process. Finally, we utilise 2D chaotic map to encrypt the marked media, aiming to achieve enhanced security and confidentiality. The experimental outcome shows satisfactory invisibility, watermark payload and robustness against image-processing attacks. Furthermore, through numerous comparative experiments with nine state-of-the-art algorithms on two standard datasets, the effectiveness of the proposed algorithm is demonstrated.

PDenoiser: A Personalized Speech Enhancement Neural Network for Pre-hospital Emergency Medical Services.

Pre-hospital emergency medical service (EMS) tasks often come with complex and diverse noise interferences, posing challenges in implementing ASR-based medical technologies and hindering efficient and accurate telephonic communication. Among the different types of noise distortion, interfering speech is especially annoying. To address these issues, our aim is to develop a technology capable of extracting the intended speech content of the target physician from noisy and mixed audio during EMS tasks. In this work, we propose a monoaural personalized speech enhancement (PSE) method called pDenoiser, which is a real-time neural network that operates in the time domain. By leveraging the prior vocalization cues of emergency physicians, pDenoiser selectively enhances target speech components while suppressing noise and nontarget speech components, thereby improving speech quality and speech recognition accuracy under noisy conditions. We demonstrate the potential value of our approach through evaluations on both public general-domain test sets and our self-collected real-world EMS test sets. The experimental results are promising, as our model effectively promotes both speech quality and ASR performance under various conditions and outperforms related methods across multiple evaluation metrics. Our methodology will hopefully elevate EMS efficiency and fortify security against nontarget speech during EMS tasks.

Innovative Stripe Noise Image Correction Method for Remote Sensing

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.

Adaptive Local Flaw Detection Based on Magnetic Flux Leakage Images With a Noise Distortion Effect for Steel Wire Ropes

Steel wire rope (SWR) is widely used in industrial scenarios because of its high strength and toughness. To avoid accidents that local flaws (LFs) can cause, SWRs should be inspected regularly. Magnetic flux leakage (MFL) image detection, which is a common SWR inspection method, is inevitably influenced by shaking noise and strand noise. Although noise-reduction methods for these two types of noise have been proposed, the phenomenon of noise distortion has not received enough attention. This paper investigates the noise distortion phenomenon on the MFL image and finds that the distorted noise severely affects the performance of existing noise-feature-oriented (NFO) denoising methods. To solve this problem, we adopt a target-feature-oriented (TFO) denoising method. Specifically, the removal of noise is avoided, instead, an LF-enhancement-based denoising process is proposed. Moreover, to localize LFs in denoised images, a three-stage adaptive localization method mainly based on disjoint region analysis is proposed. The experiment results show that the proposed TFO denoising method significantly improves the denoising performance for images that distorted noise influences. In addition, the proposed adaptive localization method improves the intelligence of the localization process and has better localization performance for images with distorted noise.

Multi-view Image Fusion Using Ensemble Deep Learning Algorithm For MRI And CT Images

Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents Enhanced Lion Swarm Optimization (ESLO) with Ensemble Deep Learning (EDL) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection. Adaptive Median Filters are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CT images are then segmented using the Region Growing–based k -Means Clustering (RKMC) algorithm to separate the images into their component regions or objects. Images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the Modified Principal Component Analysis (MPCA) to draw out the most informative aspects of the images. Then the ELSO algorithm is applied for optimal feature selection, which is computed by best fitness values. After that, multi-view image fusions of multi modal images derive lower-, middle-, and higher-level image contents. It is done by using Deep Convolution Neural Network (DCNN) and the Tissue-Aware Conditional Generative Adversarial Network (TAcGAN) algorithm, which fuses the multi-view features and relevant image features, and it is used for real-time applications. ELSO +EDL algorithm gives better results in terms of accuracy, Peak Signal-To-Noise Ratio (PSNR), and lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when compared to other existing algorithms.

GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine for accurate detection of asphalt pavement cracks

Asphalt cracks are the initial indication and major pavement structural distress in the field of civil engineering because it might potentially intimidate road traffic and highway safety. Managing and maintaining pavement structures are the two crucial issues faced eventually by road administrators. It is essential to take immediate action regarding pavement structural damages to prevent its severity. In recent times, there established numerous deep learning-based image processing methods to detect pavement cracks, but due to the presence of interference factors such as noises and road markings in the images, those methods failed to provide high accuracy and efficiency. Therefore, with the aim to yield high accuracy of crack detection, this article designs a novel asphalt pavement crack identification system “GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine” (GNet TL with IGT-KELM). The road crack images used for perusal are acquired from NHA12D dataset, in which it consists of 40 asphalt pavement images with two different viewpoints. For the purpose of data balancing, some non-crack images are gathered by field survey. The images with large noise distortions and unwanted background information influence crack detection performance so that the preprocessing steps are executed before applying it to the prediction system. The most significant crack and non-crack features from the images are extracted using GNet TL model. With the extracted features, the KELM is well-trained to detect cracks and non-cracks separately. To increase crack detection performance of the classifier, an improved gorilla troop optimizer is introduced to optimize the KELM parameters. The experimental finding reveals that the proposed detection mechanism achieves crack recognition accuracy of about 98.93%, which is considerably greater than compared baseline approaches.

Lung cancer detection via deep learning-based pyramid network with honey badger algorithm

Lung carcinoma is a fatal illness characterized by the unchecked growth of tumors in the respiratory tract. A diagnosis of cancers and their dimensions and forms are often difficult to ascertain from imaging tests because of their irregular form. And high discrepancy in health images is a severe problem in health image investigation. To overcome this issue a novel deep learning-based lung cancer detection (DL-LCD) model is proposed. The LIDC-IDRI and NIH Chest X-ray dataset was used which comprises CT scan and CXR images. Initially, these images are pre-processed using CLAHE (Contrast Limited Adaptive Histogram Equalization) by eliminating noise distortions and enhancing the image clarity. The lung images are segmented using Honey Badger Algorithm. The segmented images are classified into cancerous and non-cancerous. Based on the LIDC-IDRI database, achieves 98.5 % accuracy in normal lungs and 97.44 % accuracy in abnormal lungs. The results of the experiments indicate that the proposed approach outperforms existing approaches.

Fault Detection in a Single-Bus DC Microgrid Connected to EV/PV Systems and Hybrid Energy Storage Using the DMD-IF Method

Variations in fault currents, short times to clear the fault, and a lack of a natural current zero-crossing point are the most important challenges that DC microgrid protection faces. This challenge becomes more complicated with the presence of electric vehicles and energy storage systems due to their uncertainties. For this reason, in this paper, a new method for fault detection in DC microgrids with the presence of electric vehicles and energy storage systems is proposed. The new proposed method uses the combination of dynamic mode decomposition and instantaneous frequency for fault detection. In this method, first, a reference signal is made using the voltage and current signal sampled from the DC microgrid using the dynamic mode decomposition method. Next, in order to detect the fault, the instantaneous frequency value of the reference signal is calculated by the Hilbert transform. The simultaneous use of voltage and current signals reduces the transient effects of the control system on the proposed protection method. In order to measure voltage and current signals, only one intelligent electronic device unit is used in this paper. The proposed new method has been tested on a single-bus DC microgrid with the presence of electric vehicles and energy storage systems in MATLAB 2019b software. The results show that this method can detect all types of faults in DC microgrids, electric vehicles, and photovoltaics. Also, this method is immune to the uncertainties of the generation of distributed generation resources and the existence of noise distortions in the measured signals.

Two Residual Attention Convolution Models to Recover Underexposed and Overexposed Images

Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images.

Performance of 5G NR-polar QAM-OFDM in nonlinear distortion plus Non-Gaussian noise over Rayleigh fading channel

The polar code readily provides rate adaptation, reaching Shannon’s capacity at low encoding and decoding complexity. It has entered the 5G New Radio (5G-NR) as a result of these alluring features to improve the bit error rate (BER) performance in Gaussian noise. In challenging environments for modern broadband communication systems, the amplitude and phase distortion caused by nonlinear distortion (NLD) of high-power amplifiers (HPAs), non-Gaussian noise or impulsive noise (IN), and multipath fading channels (H) play a significant role in BER degradation in an orthogonal frequency-division multiplexing (OFDM) system. Therefore, to improve the BER degradation of the conventional NR-polar QAM-OFDM system over such environments, different novel closed-form expressions of combined NLD-HPA and IN distributions and the log-likelihood ratios (Ls) at the OFDM demodulator output with and without Rayleigh fading channel have been derived. In addition, the novel derived closed-form BER expressions validate the derived distributions using Monte-Carlo simulation in the downlink (DL) and uplink (UL) scenarios. This system demonstrates a significant performance improvement and achieves a coding gain of 28 dB and 25.5 dB compared to the traditional coded OFDM system, which achieved only 10 dB in the case of IBO = 4 dB over the Rayleigh fading channel.

SandGAN: Style-Mix Assisted Noise Distortion for Imbalanced Conditional Image Synthesis

Conditional Generative Adversarial Networks (CGANs) are well developed on balanced datasets as default standards for generating high-quality images of expected classes. However, the common problem in practice is that training datasets are imbalanced, and there is still a lack of clear conclusions of influences and solutions for imbalanced conditional image synthesis. In this paper, we first introduced a new baseline model by combining state-of-the-art CGANs with resampling and differentiable augmentation to revisit the imbalanced CGANs training. We demonstrate that class-confusion: Synthesized images mixing the attributes belonging to other classes, is the critical factor for low-quality results. To solve this, we proposed Style-Mix Assised Noise Distortion GAN (SandGAN), which explicitly learns style-controllable and style-uncontrollable features to alleviate the effect of data bias though: (i) A learnable selector layer to distort noise by combining features from noise or class-embedding for each class adaptively. (ii) An auxiliary contrastive loss constrains relative distances between sources and style-mix classes in the latent space. The style-mix-based contrastive loss assists in optimizing the distortion weights generated by the selector layer to improve the image quality and keep the intra-class diversity. Finally, experiments on imbalanced CIFAR-10, TinyImageNet and CUB-200 demonstrate that the proposed SandGAN reduces the effect of class confusion caused by data bias and achieves state-of-the-art performance.

Hybrid classical relay and advanced RISs for performance enhancement of IoT sensor networks with impaired hardware

In this paper, we propose a combination of traditional relay and advanced reconfigurable intelligent surfaces (RISs) in an Internet of things (IoT) sensor network. In particular, due to the long-range transmission from IoT sensor to receiver, a traditional relay is deployed between them. To greatly enhance the reliabilities of IoT-relay and relay-receiver communications, two RIS groups are exploited where each RIS group has multiple RISs distributed in different areas. Since the IoT, relay, and receiver are often low-cost devices, the hardware impairments (HI) are taken into account in the proposed system. We derive the closed-form expressions of average symbol error rate (SER) and capacity of the proposed hybrid RISs and relay aided IoT sensor system affected by HI and with direct IoT-relay and relay-receiver links (called as the hybrid RIS-R-HI-Wi system). Then, the performance of the hybrid RIS-R-HI-Wi system is compared with that of the relevant systems such as the hybrid RIS-R-HI system without direct links (the RIS-R-HI-Wo system), the RIS-R-ideal hardware (ID) systems with and without direct links (the RIS-R-ID-Wi and RIS-R-ID-Wo systems), and the traditional relay systems with HI and ID (the R-HI and R-ID systems). The theoretical expressions are validated through Monte-Carlo simulations. Numerical illustrations show the huge benefits of utilizing multi-RIS and relay in the hybrid RIS-R-HI-Wi system. Therefore, the performance of the RIS-R-HI-Wi system is dramatically higher than that of the RIS-R-HI-Wo and R-HI systems. Moreover, a significant performance loss caused by HI and high carrier frequencies is determined. Thus, the SER and capacity of the RIS-R-HI-Wi, RIS-R-HI-Wo, and R-HI systems are saturated in the high transmit power regime. In this circumstance, various effective methods such as reducing the distortion noises caused by the HI, using the suitable carrier frequencies, and choosing the appropriate locations of the RISs should be applied in the hybrid RIS-R-HI-Wi system for improving the performance significantly.

Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network

Matching remotely sensed multimodal images is a crucial process that poses significant challenges due to nonlinear radiometric differences and substantial image noise. To overcome these difficulties, this study presents a novel and practical template-matching algorithm specifically designed for this purpose. Unlike traditional approaches that rely on image intensity, the proposed algorithm focuses on matching multimodal images based on their geometric structure information. This approach enables the method to effectively adapt to variations in grayscale caused by radiometric differences. To enhance the matching performance, principal component analysis calculation based on the log-Gabor filter is proposed to estimate the structural feature of the image. The proposed method can estimate the structure feature accurately even under severe noise distortion. In addition, a learnable matching network is proposed for similarity measuring to adapt to the gradient reversal caused by the radiometric difference among remotely sensed multimodal images. Infrared, visible light, and synthetic aperture radar images are adopted for the evaluation, to verify the performance of the proposed algorithm. Based on the results, the proposed algorithm has a distinct advantage over other state-of-the-art template-matching algorithms.