An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network

Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the "Symmetric Convolution-based Residual Attention Network (SC-RAN)" is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

SketchTrans: Disentangled Prototype Learning With Transformer for Sketch-Photo Recognition

Matching hand-drawn sketches with photos (a.k.a sketch-photo recognition or re-identification) faces the information asymmetry challenge due to the abstract nature of the sketch modality. Existing works tend to learn shared embedding spaces with CNN models by discarding the appearance cues for photo images or introducing GAN for sketch-photo synthesis. The former unavoidably loses discriminability, while the latter contains ineffaceable generation noise. In this paper, we start the first attempt to design an information-aligned sketch transformer (SketchTrans +) via cross-modal disentangled prototype learning, while the transformer has shown great promise for discriminative visual modelling. Specifically, we design an asymmetric disentanglement scheme with a dynamic updatable auxiliary sketch (A-sketch) to align the modality representations without sacrificing information. The asymmetric disentanglement decomposes the photo representations into sketch-relevant and sketch-irrelevant cues, transferring sketch-irrelevant knowledge into the sketch modality to compensate for the missing information. Moreover, considering the feature discrepancy between the two modalities, we present a modality-aware prototype contrastive learning method that mines representative modality-sharing information using the modality-aware prototypes rather than the original feature representations. Extensive experiments on category- and instance-level sketch-based datasets validate the superiority of our proposed method under various metrics.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

A combination network of CNN and transformer for interference identification

Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed a novel network that combines both architectures, which make better use of both local and global characteristics of the signals. Additionally, recognizing the challenge of distinguishing contextual semantics within the one-dimensional signal data used in this study, we advocate the use of CNNs in place of word embedding, aligning more closely with the intrinsic features of the signal data. Furthermore, to capture the time-frequency characteristics of the signals, we integrate the proposed network with a cross-attention mechanism, facilitating the fusion of temporal and spectral domain feature information through multiple cross-attention computational layers. This innovation obviates the need for specialized time-frequency analysis. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to existing methods, highlighting its efficacy in addressing the challenge of communication interference identification in electronic warfare.

The chemical recognition of hydrogen fluoride via B24N24 nanocage: quantum chemical approach

CONTEXT

Ab initio calculations were employed in this investigation to scrutinize the adsorption characteristics of a linear chain (HF)n on a BN nanocage (B24N24), wherein the chain lengths varied (n = 1, 2, 3, and 4). The overarching aim was to assess the efficiency of this setup in detecting and adhering to (HF)n under both liquid and gaseous scenarios. This study encompassed an array of aspects, encompassing adsorption energy, optimal configuration determination, work function analysis, and charge exchange assessment. Furthermore, an exploration was conducted into the impact of HF linear chain dimensions on electrical attributes and adsorption energy. According to the values of adsorption energy, the dimer form of HF adsorbed onto BN nanocages displayed the highest stability.

METHODS

This scrutiny was undertaken utilizing density functional theory (DFT), employing the B3LYP functional and the 6-31 +  + G(d,p) basis set. Notably, the choice of the 6-31 +  + G(d,p) basis set is particularly apt for delving into nanostructure analyses. The HOMO-LUMO energy gap was significantly reduced by (HF)n upon adsorption onto the nanocage, falling from 6.48 to 5.43 eV and enhancing electrical conductivity as a result. Additionally, BN nanocages may be used as sensors to find (HF)n among other environmental pollutants.

Efficient entry point encoding and decoding algorithms on 2D Hilbert space filling curve

The Hilbert curve is an important method for mapping high-dimensional spatial information into one-dimensional spatial information while preserving the locality in the high-dimensional space. Entry points of a Hilbert curve can be used for image compression, dimensionality reduction, corrupted image detection and many other applications. As far as we know, there is no specific algorithms developed for entry points. To address this issue, in this paper we present an efficient entry point encoding algorithm (EP-HE) and a corresponding decoding algorithm (EP-HD). These two algorithms are efficient by exploiting the m consecutive 0s in the rear part of an entry point. We further found that the outputs of these two algorithms are a certain multiple of a certain bit of s, where s is the starting state of these m levels. Therefore, the results of these m levels can be directly calculated without iteratively encoding and decoding. The experimental results show that these two algorithms outperform their counterparts in terms of processing entry points.

A computational study on the mercaptopurine drug interaction with aluminum nitride nanostructures: analyzed by Marcus theory of electron-transfer

We analyzed the mercaptopurine adsorption on AlN nanostructures consisting of zero-dimensional nanoclusters, one-dimensional nanotubes, and two-dimensional nanosheets using calculations based on density functional theory (DFT). The adsorption energy, energy band gap, fluctuations in the energy band gap, charge transfers, and types of interactions that take place after mercaptopurine is adsorbed on the AlN nanostructures have all been calculated using DFT. The results show MP adsorption energies on AlN nanoparticles are -4.22, -5.95, and -8.70 eV. In this situation, MP molecules have been drawn to the surface due to the higher adsorption energies available on the AlN nanosheet (a process known as chemisorption). The Atoms in Molecules inquiry was conducted to learn more about and better comprehend the binding properties of the investigated AlN nanostructures utilizing mercaptopurine. Our findings indicate the mercaptopurine/AlN nanosheet bonding's electrostatic properties. Additionally, the electrical conductivity of the AlN nanostructures increases whenever mercaptopurine is adsorbed on them. This shows that the AlN nanoparticles might function as chemical sensors and offer an electrical signal in mercaptopurine. The following is the order of sensitivity: AlN nanosheet > AlN nanotube > AlN nanocluster. The outcomes indicate that the nanosheet has the most potential for mercaptopurine detection among the AlN nanostructures.Communicated by Ramaswamy H. Sarma.

Dual Level Adaptive Weighting for Cloth-Changing Person Re-Identification

For the long-term person re-identification (ReID) task, pedestrians are likely to change clothes, which poses a key challenge in overcoming drastic appearance variations caused by these cloth changes. However, analyzing how cloth changes influence identity-invariant representation learning is difficult. In this context, varying cloth-changed samples are not adaptively utilized, and their effects on the resulting features are overshadowed. To address these limitations, this paper aims to estimate the effect of cloth-changing patterns at both the image and feature levels, presenting a Dual-Level Adaptive Weighting (DLAW) solution. Specifically, at the image level, we propose an adaptive mining strategy to locate the cloth-changed regions for each identity. This strategy highlights the informative areas that have undergone changes, enhancing robustness against cloth variations. At the feature level, we estimate the degree of cloth-changing by modeling the correlation of part-level features and re-weighting identity-invariant feature components. This further eliminates the effects of cloth variations at the semantic body part level. Extensive experiments demonstrate that our method achieves promising performance on several cloth-changing datasets. Code and models are available at https: //github.com/fountaindream/DLAW.

Composed Image Retrieval via Cross Relation Network With Hierarchical Aggregation Transformer

Composing Text and Image to Image Retrieval (CTI-IR) aims at finding the target image, which matches the query image visually along with the query text semantically. However, existing works ignore the fact that the reference text usually serves multiple functions, e.g., modification and auxiliary. To address this issue, we put forth a unified solution, namely Hierarchical Aggregation Transformer incorporated with Cross Relation Network (CRN). CRN unifies modification and relevance manner in a single framework. This configuration shows broader applicability, enabling us to model both modification and auxiliary text or their combination in triplet relationships simultaneously. Specifically, CRN includes: 1) Cross Relation Network comprehensively captures the relationships of various composed retrieval scenarios caused by two different query text types, allowing a unified retrieval model to designate adaptive combination strategies for flexible applicability; 2) Hierarchical Aggregation Transformer aggregates top-down features with Multi-layer Perceptron (MLP) to overcome the limitations of edge information loss in a window-based multi-stage Transformer. Extensive experiments demonstrate the superiority of the proposed CRN over all three fashion-domain datasets. Code is available at github.com/yan9qu/crn.

Influenced node discovery in a temporal contact network based on common nodes

Verification is the only way to make sure if a node is influenced or not because of the uncertainty of information diffusion in the temporal contact network. In the previous methods, only $ N $ influenced nodes could be found for a given number of verifications $ N $. The target of discovering influenced nodes is to find more influenced nodes with the limited number of verifications. To tackle this difficult task, the common nodes on the temporal diffusion paths is proposed in this paper. We prove that if a node $ v $ is confirmed as the influenced node and there exist common nodes on the temporal diffusion paths from the initial node to the node $ v $, these common nodes can be regarded as the influenced nodes without verification. It means that it is possible to find more than $ N $ influenced nodes given $ N $ verifications. The common nodes idea is applied to search influenced nodes in the temporal contact network, and three algorithms are designed based on the idea in this paper. The experiments show that our algorithms can find more influenced nodes in the existence of common nodes.