The next generation of wireless networks demands transformative solutions to achieve ultra-reliable, high-capacity, and energy-efficient communications. Conventional systems based solely on relay-assisted transmission or intelligent reflecting surfaces (IRS) face inherent trade-offs in spectral efficiency, coverage, and energy consumption. In this work, we propose a novel cooperative hybrid communication system that integrates IRS with relay-assisted transmission to form a robust, scalable, and energy-aware design for beyond 5G (B5G) networks. Unlike traditional architectures, the proposed system enables mobile users to receive signals from the base station through three cooperative transmission paths: direct relay transmission, IRS reflection, and relay-assisted IRS reflection. This cooperative signal propagation enhances path diversity, improves link robustness, and increases spatial efficiency. Additionally, we introduce a joint optimization algorithm to determine the optimal IRS placement and reflecting element configuration, maximizing system throughput under practical rate constraints. Simulation results under Rayleigh fading conditions demonstrate the effectiveness of the proposed system across various deployment scenarios, including SISO, MISO-OMA, and MISO-NOMA. In the MISO-NOMA setting with 40 dBm transmit power, the system achieves a sum rate of 49.80 bits/s/Hz and an energy efficiency of 16.60 bits/Joule, outperforming benchmark hybrid relay-IRS-aided and relay-dominant cooperative hybrid systems. These findings establish the proposed system as a high-performing and energy-efficient solution for future wireless networks.

Pregnancy care often lacks continuous, personalized monitoring, which can lead to higher rates of cesarean section and preventable complications for both mother and baby. To address these challenges, we propose a federated learning-based pregnancy (FLP) care system, which offers a novel solution by integrating a mobile application with smartwatch technology to promote healthier pregnancy outcomes and support normal deliveries. Through the mobile app, FLP collects patient information, provides personalized exercise and dietary guidance, predicts early labor signs, and builds a supportive user community. The smartwatch, equipped with a fetal heart rate monitoring patch, captures fetal electrocardiogram (ECG) signals to ensure well-being, promptly alerting users to potential concerns. As patient privacy is central to FLP, we employ federated learning so that sensitive health data remain securely on the user’s device. The system’s reliability is reinforced through comprehensive development, including data preprocessing, feature extraction, model training, and validation. Results show that the proposed method has improved accuracy compared to existing methods.

In seismic data processing, denoising is one of the essential steps to identifying the earth's subsurface layer information. The noise present in the seismic data is categorized into two types: random and erratic noise. The random noise is distributed uniformly over the seismic data. The erratic noise attenuation is always challenging due to the unknown distribution of high-amplitude peaks over seismic data. The existing double sparsity dictionary learning (DSDL) method performs with analytical and adaptive transforms; both the transforms include iterative algorithms with K-singular-value decomposition (SVD); it is computationally costly, and the dictionary is initialized with trained data. To address these limitations, we propose a novel method of dictionary learning with double regularization (DLDR) to denoise both random and erratic noise from seismic data. In double regularization, we used with l1 -norm and nuclear norm. The denoised data is applied to the alternating direction method of multipliers (ADMMs) to improve denoising while preserving the signal features from seismic data while reducing the computational cost. We evaluated the performance of the proposed method using signal-to-noise ratio (SNR), mean squared error (MSE), and local similarity map. The numerical results demonstrated that the proposed method resulted in higher SNR, lower MSE, and less signal leakage from seismic data. The method gives precise interpretation from the denoised seismic data.

Hyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. Among these, hyperspectral underwater target detection (HUTD) presents unique challenges due to spectral distortions caused by water absorption and scattering. Conventional methods often struggle with spectral variability, complicating detection accuracy. This paper introduces spectral variability-aware hybrid autoencoder (SVHAE) for HUTD, a novel autoencoder-based unmixing network incorporating parallel linear and nonlinear decoders to improve underwater target detection. Our method effectively reduces the effect of spectral distortions and addresses variability using a combined loss function integrating Kullback-Leibler divergence, mean squared error, and spectral angle distance. Experimental validation on real-world and simulated datasets demonstrates that our proposed SVHAE outperformed state-of-the-art methods by achieving superior AUC values. These advancements contribute to the progressing field of HUTD, making the way for robust solutions in marine exploration and detecting targets under the water.

Hyperspectral unmixing (HU) is crucial for extracting material information from hyperspectral images (HSI) obtained through remote sensing. Although linear unmixing methods are widely used due to their simplicity, they only address linear mixing effects. Nonlinear mixing models, while more complex, often focus solely on the nonlinear aspects affecting individual pixels. However, in practice, light reflected from materials within a pixel experiences linear and nonlinear interactions, necessitating a hybrid mixing model (HMM) that leverages spatial and spectral information. This work proposes a novel deep learning-based autoencoder (AE) with dual-stream decoders to enhance spectral unmixing. Our approach employs multitask learning (MTL) to process spatial and spectral information concurrently. Specifically, one decoder stream performs linear unmixing from HSI patches, while the other stream utilizes fully connected layers to capture and model the nonlinear interactions within the data. By integrating linear and nonlinear information, our method improves the accuracy of unmixing the mixed spectrum. We validate the effectiveness of our architecture on three real-world HSI datasets and compare its performance against various baseline methods. Experimental results consistently demonstrate that our approach outperforms existing methods, as evidenced by superior spectral angle distance (SAD) and mean squared error (MSE) metrics.

In seismic data processing, denoising is one of the important steps to identify the Earth’s subsurface layer information. The erratic noise attenuation is always challenging due to the unknown distribution of high-amplitude peaks over seismic data. In literature, the double sparsity dictionary learning (DSDL) methods were used for erratic and random noise attenuation. Here, analytical and adaptive transformations are performed sequentially to attenuate erratic and random noises. However, the DSDL technique leads to a high computational cost due to K-SVD. Therefore, we propose a double dictionary learning (DDL) method to denoise both random and erratic noise by preserving the signal features from seismic data. The method uses two parallel adaptive dictionaries for simultaneous denoising, and both dictionaries are concatenated further to form a comprehensive dictionary. The regularized K-SVD was used to update the dictionary and sparse coefficients for signal preservation. The DDL method effectively reduced the computational costs. The DDL method was applied to different synthetic and field datasets for denoising. The numerical results show that the proposed method provides a higher signal-to-noise ratio (SNR), lower mean-squared error (mse), and less signal leakage than existing state-of-the-art denoising methods.

In crime investigations, rapid bloodstain identification is crucial. Hyperspectral imaging (HSI) offers a non-destructive solution. Our investigation into preprocessing techniques to improve classification accuracy and reduce computation time reveals that the best options are max normalization and mean filter.

Healthcare is a vital sector influencing societal well-being and economic stability. The COVID-19 pandemic has highlighted the critical need for innovative solutions, such as remote monitoring and real-time health tracking, to address emerging challenges. This paper examines the transformative potential of wireless technology in revolutionizing healthcare systems, emphasizing advancements in communication, remote surgeries, patient engagement, and cost efficiency. It explores the role of 6G technology in enabling high-speed data transfer, ultra-reliable connectivity, and low latency, providing the foundation for intelligent, connected healthcare ecosystems. Key challenges, including seamless connectivity, data privacy, and network scalability, are analyzed alongside strategies to overcome them, such as adopting 6G-enabled Internet of Everything (IoE), Intelligent Reflecting Surfaces (IRS) to counter signal blockages, and advanced latency reduction techniques. By reviewing state-of-the-art developments and real-world case studies, the paper demonstrates the indispensable role of wireless technology in enhancing patient outcomes, reducing healthcare costs, and ensuring universal access to high-quality care. It concludes with actionable recommendations for healthcare organizations to embrace these innovations for a resilient and efficient future.

The knowledge of hidden resources present inside the Earth layers is vital for the exploration of petroleum and hydrocarbons. However, the recorded seismic data are noisy and incomplete with missing traces that leads to misinterpretation of the Earth layers. In this manuscript, we consider seismic data with Gaussian, non-Gaussian noise distribution, regular, and irregular missing traces. We propose a method for simultaneous noise attenuation and reconstruction of the incomplete seismic data with attention-based wavelet convolutional neural network (AWUN). The wavelet transform is used as pooling layer and inverse wavelet transform (IWT) is used for upsampling layers to avoid information loss. The attention module is used to obtain weights for various feature channels with higher weights assigned to the more significant information. In addition, we propose to use hybrid loss function (logcosh + huberloss) to denoise and accurately reconstruct the seismic data. Moreover, the effect of various hyperparameters in the training process of convolutional neural networks (CNNs) is studied. Further, we tested the performance of proposed method on synthetically generated data and field data examples. The quantitative results demonstrated that our proposed deep learning (DL) method has shown improved signal-to-noise ratio (SNR) and mean-squared error (mse) when compared to the existing state-of-the-art methods.

As global competitiveness in the semiconductor sector intensifies, companies must continue to improve manufacturing techniques and productivity in order to sustain competitive advantages. In this research paper, we have used machine learning (ML) techniques on computational data collected from the sensors in the manufacturing unit to predict the wafer failure in the manufacturing of the semiconductors and then lower the equipment failure by enabling predictive maintenance and thereby increasing productivity. Training time has been greatly reduced through the proposed feature selection process with maintaining high accuracy. Logistic Regression, Random Forest Classifier, Support Vector Machine, Decision Tree Classifier, Extreme Gradient Boost, and Neural Networks are some of the model-building techniques that are performed in this work. Numerous case studies were undertaken to examine accuracy and precision. Random Forest Classifier surpassed all the other models with an accuracy of over 93.62%. Numerical results also show that the ML techniques can be implemented to predict wafer failure, perform predictive maintenance and increase the productivity of manufacturing the semiconductors.

Seismic interpretation is essential to obtain infor-mation about the geological layers from seismic data. Manual interpretation, however, necessitates additional pre-processing stages and requires more time and effort. In recent years, Deep Learning (DL) has been applied in the geophysical domain to solve various problems such as denoising, inversion, fault estimation, horizon estimation, etc. In this paper, we propose an Attention-based Deep Convolutional Neural Network (ACNN) for seismic lithology prediction. We used Continuous Wavelet Transform (CWT) to obtain the time-frequency spectrum of seismic data which is further used to train the network. The attention module is used to scale the features from the convolutional layers thus prioritizing the prominent features in the data. We validated the results on blind wells and observed that the proposed method had shown improved accuracy when compared to the existing basic CNN.

In seismic data processing, denoising and reconstruction are the two steps for identification of resources in the earth subsurface layers. The seismic data quality is affected by random noise and interference during acquisition. Further, the noisy data may be incomplete with missing traces. In this work, we propose a method for incomplete seismic data denoising and reconstruction based on double sparsity dictionary learning (DSDL) with structure oriented filtering (SOF). The main function of the DSDL step is denoising and SOF is used for residual noise attenuation and filling the missing data points. The proposed method is tested on 2-D synthetic and field datasets. The test results show that the DSDL-SOF method has better noise attenuation and reconstruction in terms of signal-to-noise ratio and mean squared error as compared to existing methods.

A method to capture three-dimensional (3D) objects image data under extremely low light level conditions, also known as Photon Counting Imaging (PCI), was reported. It is demonstrated that by combining a PCI system with computational integral imaging algorithms, a 3D scene reconstruction and recognition is possible. The resulting reconstructed 3D images often look degraded (due to the limited number of photons detected in a scene) and they, therefore, require the application of superior image restoration techniques to improve object recognition. Recently, Deep Learning (DL) frameworks have been shown to perform well when used for denoising processes. In this paper, for the first time, a fully unsupervised network (i.e., U-Net) is proposed to denoise the photon counted 3D sectional images. In conjunction with classical U-Net architecture, a skip block is used to extract meaningful patterns from the photons counted 3D images. The encoder and decoder blocks in the U-Net are connected with skip blocks in a symmetric manner. It is demonstrated that the proposed DL network performs better, in terms of peak signal-to-noise ratio, in comparison with the classical TV denoising algorithm.

Seismic inversion is an approach to obtain the physical properties of the Earth layers from the seismic data, which aids in reservoir characterization. In seismic inversion, spatially variable physical parameters, such as impedance (Z), wave velocities (Vp, Vs), and density, can be determined from the seismic data. Among these, impedance is an important parameter used for lithology interpretation. However, the inversion problem is nonlinear and ill-posed due to unknown seismic wavelet, observed data band limitation, and noise. This requires complex wave equation analysis, prior assumptions, human expert effort, and time to analyze the seismic data. To address these issues, deep learning methods were deployed to solve the seismic inversion problem. In this article, we develop a deep learning framework with an attention module for seismic impedance inversion. The relevant features from the seismic data are emphasized with the integration of the attention module into the network. First, we train the attention-based deep convolutional neural network (ADCNN) by supervised learning with predefined acoustic impedance (AI) labels. Next, we train the ADCNN in an unsupervised way with the physics of the forward problem. In the proposed method, the predicted AI is used to calculate the seismic data (calculated seismic), and error is minimized between the input seismic data and calculated seismic data. Unsupervised learning has an advantage when the labeled data are inadequate. The proposed network is trained with Marmousi 2 dataset, and the predicted experimental results show that the proposed method outperforms in comparison to the existing state-of-the-art method.

In seismic data processing, attenuation of erratic noise is a challenging task due to the unknown noise distribution. Erratic noise consists of high amplitude peaks and conventional sparse transforms based on least-square (LS) approach that is not appropriate for erratic noise attenuation. An alternative approach, where the data with erratic noise are transformed into pseudodata and then denoised based on fast discrete curvelet transform with structure-oriented space-varying median filtering, is performed and achieves better attenuation. However, the fast discrete curvelet transform with a fixed basis lacks the adaptivity for various data patterns of seismic data. Hence, in this article, we propose a double sparsity dictionary learning (DSDL) method which performs denoising and also preserves the original features of seismic data. The proposed method combines the strength of the analytical transform and adaptive transform to attenuate both random noise and erratic noise in data. The performance of proposed DSDL method is studied on synthetic datasets and field datasets. The numerical results demonstrate that the proposed method gives a better signal-to-noise ratio (SNR), a lower mean-squared error, and energy values for the denoised data in comparison to the existing methods.

In seismic data processing, denoising is one of the important steps to get the earth subsurface layers' information accurately. The dictionary learning (DL) method is one of the prominent methods to denoise the seismic data. In the DL method, there are various parameters involved for denoising such as patch size, dictionary size, number of training patches, choice of threshold, sparsity level, computational cost, and number of iterations for DL. In this work, we study each parameter and its effects on seismic denoising in terms of signal-to-noise ratio and mean square error between the true and denoised seismic data. We examined the performance of the DL method on synthetic and field seismic data for various choices of parameters.

The seismic horizons estimation from the seismic data is essential for structural and stratigraphic modeling of reservoirs. Till now, manual interpretation and semi-automated techniques were used to estimate the seismic horizon from the seismic data. However, the seismic horizon estimation takes more time and needs a human expert for analysis. To overcome those limitations, we propose a novel method to estimate the seismic horizon using a deep sparse convolutional autoencoder (DSCA). The convolutional layers in the DSCA network extract the complex features from the seismic data which improves the accuracy of seismic horizon estimation. The DSCA network is trained in supervised way with the labeled seismic data. The performance of the proposed method is tested and compared with existing method based on quantitative metrics such as Mean Squared Error (MSE), Coefficient of Determination (r2 ), and Pearson Correlation Coefficient (PCC). Experimental results prove that the proposed method has shown better results compared to the existing method.

Intelligent Reflecting Surfaces (IRS) have been recognized as presenting a highly energy-efficient and optimal solution for future fast-growing 6G communication systems by reflecting the incident signal towards the receiver. The large number of Internet of Things (IoT) devices are distributed randomly in order to serve users while providing a high data rate, seamless data transfer, and Quality of Service (QoS). The major challenge in satisfying the above requirements is the energy consumed by IoT network. Hence, in this paper, we examine the energy-efficiency (EE) of a hybrid relay-IRS-aided wireless IoT network for 6G communications. In our analysis, we study the EE performance of IRS-aided and DF relay-aided IoT networks separately, as well as a hybrid relay-IRS-aided IoT network. Our numerical results showed that the EE of the hybrid relay-IRS-aided system has better performance than both the conventional relay and the IRS-aided IoT network. Furthermore, we realized that the multiple IRS blocks can beat the relay in a high SNR regime, which results in lower hardware costs and reduced power consumption.

A simplified (i.e., single shot) method is demonstrated to generate a Fourier hologram from multiple two-dimensional (2D) perspective images (PIs) under low light level imaging conditions. It was shown that the orthographic projection images (OPIs) can be synthesized using PIs and then, following incorporation of corresponding phase values, a digital hologram can be generated. In this work, a fast dictionary learning (DL) technique, known as Sequential Generalised K-means (SGK) algorithm, is used to perform Integral Fourier hologram reconstruction from fewer samples. The SGK method transforms the generated Fourier hologram into its sparse form, which represented it with a linear combination of some basis functions, also known as atoms. These atoms are arranged in the form of a matrix called a dictionary. In this work, the dictionary is updated using an arithmetic average method while the Orthogonal Matching Pursuit algorithm is opted to update the sparse coefficients. It is shown that the proposed DL method provides good hologram quality, (in terms of peak signal-to-noise ratio) even for cases of ~ 90% sparsity.

We developed a deep stacked undercomplete autoencoder (i.e., supervised) network to denoise the noisy 3D sectional images. Results demonstrate the feasibility of our proposed model in terms of peak-signal-to-noise ratio.

Accurate estimation of battery internal model parameters and consequently SOC prediction is crucial in any battery power systems. Hence, it is a fundamental need in electric vehicles, smart grids, and energy storage systems. The accuracy of parameters identification will affect the battery management system, battery safety, characteristics, and performance which mainly depends on battery model parameters. So, to estimate the parameters accurately and easily, we require effective, simple, and robust parameters estimation algorithms. In this article, we propose a new method for estimation of parameters using least square method algorithm for Lithium-Ion Batteries (LIBs) for Electric Vehicle (EV) applications. In this, Second-order RC equivalent circuit model is considered for estimation of parameters of NMC battery. The estimation of parameters and relation between OCV-SOC nonlinear is obtained from the experimental data. This proposed method shows that the calculation of parameters is fast and efficient.

Unmanned Aerial Vehicles (UAVs) are recently used for both civilian and military applications in worldwide. Energy Efficiency (EE) is an exceptional design approach for modern communication based systems. New advance technology is needed in order to support UAV applications with reduced energy usage. In this paper, an Energy Efficiency with Hybrid Fuzzy Firefly Algorithm (EE-HFFA) method is introduced for Multiple-Input–Multiple-Output (MIMO) Amplify-And-Forward (AF) systems in which Partial Channel State Information (PCSI) estimation is existing at the relays because of the high speed mobility. A new EE-HFFA algorithm is presented in this research, by means of merging the benefits of the Firefly Algorithm (FA) as well as Differential Evolution (DE). For increasing information sharing both the techniques are implemented in parallel and as a result improve searching efficiency. The outcomes of these two techniques are based upon the fuzzy membership function. For approximation of PCSI for the source node as well as relay nodes, Branch Convolutional Neural Network (B-CNN) classifier is presented to raise the capability of cooperative MIMO-AF systems. EE-optimal source and relay precoding matrices are cooperatively enhanced by means of EE-HFFA. Simulation out comes illustrate that the presented EE-HFFA as well as B-CNN classifier could enhance the EE of MIMO-AF systems with PSCI while matched up with direct/relay link merely precoding optimization.

Real seismic data consist of multiple traces captured by an array of receivers. These multiple traces are sorted by the common midpoint between the source and the receiver, and the time-lag between different traces is corrected by a normal move-out correction process. After these preprocessing steps, the sorted traces contain the same information about the earth sublayers and are corrupted by noise. The next step, termed stacking in seismic signal processing, involves the construction of an optimum trace with an improved signal-to-noise ratio (SNR) from these sorted traces. In this letter, we present an improved method for weighted stacking, where each trace is weighed in accordance with the noise variance. Using the first-order derivative property of Ramanujan sums, we perform the estimation of noise variance in each trace. Numerical results demonstrate that the method presented in this letter has better SNR for the trace obtained after stacking in comparison with existing methods.

An accurate and effective seismic wavelet estimation technique has extreme significance in the seismic data processing for analyzing the earth's subsurface layer information. The seismic wavelet to be determined is modeled as a moving average (MA) process and assumed to be driven by a zero mean, non-Gaussian, statistically independent, and identically distributed (IID) process. In order to estimate the MA model parameter from the observed noisy seismic signal, we pose this as a blind system identification (BSI) problem. In the BSI, a set of multivariate polynomial equations is obtained by matching higher order cumulant of observed noisy data with a higher order moment of blind system's impulse response. The Groebner bases that form the solution to this set of equations are obtained using the proposed algorithm. Numerical results demonstrate that the proposed method has a lower estimation error as compared to the previously reported methods.

Denoising is one of the core steps in seismic data processing flow. The seismic gather consists of multiple traces captured at different receivers. A set of receivers observe waves which are reflected from the same reflection point. Those traces need to be grouped together as they contain the same information about the earth subsurface layers. This is done by finding a common mid-point (CMP) between the source and geophones. The time delay between CMP gathered traces are corrected by the normal move out correction method but the individual traces are corrupted by noise. In this paper we, propose a method for denoising individual traces. The set of traces can be modelled as belonging to a low-dimensional subspace of an ambient signal space. This allows for construction of sparse representations of each trace in terms of other traces in the CMP gather. The resulting sparse representations are subsequently utilised to construct approximations of individual traces and thus, noise is suppressed. We constructed, the approximations using orthogonal matching pursuit. We applied proposed method to synthetic and field seismic data, the proposed technique performs better on established benchmarks while capturing the true locations of weak reflections and effectively attenuating the random noise.