Computational Performance Research Articles

A flexible and lightweight signcryption scheme for underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) are a new research area gaining popularity. It has several key applications for instance; marine monitoring, surveillance, environmental sensing, etc. However, It has several challenges including security, node mobility, limited bandwidth, and high error rates. Thus, to solve these issues, herein we propose a new lightweight Signcryption scheme for UWSNs. The proposed scheme effectively balances computational complexity and enhances the security of UWSNS. In contrast to the other state-of-the-art cryptographic schemes, the proposed scheme consists of a single combined operation of encryption and signing processes, which significantly improves its computational and communicational performance to ensure confidence when transmitting data. We performed the experimentation, and the experimental results show that the proposed scheme performs well compared to the state-of-the-art model. In addition, the experimental results revealed that the proposed scheme had a 40% less computational cost, 30% less energy consumption, and 25% less communication overhead than the state-of-the-art methods. This makes the proposed scheme highly appropriate for resource-scarce UWSNs. The proposed scheme also showed good scalability, where the performance could be sustained from a small-scale network of 50 nodes to a bandwidth of 200 nodes. Further, the proposed model also kept the security and latency low for the mobile nodes in an environment with high node mobility over the underwater terrain. In addition, the proposed method ensures flexibility and scalability by offering compatibility with diverse network structures and seamless integration with various cryptographic approaches, making it adaptable for dynamic underwater environments and broader applications such as IoT and smart city networks.

A Novel Conjugate Gradient Algorithm as a Convex Combination of Classical Conjugate Gradient Methods

Conjugate gradient (CG) algorithms are constructive for handling large-scale nonlinear optimization problems. One optimization technique intended to address unconstrained optimization issues effectively is the hybrid conjugate gradient (HCG) algorithm. The HCG algorithm aims to improve convergence properties while keeping computations simple by merging features from other conjugate gradient techniques. In this paper, a new hybrid conjugate gradient algorithm is proposed and analyzed, which is obtained as a convex combination of the Dai-Yuan (DY), Hestenes-Stiefel (HS) and Harger-Zhan (HZ) conjugate gradient methods. The primary objective is to improve convergence efficiency and computational performance. The proposed algorithm is designed to reduce the number of iterations and computational costs compared to traditional CG methods. Numerical experiments on standard unconstrained optimization criteria show that the hybrid method achieves faster convergence, often requiring much fewer iterations to reach a specified gradient norm tolerance or objective function value. Additionally, the per-iteration computational cost remains competitive, as the convex combination framework introduces minimal overhead. Theoretical analysis proves the global convergence of the algorithm under standard assumptions. The results highlight the superior performance of the hybrid method in terms of the number of iterations and the total computational cost, especially for large-scale and unconditional problems. This work advances the development of efficient and robust CG algorithms, offering a practical solution for unconstrained optimization challenges.

Applying neural networks as direct controllers in position and trajectory tracking algorithms for holonomic UAVs

This study compares different neural networks as standalone control algorithms for position and trajectory tracking in holonomic UAVs, specifically quadcopters. The research’s novelty lies in applying these algorithms directly for control. A position-tracking algorithm based on the artificial potential field method generated extensive training and validation datasets, simulating the tracked point’s diverse trajectory shapes and velocities. The most popular neural network architectures were evaluated on the basis of their trajectory tracking accuracy and computational performance, i.e. single-layer regression networks and double-layer perceptron regression networks, deep neural networks, and residual networks. The results highlight that DNNs achieved the highest trajectory tracking accuracy, as measured by root mean squared errors (1.0830) and correlation coefficients (0.9624 given as Pearson’s correlation) while providing satisfactory results and stable flight across untrained scenarios, in opposite to other neural networks. However, simpler architectures, such as single-layer perceptrons, exhibit significantly lower latency, making them suitable for real-time applications despite slightly reduced accuracy. In contrast, ResNet architectures underperformed in terms of accuracy and latency, emphasizing the importance of selecting architectures on the basis of specific control objectives. This study demonstrates that deep neural networks can directly control quadcopters, eliminating the need for conventional control algorithms for UAV position-tracking applications, provided sufficient learning data is available. The proposed approach ensures accurate trajectory tracking, effectively handling sudden turns while maintaining stable flight. These findings highlight the potential of neural networks for UAV control, balancing computational efficiency with high precision and reliability.

Thermal colloid programming

This paper investigates the computational capabilities of colloidal systems, focusing on the integration of Boolean logic operations within gold nanoparticle suspensions under varying temperature conditions. As climate change, artificial intelligence, and privacy concerns present increasing challenges for massively parallel and low-power computing devices, there is a growing demand for novel computing substrates, and colloids offer a promising avenue for developing energy-efficient and locally deployable systems. Our research explores how temperature impacts the behavior of suspended nanoparticles and, consequently, their interactions and computational performance. Our findings demonstrate that colloidal systems can perform Boolean operations, which can be modulated through temperature changes. By showcasing the versatility of these systems, this study underscores the significance of exploring unconventional computing paradigms and lays the foundation for future research into liquid-based computational applications.

Neuromorphic algorithms for brain implants: a review

Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.

Comparative analysis of surface modeling in computer-aided design systems

This study presents a comprehensive analysis of the surface modeling capabilities of two leading contemporary computeraided design (CAD) software packages: KOMPAS-3D and SOLIDWORKS. The research focuses on evaluating their efficiency in creating complex components using surface modeling tools. As a test case, a geometrically intricate component – the VM-sinus mandrel for mold sleeves with a 125×125 mm cross-section – was selected. These copper sleeves play a crucial role in the primary cooling of molten metal within the crystallizer, ensuring the formation of a precisely shaped ingot. Given these functional demands, the mandrel requires exceptional accuracy and geometric precision. Due to the complexity of its structure, surface modeling (SM) tools represent the most practical and efficient approach to its digital design. In this study, various surface modeling tools available in both software systems were assessed, with the most suitable and functionally comparable tools selected for direct comparison. The 3D model was successfully created in both CAD environments, allowing for an objective assessment of interface usability and computational performance under identical conditions. The findings reveal several key insights: SOLIDWORKS demonstrated a simpler and faster approach to model creation, the user interface of SOLIDWORKS was more streamlined and intuitive, offering a superior user experience compared to KOMPAS-3D, in terms of computational performance and stability, both software packages exhibited equally high efficiency when handling surface modeling tasks.

Crystallinity in niobium oxides: A pathway to mitigate two-level-system defects in niobium three-dimensional resonators for quantum applications

Materials imperfections in Nb-based superconducting quantum circuits—in particular, two-level-system (TLS) defects—are a major source of decoherence, ultimately limiting the performance of quantum computation and sensing. Thus, identifying and understanding the microscopic origin of possible TLS defects in these devices will help develop strategies that eliminate them, which are key to superconducting qubit performance improvement. In this paper, we demonstrate an order-of-magnitude reduction in two-level system losses in three-dimensional superconducting radio frequency (SRF) niobium resonators by a 10-h high vacuum (HV) heat treatment at 650 °C, even after exposure to air and high-pressure rinsing (HPR). X-ray photoelectron spectroscopy (XPS) and high-resolution scanning transmission electron microscopy (STEM) reveal an alteration of the native oxide composition regrown after air exposure and HPR and the creation of nanoscale crystalline oxide regions, which correlates with the measured tenfold quality factor enhancement at low fields of the 1.3 GHz niobium resonator. Tunneling spectroscopy measurements show a pronounced proximity effect that further confirms the presence of metallic layers on the niobium surface. Published by the American Physical Society 2025

A multiclass deep learning framework for retinal disease detection using VGG16-HCCNN

Abstract Fundus imaging is a crucial diagnostic tool in ophthalmology, as it enables the detection of early signs of various ocular diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. If left untreated, these conditions can lead to vision loss or blindness. In this work, a combined VGG16 and Hybrid Centric Convolutional Neural Network (HCCNN) model is suggested that provides a balanced trade-off between computational efficiency and performance, providing a fast and precise method of classifying retinal images into one of the four classes: normal, diabetic retinopathy, glaucoma, and hypertensive retinopathy. This hybrid model seeks to improve feature extraction by integrating VGG16 feature maps with the adaptability of HCCNN, which is designed to collect domain-specific features in images. To improve the performance, this model uses regularization and dropout method that reduces complexity, and prevent overfitting. The proposed technique was assessed using a diverse dataset sourced from eight publicly available datasets (HRF, SYSU, SRM, PAPILA, DRISHTI_GS, RIM-ONE, INSPIREAVR, AVRDB), attaining an average classification accuracy of 96.93%, sensitivity of 94%, specificity of 97.96% and precision of 93.95% in detecting multiclass retinal disease. This represents a significant improvement over state-of-the-art models like VGG16, InceptionV3, and ResNet101, outperforming them by 5.46% in accuracy, 10.95% in sensitivity, and 3.65% in specificity. The model’s efficiency was also assessed using a technique called 5-fold cross-validation. This method enhances precision and is a significant resource for medical professionals.

Applying deep learning for style transfer in digital art: enhancing creative expression through neural networks

Neural style transfer (NST) has opened new possibilities for digital art by enabling the blending of distinct artistic styles with content from various images. However, traditional NST methods often need help balancing style fidelity and content preservation, and many models need more computational efficiency, limiting their applicability for real-time applications. This study aims to enhance the efficiency and quality of NST by proposing a refined model that addresses key challenges in content retention, style fidelity, and computational performance. Specifically, the research explores techniques to improve the visual coherence of style transfer, ensuring consistency and accessibility for practical use. The proposed model integrates Adaptive Instance Normalization (AdaIN) and Gram matrix-based style representation within a convolutional neural network (CNN) architecture. The model is evaluated using quantitative metrics such as content loss, style loss, Structural Similarity Index (SSIM), and processing time, along with a qualitative assessment of content and style consistency across various image pairs. The proposed model significantly improves content and style balance, with content and style loss values reduced by 15% compared to baseline models. The optimal configuration yields an SSIM score of 0.88 for medium style intensity, maintaining structural integrity while achieving stylistic effects. Additionally, the model’s processing time is reduced by 76%, making it suitable for near-real-time applications. Style fidelity scores remain high across various artistic styles, with minimal loss in content retention. The refined NST model balances style and content effectively, enhancing visual quality and computational efficiency. These advancements make NST more accessible for real-time artistic applications, providing a versatile digital art, design, and multimedia production tool.

Analysis of the efficiency of SHA algorithms based on message length

This study analyzes the efficiency of SHA (Secure Hash Algorithm) family algorithms depending on message length. The execution time of hashing processes for SHA-1 and SHA-2 (SHA-224, SHA-256, SHA-384, SHA-512) algorithms is compared across various message lengths. Python programming language is used for implementation, and the results are presented in graphical and tabular forms. The study examines the computational complexity and performance speed of these algorithms. The main objective is to determine the efficiency of each algorithm relative to message length and recommend an optimal option for practical applications. Special attention is given to balancing execution speed and computational complexity. The results highlight similarities and differences among the algorithms, providing valuable insights for selecting the most efficient algorithm in real-time systems or large-scale data processing.

Enhancing Cryptographic Solutions for Resource-Constrained RFID Assistive Devices: Implementing a Resource-Efficient Field Montgomery Multiplier

Radio Frequency Identification (RFID) assistive systems, which integrate RFID devices with IoT technologies, are vital for enhancing the independence, mobility, and safety of individuals with disabilities. These systems enable applications such as RFID navigation for blind users and RFID-enabled canes that provide real-time location data. Central to these systems are resource-constrained RFID devices that rely on RFID tags to collect and transmit data, but their limited computational capabilities make them vulnerable to cyberattacks, jeopardizing user safety and privacy. Implementing the Elliptic Curve Cryptography (ECC) algorithm is essential to mitigate these risks; however, its high computational complexity exceeds the capabilities of these devices. The fundamental operation of ECC is finite field multiplication, which is crucial for securing data. Optimizing this operation allows ECC computations to be executed without overloading the devices’ limited resources. Traditional multiplication designs are often unsuitable for such devices due to their excessive area and energy requirements. Therefore, this work tackles these challenges by proposing an efficient and compact field multiplier design optimized for the Montgomery multiplication algorithm, a widely used method in cryptographic applications. The proposed design significantly reduces both space and energy consumption while maintaining computational performance, making it well-suited for resource-constrained environments. ASIC synthesis results demonstrate substantial improvements in key metrics, including area, power consumption, Power-Delay Product (PDP), and Area-Delay Product (ADP), highlighting the multiplier’s efficiency and practicality. This innovation enables the implementation of ECC on RFID assistive devices, enhancing their security and reliability, thereby allowing individuals with disabilities to engage with assistive technologies more safely and confidently.

Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset

ABSTRACT Seafloor mapping to create a coastal DEM dataset of the oceans is significant for various applications. Land-based DEM monitoring and acquisition are more practical as compared to ocean DEM. The highest resolution free available DEM data in the Australian land area is 30 m (1 arc-second), whereas for the ocean area, it is only 450 meters (15 arc-seconds) due to the slow and costly nature of sonar data collection. Deep learning-based image super-resolution (SR) has demonstrated high computational efficiency and optimal performance in enhancing bathymetric resolution. However, direct training a robust network for the entire Australian DEM-SR remains a challenge due to the difficulty of obtaining sufficiently high-resolution ocean DEM data. Therefore, we proposed a novel method that incorporates two key measures to address this issue. First, we designed a deep gradient prior AUSDEM-SRNet network based on numerous land sample sets to acquire prior knowledge from land data. Second, we introduced transfer learning to leverage the similarities between land and bathymetric (ocean) data, compensating for the scarcity of high-resolution bathymetric information. Through experiments using bathymetric (ocean) data around Australia, the proposed method demonstrated superior performance compared to naive bicubic interpolation, achieving a 11.64% reduction in root mean square error (RMSE) and a 1.93% increase in peak signal-to-noise ratio (PSNR) averages. The AUSDEM-SRNet model has been meticulously developed and evaluated for its capability to amplify the spatial resolution of existing DEM dataset by up to 15 times without requiring additional data or information related to the original dataset. As a result, we generated an open-access Australian coastal DEM dataset with 1 arc-second, named AUSDEM_2023. This method can significantly reduce the number of sea areas or points that must be measured, thereby accelerating the detailed mapping of the coastal seabed and the creation of high-resolution sounding maps for various coastal countries.

Advancing HR-pQCT-based homogenised FE models with smooth structured hexahedral meshes.

Advancing HR-pQCT-based homogenised FE models with smooth structured hexahedral meshes.

Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models.

Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models.

Centralized and Distributed Strategies for Handover-Aware Task Allocation in Satellite Constellations

As satellite constellations grow in size, there is an increasing need for autonomous, scalable, and real-time dynamic task assignment to address the unique operational challenges of such distributed systems. In particular, a time-varying task assignment (i.e., for observing various regions of Earth) often means that the corresponding satellite has to reorient itself or its sensors, costing time and energy. However, most assignment algorithms for area requests proposed in the literature do not account for the significant cost associated with task handover in satellite constellations. In this paper, we develop a framework for solving the seemingly non-deterministic polynomial-time (NP)-hard problem of optimal dynamic task allocation while minimizing task handover. In particular, we develop Handover-Aware Assignment with Lookahead (HAAL), an algorithm with centralized and distributed variants, and solutions that provably achieve 50% of the optimal value. We then proceed to show that HAAL significantly outperforms similar heuristic methods proposed in the literature for realistic constellation experiments with up to a thousand satellites. The algorithm scales polynomially in the number of satellites/tasks and offers a smooth tradeoff between computational efficiency and performance, allowing the designer to tune the algorithm based on available computing resources, communication bandwidth, and required performance.

X-ray spectral fitting with Monte Carlo dropout neural networks

Context. The analysis of X-ray spectra often encounters challenges due to the tendency of frequentist approaches to be trapped in local minima, affecting the accuracy of spectral parameter estimation. Bayesian methods offer a solution to this issue, though computational time significantly increases, limiting their scalability. In this context, neural networks have emerged as a powerful tool for efficiently addressing these challenges, providing a balance between accuracy and computational efficiency. Aims. This work aims to explore the potential of neural networks to recover model parameters and quantify their uncertainties. We benchmark their accuracy and computational time performance against traditional X-ray spectral fitting methods based on frequentist and Bayesian approaches. This study serves as a proof of concept for data analysis of future astronomical missions, producing extensive datasets that could benefit from the proposed methodology. Methods. We applied Monte Carlo dropout to a range of neural network architectures to analyze X-ray spectra. Our networks are trained on simulated spectra derived from a multiparameter source emission model convolved with an instrument response. This allows them to learn the relationship between the spectra and their corresponding parameters while generating posterior distributions. The model parameters are drawn from a predefined prior distribution. To illustrate the method, we used data simulated with the response matrix of the X-ray instrument NICER. We focus on simple X-ray emission models with up to five spectral parameters for this proof of concept. Results. Our approach delivers well-defined posterior distributions, comparable to those produced by Bayesian inference analysis, while achieving an accuracy similar to traditional spectral fitting. It is significantly less prone to falling into local minima, thus reducing the risk of selecting parameter outliers. Moreover, this method substantially improves computational speed compared to other Bayesian approaches, with computational time reduced by roughly an order of magnitude. Conclusions. Our method offers a robust alternative to the traditional spectral fitting procedures. Despite some remaining challenges, this approach can potentially be a valuable tool in X-ray spectral analysis, providing fast, reliable, and interpretable results with reduced risk of convergence to local minima, effectively scaling with data volume.

New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification.

Microarray technology has become a vital tool in cardiovascular research, enabling the simultaneous analysis of thousands of gene expressions. This capability provides a robust foundation for heart disease classification and biomarker discovery. However, the high dimensionality, noise, and sparsity of microarray data present significant challenges for effective analysis. Gene selection, which aims to identify the most relevant subset of genes, is a crucial preprocessing step for improving classification accuracy, reducing computational complexity, and enhancing biological interpretability. Traditional gene selection methods often fall short in capturing complex, nonlinear interactions among genes, limiting their effectiveness in heart disease classification tasks. In this study, we propose a novel framework that leverages deep neural networks (DNNs) for optimizing gene selection and heart disease classification using microarray data. DNNs, known for their ability to model complex, nonlinear patterns, are integrated with feature selection techniques to address the challenges of high-dimensional data. The proposed method, DeepGeneNet (DGN), combines gene selection and DNN-based classification into a unified framework, ensuring robust performance and meaningful insights into the underlying biological mechanisms. Additionally, the framework incorporates hyperparameter optimization and innovative U-Net segmentation techniques to further enhance computational performance and classification accuracy. These optimizations enable DGN to deliver robust and scalable results, outperforming traditional methods in both predictive accuracy and interpretability. Experimental results demonstrate that the proposed approach significantly improves heart disease classification accuracy compared to other methods. By focusing on the interplay between gene selection and deep learning, this work advances the field of cardiovascular genomics, providing a scalable and interpretable framework for future applications.

THOR: inclusion of charge carriers’ creation and transport in semiconductor detectors in the PENELOPE Monte Carlo simulation code

THOR: inclusion of charge carriers’ creation and transport in semiconductor detectors in the PENELOPE Monte Carlo simulation code

A new approach for global and multi-objective optimization of fin and tube heat exchanger

A new approach for global and multi-objective optimization of fin and tube heat exchanger

Analytical and computational investigation of vibrothermoacoustic behaviour of cracks in aerostructures

Abstract This research investigates the Vibro-thermoacoustic dynamics exhibited by Aerostructures, particularly those harbouring surface cracks. Employing a novel approach, a Thermo-Acoustic field coupled Transient Heat Transfer model is developed to model the interplay among vibrations, heat dissipation, and acoustic behaviour at crack tip interfaces. Local surface flaws proximal to stress concentration sites are investigated via the Newmark Beta Method and Finite Element Analysis, which are applied to displacement and thermal mapping at crack tips through temporal and spatial discretization. Recognizing the implications of flaws in structural integrity and potential lifespan reduction, the study incorporates Analytical, Numerical and Computational analysis of surface cracks into the broader context of Thermoacoustic phenomena. 
The paper explores the application of Elastoplastic Fracture Mechanics (EPFM) to model the impact of plastic deformation on crack growth, local acoustic field and the overarching structural integrity of these components. 
In this study, we also investigate the limitations of the original Ramberg-Osgood equation, particularly in its application to modelling nonlinearities in plastic strain at crack tip interfaces. To address these shortcomings, we propose a novel modified Ramberg-Osgood formulation that incorporates Padé Approximants. This modification enables effective modelling of nonlinear stress-strain variations of high-performance materials while improving computational performance and accuracy. 
Real-world aerostructures experience varying loads, and incorporating these principles in our coupled model allows for accurate modelling of Thermoacoustic Field variations and Instability under dynamic conditions. The research offers valuable perspectives for engineering applications, providing a computational roadmap for thermoacoustic modelling in Aerostructures with cracks.