Computational Cost Research Articles

Sum-Rate Maximization in Massive MIMO with Joint Antenna Selection and Power Allocation

Telecommunications improvements in the last decades have led to an increase in the available data rate and reliability, and to reduce latency. Massive MIMO (mMIMO) has emerged as a promising solution to replace simple antenna setups. This arrangement involves a large number of transmitter antennas compared to the served terminals in the covered area, enabling higher data rates through the key characteristics of mMIMO. Despite its advantages, the utilization of such large arrays is energy-intensive. With fewer antennas, it is possible to achieve high data rates with reduced power consumption. Antenna selection is crucial for energy efficiency while maintaining computational efficiency. Additionally, power allocation in the downlink for each served terminal is an important step to consider. This work aims to propose a joint algorithm for selecting antennas and distributing energy among terminals, simplifying the process through low-complexity algorithms. The results show the penalty in sum-rate capacity for each proposed method. The two proposed algorithms demonstrate an adequate approach to the target value asymptotically as the number of transmitter antennas increases. This implies the possibility of achieving the joint process with significantly lower computational costs compared to perform antenna selection and power allocation separately.

Machine learning potential model for accelerating quantum chemistry‐driven property prediction and molecular design

AbstractQuantum chemistry (QC) calculations have significantly advanced the development of materials, drugs, and other molecular products. Molecular geometry optimization is an indispensable step for QC calculations. However, its computational cost increases dramatically with increasing molecular system complexity, hindering the large‐scale molecule screening. This work proposes a deep learning‐based molecular potential energy surface prediction tool (DeePEST) to significantly accelerate geometry optimizations. The key of DeePEST involves the development of a novel machine learning potential model for accurate and fast predictions of molecular energy and atomic forces. These predictions enable efficient molecular geometry optimizations for subsequent predictions of QC properties (single‐point energy, dipole moment, HOMO/LUMO, and 13C chemical shifts) and COSMO‐SAC‐based thermodynamic properties (activity coefficient). Moreover, DeePEST facilitates efficient computer‐aided molecular designs that involve QC‐based geometry optimizations. The utilization of DeePEST in geometry optimizations achieves high prediction accuracy approaching to rigorous QC methods while maintaining the computational efficiency of molecular mechanics methods.

Analysis of the performance of algorithms (K-Means, Farthest First, Hierarchical) Using the data analysis and modeling tool Weka

This study assesses the performance of three clustering algorithms—K-Means, Farthest First, and Hierarchical—using the Weka data mining tool. These algorithms were applied to five diverse datasets representing healthcare, industrial, and benchmark applications to evaluate their clustering accuracy, execution time, and consistency. The experimental results show that the Farthest First algorithm achieves the highest accuracy and the fastest execution time, making it suitable for real-time applications. K-Means delivers balanced performance but is sensitive to initialization and outliers, while the Hierarchical algorithm effectively captures complex relationships but incurs high computational costs. The findings highlight the importance of selecting appropriate clustering techniques based on dataset characteristics and application requirements. Future work will explore advanced clustering methods such as DBSCAN and Gaussian Mixture Models to improve scalability and performance on large datasets.

Dual-prompt complementary fusion network for RGBT tracking

RGBT target tracking is a significant downstream task in the field of object tracking. However, compared to visible light target tracking, RGBT target tracking faces the challenge of smaller datasets, making it difficult to achieve performance levels comparable to those achieved in visible light target tracking. To address how to effectively combine the complementary characteristics of visible and thermal modalities, as well as how to fully leverage the superior performance of models trained on visible light target tracking tasks, while also aiming for lower computational costs and higher tracking effectiveness, a dual-prompt complementary fusion strategy for an RGBT tracking network is proposed. Drawing on the concept of prompt learning, this network aims to extend the efficient performance of visible light target tracking to the RGBT target tracking domain. In its implementation, the prompt module inputs both visible and thermal modality information as dual prompts into the backbone network, where the network utilizes these prompts to generate new, enriched prompt information at each layer. Subsequently, an information enhancement fusion module enhances the acquired prompt information and refeeds it into the backbone network, aiming to improve the tracking accuracy and robustness. Experimental results on GTOT, RGBT234 and LasHeR datasets show that the tracking accuracy (PR) and success rate (SR) of the network reach 93.1%/76.8%, 84.4%/62.4% and 66.8%/53.8%, respectively, which is improved compared with the current mainstream RGBT target tracking network, which verifies the effectiveness of the network.

Evaluating the Effect of Surrogate Data Generation on Healthcare Data Assessment

In healthcare applications, often it is not possible to record sufficient data as required for deep learning or data-driven classification and feature detection systems due to the patient condition, various clinical or experimental limitations, or time constraints. On the other hand, data imbalance invalidates many of the test results crucial for clinical approvals. Generating synthetic (artificial or dummy) data has become a potential solution to address this issue. Such data should possess adequate information, properties, and characteristics to mimic the real-world data recorded in natural circumstances. Several methods have been proposed for this purpose, and results often show that adding surrogates improves the decision-making accuracy. This article evaluates the most recent surrogate data generation and data synthesis methods to investigate the effects of the number of surrogates on improving the classification results. It is shown that the data analysis/classification results improve with an increasing number of surrogates, but this no longer continues after a certain number of surrogates. This achievement helps in deciding on the number of surrogates for each strategy, resulting in the alleviation of the computation cost.

Using Artificial Neural Networks to Predict the Bending Behavior of Composite Sandwich Structures

The refinement of effective data generation methods has led to a growing interest in using artificial neural networks (ANNs) to solve modeling problems related to mechanical structures. This study investigates the modeling of composite sandwich structures, i.e., structures made up of two laminated composite face sheets sandwiching a lightweight honeycomb core. An ANN was utilized to predict structural deflection and face sheet stress with low computational cost. Initially, a three-point load mode was used to determine the flexural behavior of the composite sandwich structure before subsequently analyzing the sandwich structure using the Monte Carlo sampling tool. Various combinations of face sheet materials, face sheet layer numbers, core types, core thicknesses and load magnitudes were considered as design variables in data generation. The generated data were used to train a neural network. Subsequently, the predictions of the trained ANN were compared with the outcomes of a finite element model (FEM), and the comparison was extended to real structures by conducting experimental tests. A woven carbon-fiber-reinforced polymer (WCFRP) with a Nomex honeycomb core was tested to validate the ANN predictions. The predictions from the elaborated ANN model closely matched the FEM and experimental results. Therefore, this method offers a low-computational-cost technique for designing and optimizing sandwich structures in various engineering applications.

An efficient heuristic for geometric analysis of cell deformations.

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis. Recent studies have proposed various erythrocyte representation and classification methods (Jennifer et al., 2023 [1]). Since classification depends solely on cell shape, a suitable approach models erythrocytes as closed planar curves in shape space (Epifanio et al., 2020). This approach employs elastic distances between shapes, which are invariant under rotations, translations, scaling, and reparameterizations, ensuring consistent distance measurements regardless of the curves' position, starting point, or traversal speed. While previous methods exploiting shape space distances had achieved high accuracy, we refined the model by considering the geometric characteristics of healthy and sickled erythrocytes. Our method proposes (1) to employ a fixed parameterization based on the major axis of each cell to compute distances and (2) to align each cell with two templates using this parameterization before computing distances. Aligning shapes to templates before distance computation, a concept successfully applied in areas such as molecular dynamics (Richmond et al., 2004 [2]), and using a fixed parameterization, instead of minimizing distances across all possible parameterizations, simplifies calculations. This strategy achieves 96.03% accuracy rate in both supervised classification and unsupervised clustering. Our method ensures efficient erythrocyte classification, maintaining or improving accuracy over shape space models while significantly reducing computational costs.

GENES: An Efficient Recursive zk-SNARK and Its Novel Application in Blockchain

The rapid development of blockchain has significantly promoted research on zero-knowledge proofs (ZKPs), especially zero-knowledge succinct noninteractive arguments of knowledge (zk-SNARK). As is well known, protocol proof and verification time, as well as proof size, are the main obstacles that restrict the implementation of ZKPs in practical applications, so they have become the main concerns of researchers in recent years. This work achieves a new recursive zk-SNARK called GENES, which does not have a trusted setup and is secure under the standard discrete logarithm assumption. GENES is designed from the form of the rank-1 constraint system (R1CS) satisfiability problem. Recursive proof composition is achieved by merging multiple R1CS instances, which transforms the verification of numerous proofs into the verification of a single proof. Moreover, multi-helpers amortize proof commitments in this study, significantly reducing the computational pressure and time cost of proof generation. Compared with previous work, GENES effectively improves the proof time and verification time, but at the cost of larger proof sizes. We provide a blockchain Layer-1 scaling solution leveraging GENES to demonstrate its practicality.

Decentralized Energy Swapping for Sustainable Wireless Sensor Networks Using Blockchain Technology

Wireless sensor networks deployed in energy-constrained environments face critical challenges relating to sustainability and protection. This paper introduces an innovative blockchain-powered safe energy-swapping protocol that enables sensor nodes to voluntarily and securely trade excess energy, optimizing usage and prolonging lifespan. Unlike traditional centralized management schemes, the proposed approach leverages blockchain technology to generate an open, immutable ledger for transactions, guaranteeing integrity, visibility, and resistance to manipulation. Employing smart contracts and a lightweight Proof-of-Stake consensus mechanism, computational and power costs are minimized, making it suitable for WSNs with limited assets. The system is built using NS-3 to simulate node behavior, energy usage, and network dynamics, while Python manages the blockchain architecture, cryptographic security, and trading algorithms. Sensor nodes checked their power levels and broadcast requests when energy fell under a predefined threshold. Neighboring nodes with surplus power responded with offers, and intelligent contracts facilitated secure exchanges recorded on the blockchain. The Proof-of-Stake-based consensus process ensured efficient and secure validation of transactions without the energy-intensive need for Proof-of-Work schemes. The simulation results indicated that the proposed approach reduces wastage and significantly boosts network resilience by allowing nodes to remain operational longer. A 20% increase in lifespan is observed compared to traditional methods while maintaining low communication overhead and ensuring secure, tamper-proof trading of energy. This solution provides a scalable, safe, and energy-efficient answer for next-generation WSNs, especially in applications like smart cities, precision agriculture, and environmental monitoring, where autonomy of energy is paramount.

Model and mesh selection from a mCRE functional in the context of parameter identification with full-field measurements

Abstract In this paper, we propose a general deterministic framework to question the relevance, assess the quality, and ultimately choose the features (in terms of model class and discretization mesh) of the employed computational mechanics model when performing parameter identification. The goal is to exploit both modeling and data at best, with optimized model accuracy and computational cost governed by the richness of available experimental information. Using the modified Constitutive Relation Error concept based on reliability of information and the construction of optimal admissible fields, we define rigorous quantitative error indicators that point out individual sources of error contained in the identified computational model with regards to (noisy) observations. An associated adaptive strategy is then proposed to automatically select, among a hierarchical list with increasing complexity, some parameterized mathematical model and finite element mesh which are consistent with the content of experimental data. In addition, the approach is computationally enhanced by the complementary use of model reduction techniques and specific nonlinear solvers. We focus here on experimental information given by full-field kinematic measurements, e.g. obtained by means of digital image correlation techniques, even though the proposed strategy would also apply to sparser data. The performance of the approach is analyzed and validated on several numerical experiments dealing with anisotropic linear elasticity or nonlinear elastoplastic models, and using synthetic or real observations.

A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation

The uncertainty of physical parameters is a major factor contributing to poor precipitation simulation performance in Earth system models (ESMs), particularly in tropical and Pacific regions. To address the high computational cost of repetitive ESM runs, this study proposes a multilevel surrogate model-based parameter optimization framework and applies it to improve the precipitation performance of CAM5. A top-level surrogate model using gradient boosting regression trees (GBRTs) was constructed, leveraging the candidate point (CAND) approach applied to balance exploration and exploitation. A bottom-level surrogate model was then built based on a small, selected dataset; we designed a trust region approach to adjust the sampling region during the bottom-level tuning process. Experimental results demonstrate that the proposed method achieves fast convergence and significantly enhances precipitation simulation accuracy, with an average improvement of 19% in selected regions. In integrating optimization results through a nonuniform parameterization scheme and parameter smoothing, substantial improvements were observed in the South Pacific, Niño, South America, and East Asia. Comparisons with remote sensing data confirm that the optimized precipitation simulations do not introduce significant biases to other variables, validating the effectiveness and robustness of the proposed method.

Supraharmonic Distortion at the Grid Connection Point of a Network Comprising a Photovoltaic System

Grid-connected photovoltaic (PV) systems inject nonsinusoidal currents into the grid at the point of their connection. The technology of the inverter utilized for the conversion of DC power into AC is directly associated with distortion characteristics. Even though pulse-width-modulated (PWM) converters generate considerably lower harmonic distortion than their predecessors, they are responsible for the emergence of a new power quality issue in distribution grids known as supraharmonics, which can cause problems such as overheating and malfunctions of equipment. PV systems are known sources of supraharmonics, but their impact has not yet been thoroughly researched. Due to the multitude of parameters affecting their performance, a more rigorous treatment is required compared to more common nonlinear devices. In this paper, emissions from a three-phase grid-connected PV system are examined by means of a dedicated simulation tool taking into account the specifics of inverter switching action without overly increasing computational cost. The impact of environmental parameters as well as factors affecting the switch control of the converter is investigated. The dependence of the supraharmonic emission of the PV system on the converter characteristics rather than environmental conditions is demonstrated. Furthermore, simulation studies on a network comprising the PV system and an additional supraharmonic-emitting system in simultaneous operation are conducted. Their combined effect on the distortion at the connection point of the network to the grid is assessed by means of a power flow-based approach, capable of quantifying interactions within this network. From the viewpoint of the grid, an increase of supraharmonic-related disturbance at low irradiance conditions is revealed.

A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks

Recently, the application of fog-computing technology to vehicular ad hoc networks (VANETs) has rapidly advanced. Despite these advancements, challenges remain in ensuring efficient communication and security. Specifically, there are issues such as the high communication and computation load of authentications and insecure communication over public channels between fog nodes and vehicles. To address these problems, a lightweight and secure authenticated key agreement protocol for confidential communication is proposed. However, we found that the protocol does not offer perfect forward secrecy and is vulnerable to several attacks, such as privileged insider, ephemeral secret leakage, and stolen smart card attacks. Furthermore, their protocol excessively uses elliptic curve cryptography (ECC), resulting in delays in VANET environments where authentication occurs frequently. Therefore, this paper proposes a novel authentication protocol that outperforms other related protocols regarding security and performance. The proposed protocol reduced the usage frequency of ECC primarily using hash and exclusive OR operations. We analyzed the proposed protocol using informal and formal methods, including the real-or-random (RoR) model, Burrows–Abadi–Nikoogadam (BAN) logic, and automated validation of internet security protocols and applications (AVISPA) simulation to show that the proposed protocol is correct and secure against various attacks. Moreover, We compared the computational cost, communication cost, and security features of the proposed protocol with other related protocols and show that the proposed methods have better performance and security than other schemes. As a result, the proposed scheme is more secure and efficient for fog-based VANETs.

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced data requirements. Notably, delta learning trained with just 20-30% of high-level data matched or exceeded the performance of other methods trained with full data sets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large data sets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target data sets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly in thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints.

WASTER: Practical de novo phylogenomics from low-coverage short reads.

The advent of affordable whole-genome sequencing has spurred numerous large-scale projects aimed at inferring the tree of life, yet achieving a complete species-level phylogeny remains a distant goal due to significant costs and computational demands. Traditional species tree inference methods, though effective, are hampered by the need for high-coverage sequencing, high-quality genomic alignments, and extensive computational resources. To address these challenges, this study introduces WASTER, a novel de novo tool for inferring species trees directly from short-read sequences. WASTER employs a k-mer based approach for identifying variable sites, circumventing the need for genome assembly and alignment. Using simulations, we demonstrate that WASTER achieves accuracy comparable to that of traditional alignment-based methods, even for low sequencing depth, and has substantially higher accuracy than other alignment-free methods. We validate WASTER's efficacy on real data, where it accurately reconstructs phylogenies of eukaryotic species with as low depth as 1.5X. WASTER provides a fast and efficient solution for phylogeny estimation in cases where genome assembly and/or alignment may bias analyses or is challenging, for example due to low sequencing depth. It also provides a method for generating guide trees for tree-based alignment algorithms. WASTER's ability to accurately estimate trees from low-coverage sequencing data without relying on assembly and alignment will lead to substantially reduced sequencing and computational costs in phylogenomic projects.

Improving Quadrotor Tracking Performance in Wind Disturbances Using Hybrid Reinforcement Learning Control

The control performance of model-based control approaches depends on the accuracy of the system dynamics. However, due to unknown environmental disturbances, quadrotor dynamics are time-varying during real-world flight, and real-time updating of the dynamics is required to avoid degradation of control performance due to model errors. To address this challenging issue, this paper proposes a hybrid controller that combines model predictive control (MPC) with model-based reinforcement learning (MBRL) to improve quadrotor trajectory tracking accuracy in unknown wind conditions. The MPC controller uses simple dynamics identified from offline flight data. The MBRL controller learns MPC-augmented dynamics represented by the Gaussian process (GP) and the residual policy from online flight data. The RL controller outputs additional actions to compensate for MPC control errors, while MPC can ensure the safety of RL exploration. The computational cost of GP when dealing with a large dataset affects the real-time performance. We design a priority data selection criterion to maintain a small dataset, balancing model accuracy and computational time. The accurate tracking results for different reference trajectories under various wind conditions demonstrate the robustness of the proposed approach. Comparative results with nominal MPC and related baselines show the advantages of the proposed approach in terms of control and real-time performance.

ResShift-4E: Improved Diffusion Model for Super-Resolution with Microscopy Images

Blind super-resolution algorithms based on diffusion models still face significant challenges at the current stage, including high computational cost, long inference time, and limited cross domain generalization ability. This paper aims to apply super-resolution algorithms to the field of optical microscopy imaging to reveal more microscopic structures and details. Firstly, we proposed a lightweight super-resolution model called ResShift-4E, which is an optimized model from two important aspects: reducing the diffusion steps in ResShift and strengthening the influence of the original residuals on model learning. Secondly, we constructed a dataset of Multimodal High-resolution Microscopy Images (MHMI) including a total of 1220 images, which is available on line. Moreover, we extended our model to application-oriented research on blind image super-resolution of optical microscopy imaging. The experimental results demonstrate that our ResShift-4E model outperforms other models on various microscopy images.

An Efficient Petrov–Galerkin Scheme for the Euler–Bernoulli Beam Equation via Second-Kind Chebyshev Polynomials

The current article introduces a Petrov–Galerkin method (PGM) to address the fourth-order uniform Euler–Bernoulli pinned–pinned beam equation. Utilizing a suitable combination of second-kind Chebyshev polynomials as a basis in spatial variables, the proposed method elegantly and simultaneously satisfies pinned–pinned and clamped–clamped boundary conditions. To make PGM application easier, explicit formulas for the inner product between these basis functions and their derivatives with second-kind Chebyshev polynomials are derived. This leads to a simplified system of algebraic equations with a recognizable pattern that facilitates effective inversion to produce an approximate spectral solution. Presentations are made regarding the method’s convergence analysis and the computational cost of matrix inversion. The efficiency of the method described in precisely solving the Euler–Bernoulli beam equation under different scenarios has been validated by numerical testing. Additionally, the procedure proposed in this paper is more effective compared to other existing techniques.

SED-YOLO based multi-scale attention for small object detection in remote sensing

Object detection is crucial for remote sensing image processing, yet the detection of small objects remains highly challenging due to factors such as image noise and cluttered backgrounds. In response to this challenge, this paper proposes an improved network, named SED-YOLO, based on YOLOv5s. Firstly, we leverage Switchable Atrous Convolution (SAC) to replace the standard convolutions in the original C3 modules of the backbone network, thereby enhancing feature extraction capabilities and adaptability. Additionally, we introduce the Efficient Multi-Scale Attention(EMA) mechanism at the end of the backbone network to enable efficient multi-scale feature learning, which reduces computational costs while preserving crucial information. In the Neck section, an adaptive Concat method is designed to dynamically adjust the feature fusion strategy according to image content and object characteristics, strengthening the model’s ability to handle diverse objects. Lastly, the three-scale feature detection head is expanded to four by adding a small object detection layer, and incorporating the Dynamic Head(DyHead) module. This enhances the detection head’s expressive power by dynamically adjusting attention weights in feature maps. Experimental results demonstrate that this improved network achieves an mean Average Precision (mAP) of 71.6% on the DOTA dataset, surpassing the original YOLOv5s by 2.4%, effectively improving the accuracy of small object detection.

A SURVEY ON AI-CONTENT GENERATOR

This survey provides an in-depth exploration of AI-driven content generation, covering key areas such as text creation, image synthesis, video generation, and automated coding. By examining advancements in technologies like Large Language Models (LLMs), GANs, and diffusion models, the study highlights AI's role in transforming diverse fields. Text generation technologies are enabling structured, creative, and conversational outputs, while image and video synthesis models like Imagen and Phenaki are setting new benchmarks for visual quality and realism. In code generation, tools like ChatGPT and GitHub Copilot showcase AI’s ability to translate natural language into efficient, executable code. The survey delves into the mechanisms underpinning these technologies, including transformer-based architectures, hierarchical modeling, and in-context learning. It also emphasizes their practical applications, from streamlining workflows and enhancing creativity to enabling non-experts to leverage sophisticated capabilities. While showcasing these innovations, the paper discusses limitations such as bias in data, high computational costs, and the need for greater contextual understanding in outputs. By synthesizing current research and emerging challenges, this survey offers a comprehensive resource for researchers and practitioners. It underscores the transformative potential of AI in automating and enhancing content generation across domains, paving the way for future advancements in this rapidly evolving field. INDEX TERMS: AI Content Generators, Text Generation, Text-to-Image Synthesis, Text-to-Video Generation, Pre-trained Language Models, Natural Language Processing, Deep Learning, Automated Content Creation.