Accuracy Constraints Research Articles

Unlocking ultra-deep wide-field imaging with sidereal visibility averaging

abstract Producing ultra-deep high-angular-resolution images with current and next-generation radio interferometers introduces significant computational challenges. In particular, the imaging is so demanding that processing large datasets, accumulated over hundreds of hours on the same pointing, is likely infeasible in the current data reduction schemes. In this paper, we revisit a solution to this problem that was considered in the past but is not being used in modern software: sidereal visibility averaging (SVA). This technique combines individual observations taken at different sidereal days into one much smaller dataset by averaging visibilities at similar baseline coordinates. We present our method and validated it using four separate 8-hour observations of the ELAIS-N1 deep field, taken with the International LOw Frequency ARray (LOFAR) Telescope (ILT) at 140 MHz. Additionally, we assessed the accuracy constraints imposed by Earth's orbital motion relative to the observed pointing when combining multiple datasets. We find, with four observations, data volume reductions of a factor of 1.8 and computational time improvements of a factor of 1.6 compared to standard imaging. These factors will increase when more observations are combined with SVA. For instance, with 3000 hours of LOFAR data aimed at achieving sensitivities of the order of μJy beam^-1 at sub-arcsecond resolutions, we estimate data volume reductions of up to a factor of 169 and a 14-fold decrease in computing time using our current algorithm. This advancement for imaging large deep interferometric datasets will benefit current generation instruments, such as LOFAR, and upcoming instruments such as the Square Kilometre Array (SKA), provided the calibrated visibility data of the individual observations are retained. abstract

Assessing Design Space for the Device-Circuit Codesign of Nonvolatile Memory-Based Compute-in-Memory Accelerators.

Unprecedented penetration of artificial intelligence (AI) algorithms has brought about rapid innovations in electronic hardware, including new memory devices. Nonvolatile memory (NVM) devices offer one such attractive alternative with ∼2× density and data retention after powering off. Compute-in-memory (CIM) architectures further improve energy efficiency by fusing the computation operations with AI model storage. Electronic characteristics of NVM devices, like resistance in the two resistance states, directly affect the circuit designers' decisions and result in the varying performance of NVM-CIM chips. In this mini review, we assess the bounds on device resistances for accuracy and circuit performance to suggest recommendations to device engineers for frictionless device-circuit-system interactions. Furthermore, we review challenges in reliably programming NVM devices, followed by benchmarking recent NVM-CIM chips. Our literature review and analytical modeling reveal that a high resistance ratio and low variability are favored, and the resistance in a low resistance state is bound by accuracy and circuit performance constraints.

Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data.

Learning interaction potentials from the structure factor is frequently seen as impractical due to accuracy constraints of neutron and X-ray scattering experiments. This study reexamines this historic inverse problem using Bayesian inference and probabilistic machine learning on a Mie fluid to elucidate how measurement noise impacts the accuracy of recovered potentials. To perform reliable potential reconstruction, we recommend that scattering data must have noise smaller than 0.005 up to ∼30 Å-1 at a standard bin width 0.05 Å-1. At uncertainties below this threshold, Mie potentials can be determined within approximately ±1.3 for the repulsive exponent, ±0.068 Å for atomic size, and ±0.024 kcal/mol in well-depth with 95% confidence. These findings highlight the potential of uniting scattering and machine learning to overcome a century-old physics problem, infer local atomic forces to serve as a vital benchmark for model validation, and enhance the accuracy of molecular simulations.

Semantically Guided Efficient Attention Transformer for Face Super-Resolution Tasks

Face super-resolution generates high-resolution face images from low-resolution inputs, supporting face recognition in challenging environments. While deep learning methods, like Stable Diffusion and origin Transformer-based framework, have advanced super-resolution, they require heavy computation, making them difficult for tasks like face recognition. However, recognition accuracy only needs images of size 112x112, reducing the necessity for extremely large outputs. Existing methods often rely on convolutional attention, limiting receptive fields and performance. To address this, we propose SETFSR, a Semantically Guided Efficient Attention Transformer Face Super-Resolution. Our model leverages efficient self-attention for global feature extraction and incorporates face parsing constraints for structural accuracy. Experiments on Helen, CelebA, and FFHQ datasets show that SETFSR outperforms state-of-the-art models in PSNR, SSIM, and identity preservation.

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

Federated Learning (FL) has gained attention across various industries for its capability to train machine learning models without centralizing sensitive data. While this approach offers significant benefits such as privacy preservation and decreased communication overhead, it presents several challenges, including deployment complexity and interoperability issues, particularly in heterogeneous scenarios or resource-constrained environments. Over-the-air (OTA) FL was introduced to tackle these challenges by disseminating model updates without necessitating direct device-to-device connections or centralized servers. However, OTA-FL brought forth limitations associated with heightened energy consumption and network latency. In this paper, we propose a multi-attribute client selection framework employing the grey wolf optimizer (GWO) to strategically control the number of participants in each round and optimize the OTA-FL process while considering accuracy, energy, delay, reliability, and fairness constraints of participating devices. We evaluate the performance of our multi-attribute client selection approach in terms of model loss minimization, convergence time reduction, and energy efficiency. In our experimental evaluation, we assessed and compared the performance of our approach against the existing state-of-the-art methods. Our results demonstrate that the proposed GWO-based client selection outperforms these baselines across various metrics. Specifically, our approach achieves a notable reduction in model loss, accelerates convergence time, and enhances energy efficiency while maintaining high fairness and reliability indicators.

A Comparative Study Assessing the Confidence of Doctors in Conventional vs Artificial Intelligence Assisted Radiological Diagnoses for Patient Care

Introduction: The integration of Artificial Intelligence (AI) into radiology has shown promise in enhancing diagnostic accuracy and efficiency, yet the confidence of doctors in AI-assisted diagnosis remains uncertain. AI's potential to streamline workflows and detect complex abnormalities is widely acknowledged, but skepticism persists regarding its reliability and the potential disruption of traditional radiological practices. This study aims to assess global doctors' confidence in AI-assisted radiology and explore factors influencing their acceptance of AI technologies. Methods: This descriptive cross-sectional survey involved 384 doctors from diverse clinical settings worldwide. A self-administered questionnaire captured demographic data, confidence in AI versus conventional radiology, and perceptions of AI in clinical practice. Data were analyzed using descriptive statistics. Results: The majority of participants (66.7%) expressed higher confidence in conventional radiologist-led diagnoses compared to AI-assisted interpretations. Confidence in AI tools averaged 5.35/10, with limited AI training (16.9%) and lack of trust (13%) as the primary challenges. Participants with more experience reported greater confidence in interpreting radiographs independently and relied less on radiologists. Common challenges in conventional radiology included delays (35%) and limited access to radiologists (26%). AI was seen as beneficial for routine cases but not yet trusted for complex diagnoses, with only 36.7% believing it will eventually surpass human expertise. Conclusion: Doctors continue to favor conventional radiologist-led diagnostics over AI-assisted tools due to concerns about trust, reliability, and insufficient training. While AI holds potential for improving diagnostic accuracy and reducing time constraints, widespread adoption requires overcoming significant barriers. Radiologists remain crucial in clinical decision-making, and AI will likely serve as a supplementary tool until confidence in its capabilities improves.

An Interpolator for a Non-Uniform Rational B-Spline Curve with a High Efficiency and Accuracy Using Polynomial Representations

This paper introduces an efficient and accurate interpolator for NURBS (non-uniform rational B-spline) curves, addressing the challenge of regulating feedrate under machining accuracy and dynamic constraints, particularly at sensitive corners. A recursive matrix representation and polynomial conversion are utilized to enhance the computation of NURBS curve intermediate points and derivatives. An improved adaptive planning method is presented to adjust the feedrate at sensitive corners, ensuring that chord error and dynamic constraints are met. The method integrates linear acceleration and deceleration stages to mitigate abrupt changes in acceleration and jerk. Additionally, a prediction–correction scheme-based interpolator is developed, employing an asynchronous mechanism to improve computational efficiency. The proposed method’s effectiveness and correctness are validated through simulation tests and machining experiments.

Harnessing Artificial Intelligence for Wildlife Conservation

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform’s success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.

New scheme of cooperative compressed spectrum sensing

Abstract This study addresses key challenges in sparse signal recovery and compressed spectrum sensing (CSS), focusing on low signal-to-noise ratios (SNR), and the computational complexity of cooperative systems. Motivated by the need for faster and more accurate recovery techniques, we first investigate and generalize the Reduced-Set Matching Pursuit (RMP) algorithm, which overcomes the speed and accuracy limitations of conventional greedy algorithms. Secondly, we propose a novel spatial averaging technique that enhances detection performance by exploiting data from multiple users to counteract low SNR. Lastly, we integrate cooperation into CSS, further improving the detection capabilities during the recovery process. Compared to existing techniques like Joint Sparse Recovery (JSR) and CoSaMP, which face computational and accuracy constraints in real-time applications, the RMP algorithm, combined with the Virtual method (data transformation) and AND fusion rule, delivers superior performance than JSR methods. Moreover, spatial averaging significantly increases the probability of cooperative detection Q d , with SNR increasing linearly by a factor of L − 1 per channel. The results are validated through the implementation of SDR. These findings demonstrate the potential of RMP and cooperation to overcome current limitations in CSS, advancing the state-of-the-art in spectrum sensing for collaborative networks.

Data-driven Prediction of Underwater Navigation Fitness Zone Classification

This article presents a pioneering approach to enhancing underwater navigation accuracy and stealth through the utilization of gravity anomaly data. Developed by researchers from leading universities in China, the study aims to revolutionize autonomous marine navigation systems in unknown sea areas. By integrating advanced data processing, feature extraction, model construction, and optimization techniques, a cutting-edge navigation system has been formulated, showcasing remarkable improvements in navigation precision.The core innovation lies in leveraging bicubic interpolation to refine data resolution, enabling a meticulous evaluation of regional suitability and segmentation of sea zones. Subsequently, a gradient-based suitability prediction model is constructed, which is validated through migration prediction, underscoring its effectiveness and reliability. This research not only addresses the limitations of traditional terrain matching navigation methods, plagued by data accuracy and resolution constraints, but also paves the way for advancements in oceanic exploration and technological competitiveness.The introduction of gravity anomaly data, coupled with machine learning and visualization tools, marks a significant step forward in the development of adaptive navigation zones. This innovative system holds immense potential to enhance the autonomy, accuracy, and concealment of underwater vehicles, thereby facilitating safer and more efficient oceanic missions in the future.

Joint optimal PMU and DULR placement in distribution network considering fault reconstruction and state estimation accuracy

Due to the rapid development of the distribution network, there is an urgent need for high-precision state estimation and safe and stable operation, this paper proposes a joint optimal placement method for Phasor Measurement Unit (PMU) and Dual Use Line Relay (DULR) in distribution networks considering fault reconstruction and state estimation accuracy. The proposed method aims to minimize the cost of PMU and DULR configuration. Based on the known pseudo-measurements and Supervisory Control and Data Acquisition (SCADA) measurements, we use the E-optimal standard design of the state estimation error covariance matrix to form the state estimation accuracy index and take the relay protection function of Feeder Terminal Unit (FTU) device and DULR device into consideration of the fault reconstruction. By solving the mixed integer semidefinite programming (MISDP) model, the optimal placement result is obtained, so that the network under normal condition and the network after multiple fault reconstruction can meet the constraints of state estimation accuracy and load loss rate respectively. The proposed method is tested on IEEE 33-node, 123-node systems as an example. The experimental results show that the proposed method is effective and practical.Note to Practitioners—This study is stimulated by the need for optimizing the placement of Micro-PMU (µPMU) in distribution network. Since the new µPMU device DULR has the same relay protection function as FTU, and distributed generation can supply power to the island after failure, this paper adds the load loss rate to the constraint conditions to study the influence of fault reconstruction on the placement scheme. A new two-step SE method considering mixed measurements was used in this paper to improve the computational efficiency of real-time SE of large-scale system while ensuring the accuracy of the calculation results. We have conducted tests in IEEE-33 and IEEE-123 systems. After preliminary verification, compared with the single fault scenario, we find that the placement scheme considering multiple fault scenarios can effectively improve the accuracy of state estimation and reduce the load loss rate of each fault scenario and only increase the cost a little. The increase in the number of DG and contact lines in the network will also effectively improve the feasibility of the placement scheme. The increase of the number of FTU configurations can effectively reduce the configuration of DULR by adding a small amount of µPMU, reduce the total cost as a result. In future studies, we will further consider the integrated configuration of µPMU, DULR and FTU, to meet more failure scenarios while reducing costs.

Role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation.

Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes postresuscitation. This narrative review aims to explore the role of artificial intelligence (AI) in predicting neurological outcomes postcardiac resuscitation. The methodology involved a detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools, and assessing their benefit in predicting neurological outcomes in postcardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood, and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets, and provide a reliable prognosis, which only demonstrates the need for AI to help overcome challenges faced. Advanced machine learning algorithms like artificial neural networks (ANN) using supervised learning by AI have improved the accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI-powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4, and AI-based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs, and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article, including data quality, bias, privacy issues, and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method, and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in postcardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.

EMSA-IK: a real-time evolutionary multi-objective semi-analytical inverse kinematics algorithm for redundant manipulators

Purpose This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators. Design/methodology/approach Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints. Findings Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application. Originality/value The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

Modelling Hourly Thermal Energy Demand: A Machine Learning Approach of Residential District Heating Substations in Turin

This paper explores the application of the XGBoost machine learning model for forecasting the hourly thermal demand in District Heating Systems, aligning with the European Union’s ambitious sustainability targets as outlined in the Renewable Energy Directive (RED) and the Energy Efficiency Directive (EED). Accurate forecasts of thermal demand are crucial for enhancing the efficiency of district heating systems through the integration of renewable energy sources and the adoption of waste heat recovery, thereby contributing significantly to achieving climate neutrality by the year 2050. This study presents a dual approach to forecasting: at the individual building level, and at an aggregated level by considering the average characteristics of the served building stock. Through a comprehensive case study of the Turin district heating system (Italy), which comprises hourly data from approximately 200 heat exchange substations across nine heating seasons, this research evaluates the comparative effectiveness of different forecasting approaches in terms of prediction accuracy and computational efficiency. The findings aim to guide district heating operators and planners in selecting the most suitable forecasting approach based on available input information, desired accuracy, and computational constraints, contributing to the strategic planning and development of sustainable and efficient district heating systems.

Cross-Domain Optimization of Low-Power Mixed-Signal Sensor Systems Under Classification Accuracy Constraints

Cross-Domain Optimization of Low-Power Mixed-Signal Sensor Systems Under Classification Accuracy Constraints

HISTORICAL AND CULTURAL HERITAGE OF BUNDELKHAND REGION CONSERVATION, VISUALIZATION, USING G.I.S TECHNOLOGIES

This research paper investigates the conservation, visualization, and study of Bundelkhand's historical and cultural heritage using Geographic Information Systems (GIS) technologies. Located in central India, Bundelkhand is renowned for its rich historical sites, including ancient temples, forts, and palaces. These sites face significant preservation challenges due to environmental degradation, neglect, and socio-economic constraints. This study explores the application of GIS technologies in documenting, analyzing, and visualizing Bundelkhand's heritage. GIS offers powerful tools for creating detailed inventories, assessing the physical condition of structures, monitoring changes, and enhancing public engagement through immersive visualizations. The paper highlights the transformative potential of GIS in heritage conservation through case studies of the Khajuraho temples and the Orchha Fort Complex, demonstrating how GIS facilitates comprehensive documentation, condition assessment, and informed conservation strategies. While GIS technologies present numerous benefits, such as enhanced data integration and improved spatial analysis, challenges related to data accuracy, technical expertise, and resource constraints must be addressed. The study underscores the need for a balanced approach that combines traditional conservation methods with advanced GIS technologies to ensure the sustainable preservation of Bundelkhand's cultural legacy. By leveraging GIS, conservationists, researchers, and policymakers can safeguard Bundelkhand's heritage, promote sustainable tourism, and foster a deeper appreciation for the region's rich cultural history. This research contributes to the broader discourse on digital transformation in heritage conservation, emphasizing the critical role of GIS in preserving and promoting cultural heritage in the digital age.

PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRIsimulation. Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equationsusing the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulationdevelopment. The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement betweenmodels. A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRIsimulation.

Optimizing Video Queries with Declarative Clues

Video Database Management Systems (VDBMS) leverage advancements in computer vision and deep learning for efficient video data analysis and retrieval. This paper introduces the concept of user-specified Clues, allowing users to incorporate domain-specific knowledge, referred to as Clues, into query optimization. Clues are expressed as Clue types, each associated with optimization rules, and applied to queries through Clue instances. The extensible ClueVQS system we present to incorporate these ideas, optimizes queries automatically, utilizing Clues to improve processing efficiency. We also introduce algorithms to optimize queries using Clues allowing for trade-offs between speed and query accuracy. Our proposals and system address challenges such as data-dependent Clue effectiveness, limiting search space, and accuracy-efficiency trade-offs. Detailed experimental results demonstrate query speedups of up to two orders of magnitude compared to other applicable approaches, and a reduction of the query optimizer time by up to 95% while respecting user-specified accuracy constraints, showcasing the effectiveness of the proposed framework.

Efficient prediction of optical properties in hexagonal PCF using machine learning models

This research explores the use of machine learning (ML) models to forecast optical characteristics in photonic crystal fibers (PCF). Specifically, we focus on a solid core index-guided PCF with a hexagonal cladding arrangement. The primary challenges to PCF propagation analysis and predictions are accuracy, computational error, and time constraints. To address these difficulties, we have specially used ML ensemble models including Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting Regression (XGBR), and Bagging Regressor (BR). Model performance is assessed using metrics like Mean Squared Error (MSE) and R-squared (R2) through 10-fold cross-validation. Our key findings show that the GBR model outperforms other models and shows extremely low MSE and outstanding R2 values in predicting effective refractive index (Neff), effective mode area (Aeff), confinement loss, and dispersion. In addition, the study compares the performance of ML models with that of previous works using Artificial Neural Network (ANN), demonstrating improved efficiency in predicting optical characteristics for hexagonal PCFs.

Target assignment for multiple stages of weapons systems using a deep Q-learning network and a modified artificial bee colony method

Multistage Weapon Target Assignment (MWTA) in defense systems is a critical process that focuses on minimizing deviations and inaccuracies during target assignment across stages like initialization, tracking, and counterattack. The research problem lies in efficiently assigning targets to weapon systems while accounting for distractions and tracking accuracy constraints. Existing approaches often face challenges related to computational complexity, real-time engagement limitations, and scalability issues, leading to inaccuracies in various stages within the system's perceived radius. This paper proposes a novel State-based Modified Artificial Bee Colony Method (SMABCM) that integrates Q-learning to generate adaptive target assignment decisions. SMABCM aims to maximize assignment accuracy by prioritizing low deviation rewards, enabling precise one-to-one target tracking and reducing the population of the artificial bee colony. The Q-learning network dynamically defines generation and reduction states based on deviations to minimize distracting targets and enhance tracking precision. Experimental results demonstrate SMABCM's superior performance over existing approaches in target precision comparison, hit probability, increase in distracting target detection, reduced deviation factor, and computation time. As the speed increases, the proposed SMABCM improves target accuracy by 9.54 %, hit probability by 9.17 %, distracting target detection by 12.35 %, while reducing deviation by 7.39 % and computation time by 10.11 %. This approach addresses complexities in multistage weapon systems through dynamic strategies, contributing to proactive defense against evolving security threats.