Purpose: Accurate depth estimation in surgical videos is a pivotal component of numerous image-guided surgery procedures. However, creating ground truth depth maps for surgical videos is often infeasible due to challe...
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Purpose: Accurate depth estimation in surgical videos is a pivotal component of numerous image-guided surgery procedures. However, creating ground truth depth maps for surgical videos is often infeasible due to challenges such as inconsistent illumination and sensor noise. As a result, self-supervised depth and ego-motion estimation frameworks are gaining traction, eliminating the need for manually annotated depth maps. Despite the progress, current self-supervised methods still rely on known camera intrinsic parameters, which are frequently unavailable or unrecorded in surgical environments. We address this gap by introducing a self-supervised system capable of jointly predicting depth maps, camera poses, and intrinsic parameters, providing a comprehensive solution for depth estimation under such constraints. Approach: We developed a self-supervised depth and ego-motion estimation framework, incorporating a cost volume based auxiliary supervision module. This module provides additional supervision for predicting camera intrinsic parameters, allowing for robust estimation even without predefined intrinsics. The system was rigorously evaluated on a public dataset to assess its effectiveness in simultaneously predicting depth, camera pose, and intrinsic parameters. Results: The experimental results demonstrated that the proposed method significantly improved the accuracy of ego-motion and depth prediction, even when compared with methods incorporating known camera intrinsics. In addition, by integrating our cost volume based supervision, the accuracy of camera parameter estimation, including intrinsic parameters, was further enhanced. Conclusions: We present a self-supervised system for depth, ego-motion, and intrinsic parameter estimation, effectively overcoming the limitations imposed by unknown or missing camera intrinsics. The experimental results confirm that the proposed method outperforms the baseline techniques, offering a robust solution for depth estimation
This study investigates the transition control problem for a double inverted pendulum system, which has one stable and three unstable equilibrium points. We propose a method for implementing transition control using a...
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One of the pressing concerns for emerging nations is maintenance of roads, including identification and repair of pavement distress. Previous research has focused on pothole detection and lane identification, with the...
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Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used ...
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The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial....
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Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN e...
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Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key *** wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy *** proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the *** this algorithm,each node gets sorted using the K-means technique,in which the data gets distributed into various *** mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor *** position of the anchor points is calculated based on the energy distribution using the base *** this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the *** the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming *** on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.
This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have be...
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The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
In recent years, reinforcement learning (RL)-based controller design methods have emerged as a powerful alternatives to traditional methods, providing a novel paradigm that overcomes limitations associated with the ne...
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The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical ***,existing road infrastructure confronts challenges due to p...
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The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical ***,existing road infrastructure confronts challenges due to prolonged use and insufficient *** research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance,while neglecting road construction information,leading to potential time and energy inefficiencies in real-world scenarios involving infrastructure *** address this issue,a digital twin-embedded multi-objective autonomous vehicle navigation is proposed under the condition of infrastructure *** authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extrac-tion and modelling of the autonomous vehicle ***,a wavelet neural network is developed to predict real-time traffic flow,considering its inherent ***,a multi-objective brainstorm optimisation(BSO)-based method for path planning is introduced,which optimises total time-cost and energy consumption objective *** ensure optimal trajectory planning during infrastructure con-struction,the algorithm incorporates a real-time updated digital twin throughout autonomous vehicle *** effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road *** results highlight the improved performance and reli-ability of the autonomous vehicle system when equipped with the authors’approach,demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.
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