Mobile Edge Computing (MEC) is a key technology for supporting low latency applications close to the end user. Users can access application servers in MEC instead of routing to the Internet by passing through a core c...
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Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two ...
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Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.
This paper proposes A dynamic switching strategy based on Dijkstra algorithm and A ∗ algorithm. By setting a threshold, the dynamic switching algorithm according to the distance can improve the efficiency of path plan...
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Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylh...
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Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d
The world of digitization is growing exponentially;data optimization, security of a network, and energy efficiency are becoming more prominent. The Internet of Things (IoT) is the core technology of modern society. Th...
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The gannet optimization algorithm (GOA) is an effective group intelligence algorithm inspired by the foraging behavior of gannets. Despite its merits, considerable potential exists for enhancing its exploration and co...
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This paper investigates the unknown-input-observer-based fault estimation problem for a class of discrete-time-delay Markovian jump systems under the dynamic event-triggered transmission scheme. The dynamic event-trig...
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This paper investigates the unknown-input-observer-based fault estimation problem for a class of discrete-time-delay Markovian jump systems under the dynamic event-triggered transmission scheme. The dynamic event-triggered mechanism is used to decide whether the current information should be transmitted to the estimator or not to save the limited communication resources. This study aims to design an event-based fault estimator such that the estimation error is exponentially ultimately bounded in the mean square *** adopting the Lyapunov-Krasovskii functional approach, sufficient conditions are obtained to guarantee the existence of the desired estimator to achieve the prescribed performance requirement. The estimator gains are derived based on the convex optimization technique. A numerical example is provided to illustrate the effectiveness of the developed estimator design scheme.
This paper presents a novel deep neural network for designated point tracking(DPT)in a monocular RGB video,*** concretely,the aim is to track four designated points correlated by a local homography on a textureless pl...
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This paper presents a novel deep neural network for designated point tracking(DPT)in a monocular RGB video,*** concretely,the aim is to track four designated points correlated by a local homography on a textureless planar region in the *** can be applied to augmented reality and video editing,especially in the field of video *** methods predict the location of four designated points without appropriately considering the point *** solve this problem,VideoInNet predicts the motion of the four designated points correlated by a local homography within the heatmap prediction *** network refines the heatmaps of designated points through two *** the first stage,we introduce a context-aware and location-aware structure to learn a local homography for the designated plane in a supervised *** the second stage,we introduce an iterative heatmap refinement module to improve the tracking *** propose a dataset focusing on textureless planar regions,named ScanDPT,for training and *** show that the error rate of VideoInNet is about 29%lower than that of the state-of-the-art approach when testing in the first 120 frames of testing videos on ScanDPT.
In off-road scenes, the fusion of dual-LiDAR data is crucial for ensuring the accuracy of environmental perception in autonomous vehicles. The terrain in off-road scenes is complex and filled with unstructured informa...
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In off-road scenes, the fusion of dual-LiDAR data is crucial for ensuring the accuracy of environmental perception in autonomous vehicles. The terrain in off-road scenes is complex and filled with unstructured information. This leads to significant noise and a lack of distinct structural features in the point cloud data, making traditional point cloud registration methods difficult to apply. To address these issues posed by complex off-road scenes, we propose a novel point cloud fusion framework named Off-Fusion. We first filter and segment the ground in the input point cloud data, focusing on preserving the core features of the ground while effectively removing noise caused by the terrain. Next, we propose a robust and efficient feature point extraction method based on voxel division and curvature weighting, ensuring extracting meaningful and representative feature points from complex off-road scenes. Based on this, we use feature matching to calculate rough relative transformation pairs, providing a high-quality starting position for the Iterative Closest Point (ICP) algorithm, effectively avoiding local optima. Finally, by combining the kd-tree accelerated ICP algorithm, we achieve precise point cloud registration, successfully calculating the optimal rotation and translation matrix between the two LiDARs. The experimental results show that our method significantly improves the quality and speed of data fusion. Compared to some of the most advanced methods, it performs better in off-road scenes, achieving the best results. IEEE
Non-Volatile Memory(NVM) offers byte-addressability and persistency. Because NVM can be plugged into memory and provide low latency, it offers a new opportunity to build new database systems with a single-layer storag...
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Non-Volatile Memory(NVM) offers byte-addressability and persistency. Because NVM can be plugged into memory and provide low latency, it offers a new opportunity to build new database systems with a single-layer storage design. A single-layer NVM-Native DataBase(N2 DB) provides zero copy and log freedom. Hence, all data are stored in NVM and there is no extra data duplication and logging during execution. N2 DB avoids complex data synchronization and logging overhead in the two-layer storage design of disk-oriented databases and in-memory databases. Garbage Collection(GC) is critical in such an NVM-based database because memory leaks on NVM are durable. Moreover, data recovery is equally essential to guarantee atomicity, consistency, isolation, and durability properties. Without logging, it is a great challenge for N2 DB to restore data to a consistent state after crashes and recoveries. This paper presents the GC and data recovery mechanisms for N2 DB. Evaluations show that the overall performance of N2 DB is up to 3:6 higher than that of InnoDB. Enabling GC reduces performance by up to 10%,but saves storage space by up to 67%. Moreover, our data recovery requires only 0:2% of the time and half of the storage space of InnoDB.
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