Gram positive cocci occur as not only singles but also arrangements of pairs, tetorads, chains and clusters in Gram stained smears images. In this paper, we detect Gram positive cocci based on the annotation along sin...
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We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD)...
In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective ...
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In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision *** is particularly important in applications pertaining to emergency rescue and crisis *** operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and *** describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and *** information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF *** framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between *** is done through the distributed synchronization of RDF Graphs shared between ***-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team *** system is empirically validated and complexity results of the proposed algorithms are ***,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.
Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last ...
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With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreove...
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Long-term motion generation is a challenging task that requires producing coherent and realistic sequences over extended durations. Current methods primarily rely on framewise motion representations, which capture onl...
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Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various e...
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ISBN:
(数字)9798350364132
ISBN:
(纸本)9798350364149
Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition.
Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study...
Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study introduces a data integration framework that integrates data from remote sensing satellites, ground-based weather stations, and other sources to create a comprehensive weather database spanning 18 years in Alberta, Canada. Machine learning methods, including Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perceptron are employed to evaluate forest fire prediction performance, overcoming the challenge of data imbalance through changes in spatial resolution, spatio-subsamping, and downsampling techniques. XGBoost exhibits results with an ROC-AUC score of 87.2% and a sensitivity of 75%.Using meteorological data and fire history improves prediction, demonstrating big data and machine learning’s role in addressing forest fire challenges.
Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structur...
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This paper investigates use meta-heuristic to solve curve fitting problems in Optical-Diffraction Based Image Depth Reconstruction. We aim to accurately establish a relationship curve between object distance and diffr...
This paper investigates use meta-heuristic to solve curve fitting problems in Optical-Diffraction Based Image Depth Reconstruction. We aim to accurately establish a relationship curve between object distance and diffraction blur kernel through discrete data points. A mathematical model is first developed to formulate the concerned problems. Then, four meta-heuristic algorithms, particle swarm optimization, artificial bee colony, genetic algorithm and differential evolution, are employed and improved according to the problem’s feature. Finally, experiments are executed on a data set. A comparison of the similarity of the least squares method verifies the feasibility of meta-heuristics for solving curve fitting problems. Among the four meta-heuristics, differential evolution algorithm has the best competitiveness.
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