scholarly journals DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

2019 ◽  
Vol 11 (9) ◽  
pp. 1128 ◽  
Author(s):  
Maryam Rahnemoonfar ◽  
Dugan Dobbs ◽  
Masoud Yari ◽  
Michael J. Starek
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Sparse Matrix ◽  
Aerial Images ◽  
Heat Map ◽  
High Resolution Image ◽  
Current State ◽  
Density Map ◽  
Distinct Features

Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

GPU based real-time enhancement of high resolution image

2018 ◽  
Author(s):  
Yue Yu ◽  
Maosen Huang ◽  
Kuanhong Cheng ◽  
Huixin Zhou ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Resolution Image ◽  
High Resolution Image

Real Time Simulation for High Fidelity Multibody Dynamics and Mechatronic Systems

2014 ◽  
Author(s):  
William Prescott
Keyword(s):  
Real Time ◽  
Multibody Dynamics ◽  
Large Scale ◽  
State Of The Art ◽  
High Fidelity ◽  
Implicit Integration ◽  
Mechatronic Systems ◽  
Vehicle Simulation ◽  
Current State ◽  
Time Simulation

This paper will investigate the use of large scale multibody dynamics (MBD) models for real-time vehicle simulation. Current state of the art in the real-time solution of vehicle uses 15 degree of freedom models, but there is a need for higher-fidelity systems. To increase the fidelity of models uses this paper will propose the use of the following techniques: implicit integration, parallel processing and co-simulation in a real-time environment.

scholarly journals HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Juan J. Lastra-Díaz ◽  
Alicia Lara-Clares ◽  
Ana Garcia-Serrano
Keyword(s):  
Real Time ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Shortest Path ◽  
State Of The Art ◽  
Biomedical Domain ◽  
Snomed Ct ◽  
Shortest Path Algorithm ◽  
Semantic Similarity Measure ◽  
Current State

Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

2021 ◽  
Vol 15 (02) ◽  
pp. 161-187
Author(s):  
Olav A. Nergård Rongved ◽  
Steven A. Hicks ◽  
Vajira Thambawita ◽  
Håkon K. Stensland ◽  
Evi Zouganeli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Event Detection ◽  
State Of The Art ◽  
Time Estimation ◽  
High Recall ◽  
Current State ◽  
Ablation Study ◽  
Different Parts

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

scholarly journals Verification and Parameter Synthesis for Real-Time Programs using Refinement of Trace Abstraction*

2021 ◽  
Vol 178 (1-2) ◽  
pp. 31-57
Author(s):  
Franck Cassez ◽  
Peter Gjøl Jensen ◽  
Kim Guldstrand Larsen
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Timed Automata ◽  
Robustness Analysis ◽  
Hybrid Automata ◽  
Timed Systems ◽  
Current State ◽  
Parameter Synthesis ◽  
Series Of Experiments ◽  
Time Systems

We address the safety verification and synthesis problems for real-time systems. We introduce real-time programs that are made of instructions that can perform assignments to discrete and real-valued variables. They are general enough to capture interesting classes of timed systems such as timed automata, stopwatch automata, time(d) Petri nets and hybrid automata. We propose a semi-algorithm using refinement of trace abstractions to solve both the reachability verification problem and the parameter synthesis problem for real-time programs. All of the algorithms proposed have been implemented and we have conducted a series of experiments, comparing the performance of our new approach to state-of-the-art tools in classical reachability, robustness analysis and parameter synthesis for timed systems. We show that our new method provides solutions to problems which are unsolvable by the current state-of-the-art tools.

scholarly journals Improving Week-2 Forecasts with Multimodel Reforecast Ensembles

2006 ◽  
Vol 134 (8) ◽  
pp. 2279-2284 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Xue Wei ◽  
Frédéric Vitart
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Model Output ◽  
Single Model ◽  
Global Models ◽  
Operational Forecast ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.

Multivariate phase combination improves automated crystallographic model building

2010 ◽  
Vol 66 (7) ◽  
pp. 783-788 ◽  
Author(s):  
Pavol Skubák ◽  
Willem-Jan Waterreus ◽  
Navraj S. Pannu
Keyword(s):  
Model Building ◽  
State Of The Art ◽  
Standard Technique ◽  
Real Data ◽  
Data Sets ◽  
Macromolecular Crystallography ◽  
Current State ◽  
Phase Combination ◽  
Density Map ◽  
Electron Density Map

Density modification is a standard technique in macromolecular crystallography that can significantly improve an initial electron-density map. To obtain optimal results, the initial and density-modified map are combined. Current methods assume that these two maps are independent and propagate the initial map information and its accuracy indirectly through previously determined coefficients. A multivariate equation has been derived that no longer assumes independence between the initial and density-modified map, considers the observed diffraction data directly and refines the errors that can occur in a single-wavelength anomalous diffraction experiment. The equation has been implemented and tested on over 100 real data sets. The results are dramatic: the method provides significantly improved maps over the current state of the art and leads to many more structures being built automatically.

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