by jsendak | Feb 22, 2024 | Computer Science
arXiv:2304.11783v2 Announce Type: replace-cross
Abstract: Rip currents pose a significant danger to those who visit beaches, as they can swiftly pull swimmers away from shore. Detecting these currents currently relies on costly equipment and is challenging to implement on a larger scale. The advent of unmanned aerial vehicles (UAVs) and camera technology, however, has made monitoring near-shore regions more accessible and scalable. This paper proposes a new framework for detecting rip currents using video-based methods that leverage optical flow estimation, offshore direction calculation, earth camera projection with almost local-isometric embedding on the sphere, and temporal data fusion techniques. Through the analysis of videos from multiple beaches, including Palm Beach, Haulover, Ocean Reef Park, and South Beach, as well as YouTube footage, we demonstrate the efficacy of our approach, which aligns with human experts’ annotations.
The Multi-Disciplinary Nature of Rip Current Detection
Rip current detection is a complex problem that requires a multi-disciplinary approach to tackle effectively. In this research paper, the authors propose a new framework that combines concepts from computer vision, signal processing, and geographical mapping to detect rip currents using video-based methods.
The use of unmanned aerial vehicles (UAVs) and camera technology enables the monitoring of near-shore regions in a more accessible and scalable manner. By analyzing videos from multiple beaches and leveraging techniques such as optical flow estimation, offshore direction calculation, earth camera projection, and temporal data fusion, the proposed framework aims to improve rip current detection accuracy.
One of the key components of this framework is optical flow estimation, which involves tracking the motion of objects in a video sequence. By analyzing the flow patterns in the video, it becomes possible to identify regions where rip currents are likely to occur. This technique has been widely used in computer vision applications, but its adaptation for rip current detection is novel and promising.
In addition to optical flow estimation, the framework also incorporates offshore direction calculation. This involves determining the direction in which rip currents are flowing, which is crucial for accurately predicting their behavior. By combining information from multiple cameras positioned at different angles, the framework can estimate the offshore direction with higher precision.
To further enhance the accuracy of rip current detection, the proposed framework leverages earth camera projection with almost local-isometric embedding on the sphere. This technique allows for better representation of the spatial relationships between different regions of interest in the video, enabling more accurate detection and tracking of rip currents.
Integration with Multimedia Information Systems
The research presented in this paper highlights the integration of multimedia information systems with rip current detection. By leveraging video-based methods and analyzing footage from multiple sources, including YouTube, the framework expands the scope of available data for analysis. This integration with multimedia information systems enables a broader understanding of rip current patterns and behaviors, leading to more accurate detection and prediction.
Applications in Artificial Reality, Augmented Reality, and Virtual Realities
The proposed framework for rip current detection using video-based methods has significant implications for artificial reality, augmented reality, and virtual realities. By accurately detecting and predicting rip currents, this technology can be utilized to create immersive virtual environments that simulate real-world beach conditions.
For example, virtual reality simulations could provide training scenarios for lifeguards, allowing them to practice rescue operations in a safe and controlled environment. Augmented reality applications could also enhance beach safety by overlaying real-time rip current information on smartphone screens or heads-up displays, providing beachgoers with crucial alerts and guidance.
Furthermore, the integration of rip current detection technology with artificial reality, augmented reality, and virtual realities could enable novel experiences for users. Imagine a virtual beach experience where users can witness the power and danger of rip currents firsthand, providing valuable educational opportunities and promoting beach safety awareness.
Conclusion
The proposed framework for rip current detection using video-based methods demonstrates the power of a multi-disciplinary approach. By combining concepts from computer vision, signal processing, and geographical mapping, the framework aims to improve the accuracy and scalability of rip current monitoring.
The integration of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities opens up new possibilities for enhancing beach safety, training lifeguards, and creating immersive experiences. The utilization of unmanned aerial vehicles (UAVs) and camera technology will continue to play a vital role in advancing the field of rip current detection and enhancing our understanding of coastal dynamics.
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by jsendak | Feb 22, 2024 | AI
arXiv:2402.13264v1 Announce Type: new
Abstract: Fault localization is challenging in online micro-service due to the wide variety of monitoring data volume, types, events and complex interdependencies in service and components. Faults events in services are propagative and can trigger a cascade of alerts in a short period of time. In the industry, fault localization is typically conducted manually by experienced personnel. This reliance on experience is unreliable and lacks automation. Different modules present information barriers during manual localization, making it difficult to quickly align during urgent faults. This inefficiency lags stability assurance to minimize fault detection and repair time. Though actionable methods aimed to automatic the process, the accuracy and efficiency are less than satisfactory. The precision of fault localization results is of paramount importance as it underpins engineers trust in the diagnostic conclusions, which are derived from multiple perspectives and offer comprehensive insights. Therefore, a more reliable method is required to automatically identify the associative relationships among fault events and propagation path. To achieve this, KGroot uses event knowledge and the correlation between events to perform root cause reasoning by integrating knowledge graphs and GCNs for RCA. FEKG is built based on historical data, an online graph is constructed in real-time when a failure event occurs, and the similarity between each knowledge graph and online graph is compared using GCNs to pinpoint the fault type through a ranking strategy. Comprehensive experiments demonstrate KGroot can locate the root cause with accuracy of 93.5% top 3 potential causes in second-level. This performance matches the level of real-time fault diagnosis in the industrial environment and significantly surpasses state-of-the-art baselines in RCA in terms of effectiveness and efficiency.
Fault Localization in Online Micro-Services: An Analysis of KGroot
Fault localization in online micro-services is a complex and challenging task due to the various types of monitoring data, events, and interdependencies involved. The industry has traditionally relied on manual localization by experienced personnel, which is not only unreliable but also lacks automation. This manual process becomes even more difficult during urgent faults when different modules present information barriers that hinder quick alignment.
To address these inefficiencies and minimize fault detection and repair time, a more reliable and automated approach is needed. KGroot offers a potential solution by using event knowledge and the correlation between events to perform root cause analysis (RCA) through the integration of knowledge graphs (KGs) and graph convolutional networks (GCNs).
The KGroot approach involves building an online graph in real-time when a failure event occurs, based on historical data. The similarity between each knowledge graph and the online graph is then compared using GCNs to identify the fault type through ranking strategy. This method enables KGroot to automatically identify the associative relationships among fault events and propagation paths, leading to accurate root cause localization.
The multi-disciplinary nature of KGroot’s approach is noteworthy. It combines techniques from knowledge graphs, graph convolutional networks, and fault diagnosis to provide comprehensive insights into fault localization. By leveraging the power of GCNs and integrating them with knowledge graphs, KGroot surpasses existing baselines in terms of effectiveness and efficiency in RCA.
In comprehensive experiments, KGroot demonstrated an impressive accuracy of 93.5% in identifying the top 3 potential causes at the second-level. This level of performance is comparable to real-time fault diagnosis in industrial environments, highlighting the practicality and reliability of KGroot in fault localization.
Overall, KGroot presents a promising solution for automated fault localization in online micro-services. Its integration of knowledge graphs and GCNs offers a multi-faceted approach that enhances the accuracy and efficiency of root cause analysis. As the industry continues to rely on micro-services for various applications, tools like KGroot will play a crucial role in maintaining stability and minimizing downtime.
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by jsendak | Feb 22, 2024 | GR & QC Articles
arXiv:2402.12409v1 Announce Type: new
Abstract: The main objective of this paper is to investigate the impact of $f(mathcal{Q},mathcal{T})$ gravity on the geometry of anisotropic compact stellar objects, where $mathcal{Q}$ is non-metricity and $mathcal{T}$ is the trace of the energy-momentum tensor. In this perspective, we use the physically viable non-singular solutions to examine the configuration of static spherically symmetric structures. We consider a specific model of this theory to examine various physical quantities in the interior of the proposed compact stars. These quantities include fluid parameters, anisotropy, energy constraints, equation of state parameters, mass, compactness and redshift. The Tolman-Oppenheimer-Volkoff equation is used to examine the equilibrium state of stellar models, while the stability of the proposed compact stars is investigated through sound speed and adiabatic index methods. It is found that the proposed compact stars are viable and stable in the context of this theory.
The main objective of this paper is to investigate the impact of $f(mathcal{Q},mathcal{T})$ gravity on the geometry of anisotropic compact stellar objects. The authors focus on using physically viable non-singular solutions to study the configuration of static spherically symmetric structures. Specifically, they consider a specific model of $f(mathcal{Q},mathcal{T})$ gravity and examine various physical quantities in the interior of the compact stars.
The paper discusses the implications of $f(mathcal{Q},mathcal{T})$ gravity on fluid parameters, anisotropy, energy constraints, equation of state parameters, mass, compactness, and redshift of the proposed compact stars. The authors utilize the Tolman-Oppenheimer-Volkoff equation to analyze the equilibrium state of stellar models and investigate the stability of the proposed compact stars using sound speed and adiabatic index methods.
Future Roadmap
Potential Challenges:
- Theoretical Complexity: Further research may be required to fully understand the intricacies and complexities of $f(mathcal{Q},mathcal{T})$ gravity and its impact on compact stellar objects.
- Experimental Verification: Experimental tests or observations are necessary to validate the predictions and conclusions of this study.
- Generalizability: The authors focus on a specific model of $f(mathcal{Q},mathcal{T})$ gravity. Future studies could explore the generalizability of their findings by considering different models within this framework.
Potential Opportunities:
- Understanding Compact Stellar Objects: This study provides insights into the geometry and physical quantities of anisotropic compact stellar objects, which could contribute to our understanding of these astrophysical entities.
- Exploring Modified Gravity Theories: $f(mathcal{Q},mathcal{T})$ gravity is a modified theory of gravity. Further investigations into this theory may shed light on the nature of gravity itself and its implications in various astrophysical contexts.
- Advancing Stellar Structure Theory: The analysis of equilibrium states and stability of compact stars in the context of $f(mathcal{Q},mathcal{T})$ gravity can enhance our knowledge of stellar structure and the fundamental forces governing star formation and evolution.
In conclusion, this paper investigates the impact of $f(mathcal{Q},mathcal{T})$ gravity on anisotropic compact stellar objects and provides valuable insights into their geometry and physical quantities. While further research and experimental verification are needed, this study opens up opportunities for understanding compact stellar objects, exploring modified gravity theories, and advancing our knowledge of stellar structure.
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by jsendak | Feb 22, 2024 | Computer Science
In this article, the researchers introduce EyeEcho, a cutting-edge acoustic sensing system that has the potential to significantly advance the field of facial expression monitoring. By utilizing two pairs of speakers and microphones mounted on glasses, EyeEcho is able to emit encoded inaudible acoustic signals directed towards the face, capturing subtle skin deformations associated with facial expressions.
The ability of EyeEcho to continuously monitor facial expressions in a minimally-obtrusive way is a major breakthrough. Traditional methods of facial expression tracking often require the use of cumbersome and uncomfortable equipment, making it difficult to capture natural and spontaneous expressions in everyday settings. With EyeEcho, users can seamlessly wear the glasses and go about their daily activities while the system accurately tracks their facial movements.
One key technology behind EyeEcho is machine learning. The reflected signals captured by the microphones are processed through a customized machine-learning pipeline, which analyzes the data and estimates the full facial movements. This approach allows for precise and real-time tracking performance.
An impressive aspect of EyeEcho is its low power consumption. Operating at just 167 mW, EyeEcho can provide continuous facial expression monitoring without significantly impacting the battery life of the glasses. This makes it feasible for long-term usage without frequent recharging or battery replacements.
The researchers conducted two user studies to evaluate EyeEcho’s performance. The first study involved 12 participants and demonstrated that with just four minutes of training data, EyeEcho achieved highly accurate tracking performance across different real-world scenarios such as sitting, walking, and after remounting the devices. This indicates that EyeEcho can adapt well to different situations and maintain its accuracy in various contexts.
The second study involved 10 participants and evaluated EyeEcho’s performance in naturalistic scenarios while participants engaged in various daily activities. The results further validated EyeEcho’s accuracy and robustness, showcasing its potential to effectively track facial expressions in real-life situations.
One particularly exciting prospect highlighted in the article is the potential of EyeEcho to be deployed on a commercial-off-the-shelf (COTS) smartphone. By integrating this technology into smartphones, it opens up possibilities for widespread adoption and usage. Real-time facial expression tracking could have numerous applications in areas such as virtual reality, augmented reality, emotion detection, mental health monitoring, and more.
In conclusion, EyeEcho represents a significant advancement in facial expression monitoring technology. Its minimally-obtrusive design, accurate tracking performance, low power consumption, and potential for smartphone integration make it a promising solution for various industries and applications. Further research and development in this field will undoubtedly uncover more potentials and expand the possibilities offered by EyeEcho.
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by jsendak | Feb 22, 2024 | Art
