19 papers · ranked by Valyu relevance
Mateusz Pabian, Dominik Rzepka, Mirosław Pawlak, Marek Miśkowicz + 1 more
'Ryszard Sroka'] Event-driven systems can operate either on discrete-time event streams or on analog signals transformed into the event domain by a predefined encoding scheme. This paper studies the problem of optimal event-based signal encoding if data are to be processed by a machine learning model, such as the…
Karen Adam, Adam Scholefield, Martin Vetterli
—As event-based sensing gains in popularity, theoretical understanding is needed to harness this technology's potential. Instead of recording video by capturing frames, event-based cameras have sensors that emit events when their inputs change, thus encoding information in the timing of events. This creates new…
Kamilya Smagulova, Ahmed Elsheikh, Diego A. Silva, Mohammed E. Fouda + 1 more
Autonomous driving has the potential to enhance driving comfort and accessibility, reduce accidents, and improve road safety, with vision sensors playing a key role in enabling vehicle autonomy. Among existing sensors, event-based cameras offer advantages such as a high dynamic range, low power consumption, and…
Zhuowen Zou, Haleh Alimohamadi, Yeseong Kim, M. Hassan Najafi + 2 more
'Narayan Srinivasa' 'Mohsen Imani'] Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Hyper-Dimensional Computing (HDC) has shown promising results in enabling efficient and robust cognitive learning. In…
Simon F. Müller-Cleve, Vittorio Fra, Lyes Khacef, Alejandro Pequeño-Zurro + 7 more
'Alejandro Pequeño-Zurro' 'Daniel Klepatsch' 'Evelina Forno' 'Diego G. Ivanovich' 'Shavika Rastogi' 'Gianvito Urgese' 'Friedemann Zenke' 'Chiara Bartolozzi'] Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches…
Haixin Sun, Minh-Quan Dao, Vincent Frémont
—Event-based cameras can overpass frame-based cameras limitations for important tasks such as high-speed motion detection during self-driving cars navigation in low illumination conditions. The event cameras' high temporal resolution and high dynamic range, allow them to work in fast motion and extreme light scenarios.…
Abdelrahman Seleem, André F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira
Event cameras have the ability to capture asynchronous per-pixel brightness changes, called "events", offering advantages over traditional frame-based cameras for computer vision applications. Efficiently coding event data is critical for transmission and storage, given the significant volume of events. This paper…
Diego A. Silva, Kamilya Smagulova, Ahmed Elsheikh, Mohammed E. Fouda + 1 more
'Ahmed M. Eltawil'] Object detection plays a crucial role in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on conventional frame-based RGB sensors. However, these sensors face challenges such as motion blur and poor performance under extreme lighting…
Catarina Brites, João Ascenso
In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain, by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the…
Nicholas T. Franklin, Kenneth A. Norman, Charan Ranganath, Jeffrey M. Zacks + 1 more
Humans spontaneously organize a continuous experience into discrete events and use the learned structure of these events to generalize and organize memory. We introduce the Structured Event Memory (SEM) model of event cognition, which accounts for human abilities in event segmentation, memory, and generalization. SEM…
Chen Sun, Wannan Yang, Jared Martin, Susumu Tonegawa
A prevailing view is that the brain represents episodic experience as the continuous moment to moment changes in the experience. Whether the brain also represents the same experience as a sequence of discretely segmented events, is unknown. Here, we report a hippocampal CA1 “chunking code”, tracking an episode as its…
Srutarshi Banerjee, Zihao W. Wang, Henry Chopp, Oliver Cossairt + 1 more
'Aggelos K. Katsaggelos'] With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression…
Jan Niklas Adams, Gyunam Park, Sergej Levich, Daniel Schuster + 1 more
'Wil M. P. van der Aalst'] Abstract. Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple objects expressing the interaction of…
Christopher Baldassano, Janice Chen, Asieh Zadbood, Jonathan W Pillow + 2 more
During realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of neural event dynamics, we investigate how cortical structures generate event representations during continuous narratives, and how these events are stored and retrieved from long-term memory.…
Eldad Assa, Alexander Rivkind, Michael Kreiserman, Fahad Shahbaz Khan + 2 more
The fact that the eyes are constantly in motion, even during ‘fixation’, entails that the spike times of retinal outputs carry information about the visual scene even when the scene is static. Moreover, this motion implies that fine details of the visual scene could not be decoded from pure spatial retinal…
Wei Liu, Yingjie Shi, James N. Cousins, Nils Kohn + 1 more
How do we encode our continuous life experiences for later retrieval? Theories of event segmentation and integration suggest that the hippocampus binds separately represented events into an ordered narrative. Using an open-access functional Magnetic Resonance Imaging (fMRI) movie watching-recall dataset, we quantified…
Aliye Hazal Koyuncu, Jacopo Movilli, Sevil Sahin, Dmitrii V. Kriukov + 2 more
This work describes a competing activation network, which is regulated by chemical feedback at the liquid-surface interface. Feedback loops dynamically tune the concentration of chemical components in living systems, thereby controlling regulatory processes in neural, genetic, and metabolic networks. Advances in…
William Daniels, Meng Jia, Dorit Hammerling
We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas production sites that uses concentration data collected by pointin-space continuous monitoring systems (CMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and…
William Daniels, Meng Jia, Dorit Hammerling
We propose a generic, modular framework for methane emission event detection, localization, and quantification on oil and gas production sites that uses concentration and wind data collected by point-in-space continuous monitoring systems. The framework uses a gradient-based spike detection algorithm to estimate…