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Published in European Physical Society Conference on High Energy Physics, 2015
This presentation is about ATLAS FTK track trigger project based on FPGAs and a custom ASIC: the Associative Memory chip (AMchip). The AMchip is a core processor in charge of the real-time pattern recognition stage of the FTK algorithm. It includes Content Addressable Memory with advanced computation logic enabling detection of correlated data patterns (e.g. tracks made of hits) within a sparse dataset at full I/O speed.
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Published in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2016
We present the tests of a prototype system (Pattern Recognition Mezzanine) as core of pattern recognition and track fitting for HL-LHC ATLAS and CMS experiments, combining the power of both Associative Memory custom ASIC and modern Field Programmable Gate Array (FPGA) devices.
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Published in International Symposium on Electronic Imaging: Imaging Sensors and Systems, 2020
Subsurface scattering gives a distinct look to many everyday objects. We demonstrate a system that can quickly acquire the full anisotropic subsurface scattering for homogeneous materials. Unlike many existing commercial acquisition systems, our system can be assembled from off-the-shelf optical component and 3D printed/cut parts.
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Published in NVIDIA GPU Technology Conference, 2020
For past decades sports tracking was limited to a rough representation of each player by a single point and often relies on special markers integrated into sports apparel. We propose a novel modular sports tracking system comprising of independent units, each running state-of-the-art algorithms for player detection and tracking, and which provides a full skeleton representation for each player over a large game field.
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Published in United States Patent and Trademark Office, 2021
A modular tracking system is described comprising of the network of independent tracking units. Markerless computer vision algorithms are executed directly on the units and provide feedback to motorized mirror placed in front of the zoomed camera to keep tracked objects/people in its field of view. Inference from different sensor is fused in real time to reconstruct high-level events and full skeleton representation for each participant.
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Published in NeurIPS 2021 Datasets and Benchmarks Track (Round 2), 2021
Images of a real scene taken with a camera commonly differ from synthetic images of a virtual replica of the same scene, despite advances in light transport simulation and calibration. By explicitly co-developing the Structured-Light Scanning (SLS) hardware and rendering pipeline we are able to achieve negligible per-pixel difference between the real image and the synthesized image on geometrically complex calibration objects with known material properties.
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Published in MDPI Sensors, 2022
Sensor networks have dynamically expanded our ability to monitor and study the world, and their presence and need keep increasing. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework with a flexible modular API for data collection and analysis using multiple sensing modalities.
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Published in MDPI Sensors, 2023
Data collected from sensors in areas of high activity in the urban environment is valuable for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. We present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data was collected with custom REIP sensors that were designed with the ability to accurately synchronize multiple video and audio streams.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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