Search results for " computing"
showing 10 items of 2075 documents
Real-Time 3D Face Acquisition Using Reconfigurable Hybrid Architecture
2007
Acquiring 3D data of human face is a general problem which can be applied in face recognition, virtual reality, and many other applications. It can be solved using stereovision. This technique consists in acquiring data in three dimensions from two cameras. The aim is to implement an algorithmic chain which makes it possible to obtain a three-dimensional space from two two-dimensional spaces: two images coming from the two cameras. Several implementations have already been considered. We propose a new simple real-time implementation based on a hybrid architecture (FPGA-DSP), allowing to consider an embedded and reconfigurable processing. Then we show our method which provides depth map of …
Event-based encoding from digital magnetic compass and ultrasonic distance sensor for navigation in mobile systems
2016
Event-based encoding reduces the amount of generated data while keeping relevant information in the measured magnitude. While this encoding is mostly associated with spiking neuromorphic systems, it can be used in a broad spectrum of tasks. The extension of event-based data representation to other sensors would provide advantages related to bandwidth reduction, lower computing requirements, increased processing speed and data processing. This work describes two event-based encoding procedures (magnitude-event and rate-event) for two sensors widely used in industry, especially for navigation in mobile systems: digital magnetic compass and ultrasonic distance sensor. Encoded data meet Address…
Cloud masking and removal in remote sensing image time series
2017
Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clo…
Noise reduction in asteroid imaging using a miniaturized spectral imager
2021
In October 2024, European Space Agency’s Hera mission will be launched, targeting the binary asteroid Didymos. Hera will host the Juventas and Milani CubeSats, the first CubeSats to orbit close to a small celestial body performing scientific and technological operations. The primary scientific payload of the Milani CubeSat is the SWIR, NIR, and VIS imaging spectrometer ASPECT. The Milani mission objectives include mapping the global composition and the characterization of the binary asteroid surface. Onboard data processing and evaluation steps will be applied due to the limited data budget for the downlink to Earth and to perform the technological demonstration of a novel semi-autonomous h…
The Belle II Pixel Detector Data Acquisition and Background Suppression System
2017
The Belle II experiment at the future SuperKEKB collider in Tsukuba, Japan, features a design luminosity of 8 1035 cm−2s−1, which is a factor of 40 larger than that of its predecessor Belle. The pixel detector (PXD) with about 8 million pixels is based on the DEPFET technology and will improve the vertex resolution in beam direction by a factor of 2. With an estimated trigger rate of 30 kHz, the PXD is expected to generate a data rate of 20 GBytes/s, which is about 10 times larger than the amount of data generated by all other Belle II subdetectors. Due to the large beam-related background, the PXD requires a data acquisition system with high-bandwidth data links and realtime background red…
Streamlining distributed Deep Learning I/O with ad hoc file systems
2021
With evolving techniques to parallelize Deep Learning (DL) and the growing amount of training data and model complexity, High-Performance Computing (HPC) has become increasingly important for machine learning engineers. Although many compute clusters already use learning accelerators or GPUs, HPC storage systems are not suitable for the I/O requirements of DL workflows. Therefore, users typically copy the whole training data to the worker nodes or distribute partitions. Because DL depends on randomized input data, prior work stated that partitioning impacts DL accuracy. Their solutions focused mainly on training I/O performance on a high-speed network but did not cover the data stage-in pro…
The Rise of Distributed Artificial Intelligence Through Shared Data and Cloud Solutions
2021
Decision-makers of present times encounter influence by an ever-increasing emotional condition created by social media, market trends, experience, and historical facts. The concept of mixed human and artificial intelligence is one of the most underrated business drivers today, and conspiracy theories, fixed mindset, and legacy systems are slowing down collective evolution. This paper intends to contribute to the everyday awareness of data sharing through cloud solutions and services. It opens a wide range of possibilities for new solutions and insights that endorse a collaborative culture where a growth mindset paired with transparency and ethics reduces time-to-value in businesses, governm…
Distributed Real-Time Sentiment Analysis for Big Data Social Streams
2014
Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about "what-is-happening-now" with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that…
A Selective Change Driven System for High-Speed Motion Analysis.
2016
Vision-based sensing algorithms are computationally-demanding tasks due to the large amount of data acquired and processed. Visual sensors deliver much information, even if data are redundant, and do not give any additional information. A Selective Change Driven (SCD) sensing system is based on a sensor that delivers, ordered by the magnitude of its change, only those pixels that have changed most since the last read-out. This allows the information stream to be adjusted to the computation capabilities. Following this strategy, a new SCD processing architecture for high-speed motion analysis, based on processing pixels instead of full frames, has been developed and implemented into a Field …
Toward fast and accurate emergency cases detection in BSNs
2020
International audience; In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data v…