Nano-satellites are tiny spacecrafts with masses between 1 kg to 10 kg, where the Cubesat standard should be highlighted as the most relevant type of this miniaturized vehicles. The Cubesat was introduced to the field as an educational tool. The main features of the standard are the low cost (comparatively speaking) and the fast development cycle. Approaching the 20 years from its conception the platform has started to gain momentum also due to a more dynamic environment in the space sector, which is making more accessible the space for these type of vehicles. Sophisticated scientific missions are being proposed and developed in many universities and space agencies around the world. The private sector is also taking advantage of the standard to proposed new applications from space. In this presentation is discussed the current limitations and trends of this technology, the path followed in Chile by the SUCHAI program at the beginning, how the team in the Space and Planetary Exploration Laboratory at the University of Chile has been using the platform for space science and how these missions are developing knowledge toward the production, operation, maintenance and disposal of mega-constellations for science and various applications. Finally, we discuss why Chile might be a relevant partner in this sector.
Marcos Díaz Quezada is currently assistant professor in the Electrical Engineering Department at University of Chile, Santiago, Chile. He received his Electrical Engineering degree in 2001 from University of Chile, his M.S. and Ph.D. degrees in Electrical Engineering in 2004 and 2009, respectively from Boston University, USA. His research interests are related to the study of ionospheric turbulent plasma, incoherent scatter radar techniques, low-frequency-radio-astronomy/space instrumentation and nano-satellite technologies. Previously he has served as research assistant at Smithsonian Astrophysical Observatory, MIT Haystack Observatory and Electrical and Computer Engineering Department at Boston University. He is the responsible of the Space and Planetary Exploration Laboratory, a multidisciplinary Laboratory located in the Faculty of Physical and Mathematical Sciences at University of Chile, where the University´s nanosatellite-based space program is being developed.
With recent technological advances in sensor nodes, IoT (Internet of Things) enabled applications have great potential in many domains. However, sensing data may be inaccurate due to not only faults or failures in the sensor and network but also the limited resources and transmission capability available in sensor nodes. In this work, we first model streams of IoT data as a handful of sampled data in the transformed domain while assuming the information attained by those sampled data reveal different sparsity profiles between normal and abnormal. We then present a novel approach called AD2 (Anomaly Detection using Approximated Data) that applies a transformation on the original data, samples top k-dominant components, and detects data anomalies based on the disparity in k values. To demonstrate the effectiveness of AD2, we use IoT datasets (temperature, humidity, and CO) collected from real-world wireless sensor nodes deployed in farms and cities. Our experimental evaluation demonstrates that AD2 can approximate and successfully detect 64%–94% of anomalies using only 1.9% of the original data and minimize false-positive rates, which would otherwise require the entire dataset to achieve the same level of accuracy.
Seung Woo Son received the B.S. and M.S. degrees in computer engineering from Yeungnam University, South Korea, and the Ph.D. degree in computer science and engineering from the Pennsylvania State University. Before starting the Ph.D. program at Penn State, he was with ETRI (Electronics and Telecommunication Research Institute), South Korea, working on the embedded real-time operating systems and development tools for Internet devices. In 2014, he joined the Department of Electrical and Computer Engineering at University of Massachusetts Lowell. Prior to UMass Lowell, he was a postdoctoral researcher in the Electrical Engineering and Computer Science department at Northwestern University and the Math and Computer Science Division at Argonne National Laboratory. He is a recipient of the National Science Foundation CAREER award (2018) and the ECE Teaching Excellence Award (2019). His research interests include high performance computing with an emphasis on parallel I/O and storage systems; computer architecture; compilers; embedded systems; and most recently data compression for various datasets including HPC and IoT datasets, and systems and machine learning.
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