Matthias Meyer

Last Name

Meyer

First Name

Matthias

ORCID

0000-0001-6895-2823

Organisational unit

02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)

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Publications 1 - 10 of 19

Frequency Scaling as a Security Threat on Multicore Systems
Miedl, Philipp; He, Xiaoxi; Meyer, Matthias; et al. (2018)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Most modern processors use Dynamic Voltage and Frequency Scaling (DVFS) for power management. DVFS allows to optimize power consumption by scaling voltage and frequency depending on performance demand. Previous research has indicated that this frequency scaling might pose a security threat in the form of a covert channel, which could leak sensitive information. However, an analysis able to determine whether DVFS is a serious security issue is still missing. In this paper, we conduct a detailed analysis of the threat potential of a DVFS-based covert channel. We investigate two multicore platforms representative of modern laptops and hand-held devices. Furthermore, we develop a channel model to determine an upper bound to the channel capacity, which is in the order of 1 bit per channel use. Last, we perform an experimental analysis using a novel transceiver implementation. The neural network based receiver yields packet error rates between 1% and 8% at average throughputs of up to 1.83 and 1.20 bits per second for platforms representative of laptops and hand-held devices, respectively. Considering the well-known small message criterion, our results show that a relevant covert channel can be established by exploiting the behaviour of computing systems with DVFS.
Event-triggered natural hazard monitoring with convolutional neural networks on the edge
Meyer, Matthias; Farei-Campagna, Timo; Pasztor, Akos; et al. (2019)
Proceedings of the 18th International Conference on Information Processing in Sensor Networks (IPSN '19)
Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge
Meyer, Matthias; Farei-Campagna, Timo; Pasztor, Akos; et al. (2018)
arXiv
In situ observations of the Swiss periglacial environment using GNSS instruments
Cicoira, Alessandro; Weber, Samuel; Biri, Andreas; et al. (2022)
Earth System Science Data
Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022), is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management.
Acoustic and micro-seismic signal of rockfall on Matterhorn
Weber, Samuel; Beutel, Jan; Faillettaz, Jérôme; et al. (2018)
5th European Conference on Permafrost, Book of Abstracts
Cross validation of a multi-modal dataset describing temperature-induced rock slope dynamics
Weber, Samuel; Beutel, Jan; Gruber, Stephan; et al. (2019)
Rock slope destabilization due to warming or thawing permafrost poses a risk to the safety of local communities and infrastructure in populated mountain regions. The analysis of fracture kinematics in the context of local temperature evolution in the longer-term is a common approach aiming to identify its forcing (e.g. Wegmann and Gudmundsson, 1999, Matsuoka and Murton, 2008, Blikra and Christiansen, 2014). Hasler et al. (2012) and Weber et al. (2017) analyzed fracture dilatation data measured at Matterhorn Hörnligrat at 3500 m a.s.l. and suggest thawing related processes, such as meltwater percolation into fractures to cause irreversible displacement. However, this finding so far has not been backed up by data from different instruments or analysis methods. Hence, misinterpretation of the existing data can not reliably be excluded. Based on further data consisting of surface displacements measured with D-GPS, inclinometers, ambient seismic vibrations and ground resistivity captured and compiled over a period of ten years, we apply a multi-data cross validation technique to detect and quantify temperature-induced rock slope dynamics and identify the components of derived process knowledge that predict behavior across differing observation methods. The combined analysis of this multi-modal dataset allows to further develop and analyse our limited understanding of the dominant processes governing rock slope stability, in our case a steep bedrock mountain permafrost buttress. Based on this evidence we conclude that the kinematics observed at the surface in the winter/refreezing period is negligible compared to those observed during spring initiated by the thawing and mobilization of fluid water w.r.t. destabilization and precursory signs of rockfall at a larger scale. Therefore, future research should focus on the quantification of water supply, distribution and mobility both in the frozen and fluid state.
Harnessing Environmental Data at the Edge of the Cloud
Meyer, Matthias (2021)
TIK-Schriftenreihe
Global warming is a defining challenge of our time with devastating consequences for local habitats. High mountain areas are particularly affected by global warming leading to a decline of their cryosphere (glaciers, snow cover and permafrost). In high-alpine steep bedrock, permafrost thaw decreases the stability of mountain slopes leading to an increase of rockfalls and landslides and thereby putting life and built infrastructure at risk. Monitoring these environmental changes is important for natural hazard warning and understanding the geophysical processes leading to such hazards. Moreover, by providing evidence from large-scale, long-term measurements, environmental monitoring helps to bolster scientific findings and can call attention to the immediate impacts of climate change. The rise of wireless sensor networks offers a range of possibilities for environmental monitoring enabling large-scale deployments with high spatial-temporal resolution using many different sensor types. The cheap and diverse sensors can be installed at hard to reach places with little available networking or power infrastructure. However, the resulting datasets (often heterogenous and long-term measurements) require a complex data analysis. Moreover, networking or power failures often lead to an error-prone data collection and a fragmented and noisy datasets. Analyzing these datasets typically requires dedicated domain-expert knowledge which can not be scaled to long-term monitoring datasets. Machine learning provides options to extract information automatically but these techniques usually require a clean dataset for training and their performance is strongly affected by differences in the distribution of training and test data. In this dissertation, we consequently develop tools and methods applicable to heterogeneous, long-term, noisy datasets originating in wireless sensor network deployments. The main contributions of the dissertation are - A methodology to work with fragmented and noisy data from a real-world sensor network deployment at Matterhorn, Switzerland. The methodology uses active learning with human-in-the-loop and a heterogeneous set of sensors to systematically filter out unwanted influences from seismic signals. - The development and installation of an array of low-power, event-triggered micro-seismic sensors for the purpose of rockfall early warning. In addition, a machine-learning based human footstep classifier is designed and optimized for computation on memory-constraint embedded devices to detect humans in the hazard zone. - Unsupervised and semi-supervised methods designed to bridge machine learning technology and domain-expert knowledge by providing experts with automated information extraction and machine-learning algorithms with crucial information such as information about the system context. - foReal, a data analytics and visualization platform which allows to combine data from different sources. It is designed for long-term and large-scale environmental datasets and focuses on robustness against data corruption, missing data and misconfigurations during data processing as well as misinterpretations during experiment design and analysis. The tooling developed enables fast and easy exchange between experts of various domains and offers the public access to scientific data.
Kinematic observations of the mountain cryosphere using in-situ GNSS instruments
Beutel, Jan; Biri, Andreas; Buchli, Ben; et al. (2021)
Earth System Science Data Discussions
Permafrost warming is coinciding with accelerated mass movements, talking place especially in steep, mountainous topography. While this observation is backed up by evidence and analysis of both remote sensing as well as repeat terrestrial surveys undertaken since decades much knowledge is to be gained about the specific details, the variability and the processes governing these mass movements in the mountain cryosphere. This dataset collates data of continuously acquired kinematic observations obtained through in-situ Global Navigation Satellite Systems (GNSS) instruments that have been designed and implemented in a large-scale multi field-site monitoring campaign across the whole Swiss Alps. The landforms covered include rock glaciers, high-alpine steep bedrock bedrock as well as landslide sites, most of which are situated in permafrost areas. The dataset was acquired at 54 different stations situated at locations from 2304 to 4003 m a.s.l and comprises 209’948 daily positions derived through double-differential GNSS post-processing. Apart from these, the dataset contains down-sampled and cleaned time series of weather station and inclinometer data as well as the full set of GNSS observables in RINEX format. Furthermore the dataset is accompanied by tools for processing and data management in order to facilitate reuse, open alternate usage opportunities and support the life-long living data process with updates. To date this dataset has seen numerous use cases in research as well as natural-hazard mitigation and adaptation due to climate change.
Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks
Meyer, Matthias; Weber, Samuel; Beutel, Jan; et al. (2019)
Earth Surface Dynamics
Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring. In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task, obtaining an error rate of 0.79 % and an F1 score of 0.9383 by jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.
Acoustic and Microseismic Characterization in Steep Bedrock Permafrost on Matterhorn (CH)
Weber, Samuel; Faillettaz, Jérome; Meyer, Matthias; et al. (2018)
Journal of Geophysical Research: Earth Surface

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Matthias Meyer

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