Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. Additionally, prior voltage fluctuation sensors make use of proprietary vendor intellectual property and do not provide guidance on sensor migration to other vendors. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5 \(\times\) over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2 \(\times\) . As an extension to our prior work, we show that the voltage fluctuation sensor is portable to multiple FPGA vendors, and we demonstrate implementations on both Xilinx and Intel FPGA systems.

Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5× over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Finally, our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2×.

The endogenetic processes, such as earthquakes, volcanic eruptions, landslides, or more generally, deformation phenomena of the earth's surface, such as exogenetic processes like subsidence, open pit mining, deforestation can be monitored through the use of Synthetic Aperture Radar (SAR) sensors. Because, SAR signal contains amplitude and phase information, and amplitude is the strength of the radar response and phase is the fraction of one complete sine wave cycle. Interferometric SAR (InSAR) exploits the phase difference between two complex radar SAR observations of the same area, taken from slightly different sensor positions, and extracts distance information about the Earth's terrain. By using the differential interferometric synthetic aperture radar (DInSAR) technique, which compares two images acquired from different temporal baselines, we can obtain vertical displacement images of the Earth's surface, measuring earthquakes or landslides. The 2018 Hokkaido Eastern Iburi Earthquake (Mw 6.6), which occurred on September 6, 2018, in northern Japan, caused crustal deformation along with a large number of landslides and sediment-related disasters. In this study, we use the DinSAR technique with Sentinel 1 images in band C to monitor ground deformations (displacement) caused by earthquakes.

Side-channel leakage poses a major security threat in multi-tenant FPGA environments. One tenant can instantiate a voltage fluctuation sensor that measures minute changes in the power distribution network (PDN) and infer information about co-tenant computation and data. This work presents the Tunable Dual-Edged Time-to-Digital Converter (TDC) - a voltage fluctuation sensor with two unique elements: first, it has the ability to tune the sample duration, phase, and frequency to more effectively extract information about the co-located computation; second, it captures both rising and falling transitions which provides unique information about the target computation.

Various factors have been cited for forest dieback including global warming and dryness, overgrazing damage by wild animals, forest fires, and damage by insects. Hustai National Park (HNP) of Mongolia, which is 506 square kilometers (km2) in size, encompasses high mountains, hills and valleys. The land cover of HNP, includes stepe land, mountain steppe and forest steppe. In recent years, HNP forests started to dry out significantly and the entire landscape has changed from forest steppe to mountain steppe. Currently, in HNP forests cover only 13.1 km2of the park. The dominate tree species are Betula platyphylla Sukaczev (Siberian silver birch) and Populus tremula (Eurasian aspen). Siberian silver birch is drought sensitive, frost resistant and able to grow well under different environmental conditions. Siberian silver birch is distributed only on the north-facing slopes of the mountains because evapotranspiration is much higher than precipitation in Mongolia. In this study, we conducted field surveys at three sites in the park over a five-year period and sampled 1130 points. We apply a tree measurement (TM) survey to examine living and dead (dieback) trees, as well as obtain a Diameter at Breast Height (DBH) measurement. Using the DBH and height of the trees we clarify the factors that influence white birch forest. Extraction of fallen and dieback tree pixels using time series Landsat satellite data, including an analysis of microgeomorphology characteristics (slope and solar radiation) of dieback trees using 30m SRTM DEM data were employed. Results show the DBH of the birch tree with dieback was around 10–15 cm and the tree height was 2–3 m. It was found that many of the dead trees have a negative correlation with the curvature of microtopography and a positive correlation with the amount of solar radiation.

The Gobi Desert in Mongolia is characterized by sparse and patchy vegetation, interspersed with essentially bare areas. The vegetation pattern is typically formed by perennial shrubs, grasses or annually-herbaceous plant overlying a matrix composed of bare soil. Vegetation patterns, most broadly, refer to the spatial organization of vegetation in a landscape. However, since the plants in the Gobi Desert are sparsely distributed over a vast bare field, it is extremely difficult to accurately observe from satellite imagery. This is because reflectance of dry soil is very high and the reflectance of slightly distributed plants is eliminated by soil reflection. This study solves this problem by using field surveys and methods for combining different satellite sensor data and spectral un-mixing analysis. As a result, the pixel NDVI value of desert plants shows a smaller value than the ground measurement. It is shown that the fraction of the vegetation endmember after pixel un-mixing has a remarkably high correlation with the field measured values (where, R2=0.51 between NDVI of Landsat 8 imagery original pixels and un-mixed pixels and R2=0.79 between plants coverage of field measurement and un-mixed pixels percentage of vegetation endmembers).

Side channels which introduce intra-device circuit module information leakage or functional influence are of concern for the security and trust of many applications, such as multi-tenant and multi-level security single FPGA designs. Previous works utilized a sensor co-located on the same FPGA with a target module which was able to detect side channel voltage variations. We build on this by creating a sensor with more programmability and sensitivity resulting in improved recovery of bit patterns from an isolated target. We demonstrate for the first time the recovery of an unknown target frequency and data pattern length in a multi-user FPGA side channel attack. We also show increased sensitivity over previously developed voltage sensors enabling data recovery with fewer samples.

Various factors have been cited for forest dieback including global warming and dryness, overgrazing damage by wild animals, forest fires, and damage by insects. In this study, we apply a tree measurement (TM) survey to examine living and dead (dieback) trees, as well as obtain a Diameter at Breast Height (DBH) measurement. Extraction of fallen and dieback tree pixels using time series Landsat satellite data, including an analysis of microgeomorphology characteristics (slope and solar radiation) of dieback trees using 30 m Terra/ASTER DEM data were employed. Results show a majority of dieback trees in places with a large amount of insolation with a small slope, with DBH of white birch trees largest on slopes with altitudes of 1,550 m to 1,700 m.

We propose a method to visually summarize collections of networks on which a clustering of the vertices is given. Our method allows for efficient comparison of individual networks, as well as for visualizing the average composition and structure of a set of networks. As a concrete application we analyze a set of several hundred personal networks of migrants. On the individual level the network images provide visual hints for assessing the mode of acculturation of the respondent. On the population level they show how cultural integration varies with specific characteristics of the migrants such as country of origin, years of residence, or skin color.