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In this thesis, we focus on the problem of estimating receiver and sender node positions given some form of distance measurements between them. This kind of localization problem has several applications, e.g., global and indoor positioning, sensor network calibration, molecular conformations, data visualization, graph embedding, and robot kinematics. More concretely, this thesis makes contribution

Droughts affect ecosystems at multiple time scales, but their sub-seasonal legacy effects on vegetation activity remain unclear. Combining the satellite-based enhanced vegetation index MODIS EVI with a novel location-specific definition of the growing season, we quantify drought impacts on sub-seasonal vegetation activity and the subsequent recovery in the Northern Hemisphere. Drought legacy effec

Lake ice loss has been detected worldwide due to recent climate warming, yet spatially and temporally detailed information on the changes in global ice phenology does not exist. Here, we build a global lake ice phenology database comprising three lake ice phenologies—freeze-up, break-up, and ice duration—for each year across two centuries (1900–2099). The timing of all three phenologies experience

It is reported that the China–Pakistan Economic Corridor has been affected by extreme precipitation events. Since the 20th century, extreme weather events have occurred frequently, and the damage and loss caused by them have increased. In particular, the flood disaster caused by excessive extreme precipitation seriously hindered the development of the human society. Based on CRiteria Importance Th

This paper presents the hardware/software generation backend of a code generation framework. The backend aims at synthesizing complete systems based on RISC-V cores with accelerators from a single-language description. The framework takes the dataflow description of an algorithm as input and generates a combination of hardware (in Chisel) and software (in C) that interacts with the hardware. The h

With the purpose of examining biased updates in variance-reduced stochastic gradient methods, we introduce SVAG, a SAG/SAGA-like method with adjustable bias. SVAG is analyzed in a cocoercive root-finding setting, a setting which yields the same results as in the usual smooth convex optimization setting for the ordinary proximal-gradient method. We show that the same is not true for SVAG when biase

In recent decades, the Barents Sea has warmed more than twice as fast as the rest of the Arctic in winter, but the exact causes behind this amplified warming remain unclear. In this study, we quantify the wintertime Barents Sea warming (BSW, for near-surface air temperature) with an average linear trend of 1.74 °C decade-1 and an interdecadal change around 2003 based on a surface energy budget ana

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic

The MeerKAT radio telescope consists of 64 Gregorian-offset antennas located in the Karoo in the Northern Cape in South Africa. The antenna system consists of multiple subsystems working collaboratively to form a cohesive instrument capable of operating in multiple modes for defined science cases. We focus on the channelizing subsystem (F-engine), the correlation subsystem (X-engine), and the beam

This chapter focuses on the Arctic since ocean-land interactions are more important for the Arctic than the Antarctic carbon cycle. It explores the complexity of connections between the ocean and land of the North Pole region, and possible impacts on greenhouse gas exchange and lateral carbon flows thereof. Ocean-land interactions in the Arctic integrate the terrestrial and marine environments. Th

This chapter describes the Back to the Future (BTF) approach with illustrations of different data sets and their conclusions and stimulates the growth of such studies. The BTF Project included several studies that "discovered" old data sets, digitized them, carried out analyses and made data and analyses available in publications. An important aspect of the BTF approach is that the evidence of cha

BACKGROUND: Transmission of coronavirus disease 2019 (COVID-19) can occur through inhalation of fine droplets or aerosols containing infectious virus. The objective of this study was to identify situations, patient characteristics, environmental parameters, and aerosol-generating procedures (AGPs) associated with airborne severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus.METHODS:

This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The

In this thesis, we present and study a few approaches for constructing controllers for uncertain systems, using a combination of classical control theory and modern machine learning methods. The thesis can be divided into two subtopics. The first, which is the focus of the first two papers, is dual control. The second, which is the focus of the third and last paper, is multiple-input multiple-outp

Motivated by a need to characterize transient behaviors in large network systems in terms of relevant signal norms and worst-case input scenarios, we propose a novel approach based on existing theory for matrix pseudospectra. We extend pseudospectral theorems, pertaining to matrix exponentials, to an input-output setting, where matrix exponentials are pre- and post-multiplied by input and output m

The three leading modes of the North Atlantic atmospheric circulation explain about 70% of the winter climate variability. Although climate models generally can capture these modes, biases may induce large uncertainties in regional climate predictions. Here, we evaluate the leading winter modes simulated by CMIP5-PMIP3 and CMIP6-PMIP4 models from the last millennium to future scenarios in comparis

This work presents an assessment of the precipitation deficit in the São Francisco River Basin (SFRB) based on temporal, seasonal and regional analyses of two decades of precipitation data products derived from satellite missions launched since 1997. The temporal analyses were performed by means of three 3-month SPI time series, consisting of 250 values each derived from TRMM, CHIRPS and PERSIANN-

Lake level, area and volume are sensitive indicators of climate change. At present, many studies have focused on the interannual water balance of lakes, but lake level and area can change remarkably with seasons, especially for lakes with seasonal ice cover. Zhari Namco, a seasonal frozen lake, was selected as an example to investigate its seasonal water balance. Multi-source altimetry and Landsat