Tracking water pathways in steep hillslopes by d18O depth profiles of soil water

Assessing temporal variations in soil water flow is important, especially at the hillslope scale, to identify mechanisms of runoff and flood generation and pathways for nutrients and pollutants in soils. While surface processes are well considered and parameterized, the assessment of subsurface processes at the hillslope scale is still challenging since measurement of hydrological pathways is connected to high efforts in time, money and personnel work. The latter might not even be possible in alpine environments with harsh winter processes. Soil water stable isotope profiles may offer a time-integrating fingerprint of subsurface water pathways. In this study, we investigated the suitability of soil water stable isotope (d18O) depth profiles to identify water flow paths along two transects of steep subalpine hillslopes in the Swiss Alps. We applied a one-dimensional advection–dispersion model using d18O values of precipitation (ranging from _24.7 to _2.9‰) as input data to simulate the d18O profiles of soil water. The variability of d18O values with depth within each soil profile and a comparison of the simulated and measured d18O profiles were used to infer information about subsurface hydrological pathways. The temporal pattern of d18O in precipitation was found in several profiles, ranging from _14.5 to _4.0‰. This suggests that vertical percolation plays an important role even at slope angles of up to 46_. Lateral subsurface flow and/or mixing of soil water at lower slope angles might occur in deeper soil layers and at sites near a small stream. The difference between several observed and simulated d18O profiles revealed spatially highly variable infiltration patterns during the snowmelt periods: The d18O value of snow (_17.7 ± 1.9‰) was absent in several measured d18O profiles but present in the respective simulated d18O profiles. This indicated overland flow and/or preferential flow through the soil profile during the melt period. The applied methods proved to be a fast and promising tool to obtain time-integrated information on soil water flow paths at the hillslope scale in steep subalpine slopes.

Apes’ tracking of objects and collections

Recent research suggests that great apes are less vulnerable to cohesion violations than human infants are. In contrast to human infants, apes successfully track nonsolid substances or split solid objects through occlusion (Cacchione & Call, 2010a; Cacchione, Hrubesch, & Call, 2012, 2013). The present study aims to investigate whether the lower vulnerability of great apes to cohesion violations also manifests when they are tracking collections. While even very young human infants appreciate the continuous existence of solid bound objects, they fail to show similar intuitions when tracking collections of objects (Chiang & Wynn, 2000). In a manual search task inspired by recent infant research, we tested whether humans’ closest relatives, the great apes, showed a similar contrast in their reasoning about single solid objects and objects within collections. The results suggest that, in contrast to human infants, great apes appreciate the continuous existence of objects within collections and successfully track them through occlusion. This confirms the view that great apes are generally less vulnerable to cohesion violations than human infants.

Universal ontology: attentive tracking of objects and substances across languages and over development

Previous research has demonstrated that adults are successful at visually tracking rigidly moving items, but experience great difficulties when tracking substance-like ‘‘pouring’’ items. Using a comparative approach, we investigated whether the presence/absence of the grammatical count–mass distinction influences adults and children’s ability to attentively track objects versus substances. More specifically, we aimed to explore whether the higher success at tracking rigid over substance-like items appears universally or whether speakers of classifier languages (like Japanese, not marking the object–substance distinction) are advantaged at tracking substances as compared to speakers of non-classifier languages (like Swiss German, marking the object–substance distinction). Our results supported the idea that language has no effect on low-level cognitive processes such as the attentive visual processing of objects and substances. We concluded arguing that the tendency to prioritize objects is universal and independent of specific characteristics of the language spoken.

Unified detection and tracking of instruments during retinal microsurgery

Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography

Long-term electrocardiogram (ECG) signals might suffer from relevant baseline disturbances during physical activity. Motion artifacts in particular are more pronounced with dry surface or esophageal electrodes which are dedicated to prolonged ECG recording. In this paper we present a method called baseline wander tracking (BWT) that tracks and rejects strong baseline disturbances and avoids concurrent saturation of the analog front-end. The proposed algorithm shifts the baseline level of the ECG signal to the middle of the dynamic input range. Due to the fast offset shifts, that produce much steeper signal portions than the normal ECG waves, the true ECG signal can be reconstructed offline and filtered using computationally intensive algorithms. Based on Monte Carlo simulations we observed reconstruction errors mainly caused by the non-linearity inaccuracies of the DAC. However, the signal to error ratio of the BWT is higher compared to an analog front-end featuring a dynamic input ranges above 15 mV if a synthetic ECG signal was used. The BWT is additionally able to suppress (electrode) offset potentials without introducing long transients. Due to its structural simplicity, memory efficiency and the DC coupling capability, the BWT is dedicated to high integration required in long-term and low-power ECG recording systems.