Real-Time Alpine Measurement System Using Wireless Sensor Networks.

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Novel monitoring techniques for characterizing frictional interfaces in the laboratory.

A pressure-sensitive film was used to characterize the asperity contacts along a polymethyl methacrylate (PMMA) interface in the laboratory. The film has structural health monitoring (SHM) applications for flanges and other precision fittings and train rail condition monitoring. To calibrate the film, simple spherical indentation tests were performed and validated against a finite element model (FEM) to compare normal stress profiles. Experimental measurements of the normal stress profiles were within -7.7% to 6.6% of the numerical calculations between 12 and 50 MPa asperity normal stress. The film also possessed the capability of quantifying surface roughness, an important parameter when examining wear and attrition in SHM applications. A high definition video camera supplied data for photometric analysis (i.e., the measure of visible light) of asperities along the PMMA-PMMA interface in a direct shear configuration, taking advantage of the transparent nature of the sample material. Normal stress over individual asperities, calculated with the pressure-sensitive film, was compared to the light intensity transmitted through the interface. We found that the luminous intensity transmitted through individual asperities linearly increased 0.05643 ± 0.0012 candelas for an increase of 1 MPa in normal stress between normal stresses ranging from 23 to 33 MPa.

Fault healing promotes high-frequency earthquakes in laboratory experiments and on natural faults.

Faults strengthen or heal with time in stationary contact, and this healing may be an essential ingredient for the generation of earthquakes. In the laboratory, healing is thought to be the result of thermally activated mechanisms that weld together micrometre-sized asperity contacts on the fault surface, but the relationship between laboratory measures of fault healing and the seismically observable properties of earthquakes is at present not well defined. Here we report on laboratory experiments and seismological observations that show how the spectral properties of earthquakes vary as a function of fault healing time. In the laboratory, we find that increased healing causes a disproportionately large amount of high-frequency seismic radiation to be produced during fault rupture. We observe a similar connection between earthquake spectra and recurrence time for repeating earthquake sequences on natural faults. Healing rates depend on pressure, temperature and mineralogy, so the connection between seismicity and healing may help to explain recent observations of large megathrust earthquakes which indicate that energetic, high-frequency seismic radiation originates from locations that are distinct from the geodetically inferred locations of large-amplitude fault slip.

TDMA-based dual-mode communication for mobile wireless sensor networks.

Small highly mobile robots, and in particular micro air vehicles (MAVs), are well suited to the task of exploring unknown indoor environments such as buildings and caves. Such a task imposes a number of requirements on the underlying communication infrastructure, with differing goals during various stages of the mission. This work addresses those requirements with a hybrid communications infrastructure consisting of a stationary mesh network along with the mobile nodes. The combined network operates in two independent modes, coupling a highly efficient, low duty cycle, low throughput mode for routing and persistent sensing with a burst mode for high data rate communication. By strategically distributing available frequency channels between the mobile agents and the stationary nodes, the overall network provides reliable long-term communication paths while maximizing data throughput when needed.

Characterization of gait abnormalities in Parkinson's disease using a wireless inertial sensor system.

Gait analysis is important in diagnosing and evaluating certain neurological diseases such as Parkinson's disease (PD). In this paper, we show the ability of our wireless inertial sensor system to characterize gait abnormalities in PD. We obtain physical features of pitch, roll, and yaw rotations of the foot during walking, use principal component analysis (PCA) to select features, and use the support vector machine (SVM) method to create a classification model. In the binary classification task of detecting the presence of PD by distinguishing between PD and control subjects, the model performs with over 93% sensitivity and specificity, and 97.7% precision. Using a cost-sensitive learner to reflect the different costs associated with misclassifying PD and control subjects, performance of 100% specificity and precision is achieved, while maintaining sensitivity of close to 89%. In the multi-class classification task of characterizing parkinsonian gait by distinguishing among PD with significant gait disturbance, PD with no significant gait disturbance, and control subjects, 91.7% class recall for control subjects is achieved and the model performs with 84.6% precision for PD subjects with significant gait disturbance. The features selected for this classification task indicate the features of gait that are principal in discriminating gait abnormalities due to PD compared to a normal gait. These results demonstrate the ability of our wireless inertial sensor system to successfully detect the presence of PD based on physical features of gait and to identify the specific features that characterize parkinsonian gait.

Hertzian impact: experimental study of the force pulse and resulting stress waves.

Ball impact has long been used as a repeatable source of stress waves in solids. The amplitude and frequency content of the waves are a function of the force-time history, or force pulse, that the ball imposes on the massive body. In this study, Glaser-type conical piezoelectric sensors are used to measure vibrations induced by a ball colliding with a massive plate. These measurements are compared with theoretical estimates derived from a marriage of Hertz theory and elastic wave propagation. The match between experiment and theory is so close that it not only facilitates the absolute calibration the sensors but it also allows the limits of Hertz theory to be probed. Glass, ruby and hardened steel balls 0.4 to 2.5 mm in diameter were dropped onto steel, glass, aluminum, and polymethylmethacrylate plates at a wide range of approach velocities, delivering frequencies up to 1.5 MHz into these materials. Effects of surface properties and yielding of the plate material were analyzed via the resulting stress waves and simultaneous measurements of the ball's coefficient of restitution. The sensors are sensitive to surface normal displacements down to about +/-1 pm in the frequency range of 20 kHz to over 1 MHz.

Results of using a wireless inertial measuring system to quantify gait motions in control subjects.

Gait analysis is important for the diagnosis of many neurological diseases such as Parkinson's. The discovery and interpretation of minor gait abnormalities can aid in early diagnosis. We have used an inertial measuring system mounted on the subject's foot to provide numerical measures of a subject's gait (3-D displacements and rotations), thereby creating an automated tool intended to facilitate diagnosis and enable quantitative prognostication of various neurological disorders in which gait is disturbed. This paper describes the process used for ensuring that these inertial measurement units yield accurate and reliable displacement and rotation data, and for validating the preciseness and robustness of the gait-deconstruction algorithms. It also presents initial results from control subjects, focusing on understanding the data recorded by the shoe-mounted sensor to quantify relevant gait-related motions.