Diagnosing Systems through Approximated Information

This article presents a novel approach to diagnose faults in injection molding machines. A novel data-driven approach is presented to learn an approximation of dependencies between variables using Spearman correlation. It is further shown, how the approximation of the dependencies are used to create propositional logic rules for fault diagnosis. The article presents two novel algorithms: 1) to estimate dependencies from process data and 2) to create propositional logic diagnosis rules from those connections and perform consistency-based fault diagnosis. The presented approach was validated using three experiments. The first two show that the presented approach works well for injection molding machines and a simulation of a four-tank system. The limits of the presented method are shown with the third experiment containing sets of highly correlated signals.

Vital Signal Detection Using Multi-Radar for Reductions in Body Movement Effects

Vital signal detection using multiple radars is proposed to reduce the signal degradation from a subject’s body movement. The phase variation in the transceiving signals of continuous-wave radar due to respiration and heartbeat is generated by the body surface movement of the organs monitored in the line-of-sight (LOS) of the radar. The body movement signals obtained by two adjacent radars can be assumed to be the same over a certain distance. However, the vital signals are different in each radar, and each radar has a different LOS because of the asymmetric movement of lungs and heart. The proposed method uses two adjacent radars with different LOS to obtain correlated signals that reinforce the difference in the asymmetrical movement of the organs. The correlated signals can improve the signal-to-noise ratio in vital signal detection because of a reduction in the body movement effect. Two radars at different frequencies in the 5.8 GHz band are implemented to reduce direct signal coupling. Measurement results using the radars arranged at angles of 30°, 45°, and 60° showed that the proposed method can detect the vital signals with a mean accuracy of 97.8% for the subject moving at a maximum velocity of 53.4 mm/s.

Communication-Efficient Tracking of Unknown, Spatially Correlated Signals in Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for climate and environmental monitoring. In this paper, the percentage of wireless sensors that initiate a communication attempt is assumed as cost. The performance evaluation results show that the proposed spatial tracking approach is low-cost and can model the spatial signal over time with the same performance as that of spatial modeling.

Sensory and choice responses in MT distinct from motion encoding

Macaque area MT is well known for its visual motion selectivity and relevance to motion perception, but the possibility of it also reflecting non-sensory functions has largely been ignored. Manipulating subjects' temporal evidence weighting revealed multiple components of MT responses that were, surprisingly, not interpretable as behaviorally-relevant modulations of motion encoding, nor as consequences of readout of motion direction. MT's time-varying motion-driven responses were starkly changed by our strategic manipulation, but with timecourses opposite the subjects' temporal weighting strategies. Furthermore, large choice-correlated signals were represented in population activity distinctly from motion responses (even after the stimulus) with multiple phases that both lagged psychophysical readout and preceded motor responses. These results reveal multiple cognitive contributions to MT responses that are task-related but not functionally relevant to encoding or decoding of motion for psychophysical direction discrimination, calling into question its nature as a simple sensory area.

Communication-Efficient Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for environmental monitoring. In this paper, the percentage of the communication attempts of wireless sensors is assumed as cost. Performance evaluation results show that the proposed spatial tracking approach is low cost and can model the spatial signal over time with the same performance as that of spatial modeling.

Topography-correlated atmospheric signal mitigation for InSAR applications in the Tibetan plateau based on global atmospheric models

<p>Atmospheric heterogeneity mainly exposes itself as tropospheric phase delay in satellite interferometric synthetic aperture radar (InSAR) observations, which smears or even overshadows the deformation component of InSAR measurements. In this study, we estimated the performance of four global atmospheric models (GAMs), i.e. ERA5, ERA-Interim (ERA-I), MERRA2 and GACOS, for tropospheric phase delay reduction in InSAR applications in the Tibetan plateau, of which ERA5 is the latest global atmospheric model released by ECMWF. We demonstrated the effectiveness of atmospheric phase screen (APS) correction using the four GAMs for more than 700 Sentinel-1 TOPS interferograms covering two study areas in the southern (R1) and northwest margins (R2) of the Tibetan plateau. Topography-correlated signals have been widely observed in these interferograms, which are most likely due to the APS effects. We calculated the standard deviations (STD) and correlation coefficients between InSAR Line of Sight (LOS) measurements and topography before and after applying APS correction. The results show that the STDs of non-deformation areas from the GAMs decrease to ~4 mm from ~10 mm and ~12 mm originally on average for R1 and R2, respectively, and the correlation coefficients after the APS correction are reduced below 0.4 from ~0.8 for the selected interferometric pairs. In addition, as the newly released GAM, ERA5 has similar performance with GACOS products and outperforms other models generally. This suggests that GAMs, particularly ERA5, have great potentials in the APS correction for InSAR applications in the Tibetan plateau.</p>

Methodology for estimating reception efficiency of mutually correlated or in- phase signals at diversity transmission

The article describes and theoretically substantiates the potential technical capabilities of spatially separated earth stations (ES). When several ESs emit in-phase or mutually pairwise correlated signals in the direction of one received antenna of the object, the total level of the sum of emissions at its output (at the input of the receiving path) may be several times higher than the sum of the powers of these signals. In this regard, the article investigates the influence of the phase difference of the transmitted signals on the value of their total average power at the input of the receiving path. In the case of addition of common-mode signals, a formula is used to calculate in which the power of harmonic radiation is proportional to the square of the sum of the amplitude of the common-mode signals. This paradox is also valid for pairwise cross-correlated signals. The presented technique for evaluatingтthe effectivenessтallows one to establish not only the dependence of the energy ratios on the nonтenergy parameter, but also to determine the number of low-power ESs required to ensureтthe required signal level at the input of the receiving device. The use of the presented technique makes it possible to evaluate the efficiency of receiving a combined high-power signal for a differentтnumber of emitters that form in-phase or pairwise mutual correlation of signals at the receiving point.