Real-Time Sensor Networks and Systems for the Industrial IoT: What Next?

The Industrial Internet of Things (Industrial IoT—IIoT) is the emerging core backbone construct for the various cyber-physical systems constituting one of the principal dimensions of the 4th Industrial Revolution [...

Neural Networks for Indoor Person Tracking With Infrared Sensors

Indoor localization has many pervasive applications, like energy management, health monitoring, and security. Tagless localization detects directly the human body, like passive infrared sensing, and is the most amenable to different users and use cases. We evaluate the localization and tracking performance, as well as resource and processing requirements of various neural network (NN) types using directly the data from a low resolution 16-pixel thermopile sensor array in a 3 m x 3 m room. Out of the multilayer perceptron, autoregressive, 1D-CNN, and LSTM NN architectures that we test, the latter require more resources but can accurately locate and capture best the person movement dynamics, while the 1D-CNN provides the best compromise between localization accuracy (9.6 cm RMSE) and movement tracking smoothness with the least resources, and seem more suited for embedded applications

Comparative node selection-based localization technique for wireless sensor networks: A bilateration approach

Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co-operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade-off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering

An exclusion process on a tree with constant aggregate hopping rate

We introduce a model of a totally asymmetric simple exclusion process (TASEP) on a tree network where the aggregate hopping rate is constant from level to level. With this choice for hopping rates the model shows the same phase diagram as the one-dimensional case. The potential applications of our model are in the area of distribution networks; where a single large source supplies material to a large number of small sinks via a hierarchical network. We show that mean field theory (MFT) for our model is identical to that of the one-dimensional TASEP and that this mean field theory is exact for the TASEP on a tree in the limit of large branching ratio, $b$(or equivalently large coordination number). We then present an exact solution for the two level tree (or star network) that allows the computation of any correlation function and confirm how mean field results are recovered as $b\rightarrow\infty$. As an example we compute the steady-state current as a function of branching ratio. We present simulation results that confirm these results and indicate that the convergence to MFT with large branching ratio is quite rapid.Comment: 20 pages. Submitted to J. Phys.

Large deviations and dynamical phase transitions in stochastic chemical networks

Chemical reaction networks offer a natural nonlinear generalisation of linear Markov jump processes on a finite state-space. In this paper, we analyse the dynamical large deviations of such models, starting from their microscopic version, the chemical master equation. By taking a large-volume limit, we show that those systems can be described by a path integral formalism over a Lagrangian functional of concentrations and chemical fluxes. This Lagrangian is dual to a Hamiltonian, whose trajectories correspond to the most likely evolution of the system given its boundary conditions. The same can be done for a system biased on time-averaged concentrations and currents, yielding a biased Hamiltonian whose trajectories are optimal paths conditioned on those observables. The appropriate boundary conditions turn out to be mixed, so that, in the long time limit, those trajectories converge to well-defined attractors. We are then able to identify the largest value that the Hamiltonian takes over those attractors with the scaled cumulant generating function of our observables, providing a non-linear equivalent to the well-known Donsker-Varadhan formula for jump processes. On that basis, we prove that chemical reaction networks that are deterministically multistable generically undergo first-order dynamical phase transitions in the vicinity of zero bias. We illustrate that fact through a simple bistable model called the Schl\"ogl model, as well as multistable and unstable generalisations of it, and we make a few surprising observations regarding the stability of deterministic fixed points, and the breaking of ergodicity in the large-volume limit

Renyi entropy of the totally asymmetric exclusion process

The Renyi entropy is a generalisation of the Shannon entropy that is sensitive to the fine details of a probability distribution. We present results for the Renyi entropy of the totally asymmetric exclusion process (TASEP). We calculate explicitly an entropy whereby the squares of configuration probabilities are summed, using the matrix product formalism to map the problem to one involving a six direction lattice walk in the upper quarter plane. We derive the generating function across the whole phase diagram, using an obstinate kernel method. This gives the leading behaviour of the Renyi entropy and corrections in all phases of the TASEP. The leading behaviour is given by the result for a Bernoulli measure and we conjecture that this holds for all Renyi entropies. Within the maximal current phase the correction to the leading behaviour is logarithmic in the system size. Finally, we remark upon a special property of equilibrium systems whereby discontinuities in the Renyi entropy arise away from phase transitions, which we refer to as secondary transitions. We find no such secondary transition for this nonequilibrium system, supporting the notion that these are specific to equilibrium cases

The N-Myc Down Regulated Gene1 (NDRG1) Is a Rab4a Effector Involved in Vesicular Recycling of E-Cadherin

Cell to cell adhesion is mediated by adhesion molecules present on the cell surface. Downregulation of molecules that form the adhesion complex is a characteristic of metastatic cancer cells. Downregulation of the N-myc down regulated gene1 (NDRG1) increases prostate and breast metastasis. The exact function of NDRG1 is not known. Here by using live cell confocal microscopy and in vitro reconstitution, we report that NDRG1 is involved in recycling the adhesion molecule E-cadherin thereby stabilizing it. Evidence is provided that NDRG1 recruits on recycling endosomes in the Trans Golgi network by binding to phosphotidylinositol 4-phosphate and interacts with membrane bound Rab4aGTPase. NDRG1 specifically interacts with constitutively active Rab4aQ67L mutant protein and not with GDP-bound Rab4aS22N mutant proving NDRG1 as a novel Rab4a effector. Transferrin recycling experiments reveals NDRG1 colocalizes with transferrin during the recycling phase. NDRG1 alters the kinetics of transferrin recycling in cells. NDRG1 knockdown cells show a delay in recycling transferrin, conversely NDRG1 overexpressing cells reveal an increase in rate of transferrin recycling. This novel finding of NDRG1 as a recycling protein involved with recycling of E-cadherin will aid in understanding NDRG1 role as a metastasis suppressor protein

Classification of scene evolution patterns from Satellite Image Time Series based on spectro-temporal signatures

Huge quantities of medium resolution satellite images are available from various Earth observation sites. These archives enable the creation of long-term, medium resolution Satellite Image Time Series (SITS). Such SITS are large, complex data sets, embedding spatial, spectral and temporal information. The development of effective methodologies for analysis of SITS is a challenging issue

Extracting common subtrees from decision trees

This paper explores an efficient technique for the extraction of common subtrees in decision trees. The method is based on a Suffix Tree string matching process and the algorithm is applied to the problem of finding common decision rules in path planning