Downlink and Uplink Cell Association with Traditional Macrocells and Millimeter Wave Small Cells

Millimeter wave (mmWave) links will offer high capacity but are poor at penetrating into or diffracting around solid objects. Thus, we consider a hybrid cellular network with traditional sub 6 GHz macrocells coexisting with denser mmWave small cells, where a mobile user can connect to either opportunistically. We develop a general analytical model to characterize and derive the uplink and downlink cell association in view of the SINR and rate coverage probabilities in such a mixed deployment. We offer extensive validation of these analytical results (which rely on several simplifying assumptions) with simulation results. Using the analytical results, different decoupled uplink and downlink cell association strategies are investigated and their superiority is shown compared to the traditional coupled approach. Finally, small cell biasing in mmWave is studied, and we show that unprecedented biasing values are desirable due to the wide bandwidth.Comment: 30 pages, 9 figures. Submitted to IEEE Transactions on Wireless Communication

Effective interactions and large deviations in stochastic processes

We discuss the relationships between large deviations in stochastic systems, and "effective interactions" that induce particular rare events. We focus on the nature of these effective interactions in physical systems with many interacting degrees of freedom, which we illustrate by reviewing several recent studies. We describe the connections between effective interactions, large deviations at "level 2.5", and the theory of optimal control. Finally, we discuss possible physical applications of variational results associated with those theories.Comment: 12 page

Detection and Mitigation of Biasing Attacks on Distributed Estimation Networks

The paper considers a problem of detecting and mitigating biasing attacks on networks of state observers targeting cooperative state estimation algorithms. The problem is cast within the recently developed framework of distributed estimation utilizing the vector dissipativity approach. The paper shows that a network of distributed observers can be endowed with an additional attack detection layer capable of detecting biasing attacks and correcting their effect on estimates produced by the network. An example is provided to illustrate the performance of the proposed distributed attack detector.Comment: Accepted for publication in Automatic

Liquid and back gate coupling effect: towards biosensing with lowest detection limit

We employ noise spectroscopy and transconductance measurements to establish the optimal regimes of operation for our fabricated silicon nanowire field-effect transistors (Si NW FETs) sensors. A strong coupling between the liquid gate and back gate (the substrate) has been revealed and used for optimisation of signal-to-noise ratio in sub-threshold as well as above-threshold regimes. Increasing the sensitivity of Si NW FET sensors above the detection limit has been predicted and proven by direct experimental measurements.Comment: 18 pages, 6 figure

Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic

Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only inefficient but also inconsistent with their single-query nature. For problems seeking to minimize path length, the subset of states that can improve a solution can be described by a prolate hyperspheroid. We show that unless this subset is sampled directly, the probability of improving a solution becomes arbitrarily small in large worlds or high state dimensions. In this paper, we present an exact method to focus the search by directly sampling this subset. The advantages of the presented sampling technique are demonstrated with a new algorithm, Informed RRT*. This method retains the same probabilistic guarantees on completeness and optimality as RRT* while improving the convergence rate and final solution quality. We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms. We show experimentally that it outperforms RRT* in rate of convergence, final solution cost, and ability to find difficult passages while demonstrating less dependence on the state dimension and range of the planning problem.Comment: 8 pages, 11 figures. Videos available at https://www.youtube.com/watch?v=d7dX5MvDYTc and https://www.youtube.com/watch?v=nsl-5MZfwu

Optimal Geometry of CMOS Voltage-Mode and Current-Mode Vertical Magnetic Hall Sensors

Four different geometries of a vertical Hall sensor are presented and studied in this paper. The current spinning technique compensates for the offset and the sensors, driven in current-mode, provide a differential signal current for a possible capacitive integration over a defined time-slot. The sensors have been fabricated using a 6-metal 0.18-μm CMOS technology and fully experimentally tested. The optimal solution will be further investigated for bendable electronics. Measurement results of the four structures over the 10 available samples show for the best geometry an offset of 41.66 ± 8 μT and a current-mode sensitivity of 9 ± 0.1 %/T. Since the figures widely change with geometry, a proper choice secures optimal performance