A feedback control principle common to several biological and engineered systems.

Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules-additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease-are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.

Beam propagation simulation of phased laser arrays with atmospheric perturbations.

Directed energy phased array (DEPA) systems have been proposed for applications such as beaming optical power for electrical use on remote sensors, rovers, spacecraft, and future moon bases, as well as for planetary defense against asteroids and photonic propulsion up to relativistic speeds. All such scenarios involve transmission through atmosphere and beam perturbations due to turbulence that must be quantified. Numerical beam propagation and feedback control simulations were performed using an algorithm optimized for efficient calculation of real-time beam dynamics in a Kolmogorov atmosphere. Results were used to quantify the effectiveness of the system design with different degrees of atmospheric turbulence and zenith angles, and it was found that a large aperture DEPA system placed at a high altitude site can produce a stable diffraction limited spot (Strehl>0.8) on space-based targets for Fried length r0≥10cm (at 500 nm) and zenith angles up to 60 deg, depending on atmospheric conditions. We believe these results are promising for the next generation of power beaming and deep space exploration applications.

Travel in city road networks follows similar transport trade-off principles to neural and plant arbors.

Both engineered and biological transportation networks face trade-offs in their design. Network users desire to quickly get from one location in the network to another, whereas network planners need to minimize costs in building infrastructure. Here, we use the theory of Pareto optimality to study this design trade-off in the road networks of 101 cities, with wide-ranging population sizes, land areas and geographies. Using a simple one parameter trade-off function, we find that most cities lie near the Pareto front and are significantly closer to the front than expected by alternate design structures. To account for other optimization dimensions or constraints that may be important (e.g. traffic congestion, geography), we performed a higher-order Pareto optimality analysis and found that most cities analysed lie within a region of design space bounded by only four archetypal cities. The trade-offs studied here are also faced and well-optimized by two biological transport networks-neural arbors in the brain and branching architectures of plant shoots-suggesting similar design principles across some biological and engineered transport systems.

Graphene metamaterial spatial light modulator for infrared single pixel imaging.

High-resolution and hyperspectral imaging has long been a goal for multi-dimensional data fusion sensing applications - of interest for autonomous vehicles and environmental monitoring. In the long wave infrared regime this quest has been impeded by size, weight, power, and cost issues, especially as focal-plane array detector sizes increase. Here we propose and experimentally demonstrated a new approach based on a metamaterial graphene spatial light modulator (GSLM) for infrared single pixel imaging. A frequency-division multiplexing (FDM) imaging technique is designed and implemented, and relies entirely on the electronic reconfigurability of the GSLM. We compare our approach to the more common raster-scan method and directly show FDM image frame rates can be 64 times faster with no degradation of image quality. Our device and related imaging architecture are not restricted to the infrared regime, and may be scaled to other bands of the electromagnetic spectrum. The study presented here opens a new approach for fast and efficient single pixel imaging utilizing graphene metamaterials with novel acquisition strategies.

Multiplexed coded time domain sampling with metamaterials.

The far infrared region of the electromagnetic spectrum often necessitates the use of thermal detectors that, by nature, typically have poor response times and diminished sensitivities, at least compared to adjacent bands. However, many signals of interest contain frequency components far too fast to be reliably measured with such detectors, and hence expensive and inefficient alternatives are brought to bear. Here we propose and experimentally validate a new method leveraging the speed and scalability of dynamic metamaterial modulators to encode high-frequency signal components at a lower frequency, making them reliably measurable with thermal detectors that would otherwise be too slow. An optimal weighing scheme design in the time domain is realized, the result being an imaging system whose time resolution is independent of detector speed and is rather limited only by the speed of the modulator and the reproducibility of the signal of interest.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

Towards medical terahertz sensing of skin hydration.

Terahertz imaging has shown promise as a tool for noninvasive in-vivo detection of skin abnormalities, including skin cancer, burns, scars, and wounds due to its low non-ionizing photon energy and ability to penetrate clothing and gauze. This study examines whether low-level bulk differences in the water content between hyperhydrated and dehydrated skin can be detected using a scanning, reflective THz imaging system. Our results show an 8.7 x difference in the THz reflectivity between hyperhydrated and dehydrated specimens of chicken skin. The results provide further evidence that water concentration is the primary contrast mechanism in reflective THz biomedical imaging.