External Control Signals Research Articles

A network control theory pipeline for studying the dynamics of the structural connectome.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., structure and color) is needed. In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn low-cost T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications. Our code is available at https://github.com/TencentARC/T2I-Adapter.

Modelling and design of asynchronous receptive circuit for cone pathways

In this paper, asynchronous receptive circuit of retinal pathway is proposed for cone cells. Cone cells respond differently to different wavelengths of light and they are responsible for color vision. The cells present in retinal pathway namely cone photoreceptor, bipolar cell, amacrine cell and ganglion cell are mimicked using 180 nm Complementary Metal Oxide Semiconductor (CMOS) technology in strong inversion region. The proposed circuit is event based and hence asynchronous in nature. Additionally, amacrine stage is mimicked such that analog multiplexing occurs which is matching with its biological behavior. Both these features give output near to response of actual biological retinal pathway. The circuit detects wavelengths in visible spectrum of light with temporal contrast. At amacrine stage, selectivity is achieved without any external control signal which shows intelligent behavior at analog circuit level. The concept of color sensitivity of cone is considered to generate input stimulus of the circuit using Commission on Illumination (CIE) standard. The power consumed by the entire pathway circuit is 183 mW.

Fully-attentive iterative networks for region-based controllable image and video captioning

Controllable image captioning has recently gained attention as a way to increase the diversity and the applicability to real-world scenarios of image captioning algorithms. In this task, a captioner is conditioned on an external control signal, which needs to be followed during the generation of the caption. We aim to overcome the limitations of current controllable captioning methods by proposing a fully-attentive and iterative network that can generate grounded and controllable captions from a control signal given as a sequence of visual regions from the image. Our architecture is based on a set of novel attention operators, which take into account the hierarchical nature of the control signal, and is endowed with a decoder which explicitly focuses on each part of the control signal. We demonstrate the effectiveness of the proposed approach by conducting experiments on three datasets, where our model surpasses the performances of previous methods and achieves a new state of the art on both image and video controllable captioning.

Fully nonlinear Cucker–Smale model for pattern formation and damped oscillation control

We conduct a theoretical study of the phenomena associated with the self-organization of multi-agents with nonlinear interactions that form the desired spatial pattern without collision. To demonstrate this, we first adopt a singular Cucker–Smale model, which contains a discrete p-Laplacian (p>1) and external control signals. Then, we introduce a range of properties for the energy function of our model and prove that the model supports non-collision, flocking, and pattern formation. In addition, we employ numerical simulations to show that, depending on the initial data and the discrete p-Laplacian, various significant effects exist, such as the speed control of pattern positioning and amplitude adjustment of damped oscillation.

Intelligent VIV control of 2DOF sprung cylinder in laminar shear-thinning and shear-thickening cross-flow based on self-tuning fuzzy PID algorithm

A self-tuning fuzzy PID (ST-FPID) control scheme is implemented within a joint interactive (Matlab/Simulink/Fluent) co-simulation framework for effective two degrees of freedom (2DOF) vortex-induced vibration (VIV) control of an elastically-mounted circular cylinder in laminar cross-flow of incompressible non-Newtonian power-law fluids based on the control action of a single transverse force actuator. The model-free controller, which systematically tunes the control parameters online in real time based on given rules, is well-known to be highly advantageous over the previously employed conventional PID controllers. It is particularly capable of handling the intricate non-linear dynamic effects inherent in the complex fluid rheology of non-Newtonian flow past the cylinder in presence of unmodeled system dynamics, high parametric uncertainties, diverse operational conditions, and time-varying external disturbances and control signals. Extensive numerical simulations reveal that the complex shear-thinning and shear-thickening behaviors of fluid viscosity can substantially influence the cylinder dynamic response, applied hydrodynamic forces, and flow structure. In particular, effectiveness and high performance of the adopted ST-FPID control strategy in substantial suppression of the high amplitude coupled 2DOF VIV of the elastically-mounted cylinder at selected critical reduced velocities in the lock-in region are established for a wide range of power-law index parameters (e.g., up to 83% reduction in RMS value of cylinder cross-flow displacement and up to 35% reduction in RMS value of cylinder in-line displacement for n=1and U* = 5 at Re = 100). Also, the vigorous action of the error-driven ST-FPID controller in forcing the high strength vortex shedding patterns of the uncontrolled cylinder out of the lock-in condition into the classical von Kármán vortex street of 2S-type mode of moderately weaker strengths is verified.

ANALYSIS OF ACCURACY OF NONLINEAR CONTROL SYSTEMS OF MULTI-ROTOR UAVS UNDER STATIONARY RANDOM SIGNALS

A large number of articles and monographs are devoted to the development of line-ar control systems for multi-rotor UAVs, in which, based on the methods of the modern theory of automatic regulation and control, the problems of choosing the regulators that provide the required performance indices and stability margins of UAVs’ control systems are considered, taking into account structural and parametric uncertainties , nonlinearities of separate channels, various external control and disturbing signals, including determinis-tic and random signals, etc. At the same time, there are practically no works that study the issues of analyzing the dynamic accuracy of nonlinear control systems for multi-rotor UAVs under random external signals, for example, wind gusts, etc. In the article, based on the approximate method of statistic linearization, an engi-neering technique for analyzing the accuracy of nonlinear UAVs’ control systems with PID controllers in separate channels under stationary random signals with a Gaussian probabil-ity distribution and non-zero mathematical expectations are considered. It is shown that the use of the statistic linearization method allows reducing the accuracy analysis of the of independent nonlinear control channels of UAVs to a joint analysis of the accuracy of two different linear systems with respect to the mathematical expectation and the centered random component of the input signal. Analysis of the accuracy of each of these linear systems is carried out by known methods of classical control theory. A numerical example of the analysis of statistical accuracy of the control system of a quadcopter with satura-tion-type nonlinearities in the measuring devices of the system is given.

Magnetic-Field-Driven Reconfigurable Microsphere Arrays for Laser Display Pixels.

Reconfigurable microlaser arrays are essential to the construction of display panels where the individual pixel should be highly tunable in resonance mode, optical polarization, and lasing wavelength upon external control signals. Here we demonstrate a facile yet reliable approach to fabrication of organic microlaser pixels, in which the assembly of microsphere arrays on each pixel is controlled according to the near-field magnetostatic confinement. The geometrical configuration of diamagnetic microspheres could be readily modulated with the near-field potential traps by using the external field to alternate the saturation magnetization of the underneath micromagnet. The motion of microspheres can be modulated among several states upon applied field, and the reconfigurable microsphere array is thus achieved with high spatial precision and rapid temporal response. Moreover, both isolated and coupled spheres serve as low-threshold microlasers with tunable optical resonance modes, whereas the switching between the vertical and horizontal alignments of coupled spheres manipulates the polarization of lasing outputs. By repeating the magnetostatic confinement on the same substrate, the full-color laser display pixels with magnetically tunable color expression capability are successfully achieved.

ACBAM-Accuracy-Configurable Sign Inclusive Broken Array Booth Multiplier Design

Approximate computing technique has been adopted in recent years to develop low power design solutions targeted towards error-tolerant applications. Since the accuracy requirements of an application can vary dynamically at run-time, there is a justified need to design reconfigurable approximate circuits with varying power requirements proportional to computational accuracy. In this paper, we have proposed a novel accuracy reconfigurable approximate booth multiplier circuit. The design involves partial error correction through the addition of sign bits in a broken array multiplier. The design space of sign inclusive 16-bit broken array multipliers is thoroughly analyzed to include only non-redundant accuracy modes. The horizontal and vertical breaks introduced in the booth multiplier circuit are controlled using external control signals stored in a ROM. The proposed reconfigurable multiplier achieves significant power savings compared to accurate multiplier circuit and state-of-the-art accuracy configurable multiplier designs.

Nonlinear optimal control of a mean-field model of neural population dynamics.

We apply the framework of nonlinear optimal control to a biophysically realistic neural mass model, which consists of two mutually coupled populations of deterministic excitatory and inhibitory neurons. External control signals are realized by time-dependent inputs to both populations. Optimality is defined by two alternative cost functions that trade the deviation of the controlled variable from its target value against the “strength” of the control, which is quantified by the integrated 1- and 2-norms of the control signal. We focus on a bistable region in state space where one low- (“down state”) and one high-activity (“up state”) stable fixed points coexist. With methods of nonlinear optimal control, we search for the most cost-efficient control function to switch between both activity states. For a broad range of parameters, we find that cost-efficient control strategies consist of a pulse of finite duration to push the state variables only minimally into the basin of attraction of the target state. This strategy only breaks down once we impose time constraints that force the system to switch on a time scale comparable to the duration of the control pulse. Penalizing control strength via the integrated 1-norm (2-norm) yields control inputs targeting one or both populations. However, whether control inputs to the excitatory or the inhibitory population dominate, depends on the location in state space relative to the bifurcation lines. Our study highlights the applicability of nonlinear optimal control to understand neuronal processing under constraints better.

Control of spoke movement in DCMS plasmas

Spokes appear as zones of increased ionisation in magnetron sputtering discharges. They rotate in front of a 2″ target at a natural frequency between a few 10 kHz and several 100 kHz and move in or anti direction depending on plasma power. Spokes are known to cause strong gradients in plasma density and potential and can, thus, increase the ion transport from target to substrate. Here, we explore the possibility to control spokes by applying a given frequency f to a set of control probes around the plasma to lock the spoke movement. The efficiency of this locking is analyzed by diagnostic probes and energy resolved mass spectrometry, which measure the integrated ion fluxes leaving the magnetic trap region. It was found that the spoke movement could be locked to the external control signal at frequency f around the natural spoke frequencies f 0. The additional control signal affects the ion flux twofold: (i) a 15% increase in ion flux towards the substrate and a 15% reduction in radial direction irrespective of control frequency is observed, which is explained by a change in plasma confinement since electric fluctuations at the separatrix are induced; (ii) the locking at f causes an increase in ion current in normal as well as in radial direction for f < f 0 and a reduction for f > f 0. This is explained by either longer or shorter residence times of ions in the electric fields caused by the spoke, or by an enhancement of these fields caused by the control. Using this spoke controlling technique an overall increase of ion flux towards the substrate of up to 30% was realized.

Model Controlled Prediction: A Reciprocal Alternative of Model Predictive Control

Dear editor, This letter presents a reciprocal alternative to model predictive control (MPC), called model controlled prediction. More specifically, in order to integrate dynamic control signals into the transportation prediction models, a new fundamental theory of machine learning based prediction models is proposed. The model can not only learn potential patterns from historical data, but also make optimal predictions based on dynamic external control signals. The model can be used in two typical scenarios: 1) For low real-time control signals (e.g., subway timetable), we use a transfer learning method, so that the prediction models obtained from training data under the old control strategy can be predicted accurately under the new control strategy. 2) For dynamic control signals with high real-time (e.g., online ride-hailing dispatching instructions), we establish a simulation environment, design a control algorithm based on reinforcement learning (RL), and then let the model learn the mapping relationship among dynamic control signals, data, and output in the simulation environment. The experimental results show that the reasonable modeling of control signals can significantly improve the performance of the traffic prediction model.

Miller Capacitance Cancellation to Improve SiC MOSFET's Performance in a Phase-Leg Configuration

The drain to gate capacitance (Miller capacitance) of SiC MOSFETs leads to the Miller effect during switching transients. The Miller capacitance in a phase-leg configuration causes the crosstalk, the interaction between the two complementary switches, and the Miller plateau during the switching transient. The Miller effect reduces the switching speed, reduces reliability, and increases electromagnetic interference. In this article, by injecting a mirror cancellation current, the effects of Miller capacitance are canceled. The proposed technique includes a two-stage sensing and injection network to compensate for the nonlinearity of the Miller capacitance. The proposed technique can suppress both positive and negative gate voltage spikes induced by the crosstalk and reduce the switching power loss with the increased switching speed. Because no external control signals are required in the proposed technique, it can work with almost all commercial gate drivers. The detailed design for this proposed technique is presented in this article. The proposed technique was validated with both simulations and experiments.

Opto-thermally controlled beam steering in nonlinear all-dielectric metastructures

Reconfigurable metasurfaces have recently gained a lot of attention in applications such as adaptive meta-lenses, hyperspectral imaging and optical modulation. This kind of metastructure can be obtained by an external control signal, enabling us to dynamically manipulate the electromagnetic radiation. Here, we theoretically propose an AlGaAs device to control the second harmonic generation (SHG) emission at nanoscale upon optimized optical heating. The asymmetric shape of the used meta-atom is selected to guarantee a predominant second harmonic (SH) emission towards the normal direction. The proposed structure is concurrently excited by a pump beam at a fundamental wavelength of 1540 nm and by a continuous wave (CW) control signal above the semiconductor band gap. The optical tuning is achieved by a selective optimization of meta-atoms SH phase, which is modulated by the control signal intensity. We numerically demonstrate that the heating induced in the meta-atoms by the CW pump can be used to dynamically tune the device properties. In particular, we theoretically demonstrate a SH beam steering of 8° with respect to the vertical axis for an optimized device with average temperature increase even below 90° C.

A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control

We present a simple and intuitive approach for interactive control of physically simulated characters. Our work builds upon generative adversarial networks (GAN) and reinforcement learning, and introduces an imitation learning framework where an ensemble of classifiers and an imitation policy are trained in tandem given pre-processed reference clips. The classifiers are trained to discriminate the reference motion from the motion generated by the imitation policy, while the policy is rewarded for fooling the discriminators. Using our GAN-like approach, multiple motor control policies can be trained separately to imitate different behaviors. In runtime, our system can respond to external control signal provided by the user and interactively switch between different policies. Compared to existing method, our proposed approach has the following attractive properties: 1) achieves state-of-the-art imitation performance without manually designing and fine tuning a reward function; 2) directly controls the character without having to track any target reference pose explicitly or implicitly through a phase state; and 3) supports interactive policy switching without requiring any motion generation or motion matching mechanism. We highlight the applicability of our approach in a range of imitation and interactive control tasks, while also demonstrating its ability to withstand external perturbations as well as to recover balance. Overall, our approach has low runtime cost and can be easily integrated into interactive applications and games.

Design of 3–5 GHz tunable memristor-based OOK-UWB transmitter

This paper presents the design of a tunable memristor-based On-off keying (OOK) impulse-radio ultrawideband (IR-UWB) transmitter (TX), operating within the 3–5 GHz band. The proposed TX contains a CMOS-Memristor-based Ring Oscillator, an OOK modulator, a control signals circuit, and a pulse shape filter. The linear programming of the memristor device offers a smooth linear frequency variation of the ring oscillator. Accordingly, in the proposed circuit, two external control signals Vctrl and Vtune are used to tune the memristance value and the output pulse width, respectively, allowing adjusting the power spectral density (PSD) bandwidth and its central frequency. The IR-UWB TX is suitable for OOK modulation schema. It is designed and simulated using TSMC CMOS 0.18 µm technology with a 1.8 V supply voltage. The simulation results show that the proposed TX can cover both narrowband and wideband UWB PSD, depending on the number of oscillations per emitted pulse. The TX output swing is 483mVpp, and the pulse duration is 0.8 ns for narrowband and 1.74 ns for wideband. For a pulse repetition frequency of 10 MHz, the pulse generator consumes 94.8 µW and accomplishes an energy efficiency of 23.4%, while the total energy consumption is 9.48 pJ/pulse.

An Automatic Aggregator of Power Flexibility in Smart Buildings Using Software Based Orchestration.

This paper presents a software-based modular and hierarchical building energy management system (BEMS) to control the power consumption in sensor-equipped buildings. In addition, the need of this type of solution is also highlighted by presenting the worldwide trends of thermal energy end use in buildings and peak power problems. Buildings are critical component of smart grid environments and bottom-up BEMS solutions are need of the hour to optimize the consumption and to provide consumption side flexibility. This system is able to aggregate the controls of the all-controllable resources in building to realize its flexible power capacity. This system provides a solution for consumer to aggregate the controls of ‘behind-the-meter’ small loads in short response and provide ‘deep’ demand-side flexibility. This system is capable of discovery, status check, control and management of networked loads. The main novelty of this solution is that it can handle the heterogeneity of the installed hardware system along with time bound changes in the load device network and its scalability; resulting in low maintenance requirements after deployment. The control execution latency (including data logging) of this BEMS system for an external control signal is less than one second per connected load. In addition, the system is capable of overriding the external control signal in order to maintain consumer coziness within the comfort temperature thresholds. This system provides a way forward in future for the estimation of the energy stored in the buildings in the form of heat/temperature and use buildings as temporary batteries when electricity supply is constrained or abundant.

Appropriate time to apply control input to complex dynamical systems

Controlling a network structure has many potential applications many fields. In order to have an effective network control, not only finding good driver nodes is important, but also finding the optimal time to apply the external control signals to network nodes has a critical role. If applied in an appropriate time, one might be to control a network with a smaller control signals, and thus less energy. In this manuscript, we show that there is a relationship between the strength of the internal fluxes and the effectiveness of the external control signal. To be more effective, external control signals should be applied when the strength of the internal states is the smallest. We validate this claim on synthetic networks as well as a number of real networks. Our results may have important implications in systems medicine, in order to find the most appropriate time to inject drugs as a signal to control diseases.

The Orthogonal LMBA: A Novel RFPA Architecture With Broadband Reconfigurability

A novel RF power amplifier (RFPA) architecture, the orthogonal load-modulated balanced amplifier, or OLMBA, is described and demonstrated. Compared to the load-modulated balanced amplifier (LMBA), the OLMBA displays many of the same benefits, such as active adaptive tuning using the phase and amplitude of an external control signal, but with much lower power requirements on the control signal power (CSP). As such, a useful range of active tuning can be implemented with essentially no impact on overall efficiency due to the low level of a control signal. A demonstrator is described and measured, which delivers 30 W at a minimum of 50% efficiency over a 0.65-3.25-GHz bandwidth.

Thermal Control of Engineered T-cells.

Genetically engineered T-cells are being developed to perform a variety of therapeutic functions. However, no robust mechanisms exist to externally control the activity of T-cells at specific locations within the body. Such spatiotemporal control could help mitigate potential off-target toxicity due to incomplete molecular specificity in applications such as T-cell immunotherapy against solid tumors. Temperature is a versatile external control signal that can be delivered to target tissues in vivo using techniques such as focused ultrasound and magnetic hyperthermia. Here, we test the ability of heat shock promoters to mediate thermal actuation of genetic circuits in primary human T-cells in the well-tolerated temperature range of 37-42 °C, and introduce genetic architectures enabling the tuning of the amplitude and duration of thermal activation. We demonstrate the use of these circuits to control the expression of chimeric antigen receptors and cytokines, and the killing of target tumor cells. This technology provides a critical tool to direct the activity of T-cells after they are deployed inside the body.