Local Feedback Loop Research Articles

Flame dominated thermoacoustic instabilities in a system with high acoustic losses

The thermoacoustic stability behaviour of a flame is experimentally investigated in the presence of large acoustic losses. Recently it has become clear that under such conditions an instability can occur due to an intrinsic local feedbackloop at the heat source. The experimental results confirm that despite significant acoustic losses, thermoacoustic instabilities can still be present. These findings imply that the effectiveness of passive thermoacoustic damping devices is limited by the intrinsic stability properties of the flame.

Particle size characterization using a high resolution planar resonator sensor in a lossy medium

In this work, a microwave planar resonator sensor with boosted quality factor is proposed for the non-contact detection and classification of particles of different diameters in gaseous and aquatic environments. This approach can be applied to rapid particle counting, particle size sensing and classification in lossy and non-lossy medium. The presented sensor operates based on variations in the dielectric properties of materials in the vicinity of a planar open-ended microstrip resonator device. Finite Element Method (FEM) analysis is performed to study the effect of particle size on the transmission response profile of the sensor. Verification of the established FEM model and the functionality of the sensor are investigated by experiment. The resolution of the sensor may be adjusted by changing the quality factor of the resonator, which is directly related to the direct current bias voltage of the active device in the local feedback loop. The initial results show the sensor to be capable of detecting the resonant frequency variations of 1kHz over 1.5GHz, which corresponds to a detectable frequency resolution better than 1 part per million (PPM). The reported sensor operates at 1.42GHz and demonstrates particle classification capability within an overall 500kHz resonant-frequency shift for different bead-diameter sizes (40–400μm) at a distance of 5mm, in both air and water (ε’=78 and tanδ=0.15) using petroleum coke beads as a test material.

Dynamic secretion during meiotic reentry integrates the function of the oocyte and cumulus cells

The differentiation of the female gamete into a developmentally competent oocyte relies on the protected environment of the ovarian follicle. The oocyte plays a key role in establishing this microenvironment by releasing paracrine factors that control the functions of surrounding somatic cells. Growth differentiation factor 9 (GDF9) and bone morphogenetic protein 15 (BMP15) are secreted during follicle growth and play pivotal roles in this local regulation. The current view is that the function of these secreted factors declines in the periovulatory period when the oocyte reenters the meiotic cell cycle. Here, we provide evidence that oocyte reentry into meiosis is instead associated with a shift in the pattern of secretion with a new set of bioactive molecules synthesized before ovulation. Using interleukin 7 (IL7) as a prototypic secreted factor, we show that its secretion is dependent on activation of mRNA translation in synchrony with the cell cycle and that its translation is under the control of somatic cells. IL7 is part of a local feedback loop with the soma because it regulates cumulus cell replication. Similar conclusions are reached when IL7 secretion is measured in human follicular fluid during in vitro fertilization cycles. IL7 concentration in the follicular fluid correlates with the oocyte ability to reach the MII stage of maturation. These findings are consistent with the hypothesis that a new set of local factors is secreted by the oocyte during ovulation. These dynamic secretions are likely critical for promoting the final stages of maturation and oocyte developmental competence.

Recurrent Feedback Loops in Associative Learning

In this issue of Neuron, Gao et al. (2016) report on a little-studied feedback pathway from the cerebellar nuclei back to the cerebellar cortex. They find that it contributes to associative conditioning and execution of learned movements, highlighting a role for local feedback loops in the brain.

Noise canceling LNA with gain enhancement by using double feedback

In this paper we present a balun low noise amplifier (LNA) in which the gain is boosted by using a double feedback structure. The circuit is based on a conventional balun LNA with noise and distortion cancelation. The LNA is based on the combination of a common-gate (CG) stage and common-source (CS) stage. We propose to replace the load resistors by active loads, which can be used to implement local feedback loops (in the CG and CS stages). This will boost the gain and reduce the noise figure (NF). Simulation results, with a 130nm CMOS technology, show that the gain is 24dB and the NF is less than 2.7dB. The total power dissipation is only 5.4mW (since no extra blocks are required), leading to a figure-of-merit (FOM) of 3.8mW−1 using a nominal 1.2V supply. Measurement results are presented for the proposed DFB LNA included in a receiver front-end for biomedical applications (ISM and WMTS).

Local Population Synchrony and the Encoding of Eye Position in the Primate Neural Integrator

Encoding horizontal eye position in the oculomotor system occurs through temporal integration of eye velocity inputs to produce tonic outputs. The nucleus prepositus is commonly believed to be the "neural integrator" that accomplishes this function through the activity of its ensemble of predominantly burst-tonic neurons. Single-unit characterizations and labeling studies of these neurons have suggested that their collective output is achieved through local feedback loops produced by direct connections between them. If this is the case, then the ensemble of burst-tonic neurons should exhibit correlated activity. To obtain electrophysiological evidence of local interactions between neurons, we simultaneously recorded pairs (n = 29) of burst-tonic neurons in the nucleus prepositus of rhesus macaque monkeys using eight-channel linear microelectrode arrays. We computed the magnitude of synchrony between their spike trains as a function of eye position during ocular fixations and as a function of distance between neurons. Importantly, we found that neurons exhibit unexpected levels of positive synchrony, which is maximal during contralateral fixations and weakest when neurons are located far apart from one another (>300 μm). Together, our results support a role for shared inputs to ipsilateral pairs of burst-tonic neurons in the encoding of eye position in the primate nucleus prepositus.

Circulating angiopoietin-like 4 links proteinuria with hypertriglyceridemia in nephrotic syndrome

The molecular link between proteinuria and hyperlipidemia in nephrotic syndrome is not known. We show in the present study that plasma angiopoietin-like 4 (Angptl4) links proteinuria with hypertriglyceridemia through two negative feedback loops. In previous studies in a rat model that mimics human minimal change disease, we observed localized secretion by podocytes of hyposialylated Angptl4, a pro-proteinuric form of the protein. But in this study we noted high serum levels of Angptl4 (presumably normosialylated based on a neutral isoelectric point) in other glomerular diseases as well. Circulating Angptl4 was secreted by extrarenal organs in response to an elevated plasma ratio of free fatty acids (FFAs) to albumin when proteinuria reached nephrotic range. In a systemic feedback loop, these circulating pools of Angptl4 reduced proteinuria by interacting with glomerular endothelial αvβ5 integrin. Blocking the Angptl4-β5 integrin interaction or global knockout of Angptl4 or β5 integrin delayed recovery from peak proteinuria in animal models. But at the same time, in a local feedback loop, the elevated extrarenal pools of Angptl4 reduced tissue FFA uptake in skeletal muscle, heart and adipose tissue, subsequently resulting in hypertriglyceridemia, by inhibiting lipoprotein lipase (LPL)-mediated hydrolysis of plasma triglycerides to FFAs. Injecting recombinant human ANGPTL4 modified at a key LPL interacting site into nephrotic Buffalo Mna and Zucker Diabetic Fatty rats reduced proteinuria through the systemic loop but, by bypassing the local loop, without increasing plasma triglyceride levels. These data show that increases in circulating Angptl4 in response to nephrotic-range proteinuria reduces the degree of this pathology, but at the cost of inducing hypertriglyceridemia, while also suggesting a possible therapy to treat these linked pathologies.

Establishing Optimal Wave Communication Channels Automatically

We show how multiple optimal orthogonal channels for communicating or interconnecting with waves between two objects can be aligned and optimized automatically using controllable beamsplitters, detectors and simple local feedback loops, without moving parts, without device calibration, without fundamental beam splitting loss, and without calculations. Optical applications include multiple simultaneous orthogonal spatial communication channels in free space or multimode optical fibers, automatically focused power delivery with waves, multiple channel communication through scattering or lossy media, and real-time-optimized focused channels to and from multiple moving objects. The approach physically implements automatic singular value decomposition of the wave coupling between the objects, and is equivalent in its effect to the beam forming in a laser resonator with phase-conjugate mirrors with the additional benefits of allowing multiple orthogonal channels to be formed simultaneously and avoiding the need for any nonlinear optical materials.

LAURA-NoC: Local Automatic Rate Adjustment in Network-on-Chips With a Simple DVFS

In this brief, we propose local automatic rate adjustment in network-on-chips (NoC) (LAURA-NoC), a NoC with a distributed approach to dynamic voltage and frequency scaling (DVFS). The utilization of the switch buffers is used in a local feedback loop to automatically determine the appropriate clock frequency and voltage that allow the switch to sustain the rate at its input ports, without a global controller. The DVFS controller is simple and uses 2 voltage and 16 frequency values. We report a significant power saving compared to a global DVFS approach in a 45-nm CMOS technology, 33 % on average over four realistic video applications.

Reconfigurable add-drop multiplexer for spatial modes

We show how a spatial mode can be extracted from a light beam, leaving the other orthogonal modes undisturbed, and allowing a new signal to be retransmitted on that mode. The method is self-aligning, avoids fundamental splitting losses, and uses only local feedback loops on controllable beam splitters and phase shifters. It could be implemented with Mach-Zehnder interferometers in planar optics. The method can be extended to multiple simultaneous mode extractions. As a spatial reconfigurable optical add-drop multiplexer, it is hitless, allowing reconfiguration without interrupting the transmission of any channel.

A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters

In this paper, a mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) is proposed for the identification of nonlinear and time-varying systems. The MRIT2NFS uses type-2 fuzzy sets in order to enhance noise tolerance of the system. In the MRIT2NFS, the antecedent part of each recurrent fuzzy rule is defined using interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang type with interval weights. The antecedent part of MRIT2NFS forms a local internal feedback and interaction loop by feeding the rule firing strength of each rule to others including itself. The consequent is a linear combination of exogenous input variables. The learning of MRIT2NFS starts with an empty rule base and all rules are learned online via structure and parameter learning. The structure learning of MRIT2NFS uses online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by rule-ordered Kalman filter algorithm to reinforce parameter learning ability. The type-2 fuzzy sets in the antecedent and weights representing the mutual feedback are learned by the gradient descent algorithm. After the training, a weight-elimination scheme eliminates feedback connections that do not have much effect on the network behavior. This method can efficiently remove redundant recurrence and interaction weights. Finally, the MRIT2NFS is used for system identification under both noise-free and noisy environments. For this, we consider both time series prediction and nonlinear plant modeling. Compared with type-1 recurrent fuzzy neural networks, simulation results show that our approach produces smaller root-mean-squared errors using the same number of iterations.

Self-configuring universal linear optical component [Invited

We show how to design an optical device that can perform any linear function or coupling between inputs and outputs. This design method is progressive, requiring no global optimization. We also show how the device can configure itself progressively, avoiding design calculations and allowing the device to stabilize itself against drifts in component properties and to continually adjust itself to changing conditions. This self-configuration operates by training with the desired pairs of orthogonal input and output functions, using sets of detectors and local feedback loops to set individual optical elements within the device, with no global feedback or multiparameter optimization required. Simple mappings, such as spatial mode conversions and polarization control, can be implemented using standard planar integrated optics. In the spirit of a universal machine, we show that other linear operations, including frequency and time mappings, as well as non-reciprocal operation, are possible in principle, even if very challenging in practice, thus proving there is at least one constructive design for any conceivable linear optical component; such a universal device can also be self-configuring. This approach is general for linear waves, and could be applied to microwaves, acoustics and quantum mechanical superpositions.

Macrophage Migration Inhibitory Factor Is Involved in a Positive Feedback Loop Increasing Aromatase Expression in Endometriosis

Immune-endocrine interplay may play a major role in the pathogenesis of endometriosis. In the present study, we have investigated the interaction between macrophage migration inhibitory factor (MIF), a major pro-inflammatory and growth-promoting factor markedly expressed in active endometriotic lesions, and estradiol (E(2)) in ectopic endometrial cells. Our data showed a significant increase of MIF protein secretion and mRNA expression in endometriotic cells in response to E(2). MIF production was blocked by Fulvestrant, an estrogen receptor (ER) antagonist, and induced by ERα and ERβ selective agonists propyl-pyrazole-triol (PPT) and diarylpropionrile (DPN), respectively, thus demonstrating a specific receptor-mediated effect. Cell transfection with MIF promoter construct showed that E(2) significantly stimulates MIF promoter activity. Interestingly, our data further revealed that MIF reciprocally stimulates aromatase protein and mRNA expression via a posttranscriptional mRNA stabilization mechanism, that E(2) itself can upregulate aromatase expression, and that inhibition of endogenous MIF, using MIF specific siRNA, significantly inhibits E(2)-induced aromatase. Thus, the present study revealed the existence of a local positive feedback loop by which estrogen acts directly on ectopic endometrial cells to upregulate the expression of MIF, which, in turn, displays the capability of inducing the expression of aromatase, the key and rate-limiting enzyme for estrogen synthesis. Such interplay may have a considerable impact on the development of endometriosis.

On Distributed Multirate Control of Direct User-to-User Touch in Networked Haptic Systems with Passive Wave-Domain Communications

This paper investigates the stability and performance of distributed multirate control of direct touch in networked haptic systems that provide users with a remote dynamic proxy of their peer and with passive wave domain communications. The paper considers communication networks with fixed delay and with packet update rate smaller than the update rate of the users' local force feedback loops. After developing a multirate state space model of the direct touch haptic system, the paper uses eigenvalue-based stability analysis to determine the maximum contact stiffness that can be applied to users, as well as the maximum coordination gain that can be used to synchronize the user sites. The analysis predicts that both remote dynamic proxies and passive wave-domain communications make the contact stiffness robust to delay, but only passive wave-domain communications mitigate the negative impact of delay on the coordination gain. In other words, the analysis suggests that passive wave-domain communications should be employed to make the distributed multirate control of direct touch in networked haptic systems stable regardless of the fixed communication delay. However, if power-domain communications are employed, remote dynamic proxies should be used to allow rendering of stiffer contact between users. Experiments in which two users probe each other using similar haptic interfaces validate the analytical results.

Experimental implementation of a self-tuning control system for decentralised velocity feedback

Simulations have previously shown that, for broadband excitation, adjusting the gain of a local velocity feedback loop to maximise their absorbed power also tends to minimise the kinetic energy of the structure under control. This paper describes an experimental implementation of multiple velocity feedback loops on a flat panel, whose gains can be controlled automatically by an algorithm that maximises their local absorbed power. Taking care to remove excessive phase shift in the control loop allows a stable feedback gain that is high enough to experimentally demonstrate the transition in control action between optimum damping and pinning of the structure. A simple search algorithm is then used to adapt the feedback gains of two control loops to maximise their local absorbed powers, thus demonstrating self-tuning. By measuring the power absorbed by each of these loops and also estimation of the kinetic energy of the plate from velocity measurements for a wide range of the two feedback gains, it is shown that not only does the adaptive algorithm converge to a set of feedback gains that maximise total power absorbed by the two feedback loops, but also that this set of feedback gains is very close to those that minimise the measured kinetic energy of the panel.

Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model

Control schemes for powered ankle-foot prostheses would benefit greatly from a means to make them inherently adaptive to different walking speeds. Towards this goal, one may attempt to emulate the intact human ankle, as it is capable of seamless adaptation. Human locomotion is governed by the interplay among legged dynamics, morphology and neural control including spinal reflexes. It has been suggested that reflexes contribute to the changes in ankle joint dynamics that correspond to walking at different speeds. Here, we use a data-driven muscle-tendon model that produces estimates of the activation, force, length and velocity of the major muscles spanning the ankle to derive local feedback loops that may be critical in the control of those muscles during walking. This purely reflexive approach ignores sources of non-reflexive neural drive and does not necessarily reflect the biological control scheme, yet can still closely reproduce the muscle dynamics estimated from biological data. The resulting neuromuscular model was applied to control a powered ankle-foot prosthesis and tested by an amputee walking at three speeds. The controller produced speed-adaptive behaviour; net ankle work increased with walking speed, highlighting the benefits of applying neuromuscular principles in the control of adaptive prosthetic limbs.

Highly linear voltage follower based on local feedback and cascode transistor with dynamic biasing

A new highly linear voltage follower is proposed based on the local feedback loop of a flipped-voltage-follower and a cascode transistor with dynamic biasing, which outperforms classical solutions in terms of linearity. Post-layout simulations for a 0.5 µm CMOS technology validate the proposed structure.

A neurological view on reactive human stance control

During biped stance or locomotion, humans show remarkable skills in reactive balancing upon external disturbances. Mainly four types of external disturbances are relevant for stance: the field force gravity, contact forces such as a push or pull against the body, as well as body support surface rotation and translational acceleration. It is known from clinics that sensory loss severely impairs the balancing. Three sensory inputs are instrumental: vestibular, joint angle, and joint torque. System identification studies currently try to understand how humans are able to flexibly cope with changes in, and superposition of the disturbances. A solution is presented in this article. The article first describes the control problem and then reviews recent evidence for a PD (proportional-derivative) controller, for multisensory feedback, and for sensory reweighting as a key to understand the flexibility. On this basis, a recent disturbance estimation and compensation (DEC) model is introduced. It builds on two concepts from previous psychophysical studies of human self-motion perception. First, inputs from several sensory transducers are fused to establish sensors that provide explicit measures of the physical variables (sensor concept). Second, a processing level, interleaved between sensors and feedback (meta level), performs online sensory estimations of the external disturbances. These estimations are then fed into a local proprioceptive feedback loop, yielding corresponding disturbance rejections. Previous work on using the DEC model to describe human reactive balancing data is briefly reviewed. Then, novel work is presented, in which voluntary control over the reactive balancing is added to the DEC model. A prediction method for anticipating self-produced and external disturbances is suggested and corresponding software and hardware (robot) simulations are presented. The results serve as guidelines for future human experiments. Since the DEC model is very simple, we concluded that its behavioral flexibility and fault tolerance goes together with computational parsimony, an equally important biological constraint.

A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing

This paper proposes a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN.

Oscillatory Notch-pathway activity in a delay model of neuronal differentiation

Lateral inhibition resulting from a double-negative feedback loop underlies the assignment of different fates to cells in many developmental processes. Previous studies have shown that the presence of time delays in models of lateral inhibition can result in significant oscillatory transients before patterned steady states are reached. We study the impact of local feedback loops in a model of lateral inhibition based on the Notch signaling pathway, elucidating the roles of intracellular and intercellular delays in controlling the overall system behavior. The model exhibits both in-phase and out-of-phase oscillatory modes and oscillation death. Interactions between oscillatory modes can generate complex behaviors such as intermittent oscillations. Our results provide a framework for exploring the recent observation of transient Notch-pathway oscillations during fate assignment in vertebrate neurogenesis.