Feedback Signal Research Articles

System for Visually Disabled through Wearables Utilizing Arduino and Ultrasound

Vision loss is a major challenge that affects people's lives in many aspects. In order to help people with visual impairment navigate and interact with those around them, wearable electronic devices and innovative technology can be used. The visually impaired system using Arduino and ultrasound uses advanced technology to convert ultrasound waves into useful information for visual people. Wearable devices, such as glasses or mobile devices can be used to receive audio signals and convert them into notifications to the user, via vibration, sounds or even text messages, which are equipped with ultrasonic sensors. When wearable devices emit high-frequency audio signals, these signals bounce off various obstacles in the environment around the person. The feedback signals are received and converted into visual information or alert messages for the user. People with visual impairment face great challenges because they depend heavily on others for their mobility. In this paper, a model was created to support blind people, facilitate their mobility, and ensure their safety through timely alerts of any obstacles in their path. The project includes a portable device that uses Arduino and is equipped with several sensors, such as distance and temperature sensors, to detect objects whose temperature exceeds 0 degrees Celsius. This device is designed to be hand-held with the glove and belt attached at the waist. Integrating all the mentioned sensors along with the Arduino that supervises the process.

Stress pathway outputs are encoded by pH-dependent clustering of kinase components

Signal processing by intracellular kinases controls near all biological processes but how signal pathway functions evolve with changed cellular context is poorly understood. Functional specificity of c-Jun N-terminal Kinases (JNK) are partly encoded by signal strength. Here we reveal that intracellular pH (pHi) is a significant component of the JNK network and defines signal response to specific stimuli. We show pHi regulates JNK activity in response to cell stress, with the relationship between pHi and JNK activity dependent on specific stimuli and upstream kinases activated. Using the optogenetic clustering tag CRY2, we show that an increase in pHi promotes the light-induced phase transition of ASK1 to augment JNK activation. While increased pHi similarly promoted CRY2-tagged JNK2 to form light-induced condensates, this attenuated JNK activity. Mathematical modelling of feedback signalling incorporating pHi and differential contributions by ASK1 and JNK2 condensates was sufficient to delineate signal responses to specific stimuli. Taking pHi and ASK1/JNK2 signal contributions into consideration may delineate oncogenic versus tumour suppressive JNK functions and cancer cell drug responses.

Enhancing damping of low‐frequency oscillations in power networks through energy storage system‐based controller

SummaryMitigating inter‐area low‐frequency oscillations is a significant concern in multi‐machine power systems due to their adverse effects on system stability. These oscillations are intricately linked with power oscillations. So appropriate power modulation through the Battery Energy Storage System (BESS) can be an effective strategy for preserving system stability. In this paper, the maximal of all minimal residue indices under variations in power system operating conditions is utilized as the index for identifying the location for installation of the BESS, the damping control loop, and the feedback signal. A fixed‐structure scheme‐based wide‐area damping controller (WADC) is proposed for the BESS, providing sufficient damping of inter‐area oscillation modes. A modified IEEE 39‐bus system is simulated using a real‐time digital simulator as a test system in this work. The simulation results confirmed that the proposed WADC could effectively damp various inter‐area oscillation modes. Furthermore, it offers robust damping performance over contingencies associated with the system's various operating scenarios as well as the uncertainty associated with fixed and variable communication delays in the feedback signal of WADC and the integration of solar photovoltaic systems. Moreover, a comparative analysis of the proposed WADC is carried out with a BESS‐based wide‐area power system stabilizer, which is found to be more effective in mitigating system inter‐area oscillations.

Restoration of cone-circuit functionality in the regenerating adult zebrafish retina

Unlike humans, teleosts like zebrafish exhibit robust retinal regeneration after injury from endogenous stem cells. However, it is unclear if regenerating cone photoreceptors regain physiological function and integrate correctly into post-synaptic circuits. We used two-photon calcium imaging of living adult retina to examine photoreceptor responses before and after light-induced lesions. To assess functional recovery of cones and downstream outer retinal circuits, we exploited color opponency; UV cones exhibit intrinsic Off-response to blue light, but On-response to green light, which depends on feedback signals from outer retinal circuits. Accordingly, we assessed the presence and quality of Off- vs. On-responses and found that regenerated UV cones regain both Off-responses to short-wavelength and On-responses to long-wavelength light within 3months after lesion. Therefore, physiological circuit functionality is restored in regenerated cone photoreceptors, suggesting that inducing endogenous regeneration is a promising strategy for human retinal repair.

Controlling Chaotic Resonance with Extremely Local-Specific Feedback Signals

Controlling Chaotic Resonance with Extremely Local-Specific Feedback Signals

Sensory feedback and central neuronal interactions in mouse locomotion.

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyse a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behaviour to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Biomineralization-Tuned Nanounits Reprogram the Signal Transducer and Activator of Transcription 3 Signaling for Ferroptosis-Immunotherapy in Cancer Stem Cells.

Cancer stem cells (CSCs) are promising targets for improving anticancer treatment outcomes while eliminating recurrence, but their treatment remains a major challenge. Here, we report a nanointegrative strategy to realize CSC-targeted ferroptosis-immunotherapy through spatiotemporally controlled reprogramming of STAT3-regulated signaling circuits. Specifically, STAT3 inhibitor niclosamide (Ni) and an experimental ferroptosis drug (1S, 3R)-RSL3 (RSL3) are integrated into hyaluronic acid-modified amorphous calcium phosphate (ACP) nanounits through biomineralization (CaP-PEG-HA@Ni/RSL3), which could be recognized by CD44-overexpressing CSCs and released in a synchronized manner. Ni inhibits the CSC-intrinsic STAT3-PD-L1 axis to stimulate adaptive immunity and enhance interferon gamma (IFNγ) secretion by CD8+ T cells to downregulate SLC7A11 and SLC3A2 for blocking glutathione biosynthesis. Meanwhile, Ni-dependent STAT3 inhibition also upregulates ACSL4 through downstream signaling and IFNγ feedback. These effects cooperate with RSL3-mediated GPX4 deactivation to induce pronounced ferroptosis. Furthermore, CaP-PEG-HA@Ni/RSL3 also impairs the immunosuppressive M2-like tumor-associated macrophages, while Ca2+ ions released from degraded ACP could chelate with lipid peroxides in ferroptotic CSCs to avoid CD8+ T-cell inhibition, thus boosting the effector function of activated CD8+ T cells. This study offers a cooperative ferroptosis-immunotherapeutic approach for the treatment of refractory cancer.

Optimization of Predefined-Time Agent-Scheduling Strategy Based on PPO

In this paper, we introduce an agent rescue scheduling approach grounded in proximal policy optimization, coupled with a singularity-free predefined-time control strategy. The primary objective of this methodology is to bolster the efficiency and precision of rescue missions. Firstly, we have designed an evaluation function closely related to the average flying distance of agents, which provides a quantitative benchmark for assessing different scheduling schemes and assists in optimizing the allocation of rescue resources. Secondly, we have developed a scheduling strategy optimization method using the Proximal Policy Optimization (PPO) algorithm. This method can automatically learn and adjust scheduling strategies to adapt to complex rescue environments and varying task demands. The evaluation function provides crucial feedback signals for the PPO algorithm, ensuring that the algorithm can precisely adjust the scheduling strategies to achieve optimal results. Thirdly, aiming to attain stability and precision in agent navigation to designated positions, we formulate a singularity-free predefined-time fuzzy adaptive tracking control strategy. This approach dynamically modulates control parameters in reaction to external disturbances and uncertainties, thus ensuring the precise arrival of agents at their destinations within the predefined time. Finally, to substantiate the validity of our proposed approach, we crafted a simulation environment in Python 3.7, engaging in a comparative analysis between the PPO and the other optimization method, Deep Q-network (DQN), utilizing the variation in reward values as the benchmark for evaluation.

Contact-force-based closed-loop control of shell structure additive manufacturing with continuous-fiber-reinforced polymer composites

Additive manufacturing (AM) is an automated process for fabricating complex structures, especially for continuous fiber-reinforced polymer (CFRP) composites with exceptional mechanical performance and reduced weight. The extrusion-based CFRP-AM process has been widely adopted but faces challenges with insufficient impregnation/adhesion and lack of geometrical accuracy. Existing research efforts exploit various mechatronics approaches and printing parameter adjustment techniques to enhance CFRP quality. Nevertheless, most of them are static or slow-varying; thus, they cannot dynamically adjust to local geometrical features and faults, which are particularly representative of the CFRP-AM process in high-curvature regions. To enable dynamic and rapid adjustment, this study exploits contact forces as major real-time feedback signals to dynamically control the feed velocity, layer height, and fiber extrusion rate. To create a continuous feedback control environment for dynamic adjustment, the study also proposes a novel helical CFRP-AM printing trajectory generation method for closed shell structures, such that the helical angles indirectly adjust the layer height of the parts. A material extrusion CFRP-AM platform combining a co-extrusion nozzle, an industrial robot, a six-degree-of-freedom load cell, and a real-time closed-loop control system is built to verify the proposed control framework. Three representative case studies are introduced to show the enhancements of the closed-loop CFRP-AM process, particularly in layer height adjustment, high-speed printing, and large curvature regions.

Adaptation of the gain of the corrective lifting response in object manipulation transfers across the hand

Our ability to skillfully manipulate objects is supported by rapid corrective responses that are initiated when we experience perturbations that interfere with movement goals. For example, the corrective lifting response is triggered when an object is heavier than expected and fails to lift off the surface. In this situation, the absence of expected sensory feedback signalling lift off initiates, within ~ 90 ms, an increase in lifting force. Importantly, when people repeatedly lift an object that, on occasional catch trials, is heavier than expected, the gain of the corrective response, defined as the rate of force increase, adapts to the ‘catch’ weight. In the present study, we investigated whether this response adaption transfers intermanually. In the training phase, participants used either their left or right hand (counterbalanced) to repeatedly lift a 3 N object that unexpectedly increased to 9 N on catch trials, leading to an increase in the gain of the lifting response across catch trials. Participants then lifted the object with their other hand. On the first catch trial, the gain remained elevated and thus transferred across the hands. This finding suggests that the history of lifts performed by one hand updates the corrective responses for both hands.

Successful retrieval is its own reward.

The ability to successfully remember past events is critical to our daily lives, yet the neural mechanisms underlying the motivation to retrieve is unclear. Although reward-system activity and feedback-related signals have been observed during memory retrieval, whether this signal reflects intrinsic reward or goal-attainment is unknown. Adjudicating between these two alternatives is crucial for understanding how individuals are motivated to engage in retrieval and how retrieval supports later remembering. To test these two accounts, we conducted between-subjects recognition memory tasks on human participants undergoing scalp electroencephalography and varied test-phase goals (recognize old vs. detect new items). We used an independently validated feedback classifier to measure positive feedback evidence. We find positive feedback following successful retrieval regardless of task goals. Together, these results suggest that successful retrieval is intrinsically rewarding. Such a feedback signal may promote future retrieval attempts as well as bolster later memory for the successfully retrieved events.

Decentralized Retrofit Model Predictive Control of Inverter-Interfaced Small-Scale Microgrids

In recent years, small-scale microgrids have become popular in the power system industry because they provide an efficient electrical power generation platform to guarantee autonomy and independence from the power grid, which is a critical feature in cases of catastrophic events or remote areas. On the other hand, due to the short distances among multiple distribution generation systems in small-scale microgrids, the interconnection couplings among them increase significantly, which jeopardizes the stability of the entire system. Therefore, this work proposes a novel method to design decentralized robust controllers based on a retrofit model predictive control scheme to tackle the issue of instability due to the short distances among generation systems. In this approach, the retrofit model predictive controller receives the measured feedback signal from the interconnection current and generates a control command signal to limit the interconnection current to prevent instability. To design a retrofit controller, only the model of a robust closed-loop system, as well as an interconnection line, is required. The model predictive control signal is added in parallel to the control signal from the existing robust voltage source inverter controller. Simulation results demonstrate the superior performance of the proposed technique as compared with the virtual impedance and retrofit linear quadratic regulator techniques (benchmarks) with respect to peak-load demand, plug-and-play capability, nonlinear load, and inverter efficiency.

Building an Analog Circuit Synapse for Deep Learning Neuromorphic Processing

In this article, we propose a circuit to imitate the behavior of a Reward-Modulated spike-timing-dependent plasticity synapse. When two neurons in adjacent layers produce spikes, each spike modifies the thickness in the shared synapse. As a result, the synapse’s ability to conduct impulses is controlled, leading to an unsupervised learning rule. By introducing a reward signal, reinforcement learning is enabled by redirecting the growth and shrinkage of synapses based on signal feedback from the environment. The proposed synapse manages the convolution of the emitted spike signals to promote either the strengthening or weakening of the synapse, represented as the resistance value of a memristor device. As memristors have a conductance range that may differ from the available current input range of typical CMOS neuron designs, the synapse circuit can be adjusted to regulate the spike’s amplitude current to comply with the neuron. The circuit described in this work allows for the implementation of fully interconnected layers of neuron analog circuits. This is achieved by having each synapse reconform the spike signal, thus removing the burden of providing enough power from the neurons to each memristor. The synapse circuit was tested using a CMOS analog neuron described in the literature. Additionally, the article provides insight into how to properly describe the hysteresis behavior of the memristor in Verilog-A code. The testing and learning capabilities of the synapse circuit are demonstrated in simulation using the Skywater-130 nm process. The article’s main goal is to provide the basic building blocks for deep neural networks relying on spiking neurons and memristors as the basic processing elements to handle spike generation, propagation, and synaptic plasticity.

Dynamic periodic event-triggered control for nonlinear systems with output dynamic quantization

This paper is concerned with the design of stabilizing event-triggered controllers for nonlinear systems subject to dynamic quantization. The plant state is assumed to be partially known, and the feedback signal is transmitted to the controller at discrete-time instants via a digital channel. To that end, an event-triggered controller is synthesized based solely on the available plant measurement. In addition, to better adapt it to practical implementation, the event-triggering law we construct is only verified at periodic time instants, i.e., periodic event-triggering mechanism. Since the network is digital, the transmitted output signal must be quantized to be encoded using a finite number of bits. Furthermore, the initial output value is assumed to be unknown, and the plant is affected by external disturbance, which can result in quantizer saturation. To overcome these challenges, a dynamic quantization policy is developed for the output measurement such that the quantization regions are dynamically updated according to the value of the quantized signal. The combined approach of periodic event-triggering mechanism and dynamic quantization ensures the stability of the networked control system under mild conditions. The stability is studied using appropriate Lyapunov functions. Numerical simulations have been conducted to justify the proposed technique.

Touch and slippage detection in robotic hands with spiking neural networks

Significant research efforts have focused on advancing upper limb prostheses, addressing issues such as dexterity, embodiment, weight, and resistance. These key features require enhancements to mitigate prosthesis abandonment. Sensory feedback is a vital feature for improving the dexterity and embodiment of prostheses for patients. Current techniques utilize electronic signals for feedback to control systems and to partially restore tactile sensations to amputees. However, it has already been demonstrated that, in order to elicit responses from biological neurons signals must be converted into neuromorphic tactile information. Most of those demonstrations relied on algorithmic implementations of mechanoreceptor and neuron models running on classic processor cores. However, integrating real-time encoding and control strategies into such architectures is challenging due to computational complexity and high power usage. A prospective solution is to increasingly incorporate neuromorphic technologies for prosthesis sensing and control. In this work, we demonstrate the feasibility of using spiking neural networks for performing two key functions in controlling upper limb prosthesis: touch and slippage detection. We found that a simple two-layered architecture with a few tens of neurons is sufficient to identify slip and touch with nearly 100% accuracy. The first layer consists of a heterogeneous population of neurons with their parameters adjusted to model slowly and fast-adapting type 1 mechanoreceptors. An output layer classifies touch or slip events. Furthermore, simulations showed that implementing the network in ultra-low-power neuromorphic hardware, such as Loihi, has the potential to reduce power consumption by up to 162 times, with respect to a von Neumann processor.

Estrogen receptor-α signaling in tanycytes lies at the crossroads of fertility and metabolism

BackgroundEstrogen secretion by the ovaries regulates the hypothalamic-pituitary-gonadal axis during the reproductive cycle, influencing gonadotropin-releasing hormone (GnRH) and luteinizing hormone (LH) secretion, and also plays a role in regulating metabolism. Here, we establish that hypothalamic tanycytes—specialized glia lining the floor and walls of the third ventricle—integrate estrogenic feedback signals from the gonads and couple reproduction with metabolism by relaying this information to orexigenic neuropeptide Y (NPY) neurons. MethodsUsing mouse models, including mice floxed for Esr1 (encoding estrogen receptor alpha, ERα) and those with Cre-dependent expression of designer receptors exclusively activated by designer drugs (DREADDs), along with viral-mediated, pharmacological and indirect calorimetric approaches, we evaluated the role of tanycytes and tanycytic estrogen signaling in pulsatile LH secretion, cFos expression in NPY neurons, estrous cyclicity, body-weight changes and metabolic parameters in adult females. ResultsIn ovariectomized mice, chemogenetic activation of tanycytes significantly reduced LH pulsatile release, mimicking the effects of direct NPY neuron activation. In intact mice, tanycytes were crucial for the estrogen-mediated control of GnRH/LH release, with tanycytic ERα activation suppressing fasting-induced NPY neuron activation. Selective knockout of Esr1 in tanycytes altered estrous cyclicity and fertility in female mice and affected estrogen's ability to inhibit refeeding in fasting mice. The absence of ERα signaling in tanycytes increased Npy transcripts and body weight in intact mice and prevented the estrogen-mediated decrease in food intake as well as increase in energy expenditure and fatty acid oxidation in ovariectomized mice. ConclusionsOur findings underscore the pivotal role of tanycytes in the neuroendocrine coupling of reproduction and metabolism, with potential implications for its age-related deregulation after menopause. Significance statementOur investigation reveals that tanycytes, specialized glial cells in the brain, are key interpreters of estrogen signals for orexigenic NPY neurons in the hypothalamus. Disrupting tanycytic estrogen receptors not only alters fertility in female mice but also impairs the ability of estrogens to suppress appetite. This work thus sheds light on the critical role played by tanycytes in bridging the hormonal regulation of cyclic reproductive function and appetite/feeding behavior. This understanding may have potential implications for age-related metabolic deregulation after menopause.

Intracellular calcium dysregulation in heart and brain diseases: Insights from induced pluripotent stem cell studies.

The central nervous system (CNS) plays a role in regulating heart rate and myocardial contractility through sympathetic and parasympathetic nerves, and the heart can impact the functional equilibrium of the CNS through feedback signals. Although heart and brain diseases often coexist and mutually influence each other, the potential links between heart and brain diseases remain unclear due to a lack of reliable models of these relationships. Induced pluripotent stem cells (iPSCs), which can differentiate into multiple functional cell types, stem cell biology and regenerative medicine may offer tools to clarify the mechanisms of these relationships and facilitate screening of effective therapeutic agents. Because calcium ions play essential roles in regulating both the cardiovascular and nervous systems, this review addresses how recent iPSC disease models reveal how dysregulation of intracellular calcium might be a common pathological factor underlying the relationships between heart and brain diseases.

A Wearable Dual-Mode Probe for Image-Guided Closed-Loop Ultrasound Neuromodulation.

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes an image-guided platform for chronic and patient-specific FUS neuromodulation. The system uses custom wearable probes containing separate ultrasound imaging and modulation transducer arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dual-mode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam toward the target. The ultrasound transmit voltage used to excite the modulation array was optimized in real-time by automatically regulating functional feedback signals (the average rates of emulated muscle twitches detected by an on-board motion sensor) through a proportional and integral (PI) controller, thus providing robustness to inter-subject variability and probe positioning errors. The proposed closed-loop neuromodulation paradigm was experimentally demonstrated in vitro using an active tissue phantom that integrates models of the posterior tibial nerve and nearby blood vessels together with embedded sensors and actuators.

Distributed Broadcast Control of Multi-Agent Systems Using Hierarchical Coordination.

Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC.

Realization of modularized corrector magnet power supply with N+1 redundancy for TPS facilities

This paper delves into implementing multi-module parallel current output using the existing TPS storage ring correction magnet power supply. We have devised a control interface card with N+1 redundancy to facilitate bipolar high-current parallel module output. To achieve this, we have employed various current feedback methods, including external DC Current Transducer (DCCT) and internal module current feedback signals. Following PI compensation, these feedback signals are amalgamated with reference current signals to compute compensation values for each module, which are subsequently disseminated to each Corrector Magnet Power Supply (CMPS) for modulation, thereby enabling closed-loop current control. A single CMPS module can deliver ± 48 V/± 10 A output, while up to eight CMPS modules can be interconnected, yielding a maximum output of ± 80 A. Through numerous experimental measurements, the long-term output current stability remains within 0.6 mA, or 7.5 ppm, with the output current spectrum predominantly maintained within 500 μA. Furthermore, the system boasts N+1 redundancy functionality and bipolar output current characteristics. These exemplary performance attributes underscore the criticality of our design for future applications of TPS magnet power supplies.