Feedback Signal Research Articles

Feedback signal detection and drive signal compensation for electromagnetic MEMS scanning mirror based on FPGA

Feedback signal detection and drive signal compensation for electromagnetic MEMS scanning mirror based on FPGA

IoT Smart Control System: Smoke and Fire Detection Using SIM900A Module

The main focus of this system is to detect smoke and fire effectively in potential hazard situations. This system utilizes sensor technology to monitor changes in environmental conditions, with the aim of providing quick responses and notifications to users or relevant authorities via a cellular connection. This approach combines feedback signals from sensors and cellular connectivity using the SIM900A module processed with a microcontroller to increase the efficiency and reliability of hazard detection. This system was designed with the aim of providing an efficient and affordable solution for detecting potential fires or other hazardous situations in various environments, including homes, offices and industrial areas. Trial results show that this system is capable of detecting smoke and fire with high accuracy, as well as providing instant notifications via text messages or digital notifications. This innovation is expected to make a positive contribution in improving safety and security in various sectors, while leveraging the potential of IoT connectivity to provide smarter solutions in hazard detection.

Modulation of subthalamic beta oscillations by movement, dopamine, and deep brain stimulation in Parkinson’s disease

Subthalamic beta band activity (13–35 Hz) is known as a real-time correlate of motor symptom severity in Parkinson’s disease (PD) and is currently explored as a feedback signal for closed-loop deep brain stimulation (DBS). Here, we investigate the interaction of movement, dopaminergic medication, and deep brain stimulation on subthalamic beta activity in PD patients implanted with sensing-enabled, implantable pulse generators. We recorded subthalamic activity from seven PD patients at rest and during repetitive movements in four conditions: after withdrawal of dopaminergic medication and DBS, with medication only, with DBS only, and with simultaneous medication and DBS. Medication and DBS showed additive effects in improving motor performance. Distinct effects of each therapy were seen in subthalamic recordings, with medication primarily suppressing low beta activity (13–20 Hz) and DBS being associated with a broad decrease in beta band activity (13–35 Hz). Movement suppressed beta band activity compared to rest. This suppression was most prominent when combining medication with DBS and correlated with motor improvement within patients. We conclude that DBS and medication have distinct effects on subthalamic beta activity during both rest and movement, which might explain their additive clinical effects as well as their difference in side-effect profiles. Importantly, subthalamic beta activity significantly correlated with motor symptoms across all conditions, highlighting its validity as a feedback signal for closed-loop DBS.

Active vibration control in robotic grinding using six-axis acceleration feedback

Industrial robots endure severe vibrations in the grinding process due to factors such as joint flexibility, low damping, and contact force fluctuations. Additionally, the motion control of the robot primarily relies on feedback signals from the motor-side encoders, while lacking motion state feedback from the load side. This exacerbates the robot vibrations, severely compromising the surface quality of the workpieces. To address this problem, an active damping control method for robotic grinding using six-axis acceleration feedback is proposed. This method realizes a simple, reliable, and efficient active vibration control without altering the robot body. A multi-sensor fusion Cartesian space six-axis accelerometer is designed and integrated into the robot end effector, achieving accurate feedback on the robot load side vibration state. The strategy of six-axis acceleration decoupling and principle of the vibration suppression with the acceleration feedback are explained. Compared with conventional sensing schemes, this reduces joint acceleration approximation errors and end vibrations. The robotic grinding experiments are carried out on aeronautic structural parts. Extensive case studies demonstrate that the proposed approach can effectively control the vibration caused by joint flexibility in the robot grinding process, and greatly improve the surface quality of workpieces. This method has great potential application in vibration suppression during robotic grinding and polishing.

Hardware-in-the-loop simulation test platform for UAV flight control system

The flight control system is the integrated command center and core component of the UAV system. Aiming at the problems of poor accuracy and low efficiency in the field measurement of flight control system parameters, combined with the configuration of the flight attitude sensor-vertical gyroscope, hardware-in-the-loop simulation technology, and integrated automatic test methods are adopted. The hardware-in-the-loop simulation test platform for the UAV flight control system composed of a low-cost, high-precision miniaturized UAV attitude calibration platform and a control piston deflection angle test device is designed. The main controller replaces the ground master control station of the UAV and directly sends remote control commands to the aircraft. By changing the attitude of the aircraft, and measuring the voltage of the control signal, the feedback signal, and the rudder angle, the comprehensive performance test of the parameters of the flight control system is realized. The performance test of the platform shows that the platform can meet the test requirements of angular position, system voltage, and control piston deflection angle.

Progress of CO2 Dispersion Interferometer on EAST

A CO2 Dispersion Interferometer (DI) system on the Experimental Advanced Superconducting Tokamak (EAST) was successfully operated, providing plasma electron density measurements. The DI system utilizes a continuous-wave 9.3 μm CO2 laser source to measure line-averaged electron densities. This offers significant advantages, including the ability to minimize fringe jumps and insensitivity to mechanical vibrations. These characteristics are well-suited for future high-density, long-pulse plasma discharges. The DI system provides a real-time density feedback signal to the plasma control system for routine density control during long-pulse operation. Experiments with EAST demonstrated good agreement between the density obtained by the DI system and the preset densities. The DI system also exhibited stability during long-pulse discharge. Moreover, the DI system was stable during rapid density changes and high-density pellet injections. In shot No. 120594, the DI system exhibited stable density feedback during continuous projectile injection lasting over 50 seconds; the line-averaged electron density is approximately 4×1019 m-3. In contrast to the long-wavelength source interferometer, which may deflect light from the detector owing to excessive refraction angles in larger density-gradient discharges, the DI ensured accurate density measurements. The DI system on EAST is dependable for accurately measuring the electron density.

Dynamic Model on Palm Oil Production Capacity Using Variable CPO Stock for Biofuel Production

The role of biofuel for running diesel power plants is therefore indispensable for compensating various renewable plants with fluctuating output, maintaining stability to the overall power system. For expanding biofuel production, keeping the rate of investment in CPO mills is critical, as it is often driven by CPO business profit. The seasonal nature of palm-oil plantation harvest directly influences CPO production behaviour. This research investigates system dynamics model simulation for CPO production system, indicated by the feedback loop from market demand, hence influencing the daily (or monthly) CPO production to provide revenue stream to the business. Subsequently, it influences the rate of investment for increasing production capacity, forming the dynamic hypothesis for the system. The model was developed using endogenous variables, while trying to reduce the number of external inputs required to run the simulation. By substituting the price variable with the level of CPO stock at the national level, less uncertainties would affect the system such as price instability situation. Feedback signal from the level of CPO stock was utilized to control the simulated amount of CPO production, as well as the rate of re-investment for expanding the country’s production capacity. Model simulation results was able to reproduce the system behaviour for capacity increase, to meet CPO market demand. The model structure could be further replicated for efficiently developing the dynamic model for basic commodity production, where the rate of commodity production would not influence the overall market demand (decoupling of production rate from global market)

Super-enhancer-driven LIF promotes the mesenchymal transition in glioblastoma by activating ITGB2 signaling feedback in microglia.

The mesenchymal (MES) subtype of glioblastoma (GBM) is believed to be influenced by both cancer cell-intrinsic alterations and extrinsic cellular interactions, yet the underlying mechanisms remain unexplored. Identification of microglial heterogeneity by bioinformatics analysis. Transwell migration, invasion assays, and tumor models were used to determine gene function and the role of small molecule inhibitors. RNA sequencing, chromatin immunoprecipitation, and dual-luciferase reporter assays were performed to explore the underlying regulatory mechanisms. We identified the inflammatory microglial subtype of tumor-associated microglia (TAM) and found that its specific gene integrin beta 2 (ITGB2) was highly expressed in TAM of MES GBM tissues. Mechanistically, the activation of ITGB2 in microglia promoted the interaction between the SH2 domain of STAT3 and the cytoplasmic domain of ITGB2, thereby stimulating the JAK1/STAT3/IL-6 signaling feedback to promote the MES transition of GBM cells. Additionally, microglia communicated with GBM cells through the interaction between the receptor ITGB2 on microglia and the ligand ICAM-1 on GBM cells, while an increased secretion of ICAM-1 was induced by the proinflammatory cytokine leukemia inhibitory factor (LIF). Further studies demonstrated that inhibition of cyclin-dependent kinase 7 substantially reduced the recruitment of SNW1 to the super-enhancer of LIF, resulting in transcriptional inhibition of LIF. We identified notoginsenoside R1 as a novel LIF inhibitor that exhibited synergistic effects in combination with temozolomide. Our research reveals that the epigenetic-mediated interaction of GBM cells with TAM drives the MES transition of GBM and provides a novel therapeutic avenue for patients with MES GBM.

SIoV Mobility Management using SDVN-enabled Traffic Light Cooperative Framework

Social Internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, etc. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel smart traffic light controller-assisted software-defined vehicular networking-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing Software-Defined Vehicular Networking (SDVN) architecture in Vehicle to Vehicle and Vehicle to Infrastructure communication. The SDVN module is used to offer two essential services: 1) SIoV-based road-lane prioritization, and 2) congestion prevention signal generation for the smart traffic light controller. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization: 1) SIoV, and 2) the road lane with SIoV, are done. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5% to 55.2%, 1.2% to 82.7%, 1.6% to 38.4%, and 1.8% to 12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.

Simulated operant reflex conditioning environment reveals effects of feedback parameters.

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.

Signaling feedback mechanisms to promoting self-regulated learning and motivation in virtual reality transferred to real-world hands-on tasks

ABSTRACT Virtual reality (VR) provides a unique platform for interactive learning experiences, enhancing learning, particularly in hands-on courses. However, the visual load of VR and the lack of guidance and interaction from physical teachers or peers can pose challenges for learners in self-regulated learning (SRL) and learning motivation. This study designed feedback in the VR learning environment based on the signaling principles of Cognitive Theory of Multimedia Learning (CTML) to investigate learners’ SRL, learning motivation, cognitive levels, and their abilities to handle real-world scenarios. We developed a VR hands-on learning course on the Artificial Intelligence of Things (AIoT) and conducted two real-world AIoT hands-on tasks, implementing a quasi-experimental study with 71 university students. Participants were randomly assigned to either the experimental group or the control group. The experimental group received three types of feedback signals (gaze, progress, and decision) designed based on signaling principles, while the control group received no feedback. The results showed that the experimental group exhibited significantly higher levels of SRL and learning motivation compared to the control group after VR learning. The feedback group also performed better in terms of cognitive levels and real-world hands-on tasks.

Electronic Sunflower Design

Solar energy is known as the most primitive source of energy; it's clean, renewable, abundant, and widely distributed, offering a very broad prospect for utilization. However, the low efficiency of solar energy utilization has always been affecting and hindering the popularization of solar energy technology. The design of a solar tracking system offers a new approach to solving this problem, thereby greatly improving the efficiency of solar energy utilization. This design employs a photoelectric tracking method, using a stepper motor driven by photoelectric sensors, which generate feedback signals to a microprocessor based on the intensity of incident light. The microprocessor runs the program and controls the tracking mechanism to adjust the angle of the solar panels to track the sun. A single-chip microcontroller-implemented solar tracking system can effectively improve the photoelectric conversion efficiency of solar panels and has a broad application prospect.

Design and signal processing of plastic optical fiber respiratory sensors based on smartphones

A respiratory signal sensing scheme based on plastic optical fiber and smartphone is proposed. Respiratory signals are achieved by detecting the changes in abdominal circumference caused by the inhalation and exhalation of air from the lungs during breathing. A flexible clamp is prepared based on the 3D printing technology and the changes in abdominal circumference are leading to the lateral distance variation between the end face of the two plastic optical fibers in the clamp. The experiment demonstrates the scheme’s capability to detect a broad range of stable respiratory frequencies and weak signals. Finally, an automatic multi-scale peak detection algorithm is utilized to achieve real-time display of transient respiratory rate, which enables the timely feedback of apnea and abnormal signals. The proposed respiratory sensing system provides an efficient, accurate, convenient and low-cost solution for human physiological signal monitoring.

Quantization-based distributed design strategy for adaptive consensus tracking of asynchronously switched nonlinear multiagent systems

We propose a quantization-based distributed consensus tracking design to resolve the unknown control direction problem of uncertain switched nonlinear multiagent systems under a fully quantized environment. All feedback and communication signals for the local control design are quantized and the control coefficient functions and directions of agents are unknown. Differing from the previous literature results, the primary contribution of this work is to present a quantization-based distributed design solution to the unknown control direction problem of asynchronously switched agents in the consensus tracking field. The non-differentiability problem of virtual control laws using quantized feedback signals is addressed by employing the filter-based recursive method and developing the analysis technique of the quantization errors of Nussbaum functions. Quantization effects in local adaptive neural control laws are compensated via the adaptive tuning mechanism using quantized states. The practical stability of the overall closed-loop system is established by the common Lyapunov theory. Illustrative simulations verify the efficacy of the proposed quantization-based consensus tracking approach.

Progressive Irreversible Proprioceptive Piezo2 Channelopathy-Induced Lost Forced Peripheral Oscillatory Synchronization to the Hippocampal Oscillator May Explain the Onset of Amyotrophic Lateral Sclerosis Pathomechanism.

Amyotrophic lateral sclerosis (ALS) is a mysterious lethal multisystem neurodegenerative disease that gradually leads to the progressive loss of motor neurons. A recent non-contact dying-back injury mechanism theory for ALS proposed that the primary damage is an acquired irreversible intrafusal proprioceptive terminal Piezo2 channelopathy with underlying genetic and environmental risk factors. Underpinning this is the theory that excessively prolonged proprioceptive mechanotransduction under allostasis may induce dysfunctionality in mitochondria, leading to Piezo2 channelopathy. This microinjury is suggested to provide one gateway from physiology to pathophysiology. The chronic, but not irreversible, form of this Piezo2 channelopathy is implicated in many diseases with unknown etiology. Dry eye disease is one of them where replenishing synthetic proteoglycans promote nerve regeneration. Syndecans, especially syndecan-3, are proposed as the first critical link in this hierarchical ordered depletory pathomechanism as proton-collecting/distributing antennas; hence, they may play a role in ALS pathomechanism onset. Even more importantly, the shedding or charge-altering variants of Syndecan-3 may contribute to the Piezo2 channelopathy-induced disruption of the Piezo2-initiated proton-based ultrafast long-range signaling through VGLUT1 and VGLUT2. Thus, these alterations may not only cause disruption to ultrafast signaling to the hippocampus in conscious proprioception, but could disrupt the ultrafast proprioceptive signaling feedback to the motoneurons. Correspondingly, an inert Piezo2-initiated proton-based ultrafast signaled proprioceptive skeletal system is coming to light that is suggested to be progressively lost in ALS. In addition, the lost functional link of the MyoD family of inhibitor proteins, as auxiliary subunits of Piezo2, may not only contribute to the theorized acquired Piezo2 channelopathy, but may explain how these microinjured ion channels evolve to be principal transcription activators.

Rapid alkalinization factor 22 has a structural and signalling role in root hair cell wall assembly.

Pressurized cells with strong walls make up the hydrostatic skeleton of plants. Assembly and expansion of such stressed walls depend on a family of secreted RAPID ALKALINIZATION FACTOR (RALF) peptides, which bind both a membrane receptor complex and wall-localized LEUCINE-RICH REPEAT EXTENSIN (LRXs) in a mutually exclusive way. Here we show that, in root hairs, the RALF22 peptide has a dual structural and signalling role in cell expansion. Together with LRX1, it directs the compaction of charged pectin polymers at the root hair tip into periodic circumferential rings. Free RALF22 induces the formation of a complex with LORELEI-LIKE-GPI-ANCHORED PROTEIN 1 and FERONIA, triggering adaptive cellular responses. These findings show how a peptide simultaneously functions as a structural component organizing cell wall architecture and as a feedback signalling molecule that regulates this process depending on its interaction partners. This mechanism may also underlie wall assembly and expansion in other plant cell types.

Adaptive output-feedback event-triggered formation control for underactuated ships with obstacle avoidance mechanism

This paper focuses on the adaptive output-feedback event-triggered control strategy for the formation tracking and obstacle avoidance activity of unmanned surface vessels (USVs) with unmeasurable velocity, constrained communication and actuator fault. By using the neural networks and minimal learning parameter, a novel output-feedback event-triggered controller is deigned, effectively alleviates constraints related to continuous feedback signal acquisition in dynamic systems. Subsequently, by incorporating an adjustable threshold, the event-triggered condition is established, thus facilitating updates to the state and adaptive law solely within the discrete-time domain. Then, only two aperiodic adaptive laws are derived to mitigate actuator faults and system uncertainties. As for the path planning, benefiting from the artificial potential field method, a novel obstacle avoidance mechanism is developed for obstacle avoidance and waypoint-based path navigation. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded stability. Finally, two numerical simulations are illustrated to demonstrate the remarkable performance of the proposed control strategy.

Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory

The introduction of a dynamic model in robot trajectory tracking control design can significantly improve its trajectory tracking accuracy, but there are many uncertainties in the robot dynamic model which can be dealt with through robust control and adaptive control. The prevailing robust control as well as adaptive control methods require real-time computation of robot dynamics, but the extreme complexity of the robot dynamics equations makes it difficult to apply these methods in real industrial systems. To this end, this article proposes a robust adaptive control method based on the desired trajectory, which uses the desired trajectory to compute most of the control terms offline, including the robot’s nominal dynamics and regression matrices, and substantially reduces the need for real-time computation of the feedback signals. The robust term modifies the perturbation of the inertial parameters of the links, the adaptive term learns the friction coefficients of the joints online, and an additional compensation term is designed to satisfy the Lyapunov stability condition of the system. Finally, taking a universal manipulator as the experimental platform, the control performances of different control methods are compared to show the feasibility of the controller and the effective reduction in real-time computational complexity.

Interplay between secondary metabolites and plant hormones in silver nitrate-elicited Arabidopsis thaliana plants

Plants produce a myriad of specialized compounds in response to threats such as pathogens or pests and different abiotic factors. The stress-related induction of specialized metabolites can be mimicked using silver nitrate (AgNO3) as an elicitor, which application in conservation agriculture has gained interest. In Arabidopsis thaliana, AgNO3 triggers the accumulation of indole glucosinolates (IGs) and the phytoalexin camalexin as well as pheylpropanoid-derived defensive metabolites such as coumaroylagmatins and scopoletin through a yet unknown mechanism. In this work, the role of jasmonic (JA) and salicylic acid (SA) signaling in the AgNO3-triggered specialized metabolite production was investigated. To attain this objective, AgNO3, MeJA and SA were applied to A. thaliana lines impaired in JA or SA signaling, or affected in the endogenous levels of IGs and AGs. Metabolomics data indicated that AgNO3 elicitation required an intact JA and SA signaling to elicit the metabolic response, although mutants impaired in hormone signaling retained certain capacity to induce specialized metabolites. In turn, plants overproducing or abolishing IGs production had also an altered hormonal signaling response, both in the accumulation of signaling molecules and the molecular response mechanisms (ORA59, PDF1.2, VSP2 and PR1 gene expression), which pointed out to a crosstalk between defense hormones and specialized metabolites. The present work provides evidence of a crosstalk mechanism between JA and SA underlying AgNO3 defense metabolite elicitation in A. thaliana. In this mechanism, IGs would act as retrograde feedback signals dampening the hormonal response; hence, expanding the signaling molecule concept.

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters.

Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.