Feedback Path Research Articles

Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Adaptive microlearning has emerged as a crucial approach for enhancing the working skills of in-service personnel. This study introduces the design and development of an innovative adaptive microlearning (AML) system and investigates its effectiveness compared to a conventional microlearning (CML) system. The main distinguishing feature of an AML system from a CML system is its adaptive features that tailor the learning experience to individual needs, including personalized content delivery, real-time feedback, and adaptive learning paths. A quasi-experimental study involving 111 in-service personnel (NAML = 56, NCML = 55) was conducted. ANCOVA results confirmed that the AML system significantly reduced unnecessary cognitive load due to inappropriate instructional design (mean difference of -20.02, p < 0.05) and significantly improved learning adaptability (mean difference of 40.72, p < 0.05). These findings highlight the potential of adaptive microlearning systems to overcome barriers to effective learning, thereby supporting lifelong learning and professional development in various working contexts.

Adaptive control of nonlinear time-varying systems with unknown parameters and model uncertainties

This paper investigates the adaptive control problem for nonlinear time-varying systems with unknown parameters and model uncertainties. A novel class of switching functions is designed, and its construction method is detailed, along with a proof of the continuity of its n−1 order derivatives. Two simple examples are provided to illustrate how the proposed congelation of variables method handles unknown high-frequency time-varying parameters in both the feedback and input paths. A new neural network control scheme is then developed, integrating an adaptive neural network controller with a robust controller. The smooth transition between these two controllers is ensured by the novel switching function, which guarantees global system stability. Furthermore, by combining the congelation of variables method with adaptive backstepping, a new adaptive tracking control scheme is proposed. This scheme effectively handles unknown high-frequency time-varying parameters while achieving asymptotic tracking of arbitrary reference signals. Simulation results show that the proposed novel adaptive control method delivers superior control accuracy while reducing energy consumption: it achieves an order of magnitude improvement over the traditional adaptive robust control method and two orders of magnitude improvement over the conventional sliding mode control method.

A novel method for a technology enhanced learning recommender system considering changing user interest based on neural collaborative filtering

This study introduces an advanced recommender system for technology enhanced learning (TEL) that synergizes neural collaborative filtering, sentiment analysis, and an adaptive learning rate to address the limitations of traditional TEL systems. Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users. Utilizing Bidirectional Encoder Representations from Transformers for sentiment analysis, our system not only understands but also respects user privacy by processing feedback without revealing sensitive information. The adaptive learning rate, inspired by AdaGrad, allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback, ensuring a dynamic response to both positive and negative sentiments. This dual approach enhances the system’s adaptability to changing user preferences and improves its contentment understanding. Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners, facilitating a more personalized learning experience. By dynamically adjusting recommendations based on real-time user data and behavioral analysis, our system leverages the collective insights of similar users and relevant content. We validated our approach against three datasets—MovieLens, Amazon, and a proprietary TEL dataset—and saw significant improvements in recommendation precision, F-score, and mean absolute error. The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems, marking a step forward in developing more responsive and user-centric educational technologies. This study paves the way for future advancements in TEL systems, emphasizing the importance of emotional intelligence and adaptability in enhancing the learning experience.

Design of 8T DTMOS Schmitt Trigger SRAM Cell for IOT Applications

Abstract Internet of things (IoT) based systems require power-efficient circuits to raise the battery lifeline. This study presents a single-ended 8T SRAM cell. The core of the proposed 8T SRAM cell is composed of a Schmitt-Trigger circuit which a dynamic body bias technique is applied to a standard CMOS inverter through a feedback mechanism, whereby the threshold voltages of two MOSFETs can be changed, thus changing the switching voltage. Read operation of the proposed cell is conducted using the shared footer per word transistor. The write path is cut-off during the read operation, improving RSNM. A transmission gate placed in the cell core is used to cut the feedback path during write operation. To prove superiority of the proposed cell it is compared with four state-of-the-art SRAM cells under identical conditions on Cadence Virtuoso using 45nm technology at VDD=0.8 V. The proposed circuit shows a 135.74 % improvement in terms of RSNM and a 44.04 % improvement in terms of peak-to-peak power compared to the 6T DTMOS Cell.

A compact and tunable active inductor-based bandpass filter with high quality factor for Wireless LAN applications

A fully-differential compact and tunable second-order bandpass filter (BPF) with high quality factor using a novel active inductor (AI) has been proposed for 3.6 GHz WLAN applications. The proposed AI employs a differential amplifier as a feedforward transconductor and a common-source (CS) transistor as a feedback transconductor in a symmetrical configuration. The combination of a cascode scheme and a resistor in the feedback path enhances the quality factor of the AI, thereby increasing the mid-band gain of the BPF. In order to achieve independent tuning of both the center frequency and mid-band gain of the BPF, a varactor capacitor is utilized. The proposed second-order BPF is designed with a center frequency and a bandwidth of 3.675 GHz and 10 MHz, respectively. Post-layout simulation, process corner, and temperature sweep analysis results are conducted with 65 nm CMOS technology at 1.2 V supply voltage. The proposed filter has a tuning range of 3.655–3.695 GHz and attains a mid-band gain of 67.5 dB, a noise figure of 14.5 dB, and a 1-db compression point of −12.39 dBm. Additionally, the BPF consumes a DC power of 1.22 mW and occupies an active area of only 8.85 μm × 10.3 μm.

Analytical design of modified Smith predictor for second-order stable time delay plants incorporating a zero

Simplified control tuning not only enhances engineers' proficiency in control theory and its application but also contributes to their skill development. This study focuses on the control of stable second-order plus time delay (SOPTD) plants incorporating a zero, by employing a modified Smith predictor structure. A forward path PID controller and a PID controller with a lead/lag filter in the feedback path contribute to the proposed control system. The PID controller determines the set-point tracking. The load disturbance rejection is influenced by both the PID and lead/lag filter. Each controller is simply designed using pole placement, while considering a tuning parameter to make a balance between performance and robustness. Filtering of the set-point signal is implemented to enhance its response. To assess the effectiveness of the proposed technique, the study involves examining a SOPTD plant and either a negative or a positive zero. Simulation and comparison analysis show that the proposed controller is appropriate for industrial usage since it quickly tracks the set-point and efficiently rejects disturbances, resulting in a smooth control signal. Furthermore, robustness tests reveal its satisfactory degree of robustness against model parameter uncertainties. In summary, the proposed approach is straightforward and outperforms recently published design methodologies in terms of both performance and robustness.

A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.

A CMOS Inverter-Based Active Feedback Transimpedance Amplifier

This paper presents an inverter-based active feedback transimpedance amplifier (IAF-TIA), in which an active feedback is applied to a voltage-mode inverter-based TIA, and therefore, the controlled positive regeneration process enables the proposed IAF-TIA to achieve the limiting operations for input currents greater than 100 μApp. However, the active inverter feedback mechanism might be prone to instability, hence mandating a very careful optimization of the loop gain. For this purpose, a diode-connected NMOS transistor is employed as a switch in the feedback path with its gate connected to the input, which helps not only to mitigate the corresponding issue but also to accommodate large input currents up to 1.5 mApp. The proposed IAF-TIA implemented in a standard 180 nm CMOS process demonstrates a 70.5 dBΩ transimpedance gain, 1.21 GHz bandwidth, 4.3 pA/Hz noise current spectral density, 63.5 dB input dynamic range, and 23.6 mW power dissipation from a single 1.8 V supply. The chip core occupies an area of 180 × 50 μm2, including an on-chip P+/N-well/Deep N-well avalanche photodiode as an optical detector.

Low contrast stamped dates recognition for pill packaging boxes based on YOLO-SFD and image fusion

Dates sealed by embossed stamps (stamped dates) on pill packaging boxes are crucial for people to notice the expiration dates. Thus, manufacturers must ensure the accuracy of stamped dates on the pill packaging boxes. However, the low contrast between the stamped dates and the surface of the container and the lack of clear character outlines often lead to light interference, which hampers the stamped dates detections by various deep learning models. This article presents a method for detecting stamped characters that combines YOLO-Shallow Feature Detection (YOLO-SFD) and image fusion. YOLO-SFD enhances YOLOv8 by introducing a Shallow Feedback Feature Pyramid Network (SF-FPN) that includes shallow feature paths and feedback paths for the original input. This enables the model to pay more attention to the shallow features, which include a more detailed character contour. Secondly, this paper presents a lightweight convolutional structure to enhance the model's portability without sacrificing accuracy. Experimental results indicate that SF-FPN outperforms the original feature pyramid network in terms of accuracy and model size. On the stamped date dataset, YOLO-SFD achieves an average accuracy of 99.02 %, 2.57 % higher than the original model, with 29 % fewer parameters. In addition, a 1.5 % mAP improvement is achieved on the Street View House Numbers Dataset. This shows that this method can better fulfill the requirements of pill packaging box stamped date detection and provides new solutions and ideas for related character detection or low contrast image detection.

Robust constrained cosine arctangent based adaptive filtering for feedback control in hearing aids

The striking problem of acoustic feedback remains a persistent challenge in hearing aids (HAs), imposing limitations on attainable amplification and significantly deteriorating sound quality, often manifesting as disruptive howling artifacts. With the objective of feedback mitigation, the feedback path is estimated using an adaptive filter. The constrained least mean square (CLMS) technique is commonly employed in adaptive filtering applications where it is necessary to meet specific linear constraints. However, algorithm's robustness is compromised when impulsive or non-Gaussian noise interference occurs. In lieu of this, a novel algorithm known as the robust constrained cosine arctangent (CCAT) adaptive filtering is introduced to enhance the convergence behavior. This has been formulated with the inclusion of linear constraints to the suggested cosine function integrated with the arctangent technique. This algorithm aims to address the issue of feedback cancellation in hearing aids along a set of constraints. To achieve a minimal steady-state error and expedite the convergence of the method, a novel policy that adjusts the step factor has been suggested. CCAT-VSS is a modified version of CCAT that includes a variable step size (VSS). The simulation findings demonstrate that the suggested method exhibits superior performance for misalignment, added stable gain and certain voice quality assessments.

Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths.

The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path.

Integrated optical ring resonator based comb filter in silicon-on-insulator with through-to-add port feedback path showing larger free spectral range

Integrated optical ring resonator based comb filter in silicon-on-insulator with through-to-add port feedback path showing larger free spectral range

Are there interactions between the urban and rural construction land use transition? Evidence from Jiangsu province in China

The interactive feedback between the transition of urban construction land (UCL) and rural construction land (RCL) lies at the forefront of land use transition (LUT) research, and can shape the intrinsic driving forces for land system sustainable transition and urban-rural integrated development. This study innovatively investigated the interactive feedback between UCL and RCL transition. First, a theoretical analytical framework was proposed. Second, a spatial simultaneous equation model (SSEM) was employed to examine the interactive feedback between UCL and RCL transitions in Jiangsu Province, China, from 2000 to 2020. The main findings were as follows: The interactive feedback between UCL and RCL transitions followed two paths. The first interactive feedback path was driven by the interaction of urban and rural factors, whereas the second was driven by the integrated management of urban and rural land. UCL and RCL transition witnessed a positive interactive feedback relation. Specifically, a 1% increase in UCL transition led to a 0.535% increase in RCL transition, and a 1% increase in RCL transition resulted in a 0.579% increase in UCL transition. The two aspects exhibited complex spatial interaction effects. The UCL transition in the subject city had positive and negative effects on the UCL and RCL transitions of neighboring cities, respectively. The RCL transition had positive effect on the RCL transition in neighboring cities. The key to regulation is to make the mutual feedback pathways unobstructed through innovative policies in terms of both direct and indirect regulation. This study contributes to advance research on LUT and provides scientific evidence for urban–rural integrated development.

Is Green Transition in Europe Fostered by Energy and Environmental Efficiency Feedback Loops? The Role of Eco-Innovation, Renewable Energy and Green Taxation

Green transition is in the core of the European policy agenda to achieve the ambitious goal of climate neutrality following the launch of the European Green Deal. The cornerstone of the new growth strategy of Europe is resource efficiency which focuses on shifting to a more sustainable production paradigm by conserving scarce resources and by prioritizing enhanced environmental performance. Scattered efforts to investigate the drivers of resource efficiency measures have shed light on the key drivers, however, those consider resource efficiency measures in isolation neglecting for feedback loops influencing green transition. Therefore, we develop a conceptual framework to study green transition as a system of resource efficiency measures affected by feedback loops, path dependence, green technologies, and green policy tools. We mobilize the analysis by devising a unique balanced panel covering the EU-28 from 2010 through 2019, including policy efforts paving the way for green transition. Econometric results based on a system of fractional probit models, indicate that resource efficiency measures are intertwined via feedback loops, especially in the case of environmental efficiency. Green technologies affect green transition, however, rebound effects emerge in the case of energy efficiency. Past performance affects current levels pushing towards divergence. Evidence suggests that green taxation fosters energy efficiency whereas hinders environmental efficiency. The asymmetric operation of feedback loops and green taxation on energy and environmental efficiency highlights that horizontal policies hinder rather than foster green transition. This study contributes to SDGs 7, 12, 13 and 16.

Hassle - Free Load Carrying Using Automated Guided Vehicle

In today's rapidly evolving world, where efficiency and sustainability are paramount, businesses are seeking technologies that not only save time and drive profits but also align with their sustainable goals. In response to this demand, a prototype of an Automated Guided Vehicle (AGV) is developed.AGVs are versatile robotic platforms designed to navigate predefined paths while handling heavy loads of materials. The AGV system addresses these needs by utilizing cost-effective RFID tags and an RFID reader for efficient load tracking and navigation. Additionally, it incorporates two infrared (IR) sensors for path detection, an L298N motor driver for precise motor control, an Arduino microcontroller as the central processing unit, and an HC-05 Bluetooth module for wireless communication.By integrating these components, along with feedback control loops and path planning algorithms, The AGV is observed to be an efficient load carrier and can navigate reliably in dynamic environments. Above all, this solution not only saves time and enhances profitability but also promotes sustainability by optimizing resource utilization and minimizing manual intervention. Key Words: AGV(Automated Guided Vehicle),RFID(Radio Frequency Identification) tags, RFID (Radio Frequency Identification)reader, IR sensors, L298N Motor driver, Microcontroller.

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

A Direct Current-Sensing VCO-Based 2nd-Order Continuous-Time Sigma-Delta Modulator for Biosensor Readout Applications.

A second-order voltage-controlled oscillator (VCO)-based continuous-time sigma-delta modulator (CTSDM) for current-sensing readout applications is proposed. Current signals from the sensor can directly be quantized by the proposed VCO-based CTSDM, which does not require any extra trans-impedance amplifiers. With the proportional-integral (PI) structure and a VCO phase integrator, the capability of second-order noise shaping is available to reduce the in-band quantization noise. The PI structure can be simply realized by a resistor in series with the integrating capacitor, which can reduce the architecture complexity and maintain the stability of the system. The current-steering digital-to-analog converter with tail and sink current sources is used on the feedback path for the subtraction of the current-type input signal. All the components of the circuit are scaling friendly and applicable to current-sensing readout applications in the Internet of Things (IoT). The proposed VCO-based CTSDM implemented in a 0.18-μm standard CMOS process has a measured signal-to-noise and distortion ratio (SNDR) of 74.6 dB at 10 kHz bandwidth and consumes 44.8 μw only under a supply voltage of 1.2 V, which can achieve a Figure-of-Merit (FoM) of 160.76 dB.

Self‐powered HP‐LED intelligent energy management system with adaptive brightness adjustment

AbstractThe energy harvesting system achieved from one environmental energy channel is facing more and more challenges in meeting the power supply of electronic energy‐consuming devices. In this study, a combined photovoltaic (PV) and Thermoelectric Generator (TEG) energy management system is proposed with intelligent self‐powered technique for high‐power light‐emitting diodes (HP‐LED) lighting. Both the solar energy and waste heat induced by PV and HP‐LED are synchronously converted into electrical energy. Self‐powered by this gathered power, an adaptive light intensity control of HP‐LED is also realized using the established feedback path between ambient light and HP‐LED light. Experimental results show that the supply voltage of the TEG required for startup of the whole system can be decreased as 20.8 mV. Only 10 s is needed to complete the startup process under sufficient light, while 25 s in case of insufficient conditions. This energy management can provide a new technology for future smart home and greenhouse planting.

Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s.

Differential Entropy-Based Fault-Detection Mechanism for Power-Constrained Networked Control Systems.

In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach, we then discuss the finite-time approximation of the power constraints for the relevant control loop signals. The network under study is formed by two additive white Gaussian noise (AWGN) channels located on the direct and feedback paths of the closed control loop. The finite-time approximation of the controller output signal allows us to estimate its differential entropy, which is used in our proposed fault-detection mechanism. After fault detection, we propose a fault-identification mechanism that is capable of correctly discriminating faults. Finally, we discuss the extension of the contributions developed here to future research directions, such as fault recovery and control resilience.