Adaptive System Research Articles

High temperature exposure causes sexual dimorphism in immune responses against largemouth bass ranavirus (LMBV) in Micropterus salmoides

Largemouth bass (Micropterus salmoides) is an important freshwater fish species in aquaculture. Largemouth bass ranavirus (LMBV) infection at high temperature results in a considerably higher mortality rate in females than that in males. This study compared the spleen transcriptomes and analyzed the impact of sexual dimorphism on immunity and metabolism of largemouth bass infected with LMBV at high temperatures to gain a comprehensive understanding of the sex-based differences in immunity and metabolism of this species. (1) Compared with the ambient temperature group (W20), the high temperature group (W30) exhibited multiple significant differences in metabolic and immune terms, with several immunity-related pathways, including phosphatidylinositol 3-kinase-protein kinase B (PI3K-Akt) signaling pathway. Most identified pathways were associated with the estrogen signaling pathway, which also differed significantly. (2) Differences in glycolipid metabolism, immunity, and the endocrine and complex adaptive systems were distinctly different between male groups (W20M vs. W30M), whereas the female groups (W20F vs. W30F) were not significantly different. The male groups had a significantly higher number of enriched immunity-related KEGG pathways and differentially expressed genes (DEGs) and pathways related to the estrogen signaling pathway. (3) When comparing male and female samples infected with LMBV at the same temperature, no significant difference was observed between the ambient temperature groups (W20F vs. W20M). The high-temperature groups (W30F vs. W30M) had 18 immunity-related pathways among the top 25 enriched KEGG pathways, and gene expression correlation analysis of the DEGs showed positive correlations. This study demonstrates that the combined effects of high temperature and LMBV affect multiple metabolic and immune pathways in largemouth bass, thereby modulating immune responses by regulating the estrogen signaling pathway and influencing the expression of estrogen and its receptors. Under high-temperature conditions, male fish may have stronger estrogen signaling pathway-associated immune responses, which regulate immune pathways, including antigen processing and presentation, complement system, and phagocytosis. Consequently, males may have higher disease resistance than that of females. Our result contribute to mono-sex breeding and antiviral drug therapies available in largemouth bass, thereby markedly improving breeding efficiency.

Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM’s 98.8% accuracy in detecting spectrum holes and ANFIS’s 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications.

Дендроэкологическое исследование роста осины в условиях Московской области

According to V.A. Dragavtsev's concept, the reaction of the annual ring to the provocative environmental background of different years creates a “portrait” of adaptive polygenic genotype systems. By comparing the reactions of the main forest-forming species from an ecologically homogeneous habitat, it is possible to assess the level of similarity of their ecological properties. Cluster analysis of tree-ring chronologies of aspen and species growing together with it, correlation analysis of chronologies and indexed chronologies, as well as time series of meteorological factors and Fourier spectral analysis have been used for a dendroecological research into aspen growth features in the conditions of the Molokchinskiy Botanical and Entomological Reserve of the Sergiev Posad District of the Moscow Region. To compare with the chronologies of aspen from two stands, the chronologies of European white birch, English oak, grey alder and Norway spruce have been used. It has been established that the cluster analysis of indexed chronologies based on calculating the squared Euclidean distance and combining the chronologies into groups according to the rule of complete linkage distinguishes aspen chronologies into a separate cluster, very distant from the other species under consideration. According to the correlation analysis of indexed chronologies, the dynamics of radial increment of aspen is most closely related to the dynamics of radial increment of European white birch and gray alder. Moreover, the maximum correlation coefficient (0.71) is observed for the chronologies of aspen from two different stands. Among the climatic factors, the main limiting influence on aspen increment is exerted by July precipitation in the year of the annual ring formation and August precipitation in the year preceding the year of the annual ring formation. The response to precipitation in August last year is specific to aspen stands and has several options for ecophysiological interpretation. According to Fourier spectral analysis, aspen chronologies have the most pronounced cyclic component with a period of 10.4 years, which completely coincides with the cyclic component of the time series of Wolf numbers in the time interval under consideration. This distinguishes aspen from the rest of the studied forest-forming species. A conclusion has been made about the specificity of the dynamics of the radial growth of aspen in comparison with other forest-forming species under study.

Measuring aviator workload using EEG: an individualized approach to workload manipulation.

Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience. Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance. Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload. The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.

Моделювання системи стабілізації обладнання мобільного робота в умовах руху по місцевості з нахилом та нерівностями

The article is devoted to the development of a system of stabilization and guidance of equipment, which is installed on land-based small-scale mobile robots of the wheel type. During the operation of such objects, problems arise due to modes of movement, over-coming obstacles, movement on terrain with different types and profiles of the terrain. During such a movement, there are disturbances in the place of installation of the equip-ment, which leads to its deviation from the horizon plane. A stabilization system is used to ensure accuracy requirements in a wide range of movement speeds and operating condi-tions of mobile robots. In the article, the simulation of the stabilization system under the action of external disturbances caused by the influence of the movement of the mobile robot through terrain with a complex profile in combination with the inclination of the different steepness of the movement surface was carried out. A stabilization system simulation scheme with a basic structure and with an additionally introduced proportional-integrating controller was developed, realizations of various types of disturbances were generated by a combination of different inclination angles and different road profile classes. As a result of the simula-tion, the time realizations of the system’s response to the generated disturbances, which are the dependences of the stabilization error, were obtained, the root mean square devia-tions and the maximum value of the angle of deviation of the place of installation of the equipment from the horizon plane were determined. The results of the study showed that the system with an additionally introduced pro-portional-integrating regulator provides acceptable stabilization error values under the con-ditions of action of all considered types of disturbances. The obtained results will be used for the development of an adaptive digital system of stabilization of equipment installed on small-sized mobile robots of the wheel type.

The Influence of Human Capital Adaptive Capacity Strategies on Organizational Performance Achievements Mediated by Adaptive E-Procurement Systems

The Influence of Human Capital Adaptive Capacity Strategies on Organizational Performance Achievements Mediated by Adaptive E-Procurement Systems

Nonlinear disturbance observer-based finite-time prescribed performance control for MSVs with input saturation

ABSTRACT A finite-time prescribed performance control scheme based on an improved nonlinear disturbance observer is proposed for tracking the trajectory of marine surface vessels (MSVs) under the disturbance of the marine environment. The objective of this paper is to design a nonlinear disturbance observer based on state estimation error feedback that accurately estimates and compensates for the unknown disturbance problem. An adaptive law is applied to estimate the upper bound of its error, removing the requirement of a priori knowledge of the unknown lumped disturbance upper bound. This paper also introduces a non-logarithmic performance function in order to efficiently solve the problem of considering both transient and steady state MSVs performance simultaneously. In addition, the adaptive dynamic system is introduced to overcome the adverse effects caused by nonlinear saturation factors for the input-constrained problem. With this control strategy, the system output error converges to a pre-defined region within a finite amount of time and meets the prescribed performance requirements. Finally, simulation and comparison verify the control scheme's effectiveness and superiority.

Implementation of Mamdani Fuzzy Logic on PLN Electricity Sales in East Java

This research aims to provide innovation in the development of a smarter and more adaptive electricity distribution system, able to face the challenges that arise in the digital era. The results of this research can be a guide in improving the efficiency and reliability of electricity sales. The experimental method is applied by implementing mamdani fuzzy logic on PLN electricity sales data in East Java. From the results of the application of mamdani fuzzy by comparing the prediction results with the original data, the Mean Absolute Error (MAE) result is 0.17% and the Root Mean Squared Error (RMSE) value is 0.21%. So it can be concluded that fuzzy logic mamdani method can be used in predicting electricity sold and provide valuable guidance for PLN in improving the efficiency and reliability of electricity sales in the East Java region in a sustainable manner.

Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Optimization of green signal timing for each phase at a signalized intersection in an urban area is critical for efficacious traffic management and congestion mitigation. Many algorithms are developed, yet very few are for cities in developing nations where traffic is characterized by its heterogeneous nature. While some of the recent studies have explored different variants of Max Pressure (MP) and Reinforcement Learning (RL) for optimizing phase timing, the focus is limited to homogeneous traffic conditions. In developing nations, such as India, control systems, like fixed and actuated, are still predominantly used in practice. Composite Signal Control Strategy (CoSiCoSt) is also employed at a few intersections. However, there is a notable absence of advanced models addressing heterogeneous traffic behavior, which have a great potential to reduce delays and queue lengths. The present study proposes a hybrid algorithm for an adaptive traffic control system for real-world heterogeneous traffic conditions. The proposed algorithm integrates Max Pressure with Reinforcement Learning. The former dynamically determines phase order by performing pressure calculations for each phase. The latter optimizes the phase timings for each phase to minimize delays and queue lengths using the proximal policy optimization algorithm. In contrast to the past RL models, in which the phase timing is determined for all phases at once, in the proposed algorithm, the phase timings are determined after the execution of every phase. To assess the impact, classified traffic volume is extracted from surveillance videos of an intersection in Ludhiana, Punjab and simulated using Simulation of Urban Mobility (SUMO). Traffic volume data is collected over three distinct time periods of the day. The results of the proposed algorithm are compared with benchmark algorithms, such as Actuated, CoSiCoSt, and acyclic & cyclic Max Pressure, Reinforcement Learning-based algorithms. To assess the performance, queue length, delay, and queue dissipation time are considered as key performance indicators. Of actuated and CoSiCoSt, the latter performs better, and thus, the performance of the proposed hybrid algorithm is compared with CoSiCoSt. The proposed algorithm effectively reduces total delay and queue dissipation time in the range of 77.07%–87.66% and 53.95%–62.07%, respectively. Similarly, with respect to the best-performing RL model, the drop in delay and queue dissipation time range from 55.63 to 77.12% and 22.13 to 43.7%, respectively, which is significant at 99% confidence level. The proposed algorithm is deployed on a wireless hardware architecture to confirm the feasibility of real-world implementation. The findings highlight the algorithm’s potential as an efficient solution for queues and delays at signalized intersections, where mixed traffic conditions prevail.

Mode Change Management for Adaptive Cyber-physical Systems

Cyber-physical systems are usually integrated with a highly changing environment. Because of the variability of their environment, these systems need to adapt their behaviour at runtime to achieve the expected results. In the case of distributed solutions, this behaviour must be consistent across the continuum, increasing the complexity of the design due to the need to synchronise the different components distributed in the multiple nodes. A multimode application approach could be used to solve this need, assuming it is implemented as a distributed system. The approach involves applications with multiple behaviours (or operational modes) and a mechanism to change from one mode to another. This article describes an operational mode change management solution based on that approach and the reference architecture presented in the TRANSACT project. This architecture integrates CPS end devices at the edge of the network with edge computing servers and cloud computing facilities. Static and dynamic views are included to describe the solution, and specific use case scenarios are added for better understanding.

Key features of the innate immune response is mediated by the immunoproteasome in microglia.

Microglia are the resident immune cells of the central nervous system (CNS). We and others have shown that the inflammatory response of microglia is partially regulated by the immunoproteasome, an inducible form of the proteasome responsible for the generation of major histocompatibility complex (MHC) class I epitopes. While the role of the proteasome in the adaptive immune system is well established, emerging evidence suggests the immunoproteasome may have discrete functions in the innate immune response. Here, we show that inhibiting the immunoproteasome reduces the IFNγ-dependent induction of complement activator C1q, suppresses phagocytosis, and alters the cytokine expression profile in a microglial cell line and microglia derived from human inducible pluripotent stem cells. Moreover, we show that the immunoproteasome regulates the degradation of IκBα, a modulator of NF-κB signaling. Finally, we demonstrate that NADH prevents induction of the immunoproteasome, representing a potential pathway to suppress immunoproteasome-dependent immune responses.

Microneedle-Mediated Delivery of Immunomodulators Restores Immune Privilege in Hair Follicles and Reverses Immune-Mediated Alopecia.

Disorders in the regulatory arm of the adaptive immune system result in autoimmune-mediated diseases. While systemic immunosuppression is the prevailing approach to manage them, it fails to achieve long-lasting remission due to concomitant suppression of the regulatory arm and carries the risk of heightened susceptibility to infections and malignancies. Alopecia areata is a condition characterized by localized hair loss due to autoimmunity. The accessibility of the skin allowslocal rather than systemic intervention to avoid broad immunosuppression. It is hypothesized that the expansion of endogenous regulatory T cells (Tregs) at the site of antigen encounter can restore the immune balance and generate a long-lasting tolerogenic response. A hydrogel microneedle (MN) patch is therefore utilized for delivery of CCL22, a Treg-chemoattractant, and IL-2, a Tregsurvival factor to amplify them. In an immune-mediated murine model of alopecia, local bolstering of Tregnumbers is shown, leading to sustained hair regrowth and attenuation of inflammatory pathways. In a humanized skin transplant mouse model, expansion of Tregs within human skin is confirmed without engendering peripheral immunosuppression. The patch offers high-loading capacity and shelf-life stability for prospective clinical translation. By harmonizing immune responses locally, the aim is to reshape the landscape of autoimmune skin disease management.

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

Prevalence of the CRISPR-cas system and its association with antibiotic resistance in clinical Klebsiella pneumoniae isolates

Background and objective(s)CRISPR-Cas is a prokaryotic adaptive immune system that protects bacteria and archaea against mobile genetic elements (MGEs) such as bacteriophages plasmids, and transposons. In this study, we aimed to assess the prevalence of the CRISPR-Cas systems and their association with antibiotic resistance in one of the most challenging bacterial pathogens, Klebsiella pneumoniae.Materials and methodsA total of 105 K. pneumoniae isolates were collected from various clinical infections. Extended-spectrum β-lactamases (ESBLs) phenotypically were detected and the presence of ESBL, aminoglycoside-modifying enzymes (AME), and CRISPR-Cas system subtype genes were identified using PCR. Moreover, the diversity of the isolates was determined by enterobacterial repetitive intergenic consensus (ERIC)-PCR.ResultsPhenotypically, 41.9% (44/105) of the isolates were found to be ESBL producers. A significant inverse correlation existed between the subtype I-E CRISPR-Cas system’s presence and ESBL production in K. pneumoniae isolates. Additionally, the frequency of the ESBL genes blaCTX−M1 (3%), blaCTX−M9 (12.1%), blaSHV (51.5%), and blaTEM (33.3%), as well as some AME genes such as aac(3)-Iva (21.2%) and ant(2’’)-Ia (3%) was significantly lower in the isolates with the subtype I-E CRISPR-Cas system in comparison to CRISPR-negative isolates. There was a significant inverse correlation between the presence of ESBL and some AME genes with subtype I-E CRISPR-Cas system.ConclusionThe presence of the subtype I-E CRISPR-Cas system was correlated with the antibiotic-resistant gene (ARGs). The isolates with subtype I-E CRISPR-Cas system had a lower frequency of ESBL genes and some AME genes than CRISPR-negative isolates.

Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System

AbstractThe Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.

Predicting problem-solving success in an office simulation applying N-grams and a random forest to behavioral process data

Predicting students' problem-solving success in computer-based simulations at an early stage allows adaptive educational systems to provide learners with personalized support. In this paper, we predict students' problem-solving success by applying a machine-learning model, the random forest, to produce a binary classification (more vs. less successful students). During a business-related problem scenario that lasted 55 min, early behavioral data (during the first 5, 10, and 20 min) such as mouse clicks and keyboard strokes (approx. 29,800 early-window clickstreams and keystrokes during the first 20 min) of 234 trainees were recorded, mirroring the students' problem-solving behavior. We used the n-gram sequence mining technique, which was originally introduced within the emerging disciplines of natural language processing, text mining, and machine learning and has proven to be effective, particularly in the examination of online behavior. We trained the random forest model with training datasets that included all features (bigrams), as well as selected features (the most predictable bigrams explaining inter-group differences). Our results show that early predictions based on the first 10 and 20 min contained sufficient information to accurately predict problem-solving success, while predictions that are too early (based on the first 5 min) do not. As the size of the initial time window expanded, the classification performance improved. Moreover, the selection of the most predictable features improved the models' performance for all three time intervals. The model that was trained with only selected robust features that occurred in the first 20 min achieved the highest ROC AUC score of almost 0.70. This result falls within the range of accuracy scores observed in similar studies. From the instructor's perspective, predictions help in the early identification of weak students and can provide them with personalized learning prompts. For more successful students, tasks can be enriched adaptively.

Horizontal sodium density variations for laser guide star tip-tilt measurements

ABSTRACT Adaptive optics, particularly with laser guide stars, has revolutionized ground-based astronomy. However, one known limitation is the requirement of a natural guide star to retrieve tip-tilt information. This is the limiting factor in the overall achievable sky coverage with adaptive optics systems as natural guide stars are not available for all science targets. There have been numerous different techniques proposed to overcome this limitation; however, many are restricted by current technology. This work investigates the use of density perturbations in the sodium layer to retrieve the tip-tilt information. In this work, we identify atmospheric gravity waves as a mechanism that could cause perturbations in the sodium layer density. Archival hydroxyl airglow data was analysed to assess whether the density perturbations caused by atmospheric gravity waves are on the correct scales and have enough contrast to be used for laser guide star tip-tilt retrieval. This data showed a median contrast of 1.9 per cent for a laser guide star with a full width half-maximum of 25 arcsec. Then to determine if these perturbations would be visible with a laser guide star at a high enough signal-to-noise ratio, the Mathematica package LGSBloch was utilized. The CaNaPy laser guide star R&amp;D facility was chosen to model as it has a state-of-the-art 70 W sodium laser. Promising results were found when modelling this method with the CaNaPy facility as a framework. This demonstrates that if these same perturbations were present in the sodium layer, sodium density variations are a potential mechanism to retrieve tip-tilt using laser guide stars.

Using complex adaptive systems (CAS) framework to assess success factors that lead to successful organizational change: a new way to understand change implementation for success

Using complex adaptive systems (CAS) framework to assess success factors that lead to successful organizational change: a new way to understand change implementation for success

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.