Adaptive Integration Research Articles

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions.

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

On multiple impacts of functional gradient magneto-electro-elastic plates with convex and concave configurations and prediction based on PSO-BPNN

Revealing the coupling mechanism between the nonlinear effect and the multi-physical fields in the bistable plate, as well as the interaction between multiple impacts, promotes the innovative development of morphing structures. Improved neural network technology contributes to the adaptive design and functional integration of morphing structures. The multiple impacts of functional gradient magneto-electro-elastic (FG-MEE) plates with convex and concave configurations are investigated. In the Reddy high-order shear plate theory considering the bistable configuration, functional gradient models and magneto-electro-mechanical effect models are constructed. To measure the contact forces in the multi-impact process, the modified Hertz contact model is introduced. Through the Hamiltonian variational principle, the nonlinear multiple impact dynamics of the FG-MEE plates with convex and concave configurations are modeled. To obtain the bistable configuration induced by the magneto-electro-mechanical coupling effects, the two-step perturbation method is extended in multiple physical fields. Further, the two-step perturbation method and the Galerkin method are extended to acquire the higher-order truncated solutions for the multiple impact responses of FG-MEE plates with convex and concave configurations. Based on the numerical result database of multiple impact response, the nonlinear mapping relationship between the structure characteristics, load characteristics and multiple impact responses of bistable FG-MEE plate is constructed by the particle swarm optimization-back propagation (PSO-BP) technology. Ultimately, the coupling effects between the multi-physical fields and nonlinear behaviors in FG-MEE plates with convex and concave configurations are comparatively analyzed, and the interactions between multiple impacts are systematically revealed.

Adaptive light intensity aided wavelength tuning for phase‐shifting interferometric measurement system

AbstractIn phase‐shifting interferometric measurement system, using wavelength‐tuned phase‐shifting technology to measure optical surface can improve vibration errors. However, the wavelength‐tuned phase‐shifting interferometry is greatly challenged by nonlinear distortion between wavelength‐tuned light intensity and voltage modulation, which has a certain impact on phase calculation accuracy. To address this issue, an adaptive light intensity‐aided interferometric measurement scheme is proposed in this study. To improve the adjustment time of voltage response, an improved fuzzy single‐neuron adaptive proportional differential integral constant amplitude control algorithm is built. By exploiting the online detection and feedback control of light intensity, the stable output of light intensity is integrated into phase‐shifting measurement system. Experiment results verify that the improvement of light intensity fluctuation range, root mean square, and peak‐to‐valley measurement repeatability can be achieved with the proposed scheme.

Keeping up with ourselves: Multimodal processes underlying body ownership across the lifespan

The sense of a bodily self is thought to depend on adaptive weighting and integration of bodily afferents and prior beliefs. While the physical body changes in shape, size, and functionality across the lifespan, the sense of body ownership remains relatively stable. Yet, little is known about how multimodal integration underlying such sense of ownership is altered in ontogenetic periods of substantial physical changes. We aimed to study this link for the motor and the tactile domain in a mixed-realty paradigm where participants ranging from 7 to 80 years old saw their own body with temporally mismatching multimodal signals. Participants were either stroked on their hand or moved it, while they saw it in multiple trials with different visual delays. For each trial, they judged the visuo-motor/tactile synchrony and rated the sense of ownership for the seen hand. Visual dependence and proprioceptive acuity were additionally assessed. The results show that across the lifespan body ownership decreases with increasing temporal multisensory mismatch, both in the tactile and the motor domain. We found an increased sense of ownership with increasing age independent of delay and modality. Delay sensitivity during multisensory conflicts was not consistently related to age. No effects of age were found on visual dependence or proprioceptive accuracy. The results are at least partly in line with an enhanced weighting of top-down and a reduced weighting of bottom-up signals for the momentary sense of bodily self with increasing age.

Multilingualism and socioeconomic mobility: Analyzing the correlation in immigrant populations

This research investigates the relationship between multilingual abilities and socioeconomic mobility among immigrant populations. It evaluates how linguistic competencies influence employment opportunities, educational attainment, and social integration. The study aims to provide insights into how policymakers and educators can leverage multilingual education for economic and social inclusion. The phenomenon of multilingualism among immigrant populations has garnered significant scholarly attention due to its potential implications for socioeconomic mobility. This Review comprehensively analyzes the correlation between multilingualism and socioeconomic advancement among immigrant communities. Drawing upon various academic literature, this study aims to elucidate the nuanced relationship between language proficiency, cultural adaptation, and economic integration. The Review begins by contextualizing the importance of multilingualism in contemporary society, particularly in the context of globalization and increased migration flows. Multilingualism, defined as the ability to speak multiple languages proficiently, is posited as an asset for immigrants seeking to navigate new social and economic landscapes. Through a review of existing research, this Review examines how multilingualism influences socioeconomic mobility. Firstly, multilingual individuals often possess enhanced communication skills, enabling them to access a broader range of employment opportunities and interact effectively in diverse workplace environments. Furthermore, proficiency in multiple languages facilitates social integration and cultural adaptation, factors that are closely associated with socioeconomic success. Immigrants who can navigate linguistic and cultural barriers may experience greater social mobility and upward economic trajectories. Moreover, the Review explores the role of language in educational attainment and intergenerational mobility. Studies suggest that bilingualism and multilingualism can positively affect academic performance and cognitive development, potentially leading to improved educational outcomes for immigrant children. Additionally, language proficiency may facilitate intergenerational transmission of cultural capital and social networks, which are critical determinants of socioeconomic mobility. However, the relationship between multilingualism and socioeconomic mobility is complex and multifaceted. Factors such as language proficiency levels, language policy, and discrimination can significantly impact immigrant trajectories. Moreover, the intersectionality of language with other social identities, such as race, ethnicity, and class, necessitates a nuanced understanding of how language shapes opportunities and barriers for immigrant populations. In conclusion, this Review highlights the intricate interplay between multilingualism and socioeconomic mobility among immigrant communities. By examining the multifaceted dimensions of language proficiency, cultural adaptation, and economic integration, this study contributes to a deeper understanding of the factors shaping immigrant experiences and pathways to success in diverse sociocultural contexts.

Transforming evidence-based clinical guidelines into implementable clinical decision support services: the CAREPATH study for multimorbidity management.

The CAREPATH Project aims to develop a patient-centered integrated care platform tailored to older adults with multimorbidity, including mild cognitive impairment (MCI) or mild dementia. Our goal is to empower multidisciplinary care teams to craft personalized holistic care plans while adhering to evidence-based guidelines. This necessitates the creation of clear specifications for clinical decision support (CDS) services, consolidating guidance from multiple evidence-based clinical guidelines. Thus, a co-creation approach involving both clinical and technical experts is essential. This paper outlines a robust methodology for generating implementable specifications for CDS services to automate clinical guidelines. We have established a co-creation framework to facilitate collaborative exploration of clinical guidelines between clinical experts and software engineers. We have proposed an open, repeatable, and traceable method for translating evidence-based guideline narratives into implementable specifications of CDS services. Our approach, based on international standards such as CDS-Hooks and HL7 FHIR, enhances interoperability and potential adoption of CDS services across diverse healthcare systems. This methodology has been followed to create implementable specifications for 65 CDS services, automating CAREPATH consensus guideline consolidating guidance from 25 selected evidence-based guidelines. A total of 296 CDS rules have been formally defined, with input parameters defined as clinical concepts bound to FHIR resources and international code systems. Outputs include 346 well-defined CDS Cards, offering clear guidance for care plan activities and goal suggestions. These specifications have led to the implementation of 65 CDS services integrated into the CAREPATH Adaptive Integrated Care Platform. Our methodology offers a systematic, replicable process for generating CDS specifications, ensuring consistency and reliability across implementation. By fostering collaboration between clinical expertise and technical proficiency, we enhance the quality and relevance of generated specifications. Clear traceability enables stakeholders to track the development process and ensure adherence to guideline recommendations.

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.

Analysis of r - Adaptive coupling adaptive time integration strategy in FEM of seismic liquefaction

In seismic finite element analysis of saturated soils, the challenges primarily reside in liquefaction and the large deformation of soil induced by strong earthquakes. Traditional updated Lagrangian methods may result in severe mesh distortion after updating the spatial configuration of nodes. To address this issue, this study develops an r - adaptive approach based on a solid - fluid coupling finite element method (FEM) platform to mitigate mesh distortion problems. This technique optimizes mesh nodes without altering the overall topology, thereby maintaining mesh integrity. However, enhancing mesh accuracy comes at the expense of increased computational costs. To alleviate high computational expenses, this study couples two types of adaptive time stepping techniques, based on mixed error and displacement history curvature, with mesh adaptation. Through verification on two typical dynamic liquefaction models of saturated soil on embankment and subway station, this technique effectively balances the relationship between computational accuracy and costs.

Analysis of unmanned aerial vehicle communication performance with optic sensor integration

This paper presents a novel algorithmic framework to enhance unmanned aerial vehicle (UAV) communication performance through the adaptive integration of optical sensor data. The framework features a dynamic sensor integration algorithm (DSIA) that optimizes sensor bitrates based on real-time bandwidth availability and mission-driven priorities. An adaptive compression scheme works alongside the DSIA to maintain high data fidelity and resolution at lower bitrates. Together, these algorithms provide an efficient and scalable solution for managing high-volume UAV sensor data under varying conditions. The efficiency gains are demonstrated through simulations highlighting the framework’s ability to reduce packet loss and latency while maximizing image quality for a given communication bandwidth constraint. The scalable architecture ensures robustness across diverse UAV platforms, sensor types, and operational scenarios. Simulation results validate up to a 75 % reduction in latency and a 45 % improvement in image resolution compared to conventional fixed bitrate approaches. By optimizing sensor data relevancy and transmission fidelity, this framework enables more effective UAV communication and enhances mission performance.

Multi-attention fusion transformer for single-image super-resolution

Recently, Transformer-based methods have gained prominence in image super-resolution (SR) tasks, addressing the challenge of long-range dependence through the incorporation of cross-layer connectivity and local attention mechanisms. However, the analysis of these networks using local attribution maps has revealed significant limitations in leveraging the spatial extent of input information. To unlock the inherent potential of Transformer in image SR, we propose the Multi-Attention Fusion Transformer (MAFT), a novel model designed to integrate multiple attention mechanisms with the objective of expanding the number and range of pixels activated during image reconstruction. This integration enhances the effective utilization of input information space. At the core of our model lies the Multi-attention Adaptive Integration Groups, which facilitate the transition from dense local attention to sparse global attention through the introduction of Local Attention Aggregation and Global Attention Aggregation blocks with alternating connections, effectively broadening the network's receptive field. The effectiveness of our proposed algorithm has been validated through comprehensive quantitative and qualitative evaluation experiments conducted on benchmark datasets. Compared to state-of-the-art methods (e.g. HAT), the proposed MAFT achieves 0.09 dB gains on Urban100 dataset for × 4 SR task while containing 32.55% and 38.01% fewer parameters and FLOPs, respectively.

The Arabidopsis SGN3/GSO1 receptor kinase integrates soil nitrogen status into shoot development

The Casparian strip is a barrier in the endodermal cell walls of plants that allows the selective uptake of nutrients and water. In the model plant Arabidopsis thaliana, its development and establishment are under the control of a receptor-ligand mechanism termed the Schengen pathway. This pathway facilitates barrier formation and activates downstream compensatory responses in case of dysfunction. However, due to a very tight functional association with the Casparian strip, other potential signaling functions of the Schengen pathway remain obscure. In this work, we created a MYB36-dependent synthetic positive feedback loop that drives Casparian strip formation independently of Schengen-induced signaling. We evaluated this by subjecting plants in which the Schengen pathway has been uncoupled from barrier formation, as well as a number of established barrier-mutant plants, to agar-based and soil conditions that mimic agricultural settings. Under the latter conditions, the Schengen pathway is necessary for the establishment of nitrogen-deficiency responses in shoots. These data highlight Schengen signaling as an essential hub for the adaptive integration of signaling from the rhizosphere to aboveground tissues.

Integrating Internet of Things Technologies for Dynamic Sustainability in Architectural Design

The application of Internet of Things (IoT) technology is a significant step toward improving the sustainability and responsiveness of the built environment. The current work introduces the Adaptive and Sustainable IoT Integration Model (ASIIM), a novel framework designed to enhance the dynamic sustainability and adaptation of IoT in architecture in order to maximize its potential. Through a comprehensive review of the literature, this study analyzes the current state of IoT applications in architectural professions, highlighting the key benefits of IoT in improving building performance, occupant comfort, and energy efficiency. The ASIIM framework emerges as a comprehensive approach that encompasses key strategies for combining user interface systems, sustainability measures, IoT-enabled adaptive features, and fundamental design principles in order to promote a more responsive and sustainable architecture design. The paper identifies key obstacles to IoT integration, such as interoperability, data protection, and device sustainability, and offers collaborative ways to overcome them. The findings demonstrate the transformative potential of IoT in architecture, suggesting a future in which buildings will become dynamic systems that can adapt to the needs of both the surrounding environment and their occupants rather than static structures. This study contributes to the expanding body of knowledge on sustainable architecture by offering insights and a theoretical foundation for further study and practice in the integration of IoT technology in architectural design.

결혼이주여성의 문화적응과 초등학생 자녀의 학교적응 간의 구조적 관계: 학교활동참여와 양육효능감 매개를 중심으로

The purpose of this study is to examine the effects of the cultural adaptation types (assimilation and integration) of marriage immigrant women on their children's school adjustment, mediated by school activity participation and parenting efficacy. For this purpose, data from the first year of the Multicultural Adolescents Panel Study(MAPS) 2nd cohort(MAPS 2) conducted by the Korea Youth Policy Institute were utilized, involving 2,224 households with fourth-grade elementary school students. Descriptive statistics, correlation analysis, and structural equation modeling using AMOS 22.0 were conducted, and path analysis utilizing phantom variables was performed to verify the influence of each path. The main findings of this study are as follows: First, the cultural adaptation type of marriage immigrant women had a significant effect on school activity participation, but had negative effects on parenting efficacy, and had no effects on school adjustment. Second, in the relationship between the cultural adaptation assimilation type of immigrant women and school adjustment of their children, both school activity participation and parenting efficacy were found to have significant dual mediating effects. Furthermore, parenting efficacy was found to have a significant partially mediating effect in the relationship between cultural adaptation assimilation type and school adjustment of their children, while it had a significant statically mediating effect in the relationship between cultural adaptation integration type and school adjustment of their children. Based on the findings, educational implications were discussed.

Numerical Integration and Angular Spectrum Modeling of Resonant and Non-Resonant Wave Interaction with a Solid Plate

The angular spectrum (AS) model is widely used to calculate the pressure transmitted through, and reflected from, a fluid-immersed solid plate, using a baffled piston as source. However, narrow peaks (infinite peaks for the lossless case) in the piston aperture function and in the plane wave transmission and reflection coefficients, in addition to a highly oscillatory integrand, cause challenges in the numerical integration. To overcome the challenges related to the aperture function peak, the AS model is reformulated using integration by parts. The peaks in the transmission and reflection coefficients are identified as symmetric and antisymmetric Scholte modes, and a numerical integration method is provided enabling integration over these peaks. The AS-model is also used to obtain the full-wave diffraction correction for a pulse-train transmitted through, or reflected from, the plate, which is highly relevant in pulse-transmit or pulse-receive measurements. The diffraction effects caused by the plate are calculated by suppressing resonance effects in the plate and obtaining the corresponding plane wave pressure transmission and reflection coefficients. A hybrid Gauss-Filon adaptive numerical integration method is used to calculate the pressure. These calculations are conducted in APAS, a MATLAB program developed in the current work and provided as open-source supplementary material.

Charting climate adaptation integration in smart building rating systems: a comparative study

Introduction:As the world is engulfed with the growing impacts of climate change, the integration of climate adaptation measures into building performance requirements is essential. In the era of the fourth industrial revolution, smart buildings are expected to be the next frontier in the realm of building rating systems after sustainability-based one. Smart buildings can play a pivotal role in addressing the evolving challenges of changing climate due to their temporal and spatial cross-scale nature.Methods:This study assesses the integration of climate hazard adaptation options within four prominent smart building rating systems (SBRS). Using a sectoral analysis approach and a 4-point Likert scale, we systematically evaluate the extent to which these rating systems incorporate climate adaptation measures directly or indirectly across multiple building sectors. We identify strengths and weaknesses in each system’s approach, highlighting areas where adaptation options are more profoundly addressed and sectors that require further attention.Results:The evaluation results reveal variations in the comprehensiveness of climate adaptation integration among the smart building rating systems. The SRBS show a high level of integration of climate adaptation measures in the urban sectors intrinsically tied to the smart building paradigm, such as communication sector, and the human wellbeing and organization sector. Nevertheless, the study also revealed that SBRS almost universally fall short in covering other vital domains such as building envelope and structure, water and sanitation, and blue and green infrastructure.Discussions:Complementing the SBRS with sustainability rating systems (GBRS) can effectively address the limitations in climate adaptation integration within SBRS. Moreover, the inherent interconnectedness of smart buildings with their surrounding infrastructure and the broader urban environment underscores the importance of the cross-scale consideration in the building rating domain in general and in climate related topics in particular, this interconnectedness also highlights a smart building’s reliance on its surrounding context for optimal functionality and the interdependency between the building and urban scale.

Integration of climate change mitigation and adaptation in Blue Economy planning in Africa

Integration of climate change mitigation and adaptation in Blue Economy planning in Africa

Grid Tied Electric Vehicle Charging Station by Bidirectional PV Based High Gain Integrated DC-DC Converter

In the escalating concerns over carbon emissions from traditional fossil fuel vehicles, which contribute significantly to environmental degradation through phenomena like global warming and climate change, electric vehicles (EVs) have emerged as a compelling solution due to their emission free nature. This paper proposes the establishment of a grid connected electric vehicle charging station, powered by a cutting edge bidirectional PV based high-gain integrated DC-DC Cuk Landsman converter which shows 93.4 % efficiency, aimed at ensuring swift and efficient EV charging processes. To enhance dynamic performance, a Cascaded Fuzzy Controller (CFC) technique is implemented which shows 89.4 % accuracy. This technique enables rapid responses for error compensation, thereby improving the converter’s dynamic performance indices. The converter plays a pivotal role by adeptly converting the low and unregulated DC voltage generated by the PV system into a stable output at the requisite voltage level, all while attaining unity power factor and minimizing total harmonic distortion (THD). Grid synchronization is meticulously ensured through the implementation of an adaptive proportional integral (PI) controller for a single phase voltage source inverter (VSI), which meticulously compares reference power with actual power. This process ensures seamless integration with the grid, meeting stringent standards. Furthermore, an LC filter is deployed to effectively mitigate current harmonics around the switching frequency, ensuring compliance with grid regulations.

Resiliencia en adolescentes infractores con trayectorias en los sistemas chilenos de bienestar infantil y de justicia adolescente

This article delves into the processes of signification regarding resilience among adolescents who have traversed the Chilean child welfare and juvenile justice systems, as it plays a pivotal role in identifying factors conducive to adaptation and social integration. Drawing from qualitative research grounded in a phenomenological approach, the study analyzed six in-depth, semi-structured interviews with adolescents who have interacted with both systems. Findings underscore that personal resilience in adolescents is closely tied to their self-perception and learning experiences aimed at developing social skills and emotional regulation. Additionally, social and institutional resilience is marked by adolescents’ perceptions of enhancements in their quality of life and their ability to adapt socially. They emphasize the importance of establishing solid, trustworthy, and enduring relationships with counselors and educators, alongside highlighting the supportive role of intervention institutions in fostering personal, relational growth, and in shaping a proactive and adaptive life trajectory. These outcomes underscore the need to integrate resilience processes into the design of interventions targeting vulnerable children and adolescents caught in situations of legal transgression, advocating for their incorporation into public policies addressing childhood and adolescent vulnerability.

Light-responsive Au@Zn-TCPP nanozyme functionalized with cell-penetrating peptide and antisense oligonucleotide for sensing living bacteria and synergistic therapy of diabetic wounds

The persistent bacterial infections in diabetic-induced woundspose a significantsocietal burden,requiringinnovative antimicrobial agentsandeffectivebacteria biosensor. In thisstudy, we successfully developeda composite antimicrobial nanomaterial (Au@Zn-Tcpp/ASO-pVEC NSs)that combines acell-penetrating peptide-conjugated two-dimensional MOF with adaptive photo-responsive integration of biosensor diagnosis and therapy. Various in vitro antimicrobial experimentsdemonstrated that Au@Zn-Tcpp/ASO-pVEC NSsachieved99.99% inhibition against both E. coli and S. aureus under laser irradiation and glucose catalysis. Additionally, Au@Zn-Tcpp/ASO-pVEC NSsexhibitedsustained release of ROS and excellent photothermal properties.Furthermore, in the context of wound infections, Au@Zn-Tcpp/ASO-pVEC NSsshowed optimal skin wound healing,with less than 2% residual infected area against diabetic bacterial infections and significantly reduced inflammation. Moreover, these nanomaterials demonstrated the capability to effectively detect bacteria throughfluorescent polypeptide probe.Overall,this antimicrobial strategy, whichcombines sensing living bacteria with a multimodal approach comprising chemodynamic therapy (CDT), photodynamic therapy (PDT), and photothermal therapy (PTT),holds substantial potential forproviding timelyclinical treatments andreliable monitoring of bacterial infections.

Coherent Chaotic Communication Using Generalized Runge–Kutta Method

Computer simulation of continuous chaotic systems is usually performed using numerical methods. The discretization may introduce new properties into finite-difference models compared to their continuous prototypes and can therefore lead to new types of dynamical behavior exhibited by discrete chaotic systems. It is known that one can control the dynamics of a discrete system using a special class of integration methods. One of the applications of such a phenomenon is chaos-based communication systems, which have recently attracted attention due to their high covertness and broadband transmission capability. Proper modulation of chaotic carrier signals is one of the key problems in chaos-based communication system design. It is challenging to modulate and demodulate a chaotic signal in the same way as a conventional signal due to its noise-like shape and broadband characteristics. Therefore, the development of new modulation–demodulation techniques is of great interest in the field. One possible approach here is to use adaptive numerical integration, which allows control of the properties of the finite-difference chaotic model. In this study, we describe a novel modulation technique for chaos-based communication systems based on generalized explicit second-order Runge–Kutta methods. We use a specially designed test bench to evaluate the efficiency of the proposed modulation method and compare it with state-of-the-art solutions. Experimental results show that the proposed modulation technique outperforms the conventional parametric modulation method in both coverage and noise immunity. The obtained results can be efficiently applied to the design of advanced chaos-based communication systems as well as being used to improve existing architectures.