Cognitive Nodes Research Articles

The Global and Local Patterns of Reading-Related Cognitive and Ecological Variables in Chinese First-Grade Children: A Cross-Sectional Network Analysis.

In the current study, we tested a network model of reading difficulty by using state-of-the-art psychological network analysis. Four hundred and fifty-three Chinese first-grade children (about 38% female, mean age = 7.00, SD = 0.41) were divided into good (n = 154), competent (n = 147), and struggling readers (n = 152) based on their scores of Chinese character reading. The Extended Bayesian Information Criterion graphical lasso (EBICglasso) method was applied to estimate cross-sectional networks for the three groups. Each network included four cognitive nodes (homophone awareness, morphological structure awareness, phonological awareness, and vocabulary) and two ecological nodes (family socioeconomic status and the number of books at home). Chronological age and nonverbal intelligence were also included in the estimated networks. The global (i.e., global structure and global connectivity) and local patterns (i.e., the most important edges and nodes) in each network were reported. The network comparison results showed that global connectivity was significantly lower among struggling readers than for good readers, implying that a holistic impairment of bidirectional connections among multiple variables relates to the difficulty in learning to read. The theoretical and empirical implications and the significance of applying the network approach to reading research are discussed.

Depressive symptoms, cognitive functions and daily activities: An extended network analysis in monozygotic and dizygotic twins

BackgroundThe network theory posits that depression emerges as the result of individual symptoms triggering each other. Risk factors for depression can impact these between-symptoms interactions through extended networks. The study aimed to model the extended network of depressive symptoms and known depression risk factors – objective cognitive function, intellectual, physical, and social daily activities, and then, compare the observed networks between monozygotic (MZ) and dizygotic (DZ) co-twins. MethodsTwin pairs, 722 MZ and 2200 DZ, aged 40–79, were selected from the Dansh Twin Registry for having complete measures of depressive symptoms (e.g., sadness), cognitive functions (e.g., verbal memory), physical (e.g., brisk walk), intellectual (e.g., reading newspapers) and social activities (e.g., phone calls). Gaussian graphical models were used to estimate and compare the networks first between co-twins and then, between MZ to DZ twin pairs separately. ResultsSpecific intellectual, physical and social activities were central in the extended networks of depressive symptoms and, with the exception of processing speed, more central than cognition. The extended networks' structure was more homogeneous between MZ co-twins relative to DZ co-twins. Cognitive nodes were more central in MZ than DZ co-twins. LimitationsCross-sectional design, participants were middle-aged or older, mostly affective (non-somatic) depressive symptoms. ConclusionsIn depression networks, core connecting elements were intellectual, physical and social activities. The interaction between cognition and daily activities seems critical for triggering depressive symptoms. Thus, clinical interventions aimed at preventing depression and associated cognitive deficits should focus on maintenance and/or engagement in stimulating daily activities.

The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis

Deep learning has presented its spectacular potential in the jamming recognition field. Yet, sufficient samples required by normal deep learning analysis methods are not always available, especially in the 6G communication field. This situation appears to be more challenging in the communication field. In this article, Model-Agnostic Meta-Learning (MAML) is imported into the jamming recognition field in order to accomplish compound jamming recognition in the circumstances of few-shot learning. Further, the existing research on jamming recognition techniques is mostly based on single-node recognition. This technique cannot make full and efficient use of the jamming information collected. Therefore, this article adds a multi-node collaborative technique into the compound jamming recognition algorithm that is based on MAML and time-frequency analysis. Based on the fact that each cognitive node can recognize independently, the recognition results are be sent to the fusion center. The fusion center completes the fusion of the recognition results according to the majority rule. The experiments demonstrate that, with the fusion of the multi-node collaborative technique, the precision of compound jamming recognition in the condition of few-shot learning has been effectively improved.

Distributed neural representations of conditioned threat in the human brain

Detecting and responding to threat engages several neural nodes including the amygdala, hippocampus, insular cortex, and medial prefrontal cortices. Recent propositions call for the integration of more distributed neural nodes that process sensory and cognitive facets related to threat. Integrative, sensitive, and reproducible distributed neural decoders for the detection and response to threat and safety have yet to be established. We combine functional MRI data across varying threat conditioning and negative affect paradigms from 1465 participants with multivariate pattern analysis to investigate distributed neural representations of threat and safety. The trained decoders sensitively and specifically distinguish between threat and safety cues across multiple datasets. We further show that many neural nodes dynamically shift representations between threat and safety. Our results establish reproducible decoders that integrate neural circuits, merging the well-characterized ‘threat circuit’ with sensory and cognitive nodes, discriminating threat from safety regardless of experimental designs or data acquisition parameters.

APOE ε4 and cognitive networks: Centrality of episodic memory nodes in patients with mild cognitive impairment

AbstractBackgroundThe literature based on univariate models does not lead to unequivocal conclusions on the effects of APOE variability on cognition. We thus hypothesised that the impact of APOE‐ε4 on cognitive functioning can be reliably captured by graph‐theory.MethodCognitive datasets were extracted from the National Alzheimer’s Coordinating Center’s database. After the application of stringent exclusion criteria, 16,173 participants with a known APOE ε3ε3/ε4ε3/ε4ε4 genotype were retained: 8,118 controls, 3,482 MCI patients and 4,573 AD‐dementia patients. ε3ε3 individuals were classified as “non‐carriers”, while ε4ε3/ε4ε4 individuals were tagged as “carriers”. Two independent sub‐cohorts were defined based on the version of the battery used to characterise cognitive profiles. Pairwise Markov random‐field models of cognitive functioning were constructed in “R” using the “qgraph” package, following published methodological guidelines. Nine cognitive nodes were established, and partial correlations were run to calculate the statistical strength of all node‐to‐node edges. This was regularised via the LASSO penalty‐imposing method, to control graph sparsity. Differences in global and nodal metrics between ε4 carriers and non‐carriers were tested via permutation‐based Network Invariance Tests. These analyses were run separately in each sub‐cohort and within each diagnostic status. “Expected Influence” was analysed as the centrality metric of interest, to assess how influential each node is within its network.ResultA significant APOE‐dependant effect emerged from the analysis of the Logical‐Memory nodes in MCI patients in both sub‐cohorts. While non‐carriers showed equal centrality in immediate and delayed recall, the latter was significantly less central among carriers. These findings were replicated in the sub‐groups of sole amnestic‐MCI patients (n = 2,971), were independent of differences in network communities, clinical severity or other demographic factors. No effects were found in the other diagnostic groups or in global network metrics.ConclusionAPOE‐ε4 influences nodal properties of cognitive networks when patients are clinically classified as MCI. Our findings expand this area of knowledge by characterising the statistical effect of the ε4 allele in terms of its differential impact on immediate and delayed recall, and how this results in specific network effects. The study of the wider cognitive network can complement univariate approaches in characterising the effects of risk factors.

A bio-inspired robot navigation model based on the environmental cognitive mechanism of the rat brain hippocampus

Self-positioning and goal-oriented navigation behaviors in complex and variable environments are important capabilities that mammals rely on for survival in the natural world. To this end, this article proposes a robot navigation model with bio-inspired environmental cognition and autonomous localization capabilities based on the spatial cognitive mechanism of the hippocampus in the rat brain. The model constructs a cognitive map by integrating its own motion cues and visual observation features, and uses the dynamic predictive relationship between each location cognitive node to achieve a goal-oriented navigation process. The research approach in this article will provide inspirational implications for a robot navigation approach with brain-like cognitive mechanisms.

Efficient Power Control for UAV Based on Trajectory and Game Theory

Due to the fact that network space is becoming more limited, the implementation of ultra-dense networks (UDNs) has the potential to enhance not only network coverage but also network throughput. Unmanned Aerial Vehicle (UAV) communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes. A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this article. In the IoT system, the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless nodes. The quality of service (QoS) offered by the cognitive node was interpreted as a price-based utility function, which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’ net utility functions. An energy-efficient non-cooperative game theory power allocation with pricing strategy abbreviated as (EE-NGPAP) is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things (IoT) wireless nodes. It has also been demonstrated, theoretically and by the use of simulations, that the Nash equilibrium does exist and that it is one of a kind. The proposed energy harvesting approach was shown, through simulations, to significantly reduce the typical amount of power that was sent. This is taken into consideration to agree with the objective of 5G networks. In order to converge to Nash Equilibrium (NE), the method that is advised only needs roughly 4 iterations, which makes it easier to utilise in the real world, where things aren’t always the same.

ANALYSIS OF ENERGY EFFICIENCY USING IED WITH SELECTION COMBINING IN PROPOSED CSS NETWORK OVER WEIBULL CHANNEL

In the Present Scenario, a message can be transmitted to the user, if the signal in which it contains must be efficient and accurate. There are many parameters like noise signals and unknown signals etc, which shows great impact on signal efficiency and its range. The removal of unknown signals is the primary objective in order to prevent loss of information from the signal. Unknown signals which causes disturbances in effective signal transmission are detected and its energy efficiency is compared before and after removal of unknown signals. This Research Paper mainly uses a Improved Energy Detector which consists of Cognitive nodes which helps the identification of unknown signals by applying various variations (i.e fading environment). Each nodes are subjected with some number of antennas. Probability functions are used to detect the false probability alarm signals which causes interrupts in the signal. Analysis of overall channel is done throughout the process of detecting and removing of unknown signals.

Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light

This paper proposes a brain-inspired navigation model based on absolute heading for the autonomous navigation of unmanned platforms. The proposed model combined the sand ant’s strategy of acquiring absolute heading from the sky environment and the brain-inspired navigation system, which is closer to the navigation mechanism of migratory animals. Firstly, a brain-inspired grid cell network model and an absolute heading-based head-direction cell network model were constructed based on the continuous attractor network (CAN). Then, an absolute heading-based environmental vision template was constructed using the line scan intensity distribution curve, and the path integration error was corrected using the environmental vision template. Finally, a topological cognitive node was constructed according to the grid cell, the head direction cell, the environmental visual template, the absolute heading information, and the position information. Numerous topological nodes formed the absolute heading-based topological map. The model is a topological navigation method not limited to strict geometric space scale, and its position and absolute heading are decoupled. The experimental results showed that the proposed model is superior to the other methods in terms of the accuracy of visual template recognition, as well as the accuracy and topology consistency of the constructed environment topology map.

Jamming Attack Mitigation in CR-IoT Using Game Theory

Jamming attack is Cognitive radio Internet of thing network disables the spectrum sharing and reduces the spectrum utilization .detection and mitigation of such attacks is the main component in realizing cognitive radio-based spectrum sharing in cognitive radio-based internet of thing network this work proposes a game theory-based jamming attack mitigation strategy. The problem of jamming attack mitigation is modeled as a zero-sum game and solved by finding Nash equilibrium. The cognitive node which tries to share the spectrum pays the zero-sum game with the jamming attacker and selects the best strategy of selecting the best frequency without getting into the jamming attack. The result of the proposed mechanism proves that the gaming mechanism can tackle the jamming attack.

Fuzzy logic based authentication in cognitive radio networks

<p>Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity</p>

Understanding the Design Personalization of Fashion Products Using Computational Design Methods: Practical Insights into Consumer Perceptions

This paper explores user perceptions of the design personalization of fashion products using a computational design method from the designer’s point of view. As for the research methodology, ten participants were asked to design textile patterns for their own fashion clutch bags by using a design personalization tool developed using Generative Design Methods (GDM), one of the computational design methods for personalizing product design. Subsequently, in-depth interviews were conducted to collect consumer thoughts on the aforementioned design personalization experience. Their central cognitive nodes were analyzed based on the means-end chain (MEC) model. The hierarchical value analysis results of the MEC model showed core linkages among the attributes, consequences, and values levels that participants perceived prominently for a given design personalization experience. The primary cognitive paths for consumers mainly focused on hedonic values and satisfaction as a destination, triggered by inspiring and fun design experiences, as well as solving design problems for non-professionals provided by GDM-based design personalization processes. This study’s findings can provide practical insights and guidance for designers and service planners to understand how their ideas can be placed in the consumer market while planning and designing digital design personalization services.

Adaptive wideband sensing scheme based on Bayesian experimental design

In this paper, we investigate the problem of wideband sensing for cognitive radio. Due to resource constraint, a cognitive node targets at finding a given number of spectrum holes, which is studied under a novel wideband sensing architecture. Specifically, the cognitive node can dynamically focus its sensing measurements on different portions of contiguous channels. An adaptive wideband sensing scheme is proposed under the standard Bayesian experimental design framework via three steps: (1) define a Bayesian cost that incorporates the numbers of desired spectrum holes, (2) design hypothesis testing rule to detect access opportunities, and (3) identify a scheme to adapt the measurements during the sensing process. In order to facilitate implementation, we further propose an approximated Bayesian experiment design scheme to reduce the computational complexity. The performance gain of our wideband sensing scheme is demonstrated via numerical simulation.

Performance Analysis of Public Safety Cognitive Radio MANET for Diversified Traffic.

This paper presents properties of a mobile ad hoc network (MANET) with dynamic spectrum management (DSM) and is devoted to the concept and implementation of the new traffic engine that is used in a High-Fidelity simulator of MANET with cognitive nodes for special applications. The communication traffic generated by each node is defined according to its role in the hierarchical structure of the operational scenario, determining its priorities, permission to use particular real time and non-real time services. The service usage is a source based model, defined in the user’s profile containing its statistical properties, describing periodicity, duration and randomness of traffic generation. The overall traffic generated by the node is a combination of traffics related to specific services. Their statistical parameters are based on real exercises results. The model was defined in the Matlab environment and next verified using the MAENA simulator for complex, operational scenarios. The achieved results show that use of both central and distributed DSM provides a better performance of the MANET network with complex traffic.

Game Theory-Based IoT Efficient Power Control in Cognitive UAV

With the help of network densification, network coverage as well as the throughput can be improved via ultra-dense networks (UDNs). In tandem, Unmanned Aerial Vehicle (UAV) communications have recently garnered much attention because of their high agility as well as widespread applications. In this paper, a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal. Further, the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation. The quality of service (QoS) related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’ net utility function. Moreover, an energy efficiency non-cooperative game theory power allocation with pricing scheme (EE-NGPAP) is proposed to obtain an efficient power control within IoT wireless nodes. Further, uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through simulation. Simulation results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction, which is regarded to be apt with the 5G networks’ vision. Finally, the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.

Spectrum Handoff Aware AODV Routing Protocol for Cognitive Radio Vehicular Ad Hoc Networks

End-to-end application performance and throughput of vehicular cognitive transport control protocol depend on how efficiently the segments (TCP header) are being transmitted from source to destination. One way to enhance the performance of vehicular cognitive TCP protocol is by reducing the packet drops in between the source and destination. In general, packet drops occur in between the source and destination of Cognitive Radio Vehicular Ad Hoc Network (CR-VANET) because of spectrum handoff or cognitive node handoff, or network congestion. In this paper, we focus on enhancing the performance of cognitive TCP protocol through “cognitive AODV routing protocol with the spectrum handoff mechanism.” In the proposed work, channel-route control messages of cognitive AODV routing protocol are updated with the support of spectrum handoff which helps to provide the backup opportunistic channel during PU active and helps to reduce the end-to-end spectrum handoff packet drops. Simulation results reveal that the overall performance of the vehicular cognitive TCP protocol with the proposed spectrum handoff aware cognitive AODV routing protocol is enhanced as compared to the existing cognitive TCP protocol.

Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

Cooperative Communication Enabled Cognitive Radio in a Home-Care Application

In this paper, cooperative communication (CC) assisted cognitive wireless sensor network (CWSN) is presented for monitoring health and activity of an end-user in a smart indoor environment. The cooperative communication ensures better data delivery by utilizing minimum resources and also helps to track the location of the user, non-intrusively. The increase in demand for wireless sensor network insists various factors, such as spectrum sensing, sharing and optimal utilization of resources in various applications. CWSN is considered as a promising technology that improves spectrum utilization and communication quality. The study proposes a payload-centric adaptive multi-channel hopping (PC-AMCH) algorithm in CC enabled CWSN to further improve energy efficiency of conventional multi-channel wireless sensor networks, especially in a smart home-care system. All participated nodes, such as wearable cognitive node (C), relay nodes (Rn) and a destination node (D) are indigenously developed by involving CC2530 microcontroller that consists of an in-built transceiver at 2.4 GHz. Further, an adaptive relay selection scheme is used to establish a dynamic link between wearable cognitive node (C) and relay node (Rn) by analyzing factors such as, link quality indicator (LQI) and received signal strength indicator (RSSI). The performance of developed nodes is evaluated in terms of throughput, packet delivery ratio (PDR), RSSI and transmission offset. Similarly, the performance of proposed PC-AMCH is validated by measuring PDR, number of transmission, energy efficiency.

A new fast k‐nearest neighbor classification algorithm in cognitive radio networks based on parallel computing

SummaryThe field of telecommunication has undergone a very rapid technological evolution, which has forced researchers to find techniques that allow better exploitation of hardware and software. Among the proposed technologies, cognitive radio, a concept that was designed after several technologies such as software radio. Cognitive radio has been widely used for opportunistic access of the shared spectrum and has defined the cognitive nodes by their ability to intelligently adapt the environment to achieve specific objectives through advanced techniques. In this context, clustering techniques were adopted in cognitive radio networks (CRNs) due to their great advantages especially for routing. In this article, we propose a parallel mode of the k‐NN algorithm. The aim is to make a fast assignment of radio nodes in CRNs organized in the form of clusters. The obtained results are very satisfactory because we have been able to reduce to about 50% the execution time of the basic algorithm (sequential).

Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection

Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.