Cognitive Dynamic System Research Articles

Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework

Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework

Software-Defined Fiber Optic Communications for Ultrahigh-Speed Optical Pulse Transmission Systems

We introduce the cognitive dynamic system (CDS) as the brain of software defined optical communications systems (SDOCS). The cyber processing layer of smart SDOCS based on CDS can be a good solution to control quality of transmission (QoT) by cognitive symbol decision-making and finding the best trade-off between transmission reach and capacity in nonlinear SDOCS. We modify the perceptor of CDS so that the posterior can be extracted directly without using the Bayesian equation to obtain a significant reduction of the CDS complexity. The low-complexity and simple algorithmic design of the proposed cognitive decision making (CDM) can make it suitable for SDOCS applications since CDS does not require the previous knowledge of the fiber optic transmission system parameters. Then, we experimentally demonstrate the performance enhancement of ultrahigh-speed optical pulse transmission system upgraded with the preceptor of CDS as the proof-of-concept of SDOCS. Experimental results show ∼1.3 dB enhancement in Q-factor for a 1.28 Tbaud (10 Gbaud × 128 OTDM) fiber optic system with polarization-multiplexed 64 quadrature amplitude modulation (QAM) at 15 Tbit/s data rate over a 150 km long fiber link. The CDS provides good reliability over system disturbances such as clock recovery intolerance.

Research on cognitive signal waveform technology based on artificial intelligence algorithm

With the continuous development of communication measurement and control technology, the communication environment is increasingly complex and changeable, and the traditional communication technology has been unable to meet people’s communication needs. The proposal of cognitive dynamic system (CDS) provides a new direction for the development of wireless communication technology. At present, the two main application directions of cognitive system are cognitive radio (CR) and cognitive radar (CR)[1][2]. Considering the innovative combination of cognitive system and TT& C communication scene, this paper studies the cognitive transmission performance under various environmental noises based on chirp signal, and uses BP neural network to optimize the system performance. The simulation results show that the adaptive waveform technology can improve the anti-jamming ability, and the BP algorithm has good prediction effect and reduces the computational complexity of the system.

Intelligent Stress Monitoring Assistant for First Responders

This paper describes a prototype of an intelligent Stress Monitoring Assistant (SMA), – the next generation of stress detectors. The SMA is intended for the first responders and professionals coping with exposure to extreme physical and psychological stressors, e.g. firefighters, combat military personnel, explosive ordnance disposal operatives, law enforcement officers, emergency medical technicians, and paramedics. Stress impacts human behavior and decision-making, which can be propagated between the team members. The SMA is an integral part of the Decision Support System, it is a component of the decision support perception-action cycle. We model this cycle as a cognitive dynamic system. The intelligent part of the SMA is designed using $a$ ) a residual-temporal convolution network for learning data from sensors and detection of stress features, and $b$ ) a reasoning mechanism based on a causal network for fusion at various levels. The SMA prototype has been tested using a multi-factor physiological dataset WEarable Stress and Affect Detection (WESAD). In both modes, the stress recognition and stress detection , the SMA achieves an accuracy of 86% and 98% for the WESAD dataset, respectively. This performance is superior to the known results in satisfying the requirements of reliable decision support.

Natural Brain-Inspired Intelligence for Screening in Healthcare Applications

In recent years, there has been a growing interest in smart e-Health systems to improve people’s quality-of-life by enhancing healthcare accessibility and reducing healthcare costs. Continuous monitoring of health through the smart e-Health system may enable automatic diagnosis of diseases like Arrhythmia at its early onset that otherwise may become fatal if not detected on time. In this work, we developed a cognitive dynamic system (CDS)-based framework for the smart e-Health system to realize an automatic screening process in the presence of a defective or abnormal dataset. A defective dataset may have poor labeling and/or lack enough training patterns. To mitigate the adverse effect of such a defective dataset, we developed a decision-making system that is inspired by the decision-making processes in humans in case of conflict-of-opinions (CoO). We present a proof-of-concept implementation of this framework to automatically identify people having Arrhythmia from single lead Electrocardiogram (ECG) traces. It is shown that the proposed CDS performs well with the diagnosis errors of 13.2%, 9.9%, 6.6%, and 4.6%, being in good agreement with the desired diagnosis errors of 25%, 10%, 5.9%, and 2.5%, respectively. The proposed CDS algorithm can be incorporated in the autonomic computing layer of a smart-e-Health-home platform to achieve a pre-defined degree of screening accuracy in the presence of a defective dataset.

Robust Spectrum Sharing in Air-Ground Integrated Networks: Opportunities and Challenges

By integrating UAVs into terrestrial communication networks, air-ground integrated networks (AGIN) have shown great potential to revolutionize wireless communications and enhance ubiquitous wireless services. However, limited spectrum resource has been identified as a bottleneck to release the benefits of AGIN. Spectrum sharing is a powerful technique to improve spectrum utilization and poses new opportunities and challenges in AGIN. This article first elaborates the characteristics in air-ground spectrum sharing from state uncertainties and security threats perspectives. Then, a robust spectrum sharing framework is developed in which spectrum utilizing networks, spectrum monitoring networks, and spectrum clouds cooperatively form a cognitive dynamic close-loop control system. Furthermore, we discuss the key enabling technologies for robust spectrum sharing in AGIN to tackle the issues of uncertain and unknown information. Next, an exemplary robust spectrum sharing case with location uncertainty is provided. Finally, the technical challenges and open issues ahead are presented.

Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory

The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.

Cognitive Risk Control for Anti-Jamming V2V Communications in Autonomous Vehicle Networks

The future of intelligent transportation system (ITS) is expected to be composed of connected and autonomous vehicles (CAVs), the development of which will have great impact on people's everyday life. Unfortunately, this progress will be accompanied by all kinds of potential threats and attacks rising in CAV network. As a legacy from traditional wireless networks, jamming attack is still one of the major and serious threats to vehicle-to-vehicle (V2V) communications. In this paper, we investigate the anti-jamming V2V communication in CAV networks through power control in conjunction with channel selection. Bringing into play a brain-inspired research tool called cognitive dynamic system (CDS), the general structure of cognitive risk control (CRC) is well-tailored to analyze and address the jamming problem. Specifically, power control is carried out first using reinforcement learning, the result of which is then examined by a module called task-switch control. Based on the risk assessment, a multi-armed bandit (MAB) problem is formulated to perform the channel-selection process when necessary. Through continuous perception-action cycles (PACs), the feature of predictive adaptation is realized for the legitimate vehicle in its behavioral interactions with the jammer. Simulation results have shown that the proposed method has desirable performance in terms of several evaluation metrics.

Artificial Intelligence Communicates With Cognitive Dynamic System for Cybersecurity

Building on historical notes, this paper consists of three basic parts: Part One deals with environment, artificial intelligence, and cognitive dynamic system (CDS); Part Two deals with mathematics and the brain; and Part Three deals with switches. To expand this paper in a different context, the emphasis is on artificial intelligence and CDS. With switches being relatively new and highly important, we first discuss regular switches followed by the special switch, we then introduce task-switch control, and then AI and CDS for describing their individual structures. Finally, this paper finishes with conclusion.

Brain Inspired Dynamic System for the Quality of Service Control over the Long-Haul Nonlinear Fiber-Optic Link.

Brain-inspired intelligence using the cognitive dynamic system (CDS) concept is proposed to control the quality-of-service (QoS) over a long-haul fiber-optic link that is nonlinear and with non-Gaussian channel noise. Digital techniques such as digital-back-propagation (DBP) assume that the fiber optic link parameters, such as loss, dispersion, and nonlinear coefficients, are known at the receiver. However, the proposed CDS does not need to know about the fiber optic link physical parameters, and it can improve the bit error rate (BER) or enhance the data rate based on information extracted from the fiber optic link. The information extraction (Bayesian statistical modeling) using intelligent perception processing on the received data, or using the previously extracted models in the model library, is carried out to estimate the transmitted data in the receiver. Then, the BER is sent to the executive through the main feedback channel and the executive produces actions on the physical system/signal to ensure that the BER is continuously under the forward-error-correction (FEC) threshold. Therefore, the proposed CDS is an intelligent and adaptive system that can mitigate disturbance in the fiber optic link (especially in an optical network) using prediction in the perceptor and/or doing proper actions in the executive based on BER and the internal reward. A simplified CDS was implemented for nonlinear fiber optic systems based on orthogonal frequency division multiplexing (OFDM) to show how the proposed CDS can bring noticeable improvement in the system’s performance. As a result, enhancement of the data rate by 12.5% and the Q-factor improvement of 2.74 dB were achieved in comparison to the conventional system (i.e., the system without smart brain).

Cognitive Dynamic System for Future RACE Vehicles in Smart Cities: A Risk Control Perspective

As one of the largest applications for the Internet of Things in smart cities, the Internet of Vehicles has attracted increasing attention over the years due to its great potential for reshaping both transportation systems and human society. While connected and autonomous vehicles (CAVs) are currently being developed all over the world, they are unfortunately under various potential threats that could endanger the entire CAV network. In this article, we envision a new class of future vehicles, namely risk-sensitive, autonomous, connected, and electric (RACE) vehicles, to cope with uncertain attacks and potential threats. The safety, security, and privacy issues in CAV networks are identified first. Next, the cognitive dynamic system (CDS) is introduced as the supervisor of RACE vehicles for improving and coordinating multiple vehicle-mounted systems. A special function of CDS, cognitive risk control, is then described in the presence of uncertain threats. Last but not least, we present the future directions and research challenges ahead.

Brain-Inspired Intelligence for Real-Time Health Situation Understanding in Smart e-Health Home Applications

The autonomic computing layer of the smart e-Health home based on a cognitive dynamic system (CDS) can be a solution for improving health situation understanding, reducing the healthcare system costs, and improving people's quality of life. It can also be a solution for reducing the large number of sudden deaths outside of a hospital due to fatal diseases such as Arrhythmia. Towards this objective, we start from understanding the health situation, by diagnosing healthy and unhealthy persons. For this, we developed a decision-making system that is inspired by the medical doctors (MDs) decision-making processes. Our system is based on a CDS for cognitive decision-making and it can create a decision-making tree automatically. The simple, low complexity algorithmic design of the proposed system makes it suitable for real-time applications. A proof-of-concept case study of the implementation of the CDS was done on Arrhythmia disease. An accuracy of 95.4% was achieved using the proposed algorithms. Also, these algorithms can make a decision in less than 80 ms, and for one User, this includes the time for training. The proposed platform can be extended for more healthcare applications such as screening, disease class diagnosis, prevention, treatment, or monitoring healing. As a result, the proposed CDS algorithms can be an example of the first step for designing the autonomic computing layer of a smart e-Health home platform.

Cognitive Dynamic System for Control and Cyber-Attack Detection in Smart Grid

This paper introduces a new way of thinking that characterizes itself by uniting two entities, namely state estimation in the smart grid (SG) and cognitive dynamic system (CDS). False data injection (FDI) attacks are a family of new attacks that have been considered to be the most dangerous cyber-attack as it leads to cascaded bad decision making throughout the SG network, which can lead to severe repercussions. The conventional state estimation and bad data detection techniques, which have been applied to reduce observation errors and detect bad data in energy system state estimators, cannot detect FDI attacks. Here, we bring into play an objective-seeking system to act as the supervisor of the SG network. To this end, we propose to introduce a new metric for the SG: the entropic state. The entropic state has two purposes: 1) it provides an indication of the grid's health on a cycle-to-cycle basis and 2) it can be used to detect FDI attacks. Consequently, improving the entropic state is the goal of the supervisor. To achieve that objective, the supervisor dynamically optimizes the state estimation process by reconfiguring the weights of the sensors in the network. With optimality in mind, the CDS is the superior choice for the supervisory system. In this structure, the CDS interacts with the SG network, which is considered as the environment. Computer simulations are carried out on a 4-bus and the IEEE 14-bus systems to highlight the performance of the proposed approach in detecting both bad data and FDI attacks in the SG, respectively.

Brain-Inspired Cognitive Decision Making for Nonlinear and Non-Gaussian Environments

The autonomic-computing layer of the smart systems based on a cognitive dynamic system (CDS) is proposed as a solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making process. Furthermore, it is designed based on CDS for CDM and internal commands. The simple low complexity algorithmic design of the proposed system can make it suitable for real-time applications. A case study of the implementation of the CDS was done on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link. An improvement in Q-factor of 3.5 dB as well as 23.3% data rate efficiency enhancement are achieved using the proposed algorithms with an extra 20% data rate enhancement by guaranteeing to keep CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.

Cognitive Risk Control for Transmit-Waveform Selection in Vehicular Radar Systems

Cognitive dynamic system (CDS) is a structured engineering model and research tool inspired by certain features of the human brain. As a special function of CDS, cognitive risk control (CRC) actualizes the concept of predictive adaptation to bring risk under control when encountered with unexpected uncertainty. In this paper, the first experimental demonstration of CRC is presented in the practical application of vehicular radar systems, and an algorithm for transmit-waveform selection in cognitive vehicular radar (CVR) based on CRC is proposed. During each perception–action cycle, the perceptor of CVR processes new environmental inputs and provides the processed information to the executive through feedback channel for the selection of cognitive action. With the mechanism of task-switch control being functional all the time, the CVR will switch to a more capable operation mode in the face of unexpected disturbances or adverse events. In such cases, a new subsystem of executive is brought into play, in which the risk-sensitive cognitive action is finally selected and applied to the environment. Simulation results have shown the robustness and effectiveness of the proposed CVR system, which can make the next-generation vehicular radars more intelligent and play an important role in future self-driving cars.

The Cognitive Dynamic System for Risk Control [Point of View

The idea of cognitive dynamic system (CDS) was first described in a Point of View article published ten years ago. As simple as it was then, it is remarkable how far that early work on the CDS has progressed to make a big difference to engineering. The objective of this article is to highlight the recently pioneered elements of the CDS for how to bring risk under control for physical systems in the presence of uncertainties.

Smart Home: Cognitive Interactive People-Centric Internet of Things

This article proposes to integrate two entities, the Internet of Things and a cognitive dynamic system (CDS), and studies a smart home scenario as the application of interest. As a people-centric IoT, the smart home aims to enhance the intelligence level of the living environment and improve the quality of human life. Cognition, the distinct principles of which are perception?action cycle, memory, attention, intelligence, and language, can play a key role to pave the way for building truly smart homes. With cognition as its foundation, the engineering paradigm of CDS provides step-by-step guidelines for systematic development of smart homes. Hence, CDS can significantly contribute to the interactive IoT ecosystem.

Cognitive Risk Control for Physical Systems

The cognitive dynamic system (CDS) is a structured physical model and research tool inspired by certain features of the human brain. One such feature is the predictive adaptation of the organism to the future environment. From an engineering perspective, this property of the brain is of profound practical importance, particularly when the system, in the pursuit of goals or performing tasks, confronts unexpected adverse events or obstacles, which in the aggregate are commonly referred to as risk . To avert risk efficiently, much of the information processed in the past by the CDS is available for processing new information in one of the system’s components termed the perceptor . In the face of uncertainty, the perceptor will provide the processed information to the executive in order for the latter to avoid probable risk. To that effect, the executive will be fitted with Bayesian filtering mechanisms that will guide the CDS to its goal through timely risk-avoiding actions. Those mechanisms not only have unique engineering applications but also potential value for understanding the predictive-adaptation property of the brain, which modern neuroscience attributes to the prefrontal cortex.

Perception-Based $$\ell _p$$ ℓ p -Norm Minimization Approach for Nonlinear System Identification in GGD Noise

Nonlinear system identification is an important and fundamental problem in many practical applications. It becomes more challenging when the noise is non-Gaussian. Inspired by the cognitive dynamic system concept, we propose a perception-based \(\ell _p\)-norm minimization approach for nonlinear system identification in generalized Gaussian distribution noise environments. Volterra model is utilized to describe the nonlinear system. The proposed cognitive algorithm incorporates a closed feedback loop between perceptions and actions to the environments. Computer simulations have been carried out to illustrate the effectiveness of the proposed method.

Cognitive Dynamic System as the Brain of Complex Networks

This paper addresses a brand new way of thinking that distinguishes itself by integrating two entities, namely, dynamic spectrum management on the one hand and cognitive dynamic system (CDS) on the other. To be more precise, the CDS performs the role of a cognitive engine centered at the heart of wireless communications, wherein licensed bands rooted in network providers responsible for primary users and unlicensed bands rooted in other networks responsible for secondary users. Thus, these two worlds embody primary users and secondary users, which collectively function within a single complex wireless communication network empowered by the CDS for the first time ever.