The field of cognitive dynamic systems (CDSs) is an emerging area of research, whereby engineering learns from neuroscience. Under this framework, engineering systems are configured in a manner that mimics the human brain and improves the utility and performance of traditional systems. In essence, a CDS builds on Fuster’s paradigm of cognition and is fulfilled with the presence of five cognitive processes: the perception-action cycle, memory, attention, intelligence, and language. When augmented with these processes, a system can be classified as a CDS and is afforded the capabilities of processing information and learning from experience through continued interactions with the environment. Tremendous benefit from adopting the CDS framework has been observed in the literature, especially in the fields of cognitive radio and cognitive radar. More recently, the framework has been extended to other areas, such as control theory, risk control, and the Internet of Things; where the potential for drastic performance improvements has been evident in the literature. This comprehensive article presents a thorough background and exposition of the CDS framework and each field where it has been applied. In addition, we provide a comprehensive review of the recent advancements and related works in each domain by summarizing the key facts relating to the methodologies, findings, and limitations of the surveyed papers. Our novel contributions involve being the first source of centralized information on this topic and forming the foundation for future research efforts by presenting suggestions regarding worthwhile avenues for further investigation.

The volume of remote sensing data is experiencing rapid growth, primarily due to the plethora of space and air platforms equipped with an array of sensors. Due to limited hardware and battery constraints the data is transmitted back to Earth for processing. The large amounts of data along with security concerns call for new compression and encryption techniques capable of preserving reconstruction quality while minimizing the transmission cost of this data back to Earth. This study investigates image compression based on Convolutional Variational Autoencoders (CVAE), which are capable of substantially reducing the volume of transmitted data while guaranteeing secure lossy image reconstruction. CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets. The proposed model draws on the strength of the CVAE’s capability to abstract data into highly insightful latent spaces and combining it with the utilization of an entropy bottleneck is capable of finding an optimal balance between compressibility and reconstruction quality. The balance is reached by optimizing over a composite loss function that represents the rate-distortion curve.

State estimation strategies are vital for obtaining knowledge of a dynamic system’s state when faced with limited measurement capability, sensor noise, or uncertain system dynamics. The Kalman filter (KF) is one of the most widely recognized filters and provides the optimal solution for linear state estimation problems. The smooth variable structure filter (SVSF) is a model-based strategy which is also formulated as a predictor-corrector. Despite being a suboptimal estimator, the SVSF is highly robust to modeling uncertainties, errors, and system change. The combination of the SVSF with the KF (SVSF-KF) results in an adaptive estimation algorithm which provides an optimal KF estimate in normal operating conditions, and a robust SVSF estimate in the presence of faults or uncertainties. While effective in some cases, the SVSF-KF has been shown to suffer from several drawbacks associated with the time-varying smoothing boundary layer and adaptive gain used to detect system change. Several new approaches have been proposed in recent years with the aim of improving the SVSF-KF’s performance. Among these approaches is a novel gain formulation based on the normalized innovation squares, while another makes use of the interacting multiple model framework. In this paper, we review the newly proposed SVSF-KF formulations and compare their performance on an electro-hydrostatic actuator test case.