Monitoring embedded flow networks using graph Fourier transform enabled sparse molecular relays

Many embedded networks are difficult to monitor, such as water distribution networks (WDNs). A key challenge is how to use minimum sparse sensors to measure contamination and transmit contamination data to a hub for system analysis. Existing approaches deploy sensors using multi-objective optimisation and transmit the data using ground penetrating waves or fixed-line access. Here, for the first time, we introduce a novel molecular communication relay system, which is able to transmit the data report to the hub via the water-flow of WDN itself, and avoids the complex ground penetrating techniques. A water flow data-driven Graph Fourier Transform (GFT) sampling method is designed to inform the invariant orthogonal locations for deploying the molecular relay sensors. Each sensor encodes information via a DNA molecule that enables the common hub to reconstruct the full contamination information. Numerical simulation validates the proposed system, providing a pathway to integrate MC into macro-scale Digital Twin platforms for infrastructure monitoring

Molecular physical layer for 6G in wave-denied environments

The sixth generation (6G) of wireless systems are likely to operate in environments and scales that wireless services have not penetrated effectively. Many of these environments are not suitable for efficient data bearing wave propagation. Molecular signals have the potential to deliver information by exploiting both new modulation mechanisms via chemical encoding and new multi-scale propagation physics. While the fusion of biophysical models and communication theory has rapidly advanced the molecular communication field, there is a lack of real-world macro-scale applications. Here, we introduce application areas in defense and security, ranging from underwater search and rescue to covert communications; and cyber-physical systems, such as using molecular signals for health monitoring in underground networked systems. These engineering applications not only demand new wireless communication technologies ranging from DNA encoding to molecular graph signal processing, but also demonstrate the potential for molecular communication to contribute in traditional but challenging engineering areas. Together, it is increasingly believed that molecular communication can be a new physical layer for 6G, accessing and extracting data from extreme wave-denied environments

Molecular physical layer for 6G in wave-denied environments

The sixth generation (6G) of wireless systems are likely to operate in environments and scales that wireless services have not penetrated effectively. Many of these environments are not suitable for efficient data bearing wave propagation. Molecular signals have the potential to deliver information by exploiting both new modulation mechanisms via chemical encoding and new multi-scale propagation physics. While the fusion of biophysical models and communication theory has rapidly advanced the molecular communication field, there is a lack of real-world macro-scale applications. Here, we introduce application areas in defense and security, ranging from underwater search and rescue to covert communications; and cyber-physical systems, such as using molecular signals for health monitoring in underground networked systems. These engineering applications not only demand new wireless communication technologies ranging from DNA encoding to molecular graph signal processing, but also demonstrate the potential for molecular communication to contribute in traditional but challenging engineering areas. Together, it is increasingly believed that molecular communication can be a new physical layer for 6G, accessing and extracting data from extreme wave-denied environments

Sampling and inference of networked dynamics using Log-Koopman nonlinear graph fourier transform

Monitoring the networked dynamics via the subset of nodes is essential for a variety of scientific and operational purposes. When there is a lack of an explicit model and networked signal space, traditional observability analysis and non-convex methods are insufficient. Current data-driven Koopman linearization, although derives a linear evolution model for selected vector-valued observable of original state-space, may result in a large sampling set due to: (i) the large size of polynomial based observables (O(N2) , N number of nodes in network), and (ii) not factoring in the nonlinear dependency betweenobservables. In this work, to achieve linear scaling (O(N) ) and a small set of sampling nodes, wepropose to combine a novel Log-Koopman operator and nonlinear Graph Fourier Transform (NL-GFT) scheme. First, the Log-Koopman operator is able to reduce the size of observables by transforming multiplicative poly-observable to logarithm summation. Second, anonlinear GFT concept and sampling theory are provided to exploit the nonlinear dependence of observables for observability analysis using Koopman evolution model. The results demonstrate that the proposed Log-Koopman NL-GFT scheme can (i) linearize unknownnonlinear dynamics using O(N) observables, and (ii) achieve lower number of sampling nodes, compared with the state-of-the art polynomial Koopman based observability analysis

Sampling of time-varying network signals from equation-driven to data-driven techniques

Sampling and recovering the time-varying network signals via the subset of network vertices is essential for a wide range of scientific and engineering purposes. Current studies on sampling a single (continuous) time-series or a static network data, are not suitable for time-varying network signals. This will be even more challenging when there is a lack of explicit dynamic models and signal-space that indicate the time-evolution and vertex dependency. The work begins by bridging the time-domain sampling frequency and the network-domain sampling vertices, via the eigenvalues of the graph Fourier transform (GFT) operator composed by the combined dynamic equations and network topology. Then, for signals with hidden governing mechanisms, we propose a data-driven GFT sampling method using a prior signal-space. We characterize the signal dependency (among vertices) into the graph bandlimited frequency domain, and map such bandlimitedness into optimal sampling vertices. Furthermore, to achieve dynamic model and signal-space independent sensor placement, a Koopman based nonlinear GFT sampling is proposed. A novel data-driven Log-Koopman operator is designed to extract a linearized evolution model using small (M = O(N)) and decoupled observables defined on N original vertices. Then, nonlinear GFT is proposed to derive sampling vertices, by exploiting the inherent nonlinear dependence between M observables (defined on N < M vertices), and the time-evolved information presented by Log-Koopman evolution model. The work also informs the planned future work to formulate an easy-to-use and explainable neural network (NN) based sampling framework, for real-world industrial engineering and applications

Matlab

This book is a collection of 19 excellent works presenting different applications of several MATLAB tools that can be used for educational, scientific and engineering purposes. Chapters include tips and tricks for programming and developing Graphical User Interfaces (GUIs), power system analysis, control systems design, system modelling and simulations, parallel processing, optimization, signal and image processing, finite different solutions, geosciences and portfolio insurance. Thus, readers from a range of professional fields will benefit from its content

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering