Due to recent increase in deployment of Cyber-Physical Industrial Control Systems in different critical infrastructures, addressing cyber-security challenges of these systems is vital for assuring their reliability and secure operation in presence of malicious cyber attacks. Towards this end, developing a testbed to generate real-time data-sets for critical infrastructure that would be utilized for validation of real-time attack detection algorithms are indeed highly needed. This paper investigates and proposes the design and implementation of a cyber-physical industrial control system testbed where the Tennessee Eastman process is simulated in real-time on a PC and the closed-loop controllers are implemented on the Siemens PLCs. False data injection cyber attacks are injected to the developed testbed through the man-in-the-middle structure where the malicious hackers can in real-time modify the sensor measurements that are sent to the PLCs. Furthermore, various cyber attack detection algorithms are developed and implemented in real-time on the testbed and their performance and capabilities are compared and evaluated.

In light of the advancement of the technologies used in industrial control systems, securing their operation has become crucial, primarily since their activity is consistently associated with integral elements related to the environment, the safety and health of people, the economy, and many others. This work presents a distributed, machine learning based attack detection and mitigation framework for sensor false data injection cyber-physical attacks in industrial control systems. It is developed using the system’s standard operational data and validated using a hybrid testbed of a reverse osmosis plant. A MATLAB/Simulink-based simulation model of the process validated with actual data from a local plant is used. The control system is implemented using Siemens S7-1200 programmable logic controllers with 200SP Distributed Input/Output modules. The proposed solution can be adopted in the existing industrial control systems and demonstrated effective performance in real-time detection and mitigation of actual cyber-physical attacks launched by compromising the communication links between the process and the programmable logic controllers.

This paper presents a model-free fault detection and isolation (FDI) method for nonlinear dynamical systems using Koopman operator theory and linear geometric technique. The key idea is to obtain a Koopman-based reduced-order model of a nonlinear dynamical system and apply the linear geometric FDI method to detect and isolate faults in the system. Koopman operator is an infinite-dimensional, linear operator which lifts the nonlinear dynamic data into an infinite-dimensional space where the correlations of dynamic data behave linearly. However, due to the infinite dimensionality of this operator, an approximation of the operator is needed for practical purposes. In this work, the Koopman framework is adopted toward nonlinear dynamical systems in combination with the linear geometric approach for fault detection and isolation. In order to demonstrate the efficacy of the proposed FDI solution, a mathematical nonlinear dynamical system, and an experimental three-tank setup are considered. Results show a remarkable performance of the proposed geometric Koopman-based fault detection and isolation (K-FDI) technique.

Robust, reliable, low-cost, and precise target docking solutions are in high demand in the industry. This paper investigates the docking problem of two non-holonomic robots with a fork attached to one of them and a docking station attached to the other. To secure latching the fork to the docking station, it must be oriented in a particular direction and distance from the docking station. Toward this and to perform docking operations, a navigation strategy is proposed which enables orientations and distances to be matched with the desired values. In addition, the mobile robot must intercept the target at a desired closing velocity for the docking mission to latch the fork and the docking station. This method also allows the final closing velocity to be set at any arbitrary value. This paper considers the docking operation of the assembling robots, and also proposes a solution to the docking problem, when the target moves at a constant velocity. As a result, the presented method allows the assembly of the robots, while moving. In addition, since safety is a critical factor in robotic systems operating in dynamic environments, a control-barrier-functions-based technique is adopted to avoid both static and dynamic obstacles. The performance of the proposed method is validated through a simulation and comparison study, then real-world implementation is performed using QBot robots. Note to Practitioners —Autonomous target docking for mobile robots has gained prominence, driven by its diverse applications like pallet picking, parking, refueling, automatic charging, and robot assembly. This paper specifically addresses the challenges of mobile robot docking in industrial settings, where shared workspaces involve static and dynamic obstacles, including equipment and humans. Two main challenges emerge: navigating a complex environment with obstacles and the time-consuming requirement for the robot to come to a complete stop before docking, particularly in scenarios with multiple docking targets or frequent operations. The paper proposes a solution allowing mobile robots to dock with a moving target at a constant velocity. This approach not only improves efficiency by eliminating the need for stopping during docking but also enhances safety through clear navigation paths and controlled closing velocity for precise docking. The presented solution shows promise in addressing the autonomous target docking problem in industrial environments, offering possibilities for further advancements and applications. Simulation and experimental results validate the effectiveness of the proposed method.

Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HMC) in Qatar, there is a critical need of expert ECMO staff. Thus, a modular ECMO simulator is being developed to enhance the training process in a cost-effective manner. This ECMO simulator gives the instructor the ability to control the simulation modules and run common simulation scenarios through a tablet application. The core modules of the simulation system are placed in the patient unit. The unit is designed modularly such that more modules can be added throughout the simulation sessions to increase the realism of the simulation sessions. The new approach is to enclose the patient unit in a trolley, which is custom-designed and made to include all the components in a modular fashion. Each module is enclosed in a separate box and then mounted to the main blood simulation loop box using screws, quick connect/disconnect liquid fittings, and electrical plugs. This method allows fast upgrade and maintenance for each module separately as well as upgrading modules easily without modifying the trolley’s design. The prototype patient unit has been developed for portability, maintenance, and extensibility. After implementation and testing, the prototype has proven to successfully simulate the main visual and audio cues of the real emergency scenarios, while keeping costs to a minimum.

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide–copper oxide-doped MgO (GO − Cu − Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.