The growing prevalence of cybersecurity threats has increased the demand for robust intrusion detection systems (IDSs). Deep learning techniques have shown promising results in detecting and mitigating these threats, making them an increasingly popular choice in IDS design. However, evaluating the performance of deep learning-based IDSs can be challenging due to the complexity of the models and the lack of standardized evaluation metrics. This review paper presents an overview of the most common evaluation metrics used in deep learning-based IDSs, including precision, confusion metrics, accuracy, F1 score, Area Under Curve (AUC), and recall. Several studies have applied machine-learning classic algorithms like Random Forest, Decision Tree, Logistic Regression, and others, but for this paper, we used a Convolutional Neural Network (CNN) that would be independent of the features in the dataset. The studied papers did not provide AUC and none of them balanced the dataset based on the feature's proportion. The dataset utilized in this study is the CSE-CIC-IDS2018 dataset, which underwent meticulous cleansing and normalization procedures to ensure the inclusion of legitimate and useful data. Furthermore, a weighting mechanism was introduced to balance the dataset and mitigate the potential for bias in the Machine Learning process.

Stability and task accomplishment of Internet-based teleoperation systems are greatly susceptible to the network latency and uncertainty. Control of a teleoperation system aims to provide satisfactory performance of the remote task and in some cases to provide the operator with sensory feedback. This paper reviews the recent control methodologies used for teleoperation systems with model uncertainty, unknown time-varying delay, and Internet-based communication. The focus is on control algorithms that are suitable for nonlinear uncertain systems to decrease restrictions and increase application scope. The key features of these control algorithms are highlighted, and their advantages and disadvantages are comparatively discussed.

Throughout the last few decades, a breakthrough took place in the field of autonomous robotics. They have been introduced to perform dangerous, dirty, difficult, and dull tasks, to serve the community. They have been also used to address health-care related tasks, such as enhancing the surgical skills of the surgeons and enabling surgeries in remote areas. This may help to perform operations in remote areas efficiently and in timely manner, with or without human intervention. One of the main advantages is that robots are not affected with human-related problems such as: fatigue or momentary lapses of attention. Thus, they can perform repeated and tedious operations. In this paper, we propose a framework to establish trust in autonomous medical robots based on mutual understanding and transparency in decision making.

One of the key design parameters in tendon-driven continuum robots is the number of tendons and the tendon loading distribution. A load model is also helpful for avoiding slack in tendons that causes control inefficiency and inaccuracy. A quasistatic model of n-tendon continuum robots is derived using the Euler-Lagrange formulation. The model is employed to derive an analytical loading model for equidistant tendon tensions for any given beam configuration within the workspace. The model accounts for the bending and axial compliance of the manipulator as well as tendon compliance. Features of the proposed model are discussed and some of the potential applications are explained. Based on the proposed model, a slack avoidance algorithm with analytical formulation is developed to dynamically optimize the tendon loads while preventing slack in tendons for a given configuration. The proposed model is experimentally validated in a multitendon continuum robot system for four case studies of three- to six-tendon arrangements in open-loop control architecture. A stereo vision-based three-dimensional reconstruction system measures the beam configuration and properties for each of the threeto six-tendon continuum robots. The effect of number of tendons on the tension loads in n-tendon continuum robots is studied. A quantitative dimensionless relationship between the number of tendons, the maximum tendon loads, and the bending angles is developed that may be used as a design tool for tradeoff among the complexity and required force and size.

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors cannot be employed in the same way that they are in rigid robotic manipulators. In this paper, a vision-based shape sensing algorithm for real-time 3D reconstruction of catheters based on the views of two arbitrary positioned cameras is presented. Customized high-speed algorithms are developed for the segmentation and feature extraction from the images. The algorithm is experimentally validated for accuracy by measuring the tip position, bending and orientation angles and for precision by estimating known 3D circular and elliptical shapes of the catheter. Experimental results demonstrate good accuracy and performance of the proposed high speed algorithms.

The lack of haptic feedback has negatively affected the surgeon's ability to palpate and diagnose tissue and differentiate its stiffness during surgical operations with commercially available robotic assisted surgical systems. A modular surgical instrument capable of non-invasive measurement of sideways tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems is presented in this paper. The proposed force measurement technique enables the actual non-invasive measurement of the sideways interaction forces at the tip jaws. The instrument has two actuation degrees of freedom (DOF) for the tip operation and grasping orientation. The tip functionality type (e.g., grasping, cutting, and dissecting) can also be changed quickly and easily. Experiments were conducted to evaluate functionalities of the proposed instrument in palpating tissues. The results are presented and analysed here that verify the capability of the proposed instrument in accurately measuring lateral tip/tissue interaction forces.

This paper proposes a method to improve motion smoothness and decrease latency using existing ABB IRC5 robot controllers without access to any low level interface. The proposed control algorithm includes a high-level PID controller used to dynamically generate reference velocities for different travel ranges of the tool centre point (TCP) of the robot. Communication with the ABB IRC5 controller was performed utilising the ABB PC software development kit (SDK). The multitasking feature of the IRC5 controller was used in order to enhance the communication frequency between the controller and the remote application. Trajectory tracking experiments of a predefined 3D trajectory were carried out and the benefits of the proposed algorithm was demonstrated. The robot was intentionally installed on a wobbly table and its vibrations were recorded using a six degrees of freedom (DOF) force/torque sensor fitted to the tool mounting interface of the robot. The robot vibrations were used as a measure of the smoothness of the tracking movements. Experimental results demonstrating the robot tool centre point (TCP), tracking errors, and robot vibrations for different control approaches were provided and analysed. It was demonstrated that the proposed approach results in the smoothest motion with less than 0.2 mm tracking errors.

In this paper a 6-RRCRR parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS) is introduced. Remote centre-of-motion (RCM) control algorithms of PRAMiSS suitable for minimally invasive surgery and microsurgery are also presented. The programmable RCM approach is implemented in order to achieve manipulation under the constraint of moving through the fixed penetration point. Having minimised the displacements of the mobile platform of the parallel micropositioning robot, the algorithms also apply orientation constraint to the instrument and prevent the tool tip to orient due to the robot movements during the manipulation. Experimental results are provided to verify accuracy and effectiveness of the proposed RCM control algorithms for minimally invasive surgery.