Continuum robotic arm becomes the new area of scientific research nowadays. Its technology has grown and touched several vital applications included industry and agriculture thanks to many advantages made it a better choice than the conventional serial robotic manipulator. This paper represents a new designed model of continuum arm robot, which relates the motor shaft angle as the input parameter and transfers the motor torque to combined system of compression springs and results in six outputs: x,y and z 3D coordinates for the center point of the end effector and \theta,~\psi and \gamma to

Conceptual cost estimates, are prepared at the very early stages of a project, and generally before the construction drawings and specifications are available. At this stage, cost estimates are needed by the owner, contractor, designer, or funding agencies for determination of the feasibility of a project, financial evaluation of a number of alternative projects, or establishment of an initial budget. Traditional approaches rely heavily on experienced engineers. This paper presents a method using fuzzy clustering technique for pump station projects cost estimation. The proposed conceptual cost

Detection of the lane boundary is the primary task in order to control the trajectory of an autonomous car. In this paper, three methodologies for lane detection are discussed with experimental illustration: Blob analysis, Hough transformation and Birds eye view. The next task after receiving the boundary points is to apply a control law in order to trigger the steering and velocity control to the motors efficiently. In the following, a comparative analysis is made between different tuning criteria to tune PID controller for Lane Keeping Assist (LKA). In order to receive the information of the

The skid steering tracked robot is consider one of the famous robots that used in the autonomous agricultural field. The robot model is considered as a coupled nonlinear model. So, a real kinematic model is required to develop the robot motion which will improve the high quality and quantity of the cultivated crops. So, in this research a mathematical model for the skid steering mobile robot (SSMR) and a mathamtical model has been presented to simulate the robot. The model has been validated based on experimental data for the Skid Steering model. The robot motion as position and velocity has

Determining the optimum location of facilities is critical in many fields, particularly in healthcare. This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019 (COVID-19) pandemic. The used model is the most appropriate among the threemost common locationmodels utilized to solve healthcare problems (the set covering model, the maximal covering model, and the P-median model). The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints. The model is used to

Many application problems are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to optimally distribute protective materials for the newly emerged COVID-19. It is defined for a decision-maker who wants to choose a subset of candidate hospitals comprising the maximization of the distributed quantities of protective materials to a set of chosen hospitals within a specific time shift. A nonlinear binary mathematical programming model for the problem is introduced with a real

Epileptic seizures occur as a result of functional brain dysfunction and can affect the health of the patient. Prediction of epileptic seizures before the onset is beneficial for the prevention of seizures through medication. Electroencephalograms (EEG) signals are used to predict epileptic seizures using machine learning techniques and feature extractions. Nevertheless, the pre-processing of EEG signals for noise removal and extraction of features are two significant problems that have an adverse effect on both anticipation time and true positive prediction performance. Considering this, the

Recently, numerous research works in retinal-structure analysis have been performed to analyze retinal images for diagnosing and preventing ocular diseases such as diabetic retinopathy, which is the first most common causes of vision loss in the world. In this paper, an algorithm for vessel detection in fundus images is employed. First, a denoising process using the noise-estimation-based anisotropic diffusion technique is applied to restore connected vessel lines in a retinal image and eliminate noisy lines. Next, a multi-scale line-tracking algorithm is implemented to detect all the blood

Genetic algorithms (GAs) are intended to look for the optimum solution by eliminating the gene strings with the worst fitness. Hence, this paper proposes an optimized edge detection technique based on a genetic algorithm. A training dataset that consists of simple images and their corresponding optimal edge features is employed to obtain the optimum filter coefficients along with the optimum thresholding algorithm. Qualitative and quantitative performance analyses are investigated based on several well-known metrics. The performance of the proposed genetic algorithm-based cost minimization

Mental depression is associated with an increased risk of cardiovascular mortality, thus provisioning generic simple nonlinear mathematical models for normal and depressed cases using only heart rate (HR) or stroke volume (SV) as a single input to produce cardiac output (CO) as a single output instead of using both HR and SV as two inputs. The proposed models could be in the future an effective tool to investigate the effect of neuroleptic medication, especially depression, and it reduces the time of processing. Seventy-four depressed cases, 74 normal peers and autoregressive considered as a