Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

Many people have been exposed to lower extremity function losses due to neurological, pathological or traffic accidents. In the physical therapy and rehabilitation of these patients, treatment programs based on robotic systems have started to be preferred instead of conventional methods. In robotic gait rehabilitation, mobilized lower extremity exoskeletons such as Rewalk or un-mobilized lower extremity exoskeletons such as RoboGait are used. It is important to evaluate the rehabilitation process in patients with lower extremity problems. Measurement of surface electromyogram (EMG) signals during the treatment process give information about the functional activities of the muscles. Obtained information plays an important role in determining the intention of patient motion in musculoskeletal design and musculoskeletal activities of the musculoskeletal. Changes in muscle activation timing and amplitude during the use of lower extremity exoskeleton can be determined by analysis of EMG. In this study, muscles involved in walking movement during robotic rehabilitation were examined. The examined iliopsoas, gluteus maximus, gluteus medius muscles provide flexion, extension and abduction movements of the hip, while the medial gastrocnemius and tibialis anterior muscles perform flexion and dorsiflexion movements of the foot. During the gait, the knee joint patency is controlled by the Vastus Medialis and Biceps Femoris muscles. In this study, while 6 patients with lower limb dysfunction were walking on the RoboGait device, the muscle activation potentials obtained from 7 different muscle groups were transferred to the computer simultaneously and wirelessly and displayed in the Matlab environment. The EMG signals measured with the MicroCor Lab device are shaped according to the activation of the muscles during walking. The electrode placement plan is critical for the analysis of EMG signals, and an appropriate electrode placement plan was obtained as a result of the study. Examined measured signals by following with the electrode placement plan, the maximum gluteus and iliopsoas muscles responsible for the extension and flexion movements of the hips are more effective during walking. Gletous maximum muscle was found to be the most effective muscle in walking while the iliopsoas muscle group was involved in the first movement of the leg. As a result of this study, these findings will help to follow the development of the treatment process and to develop EMG controlled mobilized lower extremity exoskeletons.

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Biomedical image analysis based on metaheuristic algorithms is one of the most important research areas encountered in recent years. Due to the low contrast differences between the diseased areas and the image background in high-contrast biomedical images, effective methods are required to diagnose diseases with high accuracy. To overcome the difficulties encountered in this field, metaheuristic approaches may offer effective solutions due to their advantages such as the ability of converging to the global optimum, higher convergence rate, and having few control parameters. In this work, Jellyfish Search (JS), Marine Predators (MPA), Tunicate Swarm (TSA), Mayfly Optimization (MA), Chimp Optimization (ChOA), Slime Mould Optimization (SMA), Archimedes Optimization (AOA), and Equilibrium Optimizer (EO) algorithms, which are the most recently proposed metaheuristic algorithms in the literature, have been improved as clustering based in order to achieve vessel segmentation with high precision. Also, a detailed performance comparison of these algorithms has been realized for the rate of convergences, error values reached, CPU time, standard deviation, sensitivity, specificity, accuracy, F-score, and Wilcoxon rank sum-test. In order to present the compatibility of the results obtained with the literature, the performances of these novel algorithms have also been compared to that of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Differential Evolution (DE) algorithms. The simulation results represent that each algorithm produces similar convergence and error performance. Also, it can be emphasized from the statistical analyses that the stability and robustness of each metaheuristic approach are quite adequate in separating the vessel pixels and the background pixels of a retinal image. In general, this paper proves that although having fewer number of control parameters, the JS, MPA, TSA, MA, ChOA, SMA, AOA, and EO algorithms produce similar but a bit better results in terms of image segmentation when compared to PSO, GWO, and DE algorithms.

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The real-time vibrations occurring in a leaf spring system may cause undesirable effects, such as stresses, strains, deflections, and surface deformations over the system. In order to detect the most appropriate working conditions in which the leaf spring system will work more stably and also to design optimized leaf spring systems, these external effects have to be detected with high accuracy. In this work, artificial neural network-based estimators have been proposed to analyze the vibration effects on leaf spring systems. In the experimental studies carried out, the vibration effects of low, medium, and high-pressure values applied by a hydraulic piston on a steel leaf spring system have been analyzed by a 3-axial accelerometer. After the experimental studies, the Radial Basis Artificial Neural Network (RBANN) and Cascade-Forward Back-Propagation Artificial Neural Network (CFBANN) based nonlinear artificial neural network structures have been proposed to analyze the vibration data measured from the leaf spring system under relevant working conditions. The simulation results represent that the RBANN structure can estimate the real-time vibrations occurring on the leaf spring system with higher accuracy and reaches lower RMS error values when compared to the CFBANN structure. In general, it can be concluded that the RBANN and CFBANN network structures can successfully be used in the estimation of real-time vibration data.

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