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In order to set up a cybernetic arm, we have to transform received EMG signals to digits that represent the velocity for each sampling period (0.2 second). These numbers are inputs of a DC motor, in order to give us necessary output of position and velocity. In this paper we are trying to represent a practical method for solving this problem. By using an intermediate circuit, we will be able to make a connection between the computer and the DC motor. The circuit should move the motor in two different directions, supply a necessary current for movement, and at the same time disconnect unnecessary current when there is no motion. Now we have to see how to control the DC motor. To solve the dynamic problem of arm and DC motor, we have ignored all differential terms and will calculate the necessary voltage for the desired position and velocity. From calculated voltage we can obtain the sufficient pulse period for the DC motor. Then we can supply pulses with modulation to the DC motor (PWM). Since we are worki...

Using FES for reanimation of paralyzed arm has been seriously considered by researchers in recent years. Due to complexities of reaching movement, utilization of a precise model, virtual reality capabilities and advanced control techniques is inevitable. This paper deals with a precise model of paralyzed arm for FES researches. Model elements exactly conform to the elements of the musculoskeletal system so that it is possible to use personal information of the subject to adjust the model exactly. By applying electrical stimulation and at the same time recording elbow joint angle and stimulation pulse width, the model has been validated. Cross correlation and RMSE between experimental results and those from the adjusted model confirm the performance of the model. Simulation results also have been sent to virtual reality environment so that its valuable capabilities can be used for arm reanimation application.

In this article, two adaptive, energy-based controllers are presented for regulation problem of planar two link flexible manipulator rotating in vertical plane under gravity. The first controller consists of a joint PD controller, an adaptive proportional controller which makes use of link curvature to suppress vibration and gravity compensation term. In second one a Lyapunov-based PD controller substitutes the joint PD. the effectiveness of the proposed controllers are shown and compared in the simulation results.

Serpentine robots are multi-segmented vehicles. Based on their physical structure and design, these robots could have great mobility in their movements. This mobility can enable the robot to move around in more complex environments. The application of these kind of robots could be very useful in hard to reach places or hazardous environments. In this paper we investigated and implemented a serpentine robot. Inspired from snake motion, we implemented the structure of our robot based on the snakepsilas physical mechanism. In spite of most designed snake robots, our robot makes use of the friction between the body of the robot and the environment it encompasses to move in. This kind of movement requires a considerable amount of accuracy and synchronization among all segments. For this purpose we utilized a master and slave routine. The master is located in the head of the snake and is able to change each of the slaves status located in every segment of the snake according to the snakepsilas controlling algorithm and the feedback signals received from each slave unit. The communications to these segments are accomplished by the serial interface of the micro-controllers located in each segment. The joints between each neighboring segment are constructed of an elastic rubber and a cord, similar to the body structure of a real snake. The relative movements of the neighboring segments are the result of the wrapping and unwrapping of this cord. Utilizing this cord system, we obtained 3-degree-of freedom joints which enable the snake to move in any desired direction.

Glucose-insulin interaction in an insulin-dependent diabetic patient has been simulated using an overall model based on pharmacokinetic diagrams of insulin and glucose. Model is capable of predicting the blood glucose and insulin levels, total glucose uptake and the renal glucose excretion. The treatment strategy is based on a four-daily dose of regular insulin, which is applied through a subcutaneous route 30 min prior to each meal.

Intelligent systems can feel, learn, interpret, act and correct themselves based on their last trials or forget them completely. The major goal in an intelligent system is to model the human behavior and set it up on a machine. The main control command of a cybernetic hand should be posted by the remaining healthy organs of the disabled person. These commands can be from several channels, which their recording and detecting are different and need complicated study. In this research, we suppose that, this stage has been done or in other words, the command has been already sent and detected. So the main goal is to control a long hand, which has the part above the elbow missing, by an angle indicated by the disabled. This means that, the system input is the position desired by the disabled and the output is the elbow-joint angle variation. Therefore the goal is a suitable control design based on neural network theory in order to meet the given mapping.

Signature verification is a reliable and publicly acceptable method for authentication. Each signature is represented as a set of functional features such as coordinates of signature points, pen pressure and pen angle and therefore many features are available to the designer of signature verification system. The efficiency of any signature verification system depends mainly on the discrimination power and robustness of the features use in the system. This paper evaluates 40 dynamic features viewpoint classification error and consistency for extracting the best subset once a set of features provide maximal discrimination capability between genuine and forgery signatures. A modified distance of dynamic time warping (DTW) algorithm is proposed to improve performance of verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity-based. Second, average equal error rate (EER) for proposed algorithm in comparison with the general DTW algorithm show a 47.5% decrease. Moreover, comparative study based on different classifier with skilled forgery show that the best result has EER of 1.73% using Parzen window classifier.

Learning to control the planar three-link musculoskeletal arm by using an Actor–Critic learning algorithm during reaching movements to stationary target is presented. The arm model used in this study includes three skeletal links (hand, forearm and upper arm), three joints (wrist, elbow and shoulder without redundancy) and six nonlinear monoarticular muscles with redundancy which are modeled based on Hill model. The learning system is composed of Actor and Critic parts. For each part, a single layer neural network is used. This learning system applies six activation commands to six muscles at each instant of time. It also uses a reinforcement (reward) feedback for learning process and controlling the arm movement direction. The results showed that with a learning rate α = 0.9 and after 20 episodes, Mean square error (MSE), average reward and average time of reaching the target are gradually converged to the values: 0.0056, 0.02262 and 187 s, respectively. After the 20th episode, the learning will be completed. The research suggests a new direction for designation of learning-based controllers for functional electrical stimulation (FES) applications and for arm movement of autonomous robots.

In this paper, the control of a planar three-link musculoskeletal arm by using a revolutionary actor–critic reinforcement learning (RL) method during a reaching movement to a stationary target is presented. The arm model used in this study included three skeletal links (wrist, forearm, and upper arm), three joints (wrist, elbow, and shoulder without redundancy), and six non-linear monoarticular muscles (with redundancy), which were based on the Hill model. The learning control system was composed of actor, critic, and genetic algorithm (GA) parts. Two single-layer neural networks were used for each part of the actor and critic. This learning control system was used to apply six activation commands to six monoarticular muscles at each instant of time. It also used a reinforcement (reward) feedback for the learning process and controlling the direction of arm movement. Also, the GA was implemented to select the best learning rates for actor–critic neural networks. The results showed that mean square error (MSE) and average episode time gradually decrease and average reward gradually increases to constant values during the learning of the control policy. Furthermore, when learning was complete, optimal values of learning rates were selected.

A neuro-fuzzy based model is proposed in this paper for estimating the Lyapunov exponents of an unknown dynamical system according solely to a set of observations. Several approaches have been presented in recent years; most of them using the approximation of both the function of the trajectory of observations and the partial derivatives, to yield the Jacobian matrix of the function. The Jacobian matrix is then employed in the Jacobian-based methods that extract the Lyapunov exponents by QR-decomposition. While the accurate estimation of Lyapunov exponents has been sought, most of the related papers mainly focus on the accuracy of the trajectory approximation. In this paper, an Adaptive Neuro-Fuzzy Inference System is presented and stated to be an efficient tool for such a purpose. Structural parameters of the proposed model as the embedding dimension and the delay time are calculated by the Takens theorem and autocorrelation function, respectively. Model validation is performed by cross approximate entropy. Then, the promising performance of the proposed model as an accurate estimation of the Lyapunov exponents and its robustness to the measurement noise are finally evaluated.

To the Editor: The retina in all vertebrates is composed of three layers of nerve cell bodies and two layers of synapses. The first area of neuropil, where synaptic contacts occur, is the outer plexiform layer, in which connections between rod and cones, and vertically running bipolar cells and horizontally oriented horizontal cells, occur. 1 Experimental data suggests that some classes of spiking neurons in the first layers of sensory systems are electrically coupled via gap junctions or ephaptic interactions.

Signature verification is one of the most accepted biometric techniques, because a signature is a part of everyday life, although less accurate than biometric techniques such as using the iris. In this field, much attention has been paid to features, because a verification system should be able to overcome problems such as forgeries, insensitivity to intra-personal variability and sensitivity to inter-personal variability. In this paper, we present a simple and efficient approach to on-line signature verification, based on a discrete cosine transform, which has been applied to 44 time signals, such as position, velocity, pressure and angle of pen. Experiments are carried out on two benchmark databases, SVC2004 and SUSIG. The forward feature selection algorithm is used to search for the best performing feature subsets. The proposed system is tested with different classifiers, with skilled forgery, and equal error rates were 3.61%, 2.04% and 1.49% for SVC2004 Task1&2, Task2 and SUSIG databases, respectively.

In this paper, a potentiometric sensor based on polyaniline conducting polymer for potentiometric determination of Cr (VI) ions is reported. Polyaniline was synthesized electrochemically (cyclic voltammetry method) onto a micro pencil graphite electrode (0.7 mm diameter) in the presence of HCl and diphenylcarbazide (termed as (PGE/PAni/DPC). Some initial experiments were performed in order to find out the optimized conditions for preparation of the introduced Cr (VI) sensor electrode. The plot of E vs. log [Cr (VI)], showed a linear response in the range from 1.0 × 10 −6 to 1.0 × 10 −1 M. High repeatability with the detection limit of 8.0 × 10 −7

Background: Vitamin D supplementation during pregnancy has been supposed to defend against adverse gestational outcomes. Objective: This randomized clinical trial study was conducted to assess the effects of 50,000 IU of vitamin D every two weeks supplementation on the incidence of gestational diabetes (GDM), gestational hypertension, preeclampsia and preterm labor, vitamin D status at term and neonatal outcomes contrasted with pregnant women that received 400 IU vitamin D daily. Materials and Methods: 500 women with gestational age 12-16 weeks and serum 25 hydroxy vitamin D (25 (OH) D ) less than 30 ng/ml randomly categorized in two groups. Group A received 400 IU vitamin D daily and group B 50,000 IU vitamin D every 2 weeks orally until delivery. Maternal and Neonatal outcomes were assessed in two groups. Results: The incidence of GDM in group B was significantly lower than group A (6.7% versus 13.4%) and odds ratio (95% Confidence interval) was 0.46 (0.24-0.87) (P=0.01). The mean ± SD level of 25 (OH) D at the time of delivery in mothers in group B was significantly higher than A (37.9 ± 19.8 versus 27.2 ± 18.8 ng/ml, respectively) (P=0.001). There were no differences in the incidence of preeclampsia, gestational hypertension, preterm labor, and low birth weight between two groups. The mean level of 25 (OH) D in cord blood of group B was significantly higher than group A (37.9 ± 18 versus 29.7 ± 19ng/ml, respectively). Anthropometric measures between neonates were not significantly different. Conclusion: Our study showed 50,000 IU vitamin D every 2 weeks decreased the incidence of GDM.

In this paper, the static pull-in phenomenon is investigated in circular micro-plates subjected to capillary force. The capillary force appears in micro-scale structures due to presence of a liquid bridge. The governing equation of a circular micro-plate subjected to capillary force is presented and the static deflection of a fully-clamped circular plate is evaluated. Moreover, the effect of the normalized adhesion tension caused due to the capillary force on the static pull-in of the micro-plate is assessed.Copyright © 2011 by ASME

With the increase of communication and financial transaction through internet, on-line signature verification is an accepted biometric technology for access control and plays a significant role in authenticity and authorization in modernized society. Therefore, fast and precise algorithms for the signature verification are very attractive. The goal of this paper is modeling of velocity signal that pattern and properties is stable for persons. With using pole-zero models based on discrete cosine transform, precise method is proposed for modeling and then features is founded from strokes. With using linear, parzen window and support vector machine classifiers, the signature verification technique was tested with a large number of authentic and forgery signatures and has demonstrated the good potential of this technique. The signatures are collected from three different database include a proprietary database, the SVC2004 and the Sabanci University signature database benchmark databases. Experimental results based on Persian, SVC2004 and SUSIG databases show that our method achieves an equal error rate of 5.91%, 5.62% and 3.91% in the skilled forgeries, respectively.